Reviewerly/exHarmony huggingface dataset:
Adaptive multi-scale dual attention network for semantic segmentation

Semantic segmentation in complex traffic scenes is a challenging research topic in the field of computer vision. Algorithms based on convolutional neural network has achieved more outstanding results than traditional algorithms, but their segmentation performance needs to be further improved when faced with real scenes with complex backgrounds and variable scales. In response to this issue, this study proposes a fully convolutional network architecture based on an multi-scale attention pyramid to improve the performance of the semantic segmentation algorithm from several perspectives. Firstly, a lightweight dual attention module based on depth separable convolution is designed. This module uses depth separable convolution to simplify the modeling of semantic correlation between the spatial dimension and the channel dimension, and reduces the parameter quantity of the original dual attention module. Secondly, we constructed a multi-scale attention pyramid module, which uses feature maps of different receptive fields or different scales to output multiple prediction results. Finally, an adaptive multi-scale prediction fusion module is designed. This module adaptively fuses the prediction results of multiple different receptive fields or different scales. It further enhances the network’s predictive capabilities and generates detailed high-resolution predictive maps. Compared to the baseline DANet, we have achieved better results on the Cityscapes, PASCAL VOC 2012, and COCO Stuff datasets. We make the code publicly available at https://github.com/Exception-star/AMDANet.
Big Data, Data Science, and Causal Inference: A Primer for Clinicians

Clinicians handle a growing amount of clinical, biometric, and biomarker data. In this “big data” era, there is an emerging faith that the answer to all clinical and scientific questions reside in “big data” and that data will transform medicine into precision medicine. However, data by themselves are useless. It is the algorithms encoding causal reasoning and domain (e.g., clinical and biological) knowledge that prove transformative. The recent introduction of (health) data science presents an opportunity to re-think this data-centric view. For example, while precision medicine seeks to provide the right prevention and treatment strategy to the right patients at the right time, its realization cannot be achieved by algorithms that operate exclusively in data-driven prediction modes, as do most machine learning algorithms. Better understanding of data science and its tasks is vital to interpret findings and translate new discoveries into clinical practice. In this review, we first discuss the principles and major tasks of data science by organizing it into three defining tasks: (1) association and prediction, (2) intervention, and (3) counterfactual causal inference. Second, we review commonly-used data science tools with examples in the medical literature. Lastly, we outline current challenges and future directions in the fields of medicine, elaborating on how data science can enhance clinical effectiveness and inform medical practice. As machine learning algorithms become ubiquitous tools to handle quantitatively “big data,” their integration with causal reasoning and domain knowledge is instrumental to qualitatively transform medicine, which will, in turn, improve health outcomes of patients.
Hierarchical Connectivity-Centered Clustering for Unsupervised Domain Adaptation on Person Re-Identification

Unsupervised domain adaptation (UDA) on person Re-Identification (ReID) aims to transfer the knowledge from a labeled source domain to an unlabeled target domain. Recent works mainly optimize the ReID models with pseudo labels generated by unsupervised clustering on the target domain. However, the pseudo labels generated by the unsupervised clustering methods are often unreliable, due to the severe intra-person variations and complicated cluster structures in the practical application scenarios. In this work, to handle the complicated cluster structures, we propose a novel learnable Hierarchical Connectivity-Centered (HCC) clustering scheme by Graph Convolutional Networks (GCNs) to generate more reliable pseudo labels. Our HCC scheme learns the complicated cluster structure by hierarchically estimating the connectivity among samples from the vertex level to cluster level in a graph representation, and thereby progressively refines the pseudo labels. Additionally, to handle the intra-person variations in clustering, we propose a novel relation feature for HCC clustering, which exploits the identities from the source domain as references to represent target domain samples. Experiments demonstrate that our method is able to achieve state-of-the art performance on three challenging benchmarks.
On preserving the behavior in software refactoring: A systematic mapping study

Refactoring is the art of modifying the design of a system without altering its behavior. The idea is to reorganize variables, classes and methods to facilitate their future adaptations and comprehension. As the concept of behavior preservation is fundamental for refactoring, several studies, using formal verification , language transformation and dynamic analysis, have been proposed to monitor the execution of refactoring operations and their impact on the program semantics. However, there is no existing study that examines the available behavior preservation strategies for each refactoring operation. This paper identifies behavior preservation approaches in the research literature. We conduct, in this paper, a systematic mapping study , to capture all existing behavior preservation approaches that we classify based on several criteria including their methodology, applicability, and their degree of automation. The results indicate that several behavior preservation approaches have been proposed in the literature. The approaches vary between using formalisms and techniques, developing automatic refactoring safety tools, and performing a manual analysis of the source code. Our taxonomy reveals that there exist some types of refactoring operations whose behavior preservation is under-researched. Our classification also indicates that several possible strategies can be combined to better detect any violation of the program semantics.
Relation-aware Instance Refinement for Weakly Supervised Visual Grounding

Visual grounding, which aims to build a correspondence between visual objects and their language entities, plays a key role in cross-modal scene understanding. One promising and scalable strategy for learning visual grounding is to utilize weak supervision from only image-caption pairs. Previous methods typically rely on matching query phrases directly to a precomputed, fixed object candidate pool, which leads to inaccurate localization and ambiguous matching due to lack of semantic relation constraints. In our paper, we propose a novel context-aware weakly-supervised learning method that incorporates coarse-to-fine object refinement and entity relation modeling into a two-stage deep network, capable of producing more accurate object representation and matching. To effectively train our network, we introduce a self-taught regression loss for the proposal locations and a classification loss based on parsed entity relations. Extensive experiments on two public benchmarks Flickr30K Entities and ReferItGame demonstrate the efficacy of our weakly grounding framework. The results show that we outperform the previous methods by a considerable margin, achieving 59.27% top-1 accuracy in Flickr30K Entities and 37.68% in the ReferItGame dataset respectively <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Guiding the selection of research methodology in industry–academia collaboration in software engineering

The literature concerning research methodologies and methods has increased in software engineering in the last decade. However, there is limited guidance on selecting an appropriate research methodology for a given research study or project. Based on a selection of research methodologies suitable for software engineering research in collaboration between industry and academia, we present, discuss and compare the methodologies aiming to provide guidance on which research methodology to choose in a given situation to ensure successful industry–academia collaboration in research. Three research methodologies were chosen for two main reasons. Design Science and Action Research were selected for their usage in software engineering. We also chose a model emanating from software engineering, i.e., the Technology Transfer Model. An overview of each methodology is provided. It is followed by a discussion and an illustration concerning their use in industry–academia collaborative research. The three methodologies are then compared using a set of criteria as a basis for our guidance. The discussion and comparison of the three research methodologies revealed general similarities and distinct differences. All three research methodologies are easily mapped to the general research process describe–solve–practice, while the main driver behind the formulation of the research methodologies is different. Thus, we guide in selecting a research methodology given the primary research objective for a given research study or project in collaboration between industry and academia. We observe that the three research methodologies have different main objectives and differ in some characteristics, although still having a lot in common. We conclude that it is vital to make an informed decision concerning which research methodology to use. The presentation and comparison aim to guide selecting an appropriate research methodology when conducting research in collaboration between industry and academia.
Learning to Aggregate and Personalize 3D Face from In-the-Wild Photo Collection

Non-parametric face modeling aims to reconstruct 3D face only from images without shape assumptions. While plausible facial details are predicted, the models tend to over-depend on local color appearance and suffer from ambiguous noise. To address such problem, this paper presents a novel Learning to Aggregate and Personalize (LAP) framework for unsupervised robust 3D face modeling. Instead of using controlled environment, the proposed method implicitly disentangles ID-consistent and scene-specific face from unconstrained photo set. Specifically, to learn ID-consistent face, LAP adaptively aggregates intrinsic face factors of an identity based on a novel curriculum learning approach with relaxed consistency loss. To adapt the face for a personalized scene, we propose a novel attribute-refining network to modify ID-consistent face with target attribute and details. Based on the proposed method, we make unsupervised 3D face modeling benefit from meaningful image facial structure and possibly higher resolutions. Extensive experiments on benchmarks show LAP recovers superior or competitive face shape and texture, compared with state-of-the-art (SOTA) methods with or without prior and supervision.
Feature-based multi-criteria recommendation system using a weighted approach with ranking correlation

With the increase of online businesses, recommendation algorithms are being researched a lot to facilitate the process of using the existing information. Such multi-criteria recommendation (MCRS) helps a lot the end-users to attain the required results of interest having different selective criteria – such as combinations of implicit and explicit interest indicators in the form of ranking or rankings on different matched dimensions. Current approaches typically use label correlation, by assuming that the label correlations are shared by all objects. In real-world tasks, however, different sources of information have different features. Recommendation systems are more effective if being used for making a recommendation using multiple criteria of decisions by using the correlation between the features and items content (content-based approach) or finding a similar user rating to get targeted results (Collaborative filtering). To combine these two filterings in the multicriteria model, we proposed a features-based fb-knn multi-criteria hybrid recommendation algorithm approach for getting the recommendation of the items by using multicriteria features of items and integrating those with the correlated items found in similar datasets. Ranks were assigned to each decision and then weights were computed for each decision by using the standard deviation of items to get the nearest result. For evaluation, we tested the proposed algorithm on different datasets having multiple features of information. The results demonstrate that proposed fb-knn is efficient in different types of datasets.
An Enhanced Deep Convolutional Neural Network for Classifying Indian Classical Dance Forms

Indian classical dance (ICD) classification is an interesting subject because of its complex body posture. It provides a stage to experiment with various computer vision and deep learning concepts. With a change in learning styles, automated teaching solutions have become inevitable in every field, from traditional to online platforms. Additionally, ICD forms an essential part of a rich cultural and intangible heritage, which at all costs must be modernized and preserved. In this paper, we have attempted an exhaustive classification of dance forms into eight categories. For classification, we have proposed a deep convolutional neural network (DCNN) model using ResNet50, which outperforms various state-of-the-art approaches. Additionally, to our surprise, the proposed model also surpassed a few recently published works in terms of performance evaluation. The input to the proposed network is initially pre-processed using image thresholding and sampling. Next, a truncated DCNN based on ResNet50 is applied to the pre-processed samples. The proposed model gives an accuracy score of 0.911.
Understanding socio-technological challenges of smart classrooms using a systematic review

Smart classrooms are paradigm innovations for enhanced learning behavior in digital learning environments. These environments offer benefits for inclusive and virtual learning, underscoring the need for assessments of current practices. Although research on smart classrooms propose models and systems for enhancing socio-technological integration, knowledge on socio-technological challenges of smart classrooms remains limited. This article applies a systematic review methodology in line with the PRISMA protocol and analyzes current social and technological challenges based on 105 articles published between 2000 and 2019. The review identifies social challenges that facilitate personalization for external factors and teaching methods, stimulate learner-oriented content, instructor, peer, and technology forms of engagement, and boost interactivity depending on the willingness of learners and instructors. The review also finds technological challenges that concern designing learning environments and integrating intelligent systems, analytical tools and analysis, system models and ontology, and mobile and social media applications. The review suggests areas for future research involving smart classroom design for continuity and consistency, quality attributes of smart classrooms, efficiency and sustainability of smart classroom infrastructure, and the development of a smart classroom modelling language.
Effectiveness of a smartphone-based, augmented reality exposure app to reduce fear of spiders in real-life: A randomized controlled trial

Although in vivo exposure therapy is highly effective in the treatment of specific phobias, only a minority of patients seeks therapy. Exposure to virtual objects has been shown to be better tolerated, equally efficacious, but the technology has not been made widely accessible yet. We developed an augmented reality (AR) application (app) to reduce fear of spiders and performed a randomized controlled trial comparing the effects of our app (six 30-min sessions at home over a two-week period) with no intervention. Primary outcome was subjective fear, measured by a Subjective Units of Distress Scale (SUDS) in a Behavioural Approach Test (BAT) in a real-life spider situation at six weeks follow-up. Between Oct 7, 2019, and Dec 6, 2019, 66 individuals were enrolled and randomized. The intervention led to significantly lower subjective fear in the BAT compared to the control group (intervention group, baseline: 7.12 [SD 2.03] follow-up: 5.03 [SD 2.19] vs. control group, baseline: 7.06 [SD 2.34], follow-up 6.24 [SD 2.21]; adjusted group difference -1.24, 95 % CI -2.17 to -0.31; Cohen's d = 0.57, p = 0.010). The repeated use of the AR app reduces subjective fear in a real-life spider situation, providing a low-threshold and low-cost treatment for fear of spiders.
An empirical comparison between stochastic and deterministic centroid initialisation for K-means variations

Abstract K-Means is one of the most used algorithms for data clustering and the usual clustering method for benchmarking. Despite its wide application it is well-known that it suffers from a series of disadvantages; it is only able to find local minima and the positions of the initial clustering centres (centroids) can greatly affect the clustering solution. Over the years many K-Means variations and initialisation techniques have been proposed with different degrees of complexity. In this study we focus on common K-Means variations along with a range of deterministic and stochastic initialisation techniques. We show that, on average, more sophisticated initialisation techniques alleviate the need for complex clustering methods. Furthermore, deterministic methods perform better than stochastic methods. However, there is a trade-off: less sophisticated stochastic methods, executed multiple times, can result in better clustering. Factoring in execution time, deterministic methods can be competitive and result in a good clustering solution. These conclusions are obtained through extensive benchmarking using a range of synthetic model generators and real-world data sets.
ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning

Continual learning usually assumes the incoming data are fully labeled, which might not be applicable in real applications. In this work, we consider semi-supervised continual learning (SSCL) that incrementally learns from partially labeled data. Observing that existing continual learning methods lack the ability to continually exploit the unlabeled data, we propose deep Online Replay with Discriminator Consistency (ORDisCo) to interdependently learn a classifier with a conditional generative adversarial network (GAN), which continually passes the learned data distribution to the classifier. In particular, ORDisCo replays data sampled from the conditional generator to the classifier in an online manner, exploiting unlabeled data in a time- and storage-efficient way. Further, to explicitly overcome the catastrophic forgetting of unlabeled data, we selectively stabilize parameters of the discriminator that are important for discriminating the pairs of old unlabeled data and their pseudo-labels predicted by the classifier. We extensively evaluate ORDisCo on various semi-supervised learning benchmark datasets for SSCL, and show that ORDisCo achieves significant performance improvement on SVHN, CIFAR10 and Tiny-ImageNet, compared to strong baselines.
Interval–valued fuzzy and intuitionistic fuzzy–KNN for imbalanced data classification

Imbalanced data classification aims to categorize the complex data that have a very different distribution in the number of data samples in its classes. Since the traditional classifiers do not consider the imbalanced class distribution, they exhibit poor behavior when faced with this kind of data. There are four main kinds of solutions to deal with this problem: modifying the data distribution, modifying the learning algorithm to consider the imbalanced representation, utilizing the costs of misclassifying the data samples, and ensemble methods. In this paper, we adopt the first type of solution to resample the imbalanced data. There are two phases for resampling, in the first phase, the data is over-sampled, and then the noisy and borderline samples are chosen and removed (under–sampling phase). In the under–sampling phase, we introduce two robust extensions of KNN classifiers based on the concepts of interval-valued fuzzy and intuitionistic fuzzy sets for imbalanced data to filter the noisy and borderline samples. The process of choosing and removing these samples is an iterative procedure (i.e., the Iterative Partitioning Filter). Thus, the proposed filter exploits the classifier ensemble. We propose a new rule for voting concerning the new interval-valued intuitionistic fuzzy-based classifiers. The characteristics of the proposed method are debated in a comprehensive experimental verification comparing by SMOTE and its most well–known generalizations. The experiments on both synthetic data sets with different levels of noise and shapes of borderlines as well as real–world data sets show the potentiality of the proposed method.
Determining Pre-Service Teachers’ Intention of Using Technology for Teaching English as a Second Language (ESL)

The advancement of technology has led to a paradigm shift in the educational system, especially in classrooms. Technological tools have been used for language teaching in recent years, and it is proven that the use of technology enhances teaching and learning experiences. However, the researchers lamented the lack of use of technology by pre-service to teach English as a second language (ESL). This study aims to investigate factors that influence pre-service teachers’ intention of using technology for teaching ESL. This research applied the Unified Theory of Acceptance and Use of Technology (UTAUT) with some modifications to the constructs. Using simple random sampling, this study distributed questionnaires to 257 ESL pre-service teachers in Malaysia. Confirmatory factor analysis (CFA) and structural equation modelling (SEM) were used to analyse the data. The findings showed that technological, pedagogical, and content knowledge (TPACK) had a significant positive effect on the intention to incorporate technology in teaching. This study provides knowledge on the factors that influence ESL pre-service teachers’ intention to use technology in the classroom. It is hoped that this study can provide information for teacher training institutions to develop strategies and a new framework to address pre-service teachers’ concerns in technology utilisation for teaching ESL. As a conclusion, further study could be conducted qualitatively to better understand the subject matter.