Reviewerly/exHarmony huggingface dataset:
Multi-sensor based landslide monitoring via transfer learning

Landslides are severe geographical activities that result in large quantities of rock and debris flowing down hill-slopes, leading to thousands of casualties and billions of dollars in infrastructure damage every year worldwide. For detecting landslides, on-site sensor systems are widely applied for data collection and many existing statistical process control methods can be adopted for modeling and monitoring. However, the conventional methods may perform poorly or even inapplicable when the sensors have different set-up times and end times, especially when the system includes newly deployed sensors with limited data collected. To make effective use of such new sensors immediately after deployment, we propose a novel multi-sensor based charting scheme for dynamic landslide modeling and monitoring by using transfer learning. A regularized parameter-based transfer learning approach integrated with the ordered LASSO is first proposed to effectively transfer information from old sensors with sufficient historical data to new ones with limited data. The approach considers the similarities not only between the autoregressive coefficients of different sensors, but also between the temporal correlation patterns. A control chart is then proposed for monitoring the newly deployed sensors sequentially based on the generalized likelihood ratio. Extensive simulation results and a real data example of landslide monitoring demonstrate the effectiveness of our proposed method.
CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation

Brain lesion segmentation provides a valuable tool for clinical diagnosis, and convolutional neural networks (CNNs) have achieved unprecedented success in the task. Data augmentation is a widely used strategy that improves the training of CNNs, and the design of the augmentation method for brain lesion segmentation is still an open problem. In this work, we propose a simple data augmentation approach, dubbed as CarveMix, for CNN-based brain lesion segmentation. Like other "mix"-based methods, such as Mixup and CutMix, CarveMix stochastically combines two existing labeled images to generate new labeled samples. Yet, unlike these augmentation strategies based on image combination, CarveMix is lesion-aware, where the combination is performed with an attention on the lesions and a proper annotation is created for the generated image. Specifically, from one labeled image we carve a region of interest (ROI) according to the lesion location and geometry, and the size of the ROI is sampled from a probability distribution. The carved ROI then replaces the corresponding voxels in a second labeled image, and the annotation of the second image is replaced accordingly as well. In this way, we generate new labeled images for network training and the lesion information is preserved. To evaluate the proposed method, experiments were performed on two brain lesion datasets. The results show that our method improves the segmentation accuracy compared with other simple data augmentation approaches.
Uncertainty-Aware Clustering for Unsupervised Domain Adaptive Object Re-Identification

Unsupervised Domain Adaptive (UDA) object re-identification (Re-ID) aims at adapting a model trained on a labeled source domain to an unlabeled target domain. State-of-the-art object Re-ID approaches adopt clustering algorithms to generate pseudo-labels for the unlabeled target domain. However, the inevitable label noise caused by the clustering procedure significantly degrades the discriminative power of Re-ID model. To address this problem, we propose an uncertainty-aware clustering framework (UCF) for UDA tasks. First, a novel hierarchical clustering scheme is proposed to promote clustering quality. Second, an uncertainty-aware collaborative instance selection method is introduced to select images with reliable labels for model training. Combining both techniques effectively reduces the impact of noisy labels. In addition, we introduce a strong baseline that features a compact contrastive loss. Our UCF method consistently achieves state-of-the-art performance in multiple UDA tasks for object Re-ID, and significantly reduces the performance gap between unsupervised and supervised Re-ID. In particular, the performance of our unsupervised UCF method in the MSMT17 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\to$</tex-math></inline-formula> Market1501 task is better than that of the fully supervised setting on Market1501. The code of UCF is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Wang-pengfei/UCF</uri> .
Code Smells Detection Using Artificial Intelligence Techniques: A Business-Driven Systematic Review

Context Code smells in the software systems are indications that usually correspond to deeper problems that can negatively influence software quality characteristics. This review is a part of a R&D project aiming to improve the existing codebeat platform that help developers to avoid code smells and deliver quality code. Objective This study aims to identify and investigate the current state of the art with respect to: (1) predictors used in prediction models to detect code smells, (2) machine learning/artificial intelligence (ML/AI) methods used in prediction models to detect code smells, (3) code smells analyzed in scientific literature. Our secondary objectives were to identify (4) data sets and projects used in research papers to predict code smells, (5) performance measures used to assess prediction models and (6) improvement ideas with regard to code smell detection using ML/AI. Method We conducted a systematic review using a database search in Scopus and evaluated it using the quasi-gold standard procedure to identify relevant studies. In the data sheet used to obtain data from publications we factor research questions into finer-grained ones, which are then answered on a per-publication basis. Those are then merged over a set of publications using an automated script to obtain answers to the posed research questions. Results We have identified 45 primary studies relevant to the primary objectives of this research. The results show the prediction capability of the ML/AI techniques for predicting code smells. Conclusion Only a few smells—Blob, Feature Envy, Long Method and Data Class—have received the vast majority of interest in research community. The usage of deep learning techniques is increasing. Most researchers still use source code metrics as predictors. Precision, recall and F-measure are the go-to performance metrics. There seems to be a need for modern reference data/projects sets that reflect modern constructs of programming languages. We identified various promising paths of research that have the potential to advance the state of the art in the area of code smells prediction.
BIoT: Blockchain based Smart Agriculture with Internet of Thing

Despite what the media can have us to think, blockchain has a broad range of applications outside of cryptocurrencies. From health resources to regulation, real estate, and finance, innovation is poised to transform a wide variety of industries. In any case, agriculture is a little-studied industry that blockchain might completely transform. Our general public is supported by this massive sector, which does not be as appealing as banking or medical facilities. More importantly, it has a slew of issues that need to be addressed right away. With the use of information technologies, we expect the agricultural sector can become more secure, transparent, trustworthy, and constructive. The proposed model combines AI, IoT, and Blockchain to develop a smart and futuristic agricultural system that provides farmers with a secure and open transaction approach to rich, new, and effective decision support that can specifically create the scope of greater agricultural process productivity and help farmers maximize their benefits. And we believe that the agricultural sector will benefit from the benefits of information technology.
Developing instructional technology standards for educators: A design-based research study

Digital technologies are key tools that can be used to extend and enhance teaching and learning. However, empirical evidence show educators are unclear how to integrate technology and it is often used to support past teaching practices. Designbased research (DBR) involving a grounded theory design was used to construct a set of seven detailed education technology standards to provide guidance on how technology should be integrated and a set of 24 examples as indicators of these standards. This DBR mixed methods approach involved 2429 participants and gathered data from focus groups, surveys, and interviews. The DBR involved two macro cycles of design, implementation, analysis, and revision within the development of the standards. These standards are unique as they are the first empirically developed instructional technology standards for educators.
Privacy preservation in Distributed Deep Learning: A survey on Distributed Deep Learning, privacy preservation techniques used and interesting research directions

Distributed or Collaborative Deep Learning, has recently gained more recognition due to its major advantage of allowing two or more learning participants to contribute and enjoy better accuracy from large and varied training datasets. Despite this advantage, it also presents key privacy issues that have to be managed. In this survey paper, an overview of Distributed or Collaborative Deep Learning has been presented. We first classify Collaborative or Distributed Deep Learning into Direct, Indirect and Peer-to-peer approaches and indicate some of their related privacy issues. We then discuss general cryptographic algorithms and other techniques that can be used for privacy preservation and also indicate their advantages and disadvantages in the Distributed Deep Learning setting. Furthermore, some fundamental theories employed in this area of research have been presented which paves the way for a comprehensive review and comparison of existing privacy approaches, most of which are based on Homomorphic Encryption. Finally, we highlight some challenges in this research domain and propose future directions. Our work reveals the following: Collaborative Deep Learning is more associated with the training stage of Deep Learning than the inference stage. Homomorphic Encryption provides a good approach for preserving the privacy of training datasets in the Collaborative Deep Learning and can become more popular if some problems associated with its use such as increased communication and computation costs are brought low. Privacy preservation in the Collaborative Deep Learning has great future prospects and attempts should be made towards providing more quantum robust and collusion resistant solutions.
Confidence Guided Network For Atmospheric Turbulence Mitigation

Atmospheric turbulence can adversely affect the quality of images or videos captured by long range imaging systems. Turbulence causes both geometric and blur distortions in images which in turn results in poor performance of the subsequent computer vision algorithms like recognition and detection. Existing methods for atmospheric turbulence mitigation use registration and deconvolution schemes to remove degradations. In this paper, we present a deep learning-based solution in which Effective Nearest Neighbors (ENN) based method is used for registration and an uncertainty-based network is used for restoration. We perform qualitative and quantitative comparisons using synthetic and real-world datasets to show the significance of our work.
Towards protecting organisations’ data by preventing data theft by malicious insiders

Purpose One of the most important Information Security (IS) concerns nowadays is data theft or data leakage. To mitigate this type of risk, organisations use a solid infrastructure and deploy multiple layers of security protection technology and protocols such as firewalls, VPNs and IPsec VPN. However, these technologies do not guarantee data protection, and especially from insiders. Insider threat is a critical risk that can cause harm to the organisation through data theft. The main purpose of this study was to investigate and identify the threats related to data theft caused by insiders in organisations and explore the efforts made by them to control data leakage. Design/methodology/approach The study proposed a conceptual model to protect organisations’ data by preventing data theft by malicious insiders. The researchers conducted a comprehensive literature review to achieve the objectives of this study. The collection of the data for this study is based on earlier studies conducted by several researchers from January 2011 to December 2020. All the selected literature is from journal articles, conference articles and conference proceedings using various databases. Findings The study revealed three main findings: first, the main risks inherent in data theft are financial fraud, intellectual property theft, and sabotage of IT infrastructure. Second, there are still some organisations that are not considering data theft by insiders as being a severe risk that should be well controlled. Lastly, the main factors motivating the insiders to perform data leakage activities are financial gain, lack of fairness and justice in the workplace, the psychology or characteristics of the insiders, new technologies, lack of education and awareness and lack of management tools for understanding insider threats. Originality/value The study provides a holistic view of data theft by insiders, focusing on the problem from an organisational point of view. Organisations can therefore take into consideration our recommendations to reduce the risks of data leakage by their employees.
Lasagne: A Multi-Layer Graph Convolutional Network Framework via Node-aware Deep Architecture

Graph convolutional networks (GCNs) have been successfully applied in many different real-world tasks. However, most of the existing methods are based on shallow GCN, because multiple layers involve long-distance neighborhood information but lead to the over-smoothing problem. Actually, a similar challenge exists in the depth limitation for primitive convolutional neural networks (CNNs). As the multi-layer architecture can increase the representation ability of GCN, we study and learn from the recent progress in CNN and propose Lasagne, a novel multi-layer GCN framework, empowered by node-aware layer aggregators and factorization-based layer interactions to overcome the over-smoothing problem and realize the full potentials of the GCN model. We analyze how the node locality affects the information propagation in GCN and propose a novel node aggregation mechanism in an adaptive manner. We further demystify Lasagne from a mutual information view and evaluate it on both real-world benchmark data sets and large-scale industrial production data sets. Lasagne shows strong empirical performance on the semi-supervised node classification task and outperforms the state-of-the-art methods without considering the node locality.
Predicting Vulnerability Type in Common Vulnerabilities and Exposures (CVE) Database with Machine Learning Classifiers

Vulnerability type is not part of the standard CVE scheme so the ability to determine it only on the basis of text description would be a very useful for automated vulnerability handling. The growing number of hardware and software vulnerabilities discovered every year makes it more difficult for manual classification of the vulnerabilities types. This justifies the need for automatic machine learning classification. In this study we research the performance of base ML classifier algorithms, such as Linear Support Vector Classification, Naive Bayes, and Random Forest Classifier. To measure the performance of our classifiers, we use precision, recall, and f1-score evaluation metrics. Previous studies have focused on machine learning methods predicting platform vendor and products, vulnerability scoring, software vulnerabilities exploitation. Our study aims to show that machine learning is suitable for automated vulnerability type classification.
Deep Affine Motion Compensation Network for Inter Prediction in VVC

In video coding, it is a challenge to deal with scenes with complex motions, such as rotation and zooming. Although affine motion compensation (AMC) is employed in Versatile Video Coding (VVC), it is still difficult to handle non-translational motions due to the adopted hand-craft block-based motion compensation. In this paper, we propose a deep affine motion compensation network (DAMC-Net) for inter prediction in video coding to effectively improve the prediction accuracy. To the best of our knowledge, our work is the first attempt to deal with the deformable motion compensation based on CNN in VVC. Specifically, a deformable motion-compensated prediction (DMCP) module is proposed to compensate the current encoding block through a learnable way to estimate accurate motion fields. Meanwhile, the spatial neighboring information and the temporal reference block as well as the initial motion field are fully exploited. By effectively fusing the multi-channel feature maps from DMCP, an attention-based fusion and reconstruction (AFR) module is designed to reconstruct the output block. The proposed DAMC-Net is integrated into VVC and the experimental results demonstrate that the proposed method considerably enhances the coding performance.
Modified lightning search algorithm for optimization

This manuscript proposes a new metaheuristic optimization algorithm for solving the problems related to constraint optimization. The proposed algorithm is based on a nature-inspired algorithm like a lightning search algorithm and differential evolution algorithm. The proposed algorithm consists of two stages. In the first/exploitation/local stage, the fitness values are calculated by using a lightning search algorithm, the inclusion of weight factor in the lightning search algorithm, and differential evolution algorithm using the same population. In the second/global/exploration stage, the minimum value of fitness for each row population by the implementation of the above three algorithms is updated with the population. The first and second stage continues until certain criteria are achieved. The final minimum value is the optimized result. Validation of the proposed methodology is performed by using 60 benchmark functions. The proposed methodology is compared with seven other well-known algorithms namely the lightning search algorithm, differential evolution algorithm cuckoo search algorithm, particle swarm optimization algorithm, seagull optimization algorithm, chimp optimization algorithm, and mayfly algorithm by computing mean, standard deviation, best and worst value for 60 benchmark functions. The proposed methodology is also validated by implementing it in seven real-time constraint optimized problems. Furthermore, the efficiency of the proposed technique has been validated statistically for unimodal and multimodal test functions. The results show a better performance of the proposed optimized algorithm compared to other algorithms.
Learning-Based Quality Assessment for Image Super-Resolution

Image Super-Resolution (SR) techniques improve visual quality by enhancing the spatial resolution of images. Quality evaluation metrics play a critical role in comparing and optimizing SR algorithms, but current metrics achieve only limited success, largely due to the lack of large-scale quality databases, which are essential for learning accurate and robust SR quality metrics. In this work, we first build a large-scale SR image database using a novel semi-automatic labeling approach, which allows us to label a large number of images with manageable human workload. The resulting SR Image quality database with Semi-Automatic Ratings (SISAR), so far the largest of SR-IQA database, contains 12 600 images of 100 natural scenes. We train an end-to-end Deep Image SR Quality (DISQ) model by employing two-stream Deep Neural Networks (DNNs) for feature extraction, followed by a feature fusion network for quality prediction. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics and achieves promising generalization performance in cross-database tests. The SISAR database and DISQ model will be made publicly available to facilitate reproducible research.
A bibliometric analysis of mobile learning in the education sector

Purpose This study aims to contribute to literature on mobile learning (m-learning) by proposing four research clusters whereby scholars can expand m-learning research to facilitate effective learning experiences for students. Design/methodology/approach This study reviews student-centric literature on m-learning since 2010 and presents insights on m-learning while applying well-established bibliometric techniques. Consequently, 722 articles published in the past decade were evaluated by identifying key research areas, most influential authors, countries, journals and organisations. Most influential studies based on number of citations were also examined. Findings Through article co-citation analysis, four clusters representing m-learning literature were identified: concept of m-learning, application of m-learning in education, designing framework for model learning/acceptance and emerging technologies. Originality/value As mobile learning (m-learning) has undergone an evolution from being an emerging field to a significant teaching and research tool, it is pertinent to explore and identify the trends of m-learning research.
Explaining mispredictions of machine learning models using rule induction

While machine learning (ML) models play an increasingly prevalent role in many software engineering tasks, their prediction accuracy is often problematic. When these models do mispredict, it can be very difficult to isolate the cause. In this paper, we propose a technique that aims to facilitate the debugging process of trained statistical models. Given an ML model and a labeled data set, our method produces an interpretable characterization of the data on which the model performs particularly poorly. The output of our technique can be useful for understanding limitations of the training data or the model itself; it can also be useful for ensembling if there are multiple models with different strengths. We evaluate our approach through case studies and illustrate how it can be used to improve the accuracy of predictive models used for software engineering tasks within Facebook.
Few-Shot Segmentation With Optimal Transport Matching and Message Flow

We tackle the challenging task of few-shot segmentation in this work. It is essential for few-shot semantic segmentation to fully utilize the support information. Previous methods typically adopt masked average pooling over the support feature to extract the support clues as a global vector, usually dominated by the salient part and lost certain essential clues. In this work, we argue that every support pixel’s information is desired to be transferred to all query pixels and propose a Correspondence Matching Network (CMNet) with an Optimal Transport Matching module to mine out the correspondence between the query and support images. Besides, it is critical to fully utilize both local and global information from the annotated support images. To this end, we propose a Message Flow module to propagate the message along the inner-flow inside the same image and cross-flow between support and query images, which greatly helps enhance the local feature representations. Experiments on PASCAL VOC 2012, MS COCO, and FSS-1000 datasets show that our network achieves new state-of-the-art few-shot segmentation performance.