Reviewerly/exHarmony huggingface dataset:
HMATC: Hierarchical multi-label Arabic text classification model using machine learning

Multi-label classification assigns multiple labels to each document concurrently. Many real-world classification problems tend to employ high-dimensional label spaces, which can be naturally structured in a hierarchy. In this type of problem, each instance may belong to multiple labels and labels are organized in a hierarchical structure. It presents a more complex problem than flat classification, given that the classification algorithm has to take into account hierarchical relationships between labels and be able to predict multiple labels for the same instance. Few studies have investigated multi-label text classification for the Arabic language. Most of these studies have focused mainly on flat classification and have neglected the hierarchical structure. Therefore, this paper explores the hierarchical multi-label classification in the context of the Arabic language. It proposes a hierarchical multi-label Arabic text classification (HMATC) model with a machine learning approach. The impact of feature selection methods and feature set dimensions on classification performance are also investigated. In addition, the Hierarchy Of Multilabel ClassifiER (HOMER) algorithm is optimized via examination of different sets of multi-label classifiers, clustering algorithms and different numbers of clusters to improve the hierarchical classification. Moreover, this study contributes to existing research by introducing a hierarchical multi-label Arabic dataset in an appropriate format for hierarchical classification and making it publicly available. The results reveal that the proposed model outperforms all models considered in the experiments in terms of the computational cost, which consumed less cost (2 h) compared with other evaluated models. In addition, it shows a significant improvement compared with the state-of-the-art model (Fatwa model) in terms of Hamming loss (0.004), hierarchical loss (1.723), multi-label accuracy (0.758), subset accuracy (0.292), micro-averaged precision (0.879), micro-averaged recall (0.828), and micro-averaged F-measure (0.853).
BIM based cyber-physical systems for intelligent disaster prevention

The cyber-physical systems (CPS) has emerged in the last decade and become one of the most cutting-edge research fields. CPS is a highly integrated system of communication, computing, and control strategies. Although CPS has been studied and applied in some fields such as intelligent transportation, intelligent manufacturing, intelligent buildings, and smart cities, there is a lack of research on CPS based intelligent disaster prevention and mitigation structural system. Because structural disaster prevention and reduction have their own characteristics, it is necessary to investigate CPS based technology and system for intelligent structural disaster prevention and reduction. In this paper, a preliminary study is conducted on cyber-physical based intelligent structural disaster prevention technologies and structural systems using the Building Information Model (BIM) platform. The proposed CPS consists of the multi-source data precision sensing and intelligent control technology in the physical subsystem/layer, reliable data transmission network in the communication subsystem/layer, and real-time identification algorithm of structural state and adaptive control strategy in the information subsystem/layer. Moreover, BIM platform and Internet of things (IoT) technology are adopted for constructing the cloud architecture of CPS to achieve a high degree of integration of monitoring, identification and control, and to improve the real-time and accurate intelligent monitoring of structural disaster prevention and mitigation.
Omnisupervised Omnidirectional Semantic Segmentation

Modern efficient Convolutional Neural Networks (CNNs) are able to perform semantic segmentation both swiftly and accurately, which covers typically separate detection tasks desired by Intelligent Vehicles (IV) in a unified way. Most of the current semantic perception frameworks are designed to work with pinhole cameras and benchmarked against public datasets with narrow Field-of-View (FoV) images. However, there is a large accuracy downgrade when a pinhole-yielded CNN is taken to omnidirectional imagery, causing it unreliable for surrounding perception. In this paper, we propose an omnisupervised learning framework for efficient CNNs, which bridges multiple heterogeneous data sources that are already available in the community, bypassing the labor-intensive process to have manually annotated panoramas, while improving their reliability in unseen omnidirectional domains. Being omnisupervised, the efficient CNN exploits both labeled pinhole images and unlabeled panoramas. The framework is based on our specialized ensemble method that considers the wide-angle and wrap-around features of omnidirectional images, to automatically generate panoramic labels for data distillation. A comprehensive variety of experiments demonstrates that the proposed solution helps to attain significant generalizability gains in panoramic imagery domains. Our approach outperforms state-of-the-art efficient segmenters on highly unconstrained IDD20K and PASS datasets.
A Resampling Method for Imbalanced Datasets Considering Noise and Overlap

If there is a bias in the number of instances that make up the class in a dataset, the predicted results will be affected when applied to machine learning as training data. A method called resampling, which adjusts the number of majority and minority instances, is usually used to solve the imbalance in training data. Although resampling can eliminate imbalances, it may cause data complexity that deteriorates classification accuracy. Noise and overlap are well-known factors of data complexity. Noise is mixture of instances with features that can be classified into other classes at the time of training, and overlap represents the state in which classes cannot be linearly separated because they partially overlap each other. However, conventional methods could not consider these factors at a time, so that their classification accuracy would be not praiseworthy. In order to deal with both noise and overlap, we just need to integrate each of the methods that can deal with them. We know that there have already been established the methods to deal with each problem; however a simple integration of them may remove instances from the dataset that do not need to be removed, or may leave ones that should be removed. Therefore, we have to quantify these factors to take into account for data complexity, and have to consider more effective ways of their integration. In this paper, we propose a method for integrating well-known two resampling methods, which are called SMOTE-ENN and SMOTE-Tomek. In four out of ten datasets, our experimental result showed that our method is effective compared with the latest conventional methods.
BiCF: Learning Bidirectional Incongruity-Aware Correlation Filter for Efficient UAV Object Tracking

Correlation filters (CFs) have shown excellent performance in unmanned aerial vehicle (UAV) tracking scenarios due to their high computational efficiency. During the UAV tracking process, viewpoint variations are usually accompanied by changes in the object and background appearance, which poses a unique challenge to CF-based trackers. Since the appearance is gradually changing over time, an ideal tracker can not only forward predict the object position but also backtrack to locate its position in the previous frame. There exist response-based errors in the reversibility of the tracking process containing the information on the changes in appearance. However, some existing methods do not consider the forward and backward errors based on while using only the current training sample to learn the filter. For other ones, the applicants of considerable historical training samples impose a computational burden on the UAV. In this work, a novel bidirectional incongruity-aware correlation filter (BiCF) is proposed. By integrating the response-based bidirectional incongruity error into the CF, BiCF can Efficiently learn the changes in appearance and suppress the inconsistent error. Extensive experiments on 243 challenging sequences from three UAV datasets (UAV123, UAVDT, and DTB70) are conducted to demonstrate that BiCF favorably outperforms other 25 state-of-the-art trackers and achieves a real-time speed of 45.4 FPS on a single CPU, which can be applied in UAV Efficiently.
Characterizing and Identifying Composite Refactorings

Refactoring consists of a transformation applied to improve the program internal structure, for instance, by contributing to remove code smells. Developers often apply multiple interrelated refactorings called composite refactoring. Even though composite refactoring is a common practice, an investigation from different points of view on how composite refactoring manifests in practice is missing. Previous empirical studies also neglect how different kinds of composite refactorings affect the removal, prevalence or introduction of smells. To address these matters, we provide a conceptual framework and two heuristics to respectively characterize and identify composite refactorings within and across commits. Then, we mined the commit history of 48 GitHub software projects. We identified and analyzed 24,911 composite refactorings involving 104,505 single refactorings. Amongst several findings, we observed that most composite refactorings occur in the same commit and have the same refactoring type. We found that several refactorings are semantically related to each other, which occur in different parts of the system but are still related to the same task. Our study is the first to reveal that many smells are introduced in a program due to "incomplete" composite refactorings. Our study is also the first to reveal 111 patterns of composite refactorings that frequently introduce or remove certain smell types. These patterns can be used as guidelines for developers to improve their refactoring practices as well as for designers of recommender systems.
Developing ML/DL Models

Artificial Intelligence is becoming increasingly popular with organizations due to the success of Machine Learning and Deep Learning techniques. Using these techniques, data scientists learn from vast amounts of data to enhance behaviour in software-intensive systems. Despite the attractiveness of these techniques, however, there is a lack of systematic and structured design process for developing ML/DL models. The study uses a multiple-case study approach to explore the different activities and challenges data scientists face when developing ML/DL models in software-intensive embedded systems. In addition, we have identified seven different phases in the proposed design process leading to effective model development based on the case study. Iterations identified between phases and events which trigger these iterations optimize the design process for ML/DL models. Lessons learned from this study allow data scientists and engineers to develop high-performance ML/DL models and also bridge the gap between high demand and low supply of data scientists.
Inference Under Information Constraints II: Communication Constraints and Shared Randomness

A central server needs to perform statistical inference based on samples that are distributed over multiple users who can each send a message of limited length to the center. We study problems of distribution learning and identity testing in this distributed inference setting and examine the role of shared randomness as a resource. We propose a general-purpose simulate-and-infer strategy that uses only private-coin communication protocols and is sample-optimal for distribution learning. This general strategy turns out to be sample-optimal even for distribution testing among private-coin protocols. Interestingly, we propose a public-coin protocol that outperforms simulate-and-infer for distribution testing and is, in fact, sample-optimal. Underlying our public-coin protocol is a random hash that when applied to the samples minimally contracts the chi-squared distance of their distribution to the uniform distribution.
Cooperative Multi-Robot Navigation in Dynamic Environment with Deep Reinforcement Learning

The challenges of multi-robot navigation in dynamic environments lie in uncertainties in obstacle complexities, partially observation of robots, and policy implementation from simulations to the real world. This paper presents a cooperative approach to address the multi-robot navigation problem (MRNP) under dynamic environments using a deep reinforcement learning (DRL) framework, which can help multiple robots jointly achieve optimal paths despite a certain degree of obstacle complexities. The novelty of this work includes threefold: (1) developing a cooperative architecture that robots can exchange information with each other to select the optimal target locations; (2) developing a DRL based framework which can learn a navigation policy to generate the optimal paths for multiple robots; (3) developing a training mechanism based on dynamics randomization which can make the policy generalized and achieve the maximum performance in the real world. The method is tested with Gazebo simulations and 4 differential drive robots. Both simulation and experiment results validate the superior performance of the proposed method in terms of success rate and travel time when compared with the other state-of-art technologies.
New states of emergency: normalizing techno-surveillance in the time of COVID-19

The coronavirus has prompted states to hastily embrace sophisticated yet questionable digital surveillance tools such as contact tracing apps, turning once again to technologies as political quick fixes and rapid policy responses to the crisis. Yet, such apps raise serious concerns related to widespread surveillance, the outsourcing of expertise and sensitive data to tech giants, and the infringement of citizens' rights. The article aims to explore the state-of-play with respect to deploying tracking apps at a large scale, including potential misuses, harmful effects, and function creep leading to radical forms of state-corporate techno-surveillance. Whereas tracking apps represent a critical experiment for the role technology will play in tackling future pandemics, scepticism should surround techno-solutionism when it comes to complex problems. Hyped, and sometimes ineffective, technological silver bullets mobilized during the coronavirus state of emergency should trigger the careful assessment of the trade-offs between democratic principles and technologically mediated emergency politics.
Federated Data Cleaning: Collaborative and Privacy-Preserving Data Cleaning for Edge Intelligence

As an important driving factor of emerging Internet-of-Things (IoT) applications, machine learning algorithms are currently facing the challenge of how to “clean” data noise, that is introduced during the training process (e.g., asynchronous execution and lossy data compression and quantization). In an attempt to guarantee data quality, various data cleaning approaches have been proposed to filter out abnormal data entries based on the global data distribution. However, most existing data cleaning approaches are based on a centralized paradigm and thus cannot be applied to future edge-based IoT applications, where each edge node (EN) has only a limited view of the global data distribution. Moreover, the increasing demand for privacy preservation largely prevents ENs from combining their data for centralized cleaning. In this study, we propose a federated data cleaning protocol, coined as FedClean, for edge intelligence (EI) scenarios that is designed to achieve data cleaning without compromising data privacy. More specifically, different ENs first generate Boolean shares of their data and distribute them to two noncolluding servers. These two servers then run the FedClean protocol to privately and efficiently compute the attribute value frequency (AVF) scores of the collected data entries, which are then sorted in ascending order via a bitonic sorting network without revealing their values. As a result, data entries with lower AVF scores are considered as abnormal and filtered out. The security, efficiency, and effectiveness of the proposed approach are then demonstrated via concrete security analysis and comprehensive experiments.
Applying sentiment analysis to automatically classify consumer comments concerning marketing 4Cs aspects

With the rapid development of science and technology, consumers are used to searching online for evaluations before purchasing products. Manufacturers can also utilize such information like users' usage habits, browsed websites, comments, messages, etc. to formulate marketing strategies suitable for their products. Several researches developed opinion mining on predicting the polarity of consumers' comments, but few of them were from marketing point of view. In this regards, this study looks to establish an automated way to collect and analyze consumers' comments in social networks, automatically classify them into marketing 4Cs and non-marketing categories from a large number of consumer comments, and divide the category of marketing 4Cs articles into four types of attribute dimensions to analyze emotional polarity. Based on the marketing theory of 4Cs and LDA topic analysis, this study extracted the characteristic keywords from the collected consumer reviews for corpus classification and sentiment polarity analysis. This study further establishes a feature keyword library for specific fields, hoping to improve the accuracy of sentiment analysis through these keywords, simplify the process of consumers' searches for product evaluations, and facilitate consumers to search for helpful target information.
Managing Cyber Security Risks of the Cyber-Enabled Ship

One aspect of the digital transformation process in the shipping industry, a process often referred to as Shipping 4.0, is the increased digitization of on board systems that goes along with increased automation in and autonomy of the vessel. This is happening by integrating Information Technology with Operation Technology systems that results in Cyber Physical Systems on which the safe operations and sailing of contemporary and future vessels depend. Unavoidably, such highly interconnected and interdependent systems increase the exposure of the vessel’s digital infrastructure to cyber attacks and cyber security risks. In this paper, we leverage the STRIDE and DREAD methodologies to qualitatively and quantitatively assess the cyber risk of Cyber Physical Systems on board digitalized contemporary and future ships. Further, we propose appropriate cyber security baseline controls to mitigate such risks, by applying a systematic approach using a set of criteria that take into account the security requirements; the cyber risks; the possible attacks; and the possibly already existing controls, to select from the list of controls provided in the Industrial Control Systems (ICS) overlay of the NIST Guide to ICS Security. The results are expected to support the decision-making and the design of a security architecture for the cyber-enabled ship.
NDMF: Neighborhood-Integrated Deep Matrix Factorization for Service QoS Prediction

Quality of service (QoS) has been mostly applied to represent non-functional properties of Web services and differentiate those with the same functionality. How to accurately predict service QoS has become a key research topic. Researchers have employed neighborhood information into matrix factorization (MF) for service QoS prediction in recent years. However, they are restricted to traditional matrix factorization that may incur a couple of limitations. 1) Conventional MF for QoS prediction linearly combines the multiplication of the latent feature representation of users and services through inner product, failing to fully capture the implicit features of user and service. 2) Most of approaches integrate user or service neighborhood as heuristics into MF model, where either location context or historical invocation records are used to calculate similar users or services. Nevertheless, combining both of them together in a collaborative way is ignored for neighborhood selection that has yet to be properly explored. To deal with the challenges, we propose a novel approach for service QoS prediction called Neighborhood-integrated Deep Matrix Factorization (NDMF), which integrates user neighborhood selected by a collaborative way into an enhanced matrix factorization model via deep neural network (DNN). We implement a prototype system and conduct extensive experiments on public and real-world large Web service dataset with almost 2,000,000 service invocations called WS-DREAM which is widely used in service QoS prediction. The experimental results demonstrate that our proposed approach significantly outperforms state-of-the-art ones in terms of multiple evaluation metrics.
Image Transformation based Defense Against Adversarial Perturbation on Deep Learning Models

Deep learning algorithms provide state-of-the-art results on a multitude of applications. However, it is also well established that they are highly vulnerable to adversarial perturbations. It is often believed that the solution to this vulnerability of deep learning systems must come from deep networks only. Contrary to this common understanding, in this article, we propose a non-deep learning approach that searches over a set of well-known image transforms such as Discrete Wavelet Transform and Discrete Sine Transform, and classifying the features with a support vector machine-based classifier. Existing deep networks-based defense have been proven ineffective against sophisticated adversaries, whereas image transformation-based solution makes a strong defense because of the non-differential nature, multiscale, and orientation filtering. The proposed approach, which combines the outputs of two transforms, efficiently generalizes across databases as well as different unseen attacks and combinations of both (i.e., cross-database and unseen noise generation CNN model). The proposed algorithm is evaluated on large scale databases, including object database (validation set of ImageNet) and face recognition (MBGC) database. The proposed detection algorithm yields at-least 84.2% and 80.1% detection accuracy under seen and unseen database test settings, respectively. Besides, we also show how the impact of the adversarial perturbation can be neutralized using a wavelet decomposition-based filtering method of denoising. The mitigation results with different perturbation methods on several image databases demonstrate the effectiveness of the proposed method.