Reviewerly/exHarmony huggingface dataset:
A novel wrapper feature selection algorithm based on iterated greedy metaheuristic for sentiment classification

In recent years, sentiment analysis is becoming more and more important as the number of digital text resources increases in parallel with the development of information technology. Feature selection is a crucial sub-stage for the sentiment analysis as it can improve the overall predictive performance of a classifier while reducing the dimensionality of a problem. In this study, we propose a novel wrapper feature selection algorithm based on Iterated Greedy (IG) metaheuristic for sentiment classification. We also develop a selection procedure that is based on pre-calculated filter scores for the greedy construction part of the IG algorithm. A comprehensive experimental study is conducted on commonly-used sentiment analysis datasets to assess the performance of the proposed method. The computational results show that the proposed algorithm achieves 96.45% and 90.74% accuracy rates on average by using Multinomial Naïve Bayes classifier for 9 public sentiment and 4 Amazon product reviews datasets, respectively. The results also reveal that our algorithm outperforms state-of-the-art results for the 9 public sentiment datasets. Moreover, the proposed algorithm produces highly competitive results with state-of-the-art feature selection algorithms for 4 Amazon datasets.
Generating Fact Checking Explanations

Most existing work on automated fact checking is concerned with predicting the veracity of claims based on metadata, social network spread, language used in claims, and, more recently, evidence supporting or denying claims.A crucial piece of the puzzle that is still missing is to understand how to automate the most elaborate part of the process -generating justifications for verdicts on claims.This paper provides the first study of how these explanations can be generated automatically based on available claim context, and how this task can be modelled jointly with veracity prediction.Our results indicate that optimising both objectives at the same time, rather than training them separately, improves the performance of a fact checking system.The results of a manual evaluation further suggest that the informativeness, coverage and overall quality of the generated explanations are also improved in the multi-task model. Claim:The last major oil spill from a drilling accident in America happened over 40 years ago in 1969.Ruling Comments: (...) The last major oil spill from a drilling accident in America happened over 40 years ago in 1969.(...) The largest in volume was the Santa Barbara spill of 1969 referenced by Murdock and Johnson, in which an estimated 100,000 barrels of oil spilled into the Pacific Ocean, according to the API.The Santa Barbara spill was so big it ranked seventh among the 10 largest oil spills caused by marine well blowouts in the world, the report states.Two other U.S. spills, both in 1970, rank eighth and 10th.Fourteen marine blowouts have taken place in the U.S. between 1969 and 2007.Six of them took place after 1990 and spilled a total of nearly 13,700 barrels.(...) We interviewed three scientists who said that the impact of a spill has little to do with its volume.Scientists have proven that spills far smaller than Santa Barbara's have been devastating.Justification: While the nation's largest oil well blowout did take place in 1969, it's not factually correct to call it the "last major oil spill".First of all, two of the largest blowouts in the world took place in the U. S. the following year.More importantly, experts agree that spills far smaller in volume to the 1969 disaster have been devastating.From a scientific perspective, Johnson's decision to single out the 1969 blowout as the last "major" one makes no sense.
Federated Learning for Breast Density Classification: A Real-World Implementation

Building robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting. Seven clinical institutions from across the world joined this FL effort to train a model for breast density classification based on Breast Imaging, Reporting & Data System (BI-RADS). We show that despite substantial differences among the datasets from all sites (mammography system, class distribution, and data set size) and without centralizing data, we can successfully train AI models in federation. The results show that models trained using FL perform 6.3% on average better than their counterparts trained on an institute's local data alone. Furthermore, we show a 45.8% relative improvement in the models' generalizability when evaluated on the other participating sites' testing data.
Relation-aware Graph Attention Networks with Relational Position Encodings for Emotion Recognition in Conversations

Interest in emotion recognition in conversations (ERC) has been increasing in various fields, because it can be used to analyze user behaviors and detect fake news.Many recent ERC methods use graph-based neural networks to take the relationships between the utterances of the speakers into account.In particular, the state-of-the-art method considers self-and inter-speaker dependencies in conversations by using relational graph attention networks (RGAT).However, graph-based neural networks do not take sequential information into account.In this paper, we propose relational position encodings that provide RGAT with sequential information reflecting the relational graph structure.Accordingly, our RGAT model can capture both the speaker dependency and the sequential information.Experiments on four ERC datasets show that our model is beneficial to recognizing emotions expressed in conversations.In addition, our approach empirically outperforms the state-ofthe-art on all of the benchmark datasets.
An Unsupervised Sentence Embedding Method by Mutual Information Maximization

BERT is inefficient for sentence-pair tasks such as clustering or semantic search as it needs to evaluate combinatorially many sentence pairs which is very time-consuming.Sentence BERT (SBERT) attempted to solve this challenge by learning semantically meaningful representations of single sentences, such that similarity comparison can be easily accessed.However, SBERT is trained on corpus with high-quality labeled sentence pairs, which limits its application to tasks where labeled data is extremely scarce.In this paper, we propose a lightweight extension on top of BERT and a novel self-supervised learning objective based on mutual information maximization strategies to derive meaningful sentence embeddings in an unsupervised manner.Unlike SBERT, our method is not restricted by the availability of labeled data, such that it can be applied on different domain-specific corpus.Experimental results show that the proposed method significantly outperforms other unsupervised sentence embedding baselines on common semantic textual similarity (STS) tasks and downstream supervised tasks.It also outperforms SBERT in a setting where in-domain labeled data is not available, and achieves performance competitive with supervised methods on various tasks.
DISTILLER: Encrypted traffic classification via multimodal multitask deep learning

Traffic classification, i.e. the inference of applications and/or services from their network traffic, represents the workhorse for service management and the enabler for valuable profiling information. The growing trend toward encrypted protocols and the fast-evolving nature of network traffic are obsoleting the traffic-classification design solutions based on payload-inspection or machine learning. Conversely, deep learning is currently foreseen as a viable means to design traffic classifiers based on automatically-extracted features. These reflect the complex patterns distilled from the multifaceted (encrypted) traffic, that implicitly carries information in “multimodal” fashion, and can be also used in application scenarios with diversified network visibility for (simultaneously) tackling multiple classification tasks. To this end, in this paper a novel multimodal multitask deep learning approach for traffic classification is proposed, leading to the Distiller classifier. The latter is able to capitalize traffic-data heterogeneity (by learning both intra- and inter-modality dependencies), overcome performance limitations of existing (myopic) single-modal deep learning-based traffic classification proposals, and simultaneously solve different traffic categorization problems associated to different providers’ desiderata. Based on a public dataset of encrypted traffic, we evaluate Distiller in a fair comparison with state-of-the-art deep learning architectures proposed for encrypted traffic classification (and based on single-modality philosophy). Results show the gains of our proposal over both multitask extensions of single-task baselines and native multitask architectures.
SAFE: Scale-Adaptive Fitness Evaluation Method for Expensive Optimization Problems

The key challenge of expensive optimization problems (EOP) is that evaluating the true fitness value of the solution is computationally expensive. A common method to deal with this issue is to seek for a less expensive surrogate model to replace the original expensive objective function. However, this method also brings in model approximation error. To efficiently solve the EOP, a novel scale-adaptive fitness evaluation (SAFE) method is proposed in this article to directly evaluate the true fitness value of the solution on the original objective function. To reduce the computational cost, the SAFE method uses a set of evaluation methods (EM) with different accuracy scales to cooperatively complete the fitness evaluation process. The basic idea is to adopt the low-accuracy scale EM to fast locate promising regions and utilize the high-accuracy scale EM to refine the solution accuracy. To this aim, two EM switch strategies are proposed in the SAFE method to adaptively control the multiple EMs according to different evolutionary stages and search requirements. Moreover, a neighbor best-based evaluation (NBE) strategy is also put forward to evaluate the solution according to its nearest high-quality evaluated solution, which can further reduce computational cost. Extensive experiments are carried out on the case study of crowdshipping scheduling problem in the smart city to verify the effectiveness and efficiency of the proposed SAFE method, and to investigate the effects of the two EM switch strategies and the NBE strategy. Experimental results show that the proposed SAFE method achieves better solution quality than some baseline and state-of-the-art algorithms, indicating an efficient method for solving EOP with a better balance between solution accuracy and computational cost.
Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review

Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues.
<i>Mo<sup>2</sup>Cap<sup>2</sup> </i>: Real-time Mobile 3D <i>Mo</i>tion <i>Capture</i> with a <i>Cap</i>-mounted Fisheye Camera

We propose the first real-time system for the egocentric estimation of 3D human body pose in a wide range of unconstrained everyday activities. This setting has a unique set of challenges, such as mobility of the hardware setup, and robustness to long capture sessions with fast recovery from tracking failures. We tackle these challenges based on a novel lightweight setup that converts a standard baseball cap to a device for high-quality pose estimation based on a single cap-mounted fisheye camera. From the captured egocentric live stream, our CNN based 3D pose estimation approach runs at 60 Hz on a consumer-level GPU. In addition to the lightweight hardware setup, our other main contributions are: 1) a large ground truth training corpus of top-down fisheye images and 2) a disentangled 3D pose estimation approach that takes the unique properties of the egocentric viewpoint into account. As shown by our evaluation, we achieve lower 3D joint error as well as better 2D overlay than the existing baselines.
Path planning for intelligent robots based on deep Q-learning with experience replay and heuristic knowledge

Path planning and obstacle avoidance are two challenging problems in the study of intelligent robots. In this paper, we develop a new method to alleviate these problems based on deep Q-learning with experience replay and heuristic knowledge. In this method, a neural network has been used to resolve the “ curse of dimensionality ” issue of the Q-table in reinforcement learning. When a robot is walking in an unknown environment, it collects experience data which is used for training a neural network ; such a process is called experience replay. Heuristic knowledge helps the robot avoid blind exploration and provides more effective data for training the neural network. The simulation results show that in comparison with the existing methods, our method can converge to an optimal action strategy with less time and can explore a path in an unknown environment with fewer steps and larger average reward.
Region-Adaptive Dense Network for Efficient Motion Deblurring

In this paper, we address the problem of dynamic scene deblurring in the presence of motion blur. Restoration of images affected by severe blur necessitates a network design with a large receptive field, which existing networks attempt to achieve through simple increment in the number of generic convolution layers, kernel-size, or the scales at which the image is processed. However, these techniques ignore the non-uniform nature of blur, and they come at the expense of an increase in model size and inference time. We present a new architecture composed of region adaptive dense deformable modules that implicitly discover the spatially varying shifts responsible for non-uniform blur in the input image and learn to modulate the filters. This capability is complemented by a self-attentive module which captures non-local spatial relationships among the intermediate features and enhances the spatially varying processing capability. We incorporate these modules into a densely connected encoder-decoder design which utilizes pre-trained Densenet filters to further improve the performance. Our network facilitates interpretable modeling of the spatially-varying deblurring process while dispensing with multi-scale processing and large filters entirely. Extensive comparisons with prior art on benchmark dynamic scene deblurring datasets clearly demonstrate the superiority of the proposed networks via significant improvements in accuracy and speed, enabling almost real-time deblurring.
University students’ digital competence in three areas of the DigCom 2.1 model: A comparative study at three European universities

This study analysed the digital competence of 1,073 students at one Italian and two Spanish universities using the COBADI 2.0 (Basic Digital Competences/Registered Trademark 2970648) questionnaire. A quantitative methodology was applied to university students’ use of, and competence in, three areas of DigCom 2.1: information and data literacy, communication and collaboration, and digital content creation. The results showed that these future graduates had an upper intermediate level of competence in information and digital literacy, and communication and collaboration, but a lower intermediate level in terms of digital content creation, particularly in the creation and dissemination of multimedia content using different tools. Two student profiles were identified for time spent online: those who dedicated a lot of their time to gaming or interacting on social media, and those who used most of their online time to searching for information and completing academic work.
AssemblyNet: A large ensemble of CNNs for 3D whole brain MRI segmentation

Abstract Whole brain segmentation of fine-grained structures using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images. To address this problem, previous DL methods proposed to use a single convolution neural network (CNN) or few independent CNNs. In this paper, we present a novel ensemble method based on a large number of CNNs processing different overlapping brain areas. Inspired by parliamentary decision-making systems, we propose a framework called AssemblyNet, made of two “assemblies" of U-Nets. Such a parliamentary system is capable of dealing with complex decisions, unseen problem and reaching a relevant consensus. AssemblyNet introduces sharing of knowledge among neighboring U-Nets, an “amendment” procedure made by the second assembly at higher-resolution to refine the decision taken by the first one, and a final decision obtained by majority voting. During our validation, AssemblyNet showed competitive performance compared to state-of-the-art methods such as U-Net, Joint label fusion and SLANT. Moreover, we investigated the scan-rescan consistency and the robustness to disease effects of our method. These experiences demonstrated the reliability of AssemblyNet. Finally, we showed the interest of using semi-supervised learning to improve the performance of our method.
AI, Blockchain, and Vehicular Edge Computing for Smart and Secure IoV: Challenges and Directions

Internet of Things (IoT) is turning into an undeniably developing point of discussion in both research and industrial fields. A key area that is witnessing a quick development in the utilization of IoT devices is the Internet of Vehicles (IoV), which allows information exchange among vehicles and infrastructures. Notably, Artificial Intelligence (AI) has been widely adopted for solving challenging vehicular problems and managing the IoV infrastructure. Despite the advantages AI carries for IoV, its deployment can be negatively affected by lacking computation resources and processing unreliable data. On the other hand, Blockchain is a decentralized and distributed peer-to-peer network architecture that can be employed to empower security and resist against undesirable data modification. However, integrating both technologies (i.e., AI and Blockchain) exhausts, even more, the IoV infrastructure. Therefore, we present in this paper an overview discussing the AI and Blockchain approaches and models for IoV and propose a new Vehicular Edge Computing based architecture embedding both technologies and overcoming the aforementioned limitations. We then discuss the main challenges and give notice to the concerned parties and stakeholders about promising directions that arise from enabling the three technologies for providing smart, secure, and efficient IoV.
A deep learning framework for matching of SAR and optical imagery

SAR and optical imagery provide highly complementary information about observed scenes. A combined use of these two modalities is thus desirable in many data fusion scenarios. However, any data fusion task requires measurements to be accurately aligned. While for both data sources images are usually provided in a georeferenced manner, the geo-localization of optical images is often inaccurate due to propagation of angular measurement errors. Many methods for the matching of homologous image regions exist for both SAR and optical imagery, however, these methods are unsuitable for SAR-optical image matching due to significant geometric and radiometric differences between the two modalities. In this paper, we present a three-step framework for sparse image matching of SAR and optical imagery, whereby each step is encoded by a deep neural network. We first predict regions in each image which are deemed most suitable for matching. A correspondence heatmap is then generated through a multi-scale, feature-space cross-correlation operator. Finally, outliers are removed by classifying the correspondence surface as a positive or negative match. Our experiments show that the proposed approach provides a substantial improvement over previous methods for SAR-optical image matching and can be used to register even large-scale scenes. This opens up the possibility of using both types of data jointly, for example for the improvement of the geo-localization of optical satellite imagery or multi-sensor stereogrammetry.
Unified Named Entity Recognition as Word-Word Relation Classification

So far, named entity recognition (NER) has been involved with three major types, including flat, overlapped (aka. nested), and discontinuous NER, which have mostly been studied individually. Recently, a growing interest has been built for unified NER, tackling the above three jobs concurrently with one single model. Current best-performing methods mainly include span-based and sequence-to-sequence models, where unfortunately the former merely focus on boundary identification and the latter may suffer from exposure bias. In this work, we present a novel alternative by modeling the unified NER as word-word relation classification, namely W^2NER. The architecture resolves the kernel bottleneck of unified NER by effectively modeling the neighboring relations between entity words with Next-Neighboring-Word (NNW) and Tail-Head-Word-* (THW-*) relations. Based on the W^2NER scheme we develop a neural framework, in which the unified NER is modeled as a 2D grid of word pairs. We then propose multi-granularity 2D convolutions for better refining the grid representations. Finally, a co-predictor is used to sufficiently reason the word-word relations. We perform extensive experiments on 14 widely-used benchmark datasets for flat, overlapped, and discontinuous NER (8 English and 6 Chinese datasets), where our model beats all the current top-performing baselines, pushing the state-of-the-art performances of unified NER.
Introducing the FAIR Principles for research software

Research software is a fundamental and vital part of research, yet significant challenges to discoverability, productivity, quality, reproducibility, and sustainability exist. Improving the practice of scholarship is a common goal of the open science, open source, and FAIR (Findable, Accessible, Interoperable and Reusable) communities and research software is now being understood as a type of digital object to which FAIR should be applied. This emergence reflects a maturation of the research community to better understand the crucial role of FAIR research software in maximising research value. The FAIR for Research Software (FAIR4RS) Working Group has adapted the FAIR Guiding Principles to create the FAIR Principles for Research Software (FAIR4RS Principles). The contents and context of the FAIR4RS Principles are summarised here to provide the basis for discussion of their adoption. Examples of implementation by organisations are provided to share information on how to maximise the value of research outputs, and to encourage others to amplify the importance and impact of this work.