Reviewerly/exHarmony huggingface dataset:
CAVP: A context-aware vulnerability prioritization model

With the growing number of vulnerabilities while attacks are becoming more advanced, known software security vulnerabilities need to be managed more efficiently with prioritization and contextualization. The current industry standard approach towards vulnerability management at a large scale is limited for two reasons. First, it does not automatically capture the temporal characteristics of the Common Vulnerability Exposures (CVEs) (i.e., how CVEs change over time). Second, it requires manual labor to prioritize identified vulnerabilities. To address these limitations, this research designs a context-aware vulnerability prioritization (CAVP) model to calculate temporal-enabled vulnerability scores of CVEs and prioritize these vulnerabilities visually. The CAVP model includes an enhanced Context-Aware Vulnerability Scoring System (CAVSS) that automatically derives temporal metric values of CVEs through a set of expert-validated heuristic rules. The CAVP model is the first attempt to provide a step-by-step process of vulnerability prioritization that can be integrated within the risk management workflow of an organization. The implementation of the CAVP model in two organizations validates its usefulness.
Quantitative prediction of prospectivity for Pb–Zn deposits in Guangxi (China) by back-propagation neural network and fuzzy weights-of-evidence modelling

One significant geochemical data processing aim is to delineate anomalies associated with mineral deposits. In areas with strong surface weathering, the accumulation centres of surface geochemical anomalies are often not completely matched with locations of mineral deposits. This affects anomaly interpretation and mineral prospectivity prediction. In order to solve this challenging problem, quantitative prediction of mineral prospectivity based on multi-information fusion techniques has been one of the research hotspots in the field of data analysis in recent years. This study first summarized the geological background and metallogenic control factors of each tectonic unit in Guangxi, and then analysed the relationship between Pb–Zn deposits and Pb–Zn geochemical anomalies from 60 767 geochemical stream sediment samples. Based on the re-classified geochemical element contents, gravity, aeromagnetic data and fault, magmatic rock, magmatic rock and fault intersection buffer data as input layers, together with 302 Pb–Zn ore occurrences selected as training data sets, quantitative prediction of prospectivity for Pb–Zn ore deposits in the study area was carried out using back-propagation neural network and fuzzy weights-of-evidence methods. It was found that the Pb–Zn mineral prospectivity prediction areas based on multi-information fusion techniques can eliminate effectively the influence of secondary accumulation of elements during weathering of carbonate rocks on the recognition of deposit-associated stream sediment geochemical anomalies, and identify effectively the mineral resources closely related to rock mass and structure distribution. These analyses reveal the metallogenic regularity of Pb–Zn deposits from the perspective of data mining based on machine learning and geographical information system multi-information fusion for delineation of prospective metallogenic target areas. The purpose here was to provide new ideas for reducing the effects of secondary weathering of extensive carbonate rocks in Guangxi, and in other regions with similar landscapes, on mineral prospectivity prediction. Thematic collection: This article is part of the Applications of innovations in geochemical data analysis collection available at: https://www.lyellcollection.org/cc/applications-of-innovations-in-geochemical-data-analysis
PUNet: Novel and efficient deep neural network architecture for handwritten documents word spotting

Ancient scripts, documents and photos are valuable supports that opened a window to humanity’s past, its social and cultural treasure. However, due to the inevitable deterioration, seeking information in these documents remains a key challenge. Word Spotting remains an attractive alternative to Optical Character Recognition systems for information retrieval, especially in the case of old documents. The quantity of research work on this particular topic continues growing by extracting relevant words as an alternative method of recognizing text content. Here, we propose a new robust Deep Learning based framework for data exploration of ancient documents by applying Transfer Learning from the U-Net Network and using recent Pyramidal Histogram of Character Encryption: no special preprocessing or Data augmentation optimizers are required. Experimental results reveal its accuracy and high capacity to well classify words of various handwritten datasets.
MMSUM Digital Twins: A Multi-view Multi-modality Summarization Framework for Sporting Events

Sporting events generate a massive amount of traffic on social media with live moment-to-moment accounts as any given situation unfolds. The generated data are intensified by fans feelings, reactions, and subjective opinions towards what happens during the event, all of which are based on their individual points of view. Analyzing and summarizing this data will generate a comprehensive overview of the event in terms of how the event evolves and how fans react and view the event based on their perspectives. Previously, most of the summarization works ignore fan reactions and subjective opinions, and focus primarily on generating an objective-view summary. We believe that an effective and useful summary should consider human reactions, sentiment, and point of view, as opposed to simply describing what happens during the event. Accordingly, in this work, we propose MMSUM Digital Twins: a summarization framework that is capable of generating a multi-view multi-modal summary for sporting events in real-time. The proposed digital twins-based framework consists of four main components: sub-event recognition which detects the event’s key moments, tweet categorization, which determines which team the tweets’ writers support and assigns tweets to their teams, sentiment analysis to track fans’ state of mind, and image popularity prediction for selecting representative images. Furthermore, the MMSUM employs a visual-filtering model to address the issue of noisy images that inundate social media, compromising the summarization quality. We leverage the knowledge of sport fans to evaluate the generated multi-view summarization through an online user study. The experiment results confirm the effectiveness of our proposed approach for summarizing sporting events by considering multimedia data, sentiment, and subjective views of the event.
GAN-MVAE: A discriminative latent feature generation framework for generalized zero-shot learning

Generalized zero-shot learning (GZSL) is a challenging task that aims to recognize both seen and unseen classes. It is achieved by transferring knowledge from seen classes to unseen classes via a shared semantic space (e.g. attribute space). Recently, Generative adversarial network (GAN) have gained considerable attention in GZSL. GAN can generate missing unseen classes samples from class-specific semantic embedding for training, thereby transforming GZSL into a traditional classification task and achieving impressive results. However, due to the instability during training and the complexity of data distribution, a simple GAN framework cannot capture the real data distribution perfectly, and there is still a large gap between the generated and real sample distributions, which severely limits the performance of GZSL. Therefore, the proposed GAN-MVAE further aligns the real and generated samples by mapping them into the latent space of multi-modal reconstruction variational autoencoder (MVAE), while preserving discriminative semantic information through cross-modal reconstruction. GAN-MVAE provides some inspiration for the study of multi-modal alignment and asymmetry VAE. Extensive experiments on four GZSL benchmark datasets show that GAN-MVAE significantly outperforms the state of the arts.
An extended digital forensic readiness and maturity model

Digital forensics readiness (DFR) is an important part of the growing forensic domain. Research on DFR has been given little attention, while available DFR models have focused on theoretical investigations with inadequate input from practicing information security experts in the industry. Using feedback from practicing forensic experts in the industry and academia, this research investigates the structure required to implement and manage digital forensic readiness (DFR) within an enterprise. The research extended the DFR Commonalities framework (DFRCF) and utilised the structure to design a digital forensic maturity assessment model (DFMM) that will enable organisations to assess their forensic readiness and security incident responses. A combination of qualitative and research design approaches was utilised to perform a comparative analysis of various DFR frameworks. A top-down design approach was utilised in developing the DFMM model which was validated with forensic practitioners and academics through semi-structured interviews. The structure extracted from DFR frameworks was practical since most participants agreed with the structure of the extended DFRCF and the matrix of the maturity model. Overall, key changes were introduced to enhance both the extended DFRCF and the DFMM. The study was limited to participants who have a forensic footprint and are knowledgeable about DFR. This paper thereby provides practitioners, academics and organisations with access to a non-propriety DFMM maturity model.
Potential effect of SARS-CoV-2 on solar energy generation: Environmental dynamics and implications

The Covid-19 pandemic is exerting a significant influence on global energy markets, and continuing to hinder the growth of core technology for the implementation of renewable forms of energy. With an unprecedented effect, the new coronavirus, known as SARS-CoV2, has succeeded to seize the control of most cities of the world and led to their closure. The newly-emerged virus has also resulted in environmental changes. The present study was conducted to show the indirect positive effects of COVID-19 on the reduction of air pollution, particularly in countries such as Italy, France, and India. Our research proved the existence of meaningful relationships between probable actions, air quality improvement, and increased energy generation by photovoltaic systems (PVs). Newly-obtained data from the Copernicus Sentinel-5P satellite illustrate that some cities have experienced a 45 to 50% reduction in nitrogen dioxide (NO2) concentration compared to the same period in the past year. This reduction has provided two important and unexpected benefits, namely the reduction in environmental pollution (specifically air pollution) and, as a consequence, an increase in the amount of energy generated by PVs.
Diverse Complementary Part Mining for Weakly Supervised Object Localization

Weakly Supervised Object Localization (WSOL) aims to localize objects with only image-level labels, which has better scalability and practicability than fully supervised methods in the actual deployment. However, a common limitation for available techniques based on classification networks is that they only highlight the most discriminative part of the object, not the entire object. To alleviate this problem, we propose a novel end-to-end part discovery model (PDM) to learn multiple discriminative object parts in a unified network for accurate object localization and classification. The proposed PDM enjoys several merits. First, to the best of our knowledge, it is the first work to directly model diverse and robust object parts by exploiting part diversity, compactness, and importance jointly for WSOL. Second, three effective mechanisms including diversity, compactness, and importance learning mechanisms are designed to learn robust object parts. Therefore, our model can exploit complementary spatial information and local details from the learned object parts, which help to produce precise bounding boxes and discriminate different object categories. Extensive experiments on two standard benchmarks demonstrate that our PDM performs favorably against state-of-the-art WSOL approaches.
What’s keeping teens up at night? Reflecting on sleep and technology habits with teens

Abstract Sleep studies suggest that exams, jobs, and technologies keep teens up at night, but little research exists to engage teens in reflecting on their own sleep. We designed a set of cards and a web-based app ‘SleepBeta’ to support reflection by inviting teens to ask questions, explore, track, and experiment with sleep and related technology and lifestyle habits. Through card sorting, we invited teens to identify technology and lifestyle habits they wished to track. SleepBeta let teens track various habits and sleep whilst visualizing interrelationships between these data. Twelve teens and 11 parents participated in interviews before and after a 3-week field trial of SleepBeta. Our findings highlighted four distinct modes of reflection: reflection in preparation, reflection in action, reflection upon revisiting data, and reflection through social interaction. We discuss how our findings provide sensitizing concepts that reframe reflection from a post hoc activity with personal data, to an ongoing process that starts before technologies are used to generate data. We highlight design opportunities for scaffolding reflection in preparation, and we reflect on design choices that give teens control over their data.
Multiscale Emotion Representation Learning for Affective Image Recognition

Recognition of emotions conveyed in images has attracted increasing research attention. Recent studies show that leveraging local affective regions helps to improve the recognition performance. However, these studies do not consider features from the broad context of the local affective regions, which could provide useful information for learning improved emotion representations. In this paper, we present a region-based multiscale network that learns features for the local affective region as well as the broad context for affective image recognition. The proposed network consists of an affective region detection module and a multiscale feature learning module. The class activation mapping method is used to generate pseudo affective regions from a pretrained deep neural network to train the detection module. For the affective region outputted by the detection module, three-scale features are extracted and then encoded by a kernel-based graph attention network for final emotion classification. We show that integrating features from the broad context is effective in improving the recognition performance. We experimentally evaluate the proposed network for both multi-class emotion recognition and binary sentiment classification on different benchmark datasets. The experimental results demonstrate that the proposed network achieves improved or comparable performance as compared to previous state-of-the-art models.
Lie to Me: Shield Your Emotions from Prying Software

Deep learning approaches for facial Emotion Recognition (ER) obtain high accuracy on basic models, e.g., Ekman's models, in the specific domain of facial emotional expressions. Thus, facial tracking of users' emotions could be easily used against the right to privacy or for manipulative purposes. As recent studies have shown that deep learning models are susceptible to adversarial examples (images intentionally modified to fool a machine learning classifier) we propose to use them to preserve users' privacy against ER. In this paper, we present a technique for generating Emotion Adversarial Attacks (EAAs). EAAs are performed applying well-known image filters inspired from Instagram, and a multi-objective evolutionary algorithm is used to determine the per-image best filters attacking combination. Experimental results on the well-known AffectNet dataset of facial expressions show that our approach successfully attacks emotion classifiers to protect user privacy. On the other hand, the quality of the images from the human perception point of view is maintained. Several experiments with different sequences of filters are run and show that the Attack Success Rate is very high, above 90% for every test.
Transformer-Driven Semantic Relation Inference for Multilabel Classification of High-Resolution Remote Sensing Images

It is hard to use a single label to describe an image for the complexity of remote sensing scenes. Thus, it is a more general and practical choice to use multilabel image classification for high-resolution remote sensing (HRS) images. How to construct the relation between categories is a vital problem for multilabel classification. Some researchers use the recurrent neural network (RNN) or long short-term memory (LSTM) to exploit label relations over the last years. However, the RNN or LSTM could model such category dependence in a chain propagation manner. The performance of the RNN/LSTM might be questioned when a specific category is improperly inferred. To address this, we propose a novel HRS image multilabel classification network, transformer-driven semantic relation inference network. The network comprises two modules: semantic sensitive module (SSM) and semantic relation-building module (SRBM). The SSM locates the semantic attentional regions in the features extracted by a deep convolutional neural network and generates a discriminative content-aware category representation (CACR). The SRBM uses label relation inference from outputs of the SSM to predict final results. The characteristic of the proposed method is that it can extract semantic attentional regions relevant to the category and generate a discriminative CACR and natural and interpretable reasoning about label relations. Experiments were performed on the public UCM multilabel and MLRSNet datasets. Quantitative and qualitative analyses on state-of-the-art multilabel benchmarks proved that the proposed method could effectively locate semantic regions and build relationships between categories with better robustness.
CHChain: Secure and parallel crowdsourcing driven by hybrid blockchain

Blockchain-based crowdsourcing solutions have been widely used in the industry to solve insider threats and free-riding/false-reporting issues. However, the existing solutions suffer from three main limitations: 1) inability to concurrently achieve task privacy protection and distributed transparent storage, 2) limited transaction throughput caused by serial transaction processing, and 3) low fault-tolerance ability of their practical byzantine-fault-tolerance (PBFT) consensus protocols. In order to overcome these limitations, this paper proposes a novel hybrid-blockchain crowdsourcing platform named CHChain. The core of CHChain is a hybrid blockchain structure, which achieves distributed and transparent storage while guaranteeing task privacy by isolating the private information of each task (e.g., task response, evaluation, and feedback) into its own private task chain, and recording other common information of tasks (e.g., task ID, reward, and deadline) into a common chain. To improve transaction throughput, we then design a parallel-transaction-processing chaincode (PTPC), a chaincode running on the hybrid blockchain structure, to process transactions in parallel, which is also responsible for evaluating the reputations of platform users. For fault-tolerance enhancement, we finally design a Reputation-based PBFT (R-PBFT) consensus protocol based on the hybrid reputation values of platform users. Extensive experiment results showed that the transaction throughput of CHChain is nearly twice as much as those of the existing solutions. Moreover, the fault tolerance of R-PBFT can be approximately 98%, which is much higher than that of the PBFT, 1/3.
Deep learning based dual encoder retrieval model for citation recommendation

Citation recommendation recommends relevant documents to users based on their inputs and other information. Many traditional citation recommendation models use keywords to describe item attributes and ignore the semantics of sequences, which cause the relevance of the search results unsatisfactory. This paper proposes a deep-learning-based dual encoder retrieval (DER) model, which combines a text representation technique and a sentence pair matching approach, to improve the performance of citation recommendation. First, an input query and paper titles from publication databases are encoded to semantic vectors separately by two deep-learning-based encoders. Second, the semantic vector of the input query is matched with vectors that representing papers in the published databases by the multilayer perceptron approach to compute similarity scores. Finally, a list of documents, which are sorted in descending order of similarity scores, is generated. To validate the effectiveness of the proposed approach, it is compared with five baselines using a citation dataset. The results show that the proposed model achieves the best performance in terms of accuracy, recall, F1-measure, and AUC. In addition, we compare the DER (Glove) model with Google Scholar using a small example of twenty articles. The DER (Glove) model outperformed Google Scholar in seven recommendations, and tied in ten recommendations.
Leveraging Transfer learning techniques- BERT, RoBERTa, ALBERT and DistilBERT for Fake Review Detection

In this era of the internet, the online review system has grown tremendously, where customers share their first-hand experiences about the products or services. These reviews influence the purchasing decision of future customers and have a positive or negative financial impact on businesses. Spam reviews are written with an agenda to promote or demote a business and mislead the customers. Hence to maintain the integrity of the online review system, it is crucial to detect fake reviews. To overcome the limitations of traditional machine learning and neural network − based models, we have leveraged transfer learning and used transformer-based pre-trained models BERT, RoBERTa, ALBERT, and DistilBERT to build fake review classifier. Performance of all the models is evaluated, considering accuracy and weighted F1-source as the primary metric for evaluation. The classifier produced using RoBERTa has outperformed the baseline model in detecting fake reviews
Application of geographically weighted regression (GWR) and singularity analysis to identify stream sediment geochemical anomalies, case study, Takab Area, NW Iran

In geologically and lithologically complex areas, distinguishing weak geochemical anomalies from background and defining a valid threshold value are central issues in mineral exploration. This study benefited from using geographically weighted regression (GWR), ordinary least square regression (OLSR), singularity analysis, and a combined model of GWR and singularity analysis to identify geochemical anomalies related to Pb–Zn mineralization in the Takab area (Iran). In the analyses, major oxides were considered independent geo-variables related to upstream of stream sediment samples and on-site rock types. The results of GWR revealed the non-stationary effect of independent geo-variables on Pb–Zn geochemical anomalies, on local R2 among geo-variables, and on measured values of trace elements, which suggested a meaningful improvement in trace element prediction in comparison with OLSR. The results of singularity model showed that geochemical anomalies were closely coincident with known carbonate-hosted Pb–Zn deposits and with carbonaceous geological units in the Takab area. Anomalies delineated by singularity model illustrated another potential for follow-up mineral exploration. The results of the combined model of GWR and singularity model—based on consideration of Type I and Type II errors—exhibited not only higher coincidence with known mineral deposits but also introduced new potential exploration targets in the Takab area; that is, the combined model led to a more profound recognition of weak anomalies in a geologically and lithologically complex area.