Reviewerly/exHarmony huggingface dataset:
Multi-view approach to suggest moderation actions in community question answering sites

With thousands of new questions posted every day on popular Q&A websites, there is a need for automated and accurate software solutions to replace manual moderation. In this paper, we address the critical drawbacks of crowdsourcing moderation actions in Q&A communities and demonstrate the ability to automate moderation using the latest machine learning models. From a technical point, we propose a multi-view approach that generates three distinct feature groups that examine a question from three different perspectives: 1) question-related features extracted using a BERT-based regression model; 2) context-related features extracted using a named-entity-recognition model; and 3) general lexical features derived using statistical and analytical methods. As a last step, we train a gradient boosting classifier to predict a moderation action. For evaluation purposes, we created a new dataset consisting of 60,000 Stack Overflow questions classified into three choices of moderation actions. Based on cross-validation on the novel dataset, our approach reaches 95.6% accuracy as a multiclass task and outperforms all state-of-the-art and previously-published models. Our results clearly demonstrate the high influence of our feature generation components on the overall success of the classifier.
The triggers that open the NLP model backdoors are hidden in the adversarial samples

Deep neural networks (DNNS) have been proven to be vulnerable to adversarial attacks. But the adversarial perturbations are generated for specific input samples, and the perturbations of one sample cannot be applied to other samples. In this paper, we propose a method to search for the backdoor of the natural language processing (NLP) model under the black-box condition, and we find that the universal attack triggers exist in the adversarial samples. The method includes two steps. The first step is to extract aggressive words in the adversarial sample to form the adversarial knowledge base under the black-box condition. The second step is to generate universal attack triggers by minimizing the target prediction results of a batch of samples. When we add the generated trigger to any benign input, the prediction accuracy of the DNNS model can be reduced to close to zero. The experimental results show that our method can achieve a high attack success rate with a short trigger (e.g., more than 90% using only a trigger of length 3 when attacking BiLSTM on SST-2). In addition, experiments show that our method has higher transferability. Finally, for the backdoor vulnerabilities in the NLP models, we did two defense experiments: abnormal word detection and word frequency analysis, which improve the NLP model’s ability of resisting backdoor attacks.
Exploiting phase-based motion magnification for the measurement of subtle 3D deformation maps with FP + 2D-DIC

Phase-Based Motion Magnification (PBMM) is an effective methodology to visualise imperceptible phenomena based on magnifying periodic subtle movements on image sequences. Moreover, the integration of the optical techniques Fringe Projection and 2D Digital Image Correlation (FP + 2D-DIC) make it possible to obtain 3D displacement maps employing a single camera. In this study, the integration of PBMM with FP + 2D-DIC is exploited for the determination of subtle 3D displacements. An initial solid-rigid test validates of the combination of those techniques. A later test, employing a cantilever beam, demonstrates its correct performing in the determining of Operational Deflection Shapes. In both cases, the results are compared with those obtained with 3D-DIC and Scanning Laser Doppler Vibrometer (SLDV) to evaluate the relation between the magnification factor and the result obtained. Finally, the potential of the proposed integration for the determination of complex mode shapes is demonstrated by obtaining the ODSs of an industrial component.
Towards Automated Content-based Photo Privacy Control in User-Centered Social Networks

A large number of photos shared online often contain private user information, which can cause serious privacy breaches when viewed by unauthorized users. Thus, there is a need for more efficient privacy control that requires automatic detection of users' private photos. However, the automatic detection of users' private photos is a challenging task, since different users may have different privacy concerns and a generalized one-size-fits-all approach for private photo detection would not be suitable for most users. User-specific detection of private photos should, therefore, be investigated. Furthermore, for effective privacy control, the exact sensitive regions in private photos need to be pinpointed, so that sensitive content can be protected via different privacy control methods. In this paper, we propose a novel system, AutoPri, to enable automatic and user-specific content-based photo privacy control in online social networks. We collect a large dataset of 31, 566 private and public photos from real-world users and present important observations on photo privacy concerns. Our system can automatically detect private photos in a user-specific manner using a detection model based on a multimodal variational autoencoder and pinpoint sensitive regions in private photos with an explainable deep learning-based approach. Our evaluations show that AutoPri can effectively determine user-specific private photos with high accuracy (94.32%) and pinpoint exact sensitive regions in them to enable effective privacy control in user-centered online social networks.
High-resolution rectified gradient-based visual explanations for weakly supervised segmentation

Visual explanations for convolutional neural networks (CNNs) act as the backbone for weakly supervised segmentation with image-level labels. This paper proposes a high-resolution rectified gradient-based class activation mapping with bounding box annotations (bbox) to improve the initial seed for weakly supervised segmentation (WSS) tasks. HRCAM extends Grad-CAM by separating the gradient maps from the class activation maps from the shallow layer for higher resolution. Gradient rectified methods are proposed to improve the visualization and WSS score. Experiments and evaluations are conducted to verify the performance of HRCAM-BB on Pascal VOC 2012 and COCO datasets. On Pascal VOC 2012 set, our method achieves outstanding performance with a mean intersection over union (mIOU) of 71.6 with image-level labels and 78.2 with bbox on WSSS, and increases the WSIS mIOU (AP50) to 52.1 with image-level labels, and 61.9 with bbox. our method surpasses the previous SOTA approach in the same condition.
Sintel: A Machine Learning Framework to Extract Insights from Signals

The detection of anomalies in time series data is a critical task with many monitoring applications. Existing systems often fail to encompass an end-to-end detection process, to facilitate comparative analysis of various anomaly detection methods, or to incorporate human knowledge to refine output. This precludes current methods from being used in real-world settings by practitioners who are not ML experts. In this paper, we introduce Sintel, a machine learning framework for end-to-end time series tasks such as anomaly detection. The framework uses state-of-the-art approaches to support all steps of the anomaly detection process. Sintel logs the entire anomaly detection journey, providing detailed documentation of anomalies over time. It enables users to analyze signals, compare methods, and investigate anomalies through an interactive visualization tool, where they can annotate, modify, create, and remove events. Using these annotations, the framework leverages human knowledge to improve the anomaly detection pipeline. We demonstrate the usability, efficiency, and effectiveness of Sintel through a series of experiments on three public time series datasets, as well as one real-world use case involving spacecraft experts tasked with anomaly analysis tasks. Sintel's framework, code, and datasets are open-sourced at https://github.com/sintel-dev/.
Biometric Identification Based on Keystroke Dynamics

The purpose of the paper is to study how changes in neural network architecture and its hyperparameters affect the results of biometric identification based on keystroke dynamics. The publicly available dataset of keystrokes was used, and the models with different parameters were trained using this data. Various neural network layers-convolutional, recurrent, and dense-in different configurations were employed together with pooling and dropout layers. The results were compared with the state-of-the-art model using the same dataset. The results varied, with the best-achieved accuracy equal to 82% for the identification (1 of 20) task.
Gating augmented capsule network for sequential recommendation

Sequential recommendation has become a popular and indispensable component of various online services, which aims to predict the next interested item based on the sequence of a certain user. To deduce users’ actual interests, sequential recommenders concentrate on analyzing the complex transition dependency from the user’s recent action sequence. The key types of item transition patterns can be generally divided into item-level and factor-level. However, most existing works directly focus on a one-channel chain of interaction sequence, and only capture item co-occurrence patterns from item-level. They neglect the availability of transitions among items’ latent attributes. Toward this end, we propose a Gating Augmented Capsule Network (GAC), which models both personalized item- and factor-level transitions in a fine-grained manner. Specifically, to distill user-specific information, we present a personalized gating module to replace the convolution operation of the traditional capsule network, so as to augment the links between the user and each item. Moreover, we design an item-routing component and a factor-routing component to build a two-channel routing module for capturing item- and factor-level interactions, respectively, while preserving the relative order of items in the action sequence. Extensive experiments on four public benchmarks demonstrate the effectiveness of our proposed GAC compared to several state-of-the-art baselines.
Trust-aware and incentive-based offloading scheme for secure multi-party computation in Internet of Things

Adoption of multi-party computation in IoT provides the required processing power needed by the IoT devices to provide smart services in the shortest time. However, this requires a secure offloading scheme that is capable of fairly offloading the computations of source nodes to a different set of workers, guarantees the privacy of the source nodes, and verifies the correctness of results without a third party at a low overhead. In this paper, we formulate a secure privacy-preserving offloading scheme based on modified secret sharing to offload computations and data to a different set of workers. We also develop incentive and trust models to encourage honesty and willingness and discourage delay among workers during multi-party computation. Last, we develop a low overhead morphism-based verification technique for the self-verification of the correctness of results. We finally present the security analysis of the scheme which shows that the schemes meet up with the necessary security requirements, and the experimental results show the capability of the scheme in terms of its security functionalities, low computation cost, effective verification of results, and generation of incentives and trust values for workers during multi-party computation.
Do You Notice Me? How Bystanders Affect the Cognitive Load in Virtual Reality

In contrast to the real world, users are not able to perceive bystanders in virtual reality (VR). Bystanders may distract users and influence their cognitive load. This involves users to feel discomfort at the thought of unintentionally touching or even bumping into a physical bystander while interacting with the virtual environment. Not knowing the intentions of a bystander or whether one is present can unsettle the user. We investigate how a bystander affects a user&#x2019;s cognitive load since it has a decisive impact on applications such as VR training. In a between-subjects lab study (N = 42), three conditions were compared: 1) no bystander, 2) an invisible bystander, and 3) a visible bystander (as an avatar). Over a series of iterations, the participants were asked to memorize four pairs of letters, perform a mental rotation task and then recall the pairs of letters. The results of our study demonstrate that a bystander acting as an avatar in the virtual environment increases the user&#x2019;s cognitive load more than an invisible bystander. Moreover, the cognitive load of a VR user is significantly increased by a bystander. Therefore, our work suggests that either the examiner must be separated from the participant or the examiner&#x2019;s influence (as a bystander) must be included in the analysis.
Deep Learning Image Captioning in Construction Management: A Feasibility Study

Deep learning image captioning methods are able to generate one or several natural sentences to describe the contents of construction images. By deconstructing these sentences, the construction object and activity information can be retrieved integrally for automated scene analysis. However, the feasibility of deep learning image captioning in construction remains unclear. To fill this gap, this research investigates the feasibility of deep learning image captioning methods in construction management. First, a linguistic schema for annotating construction machine images was established, and a captioning data set was developed. Then, six deep learning image captioning methods from the computer vision community were selected and tested on the construction captioning data set. In the sentence-level evaluation, the transformer-self-critical sequence training (Tsfm-SCST) method has obtained the best performance among six methods with the bilingual evaluation (BLEU)-1 score of 0.606, BLEU-2 of 0.506, BLEU-3 of 0.427, BLEU-4 of 0.349, metric for evaluation of translation with explicit ordering (METEOR) of 0.287, recall-oriented understudy for gisting evaluation (ROUGE) of 0.585, consensus-based image description evaluation (CIDEr) of 1.715, and semantic propositional image caption evaluation (SPICE) score of 0.422. In the element-level evaluation, the Tsfm-SCST method achieved an average precision of 91.1%, recall of 83.3%, and an F1 score of 86.6% for recognition of construction machine objects by deconstructing the generated sentences. This research indicates that deep learning image captioning is feasible as a method of generating accurate and precise text descriptions from construction images, with potential applications in construction scene analysis and image documentation.
A Blockchain‐based Framework for Privacy Preservation of Electronic Health Records (EHRs)

Abstract Healthcare Records are becoming more valuable than ever due to the emergence of various new diseases and the current pandemic. These records are scattered throughout multiple healthcare providers in several places. Keeping them in physical form is time‐consuming; thus, they need to be kept in electronic form. Electronic Healthcare Records (EHRs) are mostly managed at the individual company level, creating a single point of failure and exposing users to various security risks, including amateur‐level attacks and data breaches by hackers. This article proposes a permissioned blockchain‐based framework to overcome the above issues for secured storage and sharing of EHRs over the network. For the overall implementation of the framework Hyperledger Fabric (HLF), InterPlanetary Distributed File System (IPFS), and Identity Based Proxy Re‐Encryption (IB‐PRE) algorithm are used. To create proposed blockchain network, HLF is used. To manage EHRs on the blockchain, IPFS is used where the actual encrypted massive data is stored off‐chain, and their corresponding hash values are stored on the blockchain. Further, IB‐PRE algorithm is used to share EHRs in a safe manner, where a proxy node retrieves requested data from IPFS, performs re‐encryption, and returns the re‐encrypted data to the requester thus, ensures user privacy and data integrity. Finally, the Hyperledger Caliper tool is used to run a performance test to check the system efficiency in terms of throughput and latency. Features and performance‐based comparisons of the proposed and existing related work are made to demonstrate the effectiveness.
Traditional Learning Compared to Online Learning During the COVID-19 Pandemic: Lessons Learned From Faculty’s Perspectives

This study compares university students’ performance in traditional learning to that of online learning during the pandemic, and analyses the implications of the shift to online learning from a faculty’s perspective. The Quick-Response Research method using Google Documents was used with 104 faculty members chosen on convenience sampling in five Saudi traditional (face-to-face) universities that shifted to online learning during the COVID-19 outbreak. Results showed that the performance of university students has been better via this method than traditional learning. Classroom activity was shown to positively affect overall student performance. Moreover, the adaptation of technology positively affected the validity of using online learning and student performance. We also found that time management and technology self-efficacy are mandatory to improve remote learning.
Optical Imaging and Image Restoration Techniques for Deep Ocean Mapping: A Comprehensive Survey

Abstract Visual systems are receiving increasing attention in underwater applications. While the photogrammetric and computer vision literature so far has largely targeted shallow water applications, recently also deep sea mapping research has come into focus. The majority of the seafloor, and of Earth’s surface, is located in the deep ocean below 200 m depth, and is still largely uncharted. Here, on top of general image quality degradation caused by water absorption and scattering, additional artificial illumination of the survey areas is mandatory that otherwise reside in permanent darkness as no sunlight reaches so deep. This creates unintended non-uniform lighting patterns in the images and non-isotropic scattering effects close to the camera. If not compensated properly, such effects dominate seafloor mosaics and can obscure the actual seafloor structures. Moreover, cameras must be protected from the high water pressure, e.g. by housings with thick glass ports, which can lead to refractive distortions in images. Additionally, no satellite navigation is available to support localization. All these issues render deep sea visual mapping a challenging task and most of the developed methods and strategies cannot be directly transferred to the seafloor in several kilometers depth. In this survey we provide a state of the art review of deep ocean mapping, starting from existing systems and challenges, discussing shallow and deep water models and corresponding solutions. Finally, we identify open issues for future lines of research.
Bullet Comments for 360°Video

Time-anchored on-screen comments, as known as bullet comments, are a popular feature for online video streaming. Bullet comments reflect audiences&#x2019; feelings and opinions at specific video timings, which have been shown to be beneficial to video content understanding and social connection level. In this paper, we for the first time investigate the problem of bullet comment display and insertion for 360&#x00B0; video via head-mounted display and controller. We design four bullet comment display methods and evaluate their effects on 360&#x00B0; video experiences. We further propose two controller-based methods for bullet comment insertion. Combining the display and insertion methods, the user can experience 360&#x00B0; videos with bullet comments, and interactively post new ones by selecting among existing comments. User study results revealed how the factors of display and insertion methods affect 360&#x00B0; video experience. With the experiment findings, we also discuss useful design insights for 360&#x00B0; video bullet comments.
ISeeU2: Visually interpretable mortality prediction inside the ICU using deep learning and free-text medical notes

Accurate mortality prediction allows Intensive Care Units (ICUs) to adequately benchmark clinical practice and identify patients with unexpected outcomes. Traditionally, simple statistical models have been used to assess patient death risk, many times with sub-optimal performance. On the other hand Deep Learning holds promise to positively impact clinical practice by leveraging medical data to assist diagnosis and prediction, including mortality prediction. However, as the question of whether powerful Deep Learning models attend correlations backed by sound medical knowledge when generating predictions remains open, additional interpretability tools are needed to foster trust and encourage the use of AI by clinicians. In this work we show an interpretable Deep Learning model trained on MIMIC-III to predict mortality inside the ICU using raw nursing notes, together with visual explanations for word importance based on the Shapley Value. Our model reaches a ROC of 0.8629 ( ± 0.0058), outperforming the traditional SAPS-II score and a LSTM recurrent neural network baseline while providing enhanced interpretability when compared with similar Deep Learning approaches. Supporting code can be found at https://github.com/williamcaicedo/ISeeU2 . • Deep Learning can outperform traditional scores such as SAPS-II. • Results suggest Deep Learning uses metadata to predict patient mortality. • Performance is competitive with the state of the art using simpler pre-processing. • Shapley Values offer interpretability without sacrificing predictive performance.
Hierarchical discrepancy learning for image restoration quality assessment

With the unremitting efforts of the community, great progress has been made in image restoration (IR). However, how to objectively benchmark these algorithms is much less investigated and remains a challenging problem to date, which may hinder the rapid development of IR technologies. Most of the current IR quality metrics are scenario-special and cannot be generalized to diversified scenarios. Besides, they make predict solely on basis of restored versions, without using any information from the original degraded images. Therefore, they are blind to the relative perceptual discrepancy (RPD) between the degraded-restored image (DRI) pairs, which is much more useful when comparing the performance of IR algorithms. Inspired by this, we propose a hierarchical discrepancy learning (HIDI) model for benchmarking IR algorithms. To address the small sample problem, a quality evaluation database containing a variety of simulation distortions is built to pre-train a prior evaluator. Then, the DRI pairs are constructed and used to train the fully end-to-end HIDI model. The proposed HIDI model is extensively evaluated on several public databases and experimental results demonstrate the HIDI model has higher performance than the state-of-the-arts. Besides, compared with the competitors, the HIDI model shows the best generalization ability in cross-database experiments.