Reviewerly/exHarmony huggingface dataset:
Question-Driven Span Labeling Model for Aspect–Opinion Pair Extraction

Aspect term extraction and opinion word extraction are two fundamental subtasks of aspect-based sentiment analysis. The internal relationship between aspect terms and opinion words is typically ignored, and information for the decision-making of buyers and sellers is insufficient. In this paper, we explore an aspect–opinion pair extraction (AOPE) task and propose a Question-Driven Span Labeling (QDSL) model to extract all the aspect–opinion pairs from user-generated reviews. Specifically, we divide the AOPE task into aspect term extraction (ATE) and aspect-specified opinion extraction (ASOE) subtasks; we first extract all the candidate aspect terms and then the corresponding opinion words given the aspect term. Unlike existing approaches that use the BIO-based tagging scheme for extraction, the QDSL model adopts a span-based tagging scheme and builds a question–answer-based machine-reading comprehension task for an effective aspect–opinion pair extraction. Extensive experiments conducted on three tasks (ATE, ASOE, and AOPE) on four benchmark datasets demonstrate that the proposed method significantly outperforms state-of-the-art approaches.
Operations Management teaching practices and information technologies adoption in emerging economies during COVID-19 outbreak

The objective of this article is three-fold. First, it aims at identifying the main teaching practices and information and communication technologies (ICTs) used to teach Operations Management (OM) in emerging economies during COVID-19 outbreak. Second, it investigates the effect of contextual characteristics on the adoption level of those teaching practices and ICTs. Third, this study examines the relationship between the adoption of ICTs and OM teaching practices during COVID-19 outbreak. Expectedly, schools around the world have pivoted to online learning and digital classrooms. Thus, OM lecturers and professors located in emerging economies that have been teaching during COVID-19 outbreak were surveyed. The collected data was analyzed through multivariate techniques. Findings indicate that lecturers and professors have been remarkably adopting specific teaching practices and ICTs to teach OM. Nevertheless, when considering the contextual characteristics of the universities, departments, and lecturers/professors, the adoption level of those practices and ICTs may significantly vary, especially depending on subject type and teaching experience. Moreover, we empirically verified that ICTs positively relate with OM teaching practices in emerging economies, although in a much less extent than expected. This research provides OM instructors guidelines to better plan their courses and subjects in face of extreme disruptive moments, such as the one caused by the COVID-19. Understanding how the concurrent utilization of ICTs and teaching practices helps OM programs to continue developing their activities is particularly important for universities located in emerging economies, since they are more likely to struggle with resources scarcity and more financially humble students.
Learning Intact Features by Erasing-Inpainting for Few-shot Classification

Few-shot classification aims to categorize the samples from unseen classes with only few labeled samples. To address such a challenge, many methods exploit a base set consisting of massive labeled samples to learn an instance embedding function, i.e., image feature extractor, and it is expected to possess good transferability among different tasks. Such characteristics of few-shot learning are essentially different from that of traditional image classification only pursuing to get discriminative image representations. In this paper, we propose to learn intact features by erasing-inpainting for few-shot classification. Specifically, we argue that extracting intact features of target objects is more transferable, and then propose a novel cross-set erasing-inpainting (CSEI) method. CSEI processes the images in the support set using erasing and inpainting, and then uses them to augment the query set of the same task. Consequently, the feature embedding produced by our proposed method can contain more complete information of target objects. In addition, we propose task-specific feature modulation to make the features adaptive to the current task. The extensive experiments on two widely used benchmarks well demonstrates the effectiveness of our proposed method, which can consistently get considerable performance gains for different baseline methods.
Efficient Passage Retrieval with Hashing for Open-domain Question Answering

Most state-of-the-art open-domain question answering systems use a neural retrieval model to encode passages into continuous vectors and extract them from a knowledge source.However, such retrieval models often require large memory to run because of the massive size of their passage index.In this paper, we introduce Binary Passage Retriever (BPR), a memory-efficient neural retrieval model that integrates a learning-to-hash technique into the state-of-the-art Dense Passage Retriever (DPR) (Karpukhin et al., 2020) to represent the passage index using compact binary codes rather than continuous vectors.BPR is trained with a multi-task objective over two tasks: efficient candidate generation based on binary codes and accurate reranking based on continuous vectors.Compared with DPR, BPR substantially reduces the memory cost from 65GB to 2GB without a loss of accuracy on two standard open-domain question answering benchmarks: Natural Questions and TriviaQA.
GazeBase, a large-scale, multi-stimulus, longitudinal eye movement dataset

Abstract This manuscript presents GazeBase, a large-scale longitudinal dataset containing 12,334 monocular eye-movement recordings captured from 322 college-aged participants. Participants completed a battery of seven tasks in two contiguous sessions during each round of recording, including a – (1) fixation task, (2) horizontal saccade task, (3) random oblique saccade task, (4) reading task, (5/6) free viewing of cinematic video task, and (7) gaze-driven gaming task. Nine rounds of recording were conducted over a 37 month period, with participants in each subsequent round recruited exclusively from prior rounds. All data was collected using an EyeLink 1000 eye tracker at a 1,000 Hz sampling rate, with a calibration and validation protocol performed before each task to ensure data quality. Due to its large number of participants and longitudinal nature, GazeBase is well suited for exploring research hypotheses in eye movement biometrics, along with other applications applying machine learning to eye movement signal analysis. Classification labels produced by the instrument’s real-time parser are provided for a subset of GazeBase, along with pupil area.
StyleCariGAN

We present a caricature generation framework based on shape and style manipulation using StyleGAN. Our framework, dubbed StyleCariGAN , automatically creates a realistic and detailed caricature from an input photo with optional controls on shape exaggeration degree and color stylization type. The key component of our method is shape exaggeration blocks that are used for modulating coarse layer feature maps of StyleGAN to produce desirable caricature shape exaggerations. We first build a layer-mixed StyleGAN for photo-to-caricature style conversion by swapping fine layers of the StyleGAN for photos to the corresponding layers of the StyleGAN trained to generate caricatures. Given an input photo, the layer-mixed model produces detailed color stylization for a caricature but without shape exaggerations. We then append shape exaggeration blocks to the coarse layers of the layer-mixed model and train the blocks to create shape exaggerations while preserving the characteristic appearances of the input. Experimental results show that our StyleCariGAN generates realistic and detailed caricatures compared to the current state-of-the-art methods. We demonstrate StyleCariGAN also supports other StyleGAN-based image manipulations, such as facial expression control.
Tracklet Proposal Network for Multi-Object Tracking on Point Clouds

This paper proposes the first tracklet proposal network, named PC-TCNN, for Multi-Object Tracking (MOT) on point clouds. Our pipeline first generates tracklet proposals, then refines these tracklets and associates them to generate long trajectories. Specifically, object proposal generation and motion regression are first performed on a point cloud sequence to generate tracklet candidates. Then, spatial-temporal features of each tracklet are exploited and their consistency is used to refine the tracklet proposal. Finally, the refined tracklets across multiple frames are associated to perform MOT on the point cloud sequence. The PC-TCNN significantly improves the MOT performance by introducing the tracklet proposal design. On the KITTI tracking benchmark, it attains an MOTA of 91.75%, outperforming all submitted results on the online leaderboard.
Hierarchical Object-oriented Spatio-Temporal Reasoning for Video Question Answering

Video Question Answering (Video QA) is a powerful testbed to develop new AI capabilities. This task necessitates learning to reason about objects, relations, and events across visual and linguistic domains in space-time. High-level reasoning demands lifting from associative visual pattern recognition to symbol like manipulation over objects, their behavior and interactions. Toward reaching this goal we propose an object-oriented reasoning approach in that video is abstracted as a dynamic stream of interacting objects. At each stage of the video event flow, these objects interact with each other, and their interactions are reasoned about with respect to the query and under the overall context of a video. This mechanism is materialized into a family of general-purpose neural units and their multi-level architecture called Hierarchical Object-oriented Spatio-Temporal Reasoning (HOSTR) networks. This neural model maintains the objects' consistent lifelines in the form of a hierarchically nested spatio-temporal graph. Within this graph, the dynamic interactive object-oriented representations are built up along the video sequence, hierarchically abstracted in a bottom-up manner, and converge toward the key information for the correct answer. The method is evaluated on multiple major Video QA datasets and establishes new state-of-the-arts in these tasks. Analysis into the model's behavior indicates that object-oriented reasoning is a reliable, interpretable and efficient approach to Video QA.
An Improved Equilibrium Optimizer Algorithm for Features Selection: Methods and Analysis

In the last decade, data generated from different digital devices has posed a remarkable challenge for data representation and analysis. Because of the high-dimensional datasets and the rapid growth of data volume, a lot of challenges have been encountered in various fields such as data mining and data science. Conventional machine learning classifiers are of limited ability to handle the problems of high dimensionality that includes memory limitation, computational cost, and low accuracy performance. Consequently, there is a need to reduce the dimension of datasets by choosing the most significant features that would represent the data efficiently with minimum volume. This study proposes an improved binary version of the equilibrium optimizer algorithm (IBEO) to mitigate features selection problem. Two main enhancements are added to the original equilibrium optimizer (EO) to strengthen its performance. Opposition based learning is the first advancement added to the initialization stage of EO to enhance the diversity of the population in the search space. Local search algorithm is the second advancement added to enhance the exploitation of EO. Wrapper approaches can offer premium solutions. Thus, we used k-nearest neighbour classifier and support vector machine classifiers as the most popular wrapper methods. Moreover, dealing with the problem of over-fitting is an essential task that urges on applying k-fold cross-validation to split each dataset into training and testing data. Comparative tests with different well-known algorithms such as grey wolf optimization, grasshopper optimization, particle swarm optimization, whale optimization, dragonfly, and improved salp swarm algorithms are considered. The proposed algorithm is applied to the most commonly datasets used in the field to validate the performance. Statistical analysis studies demonstrate the effectiveness of the IBEO.
Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context

Embedding based methods are widely used for unsupervised keyphrase extraction (UKE) tasks.Generally, these methods simply calculate similarities between phrase embeddings and document embedding, which is insufficient to capture different context for a more effective UKE model.In this paper, we propose a novel method for UKE, where local and global contexts are jointly modeled.From a global view, we calculate the similarity between a certain phrase and the whole document in the vector space as transitional embedding based models do.In terms of the local view, we first build a graph structure based on the document where phrases are regarded as vertices and the edges are similarities between vertices.Then, we proposed a new centrality computation method to capture local salient information based on the graph structure.Finally, we further combine the modeling of global and local context for ranking.We evaluate our models on three public benchmarks (Inspec, DUC 2001, SemEval 2010) and compare with existing state-of-the-art models.The results show that our model outperforms most models while generalizing better on input documents with different domains and length.Additional ablation study shows that both the local and global information is crucial for unsupervised keyphrase extraction tasks.
A Deep Neural Network-Based Approach for Extracting Textual Images from Deteriorate Images

INTRODUCTION:The quantity of audio and visual data is increasing exponentially due to the internet's rapid growth.The digital information in images and videos could be used for fully automated captions, indexing, and image structuring.The online image and video data system has seen a significant increase.In such a dataset, images and videos must be retrieved, explored, as well as inspected.OBJECTIVES: Text extraction is crucial for locating critical as well as important data.Disturbance is indeed a critical factor that affects image quality, and this is primarily generated during image acquisition and communication operations.An image can be contaminated by a variety of noise-type disturbances.A text in the complex image includes a variety of information which is used to recognise textual as well as non-textual particulars.The particulars in the complicated corrupted images have been considered important for individuals seeing the entire issue.However, text in complicated degraded images exhibits a rapidly changing form in an unconstrained circumstance, making textual data recognition complicated METHODS: The naïve bayes algorithm is a weighted reading technique is used to generate the correct text data from the complicated image regions.Usually, images hold some disturbance as a result of the fact that filtration is proposed during the early pre-processing step.To restore the image's quality, the input image is processed employing gradient and contrast image methods.Following that, the contrast of the source images would be enhanced using an adaptive image map.Stroke width transform, Gabor transform, and weighted naïve bayes classifier methodologies have been used in complicated degraded images to segment, features extraction, and detect textual and non-textual elements.RESULTS: Finally, to identify categorised textual data, the confluence of deep neural networks and particle swarm optimization is being used.The dataset IIIT5K is used for the development portion, and also the performance of the suggested methodology is assessed by utilizing parameters like as accuracy, recall, precision, and F1 score.It performs well enough for record collections such as articles, even when significantly distorted, and is thus suitable for creating library information system databases CONCLUSION: A combination of deep neural network and particle swarm optimization is being used to recognise classified text.The dataset IIIT5K is used for the development portion, and while high performance is achieved with parameters such as accuracy, recall, precision, and F1 score, characters may occasionally deviate.Alternatively, the same character is frequently extracted [3] multiple times, which may result in incorrect textual data being extracted from natural images.As a result, an efficient technique for avoiding such flaws in the text retrieval process must be implemented in the near future.
Next-item Recommendations in Short Sessions

The changing preferences of users towards items trigger the emergence of session-based recommender systems (SBRSs), which aim to model the dynamic preferences of users for next-item recommendations. However, most of the existing studies on SBRSs are based on long sessions only for recommendations, ignoring short sessions, though short sessions, in fact, account for a large proportion in most of the real-world datasets. As a result, the applicability of existing SBRSs solutions is greatly reduced. In a short session, quite limited contextual information is available, making the next-item recommendation very challenging. To this end, in this paper, inspired by the success of few-shot learning (FSL) in effectively learning a model with limited instances, we formulate the next-item recommendation as an FSL problem. Accordingly, following the basic idea of a representative approach for FSL, i.e., meta-learning, we devise an effective SBRS called INter-SEssion collaborativeRecommender neTwork (INSERT) for next-item recommendations in short sessions. With the carefully devised local module and global module, INSERT is able to learn an optimal preference representation of the current user in a given short session. In particular, in the global module, a similar session retrieval network (SSRN) is designed to find out the sessions similar to the current short session from the historical sessions of both the current user and other users, respectively. The obtained similar sessions are then utilized to complement and optimize the preference representation learned from the current short session by the local module for more accurate next-item recommendations in this short session. Extensive experiments conducted on two real-world datasets demonstrate the superiority of our proposed INSERT over the state-of-the-art SBRSs when making next-item recommendations in short sessions.
Ensembling EfficientNets for the Classification and Interpretation of Histopathology Images

The extended utilization of digitized Whole Slide Images is transforming the workflow of traditional clinical histopathology to the digital era. The ongoing transformation has demonstrated major potentials towards the exploitation of Machine Learning and Deep Learning techniques as assistive tools for specialized medical personnel. While the performance of the implemented algorithms is continually boosted by the mass production of generated Whole Slide Images and the development of state-of the-art deep convolutional architectures, ensemble models provide an additional methodology towards the improvement of the prediction accuracy. Despite the earlier belief related to deep convolutional networks being treated as black boxes, important steps for the interpretation of such predictive models have also been proposed recently. However, this trend is not fully unveiled for the ensemble models. The paper investigates the application of an explanation scheme for ensemble classifiers, while providing satisfactory classification results of histopathology breast and colon cancer images in terms of accuracy. The results can be interpreted by the hidden layers’ activation of the included subnetworks and provide more accurate results than single network implementations.
IS professionals’ information security behaviors in Chinese IT organizations for information security protection

Continued integration of technology for the purpose of connecting and exchanging data with other devices and systems over the Internet exposes information security (IS) to growing risks. Organizations can thus achieve a strategic advantage by securing IS as a pivotal information and intelligence asset. This study examined ways of motivating IS professionals to protect information security from potential risks, drawing on the theoretical frameworks of protection motivation theory (PMT) and the theory of planned behavior (TPB) as well as work-related organizational antecedents (e.g., organizational commitment and job satisfaction). This paper proposes structural equation modeling (SEM) in R as a framework for exploring relationships among the variables and determining the overall data fit to the hypotheses. SEM is a multivariate technique which simultaneously executes both factor analysis and aspects of multiple regression in order to estimate interrelated relationships while also allowing path analytic modeling to be performed with latent, unobserved variables. Using 804 questionnaires with SEM analysis, we find support for the following predictors' associations: (a) information security attitudes and subjective norms, as constituents of TPB, significantly influenced information security protective behaviors; (b) the coping appraisals (self-efficacy and response cost) and threat appraisals (threat susceptibility and threat severity) of PMT were significantly predictive of information security protective behaviors; and (c) organizational commitment positively impacted information security protective behaviors. However, job satisfaction and perceived behavioral control as a construct of TPB were not associated with information security behaviors. The main theoretical contribution of this research is that the addition of organizational commitment allows the behavioral science model to offer a novel understanding of IS professionals' protection motivation and actual behaviors in the Chinese context. This study has several practical implications for organizations. In order to encourage IS professionals to follow protective security behaviors, organizations should set up the belief that a close relationship with subordinates plays a vital role in ensuring information security, improve IS employees' perception and cognition of their importance to the organization, constantly highlight the importance of information security protection, and emphasize the severe consequences of information security threats during trainings.