Reviewerly/exHarmony huggingface dataset:
Why Do People Initiate an Online Firestorm? The Role of Sadness, Anger, and Dislike

Brands have become the target of online collaborative attacks performed by networked consumers who share negative brand content, leading to the so-called online firestorms (OF). Its detrimental effects on reputation, sales, and stock market performance make the understanding of OF emergence a managerial priority as well as an interesting area for academic research. We adopt a novel perspective in the analysis by drawing on the appraisal theory of emotions to dig in the emotional nature of the phenomena. Specifically, our research analyzes the role of three specific negative brand emotions (sadness, dislike, and anger) in a model that encapsulates cognitive, emotional, and motivational drivers of the initiation of an OF. The findings obtained from a sample of 303 individuals exposed to a brand misconduct episode suggest that openly expressed feelings of anger, dislike, and sadness emerge in response to outrage appraisals associated to the episode. However, only anger and dislike provided the impetus for taking revenge on the brand in the form of an OF. These findings contribute to a better understanding of the phenomenon since the role of emotions as a basis for explaining the act of initiating an OF has been overlooked. To practitioners, we offer some guidelines to monitor and manage the negative emotional content posted by individuals as mitigating them reduces the desire for revenge, which is the force behind their intentions to foster an OF.
Digital and Media Literacies in the Polish Education System—Pre- and Post-COVID-19 Perspective

Digital and media literacies refer to a specific set of skills and abilities. The range of these skills as they concern the educational process has been broadly discussed. In this paper, we analyzed the Polish educational system to determine the scope of the sorts of digital skills young people and students should achieve in order to be considered digitally and media literate. We compared sets of recommendations from the last ten years issued by different national governmental and nonprofit organizations for the Polish education system. We identified a set of skills that should be expected to be possessed by young people and students during their education. Additionally, we discussed results regarding the situation caused by the COVID-19 pandemic and the shift from regular education to distance learning.
Self-Enhanced GNN: Improving Graph Neural Networks Using Model Outputs

Graph neural networks (GNNs) have received much attention recently because of their excellent performance on graph-based tasks. However, existing research on GNNs focuses on designing more effective models without considering much about the quality of the input data. In this paper, we propose self-enhanced GNN (SEG), which improves the quality of the input data using the outputs of existing GNN models for better performance on semi-supervised node classification. As graph data consist of both topology and node labels, we improve input data quality from both perspectives. For topology, we observe that higher classification accuracy can be achieved when the ratio of inter-class edges (connecting nodes from different classes) is low and propose topology update to remove inter-class edges and add intra-class edges. For node labels, we propose training node augmentation, which enlarges the training set using the labels predicted by existing GNN models. SEG is a general framework that can be easily combined with existing GNN models. Experimental results validate that SEG consistently improves the performance of well-known GNN models such as GCN, GAT and SGC across different datasets.
Quantum Computing—From NISQ to PISQ

Given the impeding timeline of developing good quality quantum processing units, it is the moment to rethink the approach to advance quantum computing research. Rather than waiting for quantum hardware technologies to mature, we need to start assessing in tandem the impact of the occurrence of quantum computing in various scientific fields. However, to this purpose, we need to use a complementary but quite different approach than proposed by the NISQ vision, which is heavily focused on and burdened by the engineering challenges. That is why we propose and advocate the PISQ approach: Perfect Intermediate Scale Quantum computing based on the already known concept of perfect qubits. This will allow researchers to focus much more on the development of new applications by defining the algorithms in terms of perfect qubits and evaluate them on quantum computing simulators that are executed on supercomputers. It is not the long-term solution but will currently allow universities to research on quantum logic and algorithms and companies can already start developing their internal know-how on quantum solutions.
Is My Siri the Same as Your Siri? An Exploration of Users’ Mental Model of Virtual Personal Assistants, Implications for Trust

Virtual personal assistants (VPAs) are becoming so widely available that considerations are being made as to whether to begin including them in self-driving vehicles. While research has been done exploring human interactions with single VPAs, there has been a little work exploring human interactions and human mental models with interconnected systems. As companies like Amazon consider whether to integrate Alexa in their self-driving car, research needs to be done to explore whether individual's mental model of these systems is of a single system or if every embodiment of the VPA (e.g., echo) represents a different VPA. Knowing this will allow researchers and practitioners to apply existing models of trust, and predict whether high trust in the Siri that exists in an iPhone will carry over into high (and potentially miscalibrated) trust in Apple's Siri-directed self-driving vehicle. Results indicate that there is not one consistent mental model that users have, and provides the framework for greater exploration into individual differences and the determinants that affect users’ mental model.
Breast cancer intelligent analysis of histopathological data: A systematic review

For a favorable prognosis of breast cancer, early diagnosis is essential. The histopathological analysis is considered the gold standard to indicate the type of cancer. Histopathology consists of analyzing characteristics of the lesions through tissue sections stained with Hematoxylin and Eosin. During the last years, there is much interest in developing the histopathological slide analysis process. This article aims to explore recent literature related to intelligent analysis of breast cancer histopathological images, defining the taxonomy, identifying challenges, and open questions. The method is based on a systematic literature review, guided by research questions to identify relevant work and identify open problems in the literature. The present study investigates articles published in the last ten years. We are selecting and researching the most significant approaches according to pre-established criteria in the intelligent analysis of breast cancer histopathological images, resulting in a final corpus of 53 articles. As a result, we developed an updated taxonomy, identified the main challenges, public datasets, evaluation metrics, and techniques used in the studies. These results contribute to discussions about the intelligent analysis of breast cancer histopathological images and highlight some research gaps for future studies.
On the minimum weights of binary LCD codes and ternary LCD codes

Linear complementary dual (LCD) codes are linear codes that intersect with their dual codes trivially. We study the largest minimum weight d2(n,k) among all binary LCD [n,k] codes and the largest minimum weight d3(n,k) among all ternary LCD [n,k] codes. The largest minimum weights d2(n,5) and d3(n,4) are partially determined. We also determine the largest minimum weights d2(n,n−5), d3(n,n−i) for i∈{2,3,4}, and d3(n,k) for n∈{11,12,…,19}.
Citation recommendation using semantic representation of cited papers’ relations and content

Citation recommendation can help researchers quickly find supplementary or alternative references in massive academic resources. Current research on citation recommendation mainly focuses on the citing papers, resulting in the enormous cited papers are ignored, including the relations among cited papers and their citation context cited in citing papers. Moreover, cited paper’s content is often denoted with its original title and abstract, which is hard to acquire and rarely considers different citation motivations. Furthermore, the most appropriate method for semantic representation of cited papers’ relations and content is uncertain. Therefore, this paper studies citation recommendation from the perspective of semantic representation of cited papers’ relations and content. Firstly, four forms of citation context are designed and extracted as cited papers’ content considering citation motivations, as well as co-citation relationships are extracted as cited papers’ relations. Secondly, 132 methods are designed for generating semantic vector of cited paper, including four network embedding methods, 16 methods by combining four text representation algorithms with four forms of citation content, and 112 fusion methods. Finally, similarity among cited papers is calculated for citation recommendation and a quantitative evaluation method based on link prediction is designed, to find the most appropriate form of citation content and the optimal method. The result shows that doc2vecC (Document to Vector through Corruption) with the form of CS&SS (Current Sentences and Surrounding Sentences) performs best, in which the AUC (Area Under Curve) and MAP (Macro Average Precision) reach 0.877 and 0.889 and have increased by 0.462 and 0.370 compared with the worst-performing method. This performance is slightly improved by parameters adjustment, and a case study is performed whose results have further proved the effectiveness of this method. In addition, among four forms of cited papers’ content, CS&SS performs best in almost all methods. Furthermore, the fusion methods not always perform better than the single methods, where doc2vecC (CS&SS) performs better than the best fusion method GCN (Graph Convolutional Network). These results not only prove the effectiveness of citation recommendation from the perspective of cited paper, but also provide helpful and useful suggestions for method selection and citation content selection. The data and conclusions can be extended to other text mining-related tasks. Simultaneously, it is a preliminary research which needs to be further studied in other domains using emerging semantic representation methods.
How does hand gestures in videos impact social media engagement - Insights based on deep learning

With the fast improvement and development in deep learning and computer vision, the interaction between humans and computers is becoming immense. This research work aims at recognizing hand gestures performed in TEDx videos and analyse the relationship between gestures and user engagement. Here we propose a technique based on deep learning, Convolutional Neural Network (CNN), to recognize hand gestures from a video or image input. ResNeXt-101 model is used for the classification of hand gestures. Here we also used images from the Twenty Bn dataset for some gestures while we also collected gesture images from TEDx videos. Our experiments obtained high accuracy for gesture recognition on training as 95% to 99% and 94.35% for testing. Firstly a gesture is identified in each frame and then classified. We also created a count function that helped us count the number of times a gesture was performed and help us analyse these talks in a better way. Two experimental studies were carried out to analyse viewer engagement: one, the effect of suitable gestures on the viewer count, and secondly, suitable gestures on the sentiment of the viewer. Interesting results are observed that suitable gestures from talkers have an impact on increasing positive review and viewer count.
Hyperspectral Mixed Noise Removal via Spatial-Spectral Constrained Unsupervised Deep Image Prior

Recently, deep learning-based methods are proposed for hyperspectral images (HSIs) denoising.Among them, unsupervised methods such as deep image prior (DIP)-based methods have received much attention because these methods do not require any training data.However, DIP-based methods suffer from the semi-convergence behavior, i.e., the iteration of DIPbased methods needs to terminate by referring to the groundtruth image at the optimal iteration point.In this paper, we propose the spatial-spectral constrained deep image prior (S2DIP) for the HSI mixed noise removal.Specifically, we integrate the DIP, the spatial-spectral total variation regularization term, and the 1-norm sparse term to respectively capture the deep prior of the clean HSI, the spatial-spectral local smooth prior of the clean HSI, and the sparse prior of noise.The proposed S2DIP jointly leverages the expressive power brought from the deep convolutional neural network without any training data and exploits the HSI and noise structures via hand-crafted priors.Thus, our method avoids the semi-convergence behavior of DIP-based methods.Meanwhile, our method largely enhances the HSI denoising ability of DIP-based methods.To tackle the corresponding model, we utilize the alternating direction multiplier method algorithm.Extensive experiments demonstrate that our method outperforms model-based and deep learningbased state-of-the-art HSI denoising methods.
Vector and Functional Commitments from Lattices

Vector commitment (VC) schemes allow one to commit concisely to an ordered sequence of values, so that the values at desired positions can later be proved concisely. In addition, a VC can be statelessly updatable, meaning that commitments and proofs can be updated to reflect changes to individual entries, using knowledge of just those changes (and not the entire vector). VCs have found important applications in verifiable outsourced databases, cryptographic accumulators, and cryptocurrencies. However, to date there have been relatively few post-quantum constructions, i.e., ones that are plausibly secure against quantum attacks.
Towards the Unseen: Iterative Text Recognition by Distilling from Errors

Visual text recognition is undoubtedly one of the most extensively researched topics in computer vision. Great progress have been made to date, with the latest models starting to focus on the more practical "in-the-wild" setting. However, a salient problem still hinders practical deployment – prior state-of-arts mostly struggle with recognising unseen (or rarely seen) character sequences. In this paper, we put forward a novel framework to specifically tackle this “unseen” problem. Our framework is iterative in nature, in that it utilises predicted knowledge of character sequences from a previous iteration, to augment the main network in improving the next prediction. Key to our success is a unique cross-modal variational autoencoder to act as a feedback module, which is trained with the presence of textual error distribution data. This module importantly translates a discrete predicted character space, to a continuous affine transformation parameter space used to condition the visual feature map at next iteration. Experiments on common datasets have shown competitive performance over state-of-the-arts under the conventional setting. Most importantly, under the new disjoint setup where train-test labels are mutually exclusive, ours offers the best performance thus showcasing the capability of generalising onto unseen words (Figure 1 offers a summary).
An Adaptive Rank Aggregation-Based Ensemble Multi-Filter Feature Selection Method in Software Defect Prediction

Feature selection is known to be an applicable solution to address the problem of high dimensionality in software defect prediction (SDP). However, choosing an appropriate filter feature selection (FFS) method that will generate and guarantee optimal features in SDP is an open research issue, known as the filter rank selection problem. As a solution, the combination of multiple filter methods can alleviate the filter rank selection problem. In this study, a novel adaptive rank aggregation-based ensemble multi-filter feature selection (AREMFFS) method is proposed to resolve high dimensionality and filter rank selection problems in SDP. Specifically, the proposed AREMFFS method is based on assessing and combining the strengths of individual FFS methods by aggregating multiple rank lists in the generation and subsequent selection of top-ranked features to be used in the SDP process. The efficacy of the proposed AREMFFS method is evaluated with decision tree (DT) and naïve Bayes (NB) models on defect datasets from different repositories with diverse defect granularities. Findings from the experimental results indicated the superiority of AREMFFS over other baseline FFS methods that were evaluated, existing rank aggregation based multi-filter FS methods, and variants of AREMFFS as developed in this study. That is, the proposed AREMFFS method not only had a superior effect on prediction performances of SDP models but also outperformed baseline FS methods and existing rank aggregation based multi-filter FS methods. Therefore, this study recommends the combination of multiple FFS methods to utilize the strength of respective FFS methods and take advantage of filter-filter relationships in selecting optimal features for SDP processes.
Classical Simulation of Boson Sampling Based on Graph Structure

Boson sampling is a fundamentally and practically important task that can be used to demonstrate quantum supremacy using noisy intermediate-scale quantum devices. In this Letter, we present classical sampling algorithms for single-photon and Gaussian input states that take advantage of a graph structure of a linear-optical circuit. The algorithms' complexity grows as so-called treewidth, which is closely related to the connectivity of a given linear-optical circuit. Using the algorithms, we study approximated simulations for local Haar-random linear-optical circuits. For equally spaced initial sources, we show that, when the circuit depth is less than the quadratic in the lattice spacing, the efficient simulation is possible with an exponentially small error. Notably, right after this depth, photons start to interfere each other and the algorithms' complexity becomes subexponential in the number of sources, implying that there is a sharp transition of its complexity. Finally, when a circuit is sufficiently deep enough for photons to typically propagate to all modes, the complexity becomes exponential as generic sampling algorithms. We numerically implement a likelihood test with a recent Gaussian boson sampling experiment and show that the treewidth-based algorithm with a limited treewidth renders a larger likelihood than the experimental data.
Challenges of Software Requirements Quality Assurance and Validation: A Systematic Literature Review

Validation of software requirements is a primary phase in requirements engineering that ensures requirements match the target system with the intended needs of the acquirer.It aims to detect and correct errors that prevail in the specified requirements.Although there are tremendous requirements validation approaches, some software may fail because of limited or ineffective requirements validation techniques and unreliable requirements' quality characteristics.In this study, a systematic literature review of requirements validation is performed.The study analyzes the most adopted validation techniques, reports requirements quality characteristics, and discovers significant challenges of validation techniques.The review identified 66 relevant primary studies analyzed to derive deep insights into the following aspects of requirements validation: trends of requirements validation methods, including their subtechnique strengths and weaknesses, requirements quality characteristics categories, and adopted tools and datasets in these techniques.We grouped validation techniques into categories: prototyping, inspection, knowledge-oriented, test-oriented, modeling and assessment, and formal models.The analysis reported 19 validation techniques, 27 tools, new requirements validation characteristics, and several challenges that prevailed through validation techniques.The trend of validation techniques is to those methods that apply machine learning techniques with knowledge from dictionaries and ontologies.Most challenges are about how to express the requirements and how to revert clients' feedback.There is a strong relationship between validation techniques, software application domain, and requirements validation quality attributes.Thus, there is an immense need to unify the quality characteristics and domain-specific validation methods.
Pixel-Level Bijective Matching for Video Object Segmentation

Semi-supervised video object segmentation (VOS) aims to track the designated objects present in the initial frame of a video at the pixel level. To fully exploit the appearance information of an object, pixel-level feature matching is widely used in VOS. Conventional feature matching runs in a surjective manner, i.e., only the best matches from the query frame to the reference frame are considered. Each location in the query frame refers to the optimal location in the reference frame regardless of how often each reference frame location is referenced. This works well in most cases and is robust against rapid appearance variations, but may cause critical errors when the query frame contains background distractors that look similar to the target object. To mitigate this concern, we introduce a bijective matching mechanism to find the best matches from the query frame to the reference frame and vice versa. Before finding the best matches for the query frame pixels, the optimal matches for the reference frame pixels are first considered to prevent each reference frame pixel from being overly referenced. As this mechanism operates in a strict manner, i.e., pixels are connected if and only if they are the sure matches for each other, it can effectively eliminate background distractors. In addition, we propose a mask embedding module to improve the existing mask propagation method. By embedding multiple historic masks with coordinate information, it can effectively capture the position information of a target object. Code and models are available at https://github.com/suhwan-cho/BMVOS.
S-Box Design Based on 2D Multiple Collapse Chaotic Map and Their Application in Image Encryption

As an essential part of an encryption system, the performance of a chaotic map is critical for system security. However, there are many defects for the existing chaotic maps. The low-dimension (LD) ones are easily predicted and are vulnerable to be attacked, while high-dimension (HD) ones have a low iteration speed. In this paper, a 2D multiple collapse chaotic map (2D-MCCM) was designed, which had a wide chaos interval, a high complexity, and a high iteration speed. Then, a new chaotic S-box was constructed based on 2D-MCCM, and a diffusion method was designed based on the S-box, which improved security and efficiency. Based on these, a new image encryption algorithm was proposed. Performance analysis showed that the encryption algorithm had high security to resist all kinds of attacks easily.