Reviewerly/exHarmony huggingface dataset:
The impact of CIO characteristics on data breaches

The exponential rate of increase in IT security breach incidents has led governments, regulators, and practitioners to respond by introducing standards and frameworks for the disclosure and management of organizational cybersecurity risk exposure. Cybersecurity, which is a part of IT risk management, is affected by the capability and the ability of senior leadership responsible for IT-related decisions. This paper uses hand-collected data related to the Chief Information Officer (CIO) for S&P 500 firms and explores whether the presence of a CIO role, human capital characteristics of the CIO, and structural capital characteristics of the firm and the CIO are related to a firm's cybersecurity risk exposure. This study finds that firms disclosing the presence of a CIO are more likely to be breached, even after matching on the likelihood of a breach and controlling for the likelihood that a firm would choose to disclose a CIO. This study also finds predictable variations in the likelihood of a breach among CIOs based on various human capital dimensions (including past technology experience, external board memberships, firm tenure, and CIO tenure) and structural capital dimensions (including a recognized commitment to IT and charging the CIO with multiple responsibilities). Finally, this study finds evidence that the observed associations depend on both the source of the breach (external vs. internal) as well as the type of data compromised by the breach (e.g. financial, personal, etc.). The results of this study contribute to the growing body of academic breach literature, while also informing practitioners as they evaluate the costs and benefits of various methods for combating breaches.
Adversarial Continual Learning for Multi-domain Hippocampal Segmentation

Deep learning for medical imaging suffers from temporal and privacy-related restrictions on data availability. To still obtain viable models, continual learning aims to train in sequential order, as and when data is available. The main challenge that continual learning methods face is to prevent catastrophic forgetting, i.e., a decrease in performance on the data encountered earlier. This issue makes continuous training of segmentation models for medical applications extremely difficult. Yet, often, data from at least two different domains is available which we can exploit to train the model in a way that it disregards domain-specific information. We propose an architecture that leverages the simultaneous availability of two or more datasets to learn a disentanglement between the content and domain in an adversarial fashion. The domain-invariant content representation then lays the base for continual semantic segmentation. Our approach takes inspiration from domain adaptation and combines it with continual learning for hippocampal segmentation in brain magnetic resonance imaging (MRI). We showcase that our method reduces catastrophic forgetting and outperforms state-of-the-art continual learning methods.
Drivers of organizational adoption of automation

• Organizational adoption of automation is made possible by technological progress • Labor shortage, due to demographic change, livability, and labor mobility, calls for automation. • Customer characteristics and culture determine how automation is designed and implemented. • Transformational leadership is key to successful realization of business model innovation. • These factors can be employed to improve adoption of best practices of automation in tourism.
The Application of Blockchain of Custody in Criminal Investigation Process

With the advancing technologies become tools for illicit activities and avoid detection, digital forensics is necessary for law enforcement in modern criminal investigations. In order to maintain the integrity and authenticity of the evidence, a chain of custody is essential for the successful prosecution of criminals in court. However, it is a great challenge to manage the preservation and collection of digital evidence because of its fragile and volatile in nature. Blockchain has been proposed as a promising and reliable technology to provide immutability and traceability of digital content, however, the applications of blockchain to the law enforcement agencies (LEAs) requires special attention to the security issue. In this research, we proposed a blockchain of custody framework to facilitate the security and transparency of digital evidence in criminal investigation process. The framework is implement on Ethereum smart contract to support authenticity and integrity of digital evidence in preliminary investigation, case management and court phases. We also propose the role of investigator to leverage access control in evidence creation, transferring and modification. The corresponding actions with judicial process is simulated using private ethereum blockchain. The experimental results indicate that the proposed framework can prevent digital evidence been tampered or contaminated and assure its legal defensibility with rigorous privilege management. Moreover, by successfully synchronizing digital evidence transactions to multiple nodes ensures the accountability of evidence data for every involved law enforcement agency.
Cross-Encoder for Unsupervised Gaze Representation Learning

In order to train 3D gaze estimators without too many annotations, we propose an unsupervised learning framework, Cross-Encoder, to leverage the unlabeled data to learn suitable representation for gaze estimation. To address the issue that the feature of gaze is always intertwined with the appearance of the eye, Cross-Encoder disentangles the features using a latent-code-swapping mechanism on eye-consistent image pairs and gaze-similar ones. Specifically, each image is encoded as a gaze feature and an eye feature. Cross-Encoder is trained to reconstruct each image in the eye-consistent pair according to its gaze feature and the other’s eye feature, but to reconstruct each image in the gaze-similar pair according to its eye feature and the other’s gaze feature. Experimental results show the validity of our work. First, using the Cross-Encoder-learned gaze representation, the gaze estimator trained with very few samples outperforms the ones using other unsupervised learning methods, under both within-dataset and cross-dataset protocol. Second, ResNet18 pretrained by Cross-Encoder is competitive with state-of-the-art gaze estimation methods. Third, ablation study shows that Cross-Encoder disentangles the gaze feature and eye feature.
Every Cloud Has a Silver Lining: Exploring Experiential Knowledge and Assets of Family Caregivers

Family caregivers of patients with chronic conditions often subject themselves to drastic life changes. The quality of life of the caregivers often decreases noticeably at the beginning of the caregiving trajectory, because they typically reorient their lives to focus on the patient's health status. As a result, previous studies viewed caregivers primarily as people who need help and focused on how technologies can support them. However, in our study, we found that caregivers are also capable of developing their own experiential knowledge and strategies, which have been invisible in previous caregiver studies. By conducting in-home interviews with fourteen family caregivers, we present the types of new knowledge and coping strategies family caregivers have developed from their lived experiences during everyday caregiving tasks. These include 1) establishing new mindsets, 2) developing mindful activities, 3) building relationship management strategies, and 4) sharing experiences with people in their own networks. Based on our findings, we call for an asset-based approach that will help researchers notice the capabilities of caregivers. This approach could enable researchers to be more reflexive in the incorporation of caregivers' devalued knowledge within their system designs.
Modeling the Uncertainty for Self-supervised 3D Skeleton Action Representation Learning

Self-supervised learning (SSL) has been proved very effective in learning representations from unlabeled data in language and vision domains. Yet, very few instrumental self-supervised approaches exist for 3D skeleton action understanding, and directly applying the existing SSL methods from other domains for skeleton action learning may suffer from misalignment of representations and some limitations. In this paper, we consider that a good representation learning encoder can distinguish the underlying features of different actions, which can make the similar motions closer while pushing the dissimilar motions away. There exists, however, some uncertainties in the skeleton actions due to the inherent ambiguity of 3D skeleton pose in different viewpoints or the sampling algorithm in contrastive learning, thus, it is ill-posed to differentiate the action features in the deterministic embedding space. To address these issues, we rethink the distance between action features and propose to model each action representation into the probabilistic embedding space to alleviate the uncertainties upon encountering the ambiguous 3D skeleton inputs. To validate the effectiveness of the proposed method, extensive experiments are conducted on Kinetics, NTU60, NTU120, and PKUMMD datasets with several alternative network architectures. Experimental evaluations demonstrate the superiority of our approach and through which, we can gain significant performance improvement without using extra labeled data.
HelpViz: Automatic Generation of Contextual Visual Mobile Tutorials from Text-Based Instructions

We present HelpViz, a tool for generating contextual visual mobile tutorials from text-based instructions that are abundant on the web. HelpViz transforms text instructions to graphical tutorials in batch, by extracting a sequence of actions from each text instruction through an instruction parsing model, and executing the extracted actions on a simulation infrastructure that manages an array of Android emulators. The automatic execution of each instruction produces a set of graphical and structural assets, including images, videos, and metadata such as clicked elements for each step. HelpViz then synthesizes a tutorial by combining parsed text instructions with the generated assets, and contextualizes the tutorial to user interaction by tracking the user’s progress and highlighting the next step. Our experiments with HelpViz indicate that our pipeline improved tutorial execution robustness and that participants preferred tutorials generated by HelpViz over text-based instructions. HelpViz promises a cost-effective approach for generating contextual visual tutorials for mobile interaction at scale.
Deep Learning for Multiple-Image Super-Resolution of Sentinel-2 Data

Super-resolution (SR) reconstruction is a common term for techniques aimed at generating a high-resolution image from a single low-resolution image or multiple images showing the same scene. Multiple-image SR benefits from data fusion which allows for more accurate reconstruction of the underlying high-resolution information. Deep learning is extensively used for single-image SR, but its application to multiple-image SR is much less explored. Recently, several deep networks were proposed to enhance Proba-V images, and in this paper, we focus on employing them to super-resolve the Sentinel-2 images. In particular, we investigate the influence of the training data, including real and simulated low-resolution images, on the final SR outcome. Also, we make the simulated data publicly available.
Zero-Knowledge Proof Protocol for Cryptarithmetic Using Dihedral Cards

Cryptarithmetic, also known as Verbal Arithmetic or Word Addition, is a popular pencil puzzle in which the aim is to deduce which letter corresponds to which numeral, given a mathematical equation in which each numeral (from 0 to 9) has been replaced with a unique letter. The most famous instance of this puzzle is probably "SEND + MORE = MONEY", whose solution is "9567 + 1085 = 10652", i.e., S = 9, E = 5, N = 6, D = 7, M = 1, O = 0, R = 8, and Y = 2. In this study, we construct a physical zero-knowledge proof protocol for a Cryptarithmetic puzzle: That is, our protocol enables a prover who knows a solution to the puzzle to convince a verifier that he/she knows the solution without revealing any information about it. The proposed protocol uses a physical deck of "dihedral cards," which were developed by Shinagawa in 2019.
Relighting Humans in the Wild: Monocular Full‐Body Human Relighting with Domain Adaptation

Abstract The modern supervised approaches for human image relighting rely on training data generated from 3D human models. However, such datasets are often small (e.g., Light Stage data with a small number of individuals) or limited to diffuse materials (e.g., commercial 3D scanned human models). Thus, the human relighting techniques suffer from the poor generalization capability and synthetic‐to‐real domain gap. In this paper, we propose a two‐stage method for single‐image human relighting with domain adaptation. In the first stage, we train a neural network for diffuse‐only relighting. In the second stage, we train another network for enhancing non‐diffuse reflection by learning residuals between real photos and images reconstructed by the diffuse‐only network. Thanks to the second stage, we can achieve higher generalization capability against various cloth textures, while reducing the domain gap. Furthermore, to handle input videos, we integrate illumination‐aware deep video prior to greatly reduce flickering artifacts even with challenging settings under dynamic illuminations.
A Multi-Image Cryptosystem Using Quantum Walks and Chebyshev Map

The ubiquity of image and video applications in our daily lives makes data security and privacy prominent concerns for everyone. Among others, various image cryptosystems are relied upon to provide the necessary safeguards. With the inevitable realisation of quantum computing hardware, however, the anticipated quantum supremacy entails effortless violation of the integrity of even the best cryptosystems. Quantum walks (QWs) utilise the potent properties of quantum mechanics to provide randomness via stochastic transitions between states. Our study exploits these properties of QWs to design a multi-image cryptosystem. Furthermore, we infuse the symmetricity and orthogonality of Chebyshev maps into the QWs to realise a powerful cryptosystem that guarantees data integrity, authentication, and anonymity of the resulting images. These properties are validated via extensive simulation-based experiments that produce average values of NPCR as 99.606%, UACI as 33.45%, global entropy as 7.9998, and chi-square test as 238.14. Therefore, the proposed cryptosystem provides ordnance to protect images from illicit tampering during the era.
Impact of receiver imbalances on the security of continuous variables quantum key distribution

Abstract Continuous-variable quantum key distribution (CV-QKD) provides a theoretical unconditionally secure solution to distribute symmetric keys among users in a communication network. However, the practical devices used to implement these systems are intrinsically imperfect, and, as a result, open the door to eavesdropper attacks. In this work, we show the impact of receiver device imperfections on the estimated channel parameters, performance and security of a CV-QKD system. The presented results show that, due to the erroneously estimated channel parameters, non-monitored imbalances can pose a security risk or even reduce the system’s performance. Our results show the importance of monitoring these imbalances and hint at the possibility of compensating for some receiver imbalances by tuning other components.
Transciphering Framework for Approximate Homomorphic Encryption

Homomorphic encryption (HE) is a promising cryptographic primitive that enables computation over encrypted data, with a variety of applications including medical, genomic, and financial tasks. In Asiacrypt 2017, Cheon et al. proposed the \(\mathsf {CKKS}\) scheme to efficiently support approximate computation over encrypted data of real numbers. HE schemes including \(\mathsf {CKKS}\), nevertheless, still suffer from slow encryption speed and large ciphertext expansion compared to symmetric cryptography.
International insights about a holistic model of teaching competence for a digital era: the digital teacher framework reviewed

This paper qualitative examines a holistic framework for teaching in the digital era. The examination is based on teachers’ perspectives of their career, that is, what core features can be said to characterise teacher’s practice, across contexts, cultures and subjects. Semi-structured interviews of expert teachers, specifically from Australia, Europe and the Latin-America, are the main data source. The design process sought to examine teachers’ approaches to their professional definition and their agency to enact this, against a theoretically validated framework. Results indicated the emergence of three defining categories based on the validation and synthesis of the relationships between six elements in the theoretical model.
On Language Model Integration for RNN Transducer Based Speech Recognition

The mismatch between an external language model (LM) and the implicitly learned internal LM (ILM) of RNN-Transducer (RNN-T) can limit the performance of LM integration such as simple shallow fusion. A Bayesian interpretation suggests to remove this sequence prior as ILM correction. In this work, we study various ILM correction-based LM integration methods formulated in a common RNN-T framework. We provide a decoding interpretation on two major reasons for performance improvement with ILM correction, which is further experimentally verified with detailed analysis. We also propose an exact-ILM training framework by extending the proof given in the hybrid autoregressive transducer, which enables a theoretical justification for other ILM approaches. Systematic comparison is conducted for both in-domain and cross-domain evaluation on the Librispeech and TED-LIUM Release 2 corpora, respectively. Our proposed exact-ILM training can further improve the best ILM method.
Position-Augmented Transformers with Entity-Aligned Mesh for TextVQA

In addition to visual components, many images usually contain valuable text information, which is essential for understanding the scene. Thus, we study the TextVQA task that requires reading texts in images to answer corresponding questions. However, most of previous works utilize sophisticated graph structure and manually crafted features to model the position relationship between visual entities and texts in images. And traditional multimodal transformers cannot effectively capture relative position information and original image features. To address these issues in an intuitive but effective way, we propose a novel model, position-augmented transformers with entity-aligned mesh, for the TextVQA task. Different from traditional attention mechanism in transformers, we explicitly introduce continuous relative position information of objects and OCR tokens without complex rules. Furthermore, we replace the complicated graph structure with intuitive entity-aligned mesh according to perspective mapping. In this mesh, the information of discrete entities and image patches at different positions can interact with each other. Extensive experiments on two benchmark datasets (TextVQA and ST-VQA) show that our proposed model is superior to several state-of-the-art methods.
Educational Videos Subtitles’ Summarization Using Latent Dirichlet Allocation and Length Enhancement

Nowadays, people use online resources such as educational videos and courses. However, such videos and courses are mostly long and thus, summarizing them will be valuable. The video contents (visual, audio, and subtitles) could be analyzed to generate textual summaries, i.e., notes. Videos’ subtitles contain significant information. Therefore, summarizing subtitles is effective to concentrate on the necessary details. Most of the existing studies used Term Frequency–Inverse Document Frequency (TF-IDF) and Latent Semantic Analysis (LSA) models to create lectures’ summaries. This study takes another approach and applies Latent Dirichlet Allocation (LDA), which proved its effectiveness in document summarization. Specifically, the proposed LDA summarization model follows three phases. The first phase aims to prepare the subtitle file for modelling by performing some preprocessing steps, such as removing stop words. In the second phase, the LDA model is trained on subtitles to generate the keywords list used to extract important sentences. Whereas in the third phase, a summary is generated based on the keywords list. The generated summaries by LDA were lengthy; thus, a length enhancement method has been proposed. For the evaluation, the authors developed manual summaries of the existing “EDUVSUM” educational videos dataset. The authors compared the generated summaries with the manual-generated outlines using two methods, (i) Recall-Oriented Understudy for Gisting Evaluation (ROUGE) and (ii) human evaluation. The performance of LDA-based generated summaries outperforms the summaries generated by TF-IDF and LSA. Besides reducing the summaries’ length, the proposed length enhancement method did improve the summaries’ precision rates. Other domains, such as news videos, can apply the proposed method for video summarization.
Automatic feature extraction and matching modelling for highly noise near-equatorial satellite images

Abstract Feature extraction plays an important role in pattern recognition because band-to-band registration and geometric correction from different satellite images have linear image distortion. However, new near-equatorial orbital satellite system (NEqO) images is different because they have nonlinear distortion. Conventional techniques cannot overcome this type of distortion and lead to the extraction of false features and incorrect image matching. This research presents a new method by improving the performance of the Scale-Invariant Feature Transformation (SIFT) with a significantly higher rate of true extracted features and their correct matching. The data in this study were obtained from the RazakSAT satellite covering a part of Penang state, Malaysia. The method consists of many stages: image band selection, image band compression, image sharpening, automatic feature extraction, and applying the sum of absolute difference algorithm with an experimental and empirical threshold. We evaluate a refined features scenario by comparing the result of the original extracted SIFT features with corresponding features of the proposed method. The result indicates accurate and precise performance of the proposed method from removing false SIFT extracted features of satellite images and remain only true SIFT extracted features, that leads to reduce the extracted feature from using three frame size: (1) from 2000 to 750, 552 and 92 for the green and red bands image, (2) from 678 extracted control points to be 193, 228 and 73 between the green and blue bands, and (3) from 1995 extracted CPs to be 656, 733, and 556 between the green and near-infrared bands, respectively.