Reviewerly/exHarmony huggingface dataset:
Improving vulnerability remediation through better exploit prediction

Abstract Despite significant innovations in IT security products and research over the past 20 years, the information security field is still immature and struggling. Practitioners lack the ability to properly assess cyber risk, and decision-makers continue to be paralyzed by vulnerability scanners that overload their staff with mountains of scan results. In order to cope, firms prioritize vulnerability remediation using crude heuristics and limited data, though they are still too often breached by known vulnerabilities for which patches have existed for months or years. And so, the key challenge firms face is trying to identify a remediation strategy that best balances two competing forces. On one hand, it could attempt to patch all vulnerabilities on its network. While this would provide the greatest ‘coverage’ of vulnerabilities patched, it would inefficiently consume resources by fixing low-risk vulnerabilities. On the other hand, patching a few high-risk vulnerabilities would be highly ‘efficient’, but may leave the firm exposed to many other high-risk vulnerabilities. Using a large collection of multiple datasets together with machine learning techniques, we construct a series of vulnerability remediation strategies and compare how each perform in regard to trading off coverage and efficiency. We expand and improve upon the small body of literature that uses predictions of ‘published exploits’, by instead using ‘exploits in the wild’ as our outcome variable. We implement the machine learning models by classifying vulnerabilities according to high- and low-risk, where we consider high-risk vulnerabilities to be those that have been exploited in actual firm networks.
Differential evolution algorithm with multi-population cooperation and multi-strategy integration

Differential evolution with a multi-population based ensemble of mutation strategies (MPEDE) has been considered among the most efficient Evolutionary Algorithms for global optimization. Our research results reveal that the performance of MPEDE may be improved by adding an information sharing mechanism and modifying the grouping mechanism. In MPEDE, the entire population is divided into four subpopulations, and most computing resources are allocated to the best strategy, but a better strategy has the same computing resources as the worst strategy. In order to rationally distribute computational resources, a differential evolution variant with multi-population cooperation and multi-strategy integration (MPMSDE) is proposed in this paper. MPMSDE develops a new grouping method instead of the grouping method in MPEDE, and the new grouping method utilizes the ranking of strategies to assign computational resources to different strategies. Also, an information sharing mechanism is introduced in the largest sub-population to avoid falling into local optimum. In MPMSDE, a new mutation strategy, "DE/pbad-to-pbest-to-gbest/1", is used to replace the mutation strategy "DE/rand/1" in MPEDE. The new strategy not only uses personal history optimal solution and the worst solution but also uses the global best solution to update individuals. The new strategy can not only balance exploration and exploitation but also can accelerate the convergence of the algorithm. The performance of MPMSDE is compared with MPEDE and other state-of-the-art evolutionary algorithms on CEC2005 and CEC2014 benchmark functions. The experimental results show that the performance of the MPMSDE algorithm is very competitive in calculation accuracy and convergence speed.
Fintech in financial reporting and audit for fraud prevention and safeguarding equity investments

Purpose The purpose of this paper is to explore the audit-related causes of financial scandals and advice on how emerging technologies can provide solutions thereto. Specifically, this study seeks to look at the facilitators of financial statement fraud and explain specific fintech advancements that contribute to financial information reliability for equity investments. Design/methodology/approach The study uses the case studies of Enron and Arthur Andersen to document the evidence of audit-related issues in historical financial scandals. Then, a comprehensive and interdisciplinary literature review at the intersection of business, accounting and engineering, provides a foundation to propose technology advancements that can solve identified problems in accounting and auditing. Findings The findings show that blockchain, internet of things, smart contracts and artificial intelligence solutions have different functionality and can effectively solve various financial reporting and audit-related problems. Jointly, they have a strong potential to enhance the reliability of the information in financial statements and generally change how companies operate. Practical implications The proposed and explained technology advancements should be of interest to all publicly listed companies and investors, as they can help safeguard equity investments, thus build investors’ trust towards the company. Social implications Aside from implications for capital markets participants, the study findings can materially benefit various stakeholder groups, the broader company environment and the economy. Originality/value This is the first paper that seeks solutions to financial fraud and audit-related financial scandals in technology and not in implementing yet another regulation. Given the recent technology advancements, the study findings provide insights into how the role of an external auditor might evolve in the future.
Influencers and COVID-19: reviewing key issues in press coverage across Australia, China, Japan, and South Korea

As COVID-19 broke out across the Asia Pacific from December 2019, media coverage on its impacts proliferated online. Among these discourses, coverage on influencers was prominent, likely as many of the issues arising from COVID-19 contingencies – such as digitalization, public messaging, and misinformation – are cornerstones of this digital economy. In response, this cross-cultural study draws on a corpus of Australian, Chinese, Japanese, and Korean online news articles published between January and May 2020, to understand how local news ecologies were parsing the impacts of COVID-19 on influencers. From the coding of 150 news articles guided by Grounded Theory, this article focuses on the impact of the pandemic on influencers, and influencers’ engagements with and reactions to the pandemic. Our study of individual governments’ past engagements with their influencer industries suggest that local backstories and contexts are crucial to decipher why news angles tend to pitch particular stories on influencers.
Intelligent Resource Allocation for Video Analytics in Blockchain-Enabled Internet of Autonomous Vehicles With Edge Computing

Video surveillance in intelligent transportation systems (ITSs) is in the rapid growth stage, where video analytics is a potential technology to improve the safety of the Internet of Autonomous Vehicles (IoAV). However, massive video data transmission and computation-intensive video analytics bring an overwhelming burden for vehicular networks. Moreover, owing to the unstable network connection, the video data are not always reliable, which makes data sharing a lack of security and scalability in IoAV. In this work, we first propose a video analytics framework, where the multiaccess edge computing (MEC) and blockchain technologies are integrated into IoAV to optimize the transaction throughput of the blockchain system as well as reducing the latency of the MEC system. Furthermore, based on deep reinforcement learning, the joint optimization problem is modeled as a Markov decision process (MDP), and the asynchronous advantage actor–critic (A3C) algorithm is adopted to solve this problem. Simulation results demonstrate that our approach can fast converge and significantly improve the performance of blockchain-enabled IoAV with MEC.
Optical color image encryption based on Hadamard single-pixel imaging and Arnold transformation

An optical color image encryption method based on Hadamard single-pixel imaging (SPI) and Arnold transformation is proposed, in which, optical full-color image encryption with good imaging quality and high security performance can be accomplished by only one bucket detector (BD). In the encryption process, after controlling and modulating the Hadamard matrix by the coefficient matrix key, three different sets of colored Hadamard patterns are generated to be projected onto the target colored object; Subsequently, the collected experimental data: R, G, and B components are detected by a BD, which are then scrambled by Arnold transformation to further increase the security; Finally the colored ciphertext image can be recombined and obtained to transferring to the receivers. During the decryption, with the colored ciphertext and all the corrected keys, the original secret color image can be successfully decrypted by the extraction of R, G, B components, inverse Arnold transformation, the correlated computation in SPI, and inverse Hadamard transformation. The feasibility of this method and its robustness against some types of attacks are verified by both computer simulations and optical experiments.
Private-Blockchain-Based Industrial IoT for Material and Product Tracking in Smart Manufacturing

Automation in smart manufacturing depends on many parties in the supply chain and distribution networks. A smart manufacturer needs to track the location and state of materials from its suppliers and products to its customers/distributors to manage the corresponding production schedule. The need for such information can be fulfilled by the use of Industrial Internet of Things (IIoT) devices installed at each respective party. However, even though all parties may trust one another, they have confidential data produced by the IIoT devices that are not allowed to be shared with other parties, or only allowed to be exchanged with certain parties. Even though the data are not confidential, they may still be required to share partial data based on the needs of each party. On the other hand, all parties have their independent systems that differ from each other, which leads to complex integration. Regarding those situations, a private-blockchain-based IIoT is promising to bridge the need for product and material tracking information exchange between parties while maintaining the respective confidentiality.
Brands in virtual reality games: Affective processes within computer-mediated consumer experiences

Virtual reality (VR) marketing is here to stay—or at least that is what is generally believed. Head-mounted display (HMD) VR hardware is rapidly becoming more accessible to the general public, and businesses have started adopting VR as an experiential marketing platform. In this study, we examine two affective mechanisms that play a role in the workings of branded VR games in an HMD VR context: (1) virtual product appeal and (2) emotional response. We conducted a between-subjects (branded VR game vs. non-branded VR game) lab experiment (N = 81) among young adults who were recruited via the university’s lab recruitment system. During the experiment, participants used HTC Vive hardware with hand-held controllers. The results show that virtual product appeal strengthens the effect of brands in VR games on brand attitude. Moreover, brands in VR games elicit emotional responses, which subsequently drive brand attitude and purchase intention.
Machine Learning Algorithms in Quantum Computing: A Survey

Machine Learning (ML) aims at designing models that learn from previous experience, without being explicitly formulated. Applications of machine learning are inexhaustible, including recognizing patterns, predicting future trends and making decisions, and they are capable of handling sizable quantities of multi-dimensional data in the form of large vectors and tensors. To perform these operations on classical computers, however, requires vast time and computational resources. Unlike the classical computers that rely on computations using binary bits, Quantum Computers (QC) benefit from qubits which can hold combinations of 0 and 1 at the same time via superposition and entanglement. This makes QCs powerful at handling and post processing large tensors, making them a prime target for implementing ML algorithms. While several models used for ML on QCs are based on concepts from their classical computing counterparts, utilization of the QC's potential has made them the superior of the two. This paper presents an overview of the current state of knowledge in application of ML on QC, and evaluates the speed up, and complexity advantages of using quantum machines.
Multiple Instance Learning with Center Embeddings for Histopathology Classification

Histopathology image analysis plays an important role in the treatment and diagnosis of cancer. However, analysis of whole slide images (WSI) with deep learning is challenging given that the duration of pixel-level annotations is laborious and time consuming. To address this, recent methods have considered WSI classification as a Multiple Instance Learning (MIL) problem often with a multi-stage process for learning instance and slide level features. Currently, most methods focus on either instance-selection or instance prediction-aggregation that often fails to generalize and ignores instance relations. In this work, we propose a MIL-based method to jointly learn both instance- and bag-level embeddings in a single framework. In addition, we propose a center loss that maps embeddings of instances from the same bag to a single centroid and reduces intra-class variations. Consequently, our model can accurately predict instance labels and leverages robust hierarchical pooling of features to obtain bag-level features without sacrificing accuracy. Experimental results on curated colon datasets show the effectiveness of the proposed methods against recent state-of-the-art methods.
Particle swarm optimized texture based histogram equalization (PSOTHE) for MRI brain image enhancement

In this paper, a novel particle swarm optimized texture based histogram equalization (PSOTHE) technique is proposed to enhance the contrast of MRI brain images. In general, medical images are of low contrast and affected by noise. So the diagnosis process is inaccurate. To eliminate this problem, it is required to reduce the effect of the artifacts, improve the contrast and minimize the information loss. To achieve this, a novel framework for image enhancement is proposed. As per the proposed framework, the effect of non-texture region in the image is suppressed to reduce the artifacts. Probability density function (PDF) is modified to control the enhancement rate. The threshold for identification of texture region and constraint parameters for modification of PDF are selected using PSO algorithm, guided by proposed fitness function. The selection of these parameters occurred automatically without human intervention which make our proposed framework more adaptive. A novel multi-objective fitness function is proposed in order to get an optimal balanced enhanced image which includes four different objective functions such as entropy, edge contents, contrast, peak signal to noise ratio (PSNR) and each of equal weightage. The demonstration results show the supremacy of the proposed method over other existing methods.
Inference and Prediction Diverge in Biomedicine

<h2>Summary</h2> In the 20<sup>th</sup> century, many advances in biological knowledge and evidence-based medicine were supported by p values and accompanying methods. In the early 21<sup>st</sup> century, ambitions toward precision medicine place a premium on detailed predictions for single individuals. The shift causes tension between traditional regression methods used to infer statistically significant group differences and burgeoning predictive analysis tools suited to forecast an individual's future. Our comparison applies linear models for identifying significant contributing variables and for finding the most predictive variable sets. In systematic data simulations and common medical datasets, we explored how variables identified as significantly relevant and variables identified as predictively relevant can agree or diverge. Across analysis scenarios, even small predictive performances typically coincided with finding underlying significant statistical relationships, but not vice versa. More complete understanding of different ways to define "important" associations is a prerequisite for reproducible research and advances toward personalizing medical care.
A Comprehensive Survey on Local Differential Privacy

With the advent of the era of big data, privacy issues have been becoming a hot topic in public. Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while guaranteeing each individual participant’s privacy. In this paper, we present a comprehensive survey of LDP. We first give an overview on the fundamental knowledge of LDP and its frameworks. We then introduce the mainstream privatization mechanisms and methods in detail from the perspective of frequency oracle and give insights into recent studied on private basic statistical estimation (e.g., frequency estimation and mean estimation) and complex statistical estimation (e.g., multivariate distribution estimation and private estimation over complex data) under LDP. Furthermore, we present current research circumstances on LDP including the private statistical learning/inferencing, private statistical data analysis, privacy amplification techniques for LDP, and some application fields under LDP. Finally, we identify future research directions and open challenges for LDP. This survey can serve as a good reference source for the research of LDP to deal with various privacy-related scenarios to be encountered in practice.
Deep Learning for Historical Document Analysis and Recognition—A Survey

Nowadays, deep learning methods are employed in a broad range of research fields. The analysis and recognition of historical documents, as we survey in this work, is not an exception. Our study analyzes the papers published in the last few years on this topic from different perspectives: we first provide a pragmatic definition of historical documents from the point of view of the research in the area, then we look at the various sub-tasks addressed in this research. Guided by these tasks, we go through the different input-output relations that are expected from the used deep learning approaches and therefore we accordingly describe the most used models. We also discuss research datasets published in the field and their applications. This analysis shows that the latest research is a leap forward since it is not the simple use of recently proposed algorithms to previous problems, but novel tasks and novel applications of state of the art methods are now considered. Rather than just providing a conclusive picture of the current research in the topic we lastly suggest some potential future trends that can represent a stimulus for innovative research directions.
Masked Face Recognition with Generative Data Augmentation and Domain Constrained Ranking

Masked faces recognition (MFR) aims to match a masked face with its corresponding full face, which is an important task especially during the global outbreak of COVID-19. However, most existing face recognition models generalize poorly in this case, and it is hard to train a robust MFR model due to two main reasons: 1) the absence of large scale training data as well as ground truth testing data, and 2) the presence of large intra-class variation between masked faces and full faces. To address the first challenge, this paper firstly contributes a new dataset denoted as MFSR, which consists of two parts. The first part contains 9,742 masked face images with mask region segmentation annotation. The second part contains 11,615 images of 1,004 identities, and each identity has masked and full face images with various orientations, lighting conditions and mask types. However, it is still not enough for training MFR models with deep learning. To obtain sufficient training data, based on the MFSR, we introduce a novel Identity Aware Mask GAN (IAMGAN) with segmentation guided multi-level identity preserve module to generate the synthetic masked face images from the full face images. In addition, to tackle the second challenge, a Domain Constrained Ranking (DCR) loss is proposed by adopting a center-based cross-domain ranking strategy. For each identity, two centers are designed which correspond to the full face images and the masked face images respectively. The DCR forces the feature of masked faces getting closer to its corresponding full face center and vice-versa. Experimental results on the MFSR dataset demonstrate the effectiveness of the proposed approaches.
DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models

With machine learning models being increasingly applied to various decision-making scenarios, people have spent growing efforts to make machine learning models more transparent and explainable. Among various explanation techniques, counterfactual explanations have the advantages of being human-friendly and actionable-a counterfactual explanation tells the user how to gain the desired prediction with minimal changes to the input. Besides, counterfactual explanations can also serve as efficient probes to the models' decisions. In this work, we exploit the potential of counterfactual explanations to understand and explore the behavior of machine learning models. We design DECE, an interactive visualization system that helps understand and explore a model's decisions on individual instances and data subsets, supporting users ranging from decision-subjects to model developers. DECE supports exploratory analysis of model decisions by combining the strengths of counterfactual explanations at instance- and subgroup-levels. We also introduce a set of interactions that enable users to customize the generation of counterfactual explanations to find more actionable ones that can suit their needs. Through three use cases and an expert interview, we demonstrate the effectiveness of DECE in supporting decision exploration tasks and instance explanations.
Response to COVID-19 Pandemic: Where Do Public Libraries Stand?

This paper aims to identify how public libraries used Twitter in the initial months after the outbreak of the COVID-19 pandemic. Content analysis was performed on a dataset of 9,450 tweets published by 38 public libraries in New York City between December 2019 and April 2020. The study showed that 85.5% of the tweets published by NYC public libraries were related to regular library communications, such as announcements of library service updates, book recommendations, suggested readings and activities, celebrations of specific occasions or individuals, and notices of upcoming events. Meanwhile, 14.5% of the tweets published from January/April 2020 were directly and explicitly related to COVID-19. NYC public libraries posted details of the remote library services available during the closure, as well as comforting and socially supportive messages, and provided information about the financial, health, and food-related support available to the community. The findings suggest that during the pandemic, NYC public libraries mostly continued to conduct business as usual and, in doing so, may have brought a valuable feeling of normalcy to the communities they serve during those problematic and strained days.