Reviewerly/exHarmony huggingface dataset:
Identifying Tax Evasion in Mexico with Tools from Network Science and Machine Learning

Mexico has kept electronic records of all taxable transactions since 2014. Anonymized data collected by the Mexican federal government comprises more than 80 million contributors (individuals and companies) and almost 7 billion monthly-aggregations of invoices among contributors between January 2015 and December 2018. This data includes a list of almost ten thousand contributors already identified as tax evaders, due to their activities fabricating invoices for non-existing products or services so that recipients can evade taxes. Harnessing this extensive dataset, we build monthly and yearly temporal networks where nodes are contributors and directed links are invoices produced in a given time slice. Exploring the properties of the network neighborhoods around tax evaders, we show that their interaction patterns differ from those of the majority of contributors. In particular, invoicing loops between tax evaders and their clients are over-represented. With this insight, we use two machine-learning methods to classify other contributors as suspects of tax evasion: deep neural networks and random forests. We train each method with a portion of the tax evader list and test it with the rest, obtaining more than 0.9 accuracy with both methods. By using the complete dataset of contributors, each method classifies more than 100 thousand suspects of tax evasion, with more than 40 thousand suspects classified by both methods. We further reduce the number of suspects by focusing on those with a short network distance from known tax evaders. We thus obtain a list of highly suspicious contributors sorted by the amount of evaded tax, valuable information for the authorities to further investigate illegal tax activity in Mexico. With our methods, we estimate previously undetected tax evasion in the order of $10 billion USD per year by about 10 thousand contributors.
Deep learning <scp>super‐resolution</scp> electron microscopy based on deep residual attention network

Abstract Field‐emission scanning electron microscopy has become a fundamental research tool in the fields of medicine and materials science owing to its effectiveness. However, an inherent contradiction exists between the resolution of field‐emission scanning electron microscopy and its field‐of‐view. To solve this problem, we propose a deep learning‐based method for electron microscopy that can simultaneously obtain a large field‐of‐view and ultrahigh resolution. To solve the super‐resolution problem, a deep residual attention network is designed based on residual learning and the attention mechanism, wherein the backbone network employs several residual groups to effectively extract the features; moreover, attention groups append the backbone part to refine and fuse the features. Besides, a high‐frequency information retention module is added to acquire high‐frequency signals, acting as an effective complement to the deep residual attention network. Owing to the lack of super‐resolution datasets for electron microscopy, we created a microscopic butterfly wing dataset. In the experiments, MixUp was also applied to super‐resolution problem as a simple and effective data augmentation method, to provide more data to train the model. To evaluate the proposed method, we used standard and self‐made datasets with peak signal‐to‐noise ratio (PSNR) and structural similarity (SSIM) as criteria. The results show that the proposed model exhibits a state‐of‐the‐art performance with a PSNR of 25.12 dB and an SSIM of 0.64, and makes field‐emission scanning electron microscopy more practical and promising in the field of optical device optimization.
Application of Improved Self-Adaptive Weighted Median Filtering Algorithm in Neutron Radiography

In neutron radiography, the gamma ray associated with the neutron beam produced by the neutron source has a strong penetration ability. Some γ rays pass through the detected object together with neutrons, and then hit the neutron conversion screen and participate in the imaging process of the object. And some γ rays go directly through the shield to the detection surface of CCD camera. These two kinds of γ rays will cause a large number of random high brightness gamma white spots in the neutron images, which greatly interferes with the neutron imaging non-destructive detection and the subsequent quantitative analysis. Thus, eliminating gamma white spot noise in the neutron images is of great significance to the application and development of neutron radiography system. Traditional noise removal algorithms are difficult to remove the noise completely, and they may destroy the image texture information. In this paper, an improved self-adaptive weighted median filtering algorithm was proposed, which realized the dynamic detection of image noise and the weighted median filtering algorithm of self-adaptive window expansion. The weight values and spatial position of pixels in the filtering process were stored in the spiral data structure, which reduced the computational redundancy. The experimental results showed that the proposed algorithm could effectively remove the high brightness gamma white spot noise in neutron images and protect the image details. In addition, the algorithm can also be used in the application of high intensity impulse noise removal, with good stability and universality.
On Understanding the Relation of Knowledge and Confidence to Requirements Quality

[Context and Motivation] Software requirements are affected by the knowledge and confidence of software engineers. Analyzing the interrelated impact of these factors is difficult because of the challenges of assessing knowledge and confidence. [Question/Problem] This research aims to draw attention to the need for considering the interrelated effects of confidence and knowledge on requirements quality, which has not been addressed by previous publications. [Principal ideas/results] For this purpose, the following steps have been taken: 1) requirements quality was defined based on the instructions provided by the ISO29148:2011 standard, 2) we selected the symptoms of low qualified requirements based on ISO29148:2011, 3) we analyzed five Software Requirements Specification (SRS) documents to find these symptoms, 3) people who have prepared the documents were categorized in four classes to specify the more/less knowledge and confidence they have regarding the symptoms, and 4) finally, the relation of lack of enough knowledge and confidence to symptoms of low quality was investigated. The results revealed that the simultaneous deficiency of confidence and knowledge has more negative effects in comparison with a deficiency of knowledge or confidence. [Contribution] In brief, this study has achieved these results: 1) the realization that a combined lack of knowledge and confidence has a larger effect on requirements quality than only one of the two factors, 2) the relation between low qualified requirements and requirements engineers' needs for knowledge and confidence, and 3) variety of requirements engineers' needs for knowledge based on their abilities to make discriminative and consistent decisions.
SQL Injection Vulnerability Identification from Text

Increasing usage of Information Technology (IT) applications in distributed environment is leading to an increase in security exploits. Vulnerabilities related information is also available on open web in an unstructured format that developers may leverage to fix security weaknesses in their IT applications. SQL Injection (SQLi) is one of the topmost vulnerabilities impacting the security of IT applications. We propose an approach to identify information about SQLi in text using text summarization to process any length of text, and a supervised machine learning model to automate the classification of SQLi. To validate the proposed approach, we created a dataset of 100,019 entries that includes 50,010 entries of SQLi from the National Vulnerability Database, 25,010 near negatives related to other cyber security vulnerabilities, and 24,999 data entries that are unrelated to cyber security. The selected Random Forest model was also tested identify SQLi from Web and Twitter text.
Virtual machine preserving host updates for zero day patching in public cloud

Host software updates are critical to ensure the security, reliability and compliance of public clouds. Many updates require a virtualization component restart or operating system reboot. Virtual machines (VMs) running on the updated servers must either be restarted or live migrated off. Reboots can result in downtime for the VMs on the order of ten minutes, and has further impact on the workloads running in the VMs because cached state is lost. Live migration (LM) is a technology that can avoid the need to shutdown VMs. However, LM requires turn space in the form of already-patched hosts, consumes network, CPU and other resources that scale with the amount of and level of activity of VM, and has variable impact on VM performance and availability, making it too expensive and disruptive for zero-day security updates that must be applied across an entire fleet on the order of hours. We present a novel update technology, virtual machine preserving host updates (VM-PHU), that does not require turn space, consumes no network and little CPU, preserves VM state, and causes minimal VM blackout time that does not scale with VM resource usage. VM-PHU persists the memory and device state of all running guest VMs, reboots the host and virtualization components into updated code, restores the state of the VMs, and then resumes them. VM-PHU makes use of several techniques to minimize VM blackout time. One is to use kernel soft reboot (KSR) to directly transition to an updated host operating system, bypassing firmware reset of the server and attached devices. To minimize resource consumption and VM disruption, VM-PHU leaves VM memory in physical memory pages and other state in persisted pages across the soft reboot, and VM-PHU implements a mechanism called fast close to enable a reboot to proceed without waiting for the completion of in-flight VM I/Os to remote storage devices. We have implemented VM-PHU in Microsoft Azure hosting millions of servers and show results of several zero-day updates that demonstrate VM blackout times on the order of seconds. VM-PHU provides significant benefits to both customers and public cloud vendors by minimizing application downtime while enabling fast and resource efficient updates, including zero-day patches.
Cost minimization for deploying serverless functions

The costs of serverless functions increase proportional to the amount of memory reserved on the deployed server. However, increasing the amount of memory decreases the function execution time, which is also a factor that contributes to cost. We propose an automated approach for optimizing the amount of memory reserved for serverless functions. First, we measure the running time of a given function in various memory settings and derive a regression model. Then, we define an objective function and a set of constraints based on this regression model and the configuration space. Finally, we determine the optimal memory setting for minimizing cost. Our industrial case study shows that significant cost reductions can be achieved by accurate estimations of the impact of memory settings on runtime performance.
What is DOM XSS and why should you care?

For more than 10 years, cross-site scripting (XSS) has been included in OWASP's ‘Top 10 Web Application Security Risks’. 1 Although the issue is very well documented, it is sometimes overlooked by application owners. As a result, penetration testers frequently encounter this vulnerability, which takes several forms. For more than 10 years, cross-site scripting (XSS) has been included in OWASP's top web application security risks. Yet it's an issue that is often overlooked. The problem has also evolved and many people still do not understand the risks surrounding Document Object Model (DOM) or client-side XSS. Through understanding the vector itself, threat surfaces of applications will become ever-more restricted and safer for the end user. A thorough understanding of DOM-based XSS will help balance the scales of application security and usability, says Josh Hickling of Pentest People.
Unsupervised cross-domain person re-identification with self-attention and joint-flexible optimization

Unsupervised domain adaptation (UDA) for person re-identication (ReID) remains a challenging task, as the trained ReID system often fails to adapting to a new dataset. Due to the lack of supervision of real labels, the performance of the UDA models suffers from inefficient feature learning and inevitable pseudo label noise. In this work, we tackle the problems by designing an effective dual-path mutual-learning framework which can capture effective information for better feature learning and mitigate the impact of label noise. Firstly, to reduce the impact of occlusion and viewpoints, we introduce the self-attention mechanism in a two-stage strategy making the models focus on the key areas of identifying people. Secondly, considering that UDA is an open-set task, we leverage density-based spatial clustering of applications with noise (DBSCAN) to avoid manually setting the number of classes of the target domain. Thirdly, for realizing joint and flexible optimization under the supervision of soft pseudo labels and hard pseudo labels, a joint and flexible loss (JFL) is proposed to train the network. Experiments on three large-scale datasets show that our model outperforms the state-of-the-art UDA methods in both mAP and top-1 evaluation protocols by large margins. Especially on task of Duke-to-Market, our method outperforms the state-of-the-art by 6.9% mAP.
DaNetQA: A Yes/No Question Answering Dataset for the Russian Language

DaNetQA, a new question-answering corpus, follows BoolQ [] design: it comprises natural yes/no questions. Each question is paired with a paragraph from Wikipedia and an answer, derived from the paragraph. The task is to take both the question and a paragraph as input and come up with a yes/no answer, i.e. to produce a binary output. In this paper, we present a reproducible approach to DaNetQA creation and investigate transfer learning methods for task and language transferring. For task transferring we leverage three similar sentence modelling tasks: 1) a corpus of paraphrases, Paraphraser, 2) an NLI task, for which we use the Russian part of XNLI, 3) another question answering task, SberQUAD. For language transferring we use English to Russian translation together with multilingual language fine-tuning.
On the instability of embeddings for recommender systems

Most state-of-the-art top-N collaborative recommender systems work by learning embeddings to jointly represent users and items. Learned embeddings are considered to be effective to solve a variety of tasks. Among others, providing and explaining recommendations. In this paper we question the reliability of the embeddings learned by Matrix Factorization (MF). We empirically demonstrate that, by simply changing the initial values assigned to the latent factors, the same MF method generates very different embeddings of items and users, and we highlight that this effect is stronger for less popular items. To overcome these drawbacks, we present a generalization of MF, called Nearest Neighbors Matrix Factorization (NNMF). The new method propagates the information about items and users to their neighbors, speeding up the training procedure and extending the amount of information that supports recommendations and representations. We describe the NNMF variants of three common MF approaches, and with extensive experiments on five different datasets we show that they strongly mitigate the instability issues of the original MF versions and they improve the accuracy of recommendations on the long-tail.
Euclidean and hyperbolic asymmetric topological quantum codes

In the last three decades, several constructions of quantum error-correcting codes were presented in the literature. Among these codes, there are the asymmetric ones, i.e., quantum codes whose Z-distance $$d_z$$ is different from its X-distance $$d_x$$ . The topological quantum codes form an important class of quantum codes, where the toric code, introduced by Kitaev, was the first family of this type. After Kitaev’s toric code, several authors focused attention on investigating its structure and the constructions of new families of topological quantum codes over Euclidean and hyperbolic surfaces. As a consequence of establishing the existence and the construction of asymmetric topological quantum codes in Theorem 5.1, the main result of this paper, we introduce the class of hyperbolic asymmetric codes. Hence, families of Euclidean and hyperbolic asymmetric topological quantum codes are presented. An analysis regarding the asymptotic behavior of their distances $$d_x$$ and $$d_z$$ and encoding rates k/n versus the compact orientable surface’s genus is provided due to the significant difference between the asymmetric distances $$d_x$$ and $$d_z$$ when compared with the corresponding parameters of topological codes generated by other tessellations. This inherent unequal error protection is associated with the nontrivial homological cycle of the $$\{r,s\}$$ tessellation and its dual, which may be appropriately explored depending on the application, where $$r\ne s$$ and $$(r-2)(s-2)\ge 4$$ . Three families of codes derived from the $$\{7,3\}$$ , $$\{5,4\}$$ , and $$\{10,5\}$$ tessellations are highlighted.
Culture as an attribute of digital transformation

The article comprehends the concept of digital culture that appeared with the beginning of the information/digital era at the end of the last century. The paper considers digital culture from the point of view of the general resource of the knowledge society and as a new social ecology that determines the experience and capabilities of people at the present time, when the digital network environment has brought new practices, opportunities and threats. Speaking about culture in the context of information and communication technologies, the authors emphasize that its influence is even more significant, since the way it is used can affect the change in the essence of our communication and cultural models and become a support for the digital transformation of organizations. Digital culture is understood as the way that society uses to receive and process information. It is required to comprehend and create a new digital culture as a guarantor of the implementation and adoption of changes in the context of widespread digitalization and rethinking of business processes, distribution channels and relations with consumers, leading, in turn, to a change in the value proposition and the consumer segments themselves. Based on the analysis, the authors formulate and substantiate five main reasons why building a digital culture should become a key problem for the successful development of any organization: 1) digital transformation will not be complete without the development of a thriving digital culture; 2) insufficient organizational culture is detrimental to success in the digital age; 3) a strong digital culture is a competitive advantage for an organization; 4) a strong digital culture is the key to business longevity; 5) using a digital culture can increase employee engagement in the work process.
An Efficient Practical Non-Blocking PageRank Algorithm for Large Scale Graphs

PageRank algorithm is a benchmark for many graph analytics and is the underlying kernel for link predictions, recommendation systems. It is an iterative algorithm that updates ranks of pages until the value converges. Implementation of PageRank algorithm on a shared memory architecture while taking advantage of fine-grained parallelism using large-scale graphs is a challenging task. In this paper, We present parallel algorithms for computing the PageRank suitable to the shared memory systems. Initially, we present parallel implementations of page-rank algorithms using barrier and lock variants. Later, we propose new approaches which are lock-free and are barrier-less synchronization to overcome the issues of lock based methods. A detailed experimental analysis of our approach is carried out using real-world web graphs from SNAP and Synthetic Graphs from RMAT on an Intel(R) Xeon E5-2660 v4 processor architecture with 56 threads using the POSIX thread library.
Organizational Security Policy and Management during Covid-19

Protection of an organization's assets and information technology infrastructure is always crucial to any business. Securing and protecting businesses from cybersecurity threats became very challenging during the Covid-19 Pandemic. Organizations suddenly shifted towards remote work to maintain continuity and protecting against new cyber threats became a big concern for most business owners. This research looks into the following areas (i) outlining the shift from In-person to online work risks (ii) determine the cyber-attack type based on the list of 10 most prominent cybersecurity threats during the Covid-19 Pandemic (iii) and design a security policy to securing business continuity.
On ⁎-clean group rings over finite fields

A ring R is called clean if every element of R is the sum of a unit and an idempotent. Motivated by a question proposed by Lam on the cleanness of von Neumann Algebras, Vaš introduced a more natural concept of cleanness for ⁎-rings, called the ⁎-cleanness. More precisely, a ⁎-ring R is called a ⁎-clean ring if every element of R is the sum of a unit and a projection (⁎-invariant idempotent). Let F be a finite field and G a finite abelian group. In this paper, we introduce two classes of involutions on group rings of the form FG and characterize the ⁎-cleanness of these group rings in each case. When ⁎ is taken as the classical involution, we also characterize the ⁎-cleanness of FqG in terms of LCD abelian codes and self-orthogonal abelian codes in FqG.
Deep Learning Algorithms-based Object Detection and Localization Revisited

Abstract The computer vision (CV) is an emerging area with sundry promises. This communication encompasses the past development, recent trends and future directions of the CV in the context of deep learning (DL) algorithms-based object detections and localizations techniques. To identify the object location inside an image and recognize it by a computer program as fast as the human brain the machine learning and DL techniques have been evolved. However, the main limitations of the machine are related to the prolonged time consumption to handle vast amount of data to perform the same task as the human brain. To overcome these shortcomings, the convolution neural networks (NNs)-based deep NN has been developed, which detects and classifies the object with high precision. To train the deep NNs, massive amount of data (in the form of images and videos) and time is needed, making the computational cost of the CV very high. Thus, transfer learning techniques have been proposed wherein a model trained on one task can be reused on another linked task, thereby producing excellent outcomes. In this spirit, diverse DL-based algorithms have been introduced to detect and classify the object. These algorithms include the region-based convolutional NN (R-CNN), fast R-CNN, Faster R-CNN, mask E-CNN and You Only Look Once. A comparative evaluation among these techniques has been made to reveal their merits and demerits in the CV.