Reviewerly/exHarmony huggingface dataset:
Text error correction after text recognition based on MacBERT4CSC

With the wide application of text recognition technology, the text recognition model's own limitations and environmental factors interfere with the recognition error rate is high. To address the above situation, a text error correction method based on MacBERT4CSC after text recognition is proposed. Firstly, using the single-word text recognition confidence, we obtain the suspected wrong words and positions by setting thresholds for error detection of the text to be corrected, and then iterate through the constructed confusion set for text error correction in priority, and after the traversal is completed, the MacBERT4CSC model recalls the candidate words with suspected wrong positions, and finally, after the similarity of the word code and the MacBERT4CSC model score meet the set conditions, The error correction is finally completed by sorting the candidate words that meet the conditions. By imitating the error type of word recognition to build the dataset for word recognition and calling Ali's word recognition API for word recognition to obtain the test set to be corrected, the comparison experiments show that the MacBERT4CSC recall and word bar code sorting method improves in accuracy, recall rate and F1 value compared with other deep models and traditional rule error correction methods, which verifies the effectiveness of the method.
AFF-Net: a masked face recognition network based on attention and feature fusion

The context of the COVID-19 pandemic has made an increasing number of people accustomed to wearing masks to avoid getting infected. This phenomenon leads to an urgent demand for face recognition with masks, which poses serious challenge for the accuracy of face recognition. This paper seeks to produce a model called AFF-Net based on an attention mechanism and feature fusion and self-attention to address the masked face recognition by designing four compact modules to achieve the optimization of the network structure to solve the problem of unstable recognition rate and the low model generalization ability. Specifically, we fuse the features of different layers in the improved full connected module to assign weights to the channels, and formulate a specific training strategy to improve the feature learning performance. Experiments on representative datasets indicate that the proposed method can outperform the competing method significantly while maintaining high real-time efficiency of masked face recognition.
A Hybrid GCN and BiLSTM model for news text classification

News text classification is an important subtask in natural language processing. News text is not only unstructured text, but also its structure information is relatively fuzzy. Therefore, there is less useful information that can extract directly. Previous studies often assumed that the text is a linear structure by default. In this paper, we propose a text classification model based on GCN-BiLSTM and adding a Global Average Pooling layer. The hybrid neural network can capture global text information, and solve the problems of gradient disappearance. The Global Average Pooling layer can reduce the data dimension and shortens the training time. The method has a good performance on THUCNews dataset, and the F-score reaches 89.75%.
Research on real-time log analysis system based on elastic stack and Flink

The log data records the operation information of a software platform. The analysis of the log helps the manager to master the platform operation and ensure the normal operation of the platform. The rapid development of information technology has led to an explosive growth of log size. The traditional log processing architecture cannot meet the processing of massive logs. In order to process massive log data in real-time, a real-time log analysis system based on Elastic stack and Flink is designed and implemented. Based on Elastic stack, massive log data is acquired, transferred and stored in real-time. Based on Flink which is a streaming computing framework, log information is analyzed in realtime. Log analysis and statistical results are stored in MySQL database. Visualization is realized through front-end and back-end separation technology. The system has been deployed on a business platform. It can run stably against the daily average log of more than 10G, which proves the stability and reliability of the system.
An attack framework for stealing black-box based on active semi-supervised learning

Neural network models are commonly used as black-box services, but they are vulnerable to model stealing attacks, where an attacker can train a substitute model with similar performance to the original model by exploiting limited information related to the target model. This can cause significant losses to the owner of the target model and pose a serious security risk. To advance our understanding of neural networks and promote the evolution of model protection mechanisms, we conducted in-depth research on neural network model stealing attacks. In this paper, we propose a black-box stealing attack framework that combines active and semi-supervised learning, even if the target black-box only provides hard-label output, an effective attack can be achieved, generating a substitute model with the same functionality as the black-box. The framework involves selectively querying the most informative samples for black-box labeling using active learning, which significantly reduces the workload of querying the black-box and enables to achieve better performance with fewer training samples. We also apply semi-supervised learning to leverage the abundance of unlabeled data and further improve model performance. We evaluated our method on various data sets and proved that the stealing ability of our method was significantly higher than 3.86%~26.64% other methods when faced with hardlabel black-box with the same number of queries, which can achieve effective black-box function stealing.
Mitigating information anxiety in COVID-19 contact tracing for BIPOC Communities

ABSTRACTContact tracing is a crucial process of information exchange in which health institutions share guidance with the public and obtain local health data to curb the spread of disease. But this data collection can exacerbate multiple forms of anxiety, particularly for people who have historically been excluded from and exploited by public health institutions. Given this history, our article centers the perspectives of contact tracers and BIPOC tracees in two US cities: Philadelphia, PA and Providence, RI. Our analysis shows that many BIPOC tracees experience information anxiety, shaped by complex relationships between their communities and health institutions, inconsistent access to care, and privacy concerns. At the same time, contact tracers have the potential to mitigate this anxiety, though this capacity is influenced by the institutional conditions in which they work. By analyzing both tracers’ and tracees’ perspectives on contact tracing, this study contributes grounded and holistic accounts of how public health institutions can better serve local communities, particularly communities of color in urban areas, during public health crises.KEYWORDS: COVID-19contact tracinginformation anxietyracial justice Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by Social Science Research Council: [Grant Number Just Tech COVID-19 Rapid Response Grant].Notes on contributorsJenny LeeJenny Lee is a PhD student at the Annenberg School for Communication at the University of Pennsylvania.Jessa LingelJessa Lingel is an Associate Professor of Communication at the Annenberg School for Communication at the University of Pennsylvania and Core Faculty in the Gender, Sexuality, and Women's Studies department.Alexandra SanchezAlexandra Sanchez is an activist with Free Radicals.Theo Loftis is an activist with Creative Resilience Collective.Amelia MauldinAmelia Mauldin is a recent graduate of the Annenberg School of Communication at the University of Pennsylvania.
An Image Arbitrary-Scale Super-Resolution Network Using Frequency-domain Information

Image super-resolution (SR) is a technique to recover lost high-frequency information in low-resolution (LR) images. Since spatial-domain information has been widely exploited, there is a new trend to involve frequency-domain information in SR tasks. Besides, image SR is typically application-oriented and various computer vision tasks call for image arbitrary magnification. Therefore, in this article, we study image features in the frequency domain to design a novel image arbitrary-scale SR network. First, we statistically analyze LR-HR image pairs of several datasets under different scale factors and find that the high-frequency spectra of different images under different scale factors suffer from different degrees of degradation, but the valid low-frequency spectra tend to be retained within a certain distribution range. Then, based on this finding, we devise an adaptive scale-aware feature division mechanism using deep reinforcement learning, which can accurately and adaptively divide the frequency spectrum into the low-frequency part to be retained and the high-frequency one to be recovered. Finally, we design a scale-aware feature recovery module to capture and fuse multi-level features for reconstructing the high-frequency spectrum at arbitrary scale factors. Extensive experiments on public datasets show the superiority of our method compared with state-of-the-art methods.
Private simultaneous messages based on quadratic residues

Abstract Private Simultaneous Messages (PSM) model is a minimal model for secure multiparty computation. Feige, Kilian, and Naor (STOC 1994) and Ishai (Cryptology and Information Security Series 2013) constructed PSM protocols based on quadratic residues. In this paper, we define QR-PSM protocols as a generalization of these protocols. A QR-PSM protocol is a PSM protocol whose decoding function outputs the quadratic residuosity modulo p of what is computed from messages. We design a QR-PSM protocol for any symmetric function $$f: \{0,1\}^n \rightarrow \{0,1\}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>f</mml:mi> <mml:mo>:</mml:mo> <mml:msup> <mml:mrow> <mml:mo>{</mml:mo> <mml:mn>0</mml:mn> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> <mml:mo>}</mml:mo> </mml:mrow> <mml:mi>n</mml:mi> </mml:msup> <mml:mo>→</mml:mo> <mml:mrow> <mml:mo>{</mml:mo> <mml:mn>0</mml:mn> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> <mml:mo>}</mml:mo> </mml:mrow> </mml:mrow> </mml:math> of communication complexity $$O(n^2)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>n</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> . As far as we know, it is the most efficient PSM protocol for symmetric functions since the previously known best PSM protocol was of $$O(n^2\log n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>n</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mo>log</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> (Beimel et al., CRYPTO 2014). We also study the sizes of the underlying finite fields $$\mathbb {F}_p$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:mi>p</mml:mi> </mml:msub> </mml:math> in the protocols since the communication complexity of a QR-PSM protocol is proportional to the bit length of the prime p . We show that there is a prime $$p \le (1+o(1))N^22^{2N-2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>≤</mml:mo> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo> <mml:mi>o</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> <mml:msup> <mml:mi>N</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:msup> <mml:mn>2</mml:mn> <mml:mrow> <mml:mn>2</mml:mn> <mml:mi>N</mml:mi> <mml:mo>-</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> such that any length- N pattern of quadratic (non)residues appears modulo p (and hence it can be used for general QR-PSM protocols), which improves the Peralta’s known result (Mathematics of Computation 1992) by a constant factor $$(1+\sqrt{2})^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo> <mml:msqrt> <mml:mn>2</mml:mn> </mml:msqrt> <mml:mo>)</mml:mo> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:math> .
Natural language requirements boilerplates: an integrative literature review

In the field of Requirements Engineering, natural language is the most widely used form of documenting systems requirements, mainly for its expressiveness power, freedom of words, and easiness of use. On the other hand, written requirements are frequently ambiguous, incomplete, and incorrect, among other defects, impacting into the system lifecycle. To mitigate this issue, researchers have, since 1998, developed dozens of sets of boilerplates, aiding to guide natural language requirements composition. In this integrative literature review, authors searched for these sets in traditional academic databases, grey literature, and professional media, leading to a list of 54 relevant documents. Analysis of these primary works revealed that, even when boilerplate creators wish their artifacts to be used on all kinds of systems, they are generally cataloged under the Computer Science umbrella. It turned out to be evident that ambiguity is the main issue of quality of requirements addressed by boilerplates, followed by completeness and consistency. Authors mapped the requirements boilerplates definition modes, concluding that plain sentences are the foremost choice to describe these creations. It is claimed that systematization on the construction and evaluation of requirements boilerplates is still a problem to render these activities repeatable and reproducible, as the largest part of documents does not report it. Finally, the reported evaluation methods were hierarchically clustered, leading to three typical approaches for this activity.
Where's the bathroom in this “mobile home”? Adding Water, Sanitation &amp; Hygiene (WaSH) infrastructure to agendas on homelessness and digital media

Although access to water and sanitation have been recognized by the United Nations as human rights since 2010, that access continues to be a site of struggle in everyday life, especially among homeless populations. In this paper, we draw from two summers of a fieldwork-based course on homelessness and Water, Sanitation, and Hygiene in Denver, Colorado to consider whether a Google Map of public restrooms in the city might be useful for unhoused people. The paper's conceptual framework stems from Hartmann's idea of “homing”—which refers to how digital media can help unhoused people create everyday routines, exercise some control over their lives and spaces, and gradually create a safe environment—to consider whether the Google Water, Sanitation &amp; Hygiene Map might help unhoused people integrate bathroom access into routines, rounding out the so-called “mobile home” to provide relief and personal agency. The effort runs up against digital literacy issues that mitigate “access” and point to the limits of techno-solutions. This paper points to the need to consider both media literacies and inclusive design in creating digital solutions to address issues faced by unhoused people. We also argue that planners should think holistically about how digital and telecom-focused “solutions” relate to other necessary infrastructures—in this case, public toilets and water access.
Characterizing the information needs and behaviour of Egyptian street vendors

This study intends to investigate the information needs and behaviour of Egyptian street vendors in terms of their perspectives, motives, attitudes, habits, and impediments. In five focus groups, the participants (n = 54) were interviewed. The researcher employed grounded theory in this qualitative investigation. The majority of respondents were male, uneducated to possessing informal education, in their twenties and thirties, and married. A significant proportion of respondents favoured direct engagement, face-to-face meetings, or mobile and social media to share oral sources with others. Respondents sought the knowledge mostly to aid them with executing work-related duties. Numerous respondents favoured spoken or informal sources over written ones. Personal experience was one of the most important sources upon which respondents relied. Illiteracy, psychological pressure, a negative social image, a lack of awareness, a lack of skills, and a lack of time are among the major obstacles respondents face.
Trust aware cryptographic role based access control scheme for secure cloud data storage

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Study on Enhancing Training Efficiency of MARL for Swarm Using Transfer Learning

Swarm has recently become a critical component of offensive and defensive systems. Multi-agent reinforcement learning(MARL) empowers swarm systems to handle a wide range of scenarios. However, the main challenge lies in MARL’s scalability issue - as the number of agents increases, the performance of the learning decreases. In this study, transfer learning is applied to advanced MARL algorithm to resolve the scalability issue. Validation results show that the training efficiency has significantly improved, reducing computational time by 31 %.
The Quantum Density Matrix and Its Many Uses

The quantum density matrix generalises the classical concept of probability distribution to quantum theory. It gives the complete description of a quantum state as well as the observable quantities that can be extracted from it. Its mathematical structure is described, with applications to understanding quantum correlations, illustrating quantum chaos and its unravelling, and developing software simulators for noisy quantum systems with efficient quantum state tomography.
A subspace constraint based approach for fast hierarchical graph embedding

Hierarchy network, as a type of complex graphs, is widely used in many application scenarios such as social network analysis in web, human resource analysis in e-government, and product recommendation in e-commerce. Hierarchy preserving network embedding is a representation learning method that project nodes into feature space by preserving the hierarchy property of networks. Recently, researches on network embedding are devoted to mining hierarchical structures and profit a lot form it. Among these works, SpaceNE stands out of preserving hierarchy with the help of subspace constraint on the hierarchical subspace system. However, like all other existing works, SpaceNE is based on transductive learning method and is hard to generalize to new nodes. Besides, they have high time complexity and hard to be scalable to large-scale networks. This paper proposes an inductive method, FastHGE to learn node representation more efficiently and generalize to new nodes more easily. As SpaceNE, a hierarchy network is embedded into a hierarchical subspace tree. For upper communities, we exploit transductive learning by preserving inner-subspace proximity of subspace from the same ancestor. For extending to new nodes, we adopt inductive learning to learn representations of leaf nodes. The overall representation of a node is retrieved by concatenating the embedding vectors of all its ancestor communities and the leaf node. By learning the basis vectors of subspace, the computing cost is alleviated from updating many parameters of projection matrices as in SpaceNE. The performance evaluation experiments show that FastHGE outperforms with much fast speed and the same accuracy. For example, in the node classification, FastHGE is nearly 30 times faster than SpaceNE. The source code of FastHGE is available online.
Two-Stream Xception Structure Based on Feature Fusion for DeepFake Detection

Abstract DeepFake may have a crucial impact on people’s lives and reduce the trust in digital media, so DeepFake detection methods have developed rapidly. Most existing detection methods rely on single-space features (mostly RGB features), and there is still relatively little research on multi-space feature fusion. At the same time, a lot of existing methods used a single receptive field, which leads to models that cannot extract information of different scales. In order to solve the above problems, we propose a two-stream Xception network structure (Tception) that fused RGB spatial feature and noise-space feature. This network structure consists of two main parts. The first part is a feature fusion module, which can adaptively fuse RGB feature and noise-space feature generated by RGB images through SRM filters. The second part is the two-stream network structure, which utilizes a parallel structure of convolutional kernels of different sizes allowing the network to learn features of different scales. The experiments show that the proposed method improves performance compared to the Xception network. Compared to SSTNet, the detection accuracy of the Neural Textures is improved by nearly 8%.
It Happened Again: Differences Between Single and Repeat/Poly-Victimization Among Financial Fraud Victims

An increasing number of studies have focused on fraud victimization. However, fewer studies have specifically focused on financial fraud, a unique form of fraud that relies on victim participation. Using the Supplemental Fraud Survey (SFS), the prevalence and differences between single and repeat/poly-victims of financial fraud were examined utilizing state dependence and risk heterogeneity variables to identify potential differences. Overall, some support for state dependence was found, while less support was found for risk heterogeneity. Reporting the victimization to a family member and having a higher income reduced the risk of becoming a repeat/poly-victim, while identifying as a victim and having a higher total monetary loss were associated with increased risk. Implications for policy, theory, and prevention are discussed.
Security risk assessment of projects in high-risk areas based on attack-defense game model

Assessing the security risk of projects in high-risk areas is particularly important. This paper develops a security risk assessment model for projects in high-risk areas based on the target loss probability model and Bayesian game model. This model is modeled from the perspective of attack-defense confrontation and addresses the issue that traditional risk assessment focuses on the analysis of the attacker yet neglects to analyze the defender-the defender's optimum defensive information is not quantitatively determined. The risk level, optimum defensive resource value, and optimum defensive strategy of the project are determined through the analysis of a project in the high-risk area. This enables the project's risk manager to adjust the defensive resources reasonably and optimally, confirming the objectivity and feasibility of the model and offering a new benchmark for security risk assessment, which has significant practical implications.