Reviewerly/exHarmony huggingface dataset:
Analyzing Cyber Security Research Practices through a Meta-Research Framework

Sound research practices are the foundation of valid, reliable, and trustworthy research results. The discipline of meta-research critically evaluates research practices and proposes new methods to improve and refine the way in which research is conducted. In this paper, we apply the framework by Ioannidis et al. [59] for categorizing meta-research work that analyzes cyber security research practices, with the goal of gaining a better understanding on the research community's efforts to examine its own research practices. We use this framework to characterize which areas of meta-research are most commonly studied, and which areas receive the most attention in terms of developing and enforcing improved research practices. We also compare these meta-research findings with experiences from another academic community, in this case the Internet measurement community, to observe areas where academic communities can learn from each other. Our work is meant as an encouragement for the research community to continue its self-reflective practices, and we hope that it can contribute to these ongoing efforts to improve cyber security research.
Publishing of Periodical Editions in Russia in 2022

The authors of the article provide the main statistics of the Russian printed periodicals output in 2022, analyzing the typology of magazines, journals and newspapers, current state and trends in this sphere of the publishing business in Russia. The role of printed information in general and in the field of periodical publications — especially is outlined as the most reliable source of the social important information in the current conditions of the hybrid war with Western World. 13 tables with comments present the number of titles of magazines, journals and newspapers, their print runs, geography of issue, thematics and purpose. The acting normative base for the Russian state registration of printed periodicals and perspectives of its modernization is analyzed in accordance with the legislation about legal deposits, current state of publishing business and information needs of its main subjects. The article also tells about the main principles and historical traditions of the domestic statistics of printed periodicals which remain unchanged for many decades and such a situation provides the compatibility of statistical figures in this field of activities.
National (State) Bibliographic Resources on the Websites of National Libraries of the Subjects of the Russian Federation: State, Problems, Prospects

The article summarizes the results of monitoring of national (state) bibliographic resources on the websites of national libraries of the subjects of the Russian Federation. The peculiarities of the current national (state) bibliographic control in the republics of Russia are noted. The composition of the issues of state bibliographic indexes “Annals of the Press” by types of documents, differences in the periodicity of publishing, languages of the printed editions, included into “Annals”, presence of auxiliary indexes, as well as the form of presentation “Annals of the Press” (electronic editions, digitized printed editions, databases) are analyzed. The chronological framework of current and retrospective national (state) bibliographic indexes presented on the websites of national libraries of the subjects of the Russian Federation is determined.
HAT-GAE: Self-supervised graph autoencoders with hierarchical adaptive masking and trainable corruption

In recent years, self-supervised autoencoders have developed into a powerful representation learning framework in computer vision and natural language processing. However, their application to graph data has resulted in limited performance due to the non-Euclidean and complex structure of graphs compared to images or text, as well as the limitations of conventional autoencoder architectures. In this paper, we investigate factors impacting the performance of autoencoders on graph data and propose a novel autoencoder model for graph representation learning. Our model incorporates a hierarchical adaptive masking mechanism to incrementally increase the difficulty of training to mimic the process of human cognitive learning and a trainable corruption scheme to enhance the robustness of learned representations. Through extensive experimentation on ten benchmark datasets, we demonstrate the superiority of our proposed method over state-of-the-art graph representation learning models.
Cross-modal alignment and translation for missing modality action recognition

Multimodal data provide complementary information on the same context, leading to performance improvement in video action recognition. However, in reality, not all modalities are available at test time. To this end, we propose Cross-Modal Alignment and Translation (CMAT) framework for action recognition that is robust to missing modalities. Specifically, our framework first aligns representations of multiple modalities from the same video sample through contrastive learning, effectively alleviating the bias with respect to the type of missing modality. Then, CMAT learns to translate representations of one modality into that of another modality. This allows the representations of the missing modalities to be generated from the remaining modalities during the testing. Accordingly, CMAT fully utilizes multimodal information obtained through abundant interactions across modalities. The proposed CMAT achieves state-of-the-art performances in both complete and missing modality settings on NTU RGB+D, NTU RGB+D 120, and Northwestern-UCLA datasets. Moreover, extensive ablation studies demonstrate the effectiveness of our design.
Architectural Design of a Blockchain-Enabled, Federated Learning Platform for Algorithmic Fairness in Predictive Health Care: Design Science Study

Developing effective and generalizable predictive models is critical for disease prediction and clinical decision-making, often requiring diverse samples to mitigate population bias and address algorithmic fairness. However, a major challenge is to retrieve learning models across multiple institutions without bringing in local biases and inequity, while preserving individual patients' privacy at each site.This study aims to understand the issues of bias and fairness in the machine learning process used in the predictive health care domain. We proposed a software architecture that integrates federated learning and blockchain to improve fairness, while maintaining acceptable prediction accuracy and minimizing overhead costs.We improved existing federated learning platforms by integrating blockchain through an iterative design approach. We used the design science research method, which involves 2 design cycles (federated learning for bias mitigation and decentralized architecture). The design involves a bias-mitigation process within the blockchain-empowered federated learning framework based on a novel architecture. Under this architecture, multiple medical institutions can jointly train predictive models using their privacy-protected data effectively and efficiently and ultimately achieve fairness in decision-making in the health care domain.We designed and implemented our solution using the Aplos smart contract, microservices, Rahasak blockchain, and Apache Cassandra-based distributed storage. By conducting 20,000 local model training iterations and 1000 federated model training iterations across 5 simulated medical centers as peers in the Rahasak blockchain network, we demonstrated how our solution with an improved fairness mechanism can enhance the accuracy of predictive diagnosis.Our study identified the technical challenges of prediction biases faced by existing predictive models in the health care domain. To overcome these challenges, we presented an innovative design solution using federated learning and blockchain, along with the adoption of a unique distributed architecture for a fairness-aware system. We have illustrated how this design can address privacy, security, prediction accuracy, and scalability challenges, ultimately improving fairness and equity in the predictive health care domain.
Executive decision-makers: a scenario-based approach to assessing organizational cyber-risk perception

Abstract The executive leadership in corporate organizations is increasingly challenged with managing cyber-risks, as an important part of wider business risk management. Cyber-risks are complex, with the threat landscape evolving, including digital infrastructure issues such as trust in networked supply chains, and emerging technologies. Moreover, engaging organizational leadership to assess for risk management is also difficult. This paper reports on a scenario-driven, workshop-based study undertaken with executive leadership to assess for cybersecurity and cyber-risk perception related to preparation for, and response to, potential incidents. The study involves leadership members at a large public–private organization. Our approach utilizes scenarios, which are structured in their design to explore and analyse aspects of business risk, risk ownership, technological complexity, and uncertainty faced by an organizational leadership. The method offers a means to engage with leadership at real-world organizations, capturing capacity and insights to manage business risks due to cyberattacks.
Reppoints-Based Multiscale Task Enhancement Network and Sample Assignment Method for Oriented Object Detection

Unlike normal images, remote sensing images (RSI) often contain complex backgrounds and multi-scale targets with arbitrary directions. This makes existing detection methods ineffective. In contrast to the usual rotating frame RSI target detection methods, the Reppoints-based method can learn autonomously to capture target features of arbitrary pose based on the target’s characteristics. Therefore, a new Reppoints-based detector is proposed in this letter to improve the accuracy from multiple perspectives. In order to better expand the receptive fields and obtain finer features, the multi-scale task enhancement Network (MSTEN) preserves the multi-scale receptive fields and multi-scale information through the deformable convolution and the skip connection, while improving the feature extraction capability of the network for targets of arbitrary orientation. At the same time, the network adapts the features to the characteristics of different tasks in order to better suit the needs of the respective tasks. Finally, the dynamic reppoints learning (DRL) is proposed in order to select samples that perform well in both the regression and classification tasks. The experimental results on two challenging datasets, DOTA and HRSC2016, show the effectiveness of our proposed method.
Efficient Approximation of Asymmetric Shapley Values Using Functional Decomposition

Abstract Asymmetric Shapley values (ASVs) are an extension of Shapley values that allow a user to incorporate partial causal knowledge into the explanation process. Unfortunately, computing ASVs requires sampling permutations, which quickly becomes computationally expensive. We propose A-PDD-SHAP, an algorithm that employs a functional decomposition approach to approximate ASVs at a speed orders of magnitude faster compared to permutation sampling, which significantly reduces the amortized complexity of computing ASVs when many explanations are needed. Apart from this, once the A-PDD-SHAP model is trained, it can be used to compute both symmetric and asymmetric Shapley values without having to re-train or re-sample, allowing for very efficient comparisons between different types of explanations.
Understanding the Effects of Perceived Avatar Appearance on Latency Sensitivity in Full-Body Motion-Tracked Virtual Reality

Latency in virtual reality (VR) can decrease the feeling of presence and body ownership. How users perceive latency, however, is malleable and affected by the design of the virtual content. Previous work found that an avatar’s visual appearance, particularly its perceived fitness, can be leveraged to change user perception and behavior. Moreover, previous work investigating non-VR video games also demonstrated that controlling avatars that visually conform to users’ expectations associated with the avatars’ perceived characteristics increases the users’ latency tolerance. However, it is currently unknown if the avatar’s visual appearance can be used to modulate the users’ latency sensitivity in full-body motion-tracked VR. Therefore, we conducted two studies to investigate if the avatars’ appearance can be used to decrease the negative impact of latency. In the first study, 41 participants systematically determined two sets of avatars whose visual appearance is perceived to be more or less fit in two physically challenging tasks. In a second study (N = 16), we tested the two previously determined avatars (perceived to be more fit vs. perceived to be less fit) in the two tasks using VR with two levels of controlled latency (system vs. high). We found that embodying an avatar perceived as more fit significantly increases the participants’ physical performance, body ownership, presence, and intrinsic motivation. While we show that latency negatively affects performance, our results also suggest that the avatar’s visual appearance does not alter the effects of latency in VR.
Emma stop that, it's my turn now - Comparing Peer Tutoring and Thinking Aloud for Usability-Testing with Children in a school setting

The subject of this study was to explore children's ability to offer verbal feedback during usability evaluation studies. The aim is to find out whether the use of the method Peer Tutoring or Thinking Aloud can identify more usability findings in usability tests with second graders than observation. 13 Second graders tested an interactive game using two evaluation techniques. The findings indicate that the majority of verbal remarks were identified with the method of Thinking Aloud and that participants also provided more higher quality remarks. More usability findings could be identified than in a purely observational situation. Unexpectedly, the Peer Tutoring method was less beneficial for the identification of usability problems since the participants struggled to cooperate successfully.
Context-Dependent Multimodal Sentiment Analysis Based on a Complex Attention Mechanism

Multimodal sentiment analysis aims to understand people’s attitudes and opinions from different data forms. Traditional modality fusion methods for multimodal sentiment analysis con-catenate or multiply various modalities without fully utilizing context information and the correlation between modalities. To solve this problem, this article provides a new model based on a multimodal sentiment analysis framework based on a recurrent neural network with a complex attention mechanism. First, after the raw data is preprocessed, the numerical feature representation is obtained using feature extraction. Next, the numerical features are input into the recurrent neural network, and the output results are multimodally fused using a complex attention mechanism layer. The objective of the complex attention mechanism is to leverage enhanced non-linearity to more effectively capture the inter-modal correlations, thereby improving the performance of multimodal sentiment analysis. Finally, the processed results are fed into the classification layer and the sentiment output is obtained using the classification layer. This process can effectively capture the semantic information and contextual relationship of the input sequence and fuse different pieces of modal information. Our model was tested on the CMU-MOSEI datasets, achieving an accuracy of 82.04%.
Interactive cognitive maps support flexible behavior under threat

In social environments, survival can depend upon inferring and adapting to other agents' goal-directed behavior. However, it remains unclear how humans achieve this, despite the fact that many decisions must account for complex, dynamic agents acting according to their own goals. Here, we use a predator-prey task (total n = 510) to demonstrate that humans exploit an interactive cognitive map of the social environment to infer other agents' preferences and simulate their future behavior, providing for flexible, generalizable responses. A model-based inverse reinforcement learning model explained participants' inferences about threatening agents' preferences, with participants using this inferred knowledge to enact generalizable, model-based behavioral responses. Using tree-search planning models, we then found that behavior was best explained by a planning algorithm that incorporated simulations of the threat's goal-directed behavior. Our results indicate that humans use a cognitive map to determine other agents' preferences, facilitating generalized predictions of their behavior and effective responses.
Deep learning models for webcam eye tracking in online experiments

Eye tracking is prevalent in scientific and commercial applications. Recent computer vision and deep learning methods enable eye tracking with off-the-shelf webcams and reduce dependence on expensive, restrictive hardware. However, such deep learning methods have not yet been applied and evaluated for remote, online psychological experiments. In this study, we tackle critical challenges faced in remote eye tracking setups and systematically evaluate appearance-based deep learning methods of gaze tracking and blink detection. From their own homes and laptops, 65 participants performed a battery of eye tracking tasks including (i) fixation, (ii) zone classification, (iii) free viewing, (iv) smooth pursuit, and (v) blink detection. Webcam recordings of the participants performing these tasks were processed offline through appearance-based models of gaze and blink detection. The task battery required different eye movements that characterized gaze and blink prediction accuracy over a comprehensive list of measures. We find the best gaze accuracy to be 2.4° and precision of 0.47°, which outperforms previous online eye tracking studies and reduces the gap between laboratory-based and online eye tracking performance. We release the experiment template, recorded data, and analysis code with the motivation to escalate affordable, accessible, and scalable eye tracking that has the potential to accelerate research in the fields of psychological science, cognitive neuroscience, user experience design, and human-computer interfaces.
Pixyz: a Python library for developing deep generative models

With the recent rapid progress in the study of deep generative models (DGMs), there is a need for a framework that can implement them in a simple and generic way. In this research, we focus on two features of DGMs: (1) deep neural networks are encapsulated by probability distributions, and (2) models are designed and learned based on an objective function. Taking these features into account, we propose a new Python library to implement DGMs called Pixyz. This library adopts a step-by-step implementation method with three APIs, which allows us to implement various DGMs more concisely and intuitively. In addition, the library introduces memoization to reduce the cost of duplicate computations in DGMs to speed up the computation. We demonstrate experimentally that this library is faster than existing probabilistic programming languages in training DGMs.
Efficient Loss Function by Minimizing the Detrimental Effect of Floating-Point Errors on Gradient-Based Attacks

Attackers can deceive neural networks by adding human imperceptive perturbations to their input data; this reveals the vulnerability and weak robustness of current deep-learning networks. Many attack techniques have been proposed to evaluate the model's robustness. Gradient-based attacks suffer from severely overestimating the robustness. This paper identifies that the relative error in calculated gradients caused by floating-point errors, including floating-point underflow and rounding errors, is a fundamental reason why gradient-based attacks fail to accurately assess the model's robustness. Although it is hard to eliminate the relative error in the gradients, we can control its effect on the gradient-based attacks. Correspondingly, we propose an efficient loss function by minimizing the detrimental impact of the floating-point errors on the attacks. Experimental results show that it is more efficient and reliable than other loss functions when examined across a wide range of defence mechanisms.
OPE-SR: Orthogonal Position Encoding for Designing a Parameter-free Upsampling Module in Arbitrary-scale Image Super-Resolution

Arbitrary-scale image super-resolution (SR) is often tackled using the implicit neural representation (INR) approach, which relies on a position encoding scheme to im-prove its representation ability. In this paper, we introduce orthogonal position encoding (OPE), an extension of po-sition encoding, and an OPE-Upscale module to replace the INR-based upsampling module for arbitrary-scale im-age super-resolution. Our OPE-Upscale module takes 2D coordinates and latent code as inputs, just like INR, but does not require any training parameters. This parameter-free feature allows the OPE-Upscale module to directly perform linear combination operations, resulting in con-tinuous image reconstruction and achieving arbitrary-scale image reconstruction. As a concise SR framework, our method is computationally efficient and consumes less mem-ory than state-of-the-art methods, as confirmed by exten-sive experiments and evaluations. In addition, our method achieves comparable results with state-of-the-art methods in arbitrary-scale image super-resolution. Lastly, we show that OPE corresponds to a set of orthogonal basis, validating our design principle. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Project page: https://github.com/gaochao-s/ope-sr
Correlational Image Modeling for Self-Supervised Visual Pre-Training

We introduce Correlational Image Modeling (CIM), a novel and surprisingly effective approach to self-supervised visual pre-training. Our CIM performs a simple pretext task: we randomly crop image regions (exemplars) from an input image (context) and predict correlation maps between the exemplars and the context. Three key designs enable correlational image modeling as a nontrivial and meaningful self-supervisory task. First, to generate useful exemplar-context pairs, we consider cropping image regions with various scales, shapes, rotations, and transformations. Second, we employ a bootstrap learning framework that involves online and target encoders. During pre-training, the former takes exemplars as inputs while the latter converts the context. Third, we model the output correlation maps via a simple cross-attention block, within which the context serves as queries and the exemplars offer values and keys. We show that CIM performs on par or better than the current state of the art on self-supervised and transfer benchmarks. Code is available at https://github.com/weivision/Correlational-Image-Modeling.git.
Diagnostics of quantum-gate coherences deteriorated by unitary errors via end-point-measurement statistics

Abstract Quantum coherence is a central ingredient in quantum physics with several theoretical and technological ramifications. We consider a figure of merit encoding the information on how the coherence generated on average by a quantum gate is affected by unitary errors (coherent noise sources) in the form of rotation-angle and rotation-axis errors. We provide numerical evidences that such information is well captured by the statistics of local energy measurements on the output states of the gate. These findings are then corroborated by experimental data taken in a quantum optics setting.