Reviewerly/exHarmony huggingface dataset:
Dynamic Grammar Pruning for Program Size Reduction in Symbolic Regression

Grammar is a key input in grammar-based genetic programming. Grammar design not only influences performance, but also program size. However, grammar design and the choice of productions often require expert input as no automatic approach exists. This research work discusses our approach to automatically reduce a bloated grammar. By utilizing a simple Production Ranking mechanism, we identify productions which are less useful and dynamically prune those to channel evolutionary search towards better (smaller) solutions. Our objective in this work was program size reduction without compromising generalization performance. We tested our approach on 13 standard symbolic regression datasets with Grammatical Evolution. Using a grammar embodying a well-defined function set as a baseline, we compare effective genome length and test performance with our approach. Dynamic grammar pruning achieved significantly better genome lengths for all datasets, while significantly improving generalization performance on three datasets, although it worsened in five datasets. When we utilized linear scaling during the production ranking stages (the first 20 generations) the results dramatically improved. Not only were the programs smaller in all datasets, but generalization scores were also significantly better than the baseline in 6 out of 13 datasets, and comparable in the rest. When the baseline was also linearly scaled as well, the program size was still smaller with the Production Ranking approach, while generalization scores dropped in only three datasets without any significant compromise in the rest.
Contract-Based Incentive Mechanism for Redactable Proof-of-Stake Blockchains

Blockchain has received a lot of attention due to its immutability. However, the immutability characteristic prohibits editing the blocks which need to be modified. Although the existing redactable blockchain enables to manipulate blocks in a controlled way, it may suffer from the security threats if the number of honest committee members (CMs) is insufficient. Thus, to attract honest CMs for validating and voting the editing blocks in permissionless blockchain, this paper presents a contract-based incentive mechanism between contract issuer and every CM. Firstly, it models the interaction between the contract issuer and each CM in the verifying and voting process. Secondly, it builds an incentive mechanism according to the contract issuer’s cost and the committee size. Finally, it selects a sufficiently large number of CMs with an optimization method. The analysis shows that the present mechanism is secure against Sybil attack, and the simulations demonstrate that the proposed mechanism is effective.
Concentration bounds for quantum states and limitations on the QAOA from polynomial approximations

We prove concentration bounds for the following classes of quantum states: (i) output states of shallow quantum circuits, answering an open question from [DPMRF22]; (ii) injective matrix product states; (iii) output states of dense Hamiltonian evolution, i.e. states of the form $e^{\iota H^{(p)}} \cdots e^{\iota H^{(1)}} |\psi_0\rangle$ for any $n$-qubit product state $|\psi_0\rangle$, where each $H^{(i)}$ can be any local commuting Hamiltonian satisfying a norm constraint, including dense Hamiltonians with interactions between any qubits. Our proofs use polynomial approximations to show that these states are close to local operators. This implies that the distribution of the Hamming weight of a computational basis measurement (and of other related observables) concentrates. An example of (iii) are the states produced by the quantum approximate optimisation algorithm (QAOA). Using our concentration results for these states, we show that for a random spin model, the QAOA can only succeed with negligible probability even at super-constant level $p = o(\log \log n)$, assuming a strengthened version of the so-called overlap gap property. This gives the first limitations on the QAOA on dense instances at super-constant level, improving upon the recent result [BGMZ22].
A moment-matching metric for latent variable generative models

It can be difficult to assess the quality of a fitted model when facing unsupervised learning problems. Latent variable models, such as variation autoencoders and Gaussian mixture models, are often trained with likelihood-based approaches. In scope of Goodhart's law, when a metric becomes a target it ceases to be a good metric and therefore we should not use likelihood to assess the quality of the fit of these models. The solution we propose is a new metric for model comparison or regularization that relies on moments. The concept is to study the difference between the data moments and the model moments using a matrix norm, such as the Frobenius norm. We show how to use this new metric for model comparison and then for regularization. It is common to draw samples from the fitted distribution when evaluating latent variable models and we show that our proposed metric is faster to compute and has a smaller variance that this alternative. We conclude this article with a proof of concept of both applications and we discuss future work.
Transactional and Transformational Leadership Styles and Their Impact on Employees’ Acceptance of Predictive Maintenance Analytics: Evidence From an Indonesian Mining Company

This study aimed to investigate the role of leadership in the acceptance of predictive maintenance technologies in the coal mining industry. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was used to assess the acceptance, and the Multifactor Leadership Questionnaire (MLQ) 5X was used to assess the perceived leadership styles of managers. The key findings of this study revealed that transactional leadership had a significant effect on performance expectancy, effort expectancy, and social influence, whereas transformational leadership did not significantly impact these aspects. Furthermore, an examination of the subdimensions of transformational and transactional leadership showed that contingent reward, management-by-exception (active), and intellectual stimulation had a considerable influence on the core constructs of the UTAUT model. These findings suggest that for adopting new technologies, the coal mining industry may require a different approach to exert leadership, and a comprehensive approach with consideration of the various subdimensions of leadership may be more effective in promoting acceptance and application of the technologies.
Empathetic Response Generation Based on Plug-and-Play Mechanism With Empathy Perturbation

Spoken dialogue systems have rapidly developed but are often viewed as inhumane because they lack empathetic communication skills. In this study, a transformer-based language model (DialoGPT fine-tuned on the EmpatheticDialogues dataset) was combined with two proposed attribute models for affective and cognitive empathy to improve its performance. The affective empathy model ensures that the user sentence and system response have similar emotional valence, and the cognitive empathy model ensures that the system response is relevant to the user's input by using a DialoGPT-based reverse generation model to calculate the cross-entropy loss. A plug-and-play structure with these empathy attribute models was used to perturb the language generation model to increase response empathy without fine-tuning or retraining the generation model. Experiments indicated that the proposed model responses had substantially higher affective empathy, cognitive empathy, and BLEU scores than did the baseline model. Subjective evaluations also indicated that the responses of the proposed model had greater empathy, relevance, and fluency than did the baseline model. Moreover, the proposed model outperformed other similar models in A/B tests.
Critical Attacks Set Identification in Attack Graphs for Computer and SCADA/ICS Networks

Supervisory control and data acquisition/industrial control systems (SCADA/ICSs) networks are becoming more vulnerable to attacks that exploit the interdependence of security weaknesses at the atomic level to compromise system-level security. Attack graphs are an effective approach to depict these complex attack scenarios, assisting security administrators in determining how to best safeguard their systems. However, due to time and financial constraints, it is frequently not possible to address all atomic-level flaws at the same time. In this article, we propose a method for automatically detecting a minimal set of critical attacks that, when defended against, render the system secure. Finding a minimal label cut is typically an NP-complete problem. However, we propose a linear complexity approximation that uses the attack graph’s strongly connected components (SCCs) to create a simplified version of the graph in the form of a tree over the SCCs. Then, we perform an iterative backward search over this tree to find a set of backward-reachable SCCs, as well as their outward edges and labels, in order to find a cut of the tree with the fewest labels, which is a critical attack set. We put our proposed method to the test on real-world case studies, such as IT and SCADA networks for a cyber–physical system for water treatment, and outperformed previous state-of-the-art algorithms in terms of approximation accuracy and/or computational speed. Our solution provides security administrators with a practical and efficient method for prioritizing efforts to address vulnerabilities in SCADA/ICS networks.
Alleviating Exposure Bias for Neural Machine Translation via Contextual Augmentation and Self Distillation

In neural machine translation (NMT), most sequence-to-sequence (seq2seq) models are trained only with the teacher-forcing paradigm, where the ground truth history is used to predict the next ground truth word. At the inference stage, however, the decoder predicts the next token solely based on history generated from scratch. Both using ground truth history and predicting ground truth words potentially lead to exposure bias. On the one hand, to alleviate the issue of exposure bias caused by using ground truth history, we propose contextual augmentation by allowing substitution, insertion, and deletion of words. The contextual augmentation applies to target sequence to generate non-ground truth and natural history when predicting next words. On the other hand, to alleviate the exposure bias caused by predicting ground truth words, we further apply self distillation to guide the model to carry out optimization according to smoothed prediction distribution, i.e, enable the model to predict not only ground truth words, but also other potentially correct and reasonable words. Experimental results on WMT14 English <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\leftrightarrow$</tex-math></inline-formula> German and IWSLT14 German <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rightarrow$</tex-math></inline-formula> English translation tasks demonstrate that our approach achieves significant improvements over Transformer on standard benchmarks. Detailed experimental analyses further reveal the effectiveness of our proposed approach on improving the translation quality.
How to Tame Mobility in Federated Learning Over Mobile Networks?

Federated learning (FL) over mobile networks has attracted intensive attention recently. User mobility is a fundamental feature of mobile networks, which leads to dynamic network topology and wireless connectivity losses. As such, user mobility is usually considered a “trouble maker" and a great challenge to FL over mobile networks. Interestingly, we found that small user mobility can positively contribute to improving FL performance. This is because the total dataset size and the data diversity that the FL can utilize are increased by user mobility. Based on this observation, we aim to tame and exploit mobility instead of treating it as a hostile “trouble maker”. To this end, we first investigate how the FL performance changes with user mobility theoretically by jointly taking into account the positive and negative aspects of mobility. Specifically, a closed-form expression to quantify the impact of mobility on the FL loss is derived, which explains when negative or positive aspects of mobility dominate the FL performance. Next, a joint FL and communication optimization problem is formulated based on theoretical analyses to minimize the FL loss function by optimizing wireless resource allocation. Finally, we propose a two-step optimization algorithm to solve the formulated problem. The simulation results verify the theoretical analyses. It is also shown that the proposed method can significantly enhance learning performance considering users with high mobility. When the average velocity is larger than 150 km/h, the proposed method achieves more than 80% accuracy in the MNIST dataset, while the existing methods may fail during training.
Shadow-Assisted Moving Target Tracking Based on Multidiscriminant Correlation Filters Network in Video SAR

Moving targets always defocus and shift outside the scene in video synthetic aperture radar (video SAR) image sequences. However, the shadows of moving targets are immune to these issues and can reveal the true position of the moving targets. As such, by tracking the shadows of moving targets in the video SAR image sequence, it becomes feasible to keep track of these targets. Nevertheless, due to the small pixel size and time-varying characteristics of the target shadow, current prevailing tracking methods often prove insufficient for direct tracking of the shadow. In this paper, a shadow-assisted tracking method for moving targets based on multilevel discriminant correlation filters network (MDCFnet) is proposed. Primarily, we designed a Reverse Feature Pyramid Network (RFPN) that integrates multiple high-level features into low-level features to obtain multiple features with higher distinguishability and resolution, thereby enhancing the final tracking accuracy and precision. Furthermore, we devised Multi-level Discriminative Correlation Filters (MDCF) to perform filtering tracking under multiple feature maps. Real dataset processing results are provided to demonstrate that the proposed method outperforms other state-of-the-art methods.
Fast Secure Aggregation With High Dropout Resilience for Federated Learning

Federated learning has been a paradigm for privacy-preserving machine learning, but recently gradient leakage attacks threaten privacy in federated learning. Secure aggregation for federated learning is an approach to protect users’ privacy from these attacks. Especially in federated learning with large-scale mobile devices, e.g., smartphones, secure aggregation should be dropout-resilient and have low communication overhead in addition to protecting privacy. However, existing studies still suffer from performance degradation and security risk, since the number of the dropped users increases. To address these problems, we propose an effective and high dropout-resilience secure aggregation protocol based on homomorphic Pseudorandom Generator and Paillier, which can guarantee privacy while tolerating up to almost 50% of users dropping out in both the honest but curious and actively malicious settings, and the performance of aggregation in computation and communication is independent to the dropped users. To further improve performance, we reduce the number of the ciphertexts through a homomorphic Pseudorandom Generator in the multiplicative group of integers, and decrease the running time of the server-side aggregation by computing discrete logarithms fast in Paillier. Experimental evaluation shows that the proposed protocol reduces the communication overhead by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times $ </tex-math></inline-formula> while achieving <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> computation speedup over the prior dropout-resilience scheme.
Co-MDA: Federated Multisource Domain Adaptation on Black-Box Models

Federated domain adaptation (FDA) is an effective method for performing learning tasks over distributed networks, which well improves data privacy and portability in unsupervised multi-source domain adaptation (UMDA) tasks. Despite the impressive gains achieved, two common limitations exist in current FDA works. First, most previous studies require access to the model parameters or gradient details of each source party. However, the raw source data can be reconstructed from the model gradients or parameters, which may leak individual information. Second, these works assume that different parties share an identical network architecture, which is impractical and not desirable for low- or high-resource target users. To address these issues, in this work, we propose a more practical UMDA setting, called Federated Multi-source Domain Adaptation on Black-box Models (B2FDA), where all data are stored locally and only the input-output interface of the source model is available. To tackle B2FDA, we propose an effective method, termed Co2-Learning with Multi-Domain Attention (Co-MDA). Experiments on multiple benchmark datasets demonstrate the effectiveness of our proposed method. Notably, Co-MDA performs comparably with traditional UMDA methods where the source data or the trained model are fully available.
Com-STAL: Compositional Spatio-Temporal Action Localization

Spatio-temporal action localization aims to locate the spatial and temporal positions of actors and classify their actions. However, prior research overlooks the fact that human actions often interact with novel objects in real-world scenarios, which neglects the various combinations of action-object, and considerably limits the generalization of the developed models. In this paper, we study the action-object combinations by researching multi-modal vision information of them. To this end, we propose a novel compositional spatio-temporal action localization (Com-STAL) task, which features non-overlapping action-object combinations in their training and test sets. Based on this, we construct a compositional action localization dataset (Com-AD). Beyond that, we propose a simple yet effective framework, Instance-Centric Interaction Network (ICIN), to reduce invalid induction biases within the visual modality and alleviate the combined distribution bias issue by leveraging additional modal information. The extensive experiment results on Com-AD demonstrate superior action localization performance of ICIN.
Accessible tourism as an educational resource for the socialization of children with disabilities

The key factors in the new economy are formation of an accessible environment for people with disabilities, including disabled children. This issue is one of the priorities of the states included in the world educational space and is considered as the realization of the constitutional rights of citizens. This activity was initiated by a number of international and domestic documents. In our country the formation of an accessible environment is carried out in the aspect of the implementation of the State Program of the Russian Federation «Accessibility», which provides for the creation of a barrier-free environment for the effective integration of citizens with disabilities into society. This initiates pedagogical research devoted to the study of the educational opportunities of accessible tourism for the full integration of children with disabilities in society. This circumstance confirms the relevance of our research and allowed us to formulate the research problem, which is to identify the possibilities of accessible tourism as an educational resource for the socialization of children with disabilities.The purpose of the research is to substantiate the possibilities of accessible tourism as an educational resource for the socialization of children with disabilities. To achieve this goal, the following research methods were used: the study of scientific literature and information from Internet sources, analysis, generalization, description.The results of the research are as follows the role of cultural and educational tourism as an effective tool for the social rehabilitation of minors with disabilities in a specially organized accessibleenvironment has been revealed. Using the example of barrier-free inclusive tourism, the experience of using the accessible environment as an educational resource for the socialization of children with disabilities has been described.Key conclusions are: cultural and educational tourism has an impact on the education of the personality of a disabled child, developing, first of all, his emotional and value attitude to cultural heritage and creating conditions for the spiritual sublimation of his needs, which can often not be satisfied in real life; the creation of a barrier-free environment in relation to tourism activities includes augmented reality technologies (mobile applications for museums, virtual tours, tours in sign language, tactile maps, etc.). The conclusion is drawn that the development of accessible tourism is a powerful factor in the socialization of children with disabilities in a new educational environment.
Styling R <tt>ggplot2</tt> graphics with LaTeX

We present two applications of SWIGLIB: a binding to the GraphicsMagick library that under certain conditions can speed up conversion of bitmaps by up to 20% and a binding to the Ghostscript library that simplifies the integration of PostScript programs in ConT E Xt with the LuaT E X engine.Examples of TIFF conversion and barcodes in PostScript are shown.
Small Object Detection in Remote Sensing Images

Automatic interpretation of remote sensing images is a fundamental but challenging problem in the field of aerial and satellite image analysis. It plays a vital role in a wide range of applications and is receiving significant attention in recent years. Even though many great progress has been made in this field, the detection of multi-scale objects, especially small objects in high-resolution satellite (HRS) and drone images, has not been adequately explored. As a result, detection performance both in terms of detection speed and accuracy turns out to be poor. To address this problem, we propose a convolutional neural network (CNN)-based single-stage object detector for the real-time and accurate recognition of remote sensing images. Our model predicts bounding boxes and corresponding class probabilities directly from images in a single assessment. This will result in a real-time object detection of images without compromising accuracy.