Reviewerly/exHarmony huggingface dataset:
A (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1074" altimg="si826.svg"><mml:mi>w</mml:mi></mml:math>, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1079" altimg="si827.svg"><mml:mi>t</mml:mi></mml:math>, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1084" altimg="si816.svg"><mml:mi>n</mml:mi></mml:math>)-weighted threshold dynamic quantum secret sharing scheme with cheating identification

Quantum secret sharing holds a significant place in quantum cryptography. In this paper, a (w, t, n)-weighted threshold dynamic quantum secret sharing scheme with cheating identification is constructed based on the CRT for a polynomial ring. In our scheme, on the premise of not altering the private shares of the old participants, the dynamic update of the participants can be realized without other participants' help, and the shared secret will not be changed. During the secret reconstruction phase, via executing the quantum state digital signature, the proposed scheme not only can timely detect the existence of the cheating behavior, but also once exists, definitely can identify one cheater and eliminate him from the system, making the scheme more secure. Moreover, our scheme enables secret sharing among participants with different authorities, which enhances the practicability of the scheme. Finally, the security analysis shows our scheme not only is resistant to a range of typical external attacks but can detect forgery, collusion, denial and dishonest revoked participant attacks.
Adversarial gradient-based meta learning with metric-based test

The gradient-based meta learning and its approximation algorithms have been widely used in the few-shot scenarios. In practice, it is common for the trained meta-model to employ uniform settings for gradient descent steps across different tasks. However, the meta-model may be biased toward some tasks. The convergence issue occurs that some tasks may see convergence in a few steps while others fail to approach the optimum in the whole inner loop. The bias problem may cause the trained meta-model works well in some tasks but has unexpected bad performance in other tasks, which hurts the generality of the meta-model. To address this issue, in this paper, we formally establish the approximation between the metric-based strategy and gradient descent in meta-test. By directly calculating similarity to classify data, the trained meta-model avoids the convergence issue. We point out that the metric-based methods can closely approximate the gradient descent in meta-test if the representation capability of the derived features and the convergence of the inner loop during meta-training are guaranteed. Based on such observation, we propose a new meta-learning model GMT2 (Gradient-based Meta-Train with Metric-based meta-Test) by combining gradient descent in meta-training with metric-based methods in meta-test. GMT2 employs a new first-order approximation scheme using the adversarial update strategy which not only enhances the feature representation of inner layers, but also allows enough inner gradient steps without calculating second-order derivatives. Experiments show that GMT2 achieves better efficiency and competitive accuracy comparing with popular meta-learning models.
Disentangled Dynamic Deviation Transformer Networks for Multivariate Time Series Anomaly Detection

Graph neural networks have been widely used by multivariate time series-based anomaly detection algorithms to model the dependencies of system sensors. Previous studies have focused on learning the fixed dependency patterns between sensors. However, they ignore that the inter-sensor and temporal dependencies of time series are highly nonlinear and dynamic, leading to inevitable false alarms. In this paper, we propose a novel disentangled dynamic deviation transformer network (D3TN) for anomaly detection of multivariate time series, which jointly exploits multiscale dynamic inter-sensor dependencies and long-term temporal dependencies to improve the accuracy of multivariate time series prediction. Specifically, to disentangle the multiscale graph convolution, we design a novel disentangled multiscale aggregation scheme to better represent the hidden dependencies between sensors to learn fixed inter-sensor dependencies based on static topology. To capture dynamic inter-sensor dependencies determined by real-time monitoring situations and unexpected anomalies, we introduce a self-attention mechanism to model dynamic directed interactions in various potential subspaces influenced by various factors. In addition, complex temporal correlations across multiple time steps are simulated by processing the time series in parallel. Experiments on three real datasets show that the proposed D3TN significantly outperforms the state-of-the-art methods.
Image Generation from Scene Graph with Object Edges

Significant progress has been made on methods for generating images from structured semantic descriptions, but the generated images only retain semantic information, and the appearance of objects cannot be constrained and effectively represented. Therefore, we propose a scene graph structure image generation method assisted by object edge information. Our model uses two graph convolution neural networks(GCN) to process scene graphs and obtains object features as well as relation features which aggregate related information. The object bounding boxes are predicted by a method a decoupling the size and position. Where auxiliary models are added to coordinate with segmentation mask network training. Our experiments show that the introduction of object edges provides clearer object appearance information for image generation, which can constrain object shapes and improve image quality greatly. Finally, the cascaded refinement network is used to generate images. Additionally, compared with other appearance features, such as object slices, edge information occupies a smaller quantity of data, which greatly improves the image quality with less increase in the input information. This feature also benefits semantic communication systems. A large number of experiments show that our method is significantly superior to the latest Sg2im method when evaluated on Visual Genome datasets.
Specific Emitter Identification Based on Radio Frequency Fingerprint Using Multi-Scale Network

The fast development of intelligent wireless communications enables many devices to access various networks. It often leads to the security risks of malicious access of illegal devices. To ensure a secure and reliable wireless access, it is necessary to identify illegal devices and prevent their attacks accurately. To improve the performance of specific emitter identification (SEI), this paper proposes a multi-scale convolution neural network (MSCNN) based on convolution layers of three branches with different convolution kernel sizes. MSCNN extracts radio frequency fingerprints (RFF) in three receptive fields through different convolution kernels. We verify the identification accuracy using the RF signals conforming to long term evolution (LTE) standard. The experimental results show that our proposed MSCNN-based SEI method can improve the absolute accuracy by 15% and the relative accuracy by 22% in perfect communication environment. In addition, we verify the robustness of proposed MSCNN by comparing identification performance in imperfect environment. Simulation results show that the proposed MSCNN can extract more hidden features through convolution kernels of different sizes, and thus achieves better SEI performance than existing methods.
Valid Arguments as True Conditionals

Abstract This paper explores an idea of Stoic descent that is largely neglected nowadays, the idea that an argument is valid when the conditional formed by the conjunction of its premises as antecedent and its conclusion as consequent is true. As will be argued, once some basic features of our naïve understanding of validity are properly spelled out, and a suitable account of conditionals is adopted, the equivalence between valid arguments and true conditionals makes perfect sense. The account of validity outlined here, which displays one coherent way to articulate the Stoic intuition, accords with standard formal treatments of deductive validity and encompasses an independently grounded characterization of inductive validity.
99.92%-Fidelity cnot Gates in Solids by Noise Filtering

Inevitable interactions with the reservoir largely degrade the performance of entangling gates, which hinders practical quantum computation from coming into existence. Here, we experimentally demonstrate a 99.920(7)%-fidelity controlled-not gate by suppressing the complicated noise in a solid-state spin system at room temperature. We found that the fidelity limited at 99% in previous works results from considering only static classical noise, and, thus, in this work, a complete noise model is constructed by also considering the time dependence and the quantum nature of the spin bath. All noises in the model are dynamically corrected by an exquisitely designed shaped pulse, giving the resulting error below 10^{-4}. The residual gate error is mainly originated from the longitudinal relaxation and the waveform distortion that can both be further reduced technically. Our noise-resistant method is universal and will benefit other solid-state spin systems.
Reflections on Digital Maternal and Child Health Support for Mothers and Community Health Workers in Rural Areas of Limpopo Province, South Africa

Digital health support using mobile and digital technologies, such as MomConnect and WhatsApp, is providing opportunities to improve maternal and child healthcare in low- and middle-income countries. Yet, the perspective of health service providers, pregnant women, and mothers as recipients of digital health support is under-researched in rural areas.An exploratory-descriptive qualitative research approach was adopted to reflect on the experiences of mothers, community leaders, and community health workers on mobile health opportunities in the context of maternal and child health in rural areas. Purposive sampling was used to select 18 participants who participated in the two focus groups and individual semi-structured interviews for data collection about digital maternal and child health support. The thematic open coding method of data analysis assisted authors in making sense of the given reflections of mothers, community leaders, and healthcare workers about digital health support.Participants commented on different existing digital support apps and their importance for maternal and child health. For example, MoMConnect, Pregnancy+, WhatsApp, and non-digital resources were perceived as useful ways of communication that assist in improving maternal and child health. However, participants reported several challenges related to the use of digital platforms, which affect following the health instructions given to pregnant women and mothers.Participants expressed the significant role of digital support apps in maternal and child health, which is impacted by various challenges. Addressing the lack of digital resources could improve access to health instructions for pregnant women and mothers.
MKVSE: Multimodal Knowledge Enhanced Visual-semantic Embedding for Image-text Retrieval

Image-text retrieval aims to take the text (image) query to retrieve the semantically relevant images (texts), which is fundamental and critical in the search system, online shopping, and social network. Existing works have shown the effectiveness of visual-semantic embedding and unimodal knowledge exploiting (e.g., textual knowledge) in connecting the image and text. However, they neglect the implicit multimodal knowledge relations between these two modalities when the image contains information that is not directly described in the text, hindering the ability to connect the image and text with the implicit semantic relations. For instance, an image shows a person next to the “tap” but the pairing text description may only include the word “wash,” missing the washing tool “tap.” The implicit semantic relation between image object “tap” and text word “wash” can help to connect the above image and text. To sufficiently utilize the implicit multimodal knowledge relations, we propose a M ultimodal K nowledge enhanced V isual- S emantic E mbedding (MKVSE) approach building a multimodal knowledge graph to explicitly represent the implicit multimodal knowledge relations and injecting it to visual-semantic embedding for image-text retrieval task. The contributions in this article can be summarized as follows: (1) M ultimodal K nowledge G raph (MKG) is proposed to explicitly represent the implicit multimodal knowledge relations between the image and text as intra-modal semantic relations and inter-modal co-occurrence relations . Intra-modal semantic relations provide synonymy information that is implicit in the unimodal data such as the text corpus. And inter-modal co-occurrence relations characterize the co-occurrence correlations (such as temporal, causal, and logical) that are implicit in image-text pairs. These two relations help establishing reliable image-text connections in the higher-level semantic space. (2) M ultimodal G raph C onvolution N etworks (MGCN) is proposed to reason on the MKG in two steps to sufficiently utilize the implicit multimodal knowledge relations. In the first step, MGCN focuses on the intra-modal relations to distinguish other entities in the semantic space. In the second step, MGCN focuses on the inter-modal relations to connect multimodal entities based on co-occurrence correlations. The two-step reasoning manner can sufficiently utilize the implicit semantic relations between two modal entities to enhance the embeddings of the image and text. Extensive experiments are conducted on two widely used datasets, namely, Flickr30k and MSCOCO, to demonstrate the superiority of the proposed MKVSE approach in achieving state-of-the-art performances. The codes are available at https://github.com/PKU-ICST-MIPL/MKVSE-TOMM2023 .
State of Research Data Management Practices in the Top-ranked Higher Education Institutions in India

Due to the vast amounts of data generated during research activities and mandates obtained from funding agencies, higher education institutions put a high priority on research data management (RDM). RDM is prioritized by global organizations, and India is no exception. The current work aims to address the current state of RDM in the top-ranked higher education institutions (HEIs) of India and attempts to answer the research questions posed on various aspects of RDM, such as RDM services, policy implementation, responsible stakeholders, funding support for RDM strategy and development, and so on. Furthermore, unlike most of the existing literature, the development of RDM capacity is viewed as an institutional concern in this study rather than a library concern. The survey and website analysis approaches were used to conduct this study. The outcomes of the study show that RDM is still in its early stage among India’s leading institutions.
A Novel Image Encryption Algorithm Based on Multiple Random DNA Coding and Annealing

Improved encryption devices place higher demands on the randomness and security of encrypted images. Existing image encryption optimization methods based on single- or multi-objectives concentrate on selecting keys and parameters, resulting in relatively fixed parameters and keys that are susceptible to leakage and cracking. Despite the possibility of increasing security, the DNA coding encryption method does not fully take into account the large capacity of image data and the difference between pixels, resulting in a limited level of randomness. To overcome the problems above, this paper proposes a method for generating complex texture features in images using random variation of pixels. With an annealing algorithm that can find an optimal solution in a large search space, the image is optimally optimized in terms of information entropy, pixel correlation, and value of x2. Each iteration involves selecting one of 25632 combinations of DNA coding and operation. In comparison with current encryption algorithms based on optimization algorithms and DNA coding, this method is more secure and unbreakable.
Machine learning algorithms predict experimental output of chaotic lasers

We apply an artificial neural network (ANN) of 20 hidden layers and backpropagation regression to the forecast of experimental time series from a Kerr lens mode locking (KLM) Ti:sapphire laser and a Nd:vanadate with modulation losses. In both cases the neural network is able to predict up to 10 steps ahead. In the Ti:sapphire laser the prediction in pulse amplitude is accurate even when the pulse is an extreme event. In the Nd:vanadate laser we forecast both pulse amplitude and pulse-to-pulse time separation. In both cases the prediction goes beyond the Lyapunov prediction horizon.
Partial Multi-Modal Hashing via Neighbor-Aware Completion Learning

Multi-modal hashing technology can support large-scale multimedia retrieval well, because of its fast query speed and low storage consumption. Although many multi-modal hashing methods have been developed in the literature, they still suffer from three important problems: 1) Most multi-modal hashing methods assume that multi-modal data are complete. This ideal assumption limits their application to practical retrieval scenarios, where the modality-missing is common. 2) Existing partial multi-modal hashing methods directly model incomplete multi-modal data for hash learning, which may result in partial multi-modal semantics in the learned hash codes. 3) Most of the methods are based on the shallow learning framework, which inevitably suffers from limited representation capability. To solve the above problems, we propose a flexible deep partial multi-modal hash learning framework, named Neighbor-aware Completion Hashing (NCH). Our framework jointly performs the cross-modal completion learning for incomplete multi-modal data and the multi-modal hash learning. It can not only support model training with incomplete multi-modal data but also handle incomplete multi-modal queries. Besides, we design a neighbor-aware completion learning module to capture neighbor semantics and generate distribution-consistent completed features. Finally, we conduct extensive experiments to evaluate our method on both fully-paired and partial multi-modal retrieval scenarios. The experimental results verify the superiority of our proposed method over the state-of-the-art baselines. The experimental codes of our method are released at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/FutureTwT/NCH.</uri>
A Truthful and Reliable Incentive Mechanism for Federated Learning Based on Reputation Mechanism and Reverse Auction

As a distributed machine learning paradigm, federated learning (FL) enables participating clients to share only model gradients instead of local data and achieves the secure sharing of private data. However, the lack of clients’ willingness to participate in FL and the malicious influence of unreliable clients both seriously degrade the performance of FL. The current research on the incentive mechanism of FL lacks the accurate assessment of clients’ truthfulness and reliability, and the incentive mechanism based on untruthful and unreliable clients is unreliable and inefficient. To solve this problem, we propose an incentive mechanism based on the reputation mechanism and reverse auction to achieve a more truthful, more reliable, and more efficient FL. First, we introduce the reputation mechanism to measure clients’ truthfulness and reliability through multiple reputation evaluations and design a reliable client selection scheme. Then the reverse auction is introduced to select the optimal clients that maximize the social surplus while satisfying individual rationality, incentive compatibility, and weak budget balance. Extensive experimental results demonstrate that this incentive mechanism can motivate more clients with high-quality data and high reputations to participate in FL with less cost, which increases the FL tasks’ economic benefit by 31% and improves the accuracy from 0.9356 to 0.9813, and then promote the efficient and stable development of the FL service trading market.
A subgradient-based neurodynamic algorithm to constrained nonsmooth nonconvex interval-valued optimization

In this paper, a subgradient-based neurodynamic algorithm is presented to solve the nonsmooth nonconvex interval-valued optimization problem with both partial order and linear equality constraints, where the interval-valued objective function is nonconvex, and interval-valued partial order constraint functions are convex. The designed neurodynamic system is constructed by a differential inclusion with upper semicontinuous right-hand side, whose calculation load is reduced by relieving penalty parameters estimation and complex matrix inversion. Based on nonsmooth analysis and the extension theorem of the solution of differential inclusion, it is obtained that the global existence and boundedness of state solution of neurodynamic system, as well as the asymptotic convergence of state solution to the feasible region and the set of LU-critical points of interval-valued nonconvex optimization problem. Several numerical experiments and the applications to emergency supplies distribution and nondeterministic fractional continuous static games are solved to illustrate the applicability of the proposed neurodynamic algorithm.
Crooked Hillary and Sleepy Joe: name-calling’s backfire effect on candidate evaluations

Throughout his political career Donald Trump has utilized name-calling when referring to his opponents. These pejoratives are a ubiquitous part of political discourse in contemporary society. Scholarly research has yet to examine the effect that this type of incivility has on individuals' evaluations of both the attacker (i.e. the person using name-calling) and the victim. Our research aims to fill this gap by testing the effect of name-calling through the implementation of a national survey experiment. We test the effect of name-calling on candidate evaluations by randomly inserting a pejorative in front of a fictitious candidate's name in a news story. Our findings indicate that name-calling often backfires. Respondents who saw the pejorative tend to rate the attacker lower. Our findings also show an odd partisan symmetry in how respondents rate this behavior by their co-partisans, i.e. both Republicans and Democrats punish Democratic candidates that use name-calling but ignore Republicans' use of it.
Attention-guided MIL weakly supervised visual anomaly detection

The deep learning anomaly detection method that employs visual sensors as the original signal input suffers from the over-boundary problem caused by a lack of labeled datasets, resulting in unsupervised training. This paper proposes an anomaly detection method based on weakly supervised learning. First, this paper proposes a pseudo-label generation technique based on a multi-instance ranking algorithm to generate pseudo-labels, thereby transforming the weakly supervised learning problem into a fully supervised learning problem. The C3D network extracts video temporal-spatial features as the initial data stream input for the pseudo-label generator/anomaly detection model. Finally, the attention and guidance augmentation modules are combined to make the network focus on the anomaly region, improving the model’s spatial localization capability. A series of experimental results on two datasets with varied scales and scene complexity demonstrate that our method can reach a frame-level AUC of 81.48% in UCF-Crime and 94.01% in ShanghaiTech.