Reviewerly/exHarmony huggingface dataset:
On the product of translated division polynomials and Somos sequences

We consider the product sequences of the sequences $(\Psi _{n}(\mathbf P))$, $(\Phi _{n}(\mathbf P))$, and $(\overline{\Omega}_{n}(\mathbf P))$ ($n\in \mathbb N$) of values of the translated division polynomials of an elliptic curve $E/K $ evaluated at a point $\mathbf P\in $ $E(K)^{2}$. We prove that thesesequences are purely periodic when $K$ is a finite field. Then we use $p$-adic properties of these sequences to obtain $p$-adic convergence ofproduct of the Somos $4$ and Somos $5$ sequences.
Inverse Problems: A Deterministic Approach Using Physics-Based Reduced Models

These lecture notes summarize various summer schools that I have given on the topic of solving inverse problems (state and parameter estimation) by combining optimally measurement observations and parametrized PDE models. After defining a notion of optimal performance in terms of the smallest reconstruction error that any reconstruction algorithm can achieve, the notes present practical numerical algorithms based on nonlinear reduced models for which one can prove that they can deliver a performance close to optimal. We also discuss algorithms for sensor placement. The proposed concepts may be viewed as exploring alternatives to Bayesian inversion in favor of more deterministic notions of accuracy quantification.
Domain-Scalable Unpaired Image Translation Via Latent Space Anchoring

Unpaired image-to-image translation (UNIT) aims to map images between two visual domains without paired training data. However, given a UNIT model trained on certain domains, it is difficult for current methods to incorporate new domains because they often need to train the full model on both existing and new domains. To address this problem, we propose a new domain-scalable UNIT method, termed as latent space anchoring, which can be efficiently extended to new visual domains and does not need to fine-tune encoders and decoders of existing domains. Our method anchors images of different domains to the same latent space of frozen GANs by learning lightweight encoder and regressor models to reconstruct single-domain images. In the inference phase, the learned encoders and decoders of different domains can be arbitrarily combined to translate images between any two domains without fine-tuning. Experiments on various datasets show that the proposed method achieves superior performance on both standard and domain-scalable UNIT tasks in comparison with the state-of-the-art methods.
Ensemble Multi-Quantiles: Adaptively Flexible Distribution Prediction for Uncertainty Quantification

We propose a novel, succinct, and effective approach for distribution prediction to quantify uncertainty in machine learning. It incorporates adaptively flexible distribution prediction of [Formula: see text] in regression tasks. This conditional distribution's quantiles of probability levels spreading the interval (0,1) are boosted by additive models which are designed by us with intuitions and interpretability. We seek an adaptive balance between the structural integrity and the flexibility for [Formula: see text], while Gaussian assumption results in a lack of flexibility for real data and highly flexible approaches (e.g., estimating the quantiles separately without a distribution structure) inevitably have drawbacks and may not lead to good generalization. This ensemble multi-quantiles approach called EMQ proposed by us is totally data-driven, and can gradually depart from Gaussian and discover the optimal conditional distribution in the boosting. On extensive regression tasks from UCI datasets, we show that EMQ achieves state-of-the-art performance comparing to many recent uncertainty quantification methods. Visualization results further illustrate the necessity and the merits of such an ensemble model.
Prescribed Safety Performance Imitation Learning from A Single Expert Dataset

Existing safe imitation learning (safe IL) methods mainly focus on learning safe policies that are similar to expert ones, but may fail in applications requiring different safety constraints. In this paper, we propose the Lagrangian Generative Adversarial Imitation Learning (LGAIL) algorithm, which can adaptively learn safe policies from a single expert dataset under diverse prescribed safety constraints. To achieve this, we augment GAIL with safety constraints and then relax it as an unconstrained optimization problem by utilizing a Lagrange multiplier. The Lagrange multiplier enables explicit consideration of the safety and is dynamically adjusted to balance the imitation and safety performance during training. Then, we apply a two-stage optimization framework to solve LGAIL: (1) a discriminator is optimized to measure the similarity between the agent-generated data and the expert ones; (2) forward reinforcement learning is employed to improve the similarity while considering safety concerns enabled by a Lagrange multiplier. Furthermore, theoretical analyses on the convergence and safety of LGAIL demonstrate its capability of adaptively learning a safe policy given prescribed safety constraints. At last, extensive experiments in OpenAI Safety Gym conclude the effectiveness of our approach.
Quality reporting: where we are now and possible roads ahead

In the past twenty years, supplementing statistical products and processes with reports, conveying information about their quality, has turned from a long-run strategic goal to a standard practice in all statistical domains. As a result, a critical mass of work done in the field of quality reporting standards for official statistics has given food for reflection to the practitioners. The aim of this paper is therefore to review the recent efforts of revisiting the existing guidelines and making available best practices, ultimately consolidated in the ‘ESS Handbook for Quality and Metadata Reports’. In doing so, the paper will aim also to pinpoint the possible future developments in the field of quality reporting of official statistics.
PiGLET: Pixel-level Grounding of Language Expressions with Transformers

This paper proposes Panoptic Narrative Grounding, a spatially fine and general formulation of the natural language visual grounding problem. We establish an experimental framework for the study of this new task, including new ground truth and metrics. We propose PiGLET, a novel multi-modal Transformer architecture to tackle the Panoptic Narrative Grounding task, and to serve as a stepping stone for future work. We exploit the intrinsic semantic richness in an image by including panoptic categories, and we approach visual grounding at a fine-grained level using segmentations. In terms of ground truth, we propose an algorithm to automatically transfer Localized Narratives annotations to specific regions in the panoptic segmentations of the MS COCO dataset. PiGLET achieves a performance of 63.2 absolute Average Recall points. By leveraging the rich language information on the Panoptic Narrative Grounding benchmark on MS COCO, PiGLET obtains an improvement of 0.4 Panoptic Quality points over its base method on the panoptic segmentation task. Finally, we demonstrate the generalizability of our method to other natural language visual grounding problems such as Referring Expression Segmentation. PiGLET is competitive with previous state-of-the-art in RefCOCO, RefCOCO+ and RefCOCOg.
Constrained Estimation Analysis and Physical Parameters Study for Chain Tethered Formation Systems

Accurate state estimation of the tethered formation systems (TFS) is challenging due to complex dynamic coupling and elastic tether constraints. In this article, we develop a new fundamental tool to reveal the influence of tether physical parameters on the estimation performance for a class of chain-type TFS. First, we model the elastic tether as a Kelvin–Voigt material to evaluate the influence of tether stiffness and damping on estimation performance. To facilitate analysis, equilibrium configurations are used to characterize the dynamic behavior of chain TFS. Then, we extend the posterior Cramér–Rao lower bound (PCRB) to constrained subspace and derive a recursive form to calculate the constrained PCRB (C-PCRB) for discrete-time filtering with additive Gaussian noises and state equality constraints. Besides, a numerical approach using the particle filter is also developed to approximate the C-PCRB without directly calculating the complex mathematical expectations. Finally, we systematically investigate the influence of the tether stiffness and damping on estimation performance for a chain TFS by extensive simulations.
A Robust Learning Membership Scaling Fuzzy C-Means Algorithm Based on New Belief Peak

Fuzzy C-means clustering (FCM) has been a commonly used algorithm in fuzzy clustering for decades. However, it still faces two problems: how to determine the initial cluster center and how to determine the number of clusters. The recently proposed robust learning fuzzy C-means (RL-FCM) can automatically obtain the optimal number of clusters. However, it assumes that the initial cluster center is the entire dataset, which incurs a significant time cost and involves parameters that are also difficult to determine. Additionally, RL-FCM is unable to handle imbalanced datasets and datasets with a large span of sample attributes. Therefore, we propose a robust learning membership scaling fuzzy C-means algorithm based on new belief peaks (RL-MFCM). Within the framework of the confidence function, the neighbors of the sample points provide evidence for the sample points being cluster centers. Consequently, according to Jiang's combination rule, we consider the new belief peak as the initial cluster center. To avoid excessive interference of the mixing ratio of the cluster to the calculation of membership degree, we employ triangle inequality to improve the influence of the samples in the cluster in the clustering process. We analyze the time complexity of the proposed algorithm and conduct comparative experiments with existing fuzzy clustering algorithms on artificial and real datasets in the article. Experiments demonstrate that our proposed algorithm accurately estimates the number of clusters and exhibits superior clustering performance without needing initialization.
The Impact of Sound and Immersive Experience on Learners Using High-Immersion Virtual Reality and Tablet: A Mixed-Method Study

Multisensory-rich VR experiences are designed to enhance engagement, motivation, and learning. However, extensive sensory stimuli could instead compromise learning through sensory overload. This mixed-methods study addressed these issues by exploring the impact of sound on learning, enjoyment, sense of presence, and interest among museum attendees (N = 255) who used two different types of technology: high-immersion VR or a tablet. Results from a one-way MANOVA revealed that learning and presence were unaffected by the delivery mechanism unless sound was added. Using a tablet with sound lowered the sense of presence. Participants in the VR condition with or without sound had significantly higher enjoyment scores than those in either tablet condition. Interest in art was not significantly affected by any condition, regardless of whether sound was used. This study has implications for the implementation of VR into educational contexts such as museums. For instance, VR technology can enhance the sense of presence in museum exhibits, but developers need to exercise caution when combining tablets with sound because such a condition may take participants out of the immersive exhibit experience. Additional recommendations for the use of multimodal VR are provided, including suggested future research on monophonic and ambisonic audio presentations.
Enhancing Healthcare Monitoring through the Integration of IoT Networks and Machine Learning

The technology that was developed during the fourth industrial revolution has contributed to the recent surge of interest that has been seen in the field of medicine. In particular, the importance of personal medical information obtained via knowledgeable self-diagnosis is becoming more apparent. However, the disclosure of such private medical information raises several concerns regarding trustworthiness and security. Accidents involving personally identifiable medical information could happen on the computer, but more frequently than not, they take place during the process of information exchange and data transfer. So, the goal of this research is to improve the trustworthiness of managing such sensitive data by making blockchain technology better. The objective of the project was to create smart healthcare systems by utilising blockchain technology and the Internet of Things (IoT). Moreover, they utilised various measuring instruments to collect data and carry out an individual electrocardiogram assessment. Through an examination of the fused threshold, the observed biosignals were analysed to provide a tailored diagnostic. In this article, we describe the implementation of a monitoring system that analyses individual biometric information by making use of measuring devices. Machine learning has been included in the deployed system, which has resulted in better dependability and security of the system's information.
PPAuth: A Privacy-Preserving Framework for Authentication of Digital Image

Deep learning has been widely applied in many computer vision applications with remarkable success. Most of these techniques improve extraction of features from the fetched data for instance, image attributes are extracted for classifying an image. These attributes are quite useful for many tasks such as forensic investigation but it creates a threat for the privacy of the image if these attributes are leaked. Forensic authentication of digital images and videos is a crucial process in forensic investigation as they provide direct evidence. Authentication is a forensic process that involves verifying the authenticity of a digital image. In this paper, we propose a privacy-preserving framework to authenticate an image. It defends against the adversary from reconstructing the image from the extracted features and minimizes the private attribute leakage inference from the extracted features of the image. The main classifier performs the binary classification and tells whether an image is forged or authentic with an accuracy of 99%. Also, the framework maintains a very low accuracy for the adversarial classifier which aims at inferring some private attributes or even reconstruction of the image from these leaked attributes.
How Hardened is Your Hardware? Guiding ChatGPT to Generate Secure Hardware Resistant to CWEs

The development of Artificial Intelligence (AI) based systems to automatically generate hardware systems has gained an impulse that aims to accelerate the hardware design cycle with no human intervention. Recently, the striking AI-based system ChatGPT from OpenAI has achieved a momentous headline and has gone viral within a short span of time since its launch. This chatbot has the capability to interactively communicate with the designers through a prompt to generate software and hardware code, write logic designs, and synthesize designs for implementation on Field Programmable Gate Array (FPGA) or Application Specific Integrated Circuits (ASIC). However, an unvetted ChatGPT prompt by a designer with an aim to generate hardware code may lead to security vulnerabilities in the generated code. In this work, we systematically investigate the necessary strategies to be adopted by a designer to enable ChatGPT to recommend secure hardware code generation. To perform this analysis, we prompt ChatGPT to generate code scenarios listed in Common Vulnerability Enumerations (CWEs) under the hardware design (CWE-1194) view from MITRE. We first demonstrate how a ChatGPT generates insecure code given the diversity of prompts. Finally, we propose techniques to be adopted by a designer to generate secure hardware code. In total, we create secure hardware code for 10 noteworthy CWEs under hardware design view listed on MITRE site.
Hollow-Pass: A Dual-View Pattern Password Against Shoulder-Surfing Attacks

This paper presents Hollow-Pass, a developed solution that strengthens the security of pattern passwords against shoulder-surfing attacks. It is a novel approach to graphical password (GP) schemes that utilize a dual-view technology known as the global precedence effect, which eliminates the need for external devices and makes the grid and pattern invisible to potential shoulder surfers. The usability of Hollow-Pass was evaluated through an online as well as an offline user test. We recruited 30 participants from varied backgrounds, ranging in age from 20 to 80 years, for the online user test. An offline small-scale sampling test was conducted among 19 undergraduates from the Universiteit of Twente. The developed solution successfully demonstrated its ability to effectively resist shoulder-surfing attacks for simple patterns at various viewing angles (front, left-front, and right-front) and different distances (1.0 m, 1.5 m, and 2.0 m).
Midgame Attacks and Defense Against Them

In this paper, we propose the Midgame Security attack model, where it is assumed that at some point in the middle of computation with a secret key, and after some secure work (typically but not necessarily initial one), the powerful adversary sees the entire internal state and attempts key recovery/forgery. This security model is motivated by a few trends: First and primarily, it may represent a model in which part of the computation is done in a possibly insecure environment (e.g., the emerging modes of cloud server delegation, hosting environment, general pc, the cloud, etc.), where the insecure environment performs the bulk of the work, after some initial or intermediate (relatively small amount of) work at a trusted location which holds the cryptographic keys (client, co-processor, trusted hardware with leakage countermeasures, an enclave in the cloud, etc.). Secondly, from a leakage perspective, the model represents a total leakage in the system at some point after some secured work has been done without leakage (perhaps at a different location). The model is novel (though, superficially, it has a flavor of forward security), and is most meaningful to demonstrate exposures of constructions where there is an obvious lengthy progress of computation (e.g., MACing or (Authenticated) Encrypting of long messages) which is done without the cryptographic keys present, and when we want short usage of keys (to minimize their exposure). In these cases, initially in secure periods the key may be blended into the state of the computation and an attacker task is to recover that key in spite of the blending from the leakage from the environment which never hold any key. We employ the new model to analyze numerous concrete cryptosystems and mainly find key recovery or forgery attacks. We first compare HMAC based on the SHA-3 finalists in this new midgame security model. One thing we show is that the domain extension of Keccak, called the sponge construction, is exposed in a HMAC-Keccak mode, and thus if there is an exposed state, the key is recoverable. Secondly, we analyze the midgame security of several popular message authentication codes, encryption, and authenticated-encryption (AE) schemes. We show that all known (authenticated) encryption schemes based on block ciphers, and that six ECRYPT stream ciphers out of the seven we examined are not secure against the midgame attacks. We note that from the point of view of risk analysis of overall systems, midgame attacks which may use a strong (but realizable) state exposure attack, may nevertheless open the door for new exposure deserving of considerations.
Image Depth Estimation Algorithm Based on DCGAN and Prior Information

In response to the problems of low accuracy and unclear edges in the depth estimation algorithm, a new depth estimation algorithm based on DCGAN and prior information was proposed;the algorithm first added the original RGB image and prior map which calculate by three depth prior information to the input module, to replace the original Gaussian noise; secondly, in the generator module,we use the encoder-multiscale feature extraction fusion-decoder network to obtain the depth of multi-scale objects and improve the accuracy of feature extraction,also in this structure, the long-short skip connection based on the residual module and the U-Net idea can be used to avoid the network degeneration caused by the deepening of the network.Finally,the experiment shows that compared with the same type of algorithm, the proposed algorithm is reduced by 0.517 on the RMSE indicator. The accuracy of the threshold was increased by 0.01 ∼ 0.3.
Emitter identification based on multi-class spectrograms in time-frequency domain

In the identification of emitter, there are some problems such as poor identification effect and incomplete feature expression of emitter by using single signal processing method to obtain spectrogram with feature of emitter. In order to solve these problems, a method of emitter identification based on multi-class spectrograms in time-frequency domain is proposed. Specifically, three time-frequency analyses, including short-time Fourier transform (STFT), smoothed pseudo Wigner-Ville distribution (SPWVD) and reduced interference distribution with Hanning kernel (RIDHK), are performed on signal data to obtain the time-frequency spectrograms with different feature expressions. Three convolutional neural networks (CNN) were used to extract the independent features from three classes of spectrograms, and the three features were stacked to obtain more complete features of emitter, to improve the recognition effect. Experimental results show that compared with the single feature recognition method, the proposed method improves the accuracy of emitter identification effectively.
Lights flicker: Furtive Attack of Facial Recognition Systems

With the rapid development of profound learning and electronic technologies, facial recognition is becoming the world's second most popular biometric identification method after fingerprinting. Adversarial attacks have become a growing threat to neural network facial recognition systems that are gaining attention as an attack on facial recognition. Previous studies have typically used adversarial samples such as injecting noise into the database to achieve adversarial perturbations, which require a variety of attack settings and various practical applications based on the white-box. Adversarial examples are combined from pixel-level perturbed face images that alter key features of the input, leading to misclassification of the neural networks. The researchers proposed adversarial samples generated in the physical environment, such as eyeglass perturbation and infrared perturbation. However, the perturbation losses caused by complex physical environments can sometimes make existing attack methods ineffective. In this paper, we focus on designing new attacks that are effective and inconspicuous in the physical world, proposing a new light-flash-based attack scheme that physically implements adversarial attack. Due to the difference in image formation principles between cameras and human eyes and the rolling shutter of CMOS camera to achieve a higher level of concealment than traditional adversarial attack using printers by projecting stripe perturbation generated by flashing lights onto human faces. This attack models are divided into un-targeted attack 、self-model attack and targeted attack (projection of the registrant's face). The first two types of attacks are black-box attacks, where the attacker does not need to know the structure and specific parameters of the model. The latter is a white-box attack, where the attacker needs to know the model structure and specific data.