Reviewerly/exHarmony huggingface dataset:
Prompting ChatGPT in MNER: Enhanced Multimodal Named Entity Recognition with Auxiliary Refined Knowledge

Multimodal Named Entity Recognition (MNER) on social media aims to enhance textual entity prediction by incorporating image-based clues. Existing studies mainly focus on maximizing the utilization of pertinent image information or incorporating external knowledge from explicit knowledge bases. However, these methods either neglect the necessity of providing the model with external knowledge, or encounter issues of high redundancy in the retrieved knowledge. In this paper, we present PGIM — a two-stage framework that aims to leverage ChatGPT as an implicit knowledge base and enable it to heuristically generate auxiliary knowledge for more efficient entity prediction. Specifically, PGIM contains a Multimodal Similar Example Awareness module that selects suitable examples from a small number of predefined artificial samples. These examples are then integrated into a formatted prompt template tailored to the MNER and guide ChatGPT to generate auxiliary refined knowledge. Finally, the acquired knowledge is integrated with the original text and fed into a downstream model for further processing. Extensive experiments show that PGIM outperforms state-of-the-art methods on two classic MNER datasets and exhibits a stronger robustness and generalization capability.
Development &amp; Deployment of a Real-time Healthcare Predictive Analytics Platform

The deployment of predictive analytic algorithms that can safely and seamlessly integrate into existing healthcare workflows remains a significant challenge. Here, we present a scalable, cloud-based, fault-tolerant platform that is capable of extracting and processing electronic health record (EHR) data for any patient at any time following admission and transferring results back into the EHR. This platform has been successfully deployed within the UC San Diego Health system and utilizes interoperable data standards to enable portability.Clinical relevance— This platform is currently hosting a deep learning model for the early prediction of sepsis that is operational in two emergency departments.
Resilient adaptive trajectory tracking control for uncalibrated visual servoing systems with unknown actuator failures

A resilient adaptive tacking control scheme for uncalibrated visual servoing system with unknown actuator failures is proposed, where the camera parameters, the robotic physical parameters, and the position information of end-effector cannot be measured accurately. For our scheme, the nonlinear composite Jacobian matrix involving unknown parameters is estimated by an adaptive algorithm based on the depth-independent interaction matrix frame so that the image feature with time-varying depth can be tackled effectively. With the use of projection technique in the adaptive algorithm, the possible singularity of the estimated composite Jacobian matrix can be well circumvented without any stringent assumption, which is critical for establishing the closed-loop stability. Moreover, a novel adaptive resilient control scheme based on dynamic decoupling is developed to separate the design of visual servoing controller and fault compensation, improving the visual servoing system resilience against unknown actuator failures in uncalibrated environment. Through a rigorous Lyapunov argument, the asymptotic convergence of image tracking error to zero is established successfully regardless of the occurrence of unknown actuator failures. The effectiveness of the proposed visual tracking control scheme is confirmed by simulation results.
THE TRANSFIGURATION OF DON JUAN. ESSAYS ON THE DRAMATURGY OF ALEXANDER DARGOMYZHSKY’S OPERA THE STONE GUEST. DEDICATED TO THE 210TH ANNIVERSARY OF THE COMPOSER. ESSAY ONE

The essays on A.Dargomyzhsky’s opera The Stone Guest are not quite usually named - “The Transfiguration of Don Juan”, to understand the image of the protagonist. The problem of semantic content and musical dramaturgy of this experimental and absolutely brilliant phenomenon of Russian musical theatre are addressed in the study. Namely, an attempt was made to study Dargomyzhsky’s recitative opera following the primary source, the little tragedy of the same name by A.Pushkin, in terms of the authors’ spiritual worldview. In this case, spirituality is understood not only as the richness of the inner world but also as the faith-centric creativity characteristic of representatives of Russian culture of the 19th century. The research perspective reveals a lot of new information in a seemingly familiar work.
Rethinking pose estimation in crowds: overcoming the detection information bottleneck and ambiguity

Frequent interactions between individuals are a fundamental challenge for pose estimation algorithms. Current pipelines either use an object detector together with a pose estimator (top-down approach), or localize all body parts first and then link them to predict the pose of individuals (bottom-up). Yet, when individuals closely interact, top-down methods are ill-defined due to overlapping individuals, and bottom-up methods often falsely infer connections to distant bodyparts. Thus, we propose a novel pipeline called bottom-up conditioned top-down pose estimation (BUCTD) that combines the strengths of bottomup and top-down methods. Specifically, we propose to use a bottom-up model as the detector, which in addition to an estimated bounding box provides a pose proposal that is fed as condition to an attention-based top-down model. We demonstrate the performance and efficiency of our approach on animal and human pose estimation benchmarks. On CrowdPose and OCHuman, we outperform previous state-of-the-art models by a significant margin. We achieve 78.5 AP on CrowdPose and 48.5 AP on OCHuman, an improvement of 8.6% and 7.8% over the prior art, respectively. Furthermore, we show that our method strongly improves the performance on multi-animal benchmarks involving fish and monkeys. The code is available at https://github.com/amathislab/BUCTD
CoIn: Contrastive Instance Feature Mining for Outdoor 3D Object Detection with Very Limited Annotations

Recently, 3D object detection with sparse annotations has received great attention. However, current detectors usually perform poorly under very limited annotations. To address this problem, we propose a novel Contrastive Instance feature mining method, named CoIn. To better identify indistinguishable features learned through limited supervision, we design a Multi-Class contrastive learning module (MCcont) to enhance feature discrimination. Meanwhile, we propose a feature-level pseudo-label mining framework consisting of an instance feature mining module (InF-Mining) and a Labeled-to-Pseudo contrastive learning module (LPcont). These two modules exploit latent instances in feature space to supervise the training of detectors with limited annotations. Extensive experiments with KITTI dataset, Waymo open dataset, and nuScenes dataset show that under limited annotations, our method greatly improves the performance of baseline detectors: CenterPoint, Voxel-RCNN, and CasA. Combining CoIn with an iterative training strategy, we propose a CoIn++ pipeline, which requires only 2% annotations in the KITTI dataset to achieve performance comparable to the fully supervised methods. The code is available at https://github.com/xmuqimingxia/CoIn.
Holistic Label Correction for Noisy Multi-Label Classification

Multi-label classification aims to learn classification models from instances associated with multiple labels. It is pivotal to learn and utilize the label dependence among multiple labels in multi-label classification. As a result of today’s big and complex data, noisy labels are inevitable, making it looming to target multi-label classification with noisy labels. Although the importance of label dependence has been shown in multi-label classification with clean labels, it is challenging and hard to bring label dependence to the problem of multi-label classification with noisy labels. The issues are, that we do not understand why label dependence is helpful in the problem, and how to learn and utilize label dependence only using training data with noisy multiple labels. In this paper, we bring label dependence to tackle the problem of multi-label classification with noisy labels. Specifically, we first provide a high-level understanding of why label dependence helps distinguish the examples with clean/noisy multiple labels. Benefiting from the memorization effect in handling noisy labels, a novel algorithm is then proposed to learn the label dependence by only employing training data with noisy multiple labels, and utilize the learned dependence to help correct noisy multiple labels to clean ones. We prove that the use of label dependence could bring a higher success rate for recovering correct multiple labels. Empirical evaluations justify our claims and demonstrate the superiority of our algorithm.
Assessing Digital Competence in Higher Education: A Gender Analysis of DigComp 2.1 Framework in Uganda

This research paper investigated the digital competence of students in higher education in Uganda, utilising the Dig-Comp 2.1 framework as the basis for assessment. The study explores potential gender disparities in digital competence levels among students, aiming to identify areas of improvement for educational institutions and policymakers. The research utilised a quantitative approach, incorporating online surveys to collect data (N = 99) from a diverse sample of university students. Descriptive and t-test analyses were performed to analyse the significant differences in digital competencies between genders and found slight but statistically insignificant differences. The study’s findings contribute to the growing body of research on gender and digital competency and underscore the need for ongoing efforts to create inclusive and equitable digital education environments. In today's increasingly digital world, possessing adequate digital competence is crucial for personal, academic, and professional success. Higher education institutions are pivotal in equipping students with the necessary digital skills to thrive in the digital age. To enhance digital competence among all students, educators, policymakers, and institutions must focus on gender-sensitive strategies and tailor digital literacy programs to meet the diverse needs of their student population
Empirical Similarity for Absent Data Generation in Imbalanced Classification

When the training data in a two-class classification problem is overwhelmed by one class, most classification techniques fail to correctly identify the data points belonging to the underrepresented class. We propose Similarity-based Imbalanced Classification (SBIC) that learns patterns in the training data based on an empirical similarity function. To take the imbalanced structure of the training data into account, SBIC utilizes the concept of absent data, i.e. data from the minority class which can help better find the boundary between the two classes. SBIC simultaneously optimizes the weights of the empirical similarity function and finds the locations of absent data points. As such, SBIC uses an embedded mechanism for synthetic data generation which does not modify the training dataset, but alters the algorithm to suit imbalanced datasets. Therefore, SBIC uses the ideas of both major schools of thoughts in imbalanced classification: Like cost-sensitive approaches SBIC operates on an algorithm level to handle imbalanced structures; and similar to synthetic data generation approaches, it utilizes the properties of unobserved data points from the minority class. The application of SBIC to imbalanced datasets suggests it is comparable to, and in some cases outperforms, other commonly used classification techniques for imbalanced datasets.
Recovering Markov models from closed-loop data

Situations in which recommender systems are used to augument decision making are becoming prevalent in many application domains. Almost always, these prediction tools (recommenders) are created with a view to affecting behavioural change. Clearly, successful applications actuating behavioural change, affect the original model underpinning the predictor, leading to an inconsistency. This feedback loop is often not considered in standard so-called Big Data learning techniques which rely upon machine learning/statistical learning machinery. The objective of this paper is to develop tools that recover unbiased user models in the presence of recommenders. More specifically, we assume that we observe a time series which is a trajectory of a Markov chain ${R}$ modulated by another Markov chain ${S}$, i.e. the transition matrix of ${R}$ is unknown and depends on the current state of ${S}$. The transition matrix of the latter is also unknown. In other words, at each time instant, ${S}$ selects a transition matrix for ${R}$ within a given set which consists of known and unknown matrices. The state of ${S}$, in turn, depends on the current state of ${R}$ thus introducing a feedback loop. We propose an Expectation-Maximization (EM) type algorithm, which estimates the transition matrices of ${S}$ and ${R}$. Experimental results are given to demonstrate the efficacy of the approach.
Exploiting textual and visual features for image categorization

Studies show that refining real-world categories into semantic subcategories contributes to better image modeling and classification. Previous image sub-categorization work relying on labeled images and WordNet’s hierarchy is labor-intensive. To tackle this problem, in this work, we extract textual and visual features to automatically select and subsequently classify web images into semantic rich categories. The following two major challenges are well studied: (1) noise in the labels of subcategories derived from the general corpus; (2) noise in the labels of images retrieved from the web. Specifically, we first obtain the semantic refinement subcategories from the text perspective and remove the noise by using the relevance-based approach. To suppress the search error induced noisy images, we then formulate image selection and classifier learning as a multi-instance learning problem and propose to solve the employed problem by the cutting-plane algorithm. The experiments show significant performance gains by using the generated data of our approach on image categorization tasks. The proposed approach also consistently outperforms existing weakly supervised and web-supervised approaches.
Conditional channel simulation

In this work we design a specific simulation tool for quantum channels which is based on the use of a control system. This allows us to simulate an average quantum channel which is expressed in terms of an ensemble of channels, even when these channel-components are not jointly teleportation-covariant. This design is also extended to asymptotic simulations, continuous ensembles, and memory channels. As an application, we derive relative-entropy-of-entanglement upper bounds for private communication over various channels, including non-Gaussian mixtures of bosonic lossy channels. Among other results, we also establish the two-way quantum and private capacity of the so-called “dephrasure” channel.
Information system for e-GDP based on computational intelligence approach

Information system for electronic commerce could be used for selling of goods and services online. However information system could be used for gross domestic product (GDP) estimation and analyzing based on different input parameters. GDP is considered as the main indicator for economic growth and there is need for more advanced way of GDP estimation. Therefore in this study was made an attempt to design a system for GDP estimation based on several economic parameters. The system is called e-GDP (electronic GDP) system and the main part of the system is e-GDP module. The e-GDP module performs estimation of GDP based in the given inputs. The estimation and calculation of GDP is based on computational intelligence approach namely extreme learning machine — ELM. ELM present a training algorithm for neural networks which could be more precise than traditional training algorithms and training time is reduced. As the input parameters there are trade, trade in services, merchandise trade, exports and imports. The system is designed based on object-oriented methodology. Therefore the object-oriented paradigm was established to perform e-GDP designing and analyzing. Obtained results shown reliability of the used approach for the GDP estimation.
SCOTI: Science Captioning of Terrain Images for data prioritization and local image search

Planetary exploration missions with Mars rovers are complicated, which generally require elaborated task planning by human experts, from the path to take to the images to capture. NASA has been using this process to acquire over 22 million images from the planet Mars. In order to improve the degree of automation and thus efficiency in this process, we propose a system for planetary rovers to actively search for prominence of prespecified scientific features in captured images. Scientists can prespecify such search tasks in natural language and upload them to a rover, on which the deployed system constantly captions captured images with a deep image captioning network and compare the auto-generated captions to the prespecified search tasks by certain metrics so as to prioritize those images for transmission. As a beneficial side effect, the proposed system can also be deployed to ground-based planetary data systems as a content-based search engine.
Rate-distortion-perception tradeoff of variable-length source coding for general information sources

Blau and Michaeli recently introduced a novel concept for inverse problems of signal processing, that is, the perception-distortion tradeoff. We introduce their tradeoff into the rate distortion theory of variable-length lossy source coding in information theory, and clarify the tradeoff among information rate, distortion and perception for general information sources. We also discuss the fixed-length coding with average distortion criterion that was missing in the previous letter.
Classification of translation invariant topological Pauli stabilizer codes for prime dimensional qudits on two-dimensional lattices

We prove that on any two-dimensional lattice of qudits of a prime dimension, every translation invariant Pauli stabilizer group with local generators and with the code distance being the linear system size is decomposed by a local Clifford circuit of constant depth into a finite number of copies of the toric code stabilizer group (Abelian discrete gauge theory). This means that under local Clifford circuits, the number of toric code copies is the complete invariant of topological Pauli stabilizer codes. Previously, the same conclusion was obtained under the assumption of nonchirality for qubit codes or the Calderbank–Shor–Steane structure for prime qudit codes; we do not assume any of these.
KalmanFlow 2.0: Efficient Video Optical Flow Estimation via Context-Aware Kalman Filtering

Recent studies on optical flow typically focus on the estimation of the single flow field in between a pair of images but pay little attention to the multiple consecutive flow fields in a longer video sequence. In this paper, we propose an efficient video optical flow estimation method by exploiting the temporal coherence and context dynamics under a Kalman filtering system. In this system, pixel's motion flow is first formulated as a second-order time-variant state vector and then optimally estimated according to the measurement and system noise levels within the system by maximum a posteriori criteria. Specifically, we evaluate the measurement noise according to the flow's temporal derivative, spatial gradient, and warping error. We determine the system noise based on the similarity of contextual information, which is represented by the compact features learned by pre-trained convolutional neural networks. The context-aware Kalman filtering helps improve the robustness of our method against abrupt change of light and occlusion/dis-occlusion in complicated scenes. The experimental results and analyses on the MPI Sintel, Monkaa, and Driving video datasets demonstrate that the proposed method performs favorably against the state-of-the-art approaches.
An improved version of the AAG cryptographic protocol

Abstract An improved version of the Anshel–Anshel–Goldfeld (AAG) algebraic cryptographic key-exchange scheme, that is in particular resistant against the Tsaban linear span cryptanalysis, is established. Unlike the original version, that is based on the intractability of the simultaneous conjugacy search problem for the platform group, the proposed version is based on harder simultaneous membership-conjugacy search problems, and the membership problem needs to be solved for a subset of the platform group that can be easily and efficiently built to be very complicated and without any good structure. A number of other hard problems need to be solved first before start solving the simultaneous membership-conjugacy search problem to obtain the exchanged key.
Transfer Neural Trees: Semi-Supervised Heterogeneous Domain Adaptation and Beyond

Heterogeneous domain adaptation (HDA) addresses the task of associating data not only across dissimilar domains but also described by different types of features. Inspired by the recent advances of neural networks and deep learning, we propose a deep leaning model of Transfer Neural Trees (TNT), which jointly solves cross-domain feature mapping, adaptation, and classification in a unified architecture. As the prediction layer in TNT, we introduce Transfer Neural Decision Forest (Transfer- NDF), which is able to learn the neurons in TNT for adaptation by stochastic pruning. In order to handle semi-supervised HDA, a unique embedding loss term is introduced to TNT for preserving prediction and structural consistency between labeled and unlabeled target-domain data. We further show that our TNT can be extended to zero shot learning for associating image and attribute data with promising performance. Finally, experiments on different classification tasks across features, datasets, and modalities would verify the effectiveness of our TNT.
More efficient, provably-secure direct anonymous attestation from lattices

The Cloud-Edges (CE) framework, wherein small groups of Internet of Things (IoT) devices are serviced by local edge devices, enables a more scalable solution to IoT networks. The trustworthiness of the network may be ensured with Trusted Platform Modules (TPMs). This small hardware chip is capable of measuring and reporting a representation of the state of an IoT device. When connecting to a network, the IoT platform might have its state signed by the TPM in an anonymous way to prove both its genuineness and secure state through the Direct Anonymous Attestation (DAA) protocol. Currently standardised DAA schemes have their security supported on the factoring and discrete logarithm problems. Should a quantum-computer become available in the next few decades, these schemes will be broken. There is therefore a need to start developing a post-quantum DAA protocol. This paper presents a Lattice-based DAA (LDAA) scheme to meet this requirement. The security of this scheme is proved in the Universally Composable (UC) security model under the hardness assumptions of the Ring Inhomogeneous Short Integer Solution (Ring-ISIS) and Ring Learning With Errors (Ring-LWE) problems. Compared to the only other DAA scheme with conjectured post-quantum security available in related art, the storage requirements of the TPM are reduced twofold and the signature sizes 5 times. Moreover, experimental results show that the signing and verification operations are accelerated 1.1 and 2.0 times, respectively.