Reviewerly/exHarmony huggingface dataset:
Multi-objective QUBO solver

Quantum and quantum-inspired optimisation algorithms are designed to solve problems represented in binary, quadratic and unconstrained form. Combinatorial optimisation problems are therefore often formulated as Quadratic Unconstrained Binary Optimisation Problems (QUBO) to solve them with these algorithms. Moreover, these QUBO solvers are often implemented using specialised hardware to achieve enormous speedups, e.g. Fujitsu's Digital Annealer (DA) and D-Wave's Quantum Annealer. However, these are single-objective solvers, while many real-world problems feature multiple conflicting objectives. Thus, a common practice when using these QUBO solvers is to scalarise such multi-objective problems into a sequence of single-objective problems. Due to design trade-offs of these solvers, formulating each scalarisation may require more time than finding a local optimum. We present the first attempt to extend the algorithm supporting a commercial QUBO solver as a multi-objective solver that is not based on scalarisation. The proposed multi-objective DA algorithm is validated on the bi-objective Quadratic Assignment Problem. We observe that algorithm performance significantly depends on the archiving strategy adopted, and that combining DA with non-scalarisation methods to optimise multiple objectives outperforms the current scalarised version of the DA in terms of final solution quality.
Classical analogs of the covariance matrix, purity, linear entropy, and von Neumann entropy

We obtain a classical analog of the quantum covariance matrix by performing its classical approximation for any continuous quantum state, and we illustrate this approach with the anharmonic oscillator. Using this classical covariance matrix, we propose classical analogs of the purity, linear quantum entropy, and von Neumann entropy for classical integrable systems, when the quantum counterpart of the system under consideration is in a Gaussian state. As is well known, this matrix completely characterizes the purity, linear quantum entropy, and von Neumann entropy for Gaussian states. These classical analogs can be interpreted as quantities that reveal how much information from the complete system remains in the considered subsystem. To illustrate our approach, we calculate these classical analogs for three coupled harmonic oscillators and two linearly coupled oscillators. We find that they exactly reproduce the results of their quantum counterparts. In this sense, it is remarkable that we can calculate these quantities from the classical viewpoint.
Toward a General Framework for Multimodal Big Data Analysis

Multimodal Analytics in Big Data architectures implies compounded configurations of the data processing tasks. Each modality in data requires specific analytics that triggers specific data processing tasks. Scalability can be reached at the cost of an attentive calibration of the resources shared by the different tasks searching for a trade-off with the multiple requirements they impose. We propose a methodology to address multimodal analytics within the same data processing approach to get a simplified architecture that can fully exploit the potential of the parallel processing of Big Data infrastructures. Multiple data sources are first integrated into a unified knowledge graph (KG). Different modalities of data are addressed by specifying ad hoc views on the KG and producing a rewriting of the graph containing merely the data to be processed. Graph traversal and rule extraction are this way boosted. Using graph embeddings methods, the different ad hoc views can be transformed into low-dimensional representation following the same data format. This way a single machine learning procedure can address the different modalities, simplifying the architecture of our system. The experiments we executed demonstrate that our approach reduces the cost of execution and improves the accuracy of analytics.
RuArg-2022: Argument Mining Evaluation

Argumentation analysis is a field of computational linguistics that studies methods for extracting arguments from texts and the relationships between them, as well as building argumentation structure of texts. This paper is a report of the organizers on the first competition of argumentation analysis systems dealing with Russian language texts within the framework of the Dialogue conference. During the competition, the participants were offered two tasks: stance detection and argument classification. A corpus containing 9,550 sentences (comments on social media posts) on three topics related to the COVID-19 pandemic (vaccination, quarantine, and wearing masks) was prepared, annotated, and used for training and testing. The system that won the first place in both tasks used the NLI (Natural Language Inference) variant of the BERT architecture, automatic translation into English to apply a specialized BERT model, retrained on Twitter posts discussing COVID-19, as well as additional masking of target entities. This system showed the following results: for the stance detection task an F1-score of 0.6968, for the argument classification task an F1-score of 0.7404. We hope that the prepared dataset and baselines will help to foster further research on argument mining for the Russian language.
On The Benefit of Parameter-Driven Approaches for the Modeling and the Prediction of Satisfied User Ratio for Compressed Video

The human eye cannot perceive small pixel changes in images or videos until a certain threshold of distortion. In the context of video compression, Just Noticeable Difference (JND) is the smallest distortion level from which the human eye can perceive the difference between reference video and the distorted/compressed one. Satisfied-User-Ratio (SUR) curve is the complementary cumulative distribution function of the individual JNDs of a viewer group. However, most of the previous works predict each point in SUR curve by using features both from source video and from compressed videos with assumption that the group-based JND annotations follow Gaussian distribution, which is neither practical nor accurate. In this work, we firstly compared various common functions for SUR curve modeling. Afterwards, we proposed a novel parameter-driven method to predict the video-wise SUR from video features. Besides, we compared the prediction results of source-only features based (SRC-based) models and source plus compressed videos features (SRC+PVS-based) models.
iTD3-CLN: Learn to navigate in dynamic scene through Deep Reinforcement Learning

This paper proposes iTD3-CLN, a Deep Reinforcement Learning (DRL) based low-level motion controller, to achieve map-less autonomous navigation in dynamic scene. We consider three enhancements to the Twin Delayed DDPG (TD3) for the navigation task: N-step returns, Priority Experience Replay, and a channel-based Convolutional Laser Network (CLN) architecture. In contrast to the conventional methods such as the DWA, our approach is found superior in the following ways: no need for prior knowledge of the environment and metric map, lower reliance on an accurate sensor, learning emergent behavior in dynamic scene that is intuitive, and more remarkably, able to transfer to the real robot without further fine-tuning. Our extensive studies show that in comparison to the original TD3, the proposed approach can obtain approximately 50% reduction in training to get same performance, 50% higher accumulated reward, and 30–50% increase in generalization performance when tested in unseen environments. Videos of our experiments are available at https://youtu.be/BRN0Gk5oLOc (Simulation) and https://youtu.be/yIxGH9TPQCc (Real experiment).
Mediation analysis using Bayesian tree ensembles.

We present a general framework for causal mediation analysis using nonparametric Bayesian methods in the potential outcomes framework. Our model, which we refer to as the Bayesian causal mediation forests model, combines recent advances in Bayesian machine learning using decision tree ensembles, Bayesian nonparametric causal inference, and a Bayesian implementation of the g-formula for computing causal effects. Because of its strong performance on simulated data and because it greatly reduces researcher degrees of freedom, we argue that Bayesian causal mediation forests are highly attractive as a default approach. Of independent interest, we also introduce a new sensitivity analysis technique for mediation analysis with continuous outcomes that is widely applicable. We demonstrate our approach on both simulated and real data sets, and show that our approach obtains low mean squared error and close to nominal coverage of 95% interval estimates, even in highly nonlinear problems on which other methods fail. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Close the Loop: A Unified Bottom-Up and Top-Down Paradigm for Joint Image Deraining and Segmentation

In this work, we focus on a very practical problem: image segmentation under rain conditions. Image deraining is a classic low-level restoration task, while image segmentation is a typical high-level understanding task. Most of the existing methods intuitively employ the bottom-up paradigm by taking deraining as a preprocessing step for subsequent segmentation. However, our statistical analysis indicates that not only deraining would benefit segmentation (bottom-up), but also segmentation would further improve deraining performance (top-down) in turn. This motivates us to solve the rainy image segmentation task within a novel top-down and bottom-up unified paradigm, in which two sub-tasks are alternatively performed and collaborated with each other. Specifically, the bottom-up procedure yields both clearer images and rain-robust features from both image and feature domains, so as to ease the segmentation ambiguity caused by rain streaks. The top-down procedure adopts semantics to adaptively guide the restoration for different contents via a novel multi-path semantic attentive module (SAM). Thus the deraining and segmentation could boost the performance of each other cooperatively and progressively. Extensive experiments and ablations demonstrate that the proposed method outperforms the state-of-the-art on rainy image segmentation.
Robust nonadiabatic geometric quantum computation by dynamical correction

Besides the intrinsic noise resilience property, nonadiabatic geometric phases are of the fast evolution nature, and thus can naturally be used in constructing quantum gates with excellent performance, i.e., the so-called nonadiabatic geometric quantum computation (NGQC). However, previous single-loop NGQC schemes are sensitive to the operational control error, i.e., the $X$ error, due to the limitations of the implementation. Here, we propose a robust scheme for NGQC combining with the dynamical correction technique, which still uses only simplified pulses, and thus being experimental friendly. We numerically show that our scheme can greatly improve the gate robustness in previous protocols, retaining the intrinsic merit of geometric phases. Furthermore, to fight against the dephasing noise, due to the $Z$ error, we can incorporate the decoherence-free subspace encoding strategy. In this way, our scheme can be robust against both types of errors. Finally, we also propose how to implement the scheme with encoding on superconducting quantum circuits with experimentally demonstrated technology. Therefore, due to the intrinsic robustness, our scheme provides a promising alternation for the future scalable fault-tolerant quantum computation.
Asymmetric cyclic controlled quantum teleportation by using a quantum channel composed of six G states

In the existing research on cyclic teleportation protocols, it is generally discussed that the teleportation of single particle, two particle unknown quantum states with symmetrical structure or asymmetric special unknown quantum states (such as GHZ state) with three or four participants. In contrast, the scheme proposed in this paper has universality and stronger practicability. In this paper, we propose a novel scheme of asymmetric cyclic controlled teleportation. In our scheme, Alice can teleport an arbitrary single-qubit entangled state to Charlie, Charlie can teleport an arbitrary three-qubit entangled state to Bob and at the same time Bob can teleport an arbitrary two-qubit entangled state to Alice under the control of a controller David. It only needs Bell measurement and local unitary operation, and can realize three-party cyclic transmission with probability 1 by using a quantum channel composed of any six of the 16 G states.
Automatic Leukaemia Segmentation Approach for Blood Cancer Classification Using Microscopic Images

Leukaemia is a type of blood cancer that is caused by undeveloped White Blood Cells (WBC), and it is also called a blast blood cell. In the marrow of human bones, leukaemia is developed and is responsible for blood cell generation with leukocytes and WBC, and if any cell gets blasted, then it may become a cause of death. Therefore, the diagnosis of leukaemia in its early stages helps greatly in the treatment along with saving human lives. Subsequently, in terms of detection, image segmentation techniques play a vital role, and they turn out to be the important image processing steps for the extraction of feature patterns from the Acute Lymphoblastic Leukaemia (ALL) type of blood cancer. Moreover, the image segmentation technique focuses on the division of cells by segmenting a microscopic image into background and cancer blood cell nucleus, which is well-known as the Region Of Interest (ROI). As a result, in this article, we attempt to build a segmentation technique capable of solving blood cell nucleus segmentation issues using four distinct scenarios, including K-means, FCM (Fuzzy C-means), K-means with FFA (Firefly Algorithm), and FCM with FFA. Also, we determine the most effective method of blood cell nucleus segmentation, which we subsequently use for the Leukaemia classification model. Finally, using the Convolution Neural Network (CNN) as a classifier, we developed a leukaemia cancer classification model from the microscopic images. The proposed system’s classification accuracy is tested using the CNN to test the model on the ALL-IDB dataset and equate it to the current state of the art. In terms of experimental analysis, we observed that the accuracy of the model is near to 99%, and it is far better than other existing models that are designed to segment and classify the types of leukaemia cancer in terms of ALL.
Entity Alignment with Reliable Path Reasoning and Relation-aware Heterogeneous Graph Transformer

Entity Alignment (EA) has attracted widespread attention in both academia and industry, which aims to seek entities with same meanings from different Knowledge Graphs (KGs). There are substantial multi-step relation paths between entities in KGs, indicating the semantic relations of entities. However, existing methods rarely consider path information because not all natural paths facilitate for EA judgment. In this paper, we propose a more effective entity alignment framework, RPR-RHGT, which integrates relation and path structure information, as well as the heterogeneous information in KGs. Impressively, an initial reliable path reasoning algorithm is developed to generate the paths favorable for EA task from the relation structures of KGs. This is the first algorithm in the literature to successfully use unrestricted path information. In addition, to efficiently capture heterogeneous features in entity neighborhoods, a relation-aware heterogeneous graph transformer is designed to model the relation and path structures of KGs. Extensive experiments on three well-known datasets show RPR-RHGT significantly outperforms 10 state-of-the-art methods, exceeding the best performing baseline up to 8.62% on Hits@1. We also show its better performance than the baselines on different ratios of training set, and harder datasets.
AggPose: Deep Aggregation Vision Transformer for Infant Pose Estimation

Movement and pose assessment of newborns lets experienced pediatricians predict neurodevelopmental disorders, allowing early intervention for related diseases. However, most of the newest AI approaches for human pose estimation methods focus on adults, lacking publicly benchmark for infant pose estimation. In this paper, we fill this gap by proposing infant pose dataset and Deep Aggregation Vision Transformer for human pose estimation, which introduces a fast trained full transformer framework without using convolution operations to extract features in the early stages. It generalizes Transformer + MLP to high-resolution deep layer aggregation within feature maps, thus enabling information fusion between different vision levels. We pre-train AggPose on COCO pose dataset and apply it on our newly released large-scale infant pose estimation dataset. The results show that AggPose could effectively learn the multi-scale features among different resolutions and significantly improve the performance of infant pose estimation. We show that AggPose outperforms hybrid model HRFormer and TokenPose in the infant pose estimation dataset. Moreover, our AggPose outperforms HRFormer by 0.8 AP on COCO val pose estimation on average. Our code is available at github.com/SZAR-LAB/AggPose.
Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation

A cross-domain recommendation has shown promising results in solving data-sparsity and cold-start problems. Despite such progress, existing methods focus on domain-shareable information (overlapped users or same contexts) for a knowledge transfer, and they fail to generalize well without such requirements. To deal with these problems, we suggest utilizing review texts that are general to most e-commerce systems. Our model (named SER) uses three text analysis modules, guided by a single domain discriminator for disentangled representation learning. Here, we suggest a novel optimization strategy that can enhance the quality of domain disentanglement, and also debilitates detrimental information of a source domain. Also, we extend the encoding network from a single to multiple domains, which has proven to be powerful for review-based recommender systems. Extensive experiments and ablation studies demonstrate that our method is efficient, robust, and scalable compared to the state-of-the-art single and cross-domain recommendation methods.
A novel enhanced convolution neural network with extreme learning machine: facial emotional recognition in psychology practices

Facial emotional recognition is one of the essential tools used by recognition psychology to diagnose patients. Face and facial emotional recognition are areas where machine learning is excelling. Facial Emotion Recognition in an unconstrained environment is an open challenge for digital image processing due to different environments, such as lighting conditions, pose variation, yaw motion, and occlusions. Deep learning approaches have shown significant improvements in image recognition. However, accuracy and time still need improvements. This research aims to improve facial emotion recognition accuracy during the training session and reduce processing time using a modified Convolution Neural Network Enhanced with Extreme Learning Machine (CNNEELM). The system entails (CNNEELM) improving the accuracy in image registration during the training session. Furthermore, the system recognizes six facial emotions happy, sad, disgust, fear, surprise, and neutral with the proposed CNNEELM model. The study shows that the overall facial emotion recognition accuracy is improved by 2% than the state of art solutions with a modified Stochastic Gradient Descent (SGD) technique. With the Extreme Learning Machine (ELM) classifier, the processing time is brought down to 65ms from 113ms, which can smoothly classify each frame from a video clip at 20fps. With the pre-trained InceptionV3 model, the proposed CNNEELM model is trained with JAFFE, CK+, and FER2013 expression datasets. The simulation results show significant improvements in accuracy and processing time, making the model suitable for the video analysis process. Besides, the study solves the issue of the large processing time required to process the facial images.
Experiencing an art education program through immersive virtual reality or iPad: Examining the mediating effects of sense of presence and extraneous cognitive load on enjoyment, attention, and retention

Sense of presence and extraneous cognitive load (ECL) are the two psychological effects widely employed to explain the cognitive outcomes caused by high-immersive media (e. g., virtual reality). This study identified the concepts of both technological affordance (i.e., immersion) and the psychological effects of VR learning. It investigated the mechanism by which immersion leads to better or worse communication in the context of art education. We operationalized the concept of immersion into two levels: a high-immersive VR system (HTC VIVE Cosmos) and a low-immersive tablet system (iPad). Through a between-subject experiment, we found that higher immersion not only led to a greater sense of presence but also lowered extraneous cognitive load. Enjoyment and attention increased as a sense of presence rose but were not necessarily predicted by extraneous cognitive load. This study found that sense of presence was a more robust explanatory variable than ECL and that cognitive load could be lower in a high-immersive environment with content specifically designed for VR.
Uplink and Downlink Decoupled 5G/B5G Vehicular Networks: A Federated Learning Assisted Client Selection Method

Owning to the powerful support of 5G/B5G technologies, it is promising that, in a vehicular network, the ever-increasing demands for artificial intelligence applications along with privacy concerns can be fulfilled by introducing federated learning (FL) into vehicular networks. However, in such a 5G/B5G cellular-based vehicular network, distributed clients, i.e., vehicles, usually suffer from intermittent connectivity to the uplink base station (BS) due to the small cell networking and their mobility, and thereby the FL execution time and the learning performance cannot be guaranteed. In order to ensure a required execution time and the FL performance, we propose to investigate the client selection schemes in the uplink-downlink decoupled 5G/B5G networks. Due to the bursty feature of uplink transmissions, we first incorporate a flexible delay analytical tool, i.e., martingale theory, to characterize the access delay of each vehicle, and represent it in the form of delay violation probability. Then, given an energy budget, adjustable parameters are optimized to maximize the global learning performance. Supposing that all the clients participate in the FL and they can outperform the systematic FL performance requirement, the client selection methods under different criteria are further investigated. Especially, we propose a low-complexity algorithm to select clients in terms of enhancing energy efficiency. Numerical results are provided to demonstrate the effectiveness of the proposed client selection method in both guaranteeing the FL execution time and meeting the systematic learning performance. The energy efficiency of the vehicular FL system can also be enhanced by employing the proposed method.