Reviewerly/exHarmony huggingface dataset:
Block-Matching Multi-pedestrian Tracking

Target association is an extremely important problem in the field of multi-object tracking, especially for pedestrian scenes with high similarity in appearance and dense distribution. The traditional approach of combining IOU and ReID techniques with the Hungarian algorithm only partially addresses these challenges. To improve the model's matching ability, this paper proposes a block-matching model that extracts local features using a Block Matching Module (BMM) based on the Transformer model. The BMM extracts features by dividing them into blocks and mines effective features of the target to complete target similarity evaluation. Additionally, a Euclidean Distance Module (EDM) based on the Euclidean distance association matching strategy is introduced to further enhance the model's association ability. By integrating BMM and EDM into the same multi-object tracking model, this paper establishes a novel model called BWTrack that achieves excellent performance on MOT16, MOT17, and MOT20 while maintaining high performance at 7 FPS on a single GPU.
A Causality-Based Interpretable Cognitive Diagnosis Model

Cognitive diagnosis model (abbr.CDM) aims to assess students' cognitive processes during learning, enabling personalized support based on their needs. Nevertheless, deep learning-based CDMs are inherently opaque, posing challenges in providing psychological insights into the reasoning behind predicted outcomes. We address this by creating three interpretable parameters: skill mastery, exercise difficulty, and exercise discrimination. Inspired by Bayesian networks and neural networks, we use feature engineering for extraction of interpretable parameters and tree-enhanced naive Bayes classifiers for prediction. Our method balances interpretability and accuracy. Experimentally, we compare our approach to traditional and advanced models on four datasets, analyzing each feature's impact. We conduct ablation studies on each feature to examine their contribution to student performance prediction. Thus, causality-based interpretable cognitive diagnosis model (CBICDM) has great potential for providing adaptive and personalized instructions with causal reasoning in real-world educational systems.
Correlated Online k-Nearest Neighbors Regressor Chain for Online Multi-output Regression

Online multi-output regression is a crucial task in machine learning with applications in various domains such as environmental monitoring, energy efficiency prediction, and water quality prediction. This paper introduces CONNRC, a novel algorithm designed to address online multi-output regression challenges and provide accurate real-time predictions. CONNRC builds upon the k-nearest neighbor algorithm in an online manner and incorporates a relevant chain structure to effectively capture and utilize correlations among structured multi-outputs. The main contribution of this work lies in the potential of CONNRC to enhance the accuracy and efficiency of real-time predictions across diverse application domains. Through a comprehensive experimental evaluation on six real-world datasets, CONNRC is compared against five existing online regression algorithms. The consistent results highlight that CONNRC consistently outperforms the other algorithms in terms of average Mean Absolute Error, demonstrating its superior accuracy in multi-output regression tasks. However, the time performance of CONNRC requires further improvement, indicating an area for future research and optimization.
EWMIGCN: Emotional Weighting Based Multimodal Interaction Graph Convolutional Networks for Personalized Prediction

To address the challenges of information overload and cold start in personalized prediction systems, researchers have proposed graph neural network-based recommendation methods. However, existing studies have largely overlooked the shared or similar characteristics among different modal features. Moreover, there is a mismatch between the focuses of multimodal feature extraction (MFE) and user preference modeling (UPM). To tackle these issues, this paper establishes an interaction graph by extracting multimodal information and addresses the mismatch between MFE and UPM by constructing an emotion-weighted bisymmetric linear graph convolutional network (EW-BGCN). Specifically, this paper introduces a novel model called EWMIGCN, which combines multimodal information extraction using parallel CNNs to build an interaction graph, propagates the information on EW-BGCN, and predicts user preferences by summing the expressions of users and items through inner product calculations. Notably, this paper incorporates sentiment information from user comments to finely weigh the neighborhood aggregation in EW-BGCN, enhancing the overall quality of items. Experimental results demonstrate that the proposed model achieves superior performance compared to other baseline models on three datasets, as measured by HitsRatio with Normalized Discounted Cumulative Gain.
Contrastive Hierarchical Gating Networks for Rating Prediction

Review-based recommendations suffer from text noises and the absence of supervised signals. To address those challenges, we propose a novel hierarchical gated sentiment-aware model for rating prediction in this paper. Specifically, to automatically suppress the influence of noisy reviews, we propose a hierarchical gating network to select informative textual signals at different levels of granularity. Specifically, a local gating module is proposed to select reviews with personalized end-to-end differential thresholds. The aim is to gate reviews in a relatively "hard" way to minimize the information flow from noisy reviews while facilitating the model training. A global gating module is employed to evaluate the overall usefulness of the review signals by estimating the uncertainties encoded in the historical reviews. In addition, a discriminative learning module is proposed to supervise the learning of the hierarchical gating network. The essential intuition is to exploit the sentiment consistencies between the target reviews and the target ratings for developing self-supervision signals so that the hierarchical gating network can select relevant reviews related to the target ratings for better prediction. Finally, extensive experiments on public datasets and comparison studies with state-of-the-art baselines have demonstrated the effectiveness of the proposed model, additional investigations also provide a deep insight into the rationale underlying the superiority of the proposed model.
Ensemble of Randomized Neural Network and Boosted Trees for Eye-Tracking-Based Driver Situation Awareness Recognition and Interpretation

Ensuring traffic safety is crucial in the pursuit of sustainable transportation. Across diverse traffic systems, maintaining good situation awareness (SA) is important in promoting and upholding traffic safety. This work focuses on a regression problem of using eye-tracking features to perform situation awareness (SA) recognition in the context of conditionally automated driving. As a type of tabular dataset, recent advances have shown that both neural networks (NNs) and gradient-boosted decision trees (GBDTs) are potential solutions to achieve better performance. To avoid the complex analysis to select the suitable model for the task, this work proposed to combine the NNs and tree-based models to achieve better performance on the task of SA assessment generally. Considering the necessity of the real-time measure for practical applications, the ensemble deep random vector functional link (edRVFL) and light gradient boosting machine (lightGBM) were used as the representative models of NNs and GBDTs in the investigation, respectively. Furthermore, this work exploited Shapley additive explanations (SHAP) to interpret the contributions of the input features, upon which we further developed two ensemble modes. Experimental results demonstrated that the proposed model outperformed the baseline models, highlighting its effectiveness. In addition, the interpretation results can also provide practitioners with references regarding the eye-tracking features that are more relevant to SA recognition.
Mixed quantum-classical dynamics for near term quantum computers

Abstract Mixed quantum-classical dynamics is a set of methods often used to understand systems too complex to treat fully quantum mechanically. Many techniques exist for full quantum mechanical evolution on quantum computers, but mixed quantum-classical dynamics are less explored. We present a modular algorithm for general mixed quantum-classical dynamics where the quantum subsystem is coupled with the classical subsystem. We test it on a modified Shin-Metiu model in the first quantization through Ehrenfest propagation. We find that the Time-Dependent Variational Time Propagation algorithm performs well for short-time evolutions and retains qualitative results for longer-time evolutions.
Be an Excellent Student: Review, Preview, and Correction

In the letter, we propose a novel yet effective knowledge distillation scheme which mimics an all-round learning process of an excellent student from the teacher, i.e, knowledge review, knowledge preview, and knowledge correction, to acquire more informative and complementary knowledge. In the newly proposed method, to better leverage comprehensive feature knowledge from the teacher model, we propose Knowledge Review and Knowledge Preview Distillation to amalgamate multi-level features from different intermediate layers in both forward and backward pathways and fully distill them through hierarchical context loss, which greatly improves the student's feature learning efficiency. Moreover, we further present a Response Correction Mechanism to reinforce the prediction of student, which can more fully excavate the student's own knowledge, effectively alleviating the negative influence caused by the knowledge gap between the teacher and the student. We verify the effectiveness of our method with various networks on the CIFAR-100 datasets and the proposed method achieves competitive results compared with other state-of-the-art competitors. The code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/kbzhang0505/RPC</uri> .
RPUC: Semi-supervised 3D Biomedical Image Segmentation Through Rectified Pyramid Unsupervised Consistency

Deep learning models have demonstrated remarkable performance in various biomedical image segmentation tasks. However, their reliance on a large amount of labeled data for training poses challenges as acquiring well-annotated data is expensive and time-consuming. To address this issue, semi-supervised learning (SSL) has emerged as a potential solution to leverage abundant unlabeled data. In this paper, we propose a simple yet effective consistency regularization scheme called Rectified Pyramid Unsupervised Consistency (RPUC) for semi-supervised 3D biomedical image segmentation. Our RPUC adopts a pyramid-like structure by incorporating three segmentation networks. To fully exploit the available unlabeled data, we introduce a novel pyramid unsupervised consistency (PUC) loss, which enforces consistency among the outputs of the three segmentation models and facilitates the transfer of cyclic knowledge. Additionally, we perturb the inputs of the three networks with varying ratios of Gaussian noise to enhance the consistency of unlabeled data outputs. Furthermore, three pseudo labels are generated from the outputs of the three segmentation networks, providing additional supervision during training. Experimental results demonstrate that our proposed RPUC achieves state-of-the-art performance in semi-supervised segmentation on two publicly available 3D biomedical image datasets.
High-Precision State Estimator Design for the State of Gaussian Linear Systems Based on Deep Neural Network Kalman Filter

Kalman filter (KF) is highly valued in engineering for its simplicity, small storage, and real-time processing. However, KF is optimal for linear filters and not as effective for nonlinear ones. In this article, we propose a high-precision nonlinear filter, the deep neural network Kalman filter (DKF), which combines KF and a neural network model. DKF’s estimation process follows the Kalman filter approach. To maximize the use of model information, we establish DKF by merging the Kalman prediction and update outcomes as neural network input features and training the input–output nonlinear mapping model online. We also introduce a fusion filter, FDKF, based on KF and DKF. Simulation results demonstrate that, for linear Gaussian systems, DKF outperforms KF, and FDKF outperforms both DKF and KF in offline iterative prediction.
RF-CSign: A Chinese Sign Language Recognition System Based on Large Kernel Convolution and Normalization-Based Attention

Deaf people use sign language for communication, which relies on the movement gestures of body parts and plays a vital role in human-computer interaction. Most wireless sensing-based gesture recognition studies have recognized simple gestures but overlooked the recognition of complex activities, such as sign language. In addition, cross-domain recognition often requires a large amount of data to train classifiers for each environment. Therefore, we propose RF-CSign, which aims to achieve high accuracy in sign language recognition and cross-domain recognition. First, we use Radio Frequency Identification (RFID) to collect signals and obtain denoised signals through data pre-processing, so that they can be processed in a neural network. Second, the RF-CSign network is proposed with the inclusion of large kernel convolution to reduce the complexity of the model and to make the model with long-range correlations, thereby enhancing recognition accuracy. Third, RF-CSign employs a pixel Normalization-based Attention Module (NAM) to enhance the stability of the model, thereby addressing the problem of model overfitting. Finally, RF-CSign achieves high accuracy in cross-domain environments through a migration learning approach. The experimental results showed that the average recognition accuracy of RF-CSign reached 99.17%, and the average recognition accuracy for new users and new environments recorded 96.67% and 97.50%, respectively.
Domain Generalized Object Detection with Triple Graph Reasoning Network

Recent advances in Domain Adaptive Object Detection (DAOD) have vastly restrained the performance degradation caused by distribution shift. However, DAOD relies on the strong assumption of accessible target domain during the learning procedure, which is tough to be satisfied in real-world applications. Domain Generalized Object Detection (DGOD) aims to generalize the detector trained on the source domains directing to an unknown target domain without accessing the target data. Thus it is a much more challenged problem and very few contributions have been reported. Extracting domain-invariant information is the key problem of domain generalization. Considering that the topological structure of objects does not change with the domain, we present a general DGOD framework, Triple Graph Reasoning Network (TGRN) to uncover and model the structure of objects. The proposed TGRN models the topological relations of foregrounds via building refined sparse graphs on both pixel-level and semantic-level. Meanwhile, a bipartite graph is created to capture structural consistency of instances across domain, implicitly enabling distribution alignment. Experiments on our newly constructed datasets verify the effectiveness of the proposed TGRN. Codes and datasets are available at https://github.com/zjrao/tgrn .
Domain-Invariant Task Optimization for Cross-domain Recommendation

The challenge of cold start has long been a persistent issue in recommender systems. However, Cross-domain Recommendation (CDR) provides a promising solution by utilizing the abundant information available in the auxiliary source domain to facilitate cold-start recommendations for the target domain. Many existing popular CDR methods only use overlapping user data but ignore non-overlapping user data when training the model to establish a mapping function, which reduces the model's generalization ability. Furthermore, these CDR methods often directly learn the target embedding during training, because the target embedding itself may be unreasonable, resulting in an unreasonable transformed embedding, exacerbating the difficulty of model generalization. To address these issues, we propose a novel framework named Domain-Invariant Task Optimization for Cross-domain Recommendation (DITOCDR). To effectively utilize non-overlapping user information, we employ source and target domain autoencoders to learn overlapping and non-overlapping user embeddings and extract domain-invariant factors. Additionally, we use a task-optimized strategy for target embedding learning to optimize the embedding and implicitly transform the source domain user embedding to the target feature space. We evaluate our proposed DITOCDR on three real-world datasets collected by Amazon, and the experimental results demonstrate its excellent performance and effectiveness.
Deep Semantic-Level Cross-Domain Recommendation Model Based on DSV-CDRM

A deep semantic-level cross-domain recommendation model based on DSV-CDRM is proposed to address the problems of existing methods such as single modeling approach. First, review information is converted into word vectors using a TinyBERT pre-trained language model, and then two global deep semantic viewpoint matrices are used in conjunction with a gating mechanism to guide queries. An additional convolutional layer is added on top of the improved text convolution to construct auxiliary documents using similar but non-overlapping user comments. Finally, correlations between deep semantic viewpoints between different domains are learned by constructing a correlation matrix and performing semantic matching. Experiments on the Amazon public dataset demonstrate that the proposed method outperforms existing models in both MAE and MSE, and it can effectively improve the performance of cross-domain recommendation system.
Optimization of PV-system design for household

The research deals with the design of an optimal photovoltaic system for a specific location in the Czech Republic. Commercially available different types of PV panels were installed in the selected locations for the collection of data was used Arduino platform. Optimal technical and economic recommendations were developed for the four types. The work was successful in confirming some characteristic features of these types of panels according to theoretical models. In some cases, the effect of the technical performance of the measurement and the behavior of the panel was slightly different.
Hate Speech Recognition in Chilean Tweets

Hate speech, which targets specific groups based on race, religion, or sexual orientation, is a growing concern, especially on social media. Detecting hate speech is a critical research area, but most models are developed in English, leaving a gap for other languages like Spanish. Spanish presents additional challenges due to its regional variants and slang. In this paper, we introduce HateStack, the winning model of the 2022 Datathon at Universidad Técnica Federico Santa Maria, Chile, designed to detect hate speech in Chilean tweets. HateStack is a two-level ensemble model comprising a feature extraction process, five Level-l models, and a logistic regression as a second-level model. The results demonstrate that HateStack outperforms other ensemble models and RoBERTuito, a transformer-based deep learning model tailored for hate speech detection on tweets. Developing such models in non-English languages is important to detect hate speech effectively.
Prediction of optimal operating point for BPG-based lossy compression of images corrupted by Poisson noise

Lossy image compression becomes a standard way to solve the problem of the rapidly increasing image size that is faced due to better quality of sensors and spatial resolution. Compression of noise-free images is mostly considered. However, images are often corrupted by noise and this property needs to be taken into account in solving the problem of efficient lossy compression. One of the peculiarities of lossy compression of noisy images is the filtering effect that often leads to existence of the so-called optimal operation point (OOP) for which a compressed image in sense of quality is closer to noise free compared to the noisy image. OOP existence can be predicted for some simple noise models such as Gaussian noise for greyscale and color images. Here our study deals with OOP prediction for images corrupted by Poisson noise compressed by Better Portable Graphics (BPG) coder. We demonstrate the possibility of OOP prediction for such metrics as PSNR and PSNR-HVS-M and verify the proposed approach performance using images that have not been used in the preliminary “training”. In addition, the formula for the OOP position determination is presented.
Study of Methods for Improving the Meta-Algorithm of the Bagging Classifier

In this paper, we explored approaches that improve the performance of ensemble bagging classifiers for identifying the state of a computer system. The following algorithms are considered: Ensemble pruning, Advanced Voting Algorithms, Dynamic Voting Strategies, Confidence Calibration, Adaptation through Meta-Features and Meta-Learning, which allow improving the voting procedure of the bagging meta-algorithm. Artificial data was generated as initial data, which complicates the classification task and contains an increased amount of noise. In the Google Collab environment, software models of algorithms have been developed, and their quality has been assessed. It was found that the use of the Ensemble pruning and Adaptation through Meta-Features algorithm is the most qualitative. In addition, the Ensemble pruning algorithm reduces the number of basic ensemble classifiers, and, as a result, increases the efficiency of computer system identification. Based on the results of the study, a method for identifying a computer system was proposed through the integrated use of a bagging classifier and an optimization procedure based on the Ensemble pruning algorithm.