Reviewerly/exHarmony huggingface dataset:
Shapley-based feature augmentation

Improving the predictive performance of machine learning models is the desired goal in many tasks and domains. The predictive performance of the learning algorithm is directly affected by the input features it receives. Feature augmentation is aimed at enhancing the quality of models by adding informative features to the original data. Explainable AI methods are typically used to explain the results of machine learning models. Recently, these methods have also been used to improve models’ predictive performance. In this study, we examine the benefit of incorporating the explanations obtained by an explainable AI method as augmented features. In particular, we propose SFA — Shapley-Based feature augmentation, a two-stage ensemble learning method that uses out-of-fold predictions and their corresponding Shapley values as augmented features for each instance. Shapley values, which are obtained without domain expertise, reflect the importance of the original features to each prediction and consider their interactions with all other features. Experimental results demonstrate the superiority of our proposed method, SFA, against several feature augmentation methods on multiple public datasets with various characteristics.
Expert-guided contrastive learning for video-text retrieval

Transformers with collaborative experts have become a powerful framework for video-text retrieval. In specific, experts understand the specialized property of each domain (e.g., appearance, motion, and audio) from videos and the video encoder aggregates those expertise features. However, previous works implicitly guide the video transformer by solving auxiliary video-text tasks with expertise features, since concatenation for the video transformer is the only effort to exploit the knowledge of experts. In this paper, we propose an expert-guided contrastive loss in order to fully exploit expert knowledge from videos. In detail, we sample a positive bag using an expert-wise similarity matrix to learn text encoder and decompose text representation into dynamic and static factors from given videos. Through extensive experiments, we verify the effectiveness of the proposed methods. Notably, we also demonstrate that our method brings significant improvements under the expert-based framework and it can collaborate with CLIP-based architectures for further performance boosts.
Psychological recovery effects of 3D virtual tourism with real scenes -- a comparative study

This study explores the psychological recovery effects of virtual tourism on individuals. Relevant research usually tends to examine the psychological recovery effects through traditional media entailing a lesser immersive experience. Few studies focus on the psychological recovery effects of virtual tourism, and even fewer on exploring response differences depending on different landscape types. Based on a series of empirical tests and electroencephalogram (EEG) data, this study investigates the impacts of a more immersive 3D virtual tourism with real scenes on people’s relaxation (Pm), concentration (Pa), and positive and negative emotions (PA and NA). Additionally, it clarifies the differences in the psychological recovery effects of four landscape types on the abovementioned attributes. The psychological recovery effects did vary according to the type of tourist attractions. There were a few differences based on gender. For instance, men’s relaxation level changed significantly after touring lake-oriented virtual tourist attraction. Individual differences in recovery were also observed. These findings contribute to our knowledge about environmental restoration and its role in alleviating people’s anxiety, especially during situations like the COVID-19 pandemic.
Data-driven parameter estimation in stochastic dynamic neural fields by state-space approach and continuous-discrete extended Kalman filtering

This work examines the state and parameter estimation problems for Stochastic Dynamic Neural Field (SDNF) models. Unlike other studies, we suggest a general approach based on the Galerkin method that reduces the SDNF model to a standard state-space representation. This naturally yields nonlinear continuous-discrete stochastic systems with complicated nonlinear dynamics and linear measurements of a high dimension because of a spatial approximation. This in turn paves a systematic way for a strong integration of nonlinear Bayesian filtering methods into mathematical neuroscience. Because of a high resolution utilized to keep an accurate spatial approximation, a key aspect of nonlinear filters to be derived for the SDNF models is their computational budgets. In this paper, we derive the sequential Itô-Taylor-based continuous-discrete Extended Kalman filter for state and parameter estimation of the SDNF models. The novel method processes each measurement vector in a component-like manner and, hence, avoids an inversion of a large matrix. Thus, the newly-derived sequential estimator is faster and, additionally, more numerically stable compared to the previously developed SDNF-oriented estimators. Besides, the novel filter is an adaptive method, i.e. it solves both problems, which are the state and parameter estimation, in parallel. Finally, the proposed method is applied to numerical examples to provide the comparative study and to show the effectiveness of our result.
Hyper-Laplacian Regularized Multi-View Clustering with Exclusive L21 Regularization and Tensor Log-Determinant Minimization Approach

Multi-view clustering aims to capture the multiple views inherent information by identifying the data clustering that reflects distinct features of datasets. Since there is a consensus in literature that different views of a dataset share a common latent structure, most existing multi-view subspace learning methods rely on the nuclear norm to seek the low-rank representation of the underlying subspace. However, the nuclear norm often fails to distinguish the variance of features for each cluster due to its convex nature and data tends to fall in multiple non-linear subspaces for multi-dimensional datasets. To address these problems, we propose a new and novel multi-view clustering method (HL-L21-TLD-MSC) that unifies the Hyper-Laplacian (HL) and exclusive ℓ 2,1 (L21) regularization with the Tensor Log-Determinant Rank Minimization (TLD) setting. Specifically, the hyper-Laplacian regularization maintains the local geometrical structure that makes the estimation prune to nonlinearities, and the mixed ℓ 2,1 and ℓ 1,2 regularization provides the joint sparsity within-cluster as well as the exclusive sparsity between-cluster. Furthermore, a log-determinant function is used as a tighter tensor rank approximation to discriminate the dimension of features. An efficient alternating algorithm is then derived to optimize the proposed model, and the construction of a convergent sequence to the Karush-Kuhn-Tucker (KKT) critical point solution is mathematically validated in detail. Extensive experiments are conducted on ten well-known datasets to demonstrate that the proposed approach outperforms the existing state-of-the-art approaches with various scenarios, in which, six of them achieve perfect results under our framework developed in this article, demonstrating highl effectiveness for the proposed approach.
Rethinking vision transformer through human–object interaction detection

Recent works have shown that Vision Transformer models (ViT) can achieve comparable or even superior performance on image- and region-level recognition tasks, i.e., image recognition and object detection. However, can Vision Transformer perform region-level relationship reasoning with minimal information about the spatial geometry formation of input images? To answer this question, we propose the Region-level Relationship Reasoning Vision Transformer (R3ViT), a family of human–object interaction detection models based on the vanilla Vision Transformer with the fewest possible revisions, common region priors, as well as inductive biases of the objective task. Specifically, we first divide the input images into several local patches, replace the specialized [CLS ] token in vanilla ViT with extra relationship semantics carrier tokens in the entanglement-/pair-/triplet-wise manner and calculate both representations and their relevance. We assign each extra token with an individual supervision and compute the training loss in a dense manner. We find the vision transformer simply adjusted by the novel paradigm can already reason about the region-level visual relationship, e.g., R3ViT can achieve quite excellent performance on the challenging human–object interaction detection benchmark. We also discuss the impacts of adjustment schemes and model scaling strategies for Vision Transformer through R3ViT. Numerically, extensive experiments on several benchmarks demonstrate that our proposed framework outperforms most existing methods and achieves the impressive performance of 28.91 mAP on HICO-DET and 56.8 mAP on V-COCO dataset, respectively.
Toward Creating Software Architects Using Mobile Project-Based Learning Model (Mobile-PBL) for Teaching Software Architecture

Project-based learning (PBL) promotes increased levels of learning, deepens student understanding of acquired knowledge, and improves learning motivation. Students develop their ability to think and learn independently through depending on themselves in searching for knowledge, planning, exploration, and looking for solutions to practical problems. Information availability, student engagement, and motivation to learn all increase with mobile learning. The teaching process may be enhanced by combining the two styles. This paper proposes and evaluates a teaching model called Mobile Project-Based Learning (Mobile-PBL) that combines the two learning styles. The paper investigates how significantly Mobile-PBL can benefit students. The traditional lecture method used to teach the software architecture module in the classroom is not sufficient to provide students with the necessary practical experience to earn a career as software architects in the future. Therefore, the first author tested the use of the model for teaching the software architecture module at Philadelphia University’s Software Engineering Department on 62 students who registered for a software architecture course over three semesters. She compared the results of using the model for teaching with those results that were obtained when using the project-based learning (PBL) approach alone. The students’ opinions regarding the approach, any problems they had, and any recommendations for improvement were collected through a focus group session after finishing each semester and by distributing a survey to students to evaluate the effectiveness of the used model. Comments from the students were positive, according to the findings. The projects were well-received by the students, who agreed that it gave them a good understanding of several course ideas and concepts, as well as providing them with the required practical experience. The students also mentioned a few difficulties encountered while working on the projects, including student distraction from social media and the skills that educators and learners in higher education institutions are expected to have.
Performance Optimization of a Blockchain-Enabled Information and Data Exchange Platform for Smart Grids

Exchanging information and data within smart grids is crucial to improve interoperability among system users. Traditional cloud-based data exchange schemes are centralized on a single trusted third-party platform. The schemes consequently suffer from single-point failure, a lack of data protection, and uncontrolled access. Blockchain enables data exchange in a decentralised and secure manner. A new platform is proposed in this work for exchanging data within smart grids using blockchain. It allows users to securely exchange data without losing ownership. This platform provides solutions to three critical problems: privacy, scalability, and user ownership. Particularly, the blockchain-based smart contract technology gives participants the programmability to access data. All interactions are authenticated and recorded by the other participants in the tamper-resistant blockchain network. Furthermore, the performance of the proposed blockchain platform is enhanced by integrating it with an artificial neural network (ANN). The proposed method is used to predict the network’s throughput and latency, and the network administrator uses these predicted values to change the network’s settings for a high throughput and low latency. Throughout the results, the proposed model achieves performance improvements in blockchain-enabled information and data exchange and adapts well to the dynamics of smart grids.
Improved response model of a superconducting nanowire array for high photon count rate communication

In high-speed photon-counting communication where the slot duration is close to the scale of detector reset time, the detection efficiency recovery characteristics of superconducting nanowire single photon detector (SNSPD) become the key factors affecting counting and communication performance. We introduced the nonlinear detection efficiency recovery model derived from the kinetic inductance model combined with the nonlinear efficiency-current relationship into the pulse position modulation (PPM) optical communication system based on the SNSPD array, and studied the impact of the recovery characteristics on the counting and communication performance of the system. On this basis, we introduce the efficiency recovery model into the correction of the channel logarithmic likelihood ratio (LLR) to estimate the SNR of each slot more accurately. Relevant results show that compared with the existing Poisson channel model, fixed deadtime model and detection efficiency exponential recovery model, this modification combined with FEC can provide the best decoding performance, especially in scenarios with high PPM order, small array pixel number and strong noise level. Besides, the nonlinear detection efficiency recovery model also reflects the best consistency with the experimental measurements of the SNSPD array in terms of response count rates near saturation.
The Use of Evolutionary Algorithms in the Modelling of Diffuse Radiation in Terms of Simulating the Energy Efficiency of Photovoltaic Systems

In light of the rapidly growing number of photovoltaic micro-grids, the modelling of their short-term power yields based on meteorological measurements is increasing in significance. This requires the knowledge of total and diffuse instantaneous solar radiation; however, most meteorological stations conduct actinometric measurements only with regard to total solar radiation, especially on a minute scale. This paper contains an analysis of the currently used PV cell mathematical model and suggests its modification aimed at calculating PV cell power with satisfactory accuracy, without the knowledge of diffuse solar radiation. Three function families were proposed to approximate the relationship between diffuse irradiance and the total and theoretical total irradiance variance for a cloudless sky. A program has been implemented to identify functions from the aforementioned function families. It leverages an evolution strategy algorithm and a fitness function based on the least-squares point method. It was employed to calculate the desired functions based on actual measurement data. The outcome was the sought-after dependence that enables predicting diffuse irradiance based on more frequently available measurement data.
Aspect-Based Sentiment Analysis with Dependency Relation Weighted Graph Attention

Aspect-based sentiment analysis is a fine-grained sentiment analysis that focuses on the sentiment polarity of different aspects of text, and most current research methods use a combination of dependent syntactic analysis and graphical neural networks. In this paper, a graph attention network aspect-based sentiment analysis model based on the weighting of dependencies (WGAT) is designed to address the problem in that traditional models do not sufficiently analyse the types of syntactic dependencies; in the proposed model, graph attention networks can be weighted and averaged according to the importance of different nodes when aggregating information. The model first transforms the input text into a low-dimensional word vector through pretraining, while generating a dependency syntax graph by analysing the dependency syntax of the input text and constructing a dependency weighted adjacency matrix according to the importance of different dependencies in the graph. The word vector and the dependency weighted adjacency matrix are then fed into a graph attention network for feature extraction, and sentiment polarity is predicted through the classification layer. The model can focus on syntactic dependencies that are more important for sentiment classification during training, and the results of the comparison experiments on the Semeval-2014 laptop and restaurant datasets and the ACL-14 Twitter social comment dataset show that the WGAT model has significantly improved accuracy and F1 values compared to other baseline models, validating its effectiveness in aspect-level sentiment analysis tasks.
Continuous frame motion sensitive self-supervised collaborative network for video representation learning

Motion, as a feature of video that changes in temporal sequences, is crucial to visual understanding. The powerful video representation and extraction models are typically able to focus attention on motion features in challenging dynamic environments to complete more complex video understanding tasks. However, previous approaches discriminate mainly based on similar features in the spatial or temporal domain, ignoring the interdependence of consecutive video frames. In this paper, we propose the motion sensitive self-supervised collaborative network, a video representation learning framework that exploits a pretext task to assist feature comparison and strengthen the spatiotemporal discrimination power of the model. Specifically, we first propose the motion-aware module, which extracts consecutive motion features from the spatial regions by frame difference. The global–local contrastive module is then introduced, with context and enhanced video snippets being defined as appropriate positive samples for a broader feature similarity comparison. Finally, we introduce the snippet operation prediction module, which further assists contrastive learning to obtain more reliable global semantics by sensing changes in continuous frame features. Experimental results demonstrate that our work can effectively extract robust motion features and achieve competitive performance compared with other state-of-the-art self-supervised methods on downstream action recognition and video retrieval tasks.
An Improved Dual-Channel Deep Q-Network Model for Tourism Recommendation

Tourism recommendation results are affected by many factors. Traditional recommendation methods have problems such as low recommendation accuracy and lack of personalization due to sparse data. This article uses implicit features such as contextual information, time-series travel trajectories, and comment data to address these issues. First, the Long Short-Term Memory (LSTM) network is introduced as the model basis, and deals with the input data of the model such as contextual information, scenic spot information, and tourist comments and so on for feature extraction. Then, the online behavior and long-term interest preference of users are analyzed, using positive feedback and negative feedback mechanism, the Deep Q-Network (DQN) value function of dual-channel mechanism is constructed. Finally, we propose a recommendation strategy, in which, a value evaluation network and a target network are proposed for each agent to learn the optimal strategy. The model is trained on the Yelp, DP, and Tourism datasets covering multiple scenarios to provide users with tourism recommendation services. Compared with baseline models such as Ultra Simplification of Graph Convolutional Networks, DQN, Actor-Critic, and Latent Factor Model, this model has an average increase of 76.61% in accuracy compared with the comparison model, and an average increase of 43.48% in the normalized discounted cumulative gain compared with the baseline model.
Injecting Temporal-Aware Knowledge in Historical Named Entity Recognition

In this paper, we address the detection of named entities in multilingual historical collections. We argue that, besides the multiple challenges that depend on the quality of digitization (e.g., misspellings and linguistic errors), historical documents can pose another challenge due to the fact that such collections are distributed over a long enough period of time to be affected by changes and evolution of natural language. Thus, we consider that detecting entities in historical collections is time-sensitive, and explore the inclusion of temporality in the named entity recognition (NER) task by exploiting temporal knowledge graphs. More precisely, we retrieve semantically-relevant additional contexts by exploring the time information provided by historical data collections and include them as mean-pooled representations in a Transformer-based NER model. We experiment with two recent multilingual historical collections in English, French, and German, consisting of historical newspapers (19C-20C) and classical commentaries (19C). The results are promising and show the effectiveness of injecting temporal-aware knowledge into the different datasets, languages, and diverse entity types.
Parameter-Efficient Sparse Retrievers and Rerankers Using Adapters

Parameter-Efficient transfer learning with Adapters have been studied in Natural Language Processing (NLP) as an alternative to full fine-tuning. Adapters are memory-efficient and scale well with downstream tasks by training small bottle-neck layers added between transformer layers while keeping the large pretrained language model (PLMs) frozen. In spite of showing promising results in NLP, these methods are under-explored in Information Retrieval. While previous studies have only experimented with dense retriever or in a cross lingual retrieval scenario, in this paper we aim to complete the picture on the use of adapters in IR. First, we study adapters for SPLADE, a sparse retriever, for which adapters not only retain the efficiency and effectiveness otherwise achieved by finetuning, but are memory-efficient and orders of magnitude lighter to train. We observe that Adapters-SPLADE not only optimizes just 2% of training parameters, but outperforms fully fine-tuned counterpart and existing parameter-efficient dense IR models on IR benchmark datasets. Secondly, we address domain adaptation of neural retrieval thanks to adapters on cross-domain BEIR datasets and TripClick. Finally, we also consider knowledge sharing between rerankers and first stage rankers. Overall, our study complete the examination of adapters for neural IR. (The code can be found at: https://github.com/naver/splade/tree/adapter-splade .)