Reviewerly/exHarmony huggingface dataset:
A Community-Driven Deep Collaborative Approach for Recommender Systems

Recommender systems (RS) are increasingly leveraging the power of graphs to enhance accuracy.However, we stipulate that existing methods don't take into consideration the inherent behavior of communities and the interaction between all the sub-groups of the network.In this work, we develop a Deep Graph-based Collaborative Filtering recommender system (DGCF), which incorporates the concept of community profiling and leverages the power of Graph Neural Networks.DGCF utilizes multiple graphs to exploit all types of information from the different user interactions.It extracts the overlapping communities from the homophily user-user graph and also integrates the high-order information from the user-item bipartite graph.We conduct experiments and evaluate the DGCF on the MovieLens datasets(ML-100K and ML-1M), and Douban dataset.Our experiments reveal significant improvements over a number of the latest deep learning models for recommender systems.Results also support that DGCF has the potential to render better recommendations as it extracts deep relationships using the community structure.
A Comprehensive Review of Modern Object Segmentation Approaches

Image segmentation is the task of associating pixels in an image with their respective object class labels. It has a wide range of applications in many industries including healthcare, transportation, robotics, fashion, home improvement, and tourism. Many deep learning-based approaches have been developed for image-level object recognition and pixel-level scene understanding-with the latter requiring a much denser annotation of scenes with a large set of objects. Extensions of image segmentation tasks include 3D and video segmentation, where units of voxels, point clouds, and video frames are classified into different objects. We use "Object Segmentation" to refer to the union of these segmentation tasks. In this monograph, we investigate both traditional and modern object segmentation approaches, comparing their strengths, weaknesses, and utilities. We examine in detail the wide range of deep learning-based segmentation techniques developed in recent years, provide a review of the widely used datasets and evaluation metrics, and discuss potential future research directions.
Education metaverse: Innovations and challenges of the new generation of Internet education formats

&lt;p&gt;In the post-epidemic era, the shape of education has undergone significant changes, evolving continuously towards large-scale, digital, virtualized and intelligent. The metaverse has been described as the third Internet revolution, a virtual society and social network that mirrors real scenarios through a decentralized architecture. Education is the main industry scenario and innovation channel of the metaverse, and conducting research on the education metaverse is an important way to seek the path of high-quality education development, reshape the relationship between education subjects, and solve social issues such as educational equity. However, the future of the metaverse, the challenges it will face and the social implications it will have need to be considered in the light of the “hot hype”. From the perspective of technology-society interconstruction, this study analyses the background and theoretical foundation of the educational metaverse, its characteristics and application cases, with the aim of clarifying the ecological structure of the educational metaverse. The study concludes with a critical reflection on the challenges posed by the education metaverse in terms of data maintenance, digital copyright and capital coercion, in order to explore the laws of education under the new Internet education model.&lt;/p&gt;
A novel Q-learning algorithm based on improved whale optimization algorithm for path planning

Q-learning is a classical reinforcement learning algorithm and one of the most important methods of mobile robot path planning without a prior environmental model. Nevertheless, Q-learning is too simple when initializing Q-table and wastes too much time in the exploration process, causing a slow convergence speed. This paper proposes a new Q-learning algorithm called the Paired Whale Optimization Q-learning Algorithm (PWOQLA) which includes four improvements. Firstly, to accelerate the convergence speed of Q-learning, a whale optimization algorithm is used to initialize the values of a Q-table. Before the exploration process, a Q-table which contains previous experience is learned to improve algorithm efficiency. Secondly, to improve the local exploitation capability of the whale optimization algorithm, a paired whale optimization algorithm is proposed in combination with a pairing strategy to speed up the search for prey. Thirdly, to improve the exploration efficiency of Q-learning and reduce the number of useless explorations, a new selective exploration strategy is introduced which considers the relationship between current position and target position. Fourthly, in order to balance the exploration and exploitation capabilities of Q-learning so that it focuses on exploration in the early stage and on exploitation in the later stage, a nonlinear function is designed which changes the value of ε in ε-greedy Q-learning dynamically based on the number of iterations. Comparing the performance of PWOQLA with other path planning algorithms, experimental results demonstrate that PWOQLA achieves a higher level of accuracy and a faster convergence speed than existing counterparts in mobile robot path planning. The code will be released at https://github.com/wanghanyu0526/improveQL.git.
MbSRS: A multi-behavior streaming recommender system

Streaming Recommender Systems (SRSs) have emerged to deliver recommendations based on pervasive data streams, which are a sequence of user-item interactions with multiple behavior types (e.g., purchase, add-to-cart, and view). However, existing SRSs all rely on a single behavior type (e.g., purchase) to make streaming recommendations, and commonly suffer from the data sparsity problem. To address this issue, the relatively more abundant multi-behavior interactions (i.e., interactions with multiple behavior types) could be well leveraged for more accurate streaming recommendations. However, it remains a challenge on how to effectively leverage the commonly-existing and complex multi-behavior interactions for improving the accuracy of streaming recommendations. Targeting at this challenge, we propose the first Multi-behavior Streaming Recommender System in the literature, called MbSRS, to elaborately exploit multi-behavior interactions for delivering accurate recommendations in streaming scenarios. In MbSRS, we first learn instant user preferences and unified item characteristics collaboratively from multi-behavior interactions. Then, we attentively learn long-term user preferences from the historical items interacted by the corresponding users. After that, we wisely fuse the learned instant and long-term user preferences via a gate mechanism. Finally, a novel multi-behavior-specific training process is devised for more effectively learning user preferences towards items from multi-behavior interactions. Extensive experiments on three real-world datasets demonstrate that the proposed MbSRS significantly outperforms the state-of-the-art baselines.
Transfer Learning Based Neural Machine Translation of English-Khasi on Low-Resource Settings

Machine translation for low-resource language can be improved using various techniques. One such technique is the application of knowledge learned by training a model with high-resource language pair to another model with a low-resource language pair. The paper discusses the experiments and improvement of the results of neural machine translation using transfer learning for the English-Khasi language pair. Long short-term memory is used as the backbone architecture for the transfer learning model. The essential technique is the shared vocabulary, constructed utilizing the subword unit of byte pair encoding of the two pairs of languages and the subword unit of byte pair encoded datasets. Analysis and evaluation of the experimental output using human subjective evaluation, statistical evaluation, and automatic evaluation show positive results for the transfer learning system. A thorough analysis of word order agreement and comparisons of the outputs between the baseline and the transfer learning system is made. The analysis and evaluation methods portray that neural machine translation using transfer learning improves the translation accuracy for Khasi, a low-resource language.
Self-Supervised Monocular Depth Estimation: Solving the Edge-Fattening Problem

Self-supervised monocular depth estimation (MDE) models universally suffer from the notorious edge-fattening issue. Triplet loss, as a widespread metric learning strategy, has largely succeeded in many computer vision applications. In this paper, we redesign the patch-based triplet loss in MDE to alleviate the ubiquitous edge-fattening issue. We show two drawbacks of the raw triplet loss in MDE and demonstrate our problem-driven redesigns. First, we present a min. operator based strategy applied to all negative samples, to prevent well-performing negatives sheltering the error of edge-fattening negatives. Second, we split the anchor-positive distance and anchor-negative distance from within the original triplet, which directly optimizes the positives without any mutual effect with the negatives. Extensive experiments show the combination of these two small redesigns can achieve unprecedented results: Our powerful and versatile triplet loss not only makes our model outperform all previous SoTA by a large margin, but also provides substantial performance boosts to a large number of existing models, while introducing no extra inference computation at all.
Smart Analysis of Economics Sentiment in Spanish Based on Linguistic Features and Transformers

Texts related to economics and finances are characterized by the use of words and expressions whose meaning (and the sentiments they convey) substantially depend on the context.This poses a major challenge to Natural Language Processing tasks in general, and Sentiment Analysis in particular.For lowresource languages such as Spanish, this situation becomes even more acute.Yet, the latest advancements in the field, including word embeddings and transformers, have allowed to boost the performance of Sentiment Analysis solutions.In this work we explore the impact of the combination of different feature sets in the accuracy of Sentiment Analysis in Spanish financial texts.For this, a corpus with 15,915 tweets has been compiled and manually annotated as either positive, negative, or neutral.Then, feature sets based on contextual and non-contextual embeddings along with linguistic features were evaluated both individually and combined.The best results, with a weighted F1-score of 73.15880%, were obtained with a combination of feature sets by means of knowledge integration.
An Overview on Deep Leaning Application of Big Data

Big data refers to the large volumes of structured and unstructured data that are generated by businesses, organizations, and individuals on a daily basis. Deep learning is a type of machine learning that involves the use of artificial neural networks to learn patterns and relationships in data. In this paper, we discuss the applications of deep learning in the field of big data analysis. We provide an overview of deep learning and big data, and then delve into specific examples of how deep learning has been used in various domains to extract value from big data. These domains include predictive analytics, image and video analysis, natural language processing, and recommendation systems. We also discuss some of the challenges and limitations of using deep learning for big data analysis, as well as future directions for research and development in this field. Overall, deep learning has proven to be a powerful tool for extracting insights from big data, and is likely to play an increasingly important role in the field of data science.
Predicting Preservice Science Teachers’ TPACK through ICT usage

Designing effective and efficient learning environments by integrating recent educational technologies into the teaching process has become an important goal of education for nearly two decades. However, earlier studies showed that a higher level of technology knowledge does not guarantee the development of TPACK. At this point, studies guided by the transformative approach defining TPACK as a unique knowledge revealed encouraging results for a better understanding of technology-integrated instruction. This study aims to investigate to what extent ICT usage categories predict preservice science teachers’ TPACK. Totally 326 preservice science teachers with a mean age of 21.62 (SD = 1.41) from seven different universities participated. For that purpose, a correlational study was conducted. The ICT-TPACK-Science Scale and the ICT Usage Questionnaire were used to collect data. Six separate multiple regression analyses were conducted to predict TPACK measures using ICT measures. Results indicated that approximately a third of the variability in total-TPACK scores can be accounted for by three ICT measures. The relative importance of individual predictors is arranged in the following order desktop software, emerging ICTs, and hardware. As for the dimensions of the ICT-TPACK-Science Scale, the overall effect of the ICT predictors decreased in the following order: Designing, implementing, planning, proficiency, and ethics. Emerging ICTs made the highest contribution to the designing and proficiency dimensions; while desktop software made the highest contribution to the implementing, planning, and ethics dimensions. To sum up, this study describes the association between ICT usage and TPACK in the view of the transformative ICT-TPACK-Science framework. The utilization and transformation of ICT tools as a cognitive partner for effective and efficient science teaching in different TPACK dimensions needs further investigation.
Applications of Federated Learning in Mobile Health: Scoping Review

The proliferation of mobile health (mHealth) applications is partly driven by the advancements in sensing and communication technologies, as well as the integration of artificial intelligence techniques. Data collected from mHealth applications, for example, on sensor devices carried by patients, can be mined and analyzed using artificial intelligence-based solutions to facilitate remote and (near) real-time decision-making in health care settings. However, such data often sit in data silos, and patients are often concerned about the privacy implications of sharing their raw data. Federated learning (FL) is a potential solution, as it allows multiple data owners to collaboratively train a machine learning model without requiring access to each other's raw data.The goal of this scoping review is to gain an understanding of FL and its potential in dealing with sensitive and heterogeneous data in mHealth applications. Through this review, various stakeholders, such as health care providers, practitioners, and policy makers, can gain insight into the limitations and challenges associated with using FL in mHealth and make informed decisions when considering implementing FL-based solutions.We conducted a scoping review following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched 7 commonly used databases. The included studies were analyzed and summarized to identify the possible real-world applications and associated challenges of using FL in mHealth settings.A total of 1095 articles were retrieved during the database search, and 26 articles that met the inclusion criteria were included in the review. The analysis of these articles revealed 2 main application areas for FL in mHealth, that is, remote monitoring and diagnostic and treatment support. More specifically, FL was found to be commonly used for monitoring self-care ability, health status, and disease progression, as well as in diagnosis and treatment support of diseases. The review also identified several challenges (eg, expensive communication, statistical heterogeneity, and system heterogeneity) and potential solutions (eg, compression schemes, model personalization, and active sampling).This scoping review has highlighted the potential of FL as a privacy-preserving approach in mHealth applications and identified the technical limitations associated with its use. The challenges and opportunities outlined in this review can inform the research agenda for future studies in this field, to overcome these limitations and further advance the use of FL in mHealth.
Few-shot relation classification using clustering-based prototype modification

Few-shot relation classification is a natural language processing task that aims to enable models to recognize new relational categories of query instances by training on base classes with few labeled support instances. Many recently proposed prototypical-network-based approaches have exhibited excellent performance, some of which include additional information to further improve the model performance further. However, existing methods ignore the category information hidden in the query set and the hierarchical information that exists in relational hyponymy and fail to address the fundamental problem whereby the obtained prototype is always unreliable owing to the limited instances in the support set. In this study, we propose a novel prototypical-network-based model for the few-shot relation classification task to generate more precise relational prototypes by taking advantage of the category information hidden in the query set. In the model, we design a novel relational-similarity-based instance adaptive local loss that leverages relational hierarchical information to distinguish the hard instances belonging to similar relations effectively. Experiments on the FewRel dataset demonstrate that our approach achieves state-of-the-art performance.
Simulating large-size quantum spin chains on cloud-based superconducting quantum computers

Quantum computers have the potential to efficiently simulate large-scale quantum systems for which classical approaches are bound to fail. Even though several existing quantum devices now feature total qubit numbers of more than one hundred, their applicability remains plagued by the presence of noise and errors. Thus, the degree to which large quantum systems can successfully be simulated on these devices remains unclear. Here, we report on cloud simulations performed on several of IBM's superconducting quantum computers to simulate ground states of spin chains having a wide range of system sizes up to one hundred and two qubits. We find that the ground-state energies extracted from realizations across different quantum computers and system sizes reach the expected values to within errors that are small (i.e. on the percent level), including the inference of the energy density in the thermodynamic limit from these values. We achieve this accuracy through a combination of physics-motivated variational Ansatzes, and efficient, scalable energy-measurement and error-mitigation protocols, including the use of a reference state in the zero-noise extrapolation. By using a 102-qubit system, we have been able to successfully apply up to 3186 CNOT gates in a single circuit when performing gate-error mitigation. Our accurate, error-mitigated results for random parameters in the Ansatz states suggest that a standalone hybrid quantum-classical variational approach for large-scale XXZ models is feasible.
Ambiguity-aware robust teacher (ART): Enhanced self-knowledge distillation framework with pruned teacher network

Self-knowledge distillation (self-KD) methods, which use a student model itself as the teacher model instead of a large and complex teacher model, are currently a subject of active study. Since most previous self-KD approaches relied on the knowledge of a single teacher model, if the teacher model incorrectly predicted confusing samples, poor-quality knowledge was transferred to the student model. Unfortunately, natural images are often ambiguous for teacher models due to multiple objects, mislabeling, or low quality. In this paper, we propose a novel knowledge distillation framework named ambiguity-aware robust teacher knowledge distillation (ART-KD) that provides refined knowledge, that reflects the ambiguity of the samples with network pruning. Since the pruned teacher model is simply obtained by copying and pruning the teacher model, re-training process is unnecessary in ART-KD. The key insight of ART-KD lies in the predictions of a teacher model and pruned teacher model for ambiguous samples providing different distributions with low similarity. From these two distributions, we obtain a joint distribution considering the ambiguity of the samples as teacher's knowledge for distillation. We comprehensively evaluate our method on public classification benchmarks, as well as more challenging benchmarks for fine-grained visual recognition (FGVR), achieving much superior performance to state-of-the-art counterparts.
Projected PV plants in China’s Gobi Deserts would result in lower evaporation and wind

In this study, a promising photovoltaic (PV) deployment scenario is firstly designed to represent China's solar energy development in the context of its dual carbon target. Then, the potential climate effects of the designed PV plants under different emission scenarios are simulated using a newly developed weather research and forecasting (WRF)-PV regional climate model, which is initiated by the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) projections. The modeling results indicate that the projected PV plants in China's Gobi Deserts could impact the local climate, causing positive change of 3.71 ± 0.03 % in the surface relative humidity, and negative changes of 1.60 ± 1.36 W/m2 in the latent heat flux and 0.39 ± 0.00 m/s in the 10-m wind speed in July by about 2060 under the intermediate emission scenario, and these PV-induced climate effects are approximately equivalent to or even greater than the projected climate variability, but almost confined to the local area. These findings suggest that the projected PV deployment in China will lead to low carbon emissions while causing favorable climate effects, such as lower evaporation and wind speed in Gobi Deserts, which will bring environmental and ecological benefits to the PV industry.
Time-Controllable Keyword Search Scheme with Efficient Revocation in Mobile E-health Cloud

Electronic health (e-health) systems may outsource data such as patient e-health records to mobile cloud servers for efficiency gains (e.g., minimizing local storage and computation costs). However, such a move may result in privacy implications in the presence of semi-honest cloud servers. Searchable Encryption (SE) can potentially facilitate privacy-preserving searches based on keywords for encrypted data stored in the mobile cloud, but most existing SE solutions do not support temporal access control (i.e., a mechanism that grants access permissions to users for specified time ranges). Hence, in this paper we design a time-controllable keyword search scheme by using an attribute-based comparable access control. This allows users to match indexes encrypted at specified time intervals. Then, we improve the basic framework to support efficient user revocation using secret sharing. We then formally prove the security of our proposed frameworks against chosen-keyword attack and key collusion attack, as well as achieving keyword secrecy. We also evaluate the performance of our proposed approach using a real-world dataset to demonstrate their practical utility.