Reviewerly/exHarmony huggingface dataset:
Facial Emotion Recognition and Classification Using the Convolutional Neural Network-10 (CNN-10)

The importance of facial expressions in nonverbal communication is significant because they help better represent the inner emotions of individuals. Emotions can depict the state of health and internal wellbeing of individuals. Facial expression detection has been a hot research topic in the last couple of years. The motivation for applying the convolutional neural network-10 (CNN-10) model for facial expression recognition stems from its ability to detect spatial features, manage translation invariance, understand expressive feature representations, gather global context, and achieve scalability, adaptability, and interoperability with transfer learning methods. This model offers a powerful instrument for reliably detecting and comprehending facial expressions, supporting usage in recognition of emotions, interaction between humans and computers, cognitive computing, and other areas. Earlier studies have developed different deep learning architectures to offer solutions to the challenge of facial expression recognition. Many of these studies have good performance on datasets of images taken under controlled conditions, but they fall short on more difficult datasets with more image diversity and incomplete faces. This paper applied CNN-10 and ViT models for facial emotion classification. The performance of the proposed models was compared with that of VGG19 and INCEPTIONV3. The CNN-10 outperformed the other models on the CK + dataset with a 99.9% accuracy score, FER-2013 with an accuracy of 84.3%, and JAFFE with an accuracy of 95.4%.
Experimental study on the effect of dust particle deposition on photovoltaic performance of urban buildings

The output characteristics of photovoltaic (PV) modules can be negatively affected by the density of snowfall in winter and ash accumulation in summer. In order to solve this problem, an experimental platform for PV power generation with four angles of 0°, 30°, 45° and 60° was built in Shenbei New District, Shenyang City, China. The characteristics of ash accumulation on the surface of PV modules were studied and analyzed by SEM and XRD. The effects of snowfall, ash accumulation density and PV module angle on the output power and power attenuation rate of PV modules were investigated. The experimental results show that snowfall had both positive and negative effects on PV modules. The output power of the PV module decreases as the ash density increases. Based on the dust coverage density and tilt angle in the test data, empirical formulas are proposed to predict the average power reduction rate.
FAFuse: A Four-Axis Fusion framework of CNN and Transformer for medical image segmentation

Medical image segmentation is crucial for accurate diagnosis and treatment in the medical field. In recent years, convolutional neural networks (CNNs) and Transformers have been frequently adopted as network architectures in medical image segmentation. The convolution operation is limited in modeling long-range dependencies because it can only extract local information through the limited receptive field. In comparison, Transformers demonstrate excellent capability in modeling long-range dependencies but are less effective in capturing local information. Hence, effectively modeling long-range dependencies while preserving local information is essential for accurate medical image segmentation. In this paper, we propose a four-axis fusion framework called FAFuse, which can exploit the advantages of CNN and Transformer. As the core component of our FAFuse, a Four-Axis Fusion module (FAF) is proposed to efficiently fuse global and local information. FAF combines Four-Axis attention (height, width, main diagonal, and counter diagonal axial attention), a multi-scale convolution, and a residual structure with a depth-separable convolution and a Hadamard product. Furthermore, we also introduce deep supervision to enhance gradient flow and improve overall performance. Our approach achieves state-of-the-art segmentation accuracy on three publicly available medical image segmentation datasets. The code is available at https://github.com/cczu-xiao/FAFuse.
Human Pose Transfer with Augmented Disentangled Feature Consistency

Deep generative models have made great progress in synthesizing images with arbitrary human poses and transferring the poses of one person to others. Though many different methods have been proposed to generate images with high visual fidelity, the main challenge remains and comes from two fundamental issues: pose ambiguity and appearance inconsistency. To alleviate the current limitations and improve the quality of the synthesized images, we propose a pose transfer network with augmented D isentangled F eature C onsistency (DFC-Net) to facilitate human pose transfer. Given a pair of images containing the source and target person, DFC-Net extracts pose and static information from the source and target respectively, then synthesizes an image of the target person with the desired pose from the source. Moreover, DFC-Net leverages disentangled feature consistency losses in the adversarial training to strengthen the transfer coherence and integrates a keypoint amplifier to enhance the pose feature extraction. With the help of the disentangled feature consistency losses, we further propose a novel data augmentation scheme that introduces unpaired support data with the augmented consistency constraints to improve the generality and robustness of DFC-Net. Extensive experimental results on Mixamo-Pose and EDN-10k have demonstrated DFC-Net achieves state-of-the-art performance on pose transfer.
BiC-Net: Learning Efficient Spatio-temporal Relation for Text-Video Retrieval

The task of text-video retrieval aims to understand the correspondence between language and vision and has gained increasing attention in recent years. Recent works have demonstrated the superiority of local spatio-temporal relation learning with graph-based models. However, most existing graph-based models are handcrafted and depend heavily on expert knowledge and empirical feedback, which may be unable to mine the high-level fine-grained visual relations effectively. These limitations result in their inability to distinguish videos with the same visual components but different relations. To solve this problem, we propose a novel cross-modal retrieval framework, Bi-Branch Complementary Network (BiC-Net), which modifies Transformer architecture to effectively bridge text-video modalities in a complementary manner via combining local spatio-temporal relation and global temporal information. Specifically, local video representations are encoded using multiple Transformer blocks and additional residual blocks to learn fine-grained spatio-temporal relations and long-term temporal dependency, calling the module a Fine-grained Spatio-temporal Transformer (FST). Global video representations are encoded using a multi-layer Transformer block to learn global temporal features. Finally, we align the spatio-temporal relation and global temporal features with the text feature on two embedding spaces for cross-modal text-video retrieval. Extensive experiments are conducted on MSR-VTT, MSVD, and YouCook2 datasets. The results demonstrate the effectiveness of our proposed model. Our code is public at https://github.com/lionel-hing/BiC-Net .
Segment-Based Unsupervised Learning Method in Sensor-Based Human Activity Recognition

Sensor-based human activity recognition (HAR) is a task to recognize human activities, and HAR has an important role in analyzing human behavior such as in the healthcare field. HAR is typically implemented using traditional machine learning methods. In contrast to traditional machine learning methods, deep learning models can be trained end-to-end with automatic feature extraction from raw sensor data. Therefore, deep learning models can adapt to various situations. However, deep learning models require substantial amounts of training data, and annotating activity labels to construct a training dataset is cost-intensive due to the need for human labor. In this study, we focused on the continuity of activities and propose a segment-based unsupervised deep learning method for HAR using accelerometer sensor data. We define segment data as sensor data measured at one time, and this includes only a single activity. To collect the segment data, we propose a measurement method where the users only need to annotate the starting, changing, and ending points of their activity rather than the activity label. We developed a new segment-based SimCLR, which uses pairs of segment data, and propose a method that combines segment-based SimCLR with SDFD. We investigated the effectiveness of feature representations obtained by training the linear layer with fixed weights obtained by unsupervised learning methods. As a result, we demonstrated that the proposed combined method acquires generalized feature representations. The results of transfer learning on different datasets suggest that the proposed method is robust to the sampling frequency of the sensor data, although it requires more training data than other methods.
ESTUGAN: Enhanced Swin Transformer with U-Net Discriminator for Remote Sensing Image Super-Resolution

Remote sensing image super-resolution (SR) is a practical research topic with broad applications. However, the mainstream algorithms for this task suffer from limitations. CNN-based algorithms face difficulties in modeling long-term dependencies, while generative adversarial networks (GANs) are prone to producing artifacts, making it difficult to reconstruct high-quality, detailed images. To address these challenges, we propose ESTUGAN for remote sensing image SR. On the one hand, ESTUGAN adopts the Swin Transformer as the network backbone and upgrades it to fully mobilize input information for global interaction, achieving impressive performance with fewer parameters. On the other hand, we employ a U-Net discriminator with the region-aware learning strategy for assisted supervision. The U-shaped design enables us to obtain structural information at each hierarchy and provides dense pixel-by-pixel feedback on the predicted images. Combined with the region-aware learning strategy, our U-Net discriminator can perform adversarial learning only for texture-rich regions, effectively suppressing artifacts. To achieve flexible supervision for the estimation, we employ the Best-buddy loss. And we also add the Back-projection loss as a constraint for the faithful reconstruction of the high-resolution image distribution. Extensive experiments demonstrate the superior perceptual quality and reliability of our proposed ESTUGAN in reconstructing remote sensing images.
Improved Collaborative Recommendation Model: Integrating Knowledge Embedding and Graph Contrastive Learning

A recommendation algorithm combined with a knowledge graph enables auxiliary information on items to be obtained by using the knowledge graph to achieve better recommendations. However, the recommendation performance of existing methods relies heavily on the quality of the knowledge graph. Knowledge graphs often contain noise and irrelevant connections between items and entities in the real world. This knowledge graph sparsity and noise significantly amplifies the noise effects and hinders the accurate representation of user preferences. In response to these problems, an improved collaborative recommendation model is proposed which integrates knowledge embedding and graph contrastive learning. Specifically, we propose a knowledge contrastive learning scheme to mitigate noise within the knowledge graph during information aggregation, thereby enhancing the embedding quality of items. Simultaneously, to tackle the issue of insufficient user-side information in the knowledge graph, graph convolutional neural networks are utilized to propagate knowledge graph information from the item side to the user side, thereby enhancing the personalization capability of the recommendation system. Additionally, to resolve the over-smoothing issue in graph convolutional networks, a residual structure is employed to establish the message propagation network between adjacent layers of the same node, which expands the information propagation path. Experimental results on the Amazon-book and Yelp2018 public datasets demonstrate that the proposed model outperforms the best baseline models by 11.4% and 11.6%, respectively, in terms of the Recall@20 evaluation metric. This highlights the method’s efficacy in improving the recommendation accuracy and effectiveness when incorporating knowledge graphs into the recommendation process.
Redact-Chain for Health: A Scheme Based on Redactable Blockchain for Managing Shared Healthcare Data

As blockchain technology evolves, it has become a crucial component in medical data sharing. However, current needs reveal that healthcare-focused blockchain schemes increasingly require the capabilities of modification and deletion. Moreover, traditional blockchain-based systems for medical data sharing often need help with a single point of failure, which undermines the system’s robustness. To address these challenges, we propose Redact-Chain for Health, a scheme based on the redactable blockchain for managing shared healthcare data. This scheme allows users to encrypt data for privacy protection and decrypt data when sharing medical information. By substituting the SHA-256 with the chameleon hash, Redact-Chain for Health introduces a fine-grained data editing scheme, facilitating medical institutions in effectively editing and managing data on the blockchain. Moreover, Redact-Chain for Health integrates a distributed trapdoor management scheme. This scheme empowers medical institutions to manage the trapdoor of the chameleon hash effectively, thereby circumventing the issue of a single point of failure. Our scheme also incorporates a symmetric encryption-based authentication algorithm to deter potential cyberattacks. Lastly, the security analysis of our proposed system demonstrates its effectiveness in preserving patients’ privacy, while performance analysis confirms Redact-Chain for Health’s efficiency.
An Improved Nested Named-Entity Recognition Model for Subject Recognition Task under Knowledge Base Question Answering

In the subject recognition (SR) task under Knowledge Base Question Answering (KBQA), a common method is by training and employing a general flat Named-Entity Recognition (NER) model. However, it is not effective and robust enough in the case that the recognized entity could not be strictly matched to any subjects in the Knowledge Base (KB). Compared to flat NER models, nested NER models show more flexibility and robustness in general NER tasks, whereas it is difficult to employ a nested NER model directly in an SR task. In this paper, we take advantage of features of a nested NER model and propose an Improved Nested NER Model (INNM) for the SR task under KBQA. In our model, each question token is labeled as either an entity token, a start token, or an end token by a modified nested NER model based on semantics. Then, entity candidates would be generated based on such labels, and an approximate matching strategy is employed to score all subjects in the KB based on string similarity to find the best-matched subject. Experimental results show that our model is effective and robust to both single-relation questions and complex questions, which outperforms the baseline flat NER model by a margin of 3.3% accuracy on the SimpleQuestions dataset and a margin of 11.0% accuracy on the WebQuestionsSP dataset.
From distributed machine to distributed deep learning: a comprehensive survey

Abstract Artificial intelligence has made remarkable progress in handling complex tasks, thanks to advances in hardware acceleration and machine learning algorithms. However, to acquire more accurate outcomes and solve more complex issues, algorithms should be trained with more data. Processing this huge amount of data could be time-consuming and require a great deal of computation. To address these issues, distributed machine learning has been proposed, which involves distributing the data and algorithm across several machines. There has been considerable effort put into developing distributed machine learning algorithms, and different methods have been proposed so far. We divide these algorithms in classification and clustering (traditional machine learning), deep learning and deep reinforcement learning groups. Distributed deep learning has gained more attention in recent years and most of the studies have focused on this approach. Therefore, we mostly concentrate on this category. Based on the investigation of the mentioned algorithms, we highlighted the limitations that should be addressed in future research.
Minimizing of Renewable Energy Sources Impact on the Energy System Operation by the Joint Use of Solar and Wind Generation

The purpose of the study was to test the hypothesis of the existence of an optimal ratio of the installed capacity of solar and wind power plants, which allows, with a large installed capacity of renewable energy sources with a variable nature of operation, to minimize the impact of their generation on the operation of the power system and reduce the capacity of energy storage devices when they are used in the system. The following tasks were set: on the basis of meteorological data, to calculate hourly energy generation by complexes of solar and wind power plants of various combinations of capacities for several years, analyze the generation and, if the hypothesis is confirmed, find the optimal ratio of capacities. In the course of the study, modeling methods and numerical methods for solving optimization problems were used, viz. the iteration method and the nonlinear least squares method. The optimal range of the ratio of solar and wind power plants installed capacities in a separate complex and in the whole power system for the conditions of the Republic of Belarus was determined, which was 0.4:1–0.6:1 (sun:wind). Complexes of energy sources with a power ratio in the specified range can reduce the required capacity of energy storage systems compared to the capacity for only solar or only wind power plants by 2.6–4.0 times, which leads to a reduction in capital costs and the payback period of the project. Going beyond the recommended range leads to a pronounced surplus or shortage of electricity generation in different periods of the day of different periods of the year, depending on the ratio of installed capacities. Also, the optimal specific capacity of energy storage systems for complexes consisting of solar and wind power plants in the installed capacities recommended ratios was determined, which amounted to 0.4–0.8 kW×h/kW of total installed capacity.
Pedagogical models and ICT integration in entrepreneurship education: Literature review

Entrepreneurial education in different contexts has gained relevance in educational institutions, incorporating various models to enhance the development of competencies in students. This literature review aimed to analyze publications from 2014–2021 to identify the models, methods, strategies, and resources used in entrepreneurship education and to recognize the elements of Education 4.0 that were incorporated. The systematic literature review (SLR) was the method used to carry out this study, with a sample of 37 articles in the Web of Science (WoS) and Scopus databases. The search for articles was limited to those exclusively in English and in the two databases named above. The results show a diversity of models and methods used in the educational and social spheres to train entrepreneurs, mainly focused on practice. This literature review is relevant for educational institutions interested in supporting the training of entrepreneurs. It raises the desirability of Education 4.0 elements in the forefront of a student-centered education fully connected to the needs of society. It also provides value for researchers, identifying the educational gaps, the topics emphasized, and the areas necessary to deepen future research.
An Efficient Attribute-Based Encryption Scheme with Data Security Classification in the Multi-Cloud Environment

As an increasing number of people and corporations move their data to the cloud side, how to ensure efficient and secure access to data stored on the cloud side has become a key focus of current research. Attribute-Based Encryption (ABE) is largely recognized as the best access control method for safeguarding the cloud storage environment, and numerous solutions based on ABE have been developed successively. However, the majority of current research is conducted within a single cloud provider, and only the limited number of schemes for the multi-cloud environment also fail to support the data security classification on the cloud side. Therefore, we propose an efficient attribute-based encryption scheme with data security classification in the multi-cloud environment. In our scheme, the data owner’s data are divided into two security levels and stored in different cloud providers, which improves the security of outsourcing data. Moreover, based on Ciphertext-Policy Attribute-Based Encryption (CP-ABE), our scheme can not only provide a fine-grained access control for the data user, but also completely exploit the cloud side to facilitate outsourcing decryption to lighten the data user’s computing load. The security analysis showed that our scheme is effective against selective-attribute plaintext attack, as well as protects the privacy of the data. The experimental results also demonstrated that the computational overhead is obviously less than other existing schemes.
Combining uncertainty information with AI recommendations supports calibration with domain knowledge

The use of Artificial Intelligence (AI) decision support is increasing in high-stakes contexts, such as healthcare, defense, and finance. Uncertainty information may help users better leverage AI predictions, especially when combined with their domain knowledge. We conducted a human-subject experiment with an online sample to examine the effects of presenting uncertainty information with AI recommendations. The experimental stimuli and task, which included identifying plant and animal images, are from an existing image recognition deep learning model, a popular approach to AI. The uncertainty information was predicted probabilities for whether each label was the true label. This information was presented numerically and visually. In the study, we tested the effect of AI recommendations in a within-subject comparison and uncertainty information in a between-subject comparison. The results suggest that AI recommendations increased both participants’ accuracy and confidence. Further, providing uncertainty information significantly increased accuracy but not confidence, suggesting that it may be effective for reducing overconfidence. In this task, participants tended to have higher domain knowledge for animals than plants based on a self-reported measure of domain knowledge. Participants with more domain knowledge were appropriately less confident when uncertainty information was provided. This suggests that people use AI and uncertainty information differently, such as an expert versus second opinion, depending on their level of domain knowledge. These results suggest that if presented appropriately, uncertainty information can potentially decrease overconfidence that is induced by using AI recommendations.