Reviewerly/exHarmony huggingface dataset:
Exploiting Partial Observability and Optimized Simple State Representations in Deep Q-Learning

The advent of deep Q-learning has opened up new possibilities in training autonomous agents to perform intelligently in intricate settings. This research work examines the potential of deep Q-learning in the paradigmatic Snake game, which requires an agent to navigate a grid, ingest food items, and avoid collisions. We incorporated a partially observable game state, introducing a novel level of difficulty to the task. Our proposed approach utilizes a straightforward feed-forward neural network to estimate Q-values of potential actions, given the current state. The agent progressively learns to balance between exploration and exploitation, allowing it to navigate optimally in the game scenario. Most existing studies in deep Q-learning for the Snake game overlook the complexities posed by partial observability, focusing mainly on fully observable state representations. Furthermore, the majority of the investigations delve into the efficacy of complex neural network architectures, leaving a gap in the understanding of the performance of more simplistic feed-forward networks. Our study fills this gap by offering an in-depth exploration and analysis of deep Q-learning strategies, especially when applied to partially observable game states and simple neural networks. Simulations and results reveal that adopting simpler state representations can significantly boost the agent’s performance in the Snake game. A more detailed and intricate reward function is also observed to enhance the agent’s decision-making skills and overall proficiency in the game. These observations underscore the significance of optimizing state representations and reward functions for maximizing the performance of deep Q-learning agents in the Snake game.
Enhancing Personalized Healthcare via Capturing Disease Severity, Interaction, and Progression

Personalized diagnosis prediction based on electronic health records (EHR) of patients is a promising yet challenging task for AI in healthcare. Existing studies typically ignore the heterogeneity of diseases across different patients. For example, diabetes can have different complications across different patients (e.g., hyperlipidemia and circulatory disorder), which requires personalized diagnoses and treatments. Specifically, existing models fail to consider 1) varying severity of the same diseases for different patients, 2) complex interactions among syndromic diseases, and 3) dynamic progression of chronic diseases. In this work, we propose to perform personalized diagnosis prediction based on EHR data via capturing disease severity, interaction, and progression. In particular, we enable personalized disease representations via severity-driven embeddings at the disease level. Then, at the visit level, we propose to capture higher-order interactions among diseases that can collectively affect patients’ health status via hypergraph-based aggregation; at the patient level, we devise a personalized generative model based on neural ordinary differential equations to capture the continuous-time disease progressions underlying discrete and incomplete visits. Extensive experiments on two real-world EHR datasets show significant performance gains brought by our approach, yielding average improvements of 10.70% for diagnosis prediction over state-of-the-art competitors.
Emotional impact of online assessment strategies in engineering mathematics during COVID-19 confinement: A study in Mexico City

This study looks into how online testing methods in engineering math classes at a university in Mexico City affected students' emotions during the lockdown caused by the COVID-19 pandemic. It seeks to understand which specific testing techniques increased feelings of frustration, anger, anxiety, sadness, shame, and boredom among students. The research used a quantitative approach, focusing on describing and predicting outcomes, and gathered data from 324 university students chosen randomly. It examined which testing methods were most linked to negative feelings using correlation analysis and built models using ordinary least squares. The findings showed that traditional paper exams and the requirement to keep cameras on during video calls were the biggest sources of negative emotions. On the other hand, interactive methods like chat and forum participation, as well as making information organizers, had little to no emotional impact. The research also found that project-based assessments were emotionally neutral for students. When comparing these results with other studies, there's agreement that written and oral exams cause more stress in online settings. The study suggests using a variety of testing methods to avoid depending too much on any one way of assessing students and recommends making exams count for less. These suggestions aim to help improve the educational experience for students in similar situations.
Pavel Florensky and the Future of Thought

The article examines the legacy of priest Pavel Florensky in the context of European culture from antiquity to the present day. The dialogue in which Florensky is a perceiving and continuing comrade-in-arms is conducted with Plato, Aristotle, with the Fathers of the Church and Neoplatonists of different eras, with Nicholas of Cusa, Leibniz, with mathematicians — from the Pythagoreans to Kantor; Florensky’s thought echoes Kant’s constructions and builds on them. Belonging to the culture of the “Silver Age”, Father Pavel meets Nietzsche at a philosophical crossroads, becomes friends with Andrei Bely, becomes the object of criticism from Fr. Georgy Florov sky. In the space of Florensky’s creative dialogue and polemics with philosophers and theologians, the essence of his trinitology and sophiology is revealed. The most important property of the heritage of Fr. Pavel Florensky is a combination of traditionalism, consisting in strict adherence to the teachings of the church fathers and the Orthodox tradition as a whole, and freshness of ideas (up to the coincidence with the understanding of contemporary political trends), which is urgently needed in the modern Christian world both in the East and in the West.
FDA-CDM : Data Augmentation Framework for Personalized Federated Learning in Non-IID Scenarios

Federated learning generates a single global model through collaborative distributed clients without compromising data privacy. However, the statistical heterogeneity of Non-IID data across clients poses fundamental challenges to the personalization process of each client's model. This is also a research focus of many current works. Addressing this phenomenon, in this paper, we propose a data augmentation framework for personalized federated learning in Non-IID scenarios. After training a Conditional Denoising Diffusion Probabilistic Model (C-DDPM) on a central server, it is distributed to various clients to generate labeled data specifically. This enhances the client data to optimize the local data's label distribution. The experimental results indicate that when setting the data augmentation strength to 0.1 for the Cifar10 dataset with a label shift parameter of 1 and 0.02 for the Mnist dataset with a label shift parameter of 0.01, the testing accuracy improved by 7.45% and 1.25% respectively, and there was a noticeable improvement in convergence stability, providing ample evidence of the algorithm's effectiveness.
A Dynamic Printing Equipment Production Status Anomaly Detection Model

In the digital transformation of the printing industry, monitoring the equipment status during the printing production process is the key task. Researchers establish equipment anomaly detection models on historical and real-time data collected from sensors. However, in the actual production process, the data from equipment lacks clear status labels. Manually labeling the status of each time point is a challenge, it makes supervised learning methods difficult to apply. Unsupervised methods are proposed to automatically detect anomalies. But current methods are sensitive to noise and highly depend on data quality. In the realistic scenario, the timestamp intervals are uneven, and the insufficient sensor precision may produce uncorrected speed data. In response to the above issues, this article proposes a new unsupervised dynamic printing equipment production status anomaly detection model, which combined autoencoder and Gaussian model. The model, considering complexity in real scenes, is composed of three components: window feature extraction, anomaly scores measurement and confidence calculation. Compared with traditional anomaly detection using Kmeans with fixed window features, our model improves the classification accuracy of normal samples in real printed data from 90% to 93%.
A two-stage network based on edge information for visual anomaly detection

Reconstruction-based approaches attract increasing attention in unsupervised visual anomaly detection. Such methods rely on the assumption that reconstruction models trained using only normal images are not well reconstructed for anomalous data, so that the anomalous regions can be identified by computing the differences in the reconstructions. However, it's usually challenging to manage the model's generalization boundaries in practice. The model with a poor generalization capability will produce additional image differences in the normal regions, while the model with an overly strong generalization capability can even well reconstruct the abnormal regions, making the abnormal regions less distinguishable from the normal regions. In this paper, we propose a two-stage reconstruction network to solve the above problems, which can generate high-accuracy and anomaly-free reconstruction. Specifically, our two-stage anomaly detection network consists of the edge extraction sub-network and the image restoration sub-network. The first sub-network generates anomaly-free grayscale edges of the image as intermediate results, and then the second one recovers normal color and low-frequency information for the intermediate results. The proposed network can generate high-accuracy and anomaly-free reconstructions to maximize the difference between the reconstruction errors of normal and abnormal samples, which can ultimately improve the accuracy of anomaly detection. Experiments on the dataset MVTec AD show that our proposed method achieves competitive results in anomaly detection and anomaly localization (97.8% for detection and 97.7% for localization, AUROC).
Mixed Knowledge-enhance Empathetic Dialogue Generation

Empathy plays a pivotal role in human communication, and thus, it is an essential capability that any human-centered dialogue system should possess. Early research on empathetic response generation often focused on directly capturing the emotional state of the context using fixed emotion labels. However, the logical aspects exhibited in human conversations heavily rely on experiential and knowledge-based resources within the brain. This implies that whether the aim is to acquire more nuanced emotional states or to generate responses enriched with comprehensive information, the incorporation of external knowledge as supplementary information in empathetic dialogue systems is imperative. In response to this challenge, we propose a novel approach for extracting external knowledge. This is achieved by designing two components: a fine-grained knowledge graph constructed using the context and an external knowledge base, and coarse-grained knowledge acquisition based on COMET. These two scales of knowledge are then integrated with the context using methods like context refinement. This not only make the model to gain a deeper understanding of the user's context but also enhances the expression of empathy in the dialogue system. We conducted extensive experiments on the EMPATHETICDIALOGUES dataset and demonstrated the superiority of our approach over the baseline model.
Byzantine-Robust Federated Learning Based on Multi-center Secure Clustering

In the backdrop of growing social apprehensions regarding data privacy and security, Federated Learning (FL) stands out as a cornerstone in modern machine learning. However, FL grapples with challenges stemming from data heterogeneity and potential security breaches. While techniques such as FedSEM enhance multi-center aggregation by employing Expectation Maximization (EM) methods to match clients with centers, leading to improved performance in non-IID environments, there remains a critical gap in ensuring optimal privacy and security measures.
Single-image HDR Reconstruction based on Mask-aware Convolution

Due to the hardware limitations of the sensor during camera shooting, there is loss of details in underexposed and overexposed regions of an image. Inverse tone mapping is an effective approach to extend the dynamic range of the image. To correctly repair the details of overexposed regions of the image, this paper proposes a high dynamic range image reconstruction method with mask-aware convolution. First, a mask-aware convolution module is designed to efficiently learn the information of overexposed regions during the encoding process. The parameters of the convolution kernel at different positions of the features are independent and generated by the overexposure mask to minimize the interference of almost worthless information in the overexposed region. The underexposed and overexposed regions of the image are differently processed to recover more realistic overexposure information of the image. Second, the fast Fourier convolution is utilized to convert the image to the frequency domain for processing, with a high receptive field while enhancing the restoration of high-frequency details during the decoding process, while recovering as much information as possible from the underexposed and overexposed regions of the image. Extensive quantitative and qualitative experiments are conducted on the image datasets, and the experimental results show that the proposed method can better reconstruct HDR images, and better recover the overexposed and underexposed regions compared to many representative single-image HDR reconstruction methods.
Public Opinion Evolution Analysis of International Mainstream Media based onAdversarial Learning

Global media with international influence play an extremely important role in shaping international public opinion platforms related to China. They not only disseminate objective events but also shape viewpoints. Studying international public opinion orientation plays an important role in building a positive national image, improving the international public opinion environment, and resisting various ideological infiltration. We gathered millions of pieces of data from GDELT for analysis. This study combines zero-shot sentiment analysis based on adversarial learning with the LDA topic classification model to propose a new framework for public opinion analysis. Finally, based on the results of this study, suggestions are made to help maintain China's international image.
Research on Network Security in Material Support

The research on network security in material support is aimed at ensuring the security and stable operation of the material supply chain. This article conducts research and summary on network security in material support. Firstly, the basic situation of the application of the network in material support was introduced, and the network security issues and hidden dangers in material support were analyzed. Establishing a sound network security guarantee system is the foundation of the security of the material support system. On this basis, the concept of a network security system for material support was proposed, and solutions to network security issues in material support were provided. Elaborate on the serious threat posed by network attacks to the material support system, propose to study the characteristics and defense methods of network attacks, and adopt corresponding technical measures and emergency response mechanisms. In addition, protecting the security of material support information systems and ensuring the security of data are also important research topics. Conduct research on the encrypted storage and transmission of material support information, identity authentication and access control, data backup and recovery, and other aspects. At the same time, measures such as data backup and disaster recovery, data privacy protection, and data leakage prevention are taken to ensure the integrity and confidentiality of data.
Research on Real-time Detection Method of Distributed Setpoint Anomaly in Nuclear Power Industrial Control System Based on Stream Computing

The nuclear industry as a national important basic support industry, its safety is more and more attention by the state, nuclear power control system as the core of the nuclear industry, its safe and stable operation is particularly important. With the development of attack technology, more and more forms of attack from a single attack to a distributed attack, the attack has a single flow of normal characteristics, so that the existing single-point attack detection methods can not meet the malicious attack behavior detection requirements. In order to cope with the challenge, this paper takes the distributed setpoint anomaly attack as the research object, and proposes a real-time detection method security-Setpointer based on stream computing. The method constructs an aggregation model for the setpoints in the time window according to the controlled objects for the sequence of operation aggregation, and at the same time, due to the large scale of the core monitoring point, in order to meet the timeliness and real-time detection. This paper implements a distributed DTW algorithm based on stream computing, trains on a large amount of historical a priori data, realizes the feature learning and characterization of normal setpoint sequences, and adopts different DTW real-time detection tasks for different monitoring points to realize the anomaly detection and analysis of different monitoring points. Finally, in order to verify the effectiveness of the method proposed in this paper, functional and performance tests are carried out, and the test results show that the method proposed in this paper is effective.
Improved Recommendation Algorithm Based On Knowledge Graph

In order to solve the problem that It is difficult for network security operation and maintenance personnel to identify the required data in a timely and accurate manner when analyzing network security events, this paper proposed a recommendation algorithm of network security event based on knowledge graph, it uses the network threat framework ATT&CK to construct an ontology model, establishes a network threat knowledge graph based on the ontology model, extract discrete security data into interrelated security knowledge. This article extracts entity data based on knowledge graph, obtains entity vectors through TransH algorithm, and uses entity vectors to calculate data similarity between entities in network threat data. We extract network security data entities from literature on network security event handling as disposal behaviors, construct a disposal behavior matrix, and use the behavior matrix to achieve vectorized representation of network threat data. it calculates the similarity of network threat data entities based on disposal behaviors. Finally, the similarity between the network threat data and the threat data under network security event handling behavior is fused to form a data recommendation list for network security events, achieving correlation between network threat domains based on user behavior. The algorithm incorporates disposition behavior similarity on the basis of data similarity, which is closer to factual disposition behavior. Compared with other algorithms, this algorithm has a significant advantage in recall rate and accuracy within the range of recommended data volume less than 10.
Enhancing Sentiment Knowledge in Persian/Dari Language through Bidirectional GRU-CNN Model

Deep learning such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have been successfully applied to Natural Language Processing (NLP). Previous research shows that RNN and CNN achieved significant results in extracting long and short-term dependencies. However, there is a lack of research on Persian/Dari due to low resources and with many difficulties in preprocessing. In this research paper, we proposed a novel sentiment analysis model using bidirectional GRU and CNN to address the challenges and improve sentiment knowledge in the Persian/Dari language. Firstly, a preprocessor is utilized to enhance data quality by eliminating noise. Secondly, a bidirectional Gated Recurrent Unit is used to capture long-term dependencies. Thirdly, our model(named PersiSentNet) employs CNN with max pooling to extract contextual features and reduce feature dimensionality. Gaussian Noise and Dropout are applied as regularization techniques to avoid the overfitting problem. Finally, the model's performance is verified on two standard datasets, and the experimental results indicate that our model significantly outperforms the state-of-the-art models, highlighting its robustness and effectiveness in addressing the challenges of Persian/Dari language sentiment analysis.
Multilingual Entity and Relation Extraction from Unified to Language-specific Training

Entity and relation extraction is a key task in information extraction, where the output can be used for downstream NLP tasks. Existing approaches for entity and relation extraction task mainly focus on the English corpora and ignore other languages. Thus, it is critical to improve performance in a multilingual setting. Meanwhile, multilingual training is usually used to boost cross-lingual performance by transferring knowledge from languages (e.g., high-resource) to other (e.g., low-resource) languages. However, language interference usually exists in multilingual tasks as the model parameters are shared among all languages. In this paper, we propose a two-stage multilingual training method and a joint model called Multilingual Entity and Relation Extraction framework (mERE) to mitigate language interference across languages. Specifically, we randomly concatenate sentences in different languages to train a Language-universal Aggregator (LA), which narrows the distance of embedding representations by obtaining the unified language representation. Then, we separate parameters to mitigate interference via tuning a Language-specific Switcher (LS), which includes several independent submodules to refine the language-specific feature representation. After that, to enhance the relational triple extraction, the sentence representations concatenated with the relation feature are used to recognize the entities. Extensive experimental results show that our method outperforms both the monolingual and multilingual baseline methods. Besides, we also perform detailed analysis to show that mERE is lightweight but effective on relational triple extraction and mERE is easy to transfer to other backbone models of multi-field tasks, which further demonstrates the effectiveness of our method.