Reviewerly/exHarmony huggingface dataset:
Meta-learning. An Approach Applied to Clinical Data

Currently, there is a large amount of digitized data. In the field of health, an important part of the data is obtained from electronic health records, related to the health of people. This stream of data has led to a multitude of research efforts in the healthcare domain, focusing on various aspects such as disease prediction, clinical risk assessment, mortality analysis, and more. These predictions assist healthcare providers in early identification of potential risks, leading to better patient care. The objective of this work is to propose a meta-learning model applied to clinical data in which the different algorithms included can be compared and the benefits of each of them appreciated. Meta-learning involves algorithms learning about other algorithms through experience. However, the application of meta-learning to diagnosis poses considerable practical challenges. A case study is used to test the model and assess its effectiveness. In this study, the application of meta-learning showed very promising initial results, adapting the algorithm to the database used depending on the disease to be predicted.
User Multi-preferences Fusion for Conversational Recommender Systems

Conversational recommender systems (CRS) aim to provide recommendations by inferring user preferences during conversations. Many current CRS models utilize third-party information, such as reviews, to supplement the extraction of user preferences. Consequently, users develop preferences for third-party information and their own preferences extracted from original dialog data. However, the prevailing approach of combining these preferences as a unified whole for self-attention at the element level compromises their independence. In real-life decision-making, we refer to third-party information and it is important to distinguish whether the reference is from a third party or from the original dialog data. This paper emphasizes the independence of users' own preferences and third-party information. To effectively integrate multiple user preferences, we propose an Attentive Wide Deep Conversational Recommender (AWDCore). Specifically, we design an attentive wide linear module and an attentive deep neural network to capture the low-order linear and high-order nonlinear relationships between the user's own preference and third-party information, respectively. To highlight the significance of the user's current preference, we incorporate attention mechanisms and a SENet layer in the wide module and deep neural networks, respectively. The learned user preferences are then employed for recommendation and dialogue generation. Extensive experiments have demonstrated the effectiveness of our approach in both recommendation and conversation tasks.
Mani-GPT: A Generative Model for Interactive Robotic Manipulation

In real-world scenarios, human dialogues are multi-round and diverse. Furthermore, human instructions can be unclear and human responses are unrestricted. Interactive robots face difficulties in understanding human intents and generating suitable strategies for assisting individuals through manipulation. In this article, we propose Mani-GPT, a Generative Pre-trained Transformer (GPT) for interactive robotic manipulation. The proposed model has the ability to understand the environment through object information, understand human intent through dialogues, generate natural language responses to human input, and generate appropriate manipulation plans to assist the human. This makes the human-robot interaction more natural and humanized. In our experiment, Mani-GPT outperforms existing algorithms with an accuracy of 84.6% in intent recognition and decision-making for actions. Furthermore, it demonstrates satisfying performance in real-world dialogue tests with users, achieving an average response accuracy of 70%.
An Uncertainty-Adaptive Consistency Training Algorithm for Semi-Supervised Object Counting Networks

Object counting presents a significant challenge for applications demanding high levels of automation. In recently ears, semi-supervised counting methods have been explored to alleviate the burden of data annotation. Despite considerable progress, the state-of-the-art methods treat pixels with varying levels of difficulty equally during each training iteration, resulting in an inflexible consistency training that can lead to high supervision loss at the onset of training and potentially cause network collapse. To address this limitation, we propose an uncertainty-adaptive consistency training algorithm for semi-supervised object counting networks in this paper. Our approach leverages image colorization to construct colorization uncertainty, which can measure the difficulty of object pixels and background pixels. To ensure superior uncertainty estimation, we then enhance this colorization uncertainty by using density features to guide the learning of colorization. Furthermore, an uncertainty-adaptive consistency training (UACT) algorithm is introduced for the consistency training of density maps. By adaptively adjusting the uncertainty threshold, we prioritize harder pixels for selection, gradually incorporating easier ones into the consistency training. The effectiveness o f t he UACT is evaluated through extensive experiments on two datasets. Experimental results demonstrate that our method outperforms state-of-the-art semi-supervised counting methods.
Bias Reduced Methods to Q-learning

It is well known that Q-learning (QL) suffers from overestimation bias, which is caused by using the maximum action value to approximate the maximum expected action value. To solve overestimation issue, overestimation property of Q-learning is well studied theoretically and practically. In general, most work on reducing overestimation bias is to find different estimators to replace maximum estimator in order to mitigate the effect of overestimation bias. These works have achieved some improvement on Q-learning. In this work, we still focus on overestimation bias reduced methods. In these methods, we focus on M samples action values, and one of these samples estimated by remaining samples' maximum actions. We select median and max members from these new samples which are estimated by maximum actions. We call these max and median members as Bias Reduced Max Q-learning (BRMQL) and Bias Reduced Median Q-learning (BRMeQL). We first theoretically prove that BRMQL and BRMeQL suffer from underestimation bias and analyze the effect of number of M Q-functions on the performance of our algorithms. Then we evaluate the BRMQL and BRMeQL on benchmark game environments. At last, we show that BRMQL, and BRMeQL less underestimate the Q-value than Double Q-learning (DQL) and perform better than several other algorithms on some benchmark game environments.
A Partially Decentralised Protocol for a Distributed Encrypted Storage System

Decentralization is an important feature both for security and for reliability as it reduces the risks of collusion and impact of the single-point-of-failure vulnerability in a system. After having recalled the re-encryption procedure – mainly to establish notations – we will describe a modification of the distributed encrypted storage protocol by Ateniese-Fu-Green-Hohenberger. Such a protocol is a strongly centralized one since it assigns to the manager the possibility of storing all the symmetric keys involved in enciphering the resources required by the users of the system. Our goal here is to show how to reduce the role of the manager as being just a certifier of the access requests to the shared resources. We will achieve this by using standard public-key cryptography tools like the digital signature with message integrity.
Incorporating dynamic attention gating mechanism and pre-trained embedding for Chinese clinical named entity recognition

Clinical Named Entity Recognition (CNER) serves as a cornerstone in clinical research and healthcare informatics, with its primary goal being the discernment and categorization of clinical terminologies present within Electronic Medical Records (EMRs). While deep learning has significantly improved CNER's performance, these models still face challenges in identifying rare or unseen entities. Our study unveils an innovative DAGM-BiLSTM-CRF model that seamlessly integrates data-driven deep learning strategies with human knowledge to surmount this limitation. The distinctive features of this model lie in its integration of a pre-trained character embedding layer, adept at encapsulating nuanced semantic variations at the granularity of individual characters. Furthermore, it incorporates a bidirectional Long Short-Term Memory (BiLSTM) layer, proficient in deriving contextually relevant information by traversing the sequence from both endpoints. The most noteworthy aspect of our model is the inclusion of a Dynamic Attention Gating Mechanism (DAGM) that dynamically recalibrates attention weights based on the traits of the input sequence and a Conditional Random Fields (CRF) layer that considers interdependencies among output labels within a sequence. The evaluation was carried out on the CCKS-2017 CNER benchmark dataset to underscore the superiority of our model over existing cutting-edge methods, thereby validating its proficiency in deciphering the intricacies of Chinese electronic medical records.
Raise Awareness of the Environmental Impacts of Retail Food Products: A User-Centered Scenario-Based Approach

The climate is warming rapidly, and atmospheric concentrations of greenhouse gases (GHGs) are at their highest levels ever recorded. As a result of these climate changes, caused mainly by human activities, disasters have increased fivefold over the past 50 years, causing death and economic loss. Civic engagement and awareness are essential to mitigate climate change and its impacts. In this work, we proposed a user interface that makes users aware of the environmental impact of the food products they buy when shopping. A user-centered scenario-based design was followed in the development of the interface. Gamification elements were added to increase civic participation in climate action.
Federated Learning for Industry 5.0: A State-of-the-Art Review

Federated Learning (FL) and Industry 5.0’s convergence holds significant promise for changing smart systems. FL, a distributed machine learning method, allows for collaborative model training while protecting data privacy and security. The purpose of this review is to establish the current state-of-the-art on FL and its possible applications in Industry 5.0. Key accomplishments, problems, and future research paths are outlined through detailed literature review. FL has various advantages for Industry 5.0, including increased model accuracy, reduced communication overhead, and improved data privacy. Due to its decentralized nature, it allows for local model training on edge devices, facilitating real-time decision-making and lowering latency. Furthermore, the research investigates FL’s potential influence on smart cities by enabling dispersed learning across urban domains without the need for data exchange and how could improve the performance in manufacturing tasks and its role in enhancing Human-Robot Interaction (HRI), resulting in safer and more efficient industrial operations. This gives unparalleled opportunity for developing intelligent systems that address the challenges brought by Industry 5.0 paradigm. The provided state-of-the-art review aims to inform researchers and practitioners about the current advancements in FL and serves as a foundation for future studies to harness the full potential of this novel paradigm for a wide range of industries and applications.
Self-adaptive Inverse Soft-Q Learning for Imitation

As a powerful method for solving sequential decision problems, imitation learning (IL) aims to generate policy similar to expert behavior by imitating demonstrations. However, the quality of demonstrations directly limits the performance of the agent imitation policy. To solve this problem, self-adaptive inverse soft-Q learning for imitation (SAIQL) is proposed. SAIQL proposes a novel three-level buffer system by introducing an online excellent buffer based on the expert buffer and the normal buffer. Trajectories from interactions with superior performance are stored in the online excellent buffer. When the amount of data in the online excellent buffer and the expert buffer is equal, the former data will be cleaned and transferred to the latter, ensuring that demonstrations in the expert buffer are continuously optimized. Finally, we compare SAIQL with up-to-date IL methods in both the continuous control and the Atari tasks. The experimental results show the superiority of SAIQL. It improves the quality of expert demonstrations and the utilization of trajectories.
Efficient Attention for Domain Generalization

Deep neural networks suffer severe performance degradation when encountering domain shift. Previous methods mainly focus on feature manipulation in source domains to learn transferable features to unseen domains. We propose a new perspective based on the attention mechanism, which enables the model to learn the most transferable features on source domains and dynamically focus on the most discriminative features on unseen domains. To achieve this goal, we introduce a domain-specific attention module that facilitates the identification of most transferable features in each domain. Different from channel attention, spatial information is also encoded in our module to capture global structure information of samples, which is vital for generalization performance. To minimize the parameter overhead, we also introduce a knowledge distillation formulation to train a lightweight model that has the same attention capabilities as original model. So, we align the attention weights of the student model with a specific attention weights of the teacher model that corresponding to the domain of input. The results show that the distilled model performs better than its teacher and achieves the state-of-the-art performance on several public datasets, i.e. PAC, OfficeHome and VLCS. This indicates the effectiveness and superiority of our proposed approach in terms of transfer learning and domain generalization tasks.
CertifIoT: An IoT and DLT-Based Solution for Enhancing Trust and Transparency in Data Certification

Data accuracy, reliability, and integrity are of utmost importance in multiple scenarios, such as medical test, performance testing, etc.; data certification plays a critical role in providing trust, as it ensures that the data is guaranteed by trusted entities. However, traditional private and centralised approaches make it challenging to guarantee the veracity of the data or establish liability in cases of deliberate manipulation or human error. This contribution introduces CertifIoT, a Distributed Ledger Technology (DLT)-based solution for certifying data gathered from data streams produced by Internet of Things (IoT) devices. This approach adds transparency to the process and provides a mechanism for clarifying data provenance and recording methods. The foundation of our solution is the Phonendo Framework [1], which has been extended with additional features required for data certification. Furthermore, we propose an adaptable and extensible data model, to make certified data interoperable, and a protocol for trust endorsement, which aims at increasing the trust of certified data.
Design and analysis of wireless data center network topology HCDCN based on VLC

Abstract With the rapid development of the Internet of Things, big data, cloud computing and other industries, the data center network, as the core infrastructure of the computing industry, is becoming increasingly important. Wireless communication technology is developing rapidly. This paper uses visible light wireless communication technology to build a wireless data center network, solving the problems of complex wiring and expansion difficulties in the existing data center network. Aiming at the problems of insufficient spectrum, low bandwidth utilization and large energy consumption in the existing wireless data center, this paper adopts the visible light wireless communication technology based on LED, and proposes a new wireless data center network structure HCDCN. Firstly, work have been done to analyze the feasibility of introducing data centers in this technology, and transform server racks in combination with the characteristics of the technology. Secondly, the construction process of the structure is introduced and a new coding rule is proposed. In order to increase the routing efficiency and fault tolerance, the rack top link routing path is added, and an efficient routing algorithm is designed. Finally, the aggregate throughput of HCDCN under different scales is obtained through experiments, and the network performance of HCDCN structure is compared with that of OWCells and Mesh structure. Experiments have proved that HCDCN's network performance is better than the other two structures under the same conditions. The new wireless data center network proposed in this paper has largely alleviated the bandwidth shortage of hotspot servers.
Landmark-Assisted Facial Action Unit Detection with Optimal Attention and Contrastive Learning

In this paper, we propose a weakly-supervised algorithm for facial action unit (AU) detection in the wild, which combines basic facial features and attention-based landmark features as well as contrastive learning to improve the performance of AU detection. Firstly, the backbone is a weakly-supervised algorithm since AU datasets in the wild are scarce and the utilization of other public datasets can capture robust basic facial features and landmark features. Secondly, we explore and select the optimal attention-based landmark encoder to capture facial landmark features that have been shown highly related to AUs. Then, we combine basic facial features and attention-based landmark features for AU detection. Finally, we propose a weighted multi-label contrastive loss function for the further improvement of AU detection. Extensive experiments on RFAU and BP4D demonstrate that our method outperforms or is comparable with state-of-the-art weakly-supervised and supervised AU detection methods.
CRE: An Efficient Ciphertext Retrieval Scheme Based on Encoder

Searchable Encryption is utilized to address the issue of searching for outsourced encrypted data on third-party untrusted cloud servers. Traditional approaches for ciphertext retrieval are limited to basic keyword-matching queries and fall short when it comes to handling complex semantic queries. Although several semantic retrieval schemes have been proposed in recent years, their performance is inadequate. This paper introduces a semantic retrieval scheme called CRE (Ciphertext Retrieval based on Encoder), which leverages the prompt-based RoBERTa pre-trained language model to generate precise embeddings for sentences in queries and documents. Moreover, to improve retrieval speed in the face of massive high-dimensional sentence embedding vectors, we introduce the HNSW algorithm. Through experimentation and theoretical analysis, this paper demonstrates that CRE outperforms $$SSSW_2$$ and $$SSRB_2$$ in terms of retrieval speed and accuracy.
Progressive Supervision for Tampering Localization in Document Images

Tampering localization in document images plays an important role in the field of forensic and security, which has made great progress in recent years, however it is far from being solved. In this work, we aim to improve the tampering localization performance by refining both sides of the localization model. On one hand, we propose a multi-view enhancement (MVE) module at the input side, which combines RGB image, noise residual and texture information to obtain more forensic traces for tampering localization. On the other hand, at the output side, we propose both progressive supervision (PS) and detection assistance (DA) modules to enrich more detailed supervision information. Under the progressive supervision, we calculate BCE loss at each scale to extensively explore multi-scale features, which are vital for the tampering localization. To explore the tampering detection model, we adopt a KL loss to align both tampering localization and detection scores in the DA module, benefiting the estimation of global tampered probability. In the experiments, we evaluate the proposed method on the benchmark dataset DocTamper and the results demonstrate its effectiveness.
Privacy-Preserving Federated Compressed Learning Against Data Reconstruction Attacks Based on Secure Data

Federated learning is a new distributed learning framework with data privacy preserving in which multiple users collaboratively train models without sharing data. However, recent studies highlight potential privacy leakage through shared gradient information. Several defense strategies, including gradient information encryption and perturbation, have been suggested. But these strategies either involve high complexity or are susceptible to attacks. To counter these challenges, we propose to train on secure compressive measurements by compressed learning, thereby achieving local data privacy protection with slight performance degradation. A feasible method to boost performance in compressed learning is the joint optimization of the sampling matrix and the inference network during the training phase, but this may suffer from data reconstruction attacks again. Thus, we further incorporate a traditional lightweight encryption scheme to protect data privacy. Experiments conducted on MNIST and FMNIST datasets substantiate that our schemes achieve a satisfactory balance between privacy protection and model performance.
Multi-granularity Deep Vulnerability Detection Using Graph Neural Networks

The significance of vulnerability detection has grown increasingly crucial due to the escalating cybersecurity threats. Investigating automated vulnerability detection techniques to avoid high false positives and false negatives is an important issue in the current software security field. In recent years, there has been a substantial focus on deep learning-based vulnerability detectors, which have achieved remarkable success. To fill the gap in multi-granularity program representation, we propose MulGraVD, a deep learning-based vulnerability detector at the function level. MulGraVD captures the continuity and structure of the programming language by considering information at word, statement, basic block, and function granularity respectively. To overcome the constraint posed by hyperparameter layers in the information aggregation process of graph neural networks, MulGraVD serially passes information from coarse to fine granularity, which facilitates the mining of vulnerability patterns. Our experimental evaluation on FFMPeg+Qemu and ReVeal datasets shows that MulGraVD significantly outperforms existing state-of-the-art methods in terms of precision, recall, and F1 score, with an average improvement of 11.62% in precision, 27.69% in recall, and 19.71% in F1 score.
Sentiment Analysis Based on Pretrained Language Models: Recent Progress

Pre-trained Language Models (PLMs) can be applied to downstream tasks with only fine-tuning, eliminating the need to train the model from scratch. In particular, PLMs have been utilised for Sentiment Analysis (SA), a process that detects, analyses, and extracts the polarity of text sentiments. To help researchers comprehensively understand the existing research on PLM-based SA, identify gaps, establish context, acknowledge previous work, and learn from methodologies, we present a literature review on the topic in this paper. Specifically, we brief the motivation of each method, offer a concise overview of these methods, compare their pros, cons, and performance, and identify the challenges for future research.