Reviewerly/exHarmony huggingface dataset:
Joint agricultural intent detection and slot filling based on enhanced heterogeneous attention mechanism

Agricultural diseases and pests are important factors restricting global food production. The knowledge-based question-answering (Q&A) system opens a new avenue for pest and disease control. As the key issue of Q&A, Natural language understanding (NLU) mainly includes two core subtasks, intent detection and slot filling (IDSF). However, the IDSF tasks in the agricultural field were still regarded as two completely independent subtasks, which may result in challenging problems such as error propagation and correlation neglection. To tackle the abovementioned consideration, address the gap in joint IDSF in the agricultural field, and finally improve the performance of agricultural question answering, a novel joint IDSF model based on enhanced heterogeneous attention mechanism for agriculture (AgHA-IDSF) was proposed. Its improvements mainly include three aspects. First, the heterogeneous attention mechanism was proposed to enable slot semantic representation to fully integrate intention information. Then, intent embedding matrix was introduced to provide rich intent information for guiding slot filling tasks. Finally, a weighting operation was designed to improve the guiding ability of related intents to slot filling task, while weakening the interference of irrelevant intents in a pre-trained paradigm. Furthermore, the first IDSF dataset for agriculture named AGIS was also collected and annotated, which contains 22 types of intents, 10 types of slots, and 11,976 samples. In addition, two publicly available datasets ATIS and SNIPS were also considered as benchmark datasets. The experimental results, including macro, micro, and ablation study et al., showed that the proposed model has achieved the optimal overall accuracy on three datasets, which were 88.41%, 87.57%, and 90.57%, respectively, even better than bidirectional association models, indicating the effectiveness of the proposed model in joint agricultural intent detection and slot filling.
Color image quantum steganography scheme and circuit design based on DWT+DCT+SVD

This paper presents a new steganographic algorithm for quantum color images, which utilizes DWT+DCT+SVD embedding technology. Firstly, in order to improve the security of secret information, a novel three-dimensional hyperchaotic system that combines the Chebyshev map and ICMIC map using a closed-loop coupling method (3D-CICC) is proposed. In addition, this paper proposes a new steganography algorithm based on DWT+DCT+SVD, which can effectively hide the secret image in the carrier image without being detected. At the same time, an efficient quantum circuit for quantum steganography is designed. The simulation and numerical analysis results show that the algorithm has good performance in invisibility analysis, security analysis, and robustness analysis.
An efficient fine-grained vehicle recognition method based on part-level feature optimization

This paper presents an effective method for strengthening the discriminative ability of high-level deep features by enhancing and aggregating discriminative part-level features for the fine-grained vehicle recognition task. In general, the task of visual recognition concentrates more on the visual differences at the object level. However, for fine-grained object recognition, the visual differences between target objects typically exist in local discriminative areas, so it is more concerned about extracting fine-grained features from these part regions. In this context, we propose solving this issue with a novel feature extraction method from two perspectives: the generation of more feature descriptors of part regions through the learning process of deep networks and the aggregation of part-level discriminative features. This approach is designed to improve the backbone networks to generate finer-level part features through a part-level feature enhancement module and to investigate the intrinsic part-level features of the backbone networks with the help of a feature aggregation module. The enhancement module efficiently finds the finer features highly correlated to the part regions. Then the feature aggregation module builds correlations of similar part features through feature grouping and fusion. Moreover, our proposed method does not require additional parts annotations and achieves comparable performance on two widely-used benchmarks for recognizing fine-grained vehicle types. Experimental results and explainable visualizations demonstrate the effectiveness of the proposed method.
Safe multi-agent reinforcement learning for multi-robot control

A challenging problem in robotics is how to control multiple robots cooperatively and safely in real-world applications. Yet, developing multi-robot control methods from the perspective of safe multi-agent reinforcement learning (MARL) has merely been studied. To fill this gap, in this study, we investigate safe MARL for multi-robot control on cooperative tasks, in which each individual robot has to not only meet its own safety constraints while maximising their reward, but also consider those of others to guarantee safe team behaviours. Firstly, we formulate the safe MARL problem as a constrained Markov game and employ policy optimisation to solve it theoretically. The proposed algorithm guarantees monotonic improvement in reward and satisfaction of safety constraints at every iteration. Secondly, as approximations to the theoretical solution, we propose two safe multi-agent policy gradient methods: Multi-Agent Constrained Policy Optimisation (MACPO) and MAPPO-Lagrangian. Thirdly, we develop the first three safe MARL benchmarks—Safe Multi-Agent MuJoCo (Safe MAMuJoCo), Safe Multi-Agent Robosuite (Safe MARobosuite) and Safe Multi-Agent Isaac Gym (Safe MAIG) to expand the toolkit of MARL and robot control research communities. Finally, experimental results on the three safe MARL benchmarks indicate that our methods can achieve state-of-the-art performance in the balance between improving reward and satisfying safety constraints compared with strong baselines. Demos and code are available at the link (https://sites.google.com/view/aij-safe-marl/).2
A Survey on Compression Domain Image and Video Data Processing and Analysis Techniques

A tremendous amount of image and video data are being generated and shared in our daily lives. Image and video data are typically stored and transmitted in compressed form in order to reduce storage space and transmission time. The processing and analysis of compressed image and video data can greatly reduce input data size and eliminate the need for decompression and recompression, thereby achieving significant savings in memory and computation time. There exists a body of research on compression domain data processing and analysis. This survey focuses on the work related to image and video data. The papers cited are categorized based on their target applications, including image and video resizing and retrieval, information hiding and watermark embedding, image and video enhancement and segmentation, object and motion detection, as well as pattern classification, among several other applications. Key methods used for these applications are explained and discussed. Comparisons are drawn among similar approaches. We then point out possible directions of further research.
A Bibliometric Review of the Evolution of Blockchain Technologies

Bitcoin was created in 2008 as the first decentralized cryptocurrency, providing an innovative data management technology, which was later named blockchain. It ensured data validation without intervention from intermediaries. During its early stages, it was conceived as a financial technology by most researchers. It was not until 2015, when the Ethereum cryptocurrency was officially launched worldwide, along with its revolutionary technology called smart contracts, that researchers began to change their perception of the technology and look for uses outside the financial world. This paper analyzes the literature since 2016, one year after Ethereum, analyzing the evolution of interest in the technology to date. For this purpose, a total of 56,864 documents created between 2016 and 2022 from four major publishers were analyzed, providing answers to the following questions. Q1: How has interest in blockchain technology increased? Q2: What have been the major blockchain research interests? Q3: What have been the most outstanding works of the scientific community? The paper clearly exposes the evolution of blockchain technology, making it clear that, as the years go by, it is becoming a complementary technology instead of the main focus of studies. Finally, we highlight the most popular and recurrent topics discussed in the literature over the analyzed period of time.
Second-Order Side-Channel Analysis Based on Orthogonal Transform Nonlinear Regression

In recent years, side-channel analysis technology has been one of the greatest threats to information security. SCA decrypts the key information in the encryption device by establishing an appropriate leakage model. As one of many leakage models, the XOR operation leakage proposed by linear regression has typical representative significance in side-channel analysis. However, linear regression may have the problem of irreversibility of a singular matrix in the modeling stage of template analysis and the problem of poor data fit in the template analysis after the cryptographic algorithm is masked. Therefore, this paper proposes a second-order template analysis method based on orthogonal transformation nonlinear regression. The irreversibility of a singular matrix and the inaccuracy of the model are solved by orthogonal transformation and adding a negative direction to the calculation of the regression coefficient matrix. In order to verify the data fitting effect of the constructed template, a comparative experiment of template analysis based on regression, Gaussian, and clustering was carried out on SAKURA-G. The experimental results show that the second-order template analysis based on orthogonal transformation nonlinear regression can complete key recovery without sacrificing the performance of regression estimation. Under the condition of high noise and high order template analysis, the established template has good universality.
PEvaChain: Privacy-preserving ridge regression-based credit evaluation system using Hyperledger Fabric blockchain

Secure, compliant and authentic multiparty data sharing and collaborative modelling are of great significance to the accuracy of credit evaluation systems. Homomorphic encryption has the feature of supporting ciphertext calculation without sacrificing the accuracy of the model. However, the credibility and security of the existing centralized data homomorphic encryption sharing-aggregation mode have brought great hidden dangers and exacerbated the risk of private data being disclosed. Ensuring the authenticity and controllability of data are also difficulties faced by homomorphic encryption technology. To solve these problems, we propose a novel decentralized privacy-preserving credit evaluation system with trustworthy data content and calculations named PEvaChain based on Hyperledger Fabric blockchain. The PEvaChain consists of three main components: identity management, off-chain encrypted data uploading, and on-chain data security sharing-aggregation. With the help of Hyperledger Fabric's special member access mechanism, incorporating the ciphertext-policy attribute-based encryption (CP-ABE) access control scheme avoids unauthorized access. The original data are transformed by invertible random matrices off the chain, which meets data transfer agreements requirements when data uploading and eliminates the privacy disclosure concerns of data providers to a certain extent. Paillier homomorphic encryption-based data security sharing-aggregation on the chain ensures the security of multiparty data sharing and aggregation while realizing the minimum output and utilization of the original data. Security analysis demonstrates the security and compliance of PEvaChain in terms of data access, encrypted data uploading, sharing-aggregation, and storage. The experimental results show that the proposed approach is feasible, safe and efficient.
Joint Resource Allocation and Learning Optimization for UAV-Assisted Federated Learning

Aiming at the unmanned aerial vehicle (UAV)-assisted federated learning wireless-network scenario, and considering the influence of the UAV altitude on the coverage area, we propose a joint optimization algorithm of UAV placement, computation and communication resources. Considering the energy efficiency and federated learning performance, we defined the cost function of the system. Under the constraint of the total delay of federated learning completion, we formulated an optimization problem of minimizing the cost function to achieve the balance between the total energy consumption of users and the federated learning performance. Since the formulated optimization problem is a non-convex problem, in order to solve this problem, we decomposed it into three optimization subproblems: UAV horizontal placement, local accuracy and computation and communication resources. We used successive convex approximation (SCA), the Dinkelbach Method, the Bisection method and KKT condition, respectively, to solve the three subproblems, and finally obtain the optimal solutions through iteration of the three subproblems. Simulation results show that compared with the federated learning scenario under fixed UAV altitude, our proposed algorithm not only guarantees the learning performance, but also reduces more users’ total energy consumption.
Developing a Leukemia Diagnostic System Based on Hybrid and Ensemble Deep Learning Architectures

This research designed and implemented a leukemia diagnostic system targeted for a real clinical environment based on deep learning approaches. The dataset consists of 15,135 total images. This research develops four independent models (VGG19 1 epoch, VGG19 30 epochs, ResNet50 1 epoch, and ResNet50 30 epochs), two Hybrid models (trained at 1 epoch and 30 epochs), and four Ensemble models (two Ensemble models of VGG19 and ResNet50 and two Ensemble models with an additional Hybrid model). All models are pre-trained on ImageNet. By using transfer learning, the models were fine-tuned (further trained) on the leukemia domain at a much greater speed as the existing layers will have benefitted from the pre-training done on ImageNet. This research indicates that Hybrid models can help improve predictive capabilities by leveraging different feature patterns extracted from running images through two different architectures. Meanwhile, Ensemble models will take the prediction votes from multiple final model outputs to further incorporate different model capabilities and also help generalize. Among all independent models, the best model is ResNet50 30 epochs, which achieved an accuracy of 84%. Among the Hybrid models, the best model is Hybrid 30 epochs, which achieved an accuracy of 84%. Ensemble 4 (Hybrid 30 epochs, VGG19 1 epoch, and ResNet50 1 epoch) achieved an accuracy of 86%, which is 2% better than the second-best model, Ensemble 2. The diagnostic system developed in this research can be used in other medical diagnostic applications.
Listwise Explanations for Ranking Models Using Multiple Explainers

This paper proposes a novel approach towards better interpretability of a trained text-based ranking model in a post-hoc manner. A popular approach for post-hoc interpretability text ranking models are based on locally approximating the model behavior using a simple ranker. Since rankings have multiple relevance factors and are aggregations of predictions, existing approaches that use a single ranker might not be sufficient to approximate a complex model, resulting in low fidelity. In this paper, we overcome this problem by considering multiple simple rankers to better approximate the entire ranking list from a black-box ranking model. We pose the problem of local approximation as a Generalized Preference Coverage (GPC) problem that incorporates multiple simple rankers towards the listwise explanation of ranking models. Our method Multiplex uses a linear programming approach to judiciously extract the explanation terms, so that to explain the entire ranking list. We conduct extensive experiments on a variety of ranking models and report fidelity improvements of 37%–54% over existing competitors. We finally compare explanations in terms of multiple relevance factors and topic aspects to better understand the logic of ranking decisions, showcasing our explainers’ practical utility.
An Experimental Study on Pretraining Transformers from Scratch for IR

Finetuning Pretrained Language Models (PLM) for IR has been de facto the standard practice since their breakthrough effectiveness few years ago. But, is this approach well understood? In this paper, we study the impact of the pretraining collection on the final IR effectiveness. In particular, we challenge the current hypothesis that PLM shall be trained on a large enough generic collection and we show that pretraining from scratch on the collection of interest is surprisingly competitive with the current approach. We benchmark first-stage ranking rankers and cross-encoders for reranking on the task of general passage retrieval on MSMARCO, Mr-Tydi for Arabic, Japanese and Russian, and TripClick for specific domain. Contrary to popular belief, we show that, for finetuning first-stage rankers, models pretrained solely on their collection have equivalent or better effectiveness compared to more general models. However, there is a slight effectiveness drop for rerankers pretrained only on the target collection. Overall, our study sheds a new light on the role of the pretraining collection and should make our community ponder on building specialized models by pretraining from scratch. Last but not least, doing so could enable better control of efficiency, data bias and replicability, which are key research questions for the IR community.
Investigating Conversational Search Behavior for Domain Exploration

Conversational search has evolved as a new information retrieval paradigm, marking a shift from traditional search systems towards interactive dialogues with intelligent search agents. This change especially affects exploratory information-seeking contexts, where conversational search systems can guide the discovery of unfamiliar domains. In these scenarios, users find it often difficult to express their information goals due to insufficient background knowledge. Conversational interfaces can provide assistance by eliciting information needs and narrowing down the search space. However, due to the complexity of information-seeking behavior, the design of conversational interfaces for retrieving information remains a great challenge. Although prior work has employed user studies to empirically ground the system design, most existing studies are limited to well-defined search tasks or known domains, thus being less exploratory in nature. Therefore, we conducted a laboratory study to investigate open-ended search behavior for navigation through unknown information landscapes. The study comprised of 26 participants who were restricted in their search to a text chat interface. Based on the collected dialogue transcripts, we applied statistical analyses and process mining techniques to uncover general information-seeking patterns across five different domains. We not only identify core dialogue acts and their interrelations that enable users to discover domain knowledge, but also derive design suggestions for conversational search systems.
Graph Contrastive Learning with Positional Representation for Recommendation

Recently, graph neural networks have become the state-of-the-art in collaborative filtering, since the interactions between users and items essentially have a graph structure. However, a major issue with the user-item interaction graph in recommendation is the absence of the positional information of users/items, which limits the expressive power of graph recommenders in distinguishing the users/items with the same neighbours after propagating several graph convolution layers. Such a phenomenon further induces the well-known over-smoothing problem. We hypothesise that we can obtain a more expressive graph recommender through graph positional encoding (e.g., Laplacian eigenvector) thereby also alleviating the over-smoothing problem. Hence, we propose a novel model named Positional Graph Contrastive Learning (PGCL) for top-K recommendation, which aims to explicitly enhance graph representation learning with graph positional encoding in a contrastive learning manner. We show that concatenating the learned graph positional encoding and the pre-existing users/items' features in each feature propagation layer can achieve significant effectiveness gains. To further have sufficient representation learning from the graph positional encoding, we use contrastive learning to jointly learn the correlation between the pre-exiting users/items' features and the positional information. Our extensive experiments conducted on three benchmark datasets demonstrate the superiority of our proposed PGCL model over existing state-of-the-art graph-based recommendation approaches in terms of both effectiveness and alleviating the over-smoothing problem.
Adversarially Robust Decision Tree Relabeling

Decision trees are popular models for their interpretation properties and their success in ensemble models for structured data. However, common decision tree learning algorithms produce models that suffer from adversarial examples. Recent work on robust decision tree learning mitigates this issue by taking adversarial perturbations into account during training. While these methods generate robust shallow trees, their relative quality reduces when training deeper trees due the methods being greedy. In this work we propose robust relabeling, a post-learning procedure that optimally changes the prediction labels of decision tree leaves to maximize adversarial robustness. We show this can be achieved in polynomial time in terms of the number of samples and leaves. Our results on 10 datasets show a significant improvement in adversarial accuracy both for single decision trees and tree ensembles. Decision trees and random forests trained with a state-of-the-art robust learning algorithm also benefited from robust relabeling.