Reviewerly/exHarmony huggingface dataset:
AFAFed—Asynchronous Fair Adaptive Federated learning for IoT stream applications

In this paper, we design, analyze the convergence properties, address the implementation aspects, and numerically test the performance of AFAFed. This is a novel Asynchronous Fair Adaptive Federated learning framework for stream-oriented IoT application environments, which are featured by time-varying operating conditions, heterogeneous resource-limited devices (i.e., coworkers), non-i.i.d. local training data and unreliable communication links. The key new of AFAFed is the synergic co-design of: (i) two sets of adaptively tuned tolerance thresholds and fairness coefficients at the coworkers and central server, respectively; and, (ii) a distributed adaptive mechanism, which allows each coworker to adaptively tune own communication rate. The convergence properties of AFAFed under (possibly) non-convex loss functions is guaranteed by a set of new analytical bounds, which formally unveil the impact on the resulting AFAFed convergence rate of a number of Federated Learning (FL) parameters, like, first and second moments of the per-coworker number of consecutive model updates, data skewness, communication packet-loss probability, and maximum/minimum values of the (adaptively tuned) mixing coefficient used for model aggregation. Extensive numerical tests show that AFAFed is capable to improve test accuracy by up to 20% and reduce training time by up to 50%, compared to state-of-the-art FL schemes, even under challenging learning scenarios featured by deep Machine Learning (ML) models, data skewness, coworker heterogeneity and unreliable communication.
SGT: Scene Graph-Guided Transformer for Surgical Report Generation

The robotic surgical report reflects the operations during surgery and relates to the subsequent treatment. Therefore, it is especially important to generate accurate surgical reports. Given that there are numerous interactions between instruments and tissue in the surgical scene, we propose a Scene Graph-guided Transformer (SGT) to solve the issue of surgical report generation. The model is based on the structure of transformer to understand the complex interactions between tissue and the instruments from both global and local perspectives. On the one hand, we propose a relation driven attention to facilitate the comprehensive description of the interaction in a generated report via sampling of numerous interactive relationships to form a diverse and representative augmented memory. On the other hand, to characterize the specific interactions in each surgical image, a simple yet ingenious approach is proposed for homogenizing the input heterogeneous scene graph, which plays an effective role in modeling the local interactions by injecting the graph-induced attention into the encoder. The dataset from clinical nephrectomy is utilized for performance evaluation and the experimental results show that our SGT model can significantly improve the quality of the generated surgical medical report, far exceeding the other state-of-the-art methods. The code is public available at: https://github.com/ccccchenllll/SGT_master.
Stereo Depth Estimation via Self-supervised Contrastive Representation Learning

Accurate stereo depth estimation is crucial for 3D reconstruction in surgery. Self-supervised approaches are more preferable than supervised approaches when limited data is available for training but they can not learn clear discrete data representations. In this work, we propose a two-phase training procedure which entails: (1) Performing Contrastive Representation Learning (CRL) of left and right views to learn discrete stereo features (2) Utilising the trained CRL model to learn disparity via self-supervised training based on the photometric loss. For efficient and scalable CRL training on stereo images we introduce a momentum pseudo-supervised contrastive loss. Qualitative and quantitative performance evaluation on minimally invasive surgery and autonomous driving data shows that our approach achieves higher image reconstruction score and lower depth error when compared to state-of-the-art self-supervised models. This verifies that contrastive learning is effective in optimising stereo-depth estimation with self-supervised models.
A Smart-Contract Enabled Blockchain Traceability System Against Wine Supply Chain Counterfeiting

The need for effective traceability in the wine industry has dramatically increased in recent years due to the growing consumer awareness of the safety and the quality of products they buy. The loss of visibility as to the origin of a product can cause significant disruptions in the production and distribution of goods but also poses serious fraud risks. More specifically, bad or counterfeit wines may be produced and marketed when they can't be traced at all stages effectively. Fraud, such as adulteration with low-quality raw materials and wines, dilution, replacement of packaging contents and forgery of packages and labels, can be detected at the various stages of the wine supply chain. In order to combat wine supply chain counterfeiting blockchain technology has gained increased popularity as a means of enabling a secure traceability system. This paper presents the development of a smart-contracts-enabled blockchain traceability system that has been specifically designed for the wine supply chain and operates through the Ethereum Network.
Document-level event argument extraction with self-augmentation and a cross-domain joint training mechanism

Document-level event argument extraction (EAE), a critical task for event knowledge acquisition, seeks to find event arguments beyond the sentence level. Previous approaches for this task adopt supervised learning, which suffers from the data scarcity issue, especially in low-resource situations. To address this challenge, we propose a new method for document-level EAE that addresses the data scarcity from two perspectives: self-augmentation and cross-domain joint training. On the one hand, our method can generate additional training samples from an existing dataset using pre-trained language models, considerably expanding the training set; on the other hand, our method can learn with out-of-domain datasets with varying event schema, using a cross-domain joint training framework. To address data quality problems and domain mismatches, we also provide a new noise filtering method based on a teacher-student framework. The extensive experiments demonstrate that our approach delivers state-of-the-art performance on benchmark datasets, and that it is especially effective in low-resource situations. • A new approach for resolving the data sparsity issue in document-level event argument extraction from two complementing viewpoints. • A self-augmentation technique that combines pre-trained language models and a label-conditioned pre-training procedure to retain word-label consistency. • A cross-domain joint training framework for knowledge transfer from datasets of varied granularity, task specifications, and the event schema description language. • A novel noise filtering method in teacher-student framework to mitigate the data quality issue. • State-of-the-art performance on standard datasets.
A Theoretical Model Characterizing Tangle Evolution in IOTA Blockchain Network

IOTA blockchain system is lightweight without heavy proof-of-work mining phases, which is considered a promising service platform of Internet of Things applications. IOTA organizes ledger data in a directed acyclic graph (DAG), called Tangle, rather a chain structure as in traditional blockchains. With arriving messages, IOTA tangle grows in a special way, as multiple messages can be attached to the tangle at different locations in parallel. Hence, the network dynamics of an operational IOTA system would justify a thorough study, which is currently unexplored in the literature. In this article, we present the first theoretical modeling for the evolving IOTA tangle based on stochastic analysis. After analyzing snapshots of the real-world IOTA ledger data, our key finding suggests that IOTA tangle follows a rather atypical double Pareto Lognormal (dPLN) degree distribution. In contrast, typical power-law and exponential distributions do not accurately reflect the fact. For model parameter estimation, we further realize that using generic optimization solvers cannot yield quality fitting results. Thus, we design an alternative algorithm based on expectation-maximization (EM) framework. We evaluate the proposed model and fitting algorithm with official data provided by the IOTA Foundation. Quantitative comparisons confirm the fitting quality of our proposed model and algorithm. The whole analysis reveals a deeper understanding of the internal mechanism of the IOTA network.
Blockchain-based Solutions for Education Credentialing System: Comparison and Implications for Future Development

Blockchain technology is reshaping fundamental structures in many industries. Similarly, there is an increasing interest in adopting blockchain as a new solution to addressing educational credentialing problems. However, none of the existing blockchain initiatives in educational credentialing seems to meet their original goal of global adoption. Based on the Australian tertiary education credentialing industry, this paper presents an abstract credentialing workflow and involved stakeholders, identifies six practical problems in the industry, and proposes five desired attributes of ideal credentials infrastructure. In addition, the paper presents a layered framework for evaluating seven blockchain-based education projects, and provides insights into factors hindering the wide adoption of these blockchain solutions. Finally, we suggest future considerations for the application development of blockchain-based education solutions.
Realtime Weather Prediction System Using GRU with Daily Surface Observation Data from IoT Sensors

Weather is an essential part of everyone's life, but its dynamic nature makes weather forecasting difficult. In this research, we propose a system that makes daily weather predictions in Sedati, an administrative district in the Sidoarjo Regency, Indonesia. The system uses Gated Recurrent Unit (GRU) algorithm that is fed with historical weather data from the BMKG Juanda Meteorological Station. On the test data, the GRU regression model resulted in the best average RMSE for four days' worth of prediction with humidity, wind speed, temperature, sea level pressure, maximum wind speed, minimum temperature, maximum temperature, and dew point at 3.7325; 1.31; 0.64; 0.85; 2,065; 0.7125; 0.6825; and 0.705. However, the average RMSE score of those variables after we integrated the model with local IoT devices were 5.6; 2.3; 1.56; 0.57; 2.47; 3.04; 1.78; and 0.55, respectively. The sequence GRU model for rain classification achieved an accuracy of 0.88, and the accuracy after the model was integrated with the IoT devices was 0.75.
Simulation and Comparison Between Fuzzy Harmonic Search and Differential Evolution Algorithm: Type-2 Fuzzy Approach

The harmony search (HS) and differential evolution (DE) algorithms are compared in this study. Additionally, an interval type-2 fuzzy logic system (IT2FLS) allowing dynamic change of the key parameters is offered for each algorithm. The use of fuzzy systems to dynamically alter the primary parameters for each algorithm seeks to improve the performance of the associated algorithms. The optimal design of fuzzy systems for benchmark control issues, particularly in fuzzy controller design, is used to evaluate and compare each algorithm (IT2FHS and IT2FDE). Simulation results demonstrate that the FHS method outperforms the FDE approach when it comes to fuzzy controller optimization. The better errors are found with the application of fuzzy systems to enhance each proposed algorithm, according to statistics.
Towards Zero-Shot Learning: A Brief Review and an Attention-Based Embedding Network

Zero-shot learning (ZSL), an emerging topic in recent years, targets at distinguishing unseen class images by taking images from seen classes for training the classifier. Existing works often build embeddings between global feature space and attribute space, which, however, neglect the treasure in image parts. Discrimination information is usually contained in the image parts, e.g., black and white striped area of a zebra is the key difference from a horse. As such, image parts can facilitate the transfer of knowledge among the seen and unseen categories. In this paper, we first conduct a brief review on ZSL with detailed descriptions of these methods. Next, to discover meaningful parts, we propose an end-to-end attention-based embedding network for ZSL, which contains two sub-streams: the attention part embedding (APE) stream, and the attention second-order embedding (ASE) stream. APE is used to discover multiple image parts based on attention. ASE is introduced for ensuring knowledge transfer stably by second-order collaboration. Furthermore, an adaptive thresholding strategy is proposed to suppress noise and redundant parts. Finally, a global branch is incorporated for the full use of global information. Experiments on four benchmarks demonstrate that our models achieve superior results under both ZSL and GZSL settings.
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In this paper, we propose three new classes of permutation trinomials over Fq3. The first two classes are of the form x+L(xq2+q−1), where L(x)∈{x+Axq,x+Axq2}, q even, and the third class is x+Axq2−q+1+A2xq2, q odd. In fact we prove a conjecture proposed by Gong, Gao and Liu [8] about permutation trinomials of the form xq+1+L(x)∈Fq3[x] as a particular case of our results. Moreover, we determine necessary and sufficient conditions for the permutation trinomials studied. We also show that the permutation trinomials proposed in this paper are new in the sense that they are not quasi-multiplicative equivalent to permutation trinomials over Fq3 known so far.
Secure and Practical Wildcard Searchable Encryption System Based on Inner Product

As cloud computing is widely used in a variety of different applications, how to continuously improve the efficiency of processing data while protecting data privacy and security is still a problem worth investigating. Existing searchable encryption schemes mainly focus on the exact matching patterns, which are insufficient to meet and cope with the increasing demands for fuzzy search. In particular, there has been a lack of attention to the research on practical and privacy-preserving fully wildcard searchable encryption. In this article, we propose a new wildcard searchable encryption system, which is built based on a novel inner product encryption scheme that can address the above challenges simultaneously. A two-dimensional inner product relation is introduced to remove all restrictions on wildcards, allowing our system to support fully wildcard search. To resist active attacks from malicious adversaries and prevent information leakage, we enhance the security to adaptive simulation-based security under the SXDH assumption. The corresponding security proof of our construction is implicitly established on a hash proof system. Our elaborately constructed system removes redundant parameters and extra complex computations, ensuring its practicality and efficiency. Finally, our system shows better performance in more than 86% of cases in the conducted experiments.
An Improved Cascade R-CNN-Based Target Detection Algorithm for UAV Aerial Images

Target detection in the UAV aerial images is a challenging task in computer vision. Compared with traditional images, UAV images have the characteristics of small and dense targets and complex scenes, which have been a difficult task for target detection. In this paper, an improved algorithm based on cascade R-CNN is proposed to add superclass detection on top of the original one, and then fuse the regression confidence and modify the loss function to enhance the detection capability of targets. Experiments are conducted on Visdrone dataset, and the experimental results show that our designed algorithm is simple and has good performance, which can effectively improve the detection accuracy of aerial photography targets while ensuring real-time.
IRNet: An Improved RetinaNet Model for Face Detection

The current face detection methods mainly focus on overlaying network layers to improve the detection accuracy. However, in practical applications, these huge models cannot achieve the real-time detection. In order to solve the aforementioned problems, an improved RetinaNet (IRNet) model for face detection is proposed. In the field of face detection, feature fusion module is a common method to solve multi-scale detection problems. In this paper, a new feature fusion module N-FPN is introduced. The N-FPN module has been proven to improve detection accuracy. We design experiments to select the appropriate weight decay parameters. The IRNet finally achieves 91.77%, 89.26%, and 76.59% detection results on Easy, Medium, and Hard datasets of Wider Face, respectively. In addition, the IRNet obtained better detection results than RetinaNet on the FDDB dataset.
LiftChain: A Scalable Multi-Stage NFT Transaction Protocol

The Non-fungible token (NFT) market has been booming with some reports estimating the surge in the market value to over 80 Billion USD in 2025. With this surge, comes the challenge of scaling NFT transactions and providing low latency responses to end-users. Exclusive layer-1 blockchains like Bitcoin and Ethereum which focus more on security and traceability are not suitable for high throughput NFT transactions of low value due to high gas fees. Layer-2 solutions can scale but have a trade-off of losing some strong decentralization and security guarantees offered by the Mainnet. In this paper, we propose LiftChain, a multi-stage NFT transaction protocol that can scale for high volume NFT transactions and at the same time inherit the security guarantees from Mainnet. LiftChain differs from existing NFT transaction protocols in that it allows multiple NFT transactions in the off-chain before becoming consistent with the on-chain. LiftChain also uses batching for gas fee savings and better bandwidth utilization. Our evaluations show that LiftChain provides comparable performance to baseline off-chain and with batching we see more than a 5-fold improvement in gas fee savings.
Mitigating Popularity Bias for Users and Items with Fairness-centric Adaptive Recommendation

Recommendation systems are popular in many domains. Researchers usually focus on the effectiveness of recommendation (e.g., precision) but neglect the popularity bias that may affect the fairness of the recommendation, which is also an important consideration that could influence the benefits of users and item providers. A few studies have been proposed to deal with the popularity bias, but they often face two limitations. Firstly, most studies only consider fairness for one side—either users or items, without achieving fairness jointly for both. Secondly, existing methods are not sufficiently tailored to each individual user or item to cope with the varying extent and nature of popularity bias. To alleviate these limitations, in this paper, we propose FAiR , a f airness-centric model that a dapt i vely mitigates the popularity bias in both users and items for r ecommendation. Concretely, we design explicit fairness discriminators to mitigate the popularity bias for each user and item locally, and an implicit discriminator to preserve fairness globally. Moreover, we dynamically adapt the model to different input users and items to handle the differences in their popularity bias. Finally, we conduct extensive experiments to demonstrate that our model significantly outperforms state-of-the-art baselines in fairness metrics, while remaining competitive in effectiveness.
Early Detection of Obstacle to Optimize the Robot Path Planning

Robot path planning is one of the core issues in robotics and its application. Optimizing the route discovery becomes more important while dealing with the robot-based application. This paper proposes the concept of early detection of the obstacle present in the workspace of the robots. To early detect the obstacle, this paper proposes the concept of a snake algorithm along with the traditional path planning algorithms. The contour detection part is merged with the different path planning algorithms to optimize the robot traversing and benefit it in producing good results. Obstacle-free optimized path is one of the core requirements for robots in any application. With the help of path planning algorithms, robots are enabled to derive those paths in a specific environment. The presence of an obstacle makes it difficult for any path planning algorithms to derive a smooth path. The purpose of using the snake algorithm is to detect an obstacle early. This method not only perceives the obstacle but also catches out the complete boundary of the obstacle, it, thus, provides the details of obstacle coordinates to the path planning algorithm. Conceiving the complete periphery of obstacles can have multiple advantages in many application areas. A*, PRM, RRT, and RRT Smooth algorithms are considered along with the snake algorithm to validate our work in three different experimental scenarios: Maze, Random Obstacles, and Dense case. Path length, Time-taken, and Move count are parameters taken to observe the results. The result obtained using the snake algorithm with four path planning algorithms is analyzed and compared in detail with the core A*, PRM, RRT, and RRTS. Finally, the result obtained using the proposed methodology gives some encouraging results and also predicts the exploration of the robot’s path planning for more applications and fields.
Deep Learning Based Cross Domain Sentiment Classification for Urdu Language

Sentiment analysis is a widely researched area due to its various applications in customer services, brand monitoring, and market research.Automatic sentiment classification is an important but challenging task.Contrary to the English language, sentiment analysis for low-resource languages like Urdu is an under-explored research area.Most of the work on sentiment analysis in the Urdu language is domain-dependent where models are mostly trained and tested on the same dataset on limited domains.However, sentiments in different domains are expressed differently, and manually annotating the datasets for all possible domains is unfeasible.Training a sentiment classifier using annotated data on one domain and testing it on another domain results in poor performance as the terms appearing in the source domain (training data) might not appear in the target (testing data) domain.In this paper, we present a baseline method for cross-domain sentiment analysis in the Urdu language using two different domains.Feature extraction is performed using n-grams and word embedding techniques.Sentiment classification is performed using machine learning and deep learning classifiers.The proposed method achieves an accuracy, precision, recall, and F1 scores of 0.77, 0.83, 0.68, and 0.75, respectively.