Reviewerly/exHarmony huggingface dataset:
Towards Writing Style Adaptation in Handwriting Recognition

One of the challenges of handwriting recognition is to transcribe a large number of vastly different writing styles. State-of-the-art approaches do not explicitly use information about the writer’s style, which may be limiting overall accuracy due to various ambiguities. We explore models with writer-dependent parameters which take the writer’s identity as an additional input. The proposed models can be trained on datasets with partitions likely written by a single author (e.g. single letter, diary, or chronicle). We propose a Writer Style Block (WSB), an adaptive instance normalization layer conditioned on learned embeddings of the partitions. We experimented with various placements and settings of WSB and contrastively pre-trained embeddings. We show that our approach outperforms a baseline with no WSB in a writer-dependent scenario and that it is possible to estimate embeddings for new writers. However, domain adaptation using simple fine-tuning in a writer-independent setting provides superior accuracy at a similar computational cost. The proposed approach should be further investigated in terms of training stability and embedding regularization to overcome such a baseline.
A Comprehensive Overview of IoT-Based Federated Learning: Focusing on Client Selection Methods

The integration of the Internet of Things (IoT) with machine learning (ML) is revolutionizing how services and applications impact our daily lives. In traditional ML methods, data are collected and processed centrally. However, modern IoT networks face challenges in implementing this approach due to their vast amount of data and privacy concerns. To overcome these issues, federated learning (FL) has emerged as a solution. FL allows ML methods to achieve collaborative training by transferring model parameters instead of client data. One of the significant challenges of federated learning is that IoT devices as clients usually have different computation and communication capacities in a dynamic environment. At the same time, their network availability is unstable, and their data quality varies. To achieve high-quality federated learning and handle these challenges, designing the proper client selection process and methods are essential, which involves selecting suitable clients from the candidates. This study presents a comprehensive systematic literature review (SLR) that focuses on the challenges of client selection (CS) in the context of federated learning (FL). The objective of this SLR is to facilitate future research and development of CS methods in FL. Additionally, a detailed and in-depth overview of the CS process is provided, encompassing its abstract implementation and essential characteristics. This comprehensive presentation enables the application of CS in diverse domains. Furthermore, various CS methods are thoroughly categorized and explained based on their key characteristics and their ability to address specific challenges. This categorization offers valuable insights into the current state of the literature while also providing a roadmap for prospective investigations in this area of research.
Knowledge graph completion method based on quantum embedding and quaternion interaction enhancement

Knowledge graphs (KG) are used for many downstream tasks in artificial intelligence (AI). However, owing to accuracy issues associated with information extraction, KGs are often incomplete. This has led to the emergence of knowledge graph completion (KGC) tasks. Their purpose is to learn known facts to infer the missing entities in triples. Traditional embedding-based methods usually only focus on the information of individual triples and do not use the deep logical relationships of the KG. In this study, we propose a new KGC method referred to as QIQE-KGC. It uses quantum embedding and quaternion space interaction to capture the external logical relationship between triples in a KG and enhance the connection between entities and relations within a single triple to model and represent the KG. The proposed QIQE-KGC model can capture richer logical information and has more powerful and complex relationship modeling capabilities. Extensive experimental results using QIQE-KGC on 11 datasets demonstrate that the model achieves outstanding performance. Compared to the baseline models, QIQE-KGC produced the best results on most datasets.
Man or machine – or something in between? Social responses to voice assistants at work and their effects on job satisfaction

AI-enabled technology, such as digital voice assistants (VA), affects employees collaborating with it. This study, first, examines employees' perceptions of autonomy, organizational support, and psychological costs when receiving help either from an AI-enabled VA, a human co-worker, or a conventional computer (technology). Second, it analyzes the effects of these perceptions on job satisfaction. In a between-subjects online vignette experiment, 225 student participants assessed a fictitious yet realistic workplace situation of a side job where they received the same help from one of the three sources. Participants collaborating with the VA or the human co-worker reported less autonomy and more psychological costs than the group collaborating with conventional technology. Regarding perceived organizational support, participants perceived the VA and the computer as more similar to each other: Receiving help from the VA or the computer led to less perceived organizational support, compared to receiving help from a human co-worker. Further, autonomy and organizational support were significantly related to job satisfaction. Results show that the different help-sources exert distinct indirect effects on job satisfaction, mediated by differing perceptions of autonomy and organizational support. The findings have important implications for organizations' understanding of how introducing new technologies may influence employees' perceived job satisfaction.
Understanding the Success Factors of Research Software: Interviews with Brazilian Computer Science Academic Researchers

Context: Research in various domains, such as engineering, sciences, and other fields, depends on software developed in academia by researchers. However, some software becomes a successful case, and others do not. Objective: The goal of this paper is to identify and understand the factors that determine successful research software, as well as investigate the factors of non-success and challenges involved in research software to become successful. Method: An interview among twenty Brazilian academic research experts was conducted. To survey the interview, we used GQM. An analysis of the collected data was conducted according to the Grounded Theory aiming to identify factors exposed by the experts. Results: We identified sixteen success factors divided into two categories among the answers of the experts, eight non-success factors, and five challenges. Conclusion: Our study indicates that the academies can conduct more research software by being aware of the success factors, preventing the non-success, and overcoming the challenges.
The academic status of reviewers predicts their language use

Peer review plays an essential role in scientific research, but the influence of reviewers' academic status is often overlooked during this process. By accessing peer review reports, in this study we empirically investigate this effect. Specifically, we analyzed 2,580 peer review histories from eLife submissions between 2016 and 2021 to examine the relationship between reviewers' academic status and their language usage in the first round of peer review. We focused on two types of language features: emotional features (e.g., positivity and subjectivity) and linguistic features (e.g., number of long words and complex words). Our findings revealed no significant reviewer bias of academic status, such that the reviewers' comments with emotional features were not significantly associated with reviewers' awareness of being more prestigious than the last corresponding author of the manuscripts. More accomplished reviewers, however, were more likely to use longer and more complex words. Additionally, the results of linguistic features remained robust in the group where the last author served as the last corresponding author. Overall, our findings suggest that the quality of peer review remains the primary consideration for reviewers when evaluating submissions. These results have significant implications for open peer review practices and the fair assessment of the peer review process.
Joint dual-stream interaction and multi-scale feature extraction network for multi-spectral pedestrian detection

Multispectral images can provide more information, so pedestrian detection based on multispectral images has received wide attention. Existing multispectral networks mainly focus on the misalignment of image pairs and the difference between modalities. However, these network structures lack effective information interaction between two feature streams and fail to consider the scale characteristics of pedestrian objects. To deal with this issue, we propose a high-performance network structure, which is called dual-stream interaction and multi-scale feature extraction network (DSI-MSE), and contains a dual-stream feature interaction (DSI) block, a multi-scale feature extraction (MSE) block and a detection (DET) block. The DSI block extracts the features through the dual-stream interaction of RGB images and thermal images, which fuses the intra-modal information and the inter-modal information. The MSE block is designed by multiple parallel branches for matching multiple scales of pedestrian, which enhances the expressiveness of features and refines richer feature expressions at different scales. Experimental results on KAIST and CVC-14 datasets demonstrate that the proposed DSI-MSE can obtain the state-of-the-art results on multi-spectral pedestrian detection tasks.
An autonomous task assignment and decision-making method for coverage path planning of multiple pesticide spraying UAVs

As the approaching of Agriculture 4.0 era and advancements of new technologies of Unmanned Aerial Vehicles (UAVs) and particularly quadcopter UAVs, plant protection quadcopters are becoming increasingly popular and practical in pesticide spraying, fertilization, pollination, seeding, and other agricultural activities. One of the main problems for plant protection quadcopters is completing planning tasks efficiently and quickly. Therefore, this paper proposes an autonomous task assignment and decision-making method for coverage path planning by multiple cooperative quadcopters. The Sequential Quadratic Programming (SQP) method is adopted to acquire the optimal solution for the proposed problem. Then, a simulation platform by MATLAB Graphical User Interfaces (GUIs) is established using the Stateflow technique, and multiple ZY-UAV-680 quadrotor UAVs are employed to carry out the actual flight tests. Finally, simulations and actual flight tests are conducted to demonstrate the effectiveness of the autonomous optimal task assignment and decision-making method. The final results show that: the proposed method can divide multiple UAVs reasonably to several blocks; the time differences between the simulation test and real flight test are only 39.8 sec and 20.6 sec, and accounts for 6.6% and 3.9% of the spraying time spent on real flight test by three cooperative quadrotors, respectively; the optimal scheme can save 60.8 sec and 80.0 sec in the simulation tests and the real flight tests, which accounts for 10.8% and 13.2% of the time spent on average scheme, respectively. Therefore, it can be concluded that the simulation results can match the real flight test results quite well regardless of average scheme or optimal scheme, and the optimal scheme is more efficient and timesaving than the average scheme.
Steganography for medical record image

Medical record images in EHR system are users' privacy and an asset, and there is an urgent need to protect this data. Image steganography can offer a potential solution. A steganographic model for medical record images is therefore developed based on StegaStamp. In contrast to natural images, medical record images are document images, which can be very vulnerable to image cropping attacks. Therefore, we use text region segmentation and watermark region localization to combat the image cropping attack. The distortion network has been designed to take into account the distortion that can occur during the transmission of medical record images, making the model robust against communication induced distortions. In addition, based on StegaStamp, we innovatively introduced FISM as part of the loss function to reduce the ripple texture in the steganographic image. The experimental results show that the designed distortion network and the FISM loss function term can be well suited for the steganographic task of medical record images from the perspective of decoding accuracy and image quality.
Scale value guided Lite-FCOS for pointer meter reading recognition

Abstract As intelligent power grid construction advances, substation inspection becomes crucial, particularly in identifying meter readings. Existing meter reading methods are mainly based on the relationship between pointer and scale. However, these methods commonly suffer from the issue of over-relying on prior reading information, limiting readings to known meters. Hereby in this paper, we propose a method reaching more accurate and robust performance for meter reading by utilizing the unnoticed scale value. We determine the meter pointer direction with the aid of Hough transform and the pointer distribution. To detect meters from the scene as well as obtain scale values and pointers from the meter dial, we build an object detection network, named Lite-FCOS, whose backbone adopts a fast global context network that is lightweight and is of powerful feature extraction capabilities. For training these, meter dial detection dataset and dial reading information dataset are constructed. Lite-FCOS achieves 94.4 mAP50 and 96.7 mAP50 on the above two datasets with only 4.2 M parameters and 56.2 FPS. The entire pointer meter reading recognition process only spends 52 ms on an RTX 3080Ti with a successful meter reading rate (SMR rate) of 89.6%, which indicates that the proposed method achieves promising accuracy and speed.
Non-invasive hemoglobin estimation from conjunctival images using deep learning

Hemoglobin, a crucial protein found in erythrocytes, transports oxygen throughout the body. Deviations from optimal hemoglobin levels in the blood are linked to medical conditions, serving as diagnostic markers for certain diseases. The hemoglobin level is usually measured invasively with different devices using the blood sample. In the physical interpretation, some signs are traditionally used. These signs are the palms, face, nail beds, pallor of the conjunctiva, and palmar wrinkles. Studies have shown that conjunctival pallor can yield more effective results in detecting anemia than the pallor of the palms or nail beds. This study is aimed to predict the hemoglobin level by deep learning method, non-invasive, cheap, fast, high accuracy, and without creating medical waste. In this context, conjunctival images and age, weight, height, gender, and hemoglobin values were collected from 388 people who donated blood to the Turkish Red Crescent. A dataset was generated by augmenting the gathered data with body mass index data. Within the scope of this investigation, the limits of agreement (LoA) value at a 95% confidence interval was computed to be 1.23 g/dL, while the bias was established as 0.26 g/dL. The mean absolute percentage error (MAPE) values were determined to be 3.4%, and the root mean squared error (RMSE) was calculated to be 0.68 g/dL. These findings exhibit a successful outcome compared to similar investigations, signifying that this non-invasive method can be employed for hemoglobin level estimation. Furthermore, the estimated hemoglobin levels could aid in diagnosing several hemoglobin-related ailments.
SiamOHOT: A Lightweight Dual Siamese Network for Onboard Hyperspectral Object Tracking via Joint Spatial-Spectral Knowledge Distillation

Hyperspectral object tracking is aimed at tracking targets by using both the spatial information and abundant spectral information, overcoming the drawbacks of traditional RGB tracking in complex scenarios, such as the low resolution or background clutter. However, the current hyperspectral object tracking methods usually have a high computational complexity, due to the huge data volume, making them difficult to apply to real-time applications on edge devices (e.g., robots, unmanned aerial vehicles, and satellites) with limited computational resources. In this paper, a lightweight dual Siamese network for onboard hyperspectral object tracking—termed SiamOHOT—is proposed for real-time and onboard tracking. Specifically, a joint spatial-spectral knowledge distillation method is proposed to teach a lightweight dual Siamese tracker to learn from a deep tracker— SiamHYPER—so that the number of parameters can be compressed to improve the computational efficiency. In addition, a deep learning inference optimizer is introduced to fuse the layers with similar functions and quantify the parameters of the network, to further promote the processing speed when deployed on an embedded platform. The proposed lightweight model was verified using the 2021 WHISPERS Hyperspectral Object Tracking Challenge dataset, and achieved a superior efficiency and accuracy. In addition, a prototype system was built integrating a snapshot hyperspectral imager, the SiamOHOT tracking algorithm, and an artificial intelligence edge device (NVIDIA Jetson Xavier NX), to realize real-time imaging and tracking. The inference speed of the optimized SiamOHOT network is nearly doubled when compared to the teacher model on the prototype system.
Enhanced Change Detection Performance Based on Deep Despeckling of Synthetic Aperture Radar Images

Synthetic aperture radar (SAR) image change detection (CD) focuses on identifying changes between two images at different times for the same geographical region. Recently, several deep learning methods have been proposed for performing SAR based CD. However, speckle noise remains a major challenge for these methods. To address this, we propose a despeckling model (DM) that effectively suppresses speckle noise and enhances the performance of the existing CD methods. The proposed despeckling architecture is not only resilient to multi-temporal SAR acquired from one SAR imaging process (i.e., the same number of SAR images looks before and after the change) but also deals with any combination of single or multi-look images acquired prior and after the change. Moreover, as a second contribution, we have also proposed a loss function that effectively suppresses speckle noise, thereby improving the change detection accuracy. Both the despeckling model and the proposed tolerant noise loss function are evaluated extensively on three public real SAR datasets, achieving superior performance compared to existing state-of-the-art SAR CD methods in all datasets.
An Improved AdaBoost Algorithm for Highly Imbalanced Datasets in the Co-Authorship Recommendation Problem

The co-authorship recommendation problem is attractive since it helps researchers extend collaboration to improve the quality of scientific articles as well as promote innovation.This problem involves suggesting authors join research groups based on their research interests, areas of expertise, and past collaborative experiences to write scientific articles together.In this paper, we tackle the co-authorship recommendation problem by modeling it as a co-authorship network, where each author is represented as a vertex, and each collaboration between two authors is represented as an edge.Since the number of author pairs without collaboration is much larger than those with collaboration, datasets created from co-authorship networks are typically two-class imbalanced datasets.Accordingly, we propose an improved algorithm of AdaBoost combined with the W-SVM algorithm, called Im.AdaBoost.W-SVM, to solve the classification problem with two-class imbalanced datasets.To evaluate the performance of our Im.AdaBoost.W-SVM algorithm for the co-authorship recommendation problem, we collected author and article data from the website www.sciencedirect.comthrough ScienceDirect APIs and created two-class imbalanced datasets.Our experimental results for our self-built co-authorship datasets with different sizes and imbalance ratios showed that our Im.AdaBoost.W-SVM algorithm outperforms the AdaBoost.DecisionTree and AdaBoost.W-SVM algorithms for the co-authorship recommendation problem.
Multifactor Incentive Mechanism for Federated Learning in IoT: A Stackelberg Game Approach

In the era of the Internet of Things (IoT), remote sensors and endpoint appliances generate vast amounts of data. Decentralized and collaborative learning builds on these IoT data to enable classification and recognition tasks by inviting multiple data owners. Federated learning (FL), as a popular collaborative learning framework, can significantly improve the performance of models without collecting the original data. To invite data owners to participate in FL, various incentive mechanisms are designed to address this issue by researchers. However, existing solutions still face high costs and low utility due to information asymmetry, where the reputation, computation power, and data quantity of the data owners are not known in advance. Therefore, we propose a Stackelberg game-based multifactor incentive mechanism for FL (SGMFIFL). First, we design the Top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> cost selection algorithm based on reverse auction, which can reduce the cost of selecting data owners. Next, we devise a multifactor reward function based on reputation, accuracy, and reward rate, the data owners with high reputation and high accuracy will be of more reward. In particular, to ensure that SGMFIFL can provide reliable incentives in IoT, we use blockchain to provide a secure and trusted environment. Finally, we construct a two-stage Stackelberg game model for the task publisher and the data owners and derive an optimal Equilibrium solution for both stages of the whole game. Experiments conducted on two well-known data sets, MNIST and CIFAR10, demonstrate the significant performance of the proposed mechanism.