Reviewerly/exHarmony huggingface dataset:
Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning

Nowadays, deep learning methods with large-scale datasets can produce clinically useful models for computer-aided diagnosis. However, the privacy and ethical concerns are increasingly critical, which make it difficult to collect large quantities of data from multiple institutions. Federated Learning (FL) provides a promising decentralized solution to train model collaboratively by exchanging client models instead of private data. However, the server aggregation of existing FL methods is observed to degrade the model performance in real-world medical FL setting, which is termed as retrogress. To address this problem, we propose a personalized retrogress-resilient framework to produce a superior personalized model for each client. Specifically, we devise a Progressive Fourier Aggregation (PFA) at the server to achieve more stable and effective global knowledge gathering by integrating client models from low-frequency to high-frequency gradually. Moreover, with an introduced deputy model to receive the aggregated server model, we design a Deputy-Enhanced Transfer (DET) strategy at the client and conduct three steps of Recover-Exchange-Sublimate to ameliorate the personalized local model by transferring the global knowledge smoothly. Extensive experiments on real-world dermoscopic FL dataset prove that our personalized retrogress-resilient framework outperforms state-of-the-art FL methods, as well as the generalization on an out-of-distribution cohort. The code and dataset are available at https://github.com/CityU-AIM-Group/PRR-FL.
Blockchain Technology for Trustworthy Operations in the Management of Strategic Grain Reserves

Food is a daily requirement for everyone, while production patterns are seasonal. Producing sufficient nutrients is becoming more difficult because water and soil resources are already stressed and are becoming increasingly strained by climate change. Improving food security requires expertise in various areas, including sophisticated climate models, genetics research, market and household behaviour modelling, political shock modelling, and comprehensive environmental research. Additionally, governments stockpile grains to enhance national food security. These reserves should engage in markets only according to clear and transparent regulations and within defined price ranges to facilitate market functioning. It increases the demand for better technology in public administration to boost the management and distribution capacity while concentrating on improved controls and transparent governance systems. Blockchain technology emerges as a promising technology to enhance visibility, transparency, and data integrity with an immutable distributed ledger to increase trust in the parties' business transactions. This paper discusses blockchain technology and its potential role in strategic grain reserve management.
Do teachers’ educational philosophies affect their digital literacy? The mediating effect of resistance to change

This study aims to reveal the direct and indirect effects of primary school teachers' educational philosophies on their digital literacy through resistance to change. A cross-sectional research design was used in the current study. Data were collected from 298 primary school teachers working at primary schools located in a city centre in the Mediterranean region of Turkey. The relationships between the variables were tested through the use of the structural equation model. The study results indicated that the teachers' traditional and contemporary educational philosophies did not directly affect their digital literacy levels, but indirectly affected their resistance to change through the level of resistance to change. The results also suggested that teachers' traditional educational philosophies had a negative effect on their digital literacy levels through the mediating role of resistance to change, while the contemporary educational philosophies positively affected their digital literacy levels through resistance to change. Consequently, in recent conditions teachers are expected to develop both their own and students' digital skills. Due to the importance of digital literacy in this age where technology permeates every aspect of life, teachers should accept digital change and transformation without resistance.
HarrisZ+: Harris corner selection for next-gen image matching pipelines

Due to its role in many computer vision tasks, image matching has been subjected to an active investigation by researchers, which has lead to better and more discriminant feature descriptors and to more robust matching strategies, also thanks to the advent of the deep learning and the increased computational power of the modern hardware. Despite of these achievements, the keypoint extraction process at the base of the image matching pipeline has not seen equivalent progresses. This paper presents HarrisZ$^+$, an upgrade to the HarrisZ corner detector, optimized to synergically take advance of the recent improvements of the other steps of the image matching pipeline. HarrisZ$^+$ does not only consists of a tuning of the setup parameters, but introduces further refinements to the selection criteria delineated by HarrisZ, so providing more, yet discriminative, keypoints, which are better distributed on the image and with higher localization accuracy. The image matching pipeline including HarrisZ$^+$, together with the other modern components, obtained in different recent matching benchmarks state-of-the-art results among the classic image matching pipelines. These results are quite close to those obtained by the more recent fully deep end-to-end trainable approaches and show that there is still a proper margin of improvement that can be granted by the research in classic image matching methods.
Player tracking and identification in ice hockey

Tracking and identifying players is a fundamental step in computer vision-based ice hockey analytics. The data generated by tracking is used in many other downstream tasks, such as game event detection and game strategy analysis. Player tracking and identification is a challenging problem since the motion of players in hockey is fast-paced and non-linear when compared to pedestrians. There is also significant camera panning and zooming in hockey broadcast video. Identifying players in ice hockey is challenging since the players of the same team appear almost identical, with the jersey number the only consistent discriminating factor between players. To address this problem, an automated system to track and identify players in broadcast National Hockey League (NHL) videos is introduced. The system is composed of three components (1) player tracking, (2) team identification and (3) player identification. Due to the absence of publicly available datasets, the datasets used to train the three components are annotated manually. Player tracking is performed with the help of a state of the art tracking algorithm obtaining a Multi-Object Tracking Accuracy (MOTA) score of 94.5%. For team identification, away-team jerseys are grouped into a single class and home-team jerseys are grouped in classes according to their jersey color. A convolutional neural network is then trained on the team identification dataset. The team identification network obtains an accuracy of 97% on the test set. A novel player identification model is introduced that utilizes a temporal one-dimensional convolutional network to identify players from player bounding box sequences. The player identification model further takes advantage of the available NHL game roster data to obtain a player identification accuracy of 83%.
Breast Cancer Detection and Prediction using Machine Learning

Cancer death is one of humanity's major problems in the developing world. Even though there are numerous ways to prevent it from occurring in the first place, some cancer types remain unrepeatable. Due to the absence of adequate forecasting, clinicians are unable to devise a treatment plan that will improve patient mortality rate. Hence, the requisite of time is to develop the technique which gives minimum error to increment precision. Three algorithms SVM, CNN and KNN which prognosticate the breast cancer outcome have been compared in the project utilizing different datasets. All experiments are executed within a simulation environment and conducted in PyCharm, Anaconda platform. Aim of research categorizes in three domains. First domain is presage of cancer and second domain is presage of diagnosis and treatment and third domain fixates on outcome during treatment. The proposed work can be used to predict the outcome of different techniques and suitable techniques can be used depending upon requirement. This project is carried out to predict, detect and analyze the accuracy of breast cancer. Future research can be done to predict other variables, and breast cancer research can be categorized based on these variables. It is possible to draw inferences from the facts obtained in this study about whether or not the patient has a breast tumor.
Learning with Noise: Mask-Guided Attention Model for Weakly Supervised Nuclei Segmentation

Deep convolutional neural networks have been highly effective in segmentation tasks. However, high performance often requires large datasets with high-quality annotations, especially for segmentation, which requires precise pixel-wise labelling. The difficulty of generating high-quality datasets often constrains the improvement of research in such areas. To alleviate this issue, we propose a weakly supervised learning method for nuclei segmentation that only requires annotation of the nuclear centroid. To train the segmentation model with point annotations, we first generate boundary and superpixel-based masks as pseudo ground truth labels to train a segmentation network that is enhanced by a mask-guided attention auxiliary network. Then to further improve the accuracy of supervision, we apply Confident Learning to correct the pseudo labels at the pixel-level for a refined training. Our method shows highly competitive performance of cell nuclei segmentation in histopathology images on two public datasets. Our code is available at: https://github.com/RuoyuGuo/MaskGA_Net.
Accelerating Encrypted Computing on Intel GPUs

Homomorphic Encryption (HE) is an emerging encryption scheme that allows computations to be performed directly on encrypted messages. This property provides promising applications such as privacy-preserving deep learning and cloud computing. Prior works have been proposed to enable practical privacy-preserving applications with architectural-aware optimizations on CPUs, CUDA-enabled GPUs and FPGAs. However, there is no systematic optimization for the whole HE pipeline on Intel GPUs. In this paper, we present the first-ever SYCL-based GPU backend for Microsoft SEAL APIs. We perform optimizations from instruction level, algorithmic level and application level to accelerate our HE library based on the Cheon, Kim, Kim and Song (CKKS) scheme on Intel GPUs. The performance is validated on two latest Intel GPUs. Experimental results show that our staged optimizations together with optimizations including low-level optimizations and kernel fusion accelerate the Number Theoretic Transform (NTT), a key algorithm for HE, by up to 9.93X compared with the naive GPU baseline. The roofline analysis confirms that our optimized NTT reaches 79.8% and 85.7% of the peak performance on two GPU devices. Through the highly optimized NTT and the assembly-level optimization, we obtain 2.32X – 3.05X acceleration for HE evaluation routines. In addition, our all-together systematic optimizations improve the performance of encrypted element-wise polynomial matrix multiplication application by up to 3.11X.
A Deep Discontinuity-Preserving Image Registration Network

Image registration aims to establish spatial correspondence across pairs, or groups of images, and is a cornerstone of medical image computing and computer-assisted-interventions. Currently, most deep learning-based registration methods assume that the desired deformation fields are globally smooth and continuous, which is not always valid for real-world scenarios, especially in medical image registration (e.g. cardiac imaging and abdominal imaging). Such a global constraint can lead to artefacts and increased errors at discontinuous tissue interfaces. To tackle this issue, we propose a weakly-supervised Deep Discontinuity-preserving Image Registration network (DDIR), to obtain better registration performance and realistic deformation fields. We demonstrate that our method achieves significant improvements in registration accuracy and predicts more realistic deformations, in registration experiments on cardiac magnetic resonance (MR) images from UK Biobank Imaging Study (UKBB), than state-of-the-art approaches.
mDALU: Multi-Source Domain Adaptation and Label Unification with Partial Datasets

One challenge of object recognition is to generalize to new domains, to more classes and/or to new modalities. This necessitates methods to combine and reuse existing datasets that may belong to different domains, have partial annotations, and/or have different data modalities. This paper formulates this as a multi-source domain adaptation and label unification problem, and proposes a novel method for it. Our method consists of a partially-supervised adaptation stage and a fully-supervised adaptation stage. In the former, partial knowledge is transferred from multiple source domains to the target domain and fused therein. Negative transfer between unmatching label spaces is mitigated via three new modules: domain attention, uncertainty maximization and attention-guided adversarial alignment. In the latter, knowledge is transferred in the unified label space after a label completion process with pseudolabels. Extensive experiments on three different tasks - image classification, 2D semantic image segmentation, and joint 2D-3D semantic segmentation - show that our method outperforms all competing methods significantly.
Visual-Assisted Probe Movement Guidance for Obstetric Ultrasound Scanning Using Landmark Retrieval

Automated ultrasound (US)-probe movement guidance is desirable to assist inexperienced human operators during obstetric US scanning. In this paper, we present a new visual-assisted probe movement technique using automated landmark retrieval for assistive obstetric US scanning. In a first step, a set of landmarks is constructed uniformly around a virtual 3D fetal model. Then, during obstetric scanning, a deep neural network (DNN) model locates the nearest landmark through descriptor search between the current observation and landmarks. The global position cues are visualised in real-time on a monitor to assist the human operator in probe movement. A Transformer-VLAD network is proposed to learn a global descriptor to represent each US image. This method abandons the need for deep parameter regression to enhance the generalization ability of the network. To avoid prohibitively expensive human annotation, anchor-positive-negative US image-pairs are automatically constructed through a KD-tree search of 3D probe positions. This leads to an end-to-end network trained in a self-supervised way through contrastive learning.
Comprehensible Convolutional Neural Networks via Guided Concept Learning

Learning concepts that are consistent with human perception is important for Deep Neural Networks to win end-user trust. Post-hoc interpretation methods lack transparency in the feature representations learned by the models. This work proposes a guided learning approach with an additional concept layer in a CNN-based architecture to learn the associations between visual features and word phrases. We design an objective function that optimizes both prediction accuracy and semantics of the learned feature representations. Experiment results demonstrate that the proposed model can learn concepts that are consistent with human perception and their corresponding contributions to the model decision without compromising accuracy. Further, these learned concepts are transferable to new classes of objects that have similar concepts.
Learning Dual Priors for JPEG Compression Artifacts Removal

Deep learning (DL)-based methods have achieved great success in solving the ill-posed JPEG compression artifacts removal problem. However, as most DL architectures are designed to directly learn pixel-level mapping relationship-s, they largely ignore semantic-level information and lack sufficient interpretability. To address the above issues, in this work, we propose an interpretable deep network to learn both pixel-level regressive prior and semantic-level discriminative prior. Specifically, we design a variational model to formulate the image de-blocking problem and propose two prior terms for the image content and gradient, respectively. The content-relevant prior is formulated as a DL-based image-to-image regressor to perform as a de-blocker from the pixel-level. The gradient-relevant prior serves as a DL-based classifier to distinguish whether the image is compressed from the semantic-level. To effectively solve the variational model, we design an alternating minimization algorithm and unfold it into a deep network architecture. In this way, not only the interpretability of the deep network is increased, but also the dual priors can be well estimated from training samples. By integrating the two priors into a single framework, the image de-blocking problem can be well-constrained, leading to a better performance. Experiments on benchmarks and real-world use cases demonstrate the superiority of our method to the existing state-of-the-art approaches.
PDWN: Pyramid Deformable Warping Network for Video Interpolation

Video interpolation aims to generate a non-existent intermediate frame given the past and future frames. Many state-of-the-art methods achieve promising results by estimating the optical flow between the known frames and then generating the backward flows between the middle frame and the known frames. However, these methods usually suffer from the inaccuracy of estimated optical flows and require additional models or information to compensate for flow estimation errors. Following the recent development in using deformable convolution (DConv) for video interpolation, we propose a light but effective model, called Pyramid Deformable Warping Network (PDWN). PDWN uses a pyramid structure to generate DConv offsets of the unknown middle frame with respect to the known frames through coarse-to-fine successive refinements. Cost volumes between warped features are calculated at every pyramid level to help the offset inference. At the finest scale, the two warped frames are adaptively blended to generate the middle frame. Lastly, a context enhancement network further enhances the contextual detail of the final output. Ablation studies demonstrate the effectiveness of the coarse-to-fine offset refinement, cost volumes, and DConv. Our method achieves better or on-par accuracy compared to state-of-the-art models on multiple datasets while the number of model parameters and the inference time are substantially less than previous models. Moreover, we present an extension of the proposed framework to use four input frames, which can achieve significant improvement over using only two input frames, with only a slight increase in the model size and inference time.
Secrecy Offloading Rate Maximization for Multi-Access Mobile Edge Computing Networks

This letter considers a multi-access mobile edge computing (MEC) network consisting of multiple users, multiple base stations, and a malicious eavesdropper. Specifically, the users adopt the partial offloading strategy by partitioning the computation task into several parts. One is executed locally and the others are securely offloaded to multiple MEC servers integrated into the base stations by leveraging the physical layer security to combat the eavesdropping. We jointly optimize power allocation, task partition, subcarrier allocation, and computation resource to maximize the secrecy offloading rate of the users, subject to communication and computation resource constraints. Numerical results demonstrate that our proposed schemes outperform the benchmark schemes substantially and emphasize the necessity of conducting computation offloading over multiple MEC servers.
Exploiting Multi-Object Relationships for Detecting Adversarial Attacks in Complex Scenes

Vision systems that deploy Deep Neural Networks (DNNs) are known to be vulnerable to adversarial examples. Recent research has shown that checking the intrinsic consistencies in the input data is a promising way to detect adversarial attacks (e.g., by checking the object co-occurrence relationships in complex scenes). However, existing approaches are tied to specific models and do not offer generalizability. Motivated by the observation that language descriptions of natural scene images have already captured the object co-occurrence relationships that can be learned by a language model, we develop a novel approach to perform context consistency checks using such language models. The distinguishing aspect of our approach is that it is independent of the deployed object detector and yet offers very high accuracy in terms of detecting adversarial examples in practical scenes with multiple objects. Experiments on the PASCAL VOC and MS COCO datasets show that our method can outperform state-of-the-art methods in detecting adversarial attacks.
URLLC resource slicing and scheduling for trustworthy 6G vehicular services: A federated reinforcement learning approach

In the upcoming 6G era, vehicles will massively connect to the wireless network through edge access points such as roadside units (RSUs). The increasing number of connected vehicles and vehicular services will take 6G vehicular network to a new challenging security boundary, i.e., the so-called zero trust network. The traditional resource slicing and scheduling solution has to evolve to deal with the zero trust security problems. In this paper, we consider the trustworthy 6G vehicular services and focus on the typical scenario of vehicular task offloading with resource slicing and scheduling. In order to prevent vehicles from malicious attacks by untrusted edge access points, we exploit a subjective logic model to score the reputation of edge nodes. The vehicles will select the edge nodes with high reputation for task offloading. After that, we develop an federated asynchronous reinforcement learning algorithm to optimize the offloading problem. The simulation results show that our approach can efficiently schedule the slice resources and effectively protect the information security of vehicles.
Influence of Digitization on Employee Satisfaction in Small and Medium-sized Enterprises

Digitization is one of the main drivers of changing the way of work within enterprises because new business models evolve from recent digitization processes. Besides the ongoing information technologies in the world of business, it is important for small and medium-sized enterprises to care for their employees and to ensure employee satisfaction. This paper aims at assessing the influence of digitization on employee satisfaction in small and medium-sized companies. This paper is based on qualitative empirical research with focus on expert interviews. The transcribed expert interviews were evaluated using the Grounded Theory approach. Grounded Theory is a method that is particularly suitable for theory building and for identification of new and unknown relationships. The objective of this study is to provide recommendations for small and medium-sized enterprises regarding when they face increasing challenges as a result of volatile market environments and versatile digitization processes to ensure employee satisfaction. Our study identifies emerging factors which influence employee satisfaction.