Reviewerly/exHarmony huggingface dataset:
Differential evolution with an adaptive penalty coefficient mechanism and a search history exploitation mechanism

A critical issue of constrained optimization evolutionary algorithms (COEAs) is how to balance the minimization of the objective function and the reduction of the constraint violation during the optimization process. This paper proposes a differential evolution (DE) algorithm with an adaptive penalty coefficient mechanism and a search history exploitation mechanism (called APCSH) to address this issue. Specifically, APCSH includes a learning stage and an evolving stage. In the learning stage, the correlation between the constraint violation and objective function values is calculated. Then, in the evolving stage, the correlation is used with a sigmoid function to determine the upper and lower bounds of the penalty coefficient, followed by a ranking strategy to assign the penalty coefficient to each solution. Furthermore, an exponentially weighted average method is used to update the search direction of solutions to promote convergence. Additionally, the scaling factor and crossover rate of DE are tuned automatically based on the successful search history. Many numerical experiments are conducted on 18 CEC2010 benchmark problems, 28 CEC2017 benchmark problems, and 4 heat pipe constraint optimization problems to demonstrate the advantages of the proposed algorithm over some state-of-the-art COEAs.
UtilityAware: A framework for data privacy protection in e-health

Data privacy in e-health deals with the protection of sensitive medical information that is collected, stored, and analyzed in electronic health systems. Several organizations publish sensitive person-specific data for research purposes. E-health data and related domains are the loci of research. First, in publishing sensitive person-specific datasets, ensuring the privacy of user sensitive information is an issue. Secondly, ascertaining both privacy-preservation and data utility simultaneously are contradictory to each other. In addition, all transactions have the same prior belief that may result in erroneous modeling and privacy breaches. To refrain from the belief of an adversary and to solve the above discussed issues, a semantic privacy guarantee must be ensured before publishing data by any organization. This paper proposes a solution to the former issue, a framework for privacy preservation of structured datasets in ascertaining that an adversary has low confidence in extrapolation. The latter problem is also tackled by the proposed framework that combines stratified sampling with generalization to achieve representative semantic privacy-preservation with high data utility. Moreover, this study presents a mathematical proof that the proposed framework achieves differential privacy. Our experimental results show that our algorithm provides better data utility and privacy simultaneously. The proposed framework achieves 3% and 0.04% higher classification accuracy and low relative error, respectively, compared to state-of-the-art existing privacy-preservation approaches.
Exploring the Context with Factors of Cloud Computing to Digital Transformation and Innovation

As Cloud computing has been considered as key pillar of the digital transformation technology ecosystem recently, this study aims to explore the factor of cloud computing to digital innovation as well as the relationships among them. A hybrid approach combining with content analysis and formal conceptual analysis was used to explore the relevant factors and context of 174 use cases which including cloud natives and startups, the research design supports the researchers on how to come across the research findings. The results indicate that relative advantages, financial costs, ease of use, security and privacy, and supplier computing support are significant in cloud computing on digital innovation, especially in startups. The context of cloud computing with our proposed research model and approach provides objective findings with evidence to demonstrate the critical position of cloud computing to digital transformation and provide managerial implications for both of academical and practical.
Single‐image super‐resolution using lightweight transformer‐convolutional neural network hybrid model

Abstract With constant advances in deep learning methods as applied to image processing, deep convolutional neural networks (CNNs) have been widely explored in single‐image super‐resolution (SISR) problems and have attained significant success. These CNN‐based methods cannot fully use the internal and external information of the image. The authors add a lightweight Transformer structure to capture this information. Specifically, the authors apply a dense block structure and residual connection to build a residual dense convolution block (RDCB) that reduces the parameters somewhat and extracts shallow features. The lightweight transformer block (LTB) further extracts features and learns the texture details between the patches through the self‐attention mechanism. The LTB comprises an efficient multi‐head transformer (EMT) with small graphics processing unit (GPU) memory footprint, and benefits from feature preprocessing by multi‐head attention (MA), reduction, and expansion. The EMT significantly reduces the use of GPU resources. In addition, a detail‐purifying attention block (DAB) is proposed to explore the context information in the high‐resolution (HR) space to recover more details. Extensive evaluations of four benchmark datasets demonstrate the effectiveness of the authors’ proposed model in terms of quantitative metrics and visual effects. The proposed EMT only uses about 40% as much GPU memory as other methods, with better performance.
Artificial Intelligence for Clinical Flow Cytometry

In this review, the authors discuss the fundamental principles of machine learning. They explore recent studies and approaches in implementing machine learning into flow cytometry workflows. These applications are promising but not without their shortcomings. Explainability may be the biggest barrier to adoption, as they contain "black boxes" in which a complex network of mathematical processes learns features of data that are not translatable into real language. The authors discuss the current limitations of machine learning models and the possibility that, without a multiinstitutional development process, these applications could have poor generalizability. They also discuss widespread deployment of augmented decision-making.
Attentive continuous generative self-training for unsupervised domain adaptive medical image translation

Self-training is an important class of unsupervised domain adaptation (UDA) approaches that are used to mitigate the problem of domain shift, when applying knowledge learned from a labeled source domain to unlabeled and heterogeneous target domains. While self-training-based UDA has shown considerable promise on discriminative tasks, including classification and segmentation, through reliable pseudo-label filtering based on the maximum softmax probability, there is a paucity of prior work on self-training-based UDA for generative tasks, including image modality translation. To fill this gap, in this work, we seek to develop a generative self-training (GST) framework for domain adaptive image translation with continuous value prediction and regression objectives. Specifically, we quantify both aleatoric and epistemic uncertainties within our GST using variational Bayes learning to measure the reliability of synthesized data. We also introduce a self-attention scheme that de-emphasizes the background region to prevent it from dominating the training process. The adaptation is then carried out by an alternating optimization scheme with target domain supervision that focuses attention on the regions with reliable pseudo-labels. We evaluated our framework on two cross-scanner/center, inter-subject translation tasks, including tagged-to-cine magnetic resonance (MR) image translation and T1-weighted MR-to-fractional anisotropy translation. Extensive validations with unpaired target domain data showed that our GST yielded superior synthesis performance in comparison to adversarial training UDA methods.
A Multi-View Filter for Relation-Free Knowledge Graph Completion

As knowledge graphs are often incomplete, knowledge graph completion methods have been widely proposed to infer missing facts by predicting the missing element of a triple given the other two elements. However, the assumption that the two elements have to be correlated is strong. Thus in this paper, we investigate relation-free knowledge graph completion to predict relation-tail(r-t) pairs given a head entity. Considering the large scale of candidate relation-tail pairs, previous work proposed to filter r-t pairs before ranking them relying on entity types, which fails when entity types are missing or insufficient. To tackle the limitation, we propose a relation-free knowledge graph completion method that can cope with knowledge graphs without additional ontological information, such as entity types. Specifically, we propose a multi-view filter, including two intra-view modules and an inter-view module, to filter r-t pairs. For the intra-view modules, we construct head-relation and tail-relation graphs based on triples. Two graph neural networks are respectively trained on these two graphs to capture the correlations between the head entities and the relations, as well as the tail entities and the relations. The inter-view module is learned to bridge the embeddings of entities that appeared in the two graphs. In terms of ranking, existing knowledge graph embedding models are applied to score and rank the filtered candidate r-t pairs. Experimental results show the efficiency of our method in preserving higher-quality candidate r-t pairs for knowledge graphs and resulting in better relation-free knowledge graph completion.
SOLARNet: A single stage regression based framework for efficient and robust object recognition in aerial images

Object recognition and localization play a crucial role in aerial images and their applications. The aerial images are challenging due to the large aspect ratio, arbitrary orientation, variation in scales, and non-uniform and cluttered object distribution. To address these challenges, we propose an efficient and robust model called the Simultaneous Object Localization and Recognition Network (SOLARNet), which is a fusion network that integrates two different sub-networks: PixelAttentionDetector (PD) and RotationDetector (RD). The PD considers features from different scales and cluttered objects, while RD handles rotation invariance, giving horizontal and oriented object detection results. The state-of-the-art model fails to improve accuracy when images are adversarially attacked. SOLARNet is not only efficient in terms of accuracy but also robust concerning to Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), Jacobian Based Saliency Map (JSM) adversarial attacks, which is the crucial factor for any mission-critical system. We have performed experiments and achieved the accuracy on the publicly available DOTA dataset (75.00% mAP, 66.40% mAP) and DIOR dataset (88.60% mAP, 81.50% mAP) for horizontal and oriented object recognition tasks respectively while having high inference speed. Qualitative and quantitative results reported in this paper substantiate the superiority of SOLARNet over other state-of-the-art methodologies.
Fine-Grained Hard-Negative Mining: Generalizing Mitosis Detection with a Fifth of the MIDOG 2022 Dataset

Making histopathology image classifiers robust to a wide range of real-world variability is a challenging task. Here, we describe a candidate deep learning solution for the Mitosis Domain Generalization Challenge 2022 (MIDOG) to address the problem of generalization for mitosis detection in images of hematoxylin-eosin-stained histology slides under high variability (scanner, tissue type and species variability). Our approach consists in training a rotation-invariant deep learning model using aggressive data augmentation with a training set enriched with hard negative examples and automatically selected negative examples from the unlabeled part of the challenge dataset. To optimize the performance of our models, we investigated a hard negative mining regime search procedure that lead us to train our best model using a subset of image patches representing 19.6% of our training partition of the challenge dataset. Our candidate model ensemble achieved a $$\textrm{F}_{1}$$ -score of .697 on the final test set after automated evaluation on the challenge platform, achieving the third best overall score in the MIDOG 2022 Challenge.
Information behaviour of prison inmates in Ghana

Information provision and services in prisons have been demonstrated to be a critical component of successful rehabilitation and reintegration. The study therefore examined the information behaviour of inmates in Ghana prisons with the aim of understanding their information needs, sources and challenges towards improving the information provision and services available in Ghana prisons. In all, qualitative data were collected from 27 participants comprising 19 inmates and eight Correctional Officers from four prisons in Ghana: Ankaful Maximum Security, Nsawam Medium Security, Kumasi Central and Nsawam Female prisons. The study found that inmates had information needs related to welfare, legal, support among others and it was evident that a number of their information needs were not met. The study further revealed poor library systems in Ghana prisons and inmates’ inability to access modern ICT tools as some of the challenges in accessing information in Ghana prisons. The study recommends the need for stakeholders and policy makers to invest in information provisions and services in Ghana prisons.
Low-Light Robust Face Image Super-Resolution via Neuro-Fuzzy Inferencing-Based Locality Constrained Representation

Face super-resolution (FSR) has recently become an interesting topic of study in the fields of image processing and computer vision. However, current FSR methods are still incapable of dealing with those faces suffering from low-light issues, which are frequently caused by uneven lighting conditions in the environment. Therefore, a novel neuro-fuzzy inferencing-based locality-constrained representation (NFILcR) method for low-light robust FSR is proposed in this paper. In NFILcR, the adaptive neuro-fuzzy inferencing system and locality-constrained representation approach are integrated into one unified framework to super resolve the captured low-light and low-resolution faces. Specifically, an adaptive neuro-fuzzy inferencing-based low-light factor estimator (ANFILLE) model is introduced that estimates the low-light factor in the test images. Further, the estimated low-light factor is incorporated into the objective function of the proposed NFILcR method. These contributions make the proposed method robust against the low-light issue. The experimental results on benchmark face datasets and real-world images show that the proposed NFILcR method outperforms the other FSR methods that have been compared.
A Policy-Hiding Attribute-Based Access Control Scheme in Decentralized Trust Management

Internet of Medical Things (IoMT) technologies significantly improve the quality of health care, especially at the time when COVID-19 is becoming a worldwide pandemic. Due to the complexity of devices and user nodes in the IoMT system, there should be some ways to ensure the security and quality of the service or information. Decentralized trust management techniques are efficient means of promoting application security and reliability in these cases. However, the majority of currently utilized access control schemes cannot be applied in decentralized trust management systems or perform poorly owing to the numerous restrictions of decentralized systems. In this article, we present a policy-hiding and multiauthority key generation CP-ABE scheme (PM-CPABE) for decentralized trust management systems, which could provide fine-grained access control capabilities. Meanwhile, the proposed scheme does not require any fully trusted entity, thus it can be well adapted to decentralized trust management systems. The scheme also implements policy hiding to protect user privacy. In addition, it supports large universe and outsourced decryption. The security analyses and performance comparisons give evidence of our scheme is secure and efficient.
Ownership Verification Protocols for Deep Neural Network Watermarks

To protect deep neural networks as intellectual properties, it is necessary to accurately identify their author or registered owner. Numerous techniques, spearheaded by the watermark, have been proposed to establish the connection between a deep neural network and its owner; however, it is until that such connection is provably unambiguous and unforgeable that it can be leveraged for copyright protection. The ownership proof is feasible only after multiple parties, including the owner, the adversary, and the third party to whom the owner wants to present a proof operate under deliberate protocols. The design of these ownership verification protocols requires more careful insight into the knowledge and privacy concerns of participants, during which process several extra security risks emerge. This chapter briefly reviews ordinary security requirements in deep neural network watermarking schemes, formulates several additional requirements regarding ownership proof under elementary protocols, and puts forward the necessity of analyzing and regulating the ownership verification procedure.
FedIPR: Ownership Verification for Federated Deep Neural Network Models

In federated learning, multiple clients collaboratively develop models upon their private data. However, IP risks including illegal copying, re-distribution, and free-riding threat the collaboratively built models in federated learning. To address IP infringement issues, in this chapter, we introduce a novel deep neural network ownership verification framework for secure federated learning that allows each client to embed and extract private watermarks in federated learning models for legitimate IPR. In the proposed FedIPR scheme, each client independently extracts the watermarks and claims ownership on the federated learning model while keep training data and watermark private.
Automatic speaker verification system for dysarthric speakers using prosodic features and out-of-domain data augmentation

A communication disorder is an impairment of a person's ability to talk or communicate appropriately. Dysarthria is a common neuro-motor speech communication disorder that can be caused by neurological damage. Dysarthria may affect the articulation, phonation, and prosody of a speaker. Dysarthria patients have poor neuromotor coordination and other physical impairments, making it difficult to utilize an interactive keyboard or other user interfaces. The ASV system can make biometric applications more accessible to dysarthric speakers by eliminating the need for them to remember cumbersome and unique authentication numbers and passwords. In this paper, we presented a study on developing an automatic speaker verification (ASV) system for dysarthria patients with varying speech intelligibility to assist them in remote access control and voice-based biometric applications. In the initial part of our proposed approach, we included a duration modification-based data augmentation module in the front end of the ASV system. Since prosody deficits are one of the early indicators of dysarthria, we investigated the role of prosodic variables in combination with the traditional Mel-frequency cepstral coefficients (MFCC). The prosodic variables explored in this study include pitch, loudness, and voicing probability. Separate i-vector and x-vector models are trained and compared using individual MFCC, prosodic variables, and their combinations. The experimental results showed that the proposed approach based on combining MFCC and prosody features along with duration-modification-based data augmentation produced promising results.
Private set intersection: A systematic literature review

Secure Multi-party Computation (SMPC) is a family of protocols which allow some parties to compute a function on their private inputs, obtaining the output at the end and nothing more. In this work, we focus on a particular SMPC problem named Private Set Intersection (PSI). The challenge in PSI is how two or more parties can compute the intersection of their private input sets, while the elements that are not in the intersection remain private. This problem has attracted the attention of many researchers because of its wide variety of applications, contributing to the proliferation of many different approaches. Despite that, current PSI protocols still require heavy cryptographic assumptions that may be unrealistic in some scenarios. In this paper, we perform a Systematic Literature Review of PSI solutions, with the objective of analyzing the main scenarios where PSI has been studied and giving the reader a general taxonomy of the problem together with a general understanding of the most common tools used to solve it. We also analyze the performance using different metrics, trying to determine if PSI is mature enough to be used in realistic scenarios, identifying the pros and cons of each protocol and the remaining open problems.
A Nested Attention Guided UNet++ Architecture for White Matter Hyperintensity Segmentation

White Matter Hyperintensity (WMH) is a common finding in Magnetic Resonance Imaging (MRI) of patients with cerebral infarction and is associated with poor prognosis.Accurate and rapid segmentation of WMH lesions is critical for clinicians to assess the risk of rebleeding and the long-term prognosis of thrombolytic patients.However, segmentation can be challenging due to the erratic signals of WMH in MRI, leading to imprecise results.Deep learning-based approaches have been proposed, but the dice similarity coefficient remains low.Atlas images are navigation maps that integrate various medical information expressions.In this study, we propose a nested attention-guided UNet++ framework that employs attention mechanisms to capture local and global features of WMH lesions using atlas images for segmentation.The framework consists of two modules, the atlas attention module, and the nested attentionguided nested U-Net module.The atlas attention module generates the atlas attention map, which is used as the input for the nested attention-guided nested U-Net module that generates the segmentation map of the FLAIR image.Experimental results demonstrate that the proposed NAUNet++ framework converges faster than conventional UNet and UNet++ approaches.Moreover, the nested architecture enhances recall and f1 scores of the segmentation results compared to the attention-guided approach.