Reviewerly/exHarmony huggingface dataset:
Breast Mass Detection and Classification Using Deep Convolutional Neural Networks for Radiologist Diagnosis Assistance

Several developments in computational image processing methods assist the radiologist in detecting abnormal breast tissue in recent years. Consequently, deep learning-based models have become crucial for early screening and interpretation of mammographic images for breast masses diagnosis, helping for successful treatment. Breast masses and calcification is an essential parameter for the prognosis of breast cancer. However, the mammographic image’s mass detection needs a deeper investigation due to the breast masses’ heterogeneity and anomalies’ characteristics that are easily confused with other objects present in the image. Hence, this study proposed a deep learning-based convolutional neural network (ConvNet) that will incorporate both mammography and clinical variables to predict and classify breast masses to assist the expert’s decision-making processes. We trained our proposed model with 322 scanned digital mammographic images of the MIAS (Mammogram Image Analysis Society) dataset and 580 images of the private dataset to evaluate the performance, which is highly imbalanced. This study aimed to perform an automatic and comprehensive characterization of breast masses using appropriate layers deep ConvNet model with high accuracy true-positive rate, decreased error rate and applying data-augmentation techniques. We obtained a classification accuracy of 97% applying the filtered deep features, which is the best performance from the existing approaches.
Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation

In the assistive research area, human-computer interface (HCI) technology is used to help people with disabilities by conveying their intentions and thoughts to the outside world. Many HCI systems based on eye movement have been proposed to assist people with disabilities. However, due to the complexity of the necessary algorithms and the difficulty of hardware implementation, there are few general-purpose designs that consider practicality and stability in real life. Therefore, to solve these limitations and problems, an HCI system based on electrooculography (EOG) is proposed in this study. The proposed classification algorithm provides eye-state detection, including the fixation, saccade, and blinking states. Moreover, this algorithm can distinguish among ten kinds of saccade movements (i.e., up, down, left, right, farther left, farther right, up-left, down-left, up-right, and down-right). In addition, we developed an HCI system based on an eye-movement classification algorithm. This system provides an eye-dialing interface that can be used to improve the lives of people with disabilities. The results illustrate the good performance of the proposed classification algorithm. Moreover, the EOG-based system, which can detect ten different eye-movement features, can be utilized in real-life applications.
Named Entity Recognition in Natural Language Processing: A Systematic Review

The enormous growth and availability of data poses a great challenge for extracting useful information from documents written in natural language. The information extraction task has become a vital activity in all domains. The process of identifying the names of organization, people, locations or other entities in text is called named entity recognition (NER). It is the subtask and plays an important part to discover and classify the names such as organization name, person name or the location. This is one of the trending fields and most important step in the natural language processing (NLP) for analysis of text. Research on NER changed a lot in the recent decade. NER can consequently examine the entire articles and reveal the individuals, associations, and spots talked about in text. Knowing the applicable labels for every single article help in naturally arranging the articles in all around characterized progressive systems and endorse smooth content disclosure. The pretension of this paper is to present survey on NER. The prime contribution of this research to present state-of-the-art NER is systematically reviewed according to techniques used in NER. This paper also provides tools, datasets, techniques, challenges and future directions in the field of NER with the aim of providing researchers the substantial knowledge for further work.
SIA-GAN: Scrambling Inversion Attack Using Generative Adversarial Network

This paper presents a scrambling inversion attack using a generative adversarial network (SIA-GAN).This method aims to evaluate the privacy protection level achieved by image scrambling method.For privacy-preserving machine learning, scrambled images are often used to protect visual information, assuming that searching the scramble parameters is highly difficult for an attacker due to the application of complex image scrambling operations.However, the security of such methods has not been thoroughly investigated.SIA-GAN learns the mapping between pairs of scrambled images and original images, then attempts to invert image scrambling.Therefore, the attacker is assumed to have real images whose domain is the same as that of scrambled images.Experimental results demonstrate that scrambled images cannot be recovered if block shuffling is applied as a scrambling operation.The experimental code of SIA-GAN is publicly available after the paper is accepted.
Learning Unsupervised Parameter-Specific Affine Transformation for Medical Images Registration

Affine registration has recently been formulated using deep learning frameworks to establish spatial correspondences between different images. In this work, we propose a new unsupervised model that investigates two new strategies to tackle fundamental problems related to affine registration. More specifically, the new model 1) has the advantage to explicitly learn specific geometric transformation parameters (e.g. translations, rotation, scaling and shearing); and 2) can effectively understand the context between the images via cross-stitch units allowing feature exchange. The proposed model is evaluated on two two-dimensional X-ray datasets and a three-dimensional CT dataset. Our experimental results show that our model not only outperforms state-of-art approaches and also can predict specific transformation parameters. Our core source code is made available online\(^{1}\)(\(^{1}\)https://github.com/xuuuuuuchen/PASTA).
IoT Inspired Intelligent Monitoring and Reporting Framework for Education 4.0

Education 4.0 contemplates the involvement of emerging technologies like IoT (Internet of Things), Fog and cloud computing. Ubiquitous real-time monitoring under the challenging environment of an educational institution is the key requirement for proper implementation of Education 4.0 around the world. Conspicuously, this research presents a novel monitoring and irregularity detection framework for educational institutions. In this research, acquisition of data while deploying IoT is being suggested in the comprehensive environs of the educational institutions. Pre-processing of acquired data for feature extraction is implemented using the Fog-Cloud nodes. Subsequently, the Temporal Chunk (TC)which is articulated with Temporal Data mining, is further employed to detect irregularities based on SoTL (Set of threshold limits)and HAA (Historical Adversity Approximation). Successively, Multi-Layered Bi-Directional Long Short Term Memory (M-Bi-LSTM) oriented irregularity prediction model is deployed. Furthermore, a fog-inspired alert generation and reporting module are employed for real-time reporting to notify the concerned stakeholder for in-time preventive action corresponding to predicted irregularity. To authenticate the proposed framework and to avoid the experimental implementation cost, the experimental simulations are performed. The experimental results verify that the proposed framework is capable to perform better in comparison to other contemporary decision-making methods for Delay Efficiency, Data Classification, Irregularity Prediction and System Stability. Moreover, the proposed framework can successfully estimate most of the irregularities in an educational environment and provide support for the proper implementation of Education 4.0 in a proficient and realistic manner.
LGGD+: Image Retargeting Quality Assessment by Measuring Local and Global Geometric Distortions

Numerous image retargeting algorithms have been proposed to achieve adaptive image resizing during the past years. To compare different image retargeting algorithms, reliable objective image retargeting quality assessment (IRQA) metrics are highly desired. Given that image retargeting usually introduces geometric distortions, this paper presents an objective IRQA metric by measuring both local and global geometric distortions (LGGD). Since human visual system perception is highly dependent on edges and the geometric distortions caused by image retargeting usually cause edge deformation, a sketch token-based local edge descriptor (ST-LED) is introduced to represent geometric-aware features in LGGD. First, ST-LED is first applied on both source and retargeted images for edge pattern representation. Second, pixel-level backward registration is conducted to enable estimating local geometric distortion (LGD) and a spatial pyramid-improved Bag-of-Token (BoT) model is built to enable estimating global geometric distortion (GGD). Since the proposed LGGD metric only focuses on geometric distortion while image retargeting quality is related with more aspects, we further fuse LGGD and an existing (EXT) IRQA metric to build a final version called LGGD+ for IRQA. Experiments on two benchmark databases demonstrate the superiority of LGGD+ and the excellent compatibility of our proposed LGGD for further improving a wide range of existing IRQA metrics (including both geometric distortion and non-geometric distortion metrics). In addition, the effectiveness of our LGGD metric is also demonstrated in another relevant task, i.e., quality evaluation of depth-image-based rendering (DIBR)-synthesized images, which also calls for accurate estimation of geometric distortion.
Semi-supervised Anomaly Detection on Attributed Graphs

We propose a simple yet effective method for detecting anomalous instances on an attribute graph with label information of a small number of instances. Although standard anomaly detection methods usually assume that instances are independent and identically distributed, in many real-world applications, instances are often explicitly connected, resulting in so-called attributed graphs. The proposed method embeds nodes (instances) on the attributed graph in a latent space by taking into account their attributes as well as the graph structure on the basis of graph convolutional networks (GCNs). To learn node embeddings specialized for anomaly detection, in which there is a class imbalance due to the rarity of anomalies, the parameters of a GCN are trained to minimize the volume of a hypersphere that encloses the node embeddings of normal instances while embedding anomalous ones outside the hypersphere. This enables us to detect anomalies by simply calculating the distances between the node embeddings and hypersphere center. The proposed method can effectively propagate label information on a small amount of nodes to unlabeled ones by taking into account the node's attributes, graph structure, and class imbalance. In experiments with five real-world attributed graph datasets, we demonstrate that the proposed method outperforms various existing anomaly detection methods.
FFAVOD: Feature fusion architecture for video object detection

A significant amount of redundancy exists between consecutive frames of a video. Object detectors typically produce detections for one image at a time, without any capabilities for taking advantage of this redundancy. Meanwhile, many applications for object detection work with videos, including intelligent transportation systems, advanced driver assistance systems and video surveillance. Our work aims at taking advantage of the similarity between video frames to produce better detections. We propose FFAVOD, standing for feature fusion architecture for video object detection. We first introduce a novel video object detection architecture that allows a network to share feature maps between nearby frames. Second, we propose a feature fusion module that learns to merge feature maps to enhance them. We show that using the proposed architecture and the fusion module can improve the performance of three base object detectors on two object detection benchmarks containing sequences of moving road users. Additionally, to further increase performance, we propose an improvement to the SpotNet attention module. Using our architecture on the improved SpotNet detector, we obtain the state-of-the-art performance on the UA-DETRAC public benchmark as well as on the UAVDT dataset. Code is available at https://github.com/hu64/FFAVOD.
Automatic online multi-source domain adaptation

Knowledge transfer across several streaming processes remain challenging problem not only because of different distributions of each stream but also because of rapidly changing and never-ending environments of data streams. Albeit growing research achievements in this area, most of existing works are developed for a single source domain which limits its resilience to exploit multi-source domains being beneficial to recover from concept drifts quickly and to avoid the negative transfer problem. An online domain adaptation technique under multi-source streaming processes, namely automatic online multi-source domain adaptation (AOMSDA), is proposed in this paper. The online domain adaptation strategy of AOMSDA is formulated under a coupled generative and discriminative approach of denoising autoencoder (DAE) where the central moment discrepancy (CMD)-based regularizer is integrated to handle the existence of multi-source domains thereby taking advantage of complementary information sources. The asynchronous concept drifts taking place at different time periods are addressed by a self-organizing structure and a node re-weighting strategy. Our numerical study demonstrates that AOMSDA is capable of outperforming its counterparts in 5 of 8 study cases while the ablation study depicts the advantage of each learning component. In addition, AOMSDA is general for any number of source streams. The source code of AOMSDA is shared publicly in https://github.com/Renchunzi-Xie/AOMSDA.git.
Fast and Efficient Method for Large-Scale Aerial Image Stitching

Recent studies on image stitching have been extensively conducted to stitch panoramic or 360 • images using a small number of input images.The stitching of aerial images, that are captured by unmanned aerial vehicles (UAVs), has various practical applications.In this paper, we propose a fast adaptive stitching algorithm for handling numerous aerial images.First, the proposed method analyzes the relative positions and overlapping regions of the UAV image footprints by exploiting their geotag information.Based on the analysis, an adaptive selection algorithm is proposed to eliminate the densely overlapped images from among all the UAV images.Then, the proposed method sequentially performs fast feature extraction and feature matching.Finally, a local warp method, with a smooth transition for overlapping regions, is introduced to alleviate the blurring artifacts and achieve highly accurate image alignment.The experiments are conducted for various scenarios to generate seamless terrestrial mosaic images of large areas.The proposed method improves the visual quality of the stitched image, by decreasing the estimated reprojection error and the number of observed visual distortions.In addition, the proposed method can substantially reduce the processing time compared with conventional stitching methods.
A Blockchain-IoT Platform for the Smart Pallet Pooling Management

Pallet management as a backbone of logistics and supply chain activities is essential to supply chain parties, while a number of regulations, standards and operational constraints are considered in daily operations. In recent years, pallet pooling has been unconventionally advocated to manage pallets in a closed-loop system to enhance the sustainability and operational effectiveness, but pitfalls in terms of service reliability, quality compliance and pallet limitation when using a single service provider may occur. Therefore, this study incorporates a decentralisation mechanism into the pallet management to formulate a technological eco-system for pallet pooling, namely Pallet as a Service (PalletaaS), raised by the foundation of consortium blockchain and Internet of things (IoT). Consortium blockchain is regarded as the blockchain 3.0 to facilitate more industrial applications, except cryptocurrency, and the synergy of integrating a consortium blockchain and IoT is thus investigated. The corresponding layered architecture is proposed to structure the system deployment in the industry, in which the location-inventory-routing problem for pallet pooling is formulated. To demonstrate the values of this study, a case analysis to illustrate the human–computer interaction and pallet pooling operations is conducted. Overall, this study standardises the decentralised pallet management in the closed-loop mechanism, resulting in a constructive impact to sustainable development in the logistics industry.
Blockchain-based decentralized architecture for cloud storage system

Cloud storage system provides on-demand and pay-per-use storage model with low computing cost. However, this storage system suffers from various security risks. Blockchain technology is an advanced technique that stores data in a distributed manner, and the data, once stored, cannot be altered. Therefore, we propose a blockchain-based decentralized architecture for the cloud storage system. The proposed architecture includes access control and integrity checking mechanism to provide a more secure environment. Moreover, it provides four primary services to ensure security features in a cloud storage system: (i) A registration process is designed to register data owners and users using a key generation technique to provide an authentication feature; (ii) The data owners save the user data Meta details in the blockchain structure and set access rules to maintain the authorization feature; (iii) The cloud storage system stores the original data and uses the optimization algorithm to reduce the transaction processing time; (iv) Data owners maintain the data integrity using the Merkle root concept. The experimental results, analysis, and performance evaluation show that our proposed architecture provides a feasible and reliable cloud environment.
Domain Composition and Attention for Unseen-Domain Generalizable Medical Image Segmentation

Domain generalizable model is attracting increasing attention in medical image analysis since data is commonly acquired from different institutes with various imaging protocols and scanners. To tackle this challenging domain generalization problem, we propose a Domain Composition and Attention-based network (DCA-Net) to improve the ability of domain representation and generalization. First, we present a domain composition method that represents one certain domain by a linear combination of a set of basis representations (i.e., a representation bank). Second, a novel plug-and-play parallel domain preceptor is proposed to learn these basis representations and we introduce a divergence constraint function to encourage the basis representations are as divergent as possible. Then, a domain attention module is proposed to learn the linear combination coefficients of the basis representations. The result of liner combination is used to calibrate the feature maps of an input image, which enables the model to generalize to different and even unseen domains. We validate our method on public prostate MRI dataset acquired from six different institutions with apparent domain shift. Experimental results show that our proposed model can generalizes well on different and even unseen domains and it outperforms state-of-the-art methods on the multi-domain prostate segmentation task. Code is available at https://github.com/HiLab-git/DCA-Net.
Recommender Systems: An Explainable AI Perspective

In recent years, in the era of information overload development, the need for recommender systems that make personalized suggestion systems has become a very exciting field for researchers. To develop models that generate high-quality recommendations, the explainable recommendation has been introduced, proposing to develop intuitive and trustworthy explanations. The problem that the explainable recommendation wants to solve is to let people understand why certain elements rather than other are recommended by the system. This paper briefly overviews the short history of explainable AI and then it presents its role and applicability in the domain of recommender systems. Our work contributes to understanding the concept of explainable recommendation and what it should accomplish to increase its acceptability and to enable its accurate evaluation.
Disentangled Representation for Age-Invariant Face Recognition: A Mutual Information Minimization Perspective

General face recognition has seen remarkable progress in recent years. However, large age gap still remains a big challenge due to significant alterations in facial appearance and bone structure. Disentanglement plays a key role in partitioning face representations into identity-dependent and age-dependent components for age-invariant face recognition (AIFR). In this paper we propose a multi-task learning framework based on mutual information minimization (MT-MIM), which casts the disentangled representation learning as an objective of information constraints. The method trains a disentanglement network to minimize mutual information between the identity component and age component of the face image from the same person, and reduce the effect of age variations during the identification process. For quantitative measure of the degree of disentanglement, we verify that mutual information can represent as metric. The resulting identity-dependent representations are used for age-invariant face recognition. We evaluate MT-MIM on popular public-domain face aging datasets (FG-NET, MORPH Album 2, CACD and AgeDB) and obtained significant improvements over previous state-of-the-art methods. Specifically, our method exceeds the baseline models by over 0.4% on MORPH Album 2, and over 0.7% on CACD subsets, which are impressive improvements at the high accuracy levels of above 99% and an average of 94%.