Reviewerly/exHarmony huggingface dataset:
Accurate Gastric Cancer Segmentation in Digital Pathology Images Using Deformable Convolution and Multi-Scale Embedding Networks

Automatic gastric cancer segmentation is a challenging problem in digital pathology image analysis.Accurate segmentation of gastric cancer regions can efficiently facilitate clinical diagnosis and pathological research.Technically, this problem suffers from various sizes, vague boundaries, and the nonrigid characters of cancerous regions.For addressing these challenges, we use a deep learning based method and integrate several customized modules.Structurally, we replace the basic form of convolution with deformable and Atrous convolutions in specific layers, for adapting to the non-rigid characters and larger receptive field.We take advantage of the Atrous Spatial Pyramid Pooling module and encoder-decoder based semantic-level embedding networks for multi-scale segmentation.In addition, we propose a lightweight decoder to fuse the contexture information, and utilize the dense upsampling convolution for boundary refinement at the end of the decoder.Experimentally, sufficient comparative experiments are enforced on our own gastric cancer segmentation dataset, which is delicately annotated to pixel-level by medical specialists.The quantitative comparisons against several prior methods demonstrate the superiority of our approach.We achieve 91.60% for pixel-level accuracy and 82.65% for mean Intersection over Union.
Automatic digital modulation classification using extreme learning machine with local binary pattern histogram features

Discrimination of the Local Binary Pattern (LBP) in the classification of different digital modulation types was investigated in this study. It has been shown that LBP can be used as a feature extraction method for AMC schemes. A new AMC scheme is proposed using Extreme Learning Machine (ELM) as a classifier, which has a faster learning process and better generalization performance than conventional machine learning methods. The study also investigated the stability of the proposed AMC scheme, which is affected by variation in the values of the roll-off factor, frequency and phase offset that can affect the stability and performance of the system. Through simulation, a classification accuracy of over 95% was achieved at low SNR levels such as −2 dB. It was also shown that the proposed AMC scheme is more successful under similar conditions when making comparisons to other studies.
Critical Reflections on Visualization Authoring Systems

An emerging generation of visualization authoring systems support expressive information visualization without textual programming. As they vary in their visualization models, system architectures, and user interfaces, it is challenging to directly compare these systems using traditional evaluative methods. Recognizing the value of contextualizing our decisions in the broader design space, we present critical reflections on three systems we developed -- Lyra, Data Illustrator, and Charticulator. This paper surfaces knowledge that would have been daunting within the constituent papers of these three systems. We compare and contrast their (previously unmentioned) limitations and trade-offs between expressivity and learnability. We also reflect on common assumptions that we made during the development of our systems, thereby informing future research directions in visualization authoring systems.
Semantic Textual Similarity with Siamese Neural Networks

Calculating the Semantic Textual Similarity (STS) is an important research area in natural language processing which plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction.This paper evaluates Siamese recurrent architectures, a special type of neural networks, which are used here to measure STS.Several variants of the architecture are compared with existing methods.
A Novel Hybrid Deep Neural Network to Predict Pre-impact Fall for Older People Based on Wearable Inertial Sensors

Falls in the elderly is a major public health concern due to its high prevalence, serious consequences and heavy burden on the society. Many falls in older people happen within a very short time, which makes it difficult to predict a fall before it occurs and then to provide protection for the person who is falling. The primary objective of this study was to develop deep neural networks for predicting a fall during its initiation and descending but before the body impacts to the ground so that a safety mechanism can be enabled to prevent fall-related injuries. We divided the falling process into three stages (non-fall, pre-impact fall and fall) and developed deep neutral networks to perform three-class classification. Three deep learning models, Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), and a novel hybrid model integrating both Convolution and Long Short Term Memory (ConvLSTM) were proposed and evaluated on a large public dataset of various falls and activities of daily living acquired with wearable inertial sensors (accelerometer and gyroscope). 5-fold cross validation results showed that the hybrid ConvLSTM model had mean sensitivities of 93.15%, 93.78% and 96.00% for non-fall, pre-impact fall and fall respectively, which were higher than both LSTM (except the fall class) and CNN models. ConvLSTM model also showed higher specificities for all three classes (96.59%, 94.49% and 98.69%) than LSTM and CNN models. In addition, latency test on a microcontroller unit showed that ConvLSTM model had a short latency of 1.06ms, which was much lower than LSTM model (3.15ms) and comparable with CNN model (0.77ms). High prediction accuracy (especially for pre-impact fall) and low latency on the microboard indicated that the proposed hybrid ConvLSTM model outperformed both LSTM and CNN models. These findings suggest that our proposed novel hybrid ConvLSTM model has great potential to be embedded into wearable inertial sensor-based systems to predict pre-impact fall in real-time so that protective devices could be triggered in time to prevent fall-related injuries for older people.
Searching Multi-Rate and Multi-Modal Temporal Enhanced Networks for Gesture Recognition

Gesture recognition has attracted considerable attention owing to its great potential in applications. Although the great progress has been made recently in multi-modal learning methods, existing methods still lack effective integration to fully explore synergies among spatio-temporal modalities effectively for gesture recognition. The problems are partially due to the fact that the existing manually designed network architectures have low efficiency in the joint learning of multi-modalities. In this paper, we propose the first neural architecture search (NAS)-based method for RGB-D gesture recognition. The proposed method includes two key components: 1) enhanced temporal representation via the proposed 3D Central Difference Convolution (3D-CDC) family, which is able to capture rich temporal context via aggregating temporal difference information; and 2) optimized backbones for multi-sampling-rate branches and lateral connections among varied modalities. The resultant multi-modal multi-rate network provides a new perspective to understand the relationship between RGB and depth modalities and their temporal dynamics. Comprehensive experiments are performed on three benchmark datasets (IsoGD, NvGesture, and EgoGesture), demonstrating the state-of-the-art performance in both single- and multi-modality settings. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ZitongYu/3DCDC-NAS</uri> .
Medical Internet-of-Things Based Breast Cancer Diagnosis Using Hyperparameter-Optimized Neural Networks

In today’s healthcare setting, the accurate and timely diagnosis of breast cancer is critical for recovery and treatment in the early stages. In recent years, the Internet of Things (IoT) has experienced a transformation that allows the analysis of real-time and historical data using artificial intelligence (AI) and machine learning (ML) approaches. Medical IoT combines medical devices and AI applications with healthcare infrastructure to support medical diagnostics. The current state-of-the-art approach fails to diagnose breast cancer in its initial period, resulting in the death of most women. As a result, medical professionals and researchers are faced with a tremendous problem in early breast cancer detection. We propose a medical IoT-based diagnostic system that competently identifies malignant and benign people in an IoT environment to resolve the difficulty of identifying early-stage breast cancer. The artificial neural network (ANN) and convolutional neural network (CNN) with hyperparameter optimization are used for malignant vs. benign classification, while the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) were utilized as baseline classifiers for comparison. Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models. We employ a particle swarm optimization (PSO) feature selection approach to select more satisfactory features from the breast cancer dataset to enhance the classification performance using MLP and SVM, while grid-based search was used to find the best combination of the hyperparameters of the CNN and ANN models. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used to test the proposed approach. The proposed model got a classification accuracy of 98.5% using CNN, and 99.2% using ANN.
A new metaheuristic optimization based on K-means clustering algorithm and its application to structural damage identification

This paper develops a new metaheuristic optimization algorithm named K-means Optimizer (KO) to solve a wide range of optimization problems from numerical functions to real-design challenges. First, the centroid vectors of clustering regions are established at each iteration using K-means algorithm, then KO proposes two movement strategies to create a balance between the ability of exploitation and exploration. The decision on the movement strategy for exploration or exploitation at each iteration depends on a parameter that will be designed to recognize if each search agent is too long in the region visited with no self-improvement. To demonstrate the effectiveness and reliability of KO, twenty-three classical benchmark functions, CEC2005 and CEC2014 benchmark functions, are employed as a first example and compared with other algorithms. Then, three well-known engineering problems are also considered and their results are compared to the results obtained by the other algorithms. Finally, KO is applied to structural damage identification (SDI) problem of a complex 3D concrete structure including seven stories building having a 25.2 m total height. For this purpose, SAP2000 is used to solve the finite element (FE) model of this structure. Then, for the first time, we successfully developed a sub-program that allows two-way data exchange between SAP2000 and MATLAB through the Open Application Programming Interface (OAPI) library to update the FE model. From the results, we found that KO has the best performance for the considered benchmark functions based on the Wilcoxon rank-sum test and Friedman ranking test. The results obtained in this work have proved the effectiveness and reliability of KO in solving optimization problems, especially for SDI. Source codes of KO is publicly available at http://goldensolutionrs.com/codes.html.
Deep learning for Alzheimer's disease diagnosis: A survey

Alzheimer's Disease (AD) is an irreversible neurodegenerative disease that results in a progressive decline in cognitive abilities. Since AD starts several years before the onset of the symptoms, its early detection is challenging due to subtle changes in biomarkers mainly detectable in different neuroimaging modalities. Developing computer-aided diagnostic models based on deep learning can provide excellent opportunities for the analysis of different neuroimage modalities along with other non-image biomarkers. In this survey, we perform a comparative analysis of about 100 published papers since 2019 that employ basic deep architectures such as CNN, RNN, and generative models for AD diagnosis. Moreover, about 60 papers that have applied a trending topic or architecture for AD are investigated. Explainable models, normalizing flows, graph-based deep architectures, self-supervised learning, and attention mechanisms are considered. The main challenges in this body of literature have been categorized and explained from data-related, methodology-related, and clinical adoption aspects. We conclude our paper by addressing some future perspectives and providing recommendations to conduct further studies for AD diagnosis.
MCI-Net: Multi-scale context integrated network for liver CT image segmentation

Owing to the various object scales and high similarity with the surrounding organs (e.g., kidney, stomach, and spleen), it is difficult to accurately segment the liver region from the abdominal computed tomography images. In this study, we propose a multi-scale context integration network called MCI-Net for liver image segmentation. Specifically, we first design a simplified residual module to prevent network degradation. Given the scale variability of objects, we propose a multi-scale context extraction module by combining four cascaded branches of hybrid dilated convolutions to capture broader and deeper features. In addition, we introduce an external attention mechanism based on two external, learnable and shared memory units, which helps to perceive the most discriminative information and suppress redundant features. Finally, we provide a boundary correction block to further improve the localization ability of boundary information. Extensive experiments on two liver CT image benchmark datasets qualitatively and quantitatively illustrate that our method is effective in improving liver segmentation accuracy and outperforms several state-of-the-art methods.
Employing Lexicalized Dependency Paths for Active Learning of Relation Extraction

Active learning methods which present selected examples from the <i>corpus</i> for annotation provide more efficient learning of supervised relation extraction models, but they leave the developer in the unenviable role of a passive informant. To restore the developer's proper role as a partner with the system, we must give the developer an ability to inspect the extraction model during development. We propose to make this possible through a representation based on lexicalized dependency paths (LDPs) coupled with an active learner for LDPs. We apply LDPs to both simulated and real active learning with ACE as evaluation and a year's newswire for training and show that simulated active learning greatly reduces annotation cost while maintaining similar performance level of supervised learning, while real active learning yields comparable performance to the state-of-the-art using a small number of annotations.
Zebra Optimization Algorithm: A New Bio-Inspired Optimization Algorithm for Solving Optimization Algorithm

In this paper, a new bio-inspired metaheuristic algorithm called Zebra Optimization Algorithm (ZOA) is developed; its fundamental inspiration is the behavior of zebras in nature.ZOA simulates the foraging behavior of zebras and their defense strategy against predators' attacks.The ZOA steps are described and then mathematically modeled.ZOA performance in optimization is evaluated on sixty-eight benchmark functions, including unimodal, high-dimensional multimodal, fixed-dimensional multimodal, CEC2015, and CEC2017.The results obtained from ZOA are compared with the performance of nine well-known algorithms.The simulation results show that ZOA can solve optimization problems by creating a suitable balance between exploration and exploitation and has a superior performance compared to nine competitor algorithms.ZOA's ability to solve real-world problems has been tested on four engineering design problems, namely, tension/compression spring, welded beam, speed reducer, and pressure vessel.The optimization results show that ZOA is an effective optimizer in determining the values of the design variables of these problems compared to the nine competitor algorithms.
On Demand Fog Federations for Horizontal Federated Learning in IoV

Federated learning using fog computing can suffer from the dynamic behavior of some of the participants in its training process, especially in Internet-of-Vehicles where vehicles are the targeted participants. For instance, the fog might not be able to cope with the vehicles’ demands in some areas due to resource shortages when the vehicles gather for events, or due to traffic congestion. Moreover, the vehicles are exposed to unintentionally leaving the fog coverage area which can result in the task being dropped as the communications between the server and the vehicles weaken. The aforementioned limitations can affect the federated learning model accuracy for critical applications, such as autonomous driving, where the model inference could influence road safety. Recent works in the literature have addressed some of these problems through active sampling techniques, however, they suffer from many complications in terms of stability, scalability, and efficiency of managing the available resources. To address these limitations, we propose a horizontal-based federated learning architecture, empowered by fog federations, devised for the mobile environment. In our architecture, fog computing providers form stable fog federations using a Hedonic game-theoretical model to expand their geographical footprints. Hence, providers belonging to the same federations can migrate services upon demand in order to cope with the federated learning requirements in an adaptive fashion. We conduct the experiments using a road traffic signs dataset modeled with intermodal traffic systems. The simulation results show that the proposed model can achieve better accuracy and quality of service than other models presented in the literature.
Quantum error correction with silicon spin qubits

Abstract Future large-scale quantum computers will rely on quantum error correction (QEC) to protect the fragile quantum information during computation 1,2 . Among the possible candidate platforms for realizing quantum computing devices, the compatibility with mature nanofabrication technologies of silicon-based spin qubits offers promise to overcome the challenges in scaling up device sizes from the prototypes of today to large-scale computers 3–5 . Recent advances in silicon-based qubits have enabled the implementations of high-quality one-qubit and two-qubit systems 6–8 . However, the demonstration of QEC, which requires three or more coupled qubits 1 , and involves a three-qubit gate 9–11 or measurement-based feedback, remains an open challenge. Here we demonstrate a three-qubit phase-correcting code in silicon, in which an encoded three-qubit state is protected against any phase-flip error on one of the three qubits. The correction to this encoded state is performed by a three-qubit conditional rotation, which we implement by an efficient single-step resonantly driven iToffoli gate. As expected, the error correction mitigates the errors owing to one-qubit phase-flip, as well as the intrinsic dephasing mainly owing to quasi-static phase noise. These results show successful implementation of QEC and the potential of a silicon-based platform for large-scale quantum computing.
Integrated multimodal artificial intelligence framework for healthcare applications

Artificial intelligence (AI) systems hold great promise to improve healthcare over the next decades. Specifically, AI systems leveraging multiple data sources and input modalities are poised to become a viable method to deliver more accurate results and deployable pipelines across a wide range of applications. In this work, we propose and evaluate a unified Holistic AI in Medicine (HAIM) framework to facilitate the generation and testing of AI systems that leverage multimodal inputs. Our approach uses generalizable data pre-processing and machine learning modeling stages that can be readily adapted for research and deployment in healthcare environments. We evaluate our HAIM framework by training and characterizing 14,324 independent models based on HAIM-MIMIC-MM, a multimodal clinical database (N=34,537 samples) containing 7,279 unique hospitalizations and 6,485 patients, spanning all possible input combinations of 4 data modalities (i.e., tabular, time-series, text, and images), 11 unique data sources and 12 predictive tasks. We show that this framework can consistently and robustly produce models that outperform similar single-source approaches across various healthcare demonstrations (by 6-33%), including 10 distinct chest pathology diagnoses, along with length-of-stay and 48-hour mortality predictions. We also quantify the contribution of each modality and data source using Shapley values, which demonstrates the heterogeneity in data modality importance and the necessity of multimodal inputs across different healthcare-relevant tasks. The generalizable properties and flexibility of our Holistic AI in Medicine (HAIM) framework could offer a promising pathway for future multimodal predictive systems in clinical and operational healthcare settings.
A Survey on Negative Transfer

Transfer learning (TL) utilizes data or knowledge from one or more source domains to facilitate the learning in a target domain. It is particularly useful when the target domain has very few or no labeled data, due to annotation expense, privacy concerns, etc. Unfortunately, the effectiveness of TL is not always guaranteed. Negative transfer (NT), i.e., leveraging source domain data/knowledge undesirably reduces the learning performance in the target domain, has been a long-standing and challenging problem in TL. Various approaches have been proposed in the literature to handle it. However, there does not exist a systematic survey on the formulation of NT, the factors leading to NT, and the algorithms that mitigate NT. This paper fills this gap, by first introducing the definition of NT and its factors, then reviewing about fifty representative approaches for overcoming NT, according to four categories: secure transfer, domain similarity estimation, distant transfer, and NT mitigation. NT in related fields, e.g., multi-task learning, lifelong learning, and adversarial attacks, are also discussed.