Reviewerly/exHarmony huggingface dataset:
Peripheral Blood Smear Analysis Using Automated Computer-Aided Diagnosis System to Identify Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a cancer of the myeloid line of cells caused due to the rapid increase of abnormal cells that later interfere with healthy cells. One of the main reasons for the increase in mortality is the cost of the devices used for the determination and late diagnosis. The most effective treatment option can be provided by accurate medical diagnosis. Automated segmentation of blood smear images plays a crucial role in the identification of the AML. This article proposes a new computer-aided diagnosis model to segment the blood smear images and identifies the stage of AML. The methodology presented in this work consists of various stages: Image acquisition, image segmentation, feature extraction/selection, and classification. The model is trained using 800 blood smear images collected from Kasturba Medical College Manipal, and 200 images collected from the dataset of microscopic peripheral blood cell images for the development of automatic recognition systems. The model is tested on 500 images. A novel algorithm is designed to accurately segment the blood smear images to identify AML and its stages. The segmentation algorithm addresses critical issues in blast cell detection, including identifying the blast cell and extracting the cytoplasm of the cell without involving manual intervention. It can identify the multiple lobes and the nucleated red blood cells (NRBCs), separate the overlapped erythrocytes from white blood cells (WBCs), and discover the presence of Auer rods and granules. The feature selection is performed using the InfoGainAttributeEval and the ranker search method. This article compares the performance of the various machine learning algorithms exploited for the classification of different types of cells and hence determines AML. The model successfully differentiated between NRBC and WBC with an accuracy of 99.81%. The model obtained a classification accuracy of 99.48 %. It achieved a prediction accuracy of 99.2% while predicting the unknown stage of AML. The algorithm’s efficiency proves that it can be used by pathologists to form a prognosis.
A method of network robustness under strategic goals for project portfolio selection

Selecting a rational project portfolio is a key step in achieving the strategic goals of an enterprise. However, the selection is challenging due to complicated relationships in the project portfolio network. This paper proposes a method based on complex network theory to select a robust project portfolio under strategic goals. The proposed method consists of three phases. In the first phase, the strategic goal of the enterprise is divided into several sub-goals, and the approach degrees between projects and sub-goals are determined by the Euclid approach degree. In the second phase, the coupling coordination degree (CCD) of the project portfolio is determined by using the modified CCD model. The optimal project portfolio that considers the robustness of the project portfolio network under random attack is obtained in the last phase. A numerical example demonstrates how the project portfolio selection method fits in an enterprise and proves the model's efficiency via selection results obtained with different attack intensities. This research provides theoretical guidance and practical applied value for theoretical researchers and companies.
Online learnable keyframe extraction in videos and its application with semantic word vector in action recognition

Video processing has become a popular research direction in computer vision due to its various applications such as video summarization, action recognition, etc. Recently, deep learning-based methods have achieved impressive results in action recognition. However, these methods need to process a full video sequence to recognize the action, even though many of the frames in the video sequence are similar and non-essential to recognizing a particular action. Additionally, these non-essential frames increase the computational cost and can confuse a method in action recognition. Instead, the important frames called keyframes not only are helpful in recognizing an action but also can reduce the processing time of each video sequence in classification or in other applications, e.g. summarization. As well, current methods in video processing have not yet been demonstrated in an online fashion. Motivated by the above, we propose an online learnable module for keyframe extraction. This module can be used to select key shots in video and thus, can be applied to video summarization. The extracted keyframes can be used as input to any deep learning-based classification model to recognize action. We also propose a plugin module to use the semantic word vector as input along with keyframes and a novel train/test strategy for the classification models. To our best knowledge, this is the first time such an online module and train/test strategy have been proposed. The experimental results on many commonly used datasets in video summarization and in action recognition have demonstrated the effectiveness of the proposed module.
Ten Open Challenges in Medical Visualization

The medical domain has been an inspiring application area in visualization research for many years already, but many open challenges remain. The driving forces of medical visualization research have been strengthened by novel developments, for example, in deep learning, the advent of affordable VR technology, and the need to provide medical visualizations for broader audiences. At IEEE VIS 2020, we hosted an Application Spotlight session to highlight recent medical visualization research topics. With this article, we provide the visualization community with ten such open challenges, primarily focused on challenges related to the visualization of medical imaging data. We first describe the unique nature of medical data in terms of data preparation, access, and standardization. Subsequently, we cover open visualization research challenges related to uncertainty, multimodal and multiscale approaches, and evaluation. Finally, we emphasize challenges related to users focusing on explainable AI, immersive visualization, P4 medicine, and narrative visualization.
Optimization framework for Best Approver Selection Method (BASM) and Best Tip Selection Method (BTSM) for IOTA tangle network: Blockchain-enabled next generation Industrial IoT

We develop an optimization framework to increase network scalability and to incorporate a priority level for essential transactions based on the Internet of Things and Application (IOTA) Tangle network for a blockchain-enabled next-generation industrial network. Firstly, we propose a model to count newly arrived tips (transactions) into a selected timeframe and calculate the transaction time before joining the Tangle network as a new tip. Selecting secure transactions for approval requires an accurate tip selection method. Secondly, we develop the Best Tip Selection Method (BTSM) to increase network security and to incorporate the priority level for essential or prioritized IoT transactions. Thirdly, we develop a multiple-objective unconstrained optimization strategy, the Best Approver Selection Method (BASM). The objective of BASM is to optimize the trade-off between the multiplication of network latency and the cumulative weight of transactions and approvers’ Tangle time after joining the network. Finally, we estimate the probability of malicious attacks on a Tangle network by assuming the transactions’ cumulative weight increases linearly with transaction arrival speed to a Tangle. We adapt the fault-tolerance relationship to secure the system. We then analyze our model to decide that requires us to consider the trade-off between these two factors to obtain the optimal value. Our results show that BTSM and BASM are viable solutions for next-generation Industrial IoT networks with fast machine-to-machine communications. Compared with IOTA and other existing strategies, our proposed model offers the advantage of identifying the probability of malicious attacks with high accuracy; thus minimizing malicious attacks and optimizing the network latency for next-generation Industrial IoT networks. In summary, the proposed mechanism would allow adding more transactions from IoT devices to the IOTA Tangle and discourage existing malicious transactions from verifying new transactions. The proposed mechanism could be utilized in real-world applications, including enabling preference levels for essential or prioritized activities and fault-tolerance adaptation. IOTA aims to play a vital role in the next-generation industrial revolution; our study advances the IOTA Tangle to move a step forward towards this direction.
Particle Swarm Optimization with New Initializing Technique to Solve Global Optimization Problems

Particle Swarm Optimization (PSO) is a well-known extensively utilized algorithm for a distinct type of optimization problem.In meta-heuristic algorithms, population initialization plays a vital role in solving the classical problems of optimization.The population's initialization in meta-heuristic algorithms urges the convergence rate and diversity, besides this, it is remarkably beneficial for finding the efficient and effective optimal solution.In this study, we proposed an enhanced variation of the PSO algorithm by using a quasi-random sequence (QRS) for population initialization to improve the convergence rate and diversity.Furthermore, this study represents a new approach for population initialization by incorporating the torus sequence with PSO known as TO-PSO.The torus sequence belongs to the family of low discrepancy sequence and it is utilized in the proposed variant of PSO for the initialization of swarm.The proposed strategy of population's initialization has been observed with the fifteen most famous unimodal and multimodal benchmark test problems.The outcomes of our proposed technique display outstanding performance as compared with the traditional PSO, PSO initialized with Sobol Sequence (SO-PSO) and Halton sequence (HO-PSO).The exhaustive experimental results conclude that the proposed algorithm remarkably superior to the other classical approaches.Additionally, the outcomes produced from our proposed work exhibits anticipation that how immensely the proposed approach highly influences the value of cost function, convergence rate, and diversity.
A Comprehensive Survey on Auction Mechanism Design for Cloud/Edge Resource Management and Pricing

Driven by the vision of cloud and edge computing as the 5th utility, a major challenge is the efficient resources management and pricing taking into account incentives and preferences of cloud/edge providers, users, and the system. In recent years, auction mechanism design has gained wide attention as a tool for addressing this challenge. Here existing surveys lack detailed studies in this area, hence this work presents a comprehensive survey on the state-of-the-art auction-based mechanisms in the field of cloud/edge computing. First, the problem statements and background are presented for cloud/edge computing and auction theory. This is followed by a collaboration model on the benefits of auction mechanism designs to cloud/edge computing. Next, various auction mechanisms in cloud computing are reviewed based upon the following aspects: (1) direction of bids—forward, reverse, and two-sided auctions, (2) the heterogeneity of resources—combinatorial auctions, (3) the number and types of adopted attributes in winner determination—multi-attribute auctions, (4) the setting of auction—offline, online, or sequential auctions, and (5) the interoperability among cloud providers—inter-clouds. For each aspect, existing studies are summarized along with their saliencies and drawbacks. Moreover, existing auction mechanisms in the context of edge computing are also presented, followed by existing challenges and research directions.
Analysis of Deep Neural Networks for Human Activity Recognition in Videos—A Systematic Literature Review

From the past few decades, Human activity recognition (HAR) is one of the vital research areas in computer vision in which much research is ongoing. The researcher’s focus is shifting towards this area due to its vast range of real-life applications to assist in daily living. Therefore, it is necessary to validate its performance on standard benchmark datasets and state-of-the-art systems before applying it in real-life applications. The primary objective of this Systematic Literature Review (SLR) is to collect existing research on video-based human activity recognition, summarize, and analyze the state-of-the-art deep learning architectures regarding various methodologies, challenges, and issues. The top five scientific databases (such as ACM, IEEE, ScienceDirect, SpringerLink, and Taylor & Francis) are accessed to accompany this systematic study by summarizing 70 different research articles on human activity recognition after critical review. Human activity recognition in videos is a challenging problem due to its diverse and complex nature. For accurate video classification, extraction of both spatial and temporal features from video sequences is essential. Therefore, this SLR focuses on reviewing the recent advancements in stratified self-deriving feature-based deep learning architectures. Furthermore, it explores various deep learning techniques available for HAR, challenges researchers to face to build a robust model, and state-of-the-art datasets used for evaluation. This SLR intends to provide a baseline for video-based human activity recognition research while emphasizing several challenges regarding human activity recognition accuracy in video sequences using deep neural architectures.
TarGAN: Target-Aware Generative Adversarial Networks for Multi-modality Medical Image Translation

Paired multi-modality medical images, can provide complementary information to help physicians make more reasonable decisions than single modality medical images. But they are difficult to generate due to multiple factors in practice (e.g., time, cost, radiation dose). To address these problems, multi-modality medical image translation has aroused increasing research interest recently. However, the existing works mainly focus on translation effect of a whole image instead of a critical target area or Region of Interest (ROI), e.g., organ and so on. This leads to poor-quality translation of the localized target area which becomes blurry, deformed or even with extra unreasonable textures. In this paper, we propose a novel target-aware generative adversarial network called TarGAN, which is a generic multi-modality medical image translation model capable of (1) learning multi-modality medical image translation without relying on paired data, (2) enhancing quality of target area generation with the help of target area labels. The generator of TarGAN jointly learns mapping at two levels simultaneously—whole image translation mapping and target area translation mapping. These two mappings are interrelated through a proposed crossing loss. The experiments on both quantitative measures and qualitative evaluations demonstrate that TarGAN outperforms the state-of-the-art methods in all cases. Subsequent segmentation task is conducted to demonstrate effectiveness of synthetic images generated by TarGAN in a real-world application. Our code is available at https://github.com/cs-xiao/TarGAN.
A diagnostic prediction framework on auxiliary medical system for breast cancer in developing countries

Due to the complexity of the tumor and a large amount of patient information, an intelligent system is used to filter and extract hidden information, which will be beneficial to make accurate diagnostic decisions. The treatment and prognosis of breast cancer depend on the tumor stage. The PET-CT image can clearly show the lesion area and lesion range, especially for advanced-stage patients. Images and blood tests are crucial for accurate staging, tumor monitoring, and providing guided treatment plans. Multi-source data collaborative analysis can mine hidden attributes to provide a more intelligent treatment plan. This paper proposes a framework for predicting the diagnosis of the patient's disease by combining images and labeling parameters. The blood test data and image data of patients are filtered based on the establishment of the medical decision-making module. The module selects detection indicators that correlate with tumor staging for analysis and trains a prediction model to assist doctors in providing a second diagnosis. The proposed framework for breast cancer diagnosis was tested on 5470 patient data from three well-known hospitals in China. The test results indicate that it performs well in diagnosing cancer staging with a prediction accuracy of 0.88.
Machine learning and multi-sensor data fusion for mapping lithology: A case study of Kowli-kosh area, SW Iran

Lithological maps derived from traditional pixel-based image analysis are widely established in remote sensing. Recent development of new segmentation-based approaches such as object-based image analysis (OBIA) has led to significant improvements of accuracy in land cover classifications. However, additional enhancements have been reported by coupling OBIA with machine learning (ML) techniques. ML which automates computer algorithms and model building, enables learning, reasoning, and decision making through analysis and interpretation of data patterns and structures. This study is an attempt for evaluating and comparing the performances of four ML classifiers, including support vector machine (SVM), normal Bayes (NB), k-nearest neighbor (k-NN), and random forest (RF). This approach was tested with medium resolution imagery using an object-based classification procedure in a metamorphic complex, SW Iran. The multi-band input datasets were assembled form individual and multi-sensor layers from ASTER, Landsat 8 OLI, Sentinel-1 and Sentinel-2A. Results revealed that combined data sets obtain more reasonable classes for lithology, and combination of ASTER + simulated panchromatic of Sentinel-2A data shows the most efficient results. We also showed that, in general, RF and SVM algorithms were the most advantageous classifiers when using an object-based image analysis approach. When integrating the data sets aiming improvement of spatial resolution, the maximum accuracies of RF, SVM, k-NN and NB algorithms increased from 90% to 98%, 81% to 97%, 52 to 75%, and 40% to 64%, respectively. We also revealed that the integration of Sentinel-1 and Sentinel-2A data was promising and allowed for additional extraction of rock relief.
Incremental Predictive Process Monitoring: The Next Activity Case

Next-activity prediction methods for business processes are always introduced in a static setting, implying a single training phase followed by the application of the learned model during the test phase. Real-life processes, however, are often dynamic and prone to changes over time. Therefore, all state-of-the-art methods need regular retraining on new data to be kept up to date. It is, however, not straightforward to determine when to retrain nor what data to use; for instance, should all historic data be included or only new data? Updating models that still perform at an acceptable level wastes a potentially large amount of computational resources while postponing an update too much will deteriorate model performance. In this paper, we present incremental learning strategies for updating these existing models that do not require fully retraining them, hence reducing the number of computational resources needed while still maintaining a more consistent and correct view of the process in its current form. We introduce a basic neural network method consisting of a single dense layer. This architecture makes it easier to perform fast updates to the model and enables us to perform more experiments. We investigate the differences between our proposed incremental approaches. Experiments performed with a prototype on real-life data show that these update strategies are a promising way forward to further increase the power and usability of state-of-the-art methods.
Robust CSI-Based Human Activity Recognition With Augment Few Shot Learning

With the development of Internet of Things technology, Channel State Information (CSI) based human activity recognition (HAR) plays an important role in Human-Computer Interaction and achieves considerable advancements over recent years. However, when the trained model is applied to recognize new activity categories or recognizing new users in new scenarios, the recognition performance of general methods will dramatically decline. And re-collecting adequate new activity categories’ samples to train the HAR model to adapt to the new situation will consume a lot of time and human effort. To overcome this challenge, we propose a framework, Augment Few Shot Learning-based Human Activity Recognition (AFSL-HAR), which can achieve significant performance in recognizing new categories through a small amount of samples to fine-tune the model parameters and avoid retraining the network from scratch again. And besides, in order to improve the robustness of AFSL-HAR, we design a Feature Wasserstein Generative Adversarial Network (FWGAN) module, which can synthesize diverse samples to help the recognition model learn more sharper classification boundaries. Specifically, the FWGAN module incorporates a feature extractor to realize converging with a fewer number of training samples, and takes an improved discriminator to enhance system performance. The experimental results demonstrate that AFSL-HAR can achieve accuracy of 98.9% and 94.7% when recognizing new activities using few samples for each category on the public data set and self-made data set, respectively.
“Serving Each User”: Supporting Different Eating Goals Through a Multi-List Recommender Interface

Food recommender systems optimize towards a user's current preferences. However, appetites may vary, in the sense that users might seek healthy recipes today and look for unhealthy meals tomorrow. In this paper, we propose a novel approach in the food domain to diversify recommendations across different lists to 'serve' different users goals, compiled in a multi-list food recommender interface. We evaluated our interface in a 2 (single list vs multiple lists) x 2 (without or with explanations) between-subject user study (N = 366), linking choice behavior and evaluation aspects through the user experience framework. Our multi-list interface was evaluated more favorably than a single-list interface, in terms of diversity and choice satisfaction. Moreover, it triggered changes in food choices, even though these choices were less healthy than those made in the single-list interface.
Is a Single Model Enough? MuCoS: A Multi-Model Ensemble Learning Approach for Semantic Code Search

Recently, deep learning methods have become mainstream in code search since they do better at capturing semantic correlations between code snippets and search queries and have promising performance. However, code snippets have diverse information from different dimensions, such as business logic, specific algorithm, and hardware communication, so it is hard for a single code representation module to cover all the perspectives. On the other hand, as a specific query may focus on one or several perspectives, it is difficult for a single query representation module to represent different user intents. In this paper, we propose MuCoS, a multi-model ensemble learning architecture for semantic code search. It combines several individual learners, each of which emphasizes a specific perspective of code snippets. We train the individual learners on different datasets which contain different perspectives of code information, and we use a data augmentation strategy to get these different datasets. Then we ensemble the learners to capture comprehensive features of code snippets. The experiments show that MuCoS has better results than the existing state-of-the-art methods. Our source code and data are anonymously available at https://github.com/Xzh0u/MuCoS.