Reviewerly/exHarmony huggingface dataset:
A convolution neural network with multi-level convolutional and attention learning for classification of cancer grades and tissue structures in colon histopathological images

A clinically comparable Convolutional Neural Network framework-based technique for performing automated classification of cancer grades and tissue structures in hematoxylin and eosin-stained colon histopathological images is proposed in this paper. It comprised of Enhanced Convolutional Learning Modules (ECLMs), multi-level Attention Learning Module (ALM), and Transitional Modules (TMs). The ECLMs perform a dual mechanism to extract multi-level discriminative spatial features and model cross-channel correlations with fewer computations and effectual avoidance of vanishing gradient issues. The ALM performs focus-refinement through the channel-wise elemental attention learning to accentuate the discriminative channels of the features maps specifically belonging to the important pathological regions and the scale-wise attention learning to facilitate recalibration of features maps at diverse scales. The TMs concatenate the output of these two modules, infuse deep multi-scalar features and eliminate resolution degradation issues. Varied pre-processing techniques are further employed to improvise the generalizability of the proposed network. For performance evaluation, four diverse publicly available datasets (Gland Segmentation challenge(GlaS), Lung Colon(LC)-25000, Kather_Colorectal_Cancer_Texture_Images (Kather-5k), and NCT_HE_CRC_100K(NCT-100k)) and a private dataset Hospital Colon(HosC) are used that further aids in building network invariance against digital variability that exists in real-clinical data. Also, multiple pathologists are involved at every stage of the proposed research and their verification and approval are taken for each step outcome. For the cancer grade classification, the proposed model achieves competitive results for GlaS (Accuracy(97.5%), Precision(97.67%), F1-Score(97.67%), and Recall(97.67%)), LC-25000 (Accuracy(100%), Precision(100%), F1-Score(100%), and Recall(100%)), and HosC(Accuracy(99.45%), Precision(100%), F1-Score(99.65%), and Recall(99.31%)), and while for the tissue structure classification, it achieves results for Kather-5k(Accuracy(98.83%), Precision(98.86%), F1-Score(98.85%), and Recall(98.85%)) and NCT-100k(Accuracy(97.7%), Precision(97.69%), F1-Score(97.71%), and Recall(97.73%)). Furthermore, the reported activation mappings of Gradient-Weighted Class Activation Mappings(Grad-CAM), Occlusion Sensitivity, and Local Interpretable Model-Agnostic Explanations (LIME) evidence that the proposed model can itself learn the similar patterns considered pertinent by the pathologists exclusive of any prerequisite for annotations. In addition, these visualization results are inspected by multiple expert pathologists and provided with a validation score as (GlaS(9.251), LC-25000(9.045), Kather-5k(9.248), NCT-100k(9.262), and HosC(9.853)). This model will provide a secondary referential diagnosis for the pathologists to ease their load and aid them in devising an accurate diagnosis and treatment plan.
A Two-Step Data Normalization Approach for Improving Classification Accuracy in the Medical Diagnosis Domain

Data normalization is a data preprocessing task and one of the first to be performed during intellectual analysis, particularly in the case of tabular data. The importance of its implementation is determined by the need to reduce the sensitivity of the artificial intelligence model to the values of the features in the dataset to increase the studied model’s adequacy. This paper focuses on the problem of effectively preprocessing data to improve the accuracy of intellectual analysis in the case of performing medical diagnostic tasks. We developed a new two-step method for data normalization of numerical medical datasets. It is based on the possibility of considering both the interdependencies between the features of each observation from the dataset and their absolute values to improve the accuracy when performing medical data mining tasks. We describe and substantiate each step of the algorithmic implementation of the method. We also visualize the results of the proposed method. The proposed method was modeled using six different machine learning methods based on decision trees when performing binary and multiclass classification tasks. We used six real-world, freely available medical datasets with different numbers of vectors, attributes, and classes to conduct experiments. A comparison between the effectiveness of the developed method and that of five existing data normalization methods was carried out. It was experimentally established that the developed method increases the accuracy of the Decision Tree and Extra Trees Classifier by 1–5% in the case of performing the binary classification task and the accuracy of the Bagging, Decision Tree, and Extra Trees Classifier by 1–6% in the case of performing the multiclass classification task. Increasing the accuracy of these classifiers only by using the new data normalization method satisfies all the prerequisites for its application in practice when performing various medical data mining tasks.
K-nearest neighbour-based smart contract for internet of medical things security using blockchain

Due to the fast-pacing development of technology in the healthcare domain, many problems arise surrounding the security and privacy preservation of medical data. Secure authentication on the Internet of Medical Things (IoMT) is essential. The lack of security in critical and sensitive information of IoMT may lead to high-risk issues in patient privacy. When new data is transmitted from the sensor node, it cannot be assured as authenticated data. Therefore, a blockchain-based system is needed. Such a system allows healthcare providers to access the health records of patients in a more secured authentication-based approach across various network connections. In this paper, a new secure authentication approach using machine learning is proposed. To identify the dynamic time attack detection and authentication in an IoMT environment, this work implements K-Nearest neighbour (KNN) and machine learning using smart contract (KNN-MLSC). It improves security, reduces latency, and maintains health data privacy for both physicians and patients. The accuracy of KNN-MLSC got 0.96 compared with KNN using a smart contract. Also, the results showed that KNN-MLSC has the minimum computation time.
Boundary-rendering network for breast lesion segmentation in ultrasound images

Breast Ultrasound (BUS) has proven to be an effective tool for the early detection of cancer in the breast. A lesion segmentation provides identification of the boundary, shape, and location of the target, and serves as a crucial step toward accurate diagnosis. Despite recent efforts in developing machine learning algorithms to automate this process, problems remain due to the blurry or occluded edges and highly irregular nodule shapes. Existing methods often produce over-smooth or inaccurate results, failing the need of identifying detailed boundary structures which are of clinical interest. To overcome these challenges, we propose a novel boundary-rendering framework that explicitly highlights the importance of boundary for automated nodule segmentation in BUS images. It utilizes a boundary selection module to automatically focuses on the ambiguous boundary region and a graph convolutional-based boundary rendering module to exploit global contour information. Furthermore, the proposed framework embeds nodule classification via semantic segmentation and encourages co-learning across tasks. Validation experiments were performed on different BUS datasets to verify the robustness of the proposed method. Results show that the proposed method outperforms states-of-art segmentation approaches (Dice=0.854, IOU=0.919, HD=17.8) in nodule delineation, as well as obtains a higher classification accuracy than classical classification models.
Parameter and strategy adaptive differential evolution algorithm based on accompanying evolution

Differential evolution (DE) is an intelligent optimization algorithm inspired by biological evolution. Setting a mutation strategy and control parameters that meet the optimization requirements are the premise for DE to achieve good performance. This paper proposes a parameter and strategy adaptive differential evolution algorithm based on accompanying evolution (APSDE). Through the accompanying population, in which individuals are composed of suboptimal solutions, the mutation strategy and control parameters are optimized to realize the adaptation of the strategy and parameters of the main population. Population diversity is enhanced in evolution by generating reverse individuals. In addition, radial spatial projection technology is utilized to track the change in evolution direction with optimization. The performance of APSDE is validated under four sets of benchmark problem suites from the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC), and compared with state-of-the-art optimization algorithms. The results show that the proposed algorithm has better optimization performance than the competitive algorithms because of its efficient adaptive mechanism and its excellent population diversity.
Improved Analytic Expansions in Hybrid A-Star Path Planning for Non-Holonomic Robots

In this study, we concisely investigate two phases in the hybrid A-star algorithm for non-holonomic robots: the forward search phase and analytic expansion phase. The forward search phase considers the kinematics of the robot model in order to plan continuous motion of the robot in discrete grid maps. Reeds-Shepp (RS) curve in the analytic expansion phase augments the accuracy and the speed of the algorithm. However, RS curves are often produced close to obstacles, especially at corners. Consequently, the robot may collide with obstacles through the process of movement at these corners because of the measurement errors or errors of motor controllers. Therefore, we propose an improved RS method to eventually improve the hybrid A-star algorithm’s performance in terms of safety for robots to move in indoor environments. The advantage of the proposed method is that the non-holonomic robot has multiple options of curvature or turning radius to move safer on pathways. To select a safer route among multiple routes to a goal configuration, we introduce a cost function to evaluate the cost of risk of robot collision, and the cost of movement of the robot along the route. In addition, generated paths by the forward search phase always consist of unnecessary turning points. To overcome this issue, we present a fine-tuning of motion primitive in the forward search phase to make the route smoother without using complex path smoothing techniques. In the end, the effectiveness of the improved method is verified via its performance in simulations using benchmark maps where cost of risk of collision and number of turning points are reduced by up to around 20%.
Mirror Detection With the Visual Chirality Cue

Mirror detection is challenging because the visual appearances of mirrors change depending on those of their surroundings. As existing mirror detection methods are mainly based on extracting contextual contrast and relational similarity between mirror and non-mirror regions, they may fail to identify a mirror region if these assumptions are violated. Inspired by a recent study of applying a CNN to help distinguish whether an image is flipped or not based on the visual chirality property, in this paper, we rethink this image-level visual chirality property and reformulate it as a learnable pixel level cue for mirror detection. Specifically, we first propose a novel flipping-convolution-flipping (FCF) transformation to model visual chirality as learnable commutative residual. We then propose a novel visual chirality embedding (VCE) module to exploit this commutative residual in multi-scale feature maps, to embed the visual chirality features into our mirror detection model. Besides, we also propose a visual chirality-guided edge detection (CED) module to integrate the visual chirality features with contextual features for detection refinement. Extensive experiments show that the proposed method outperforms state-of-the-art methods on three benchmark datasets.
RecBole 2.0: Towards a More Up-to-Date Recommendation Library

In order to support the study of recent advances in recommender systems, this paper presents an extended recommendation library consisting of eight packages for up-to-date topics and architectures. First of all, from a data perspective, we consider three important topics related to data issues (ie sparsity, bias and distribution shift ), and develop five packages accordingly, including meta-learning, data augmentation, debiasing, fairness and cross-domain recommendation. Furthermore, from a model perspective, we develop two benchmarking packages for Transformer-based and graph neural network~(GNN)-based models, respectively. All the packages (consisting of 65 new models) are developed based on a popular recommendation framework RecBole, ensuring that both the implementation and interface are unified. For each package, we provide complete implementations from data loading, experimental setup, evaluation and algorithm implementation. This library provides a valuable resource to facilitate the up-to-date research in recommender systems. The project is released at the link: \urlhttps://github.com/RUCAIBox/RecBole2.0.
Artificial Intelligence and Robots in Libraries: Opportunities in LIS Curriculum for Preparing the Librarians of Tomorrow

The use of AI and robots in library and information science is garnering attention due to early applications and their potential to contribute to the digital transformation of the information professions. This paper assesses the challenges and opportunities for LIS education in these topics. To achieve this aim, this paper reviews the curriculum, through subject descriptions, of five ALIA accredited LIS courses in Australia and the ALIA foundation knowledge documentation. Content analysis is employed to identify and assess the framing of AI, robotics and related themes in the documentation. Findings indicate only one subject mentions AI to position subject content and none mention robotics. An analysis of the framing of related themes, such as digital technology, data, and information ethics, is discussed. Findings also indicate multiple areas for the inclusion of these topics within the five categories of the ALIA foundation knowledge, while allowing for differentiation among programmatic and institutional foci. This paper argues that a form of integration of these topics in LIS professional education will be necessary in order to meet future skills needs. This paper concludes with opportunities for LIS education in Australia.
Diving into the uncertainties of open innovation: A systematic review of risks to uncover pertinent typologies and unexplored horizons

The open innovation paradigm has created substantial new opportunities for firms in various sectors. However, scholars have long expressed concern that open innovation also entails a dark side, which can result in value co-destruction. This concern highlights the importance of devoting attention to the perils of pursuing open innovation. Existing scholarship has given due credence to these perils by examining various associated risks and uncertainties. We observe that the extant literature is siloed and unorganized, which impedes future research. Positing that an endeavor to organize existing studies may enhance the pace of research in the area, we attempt to address this gap by reviewing the relevant literature. We thus utilize the systematic literature review approach to identify, synthesize, and critically analyze 80 related research articles. Based on this analysis, we present the bibliometric profile of the extant research and a typology of five risks in open innovation: data-related risks, people-related risks, firm-level risks, outcome risks, and other risks. In addition, we discuss a specific risk management approach for each of the identified risks. Beyond providing a lucid narrative of the extant literature, we also identify unexplored avenues and offer an overarching framework to conceptualize future research potential in the area. From a practical perspective, managers can utilize this framework as a risk assessment tool when engaging in open innovation. In sum, this review—one of the first of its kind—offers a valuable consolidation of the state of the art of open innovation risk research, which can meaningfully advance theory and practice in the area.
A Survey of Human Action Recognition and Posture Prediction

Human action recognition and posture prediction aim to recognize and predict respectively the action and postures of persons in videos. They are both active research topics in computer vision community, which have attracted considerable attention from academia and industry. They are also the precondition for intelligent interaction and human-computer cooperation, and they help the machine perceive the external environment. In the past decade, tremendous progress has been made in the field, especially after the emergence of deep learning technologies. Hence, it is necessary to make a comprehensive review of recent developments. In this paper, firstly, we attempt to present the background, and then discuss research progresses. Secondly, we introduce datasets, various typical feature representation methods, and explore advanced human action recognition and posture prediction algorithms. Finally, facing the challenges in the field, this paper puts forward the research focus, and introduces the importance of action recognition and posture prediction by taking interactive cognition in self-driving vehicle as an example.
Emerging research on blockchain technology in finance; a conveyed evidence of bibliometric-based evaluations

The emergence of blockchain technology is reshaping several aspects of economies, opening a wider spectrum for the research communities, and making it critical to conduct studies that comprehensively analyze this body of knowledge. From the Web of Sciences' database, a bibliometric study using VOS viewer and biblioshiny tool is provided on the evolving research on blockchain technology in finance. Blockchain in finance has provided enabled applications such as tokens, initial coin offerings, crowdfunding, smart contracts, prediction marketplace systems, and digital currencies like Bitcoin and Ethereum. In this study, we convey a detailed bibliometric-based evaluation of scientific research publications from 2008 to mid-2020. With 157 articles retrieved, our findings show the global trends, current tendencies, and frontiers. We report that blockchain technology research in finance has evolved and is predominated by studies on crowdfunding/equity crowdfunding, entrepreneurial finance, bitcoin, entrepreneurship, fintech, and venture capital. However, several of these studies are contextually based, with scantily used cases leading to the design of frameworks and architectures. Additionally, the majority of the studies could not afford sufficient details on the practical discharge of the few blockchain-enabled prototypes. • Bibliometric evaluation of articles on blockchain in finance from 2008 to mid-2020. • It describes key periodicals, references, and how studies have evolved. • The study describes the prevailing trends of blockchain in finance. • It outlines the essential features of blockchain on finance-related publications. • The study proposes new frontiers that can be exploited in further studies.
Transferable adaptive channel attention module for unsupervised cross-domain fault diagnosis

Domain adaptation methods are widely applied to unsupervised cross-domain fault diagnosis. However, the existing studies always treat the extracted features equally and thus cannot effectively tackle the negative transfer caused by those non-transferable features. Besides, complex actual diagnosis scenarios impose higher generalization performance requirements on traditional domain adaptation models. Given all this, we develop a transferable adaptive channel attention module to enhance the positive transfer and improve models' performance. As a practical plug-and-play component, it can be universally applicable to any one of most domain adaptation models with different network structures. To actively guide domain adaptation, the transferable adaptive channel attention module continuously recalibrates the feature maps based on their transferability during training to improve shared features' domain-invariance and category-discriminability. Moreover, by establishing adaptive selection of feature group size and third-order statistical moment matching strategies, the effectiveness and broad applicability of the proposed module are further improved. Without bells and whistles, the results of two transfer diagnosis cases demonstrate the advantages of the transferable adaptive channel attention module for improving various domain adaptation models' accuracy and generalization performance.
Blockchain Technology for Renewable Energy: Principles, Applications and Prospects

Blockchain, or distributed ledger, is an innovative technology that is emerging in various sectors and industries across the globe. It has attracted the attention of different interest groups such as energy companies, SMEs and start-ups, information technology developers, financial institutions, national authorities, and the university community. Through, for example, the decentralization of authority in transactions, Internet of Things (IoT) implementation, and smart contracting, the improvement of the daily business operations is firmly forecasted. In the energy sector, digitalization is already present in solutions such as smart grids, smart meters, electric vehicles, etc. Moreover, a new concept of the Internet of Energy (IoE) has been introduced in the academic literature. In this article, the level of trust and maturity of Blockchain technology implementation is investigated through the Blockchain Maturity Questionnaire, developed by the authors. The database consists of responses from upper management professionals from the renewable energy industry. The analysis reveals the state of know-how about Blockchain, the main benefits and bottlenecks associated with its implementation as well as willingness to integrate this technology in the case companies’ future operations. The insight from the industry experts helped to provide a “Roadmap for Blockchain Adoption” in future energy systems. This curiosity study yields numerous applications not only for the renewable energy industry experts but also for the interest groups coming from different industries, as well as public authorities and researchers scrutinizing the fields taken into its scope.