Reviewerly/exHarmony huggingface dataset:
A multilayer perceptron-based model applied to histopathology image classification of lung adenocarcinoma subtypes

Lung cancer is one of the most common malignant tumors in humans. Adenocarcinoma of the lung is another of the most common types of lung cancer. In clinical medicine, physicians rely on the information provided by pathology tests as an important reference for the fifinal diagnosis of many diseases. Thus, pathological diagnosis is known as the gold standard for disease diagnosis. However, the complexity of the information contained in pathology images and the increase in the number of patients far exceeds the number of pathologists, especially in the treatment of lung cancer in less-developed countries.This paper proposes a multilayer perceptron model for lung cancer histopathology image detection, which enables the automatic detection of the degree of lung adenocarcinoma infifiltration. For the large amount of local information present in lung cancer histopathology images, MLP IN MLP (MIM) uses a dual data stream input method to achieve a modeling approach that combines global and local information to improve the classifification performance of the model. In our experiments, we collected 780 lung cancer histopathological images and prepared a lung histopathology image dataset to verify the effectiveness of MIM.The MIM achieves a diagnostic accuracy of 95.31% and has a precision, sensitivity, specificity and F1-score of 95.31%, 93.09%, 93.10%, 96.43% and 93.10% respectively, outperforming the diagnostic results of the common network model. In addition, a number of series of extension experiments demonstrated the scalability and stability of the MIM.In summary, MIM has high classifification performance and substantial potential in lung cancer detection tasks.
Blockchain technology as a driver of economic development in small economies: a dynamic capabilities framework

ABSTRACTABSTRACTBlockchain can enable small countries to overcome systemic constraints and strengthen their economies. Based on dynamic capabilities theory, we develop a conceptual framework that explores the applicability of blockchain for fostering economic development. Specifically, we postulate that the agent's role in dynamic capabilities theory can be extended from the organisation level to the country level. Our proposed framework integrates blockchain as an economic driver and dynamic capabilities on higher-order and lower-order levels (i.e., business and investment development, human capital development, financial system enhancement, regulatory framework improvement, and systems and infrastructure improvement) to show their impacts on different aspects of economic development. International regulatory and political factors serve as moderators that determine whether a national blockchain-based strategy will ultimately be successful. In summary, we illustrate how blockchain as an enabler of dynamic capabilities can contribute to a small country's economic development.KEYWORDS: Blockchaindistributed ledger technologydynamic capabilitieseconomic developmentemerging technologytechnology adoption Disclosure statementNo potential conflict of interest was reported by the authors.
KTPGN: Novel event-based group recommendation method considering implicit social trust and knowledge propagation

Groups where like-minded people gather to share interests, comments, or participate in activities have recently gained popularity in well-known social platforms, such as Meetup and Douban. Group recommendations are essential in social media to help users identify their preferred groups. Our concern is event-based group recommendations, based on which users join a group primarily to participate in offline events hosted by group members. Traditional methods for group recommendation do not work well in addressing this issue, because they ignore the social relationships among users or multiple attributes of groups. The knowledge-enhanced trust propagation graph neural network (KTPGN) presented in this study exploits social trust, multiple attributes of groups, and user-group interaction history in a unified framework to predict user preferences for groups. First, an implicit social trust graph is constructed by considering two aspects of credibility, because explicit trust relationships are not always available in most real recommender systems. Furthermore, a graph neural network (GNN)-based model is proposed to learn group embeddings by iteratively aggregating multiple linked attributes and user embeddings through recursive propagation of social trust and historical interest. Experimental results on several datasets from Meetup demonstrate that KTPGN significantly outperforms state-of-the-art recommendation methods.
A deep actor critic reinforcement learning framework for learning to rank

In this paper, we propose a Deep Reinforcement learning based approach for Learning to rank task. Reinforcement Learning has been applied in the ranking task with good success, but the existing Policy Gradient based approaches suffer from noisy gradients and high variance, resulting in unstable learning. The natural policy gradient methods like REINFORCE perform Monte Carlo sampling, thus taking samples randomly, which leads to high variance. As the action space becomes large, i.e., with a very large number of documents, traditional RL techniques lack the complex model required in the scenario to deal with a large number of items. We propose a Deep Reinforcement learning based approach for learning to rank task in this paper to address these issues. By combining Deep learning with the Reinforcement Learning framework, our approach can learn a complex function as deep neural networks can provide significant function approximation. We used Actor-Critic framework where the critic network can reduce variance by utilizing techniques such as clipped delayed policy updates, clipped double q learning, etc. Also, due to the enormous space of the web, the most relevant results are needed to be returned for the corresponding query from within a large action space. Policy gradient algorithms have been effectively applied to problems in large action spaces(items) with deep neural networks as they don’t rely on finding value for each action(item) as in value-based methods. Further, we use an actor-network with a CNN layer in the ranking process to capture the sequential patterns among the documents. We utilize the TD3 method to train our Reinforcement Learning agent with a listwise loss function, which performs delayed policy updates resulting in value estimates with lower variance. To the best of our knowledge, this is the first Deep reinforcement learning method applied in Learning to Rank for document retrieval. We performed experiments on the various Letor datasets and showed that our method outperforms various state-of-the-art baselines.
Debiasing backdoor attack: A benign application of backdoor attack in eliminating data bias

Backdoor attack which carries out a threat to model training has received increasing attention in recent years. Reviewing the previous research on adversarial attacks posing risk at the testing stage while at the same time facilitating the understanding of model predictions, we argue that the backdoor attack also has the potential to probe into the model learning process and help improve model performance. We started by attributing the phenomenon of Clean Accuracy Drop (CAD) in backdoor attack as the result of pseudo-deletion to the training data. Then an explanation from the perspective of model classification boundary is provided to explain this phenomenon that backdoor attack has advantages over undersampling in the data debiasing problem. Based on the above findings, we proposed Debiasing Backdoor Attack (DBA), employing backdoor attacks to address the data bias problem. Experiments demonstrate the effectiveness of backdoor attacks in debiasing tasks, with the envisioning of a broader range of benign application scenarios. Our code for the study can be found at https://github.com/KirinNg/DBA.
How do we elicit more user feedback in the social Q&amp;A community? A consideration of the expertise-required question

Purpose The current study aims to investigate the factors that impact the feedback received on answers to questions in social Q&amp;A communities and whether the expertise-required question influences the role of these factors on the feedback. Design/methodology/approach To understand the antecedents and consequences that influence the feedback received on answers to online community questions, the elaboration likelihood model (ELM) is applied in this study. The authors use web data crawling methods and a combination of quantitative analyses. The data for this study came from Zhihu; in total, 353,775 responses were obtained to 1,531 questions, ranging from 49 to 23,681 responses per question. Each answer received 0 to 113,892 likes and 0 to 6,250 comments. Findings The answers' cognitive and emotional components and the answerer's influence positively affect user feedback behavior. In addition, the expertise-required question moderates the effects of the answer's cognitive component and emotional component on the user feedback, moderating the effects of the answerer's influence on the user approval feedback. Originality/value This study builds upon a limited yet growing body of literature on a theme of great relevance to scholars, practitioners and social media users concerning the effects of the connotation of answers (i.e. their cognitive and emotional components) and the answerer's influence on user feedback (i.e. approval and collaborative feedback) in social Q&amp;A communities. The authors further consider the moderating role of the domain expertise required by the question (expertise-required question). The ELM model is applied to explore the relationships between questions, answers and feedback. The findings of this study add a new perspective to the research on user feedback and have implications for the management of social Q&amp;A communities.
Using the Whale Optimization Algorithm to Solve the Optimal Reactive Power Dispatch Problem

The optimal reactive power dispatch (ORPD) is a complex, optimal non-meritorious control issue with continuous and discontinuous control variables. This article exhibits a whale optimization algorithm (WOA) motivated by the whale’s bubble-net hunting tactic to resolve ORPD. The intention is to comply with certain constraints to promote the voltage transmission quality by adequately altering the parameters. The WOA not only equalizes exploitation and exploration to maximize the overall performance and eliminate search stagnation but also has remarkable sustainability and robustness to accomplish superior convergence speed and computation accuracy. The WOA is contrasted with MFO, BA, GOA, GWO, MDWA, SMA, SPBO and SSA by diminishing the fitness value to highlight the superiority and stability. The experimental results reveal that WOA exhibits a superior convergence level and computation precision to accomplish the minimum active power loss and superior control variables.
Unsupervised outlier detection for time-series data of indoor air quality using LSTM autoencoder with ensemble method

Abstract The proposed framework consists of three modules as an outlier detection method for indoor air quality data. We first use a long short-term memory autoencoder (LSTM-AE) based reconstruction error detector, which designs the LSTM layer in the shape of an autoencoder, to build a reconstruction error-based outlier detection model and extract latent features. The latent feature class-assisted vector machine detector constructs an additional outlier detection model using previously extracted latent features. Finally, the ensemble detector combines the two independent classifiers to define a new ensemble-based decision rule. Furthermore, because real-time anomaly detection proceeds with unsupervised learning, more stable and consistent external detection rules are defined than when using a single ensemble model. Laboratory tests with five random cases were performed for objective evaluation. Thus, we propose a framework that can be applied to various industrial environments by detecting and defining stable outlier decision rules.
Using 4-gram to Obtain Factor Scores of Japanese Statements Posted at Q&amp;A Sites

With the aim of clearing the issues of mismatches between the intentions of questioners and respondents of Question and Answer (Q&A) sites, nine factors of impressions for Q&A statements have been obtained through impression evaluation experiments. Then by means of multiple regression analysis, factor scores have been estimated by using the feature values of statements e.g., syntactic information, etc. The factor scores estimated and obtained have been subsequently employed for inspecting their potential to detect respondents who are likely to post appropriate answers to a posted question. However, our method has considerably depended on the syntactic information extracted through morphological analysis. Furthermore, this method has a critical downside of numerous explanatory variables and complicated multiple regression equation to estimate factor scores. Thus, we have shifted our principle to use N-gram on top of morphological analysis. In this paper, 4-gram is applied to the feature values, in a similar way as the previous analysis using 2-gram and 3-gram. Further analysis has shown that 4-gram is also applicable to our method. As a result of the analysis, 2-gram, 3-gram and 4-gram also outscore morphological analysis in terms of estimation accuracy; above all 2-gram shows the best accuracy. By and large, it could be suggested that N-gram as well as morphological analysis should be more sufficient than mere morphological analysis as the syntactic information of the feature values to estimate factor scores.
SC-CNN: Effective Speaker Conditioning Method for Zero-Shot Multi-Speaker Text-to-Speech Systems

This letter proposes an effective speaker-conditioning method that is applicable to zero-shot multi-speaker text-to-speech (ZSM-TTS) systems. Based on the inductive bias in the speech generation task, in which local context information in text/phoneme sequences heavily affect the speaker characteristics of the output speech, we propose a Speaker-Conditional Convolutional Neural Network (SC-CNN) for the ZSM-TTS task. SC-CNN first predicts convolutional kernels from each learned speaker embedding, then applies 1-D convolutions to phoneme sequences with the predicted kernels. It utilizes the aforementioned inductive bias and effectively models the characteristic of speech by providing the speaker-specific local context in phonetic domain. We also build both FastSpeech2 and VITS-based ZSM-TTS systems to verify its superiority over conventional speaker conditioning methods. The results confirm that the models with SC-CNN outperform the recent ZSM-TTS models in terms of both subjective and objective measurements.
Energy-Aware, Device-to-Device Assisted Federated Learning in Edge Computing

The surging of deep learning brings new vigor and vitality to shape the prospect of intelligent Internet of Things (IoT), and the rise of edge intelligence enables provisioning real-time deep neural network (DNN) inference services for mobile users. To perform efficient and effective DNN model training in edge computing environments while preserving training data security and privacy of IoT devices, federated learning has been envisioned as an ideal learning paradigm for this purpose. In this article, we study energy-aware DNN model training in edge computing. We first formulate a novel energy-aware, Device-to-Device (D2D) assisted federated learning problem with the aim to minimize the global loss of a training DNN model, subject to bandwidth capacity on an edge server and energy capacity on each IoT device. We then devise a near-optimal learning algorithm for the problem when the training data follows the i.i.d. data distribution. The crux of the proposed algorithm is to explore using the energy of neighboring devices of each device for its local model uploading, by reducing the problem to a series of weighted maximum matching problems in corresponding auxiliary graphs. We also consider the problem without the assumption of the i.i.d. data distribution, for which we propose an efficient heuristic algorithm. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results show that the proposed algorithms are promising.
Large-Scale Clustering With Structured Optimal Bipartite Graph

The widespread arising of data size gives rise to the necessity of undertaking large-scale data clustering tasks. To do so, the bipartite graph theory is frequently applied to design a scalable algorithm, which depicts the relations between samples and a few anchors, instead of binding pairwise samples. However, the bipartite graphs and existing spectral embedding methods ignore the explicit cluster structure learning. They have to obtain cluster labels by using post-processing like K-Means. More than that, existing anchor-based approaches always acquire anchors by using centroids of K-Means or a few random samples, both of which are time-saving but performance-unstable. In this paper, we investigate the scalability, stableness and integration in large-scale graph clustering. We propose a cluster-structured graph learning model, thus obtaining a c-connected ( c is the cluster number) bipartite graph and also getting discrete labels straightforward. Taking data feature or pairwise relation as a start point, we further design an initialization-independent anchor selection strategy. Experimental results reported for synthetic and real-world datasets demonstrate the proposed method outperforms its peers.
DRL-Based Secure Aggregation and Resource Orchestration in MEC-Enabled Hierarchical Federated Learning

Federated learning (FL) provides a new paradigm for protecting data privacy by enabling model training at devices and model aggregation at servers. However, data information may be leaked to honest-but-curious aggregation servers by updated model parameters. The existing secure methods do not fully exploit the potentiality of data characteristics in enhancing security, which makes it impossible to optimize limited system resources overall to achieve secure, fair, and efficient FL systems. In this article, a DRL-based joint secure aggregation and resource orchestration scheme is proposed to guarantee security and fairness, and improve efficiency for hierarchical FL (HFL) assisted by untrusted mobile-edge computing (MEC) servers. We formulate a joint optimization problem of data size, payment, and resource orchestration, to maximize the long-term social welfare subject to secure aggregation and limited resources. Since the formulated problem is a complex mixed integer dynamic optimization problem with NP-hardness, where multiple mixed integer optimization variables are highly coupled in time-varying constraints and objective function, it is difficult to obtain its optimal solution via traditional optimization methods. Thus, we propose a hierarchical reward function-based DRL algorithm (MATD3) to guide the agents to approach the optimal policy of secure aggregation and resource orchestration. Simulation results show that the proposed algorithm MATD3 can achieve superior performance over comparison algorithms and the MEC-enabled HFL framework outperforms two-layer FL frameworks.
Deepfakes Catcher: A Novel Fused Truncated DenseNet Model for Deepfakes Detection

In recent years, we have witnessed a tremendous evolution in generative adversarial networks resulting in the creation of much realistic fake multimedia content termed deepfakes. The deepfakes are created by superimposing one person’s real facial features, expressions, or lip movements onto another one. Apart from the benefits of deepfakes, it has been largely misused to propagate disinformation about influential persons like celebrities, politicians, etc. Since the deepfakes are created using different generative algorithms and involve much realism, thus it is a challenging task to detect them. Existing deepfakes detection methods have shown lower performance on forged videos that are generated using different algorithms, as well as videos that are of low resolution, compressed, or computationally more complex. To counter these issues, we propose a novel fused truncated DenseNet121 model for deepfakes videos detection. We employ transfer learning to reduce the resources and improve effectiveness, truncation to reduce the parameters and model size, and feature fusion to strengthen the representation by capturing more distinct traits of the input video. Our fused truncated DenseNet model lowers the DenseNet121 parameters count from 8.5 to 0.5 million. This makes our model more effective and lightweight that can be deployed in portable devices for real-time deepfakes detection. Our proposed model can reliably detect various types of deepfakes as well as deepfakes of different generative methods. We evaluated our model on two diverse datasets: a large-scale FaceForensics (FF)++ dataset and the World Leaders (WL) dataset. Our model achieves a remarkable accuracy of 99.03% on the WL dataset and 87.76% on the FF++ which shows the effectiveness of our method for deepfakes detection.
Weightwise Perfectly Balanced Functions and Nonlinearity

In this article we realize a general study on the nonlinearity of weightwise perfectly balanced (WPB) functions. First, we derive upper and lower bounds on the nonlinearity from this class of functions for all n. Then, we give a general construction that allows us to provably provide WPB functions with nonlinearity as low as $$2^{n/2-1}$$ and WPB functions with high nonlinearity, at least $$2^{n-1}-2^{n/2}$$ . We provide concrete examples in 8 and 16 variables with high nonlinearity given by this construction. In 8 variables we experimentally obtain functions reaching a nonlinearity of 116 which corresponds to the upper bound of Dobbertin’s conjecture, and it improves upon the maximal nonlinearity of WPB functions recently obtained with genetic algorithms. Finally, we study the distribution of nonlinearity over the set of WPB functions. We examine the exact distribution for $$n=4$$ and provide an algorithm to estimate the distributions for $$n=8$$ and 16, together with the results of our experimental studies for $$n=8$$ and 16.
Deep Learning Technique to Identify Abrupt Movements in Human-Robot Collaboration

According to the ninth Sustainable Development Goal (SDG9) of the 2030 Agenda, an upgrade of technological capabilities promotes the development of a sustainable and inclusive industrialization. In this context, a fundamental requirement is represented by the operator's safety inside the workspace, especially when it is shared with a collaborative robot. Even if typical collaborative tasks are usually characterized by repetitive and controlled kinematics and dynamics, external disturbances and environmental factors can make the operator executing unexpected and abrupt gestures which are highly variable. The current study aimed at identifying human unexpected movements measuring upper body accelerations through wearable magneto-inertial measurement units. An experimental pick and place task was performed by five subjects, combining both routine and abrupt movements. Recurrent neural network was exploited to distinguish between normal and unexpected gestures. Overall, the chosen deep learning network and the developed pre-classification method for accelerations proved to be suitable for the identification of human abrupt movements in interaction with machines.