Reviewerly/exHarmony huggingface dataset:
An Enhanced Residual Networks Based Framework for Early Alzheimer’s Disease Classification and Diagnosis

Alzheimer's disease (AD) is a long-lasting, degenerative brain illness for which there is now no successful treatment. However, there are medications that can delay its growth and stop its tracks. Therefore, stopping and regulating the progression of AD depends greatly on its early diagnosis. Computer-aided diagnosis (CAD) in health care has become more and more dependent on deep learning technologies. This has help in early diagnosis and detection of various disease in health care. New techniques for the early diagnosis of neurocognitive abnormalities are now possible thanks to advancements in medical imaging and computational capacity in an effort to stop or slow down cognitive aging. Therefore, this study proposes a residual network (ResNet) type 50 for early diagnosis of AD. Convolutional neural networks (CNNs) are first used in the study for features extraction and then make the ultimate diagnosis using the ResNet-50. To create a better architecture, the problem of AD diagnosis will be addressed by utilizing and incorporating ResNet-50, which improves the current categorization problem's accuracy to its maximum. With a 98.8% accuracy rate, the proposed model using ResNet-50 produces encouraging findings for AD prediction. The study reveals that the model can effectively circumvent CNNs for the diagnostic issue with AD.
Blockchain-Enabled Reward System for Sustainable Last-Mile Delivery in Urban Logistics: A Case Study on SimFleet Platform

To promote green transportation within cities, the concept of active mobility has gained attraction in the research community. In that sense, the use of IoT sensors along with open fleets, sharing vehicles, and pay-per-use mobility services, allow the development and deployment of new platforms that have the potential to offer active transportation to the citizens. On the other hand, the extreme dependence on the data generated by the sensors, and the need to share information between public and private companies, have arisen challenges in the traceability of the information generated, along with the security of the communications between devices. In that sense, the incorporation of blockchain technology seems the key aspect to allow the real implementation of these systems in the real world, protecting the communications between machines from external attacks, while implementing a service for the trackability of the information generated within the platform. This paper makes use of a tool developed by the authors to simulate a scenario in which the model proposed allows the optimization of last-mile delivery. Besides, the use of blockchain technology in these systems, also increases the transparency in the platform and trust of the users by being able to track the source of the information for the decision-making algorithm.
Comparing Performance of Feature Extraction Methods and Machine Learning Models in Automatic Essay Scoring

This study used Kaggle data, the ASAP data set, and applied NLP and Bidirectional Encoder Representations from Transformers (BERT) for corpus processing and feature extraction, and applied different machine learning models, both traditional machine-learning classifiers and neural-network-based approaches. Supervised learning models were used for the scoring system, where six out of the eight essay prompts were trained separately and concatenated. Compared with previous study, we found that adding more features such as readability scores using Spacy Textsta improved the prediction results for the essay scoring system. The neural network model, trained on all prompt data and utilizing NLP for corpus processing and feature extraction, performed better than other models with an overall test quadratic weighted kappa (QWK) of 0.9724. It achieved the highest QWK score of 0.859 for prompt 1 and an average QWK of 0.771 across all 6 prompts, making it the best-performing machine learning model that was tested.
M2SA: a novel dataset for multi-level and multi-domain sentiment analysis

People have more channels to express their opinions and feelings about events, products, and celebrities because of the development of social networks. They are becoming rich data sources, gaining attention for many practical applications and in the field of research. Sentiment analysis (SA) is one of the most common uses of this data source. Of the currently available SA datasets, most are only suitable for use in SA corresponding to a specific level, such as document, sentence, or aspect levels. This renders it difficult to develop practical systems that require a combination of sentiment analyzes at all three levels. Additionally, the previous datasets included opinions on only a single domain, although many people often mention multiple domains when expressing their views. This study introduces a new dataset called multi-level and multi-domain (M2SA) for SA. Each sample in M2SA contains a short text with at least two sentences and two aspects with different domains and sentiment polarities. The release of the M2SA dataset will contribute to the promotion of research in the field of SA, primarily by promoting the development and improvement of methods for multi-level SA or multi-aspect, multi-domain SA. The M2SA dataset was tested using state-of-the-art SA methods and was compared with other standard datasets. The results demonstrate that the M2SA dataset is better than the previous datasets in supporting to improve of the performance of SA methods.
Multi-level adversarial attention cross-modal hashing

Deep cross-modal hashing has made great progress in recent years due to the development of deep learning and efficient hashing algorithms. However, most of the existing methods only focus on the feature distribution between modalities, and ignore the fine grain information in each modality. To solve this problem, we propose a multi-level adversarial attention cross-modal hashing (MAAH). First, we design a modality-attention module to find the fine-grained information of each modality. Specifically, we use the channel attention mechanism to divide modality information into relevant and irrelevant representation, in which the irrelevant representation is the fine-grained information of the modality. Then, we design a modality-adversary module to supplement the fine-grained information of each modality. In this module, intra-modal adversarial learning can supplement the relevant representation of modalities, and inter-modal adversarial learning can make the distribution of the relevant representation of each modality more uniform. Experimental results on three widely used datasets demonstrate the superiority of the proposed method.
Quantum image scaling with applications to image steganography and fusion

A quantum hue, saturation, and lightness model is proposed in which a triple qubit sequence (QHTS) is encoded and used as a data model for the implementation of quantum image scaling. The preparation and retrieval of QHTS images is presented, in which only q+2 qubits (where q is the bit depth) are required to encode color information while retaining relevant HSL image features and operability. A conventional nearest neighbor interpolation was adopted to implement quantum image up-scaling and down-scaling operations, from which two other scaling applications were developed. One such technique is a form of quantum steganography based on end-to-end encryption, which provides high capacity while ensuring the security of carrier images and secret messages. The other is a spatial remote sensing image fusion algorithm, based on QHTS images, which pioneers quantum pseudocolor composites of multi-spectral and panchromatic images. Simulation experiments demonstrated the proposed methodology provides an embedding capacity more than double that of existing quantum image steganography algorithms. In addition, a complexity analysis demonstrated the efficiency of the two proposed quantum image scaling applications, which take full advantage of quantum parallelism.
Partially overlapped grain shape restoration of the granular rock images

This paper introduces a novel method for restoring each grain's original shape from an image of a granular rock sample whose grains partially overlap with each other. Image-based grain shape restoration provides a promising way to understand the generation of the rock that involves sedimentary and diagenesis processes. The difficulties of this task are: 1) how to effectively identify the region of each grain that is partially overlapped with its neighbor grains; and 2) how to recover the overlapped part of a grain based on the residual part. To solve the first problem, a differential erosion strategy that combines concave-points-constraint and pore-skeleton-extension-erosion is proposed to overcome the under-partitioning issue that popularly occurs in the current erosion-dilation grain partitioning method. To address the second problem, closed B-spline interpolation is applied to recover the original shape of the grains based on the partially presented grain edges. The proposed method successfully processed some synthetic and real granular rock images and presented a competent ability to deal with the moderately compressed granular rock samples consisting of relatively high-roundness grains that each grain can be treated as a convex polygon.
Bi-level ensemble method for unsupervised feature selection

Unsupervised feature selection is an important machine learning task and thus attracts increasingly more attention. However, due to the absence of labels, unsupervised feature selection often suffers from stability and robustness problems. To tackle these problems, some works try to ensemble multiple feature selection results to obtain a consensus result. Most of the existing methods do the ensemble on the feature level, i.e., they directly ensemble feature selection results by feature ranking or voting aggregation, without paying any attention to the following downstream tasks. In this paper, we take clustering as the downstream task and wish to ensemble the base results to select features which are appropriate for clustering. To this end, we propose a novel bi-level feature selection ensemble method, which ensembles on two levels: the feature level and the clustering level. Together with feature level ensemble, we also learn a consensus clustering result from base feature selection results with self-paced learning. Then, we apply the consensus clustering result to guide the feature selection in turn. Extensive experiments are conducted to demonstrate that the proposed method outperforms other state-of-the-art feature selection and feature selection ensemble methods in the clustering task. The codes of this paper are released in https://doctor-nobody.github.io/codes/BLFSE.zip.
Mobile money innovations and health performance in sub-Saharan Africa

This study assesses nexuses between mobile money innovations and health performance in terms of total life expectancy in 43 countries in Sub-Saharan Africa employing data for the period 2004–2018. Four mobile money innovation dynamics are proxied with registered mobile money agents and active mobile money agents. The empirical evidence is based on quantile regressions. The findings overwhelmingly show that mobile money innovations are relevant in improving health performance or total life expectancy exclusively in bottom quantiles of the conditional distribution of total life expectancy. In other words, countries with below-median levels of total life expectancy are more susceptible to benefit from mobile money innovations compared to countries with above-median levels of total life expectancy. It follows that common or general policy measures on the linkage between mobile money innovations and health performance are unlikely to succeed unless attendant policies are contingent on initial levels of health performance and hence, tailored differently across countries with various initial levels of health performance. More policy implications are discussed.
New machine learning techniques for simulation-based inference: InferoStatic nets, kernel score estimation, and kernel likelihood ratio estimation

We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio estimators in cases when the probability density can be sampled but not computed directly. The ISN uses a backend neural network that models a scalar function called the inferostatic potential \varphi <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>φ</mml:mi></mml:math> . In addition, we introduce new strategies, respectively called Kernel Score Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the score and the likelihood ratio functions from simulated data. We illustrate the new techniques with some toy examples and compare to existing approaches in the literature. We mention en passant some new loss functions that optimally incorporate latent information from simulations into the training procedure.
Gendered Language Features Based on Perceptions of Malaysian Millennials in Facebook Communication

The study examined gendered language features based on perceptions of Malaysian millennials in Facebook communication. The data were collected from 60 millennials who were Facebook users living in Malaysia. In the study, the millennials were asked to read 14 comments by other Facebook users and asked to state whether they were written by female or male users and to justify their identification of gender. An analysis framework made based on past findings on gendered language features was used to code the 14 Facebook comments as female or male features. The analysis showed that the accuracy of gender identification was about 50%. Comments identified as written by males were those containing straightforward and short comments, harsh language, male discussion topics, and societal roles. Conversely, comments identified as written by females were those containing advice, expression of emotions, empathy, female discussion topics, and polite language. The findings suggest that some language features are losing a clear gender identification. The gender-neutral features emerge from female users who also use words with profanities and insults, and write in an autonomous or directive manner, and male users who engage in attenuation/experience sharing, and interpersonal orientation/supportiveness. The study indicates that despite the association of Facebook comment features with gender, gender identity lines are getting blurred among millennial Facebook users, making gender identification more difficult. Keywords: Gendered language features, Malaysian millennials, Facebook, Facebook communication, online gender identification.
IoT forensics: Analysis of a HIKVISION's mobile app

CCTV surveillance systems are ubiquitous IoT products. These CCTV systems can be remotely operated using either a mobile or a desktop application. HIKVISION is a well-known manufacturer of such devices that offers a variety of applications that allow remote usage of their products. Research regarding digital forensics of HIKVISION's CCTV systems is scarce and currently only limited to recovering video footage from the devices themselves, skipping all valuable artifacts that could reside within the applications' data that were utilized to access them. This unexplored piece of evidence is currently not parsed by either commercial or open source software yet it can hide vital information for a number of investigative questions. In this paper, a HIKVISION's mobile application is thoroughly analyzed, in both Android and iOS operating systems, in pursuit of evidentiary data that could reside within. Exploiting the findings of this study authors contributed to FOSS with the aim of assisting investigators with their examinations. In particular, they used their findings to develop relevant parsers for ALEAPP and iLEAPP.
The Influence of Modern Trends in the Development of the Creative Economy on the Education System

The aim of the study is an integration approach in the implementation of current business risk management problems through a new management technology that is becoming increasingly in demand in Russian practice. Research objectives: the theoretical provisions of the unit economy composition are substantiated; the indicators for assessing the business at the stage of creation are determined; the influence of the unit economy as an integral part of the business model is disclosed in order to ensure the viability of client–oriented entrepreneurship.&#x0D; Results and practical significance. The presented research reveals the theoretical foundations and reveals the specific features inherent in the unit-economy system. Determines the economic content of the indicators of the new management model and the relationship between them. Practical approaches to the use of unit economics in the conditions of economic crisis are illustrated and the connection of unit economics with the management accounting system is substantiated. The work uses general scientific methods, in particular, the method of systematization and critical evaluation, practical methods of generalization and system reasoning. The historical prerequisites for the emergence of the unit economy are determined. The methodological features of the unit-economy, which formalized it into an independent management technology, are revealed.&#x0D; The results obtained reflect the scientific novelty: the provisions that explain the use of unit economics as an integral part of the business model in a crisis economy are substantiated and the procedures for further development of new management technology for the purpose of efficiency and stimulation of entrepreneurial activity are predicted.&#x0D; The results of the study can be used as recommendations on business risk management in practical activities by entrepreneurs, as well as a theoretical basis for further research on this topic.
Users’ intention to adopt artificial intelligence-based chatbot: a meta-analysis

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Advances in AI‐based cancer cytopathology

Abstract Cytopathological examination plays a crucial role in cancer diagnosis as it reflects the cellular pathology of cancer. However, this process traditionally relies on the visual examination by cytopathologists. Recent advancements in computer and digital imaging technologies have enabled the application of artificial intelligence (AI)‐based models to identify tumor cells in images, thereby assisting cytopathologists in achieving enhanced performance. AI‐based models can improve the accuracy and reproducibility of image evaluation and streamline clinical workflows. Moreover, AI‐based models can analyze a diverse range of sample types, including peripheral blood, urine, ascites, and bone marrow. AI‐based cytopathological recognition can help clinicians screen and diagnose cancer, predict prognosis and recurrence of cancers, such as leukemia, cervical cancer, urothelial carcinoma, and gastric cancer. Additionally, AI‐based models can predict the types of mutations in leukemia. A growing number of studies emphasize the potential of computational image analysis and deep learning‐based AI to build novel diagnostic tools that are conducive to the biomedical field. This review describes the recent developments in AI‐based cytopathological recognition and offers a perspective on how AI tools of cytopathology can help improve cancer diagnosis and prognosis prediction. Future developments in AI model applications can further contribute to the improvement of human health.
Knowledge-Graph- and GCN-Based Domain Chinese Long Text Classification Method

In order to solve the current problems in domain long text classification tasks, namely, the long length of a document, which makes it difficult for the model to capture key information, and the lack of expert domain knowledge, which leads to insufficient classification accuracy, a domain long text classification model based on a knowledge graph and a graph convolutional neural network is proposed. BERT is used to encode the text, and each word’s corresponding vector is used as a node for the graph convolutional neural network so that the initialized vector contains rich semantic information. Using the trained entity–relationship extraction model, the entity-to-entity–relationships in the document are extracted and used as the edges of the graph convolutional neural network, together with syntactic dependency information. The graph structure mask is used to learn about edge relationships and edge types to further enhance the learning ability of the model for semantic dependencies between words. The method further improves the accuracy of domain long text classification by fusing knowledge features and data features. Experiments on three long text classification datasets—IFLYTEK, THUCNews, and the Chinese corpus of Fudan University—show accuracy improvements of 8.8%, 3.6%, and 2.6%, respectively, relative to the BERT model.
Dynamic Weighted Multitask Learning and Contrastive Learning for Multimodal Sentiment Analysis

Multimodal sentiment analysis (MSA) has attracted more and more attention in recent years. This paper focuses on the representation learning of multimodal data to reach higher prediction results. We propose a model to assist in learning modality representations with multitask learning and contrastive learning. In addition, our approach obtains dynamic weights by considering the homoscedastic uncertainty of each task in multitask learning. Specially, we design two groups of subtasks, which predict the sentiment polarity of unimodal and bimodal representations, to assist in learning representation through a hard parameter-sharing mechanism in the upstream neural network. A loss weight is learned according to the homoscedastic uncertainty of each task. Moreover, a training strategy based on contrastive learning is designed to balance the inconsistency between training and inference caused by the randomness of the dropout layer. This method minimizes the MSE between two submodels. Experimental results on the MOSI and MOSEI datasets show our method achieves better performance than the current state-of-the-art methods by comprehensively considering the intramodality and intermodality interaction information.
A Survey of Non-Autoregressive Neural Machine Translation

Non-autoregressive neural machine translation (NAMT) has received increasing attention recently in virtue of its promising acceleration paradigm for fast decoding. However, these splendid speedup gains are at the cost of accuracy, in comparison to its autoregressive counterpart. To close this performance gap, many studies have been conducted for achieving a better quality and speed trade-off. In this paper, we survey the NAMT domain from two new perspectives, i.e., target dependency management and training strategies arrangement. Proposed approaches are elaborated at length, involving five model categories. We then collect extensive experimental data to present abundant graphs for quantitative evaluation and qualitative comparison according to the reported translation performance. Based on that, a comprehensive performance analysis is provided. Further inspection is conducted for two salient problems: target sentence length prediction and sequence-level knowledge distillation. Accumulative reinvestigation of translation quality and speedup demonstrates that non-autoregressive decoding may not run fast as it seems and still lacks authentic surpassing for accuracy. We finally prospect potential work from inner and outer facets and call for more practical and warrantable studies for the future.