Reviewerly/exHarmony huggingface dataset:
Automatic Lymph Node Classification with Convolutional Neural Network

Manual lymph node classification is a tedious and time-consuming task. It requires a histopathologist to discriminate a lymph node from other look-alike kinds of tissues. The lymph node is easily misunderstood with other tissues because its shape and color might be similar to the others tissue around it. To automate this task, we present an automatic lymph node classification with convolutional neural network (CNN). In addition, we compared eight existing CNNs to ensure that we discover the best architecture for discriminating lymph node. DenseNet architecture provided the highest performance among AlexNet, VGG, GoogLeNet, ResNet, SqueezeNet, MobileNet, and EfficientNet, the highest accuracy at 0.994 and an F1score of 0.996. DenseNet accomplished the highest performance from two advantages: (i) fewer parameters and (ii) Dense connectivity.
Closed-Form Models of Accuracy Loss due to Subsampling in SVD Collaborative Filtering

We postulate and analyze a nonlinear subsampling accuracy loss (SSAL) model based on the root mean square error (RMSE) and two SSAL models based on the mean square error (MSE), suggested by extensive preliminary simulations. The SSAL models predict accuracy loss in terms of subsampling parameters like the fraction of users dropped (FUD) and the fraction of items dropped (FID). We seek to investigate whether the models depend on the characteristics of the dataset in a constant way across datasets when using the SVD collaborative filtering (CF) algorithm. The dataset characteristics considered include various densities of the rating matrix and the numbers of users and items. Extensive simulations and rigorous regression analysis led to empirical symmetrical SSAL models in terms of FID and FUD whose coefficients depend only on the data characteristics. The SSAL models came out to be multi-linear in terms of odds ratios of dropping a user (or an item) vs. not dropping it. Moreover, one MSE deterioration model turned out to be linear in the FID and FUD odds where their interaction term has a zero coefficient. Most importantly, the models are constant in the sense that they are written in closed-form using the considered data characteristics (densities and numbers of users and items). The models are validated through extensive simulations based on 850 synthetically generated primary (pre-subsampling) matrices derived from the 25M MovieLens dataset. Nearly 460 000 subsampled rating matrices were then simulated and subjected to the singular value decomposition (SVD) CF algorithm. Further validation was conducted using the 1M MovieLens and the Yahoo! Music Rating datasets. The models were constant and significant across all 3 datasets.
Digital mapping and GIS-based spatial analyses of the Pur-Banera Group in Rajasthan, India, with special reference to the structural control on base-metal mineralization

GIS-based ‘digital’ maps help to store geological field data in a georeferenced framework that can be easily displayed at map-scale to outcrop-scale, without the need to map an area separately at different scales. In the iron and base-metal (Pb–Zn ± Cu) prospect of Pur-Banera belt in Rajasthan, India, multiple phases of folding and shearing of the host rocks have led to a complex structural pattern. The relationship between deformation and ore (especially the base-metal ore) localization is not well understood, although it is very important for mineral prospecting and mining. To facilitate exploration targeting, a large part of the Pur-Banera belt is mapped by us at different scales, using the ArcGIS™ platform. The resulting map is ‘intelligent’ in nature, as it can display geological field data at different scales by just zooming in and out, and it is possible to retrieve any type of geological data from any outcrop/sector through a specific GIS-query. Photographs and virtual 3D models of an outcrop can be opened through hyperlinks. Geospatially referenced mapping of the Pur-Banera belt demonstrates four phases of folding, of which the F3 folds define the regional structural pattern. Detailed mapping of mineralized bands and gossans indicate that the originally stratiform iron ore is best mined at the F3 hinge zones where these layers are thickest due to repeated folding. In contrast, the primarily stratabound Pb–Zn ± Cu ore has largely remobilized during F2 folding, becoming concentrated in the hinge zone and/or along the axial plane of F2 folds, which are consequently now the best prospect sites for base metals.
Leukemia can be Effectively Early Predicted in Routine Physical Examination with the Assistance of Machine Learning Models

The diagnosis of leukemia relies very much on the results of bone marrow examinations, which is never generally performed in routine physical examination. In many rural areas even community hospitals and primary care clinics, the lack of hematological specialist and facility does not allow a definite diagnosis of leukemia. Thus, there will be a significant benefit if machine learning (ML) models could help early predict leukemia using preliminary blood test data in a routine physical examination in community hospitals to save time before a definite diagnosis.We collected the routine physical examination data of 1230 newly diagnosed leukemia patients and 1300 healthy people. We trained and tested 3 machine learning (ML) models including linear support vector machine (LSVM), random forest (RF), and XGboost models. We not only examined the accordance between model results and statistical analysis of the input data but also examined the consistency of model accuracy scores and relative importance order of model factors with regard to different input data sets and different model arguments to check the applicability of both the models and the input data.Generally, the RF and XGboost models give more identical, consistent, and robust relative importance order of factors that is also accordant with the statistical analysis, while the LSVM gives much different and nonsense orders for different inputs. Results of the RF and XGboost models show that (1) generally, the models achieve accuracy scores above 0.9, indicating effective identification of leukemia, and (2) the top three factors that contribute most to the identification of leukemia include red blood cell (RBC), hematocrit (HCT), and white blood cell (WBC), while the other factors contribute relatively less.This study shows a feasible case example for early identification of leukemia using routine physical examination data with the assistance of ML models, which can be conveniently, cheaply, and widely applied in community hospitals or primary care clinics to save time before definite diagnosis; however, more studies are still needed to validate the applicability of more ML models to a larger variety of input data sets.
Accounting and Tax Regulation of Cryptocurrencies

The digital age creates new concepts, methods, and rules that are affecting the accounting process which presents new challenges in accounting practice.One of the most significant changes and innovations within accounting processing and the financial industry is the emergence of cryptocurrencies that create a completely new financial business concept.Cryptocurrency is the equivalent of electronic money and is completely digital.Cryptocurrencies and transactions with cryptocurrencies are frequent appearances in todayʼs modern business but are not fully regulated.Accounting and tax treatment in the Republic of Croatia and other European Union countries but also beyond is not fully established and adjusted for doing business with cryptocurrencies.Due to the mentioned lack, cryptocurrencies and their transactions require strict regulation and adjustment operations.This article aims to investigate the development of cryptocurrencies and their involvement in the financial industry and business environment, with a focus on the accounting and tax treatment of cryptocurrencies within the accounting profession.Cryptocurrencies are rapidly developing on a daily basis and pose a significant challenge to the financial industry and especially their regulation within the accounting profession.A systematic literature review was undertaken to provide an overview of the related area of cryptocurrencies and their accounting and tax treatment.The terms ("cryptocurrency") and ("accounting" OR "tax") were used to collect data.The papers were reviewed to investigate whether a cryptocurrency control system has been established from an accounting and tax point of view and whether it functions as such but also its impact on the financial industry.Results indicate that the main problem that arises around the concept of cryptocurrencies and their accounting and tax treatment is insufficient regulation and inconsistencies in the implementation, monitoring, and evaluation of cryptocurrencies in the field of accounting at the local but also global levels.
Precision and accuracy of measuring finger motion with a depth camera: a cross-sectional study of healthy participants

The purpose of this cross-sectional study was to determine the precision and accuracy of the measurement of finger motion with a depth camera. Fifty-five healthy adult hands were included. Measurements were done with a depth camera and compared with traditional manual goniometer measurements. Repeated measuring showed that the overall repeatability and reproducibility of extension measured with the depth camera were within 3° and 4° and that of flexion were within 13° and 14°. Compared with traditional manual goniometry, biases of extension of all finger joints and flexion of metacarpophalangeal joints were less than 5°, and the average bias of flexion of proximal and distal interphalangeal joints was 29°. We conclude that the measurement of finger extension and flexion of the metacarpophalangeal joints with a depth camera was reliable, but improvement is required in the precision and accuracy of interphalangeal joint flexion.
Semi-supervised named entity recognition in multi-level contexts

Named entity recognition is a critical task in the natural language processing field. Most existing methods for this task can only exploit contextual information within a sentence. However, their performance on recognizing entities in limited or ambiguous sentence-level contexts is usually unsatisfactory. Fortunately, other sentences in the same document can provide supplementary document-level contexts to help recognize these entities. In addition, words themselves contain word-level contextual information since they usually have different preferences of entity type and relative position from named entities. In this paper, we propose a semi-supervised unified framework to incorporate multi-level contexts for named entity recognition. We use bi-directional gated recurrent units and incorporate pre-trained language model embeddings to capture sentence-level contextual information. To incorporate document-level contexts, we propose to capture interactions between sentences via a multi-head self attention network. To mine word-level contexts, we propose an auxiliary task to predict the type of each word to capture its type preference. We jointly train our model in entity recognition and the auxiliary classification task via multi-task learning. We conduct experiments on two widely-used sequence taggers: CRF tagger and boundary tagger. The experimental results on the CoNLL dataset in English, Dutch, and German validate the effectiveness of our method.
Consolidated Adversarial Network for Video De-raining and De-hazing

The performance of recent video enhancement methods is superior in specific hazy, rainy, snowy, and foggy weather conditions. However, these approaches can handle degradation rendered by single weather. We propose an integrated lightweight adversarial learning network to handle the degradations induced by different weather conditions. This is a unique approach to mitigate the problem of video restoration for multi- weather degraded videos using single network. The proposed architecture combines the idea of multi-resolution analysis with a multi-scale encoder and domain-specific feature learning is achieved using domain-aware filtering modules. The architecture provides recurrent feature sharing for temporal consistency, achieved by feeding the previous frame output as feedback. Substantial experiments on various datasets demonstrate that the proposed method performs competitively with the existing state-of-the-art approaches for video restoration in multi-weather conditions.
Exploring attention on faces: similarities between humans and Transformers

Attention in Machine Learning allows a model to selectively up-weight informative parts of an input in relation to others. The Vision Transformer (ViT) is entirely based on attention. ViTs have shown state of the art performance in multiple fields including person re-identification, presentation attack detection and object recognition. Several works have shown that embedding human attention into a Machine Learning pipeline can improve performance or compensate for a lack of data. However the correlation between computer vision models and human attention has not yet been investigated. In this paper we explore the intersection of human and Transformer attention. For this we collect a new dataset: the University of Sassari Face Fixation Dataset (Uniss-FFD) of human fixations and show through a quantitative analysis that correlations exist between these two modalities. The dataset described in this paper is available at https://github.com/ CVLab-Uniss/Uniss-FFD.
Integrated Development of Artificial Intelligence and Economic Management

The world economy is developing rapidly. Behind the rapid development, the management of economic regions is very important. In the process of economic management, the government plays the role of macrocontrol and is responsible for the management of various economic affairs and social and economic services. While undertaking infrastructure construction, it creates a good environment for economic development. However, with the deepening of economic development and the more and more complex economic data, the current economic management has gradually exposed a range of issues that arise during the process of economic development, and these problems need to be solved urgently. At this time, the application scope of artificial intelligence in the economic field is getting wider and wider, and it has a great positive effect on economic development. Therefore, in order to solve the problem of economic management in the process of economic development, this paper proposes a development path that integrates AI and economic management and provides intelligent technology support for the development of economic management to help the smooth operation of economic development. In addition, this paper shows through experiments that the path of integration of AI and economic management can promote the development and smooth operation of the economy, and AI has a positive impact on economic management.
MovableBag: Substitutional Robot for Enhancing Immersive Boxing Training with Encountered-Type Haptic

Boxing is a combat sport that involves fierce movement and close body contact between two boxers. In addition to the boxer, the coach is also essential for training. The coach could be injured or too tired to continue consecutive training sessions for many boxers. Thus, we present MovableBag, a substitutional robot that provides room-scale mobility and the ability to carry sport props with an easier tracking setup to simulate the boxing opponent or coach in virtual reality with encountered-type haptic feedback. In addition, we integrate our system with a wireless VR headset and develop a VR boxing application to show the capacity of the device and inform future studies.
Attention-adaptive multi-scale feature aggregation dehazing network

In this paper, we propose an end-to-end Attention-adaptive Multi-scale Feature Aggregation Dehazing Network (AMA-Net). The AMA-Net is based on U-Net and designs with three attention-driven modules, Joint Attention Residual Block (JAB), Joint Attention Feature Aggregation Group (JAAG), and Layer Adaptive Attention Feature Aggregation Module (LAA). To be more specific, considering the unevenly distributed haze in images, we introduce the JAB, which adaptively assigns weights to make networks pay attention to important features; to fully utilize the residual features, we propose the residual aggregation (via three JABs) in JAAG; since most feature aggregation methods for dehazing networks do not filter and refine features at different layers, we add LAA to the decoder to weight the features at different layers for aggregation. Through the ablation studies, we verify the effectiveness of the JAB, JAAG, and LAA. Experimental results on synthetic and real-world datasets show that the proposed AMA-Net outperforms relevant state-of-the-art methods.
Dynamic virtual machine relocation system for energy‐efficient resource management in the cloud

Summary Cloud services can be expanded and offered in a flexible manner depending on the needs of the users, and cloud computing has become more popular. The energy consumption to provide users with service quality is the biggest concern in virtualization technology. Therefore, reducing energy consumption is a key step in reducing the operating costs of various organizations' data centers. In this article, we propose a dynamic VM relocation (DVMR) system used inside data centers to study virtualization technologies for reducing energy consumption in cloud computing environments. The DVMR system provides a method for predicting PM load by using and selecting loaded and unloaded PM, thereby effectively managing virtualization and energy saving. Experimental evaluation is done by implementing load threshold‐based relocation. The results show that the proposed system can efficiently balance the load between different PMs and efficiently decrease the energy consumption of the entire system.
Assessing the Innovation of Mobile Pedagogy from Teacher Perspective

This paper focuses on the use of mobile technology to assist teaching and learning in distance education. It aims to investigate teaching behaviour in mobile pedagogy and examine the impact of technology on current education. A case study was conducted through semi-structured interviews with a cohort of 30 Chinese lecturers who taught English through online tutoring. Thematic analysis was used to analyse the interview data and the assessment was based on teacher perceptions of mobile pedagogy. The impact of technology on the current educational environment is discussed through an analysis of mobile pedagogy and teacher perceptions. The findings show that mobile pedagogy is highly regional in practice and nature and features in-country software applications and social communication tools. Despite the attributes of connectivity and flexibility, mobile pedagogy only disrupted traditional teaching methods, leading to minimal changes to the education system. This study provides recommendations for the sustainable development of mobile pedagogy for future education systems in the digital age.
Adaptive horizontal scaling method for speckle-assisted fringe projection profilometry

Phase-shifting method is widely used in fringe projection profilometry to obtain high-precision wrapped phase maps. The wrapped phase map needs to be converted to an absolute phase map to recover 3D information. The speckle pattern based phase unwrapping method requires only one additional auxiliary pattern, showing great potential for fast 3D measurements. In this paper, a speckle assisted four-steps phase-shifting method was proposed for 3D measurements. This method requires five structured light patterns to complete 3D measurements, including four-steps phase-shifting fringe patterns and a speckle pattern which is used to remove phase ambiguity. The main challenge of speckle based phase unwrapping method is to overcome the mismatch problem which often occurs in some very steep surfaces. In order to improve the speckle matching accuracy, an adaptive horizontal scaling method was proposed. A voting strategy based on phase-connected regions was proposed to reduce the computational overhead. The experiments demonstrate its superior performance, and an accuracy of 0.21 mm was achieved.
A novel multi‐objective immune optimization algorithm for under sampling software defect prediction problem

Summary Data imbalance and parameter selection have always been two important factors affecting the accuracy of software defect prediction. However, the existing methods have been poor in balancing these two factors. To address this challenge, a multi‐objective software defect prediction model is employed to describe the under sampled software defect prediction problem. And in this model, defect detection rate and defect false alarm rate are deemed as two objectives which should be optimized. Simultaneously, we design a novel multi‐objective immune optimization algorithm based on the comprehensive fitness of evaluation mechanism to effectively address the employed model. In the algorithm, the original mechanism based on neighborhood individual selection is replaced based on comprehensive fitness evaluation,which have better selection ability to attaining the predicted effect of software to improving in the process of selection solution of population evolution and further effectively help decision makers choose better scheme that meets requirements. In addition, in order to verify the effectiveness of the designed algorithm, the proposed algorithm is compared on eight different public data sets. Simulation results show that the proposed algorithm has better performance in handling with the multi‐objective under sampling software defect prediction problem.
Algorithm For Intelligent Systems

In the 21st-century, machines are becoming more and more intelligent. Terms like artificial intelligence, automation, robotics, etc., are becoming the new normal in today's tech-savvy world. All of this is made possible because of complex programs (algorithms) which are able to perform such difficult tasks.&lt;br&gt;&lt;br&gt;Intelligent systems are self-taught machines intended for a particular task. Intelligence is the ability to learn and use new information and skills. As the person learns from past data, the system can be programmed using various algorithms to make it intelligent.&lt;br&gt;&lt;br&gt;In this chapter, we will be discussing some of the algorithms in brief, like- Reinforcement learning, Game theory, Machine Learning, Decision Tree, Artificial Neural Networks, Swarm Intelligence, and Natural Language Processing and its applications.