Reviewerly/exHarmony huggingface dataset:
An accurate classification method based on multi-focus videos and deep learning for urinary red blood cell

Generally, there are multitudinous red blood cells in the urine of patients with urinary system diseases (USDs). Classifying urinary red blood cells (U-RBCs) is an important diagnostic method for USDs. Because the U-RBCs classification relying on professional microscopist is difficult to satisfy the growing urinalysis demand of patients, researchers have proposed several U-RBC classification methods based on artificial intelligence technologies. However, the existing methods mainly using single-focus images are unable to accurately classify U-RBCs because the shapes of U-RBCs may deform significantly with the change of microscope focus. Therefore, the research in this paper aims to develop a high-accuracy U-RBCs classification method by the combination of innovative multi-focus videos and mature Inflate 3D Convnet model for video classification. Experimental results show that the proposed method achieves overall accuracy of 82.1% in classifying six categories of U-RBCs, which is obviously higher than traditional method with overall accuracy of 75.9%. Comprehensively, the proposed method is proved to be successful and significant for its advantages of accuracy, robustness and low cost of data collection. In the future works, this method can also be extended to accurately classify other medical targets under the microscope, and its classification performance would be further improved by introducing the multi-instance learning model.
In Defense of LSTMs for Addressing Multiple Instance Learning Problems

LSTMs have a proven track record in analyzing sequential data. But what about unordered instance bags, as found under a Multiple Instance Learning (MIL) setting? While not often used for this, we show LSTMs excell under this setting too. In addition, we show that LSTMs are capable of indirectly capturing instance-level information using only bag-level annotations. Thus, they can be used to learn instance-level models in a weakly supervised manner. Our empirical evaluation on both simplified (MNIST) and realistic (Lookbook and Histopathology) datasets shows that LSTMs are competitive with or even surpass state-of-the-art methods specially designed for handling specific MIL problems. Moreover, we show that their performance on instance-level prediction is close to that of fully-supervised methods.
On the Selection of the Competence Measure for Dynamic Regressor Selection

Dynamic regressor selection (DRS) systems work by selecting the most competent regressors from an ensemble to predict the target value of a query pattern. This competence is calculated using the performance of the regressors in a local region of the feature space around the query pattern that is called the region of competence. Nonetheless, defining the correct measure to compute the degree of competence of the regressors is a hard task. In this work, we propose a new technique to DRS that selects the best competence measure for a given dataset. To validate our technique, we perform a set of comprehensive experiments on 15 regression datasets. The proposed technique can operate in three different fashions: (i) selection of the most competent regressor; (ii) combination of all regressors from the ensemble; and (iii) selection of a subset composed of the most competent ones and combine them. The proposals are compared against DRS algorithms, individual regressors, and static systems that use the Mean and the Median as a fusion strategy. The results show that the proposed technique, which chooses a different competence measure per task, outperforms literature techniques.
Learning performance of LapSVM based on Markov subsampling

It has become common to collect massive datasets in modern applications. The massive and highly noise contaminated data pose serious challenges to conventional semi-supervised learning methods. To tackle such challenges from the large-quantity-low-quality situation, we propose a distribution-free Markov subsampling strategy based on Laplacian support vector machine (LapSVM) to achieve robust and effective estimation. The core idea is to construct an informative subset which allows us to conservatively correct a rough initial estimate towards the true classifier. Specifically, the proposed subsampling strategy selects samples with small losses via a probabilistic procedure, constructing a subset which stands a good chance of excluding the noise data and providing a safe improvement over the rough initial estimate. Theoretically, we show that the obtained classifier is statistically consistent and can achieve fast learning rate under mild conditions. The promising performance is also supported by simulation studies and real data examples.
Online Keyframe Selection Scheme for Semantic Video Segmentation

In this paper, we propose an online keyframe selection scheme for video analysis tasks based on deep reinforcement learning. Optimal keyframe selection is a crucial job for preserving the temporal updates in the video sequence since it affects the accuracy and throughput of the whole video analysis framework to a great degree. Our scheme is generic and can be used for the simultaneous decision of keyframe in any real-time video based task. We employ policy gradient reinforcement strategy to learn policy function for maximizing the expected reward. The proposed selection network has two actions (key and non-key) in the action space. State information is derived from the element-wise difference image of keyframe and the current frame. We adopt deep feature flow approach for feature propagation between keyframe and the current frame. We evaluate our scheme on the Cityscapes dataset with DeepLabv3 as segmentation network and LiteFlowNet for computing flow fields.
Tree Bounds for Sums of Bernoulli Random Variables: A Linear Optimization Approach

We study the problem of computing the tightest upper and lower bounds on the probability that the sum of n dependent Bernoulli random variables exceeds an integer k. Under knowledge of all pairs of bivariate distributions denoted by a complete graph, the bounds are NP-hard to compute. When the bivariate distributions are specified on a tree graph, we show that tight bounds are computable in polynomial time using a compact linear program. These bounds provide robust probability estimates when the assumption of conditional independence in a tree-structured graphical model is violated. We demonstrate, through numericals, the computational advantage of our compact linear program over alternate approaches. A comparison of bounds under various knowledge assumptions, such as univariate information and conditional independence, is provided. An application is illustrated in the context of Chow–Liu trees, wherein our bounds distinguish between various trees that encode the maximum possible mutual information.
Drivers and Impediments: Experts Opinions on Blockchain Adoption in the Netherlands

There is an ongoing interest in blockchain by organisations and governments in the Netherlands. In this research we aim to determine what positive and negative factors influence blockchain adoption in the Netherlands. We achieve this by interviewing eight blockchain experts and by analysing the factors that are top of mind among these experts. We anticipated that the majority of the experts would mention the technological advantages of blockchain as a prevalent factor that drives adoption. Surprisingly, our research suggests a disconnect between blockchain adoption and its technological advantages. By contrast, a prevalent factor that drives blockchain adoption is competitive pressure, according to the majority of experts. Also, based on the analysis of the interviews we introduce two new drivers, 1. Being willing to collaborate, and 2. Problem match. We also identify two factors that are an impediment for blockchain adoption according to the majority of the experts, 1. Lack of regulation on blockchain, and 2. Lack of knowledge about blockchain properties by non-experts. Our work is useful as organisations and governments may want to reconsider their rationale for adopting blockchain. If a rationale exists for adopting blockchain then these organisations and governments can focus on the impediments of blockchain adoption.
Efficient Data Preprocessing approach for Imbalanced Data in Email Classification System

Email is one of the important means of communication over the Internet. Due to the rapid growth of Internet, usage of email communication for business, personal and other works has resulted into generation of electronic data in exponential order. Applying machine learning techniques on the huge raw data may degrade the performance. Hence, the data has to be prepared for better performance of the machine learning techniques. The preprocessing phase in machine learning applications such as classification, clustering and prediction is intended to reduce the size of data. This paper proposes a new data preprocessing approach for imbalanced data in email classification domain to measure the effects of various preprocessing methods on different machine learning classifiers. Contribution of various preprocessing methods on the imbalanced dataset is discussed. Accuracy analysis reveals that the proposed approach significantly improves the accuracy of all the machine learning classifiers used in this work. The outcome of this work showed that, success rate of logistic regression achieved 90.39% accuracy in the proposed approach.
Genuine Quadripartite Continuous‐Variable Entanglement in Symmetric Cascaded Four‐Wave Mixing Process

Abstract The symmetric cascaded four‐wave mixing process is a phase‐insensitive and easy‐to‐implement technology to prepare quadripartite entanglement. However, it has not yet been discussed whether the state generated by this method is genuinely entangled. Here, it is shown that the genuine quadripartite entanglement criteria previously derived are not applicable to the symmetric cascaded scheme. Thus some more effective sufficient conditions are derived for detecting genuine entanglement in this work. The existence of genuine quadripartite entanglement is then confirmed through different entanglement witnesses. Under the premise that the quadripartite state is genuinely entangled, the quantum gain of the first‐stage determines the optimal range of the quantum gain of second‐stage. Finally, it is show that the four output modes are genuinely entangled within the appropriate quantum gain ranges when the optical loss of each step of the cascaded system is less than 10%. The results pave the way for the experimental generation of genuine quadripartite entanglement in symmetric cascaded scheme.
Photovoltaic power plant production operational forecast based on its short-term forecasting model

This paper addresses the study of operational photovoltaic power plant forecasting based on the results of short-term forecasts, thus providing the multi-level hierarchical system of solar power plant generation planning. The study provides the comparison between naive persistence, autoregressive and autoregressive moving average models with the corresponding parameters tuning in order to identify the most effective way to implement intra-day forecasting option. The case study is based on real photovoltaic power plant operational data in order to verify the opportunity of the presented approach practical implementation.
Sizing of a Standalone PV System with Battery Storage for a Dairy: A Case Study from Chile

In this paper, a stochastic simulation model for a standalone PV system sizing is replicated and extended to supply a dairy’s power demand. A detailed hourly-based simulation is conducted considering an hourly load profile and global solar radiation prediction model. The stochastic simulation model is based on a thorough statistical analysis of the solar radiation data and simulates the energy yield, the excess energy curtailed, and the state of charge of the batteries for the sizing month and the whole year, providing the designer autonomy factor values <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>d</mi> </math> to properly size the PV system, finding the optimum combination of installed peak power <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mfenced open="(" close=")" separators="|"> <mrow> <msub> <mrow> <mi>P</mi> </mrow> <mrow> <mtext>m</mtext> </mrow> </msub> </mrow> </mfenced> </math> and battery storage capacity <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mfenced open="(" close=")" separators="|"> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mtext>L</mtext> </mrow> </msub> </mrow> </mfenced> </math> that meets the application load requirements, considering a preset reliability level at minimum cost. The model makes use of the NASA’S Surface Meteorology and Solar Energy database to obtain solar radiation data. Results show a substantial reduction of 44% in installed peak power and battery storage capacity when compared to conventional methodologies, considering three days of autonomy, and an 85% reduction considering four days. Considering the goodness of fit test results, the Wakeby distribution best represents the behavior of historical solar radiation data for the site in almost half of the months. This article seeks to contribute to the literature gap in the application of methodologies for the multicomponent power supply in the dairy industry through the use of renewable energy.
On the Reliability and Validity of Digital Competence Evaluation in Higher Education

This paper aims to provide an overview of the reliability and validity of the instruments used to measure digital competence in higher education. Three research questions were addressed about: 1) the demographic and methodological characteristics of the literature, 2) typical assessments and reporting practices, and 3) the association between studies' characteristics and their reporting practices. Through a systematic mapping study, 60 primary studies published in the period 2018-2020 were analyzed and classified. The results indicated that most of the studies originated in Europe and Asia; they focused on university students from Social Sciences; they administered instruments composed of cognitive items for diagnosing digital competence; they were published in journals not indexed by the Journal Citation Report; and about 50% did not report on the reliability and validity of the instrument used. Studies published in low-quality journals or focused on administration only are more susceptible to not reporting these psychometric assessments.
An Overview of Applications of Artificial Intelligence Using Different Techniques, Algorithms, and Tools

Technology is currently a crucial benchmark in any application area. In general, society is immersed in the era of digitalization; therefore, incorporating digital technology in different application areas has been more accessible. Nowadays, claiming that adopting artificial intelligence systems in any area is already an emerging need. In this chapter, several artificial intelligence techniques are presented, as well as algorithms and tools that have been used to provide a variety of solutions such as artificial neural networks, convolutional neural networks architecture, AI models, machine learning, deep learning, and bio-inspired algorithms focused mainly on ant colony optimization, response threshold models, and stochastic learning automata. Likewise, the main applications that use AI techniques are described, and the main trends in this discipline are mentioned. This chapter ends with a critical discussion of artificial intelligence advances.
RETRACTED: A Comprehensive Study on the Various Applications of Deep Learning

Abstract Machine learning, using its analysis, is a region of computer technology that progressed stemming from examining style recognition as well as likewise computational learning concepts in an expert system. It is the learning as well as likewise building of protocols that might benefit from in addition to produce prophecies on data collections. These procedures operate by building and construction of a style coming from example inputs so regarding generating data-driven revelations or options rather than noting agency static device suggestions.
A Study on Calendar Anomalies in the Cryptocurrency Market

Cryptocurrencies are sub-classes of digital currencies. Trading of these currencies have gained momentum during the past few years and have become new investment avenues for investors. An understanding on the market anomalies, which are patterns in asset prices would help the investors to adopt suitable strategies while trading in this asset class. This study aims to examine the presence of three calendar anomalies, day of the week, turn of the month, and year end effect in the cryptocurrencies. The top five cryptocurrencies which constitute a major share of the market capitalization value are selected for the study and the period of study is from July 23, 2017 to July 9, 2020. Dummy Variable Regression using GARCH (1, 1) model was employed on the log value of returns of the cryptocurrencies. The study provides evidence on the existence of anomalies during Thursdays, the months March and April, and at the turn of the year.
Span-core Decomposition for Temporal Networks

When analyzing temporal networks, a fundamental task is the identification of dense structures (i.e., groups of vertices that exhibit a large number of links), together with their temporal span (i.e., the period of time for which the high density holds). In this article, we tackle this task by introducing a notion of temporal core decomposition where each core is associated with two quantities, its coreness, which quantifies how densely it is connected, and its span, which is a temporal interval: we call such cores span-cores . For a temporal network defined on a discrete temporal domain T , the total number of time intervals included in T is quadratic in | T |, so that the total number of span-cores is potentially quadratic in | T | as well. Our first main contribution is an algorithm that, by exploiting containment properties among span-cores, computes all the span-cores efficiently. Then, we focus on the problem of finding only the maximal span-cores , i.e., span-cores that are not dominated by any other span-core by both their coreness property and their span. We devise a very efficient algorithm that exploits theoretical findings on the maximality condition to directly extract the maximal ones without computing all span-cores. Finally, as a third contribution, we introduce the problem of temporal community search , where a set of query vertices is given as input, and the goal is to find a set of densely-connected subgraphs containing the query vertices and covering the whole underlying temporal domain T . We derive a connection between this problem and the problem of finding (maximal) span-cores. Based on this connection, we show how temporal community search can be solved in polynomial-time via dynamic programming, and how the maximal span-cores can be profitably exploited to significantly speed-up the basic algorithm. We provide an extensive experimentation on several real-world temporal networks of widely different origins and characteristics. Our results confirm the efficiency and scalability of the proposed methods. Moreover, we showcase the practical relevance of our techniques in a number of applications on temporal networks, describing face-to-face contacts between individuals in schools. Our experiments highlight the relevance of the notion of (maximal) span-core in analyzing social dynamics, detecting/correcting anomalies in the data, and graph-embedding-based network classification.
WDN: A Wide and Deep Network to Divide-and-Conquer Image Super-Resolution

Divide and conquer is an established algorithm design paradigm that has proven itself to solve a variety of problems efficiently. However, it is yet to be fully explored in solving problems with a neural network, particularly the problem of image super-resolution. In this work, we propose an approach to divide the problem of image super-resolution into multiple subproblems and then solve/conquer them with the help of a neural network. Unlike a typical deep neural network, we design an alternate network architecture that is much wider (along with being deeper) than existing networks and is specially designed to implement the divide-and-conquer design paradigm with a neural network. Additionally, a technique to calibrate the intensities of feature map pixels is being introduced. Extensive experimentation on five datasets reveals that our approach towards the problem and the proposed architecture generate better and sharper results than current state-of-the-art methods.
Eye Gaze Analysis of Students in Educational Systems

Eye gaze provides indicative information about the status and the behavior of a person and can be very assistive in human-computer interaction. Eye-gaze analysis is very helpful in a variety of applications in order to understand the interest of the users, their behavior or even to unveil distractions. However, the accurate eye-gaze estimation is a very challenging process. In this paper, we present an eye gaze estimation work that relies on convolutional neural networks which imitate the LeNet’s architecture. They analyze eye gaze and provide a 2D vector that concerns the coordinates of the specific pixel inside the 2D screen’s space, in which the user is looking at. Also, a system capable of working under various real-world conditions such as light, angle and distance differentiations was designed and developed. An evaluation study was performed and the results are quite promising pointing out that the system is scalable and accurate in estimating the eye gaze of the users.
Heuristics-based mediation for building smart architectures at run-time

Smart architectures are increasingly being used in current software development. Smart user interfaces, smart homes, or smart buildings are becoming common examples in the new era of smart cities. Software architectures usually related to these domains need to be adapted and reconfigured at run-time, for example, to provide new services, react to user interaction, or due to changes decided from the business logic of the application. Component-based techniques are a suitable way to carry out this kind of adaptation, as dynamic reconfiguration operations can be applied to the architecture. In this paper, we address run-time generation of component-based applications, taking the abstract definitions of their architecture as a reference, in addition to a set of available components. The process calculates the best configuration of components from the abstract definition by applying a trading approach based on an adapted A* algorithm. This algorithm uses heuristics based on syntactic and semantic information obtained from the component definitions. A case study related to mashup user interfaces formed by coarse-grained components is also explained. In short, the results show the usefulness of heuristics and suitable execution times for building the best configurations.
High-level representations through unconstrained sensorimotor learning

How the sensorimotor experience of an agent can be organized into abstract symbol-like structures to enable effective planning and control is an open question. In the literature, there are many studies that start by assuming the existence of some symbols and `ground' those onto continuous sensorimotor signals. There are also works that aim to facilitate the emergence of symbol-like representations by using specially designed machine learning architectures. In this paper, we investigate whether a deep reinforcement learning system that learns a dynamic task would facilitate the formation of high-level neural representations that might be considered as precursors of symbolic representation, which could be exploited by higher level neural circuits for better control and planning. The results indicate that without even explicit design to promote such representations, neural responses emerge that may serve as the basis of abstract symbol-like representations.