Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence for the Future of Medicine

Since its origins, medicine has been more linked to the cure of diseases than to their prevention. This is due to multiple factors, training of health professionals aimed at curing diseases, lack of quality data, processing capacity, poor multidisciplinary approach, etc. However, this paradigm is changing, focusing on maintaining the health of individuals to avoid diseases, improving social welfare. To achieve this, the new approach proposes that medicine must be Preventive, Participatory, Predictive, and Personalized (P4 Medicine). In this chapter, we will analyze how artificial intelligence can convincingly contribute to the construction of P4 Medicine, through the processing of key data such as DNA, electronic medical records and environmental variables to which people have been exposed. Here we can find complex data such as Computed Tomography images, electroencephalograms, free text in electronic medical records, pharmacological data, etc. These data have grown exponentially and efforts to improve their quality are already paying off. However, it is no longer possible for a health professional to analyze them to provide a better diagnosis or carry out preventive work on diseases, requiring the formation of multidisciplinary teams to find new solutions to ancient problems, such as healthcare, where data processing, knowledge extraction and its subsequent parameterization in support systems for medical decision-making are vital to save lives. In this sense, artificial intelligence, together with new methods for processing complex data and computational resources to process massive data, will be key to improving the humanity health.
How to Elicit Complex-Valued Fuzzy Degrees

In traditional fuzzy logic, an expert’s degree of certainty in a statement is described by a single number from the interval [0, 1]. However, there are situations when a single number is not sufficient: e.g., a situation when we know nothing and a situation in which we have a lot of arguments for a given statement and an equal number of arguments against it are both described by the same number 0.5. Several techniques have been proposed to distinguish between such situations. The most widely used are interval-valued techniques, where we allow the expert to describe his/her degree of certainty by a subinterval of the interval [0, 1]. Eliciting an interval-valued degree is straightforward. On the other hand, in many practical applications, another technique has been useful: complex-valued fuzzy degrees. Unfortunately, in the complex-valued case, there is no direct way to elicit such degrees. In this paper, we explain a reasonable natural way to elicit these degrees indirectly.
Night Vision Object Tracking System Using Correlation Aware LSTM-Based Modified Yolo Algorithm

Improved picture quality is critical to the effectiveness of object recognition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions, such as mist, fog, dust etc.The pictures then shift in intensity, colour, polarity and consistency.A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient environments.In recent years, target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance capabilities.However, the identification of objects in the night world also poses further problems because of the distorted backdrop and dim light.The Correlation aware LSTM based YOLO (You Look Only Once) classifier method for exact object recognition and determining its properties under night vision was a major inspiration for this work.In order to create virtual target sets similar to daily environments, we employ night images as inputs; and to obtain high enhanced image using histogram based enhancement and iterative wiener filter for removing the noise in the image.The process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding (AILE) and uplift linear discriminant analysis (ULDA).The region of interest mask can be segmented using the Recurrent-Phase Level set Segmentation.Finally, we use deep convolution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based (LSTM) with YOLO method for object tracking system.A range of experimental findings demonstrate that our technique achieves high average accuracy with a precision of 99.7% for object detection of SSAN datasets that is considerably more than that of the other standard object detection mechanism.Our approach may therefore satisfy the true demands of night scene target detection applications.We very much believe that our method will help future research.
An Intelligent Cardiovascular Diseases Prediction System Focused on Privacy

Machine learning (ML) and cloud computing have now evolved to the point where they are able to be used effectively.Further improvement, however, is required when both of these technologies are combined to reap maximum benefits.A way of improving the system is by enabling healthcare workers to select appropriate machine learning algorithms for prediction and, secondly, by preserving the privacy of patient data so that it cannot be misused.The purpose of this paper is to combine these promising technologies to maintain the privacy of patient data during the disease prediction process.Treatment of heart failure may be improved and expedited with this framework.We used the following machine learning algorithms to make predictions: Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbors (KNN), Decision Tree (DT) and Support Vector Machines (SVM).These techniques, combined with cloud computing services, improved the process of deciding whether to treat a patient with cardiac disease.Using our classifiers, we classified cardiac patients according to their features, which are grouped into single features, combinations of selected features, and all features.In experiments using all clinical features, machine learning classifiers SVM, DT, and KNN outperformed the rest, whereas in experiments using minimal clinical features, SVM and KNN were the most accurate.Internet of Things (IoT) devices allow family physicians to share diagnostic reports on the cloud in a secure manner.Ring signatures are particularly useful for verifying the integrity of data exchange.Our system keeps the physician's identity confidential from all authorized users, who can still access medical reports publicly.Our proposed mechanism has been shown to be both effective and efficient when it comes to obtaining patient reports from cloud storage.
Robust Frequency Estimation Under Additive Symmetric α-Stable Gaussian Mixture Noise

Here the estimating problem of a single sinusoidal signal in the additive symmetric α-stable Gaussian (ASαSG) noise is investigated.The ASαSG noise here is expressed as the additive of a Gaussian noise and a symmetric α-stable distributed variable.As the probability density function (PDF) of the ASαSG is complicated, traditional estimators cannot provide optimum estimates.Based on the Metropolis-Hastings (M-H) sampling scheme, a robust frequency estimator is proposed for ASαSG noise.Moreover, to accelerate the convergence rate of the developed algorithm, a new criterion of reconstructing the proposal covariance is derived, whose main idea is updating the proposal variance using several previous samples drawn in each iteration.The approximation PDF of the ASαSG noise, which is referred to the weighted sum of a Voigt function and a Gaussian PDF, is also employed to reduce the computational complexity.The computer simulations show that the performance of our method is better than the maximum likelihood and the lp-norm estimators.
Generating Interval Type-2 Fuzzy Inputs from Smoothed Data for Fuzzy Rule-Based Systems

Non-Singleton fuzzy logic systems (NSFLSs) have two main advantages over singleton ones: If there is prior knowledge regarding the uncertainty in the input data, e.g., if the measurement error is known, then one is in a position to model non-singleton input fuzzy sets directly, e.g., using intervals. Otherwise, one usually chooses certain types of input type-1 or type-2 fuzzy sets and attempts to estimate their parameters. Usually, one assumes that the training data are noise free. In this paper, we first consider the scenario where both the training and the testing data are corrupted by Gaussian noise with zero mean with an unknown variance. In this case, we smooth the training data using a statistical method before generating the rules. The input fuzzy sets in the testing phase are interval type-2 Gaussians that are centered at the filtered singleton inputs. We propose a method to estimate their parameters. This method also can be applied to the scenario where the testing data are corrupted by arbitrary (stable or varying) levels of noise and the training data is either noisy or noise free. We apply the approaches introduced in this paper to the prediction of chaotic non-linear time series and present some experimental results.
Deep Transfer Learning Approach for Robust Hand Detection

Human hand detection in uncontrolled environments is a challenging visual recognition task due to numerous variations of hand poses and background image clutter.To achieve highly accurate results as well as provide real-time execution, we proposed a deep transfer learning approach over the state-of-theart deep learning object detector.Our method, denoted as YOLOHANDS, is built on top of the You Only Look Once (YOLO) deep learning architecture, which is modified to adapt to the single class hand detection task.The model transfer is performed by modifying the higher convolutional layers including the last fully connected layer, while initializing lower non-modified layers with the generic pretrained weights.To address robustness issues, we introduced a comprehensive augmentation procedure over the training image dataset, specifically adapted for the hand detection problem.Experimental evaluation of the proposed method, which is performed on a challenging public dataset, has demonstrated highly accurate results, comparable to the state-of-the-art methods.
Delving into Light-Dark Semantic Segmentation for Indoor Scenes Understanding

State-of-the-art segmentation models are mostly trained with large-scale datasets collected under favorable lighting conditions, and hence directly applying such trained models to dark scenes will result in unsatisfactory performance. In this paper, we present the first benchmark dataset and evaluation methodology to study the problem of semantic segmentation under different lighting conditions for indoor scenes. Our dataset, namely LDIS, consists of samples collected from 87 different indoor scenes under both well-illuminated and low-light conditions. Different from existing work, our benchmark provides a new task setting, namely Light-Dark Semantic Segmentation (LDSS), which adopts four different evaluation metrics that assess the performance of a model from multiple aspects. We perform extensive experiments and ablation studies to compare the effectiveness of different existing techniques with our standardized evaluation protocol. In addition, we propose a new technique, namely DepthAux, that utilizes the consistency of depth images under different lighting conditions to help a model learn a unified and illumination-invariant representation. Our experimental results show that the proposed DepthAux can provide consistent and significant improvements when applied to a variety of different models. Our dataset and other resources are publicly available on our project page: http://mercy.cse.lehigh.edu/LDIS/
Security Education – The Main Tool for Strengthening the Security Culture

Abstract Nowadays, there is a continuous and significant change of the international security environment. More and more complex and diversified types of threats are present and the regional security and safety are in danger. The technological evolution from the last decade produced what security experts call the Revolution in Military Affairs. At the same time, it also allowed opponents to use a wide range and diversified means of action. The use of technology offers advantages and disadvantages on both sides. In this regard, it is everyone’s responsibility to identify possible threats or vulnerabilities. It is also everyone’s responsibility to prevent, limit or eliminate the negative impact. Proper establishment of an appropiate security culture, and, at the same time an effective security education, in accordance with the existing threats, are essential for the state, in its effort of creating and maintaining a stable and secure environment for its citizens. Starting from the research hypothesis that there appear to be new threats in the contemporary security environment, I have developed three directions on which I focused my research effort. I intended to use a deductive approach and I aimed to identify the positive aspects that can be maintained, but also other measures and actions that can be integrated into security education so that the individual as part of the state, to achieve the appropriate level of security. For this I have applied multiple research methods specific to the security field, such as the theoretical analysis, comparative analysis, observation, longitudinal method, and also the case study.
Analysis of applying blockchain technology to hospitality operation

Blockchain was first conceptualised in 2008 and drew attention from the general public since the introduction of one of its applications, cryptocurrency, in 2014. Blockchain consists of several revolutionary characteristics and has already been applied in different industries. Therefore, it is also deemed as a computer technology that has the potential to change the traditional business model. The hospitality industry is one of the significant economic supports to New Zealand; this industry could take advantage of the implementation of blockchain technology. Moreover, applying blockchain technology in various industries is a new arena in the existing research. The results of qualitative research indicate that blockchain technology could benefit the hospitality industry by building a more trustable reputation system, smoothening operation flow, reducing operation cost, enhancing customer experiences, enhancing food safety, and providing convenient payment methods without the necessity of intermediaries. This chapter is focused on investigating how the implementation of blockchain technology benefits the hospitality industry, together with its potential drawbacks.
Secure IoT data using blockchain

Internet of things (IoT) is a network of physical objects with embedded systems that are able to communicate with each other via the Internet. It leads to a distributed network of devices that can communicate with humans and objects. The implementation of IoT will provide benefits, but it also raises security issues that affect data integrity and privacy of the companies. In order to overcome security issues, blockchain technology is used. This chapter is based on up-to-date research on the blockchain as well as IoT with the purpose of studying the blockchain as potential solutions to secure IoT data. Both blockchain and IoT are new research areas into existing research, which support the use of a qualitative research method. The results indicate that blockchain is used to secure data management, but it is not a complete solution. Most of the security issues are related to devices, and blockchain technology handles information and secures identities, stock traceability and transactions, which are made without human interaction and with automated storage management. There are still some barriers to creating benefits work into reality. This chapter is focused on investigating the integration of IoT and blockchain to analyze and improve security issues.
Internet of Things Information System and Clothing Computer Renderings Digital Art

The Internet of Things (IoT) is developing rapidly and is integrated into all aspects of life. Clothing is an indispensable part of meeting the basic needs of the human body, and its traditional functions include warmth, health care, decoration, and beauty. While as a special type of clothing that integrates multidisciplinary technology, smart clothing has a wider scope of action. With the update and iteration of science and technology, many technologies that originally belonged to nonclothing disciplines have also been applied to the clothing field. Accordingly, for the special clothing category of smart clothing based on embedded system, a set of design process that can be used for this category of smart clothing was proposed. Using this design process, an intelligent fire suit that can be used to protect the personal safety of firefighters and assist firefighters to cooperate was designed and implemented. Starting from the daily working environment and working characteristics of firefighters, this firefighting obedience analyzed the design points from the perspectives of clothing comfort, warning, toxic and harmful gas monitoring, and firefighters’ cooperation. After testing, the test results of the outer layer fabric, waterproof and moisture-permeable layer fabric, and thermal insulation and comfort layer fabric of the fire suit all met the corresponding national standards; the monitoring sensitivity of harmful gases was high; it could achieve a “good” warning effect in a dark environment. Compared with ordinary firefighting suits, it was more comfortable under the subjective and objective test and scored 0.669 higher under the seven-point scale; its interactive performance met the actual needs. The clothing has complete functions and a complete feedback mechanism, which has a positive effect on ensuring the personal safety of firefighters.
Advancing Access to Cutting-Edge Tabletop Science

Hands-On Research in Complex Systems Schools provide an example of how graduate students and young faculty working in resource-constrained environments can apply key mindsets and methods of tabletop experiments to problems at the frontiers of science. Each day during the Schools’ two-week program, participants work in small groups with experienced tabletop scientists in interactive laboratories on topics drawn from diverse disciplines in science and technology. Using modern low-cost tools, participants run experiments and perform associated data analysis together with mathematical and computational modeling. Participants also engage in other scientific professional activities; in particular, they learn best practices for communicating their results visually, orally, and in writing. In this way, the Hands-On Schools foster the development of scientific leaders in low- and middle-income countries.
A Method of Early Warning for Course Learning Based on SMOTE and OCSVM

With the application of big data, artificial intelligence, and other related technologies in the field of education, using machine learning to carry out early warning for course learning has become an effective means to improve teaching quality. However, in the scene of early warning, the samples are significantly less than the ordinary samples, and the general clustering or classification methods are difficult to achieve good results. Therefore, this paper proposes an early warning for course learning method based on SMOTE and OCSVM. First, collect and preprocess students’ college entrance examination information and online course learning information data. Second, use SMOTE algorithm to expanding the samples. Then, the OCSVM model is designed, the Gaussian kernel function is used, and the Lagrange multiplier is used to solve the optimization problem for the optimization objective. The qualified student samples are selected for learning, and the classifier is trained, so as to classify the student data and realize the early warning of course learning. Select recall and F1_Score to evaluate the model, and comparative experiments are carried out. From the experiment, it is clear that in most cases, the method proposed in this paper is superior to the original sample and traditional methods in recall rate and F1_Score.
Research on Evaluation of Art Education Effect in Colleges and Universities Based on Big Data Technology

In order to improve the automatic evaluation ability of fine arts education effect in colleges and universities, this paper proposes the evaluation research of fine arts education effect based on big data technology. We build university fine arts education effect data analysis model, considering the characteristics of the college art education effect; can reflect the effect of art education in colleges and universities’ selected index system, with the effect of art education in colleges and universities of the decision about the elements of the decomposed into goals, standards, and plan level, such as university fine arts education effect evaluation of qualitative and quantitative analysis; find out the hidden representative university fine arts education effect evaluation factors; build fine arts education in colleges and universities’ effect-associated distribution rules of the data, through unsupervised learning method, the effect of art education in colleges and universities’ data feature extraction in the process of adaptive learning, by fuzzy comprehensive evaluation of big data; and realize the effect of art education in colleges and universities. The test results show that the fitness level of using this method to evaluate the effect of art education in colleges and universities is high, the score of the evaluation effect of art education in colleges and universities is significant, and it is in a good state in the evaluation index score table, indicating that the evaluation effect is accurate and reliable.
Entanglement criterion of &lt;i&gt;N&lt;/i&gt; qubit system

In previous paper (&lt;ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://doi.org/10.1142/S0217979219501972"&gt;2019 &lt;i&gt;Int. J. Mod. Phys. B&lt;/i&gt; &lt;b&gt;33&lt;/b&gt; 1950197&lt;/ext-link&gt;; &lt;ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://doi.org/10.1142/S0217979220500228"&gt;2020 &lt;i&gt;Int. J. Mod. Phys. B&lt;/i&gt; &lt;b&gt;34&lt;/b&gt; 2050022&lt;/ext-link&gt;), we presented a method to judge the entanglement of 2-qubit system. The necessary and sufficient conditions for the 2qubit system being separable are that if the relevant coefficients is positive and the principal density matrix is separable, then the system is separable, otherwise it is entangled. Now in this paper, we try to generalize this criterion to a 3-qubit system, and then, we further generalize the criterion of 3-qubit system to an &lt;i&gt;N-&lt;/i&gt;qubit system. This is a complicated and interesting issue.
Evolutionary Computing to solve product inconsistencies in Software Product Lines

In Software Product Lines (SPLs), multiple design teams work collectively to configure products. Often, having multiple sub-designs leads to inconsistencies which prevent and delay the configuration of the final product at the end. Solutions are available to identify and resolve these inconsistencies for limited scale SPLs. For large scale and industrial SPLs, resolution of inconsistencies is still a potential research topic. In this paper, we propose, implement and validate an application of evolutionary computation, particularly genetic algorithms, to resolve inconsistencies in large scale SPL products. We label our approach ga-SPLIS (genetic algorithm-Software Product LIne Solution). Using a concrete experimental methodology with different feature set sizes and multiple standard performance metrics, we generate results which validate the applicability of ga-SPLIS for the SPL community.