Reviewerly/exHarmony huggingface dataset:
Hybridizing Bees Algorithm with Firefly Algorithm for Solving Complex Continuous Functions

In this article, two hybrid schemes using the Bees Algorithm (BA) and the Firefly Algorithm (FA) are presented for numerical complex problem resolution. The BA is a recent population-based optimization algorithm, which tries to imitate the natural behaviour of honey bees foraging for food. The FA is a swarm intelligence technique based upon the communication behaviour and the idealized flashing features of tropical fireflies. The first approach, called the Hybrid Bee Firefly Algorithm (HBAFA), centres on improvements to the BA with FA during the local search thus increasing exploitation in each research zone. The second one, namely the Hybrid Firefly Bee Algorithm (HFBA), uses FA in the initialization step for a best exploration and detection of promising areas in research space. The performance of the novel hybrid algorithms was investigated on a set of various benchmarks and compared with standard BA, and other methods found in the literature. The results show that the proposed algorithms perform better than the Standard BA, and confirm their effectiveness in solving continuous optimization functions.
Automatic Viewpoint Switching for Multi-view Surgical Videos

Recording medical surgery operations is important for sharing the various operating techniques. In most operating rooms, fixed surgery cameras are already installed, but it is almost impossible to capture the surgical field because of occlusion by the surgeon's head and body. In order to capture the surgical field in real surgery operations, we proposed the installation of multiple cameras in a surgical lighting system, so that at least one camera can capture the target surgical field even when the surgeon's head and body occlude other cameras. In this paper, we present a method for automatic viewpoint selection from multi-view surgical videos, so that the surgical field can always be recorded in the output video. We employ a method for learning-based object detection from videos for automatic evaluation of the surgical field area from multiple input videos. By selecting the viewpoint with the largest area of the surgical field, we can virtually reduce the area of the surgeon's head and hands. In general, frequent camera switching degrades the video quality of view (QoV). Therefore, we apply the Dijkstra method widely used in the shortest path problem as a combinatorial optimization method for this problem. Our camera scheduling method is that the camera switching is not performed for a certain period of time, and the surgical field observed in the entire video is maximized.
Pegg–Barnett coherent states

In this paper, we show that the Pegg–Barnett formalism accepts coherent states constructed as eigenstates of the annihilation operator, considering both the number and the phase. These operators are defined within a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mo stretchy="false">(</mml:mo> <mml:mi>s</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> <mml:mo stretchy="false">)</mml:mo> </mml:math> -dimensional Hilbert space <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi class="MJX-tex-caligraphic" mathvariant="script">H</mml:mi> </mml:mrow> <mml:mi>s</mml:mi> </mml:msub> </mml:mrow> </mml:math> and with periodic conditions. The coherent states that we find are determined by the eigenvalue of the annihilation operator, which leads to a discrete spectrum. This approach allows calculation of the discrete-finite counterpart of the Wigner function in a phase space defined by the variables of number and phase.
On the Composability of Statistically Secure Random Oblivious Transfer

We show that random oblivious transfer protocols that are statistically secure according to a definition based on a list of information-theoretical properties are also statistically universally composable. That is, they are simulatable secure with an unlimited adversary, an unlimited simulator, and an unlimited environment machine. Our result implies that several previous oblivious transfer protocols in the literature that were proven secure under weaker, non-composable definitions of security can actually be used in arbitrary statistically secure applications without lowering the security.
Convolution Neural Network-based Action Recognition for Fall Event Detection

Action recognition is a challenging and essential task in computer vision particularly in monitoring elderly activities in daily living.This paper presents an efficient normal and abnormal actions recognition model, particularly for fall event detection using the convolution neural network; CNN-AlexNet.In this study, the model was trained and tested using an established CASIA and Le2i datasets that cover various application domains.Results showed that the model performed well with classification accuracy, F-score, sensitivity and specificity rates of 99.97%, 99.97%, 99.94% and 100%, respectively.Therefore, the CNN-AlexNet based action recognition model can be considered as a high-performance classifier to differentiate between normal and abnormal actions to be adapted in a smart camera-based surveillance system.
Forecasting the Translation Profession Development: Foresight Technology

The article describes the results of the conducted research based on the foresight technology that is widely used for long-term forecasting, representing a way of building a coherent and balanced image of the future. It was the first Foresight session that was devoted to the translator’s profession in Russia. The participants tried to predict the future trends, technologies, possibilities and risks of the translator’s profession in the future (up to 2030). The session was not limited to common forecasting: the task was not only to imagine the future of the translation profession but also to suggest the actions needed to achieve positive results, i.e., participants not only designed the future but also considered possible ways of developing and stabilizing the translator’s profession.The analysis of different foresight sessions in the field of education that were held in Russia, European Union, Great Britain, Canada, and the USA are also presented in the article.
Web application for self-diagnosis and drug recommendation based on user symptoms

Throughout the evolution of the Internet and social networks, forums and online platforms have a vital role in sharing information, along with the creation and engagement of virtual communities.Such websites represent great resources, and they are the irst step in the adoption of e-health services.When the persons are ill, many of them use search engines for self-diagnosis and gather possible treatment ideas before asking for a doctor's opinion.This takes a lot of time because the information is scattered across various forums and websites.In this paper is presented an application that aims to provide an online self-diagnosis and drug recommendation tool based on natural language processing of the symptoms described by the user.Over 2,200 medicines are stored in the database, each having a set of keywords according to their usage.Thus, the platform automates the search process, and provides the user with the most relevant information, eliminating the need for manual data interpretation.The results are ranked according to the conidence score obtained after the execution of the fuzzy search algorithm.The platform does not provide medical advice.Thus it is intended for informational purposes only.The developed platform is not a substitute for professional medical advice, diagnosis or treatment.Another feature of the platform is that it enables users to ind hospitals and clinics around them, so that they can receive professional healthcare service.The ield of medicine is a sensitive topic due to the fact that one mistake could lead to the loss of countless human lives.It was a long and dificult journey to reach the current state of medicine, but technology was and will remain essential to the evolution of the healthcare system.The proposed solution of this paper is customized for Romania but it can be adapted for other countries by replacing the drug database.
Automated Trainability Evaluation for Smart Software Functions

More and more software-intensive systems employ machine learning and runtime optimization to improve their functionality by providing advanced features (e. g. personal driving assistants or recommendation engines). Such systems incorporate a number of smart software functions (SSFs) which gradually learn and adapt to the users' preferences. A key property of SSFs is their ability to learn based on data resulting from the interaction with the user (implicit and explicit feedback)-which we call trainability. Newly developed and enhanced features in a SSF must be evaluated based on their effect on the trainability of the system. Despite recent approaches for continuous deployment of machine learning systems, trainability evaluation is not yet part of continuous integration and deployment (CID) pipelines. In this paper, we describe the different facets of trainability for the development of SSFs. We also present our approach for automated trainability evaluation within an automotive CID framework which proposes to use automated quality gates for the continuous evaluation of machine learning models. The results from our indicative evaluation based on real data from eight BMW cars highlight the importance of continuous and rigorous trainability evaluation in the development of SSFs.
Comparison of neural networks and regression time series in predicting export from Czech Republic into People´s Republic of China

The People´s Republic of China is one of the largest, but also the most demanding markets in the world. The trade is limited by a number of barriers, strong competition and unusual environment for trades from other parts of the world. Despite those limitations, Czech exporters are able to establish themselves in the Chinese market, exporting mainly machines and vehicles. To predict future export trends is very difficult; however, these predictions can be crucial not only for individual exporters but also for the whole national economy. For predictions, economists use causal, intuitive or statistical methods. The objective of the contribution is to compare the accuracy of equalizing time series by means of regression analysis and artificial neural networks for a possible prediction of future export trends on the example of the Czech Republic export to the People´s Republic of China. For the purposes of analysis by means of statistical methods, the data obtained from monthly statements from the period starting from the year 2000 and ending in July 2018. First, a linear regression is carried out and subsequently, neural networks are used for regression. Finally, the results are compared. It appeared that in practice, mainly all retained neural networks are applicable. However, the first of them showed significant deviations within a very short period of time.
An Information-theoretic Approach to Unsupervised Feature Selection for High-Dimensional Data

In this talk, we propose an information theoretic approach to design the functional representations to extract the hidden common structure shared by a set of random variables. The main idea is to measure the common information between the random variables by the Watanabe's total correlation, and then find the hidden attributes of these random variables such that common information between these random variables is reduced the most given these hidden attributes. We show that these hidden attributes can be characterized by an exponential family specified by the eigen-decomposition of some pairwise joint distribution matrix. Then, we adopt the log-likelihood functions for estimating these hidden attributes as the desired functional representations of the random variables, and show that these functional representations are informative to describe the common structure. Moreover, we design both the multivariate alternative conditional expectation (MACE) algorithm to compute the proposed functional representations for discrete data, and a novel neural network training scheme for continuous or high-dimensional data. Finally, the performances of our algorithms are validated by numerical simulations in the MNIST digital recognition.
Penggunaan Algoritma Nearest Neighbor Pada Sistem Penalaran Berbasis Kasus Untuk Diagnosis Penyakit ISPA

&lt;p&gt;Acute Respiratory Infection is defined as an acute respiratory disease caused by an infectious agent that is transmitted from human to human. Symptoms include shortness of breath, difficulty breathing, sore throat, fever, wheezing, runny nose, and cough. This research implements CBR to help diagnose ARI. The diagnosis process is done by entering a new problem that contains the symptoms and risk factors that will be diagnosed in the system. The normalized nearest neighbor method with expert confidence is used to calculate the similarity between new problems and cases stored on a case basis. The results of testing the system using 112 case data with 78 case stored in a case base and 34 data used as new cases data, the system has identified four types of ARI disease with a system performance measurement 97.06%.&lt;/p&gt;
Upgraded to Obsolescence: Age Intervention in the Era of Biohacking

Popularized by DIY scientists and quantified-selfers, the language of “biohacking” has become increasingly prevalent in anti-aging discourse. Presented with speculative futures of superhuman health and longevity, consumers and patients are invited to “hack” the aging process, reducing age to one of the many programs, or rather “bugs” that can be re-written, removed, and rendered obsolete. Drawing on recent examples from popular media and anti-aging promotional materials, I explore how the language of biohacking signals an orientation to the body that denies the acceptability of a body that is anything but optimal. In the endless strive towards the latest and greatest, the language of biohacking renders the old body obsolete, standing as nothing more than a relic of an outdated operating system.
Vehicle Network Security Situation Assessment Method Based on Attack Tree

Abstract In order to solve the problem that the current vehicle network security situation assessment methods are seriously few, and the existing methods have low accuracy and are greatly affected by subjective factors, an attack tree-based vehicle network security situation assessment method is proposed. Firstly, the attack tree models are established to describe the network attacks the internet of vehicles may suffer from. Secondly, the security attributes of the attack path are determined, and each attack path is scored. At the same time, the fuzzy analytic hierarchy process is used to solve the weight values of each attack path. After that, weights and ratings are used to assess the security situation level of the threat. Finally, a comparative analysis of the security situation assessment with the analytic hierarchy process shows that the fuzzy analytic hierarchy process method can reduce the influence of human subjective factors and improve the credibility of the security situation assessment.
PENGARUH PEMANFAATAN E-LEARNING, LINGKUNGAN TEMAN SEBAYA, DAN MOTIVASI BELAJAR TERHADAP PRESTASI BELAJAR

&lt;p&gt;&lt;span class="fontstyle0"&gt;This study aims to examine the influence of the use of e-learning, peer group and learning motivation towards learning outcomes of Accounting in class X Accounting of SMK Hidayah Semarang. The sample of this study was students of class X Accounting SMK Hidayah Semarang with total of 41 students. A self-administered questionnaires and multiple regression analysis were used to test the research question. In addition, the results showed that there is a positive and significant effect both simultaneously and partially from the use of e-learning, peer group and learning motivation towards learning outcomes in class X Accounting at SMK Hidayah Semarang. The improvement and intensity in using e-learning strategies, teacher's attention to the students’ peer environment specifically on cooperation and competition among students, and students' intelligence in interacting and establishing fair cooperation in the academic context with their peers are very important factors towards learning outcomes&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt; &lt;span class="fontstyle0"&gt;Keywords: learning outcomes; e-learning; peer group; motivation&lt;/span&gt; &lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;&lt;/em&gt;&lt;br /&gt;&lt;/strong&gt;&lt;/p&gt;
The Evolution of Adjacency Matrices for Sparsity of Connection in DenseNets

Image classification is a task in which convolutional neural networks hold state-of-the-art results. DenseNets, in particular, have been shown to provide state-of-the-art results with reduced model complexity when compared with other convolutional neural network structures. However, DenseNets themselves have opportunities for model simplification, which can not only make DenseNets smaller, but also improve the accuracy of resulting networks. This work develops a novel genetic algorithm, Matrix Evolution for sparse Dense Blocks (ME-SDB), to find such DenseNets, by removing unneeded connections within DenseNets. Our Sparse DenseNets improved the classification accuracy and reduced the number of trainable parameters by up to 33% on CIFAR10, while providing promising results on CIFAR100.
A Clinical Decision Support System as a Tool to Improve the Accuracy of Nursing Diagnoses

Introduction: Nursing diagnosis is an important part of the nursing process and it influences the quality of nursing care. Accuracy in establishing nursing diagnoses is one of the factors that determines the accuracy of the nursing intervention program and the speed of healing of the patients. The Clinical Decision Support System is an information system built to assist health workers in making clinical decisions including the enforcement of nursing diagnoses. Through this systematic review, the author wants to explain the clinical decision support system as a tool to increase the accuracy of the enforcement of nursing diagnoses.Methods: The search for journals using the PICOT framework was conducted in the Scopus, Proquest, PubMed and Cinahl databases with a 10-year publication time limit (2008 - 2018). There were 8 international journals reviewed in full.Results: Generally the journals reviewed stated that the use of the Clinical Decision Support System in establishing nursing diagnoses has been shown to improve the accuracy of nursing diagnoses.Conclusion: This systematic review concludes that the use of clinical decision support systems can help nurses or nursing students in establishing accurate nursing diagnoses.
An Advanced Reversible Data Hiding Algorithm Using Local Similarity, Curved Surface Characteristics, and Edge Characteristics in Images

In this paper, we proposed methods to accurately predict pixel values by effectively using local similarity, curved surface characteristics, and edge characteristics present in an image. Furthermore, to hide more confidential data in a cover image using the prediction image composed of precisely predicted pixel values, we proposed an effective data hiding technique that applied the prediction image to the conventional reversible data hiding technique. Precise prediction of pixel values greatly increases the frequency at the peak point in the histogram of the difference sequence generated using the cover and prediction images. This considerably increases the amount of confidential data that can be hidden in the cover image. The proposed reversible data hiding algorithm (ARDHA) can hide up to 24.5% more confidential data than the existing algorithm. Moreover, it is not possible to determine the presence of hidden confidential data in stego-images, as they possess excellent visual quality. The confidential data can be extracted from the stego-image without loss, and the original cover image can be restored from the stego-image without distortion. Therefore, the proposed algorithm can be effectively used in digital image watermarking, military, and medical applications.
Proof assistants at the hardware-software interface (invited talk)

Some of the earliest applications of proof assistants were to correctness of digital hardware designs, but the subject doesn’t come up too frequently today at venues like CPP. I will try to make the case that proof assistants are a crucial tool for resolving both classical problems and new ones at the hardware-software interface. That is, it is important to understand exactly what guarantees a processor exports to software, it is important to verify that hardware exports those guarantees correctly, and it is important to prove end-to-end theorems covering both hardware and software. A few social developments make this an exciting time to tackle these problems: open instruction sets and open-source hardware designs are growing in real-world relevance, and surprising new classes of security vulnerabilities have gotten more practitioners thinking about precise hardware-software contracts. I will sketch the state of the research area and go into detail on a few of my own related projects. An ancillary goal is to convey that programming or proving digital hardware is a lot like programming or proving software, with a few fun distinctions, so more of the CPP crowd might want to give it a try!
TOURIST SATISFACTION: AN ANALYSIS OF PUSH AND PULL FACTORS - A CASE OF QATAR TOURISM

In the light of rising competition in providing tourism experience amongst Gulf Cooperation Council (GCC) and countries, how efficient Qatar has been in creating tourist satisfaction and tourist motivation in order to create tourist loyalty and promote tourism to global standards.Measuring tourist satisfaction plays an important role in marketing tourism products and services.The purpose of this study is to examine the push and pull factors of tourists visit; their motivation and the activities carry out at the destination.This study explore to understand what comprises tourists are satisfied to transmit their positive experience to third persons as well as repeating their visit to Qatar.The result was drawn from questionnaire survey of 494 international visitors who visited Qatar in 2018.The data were analyzed using matrices which shown the mean scores of expectations, the mean score of experience and the mean of difference between experience and expectation scores.The mean scores of expectation were then plotted against experience in a two dimensional map for positive and negative attributes.The t-test was performed to identify the significant result of each attributes at 1:1000 levels.The findings provide Qatar Tourism Authorities with insights about the level of satisfactions among international tourists and call for better improvements strategies for future tourism development in Qatar.
Paleoindian Settlement in the Southeastern United States

The authors cite two large research open-access databases developed in recent years, popularly known by their acronyms PIDBA (Paleoindian Database of the Americas) and DINAA (Digital Index of North American Archaeology), for aiding in their study of early human settlements in Southeastern North America. With nearly half a million sites indexed so far, many with complex data records listing hundreds of attributes, DINAA has several orders of magnitude more information than PIDBA. PIDBA, however, encompasses image and attribute data on ca. 30,000 Paleoindian artifacts. Information recorded in both PIDBA and DINAA documents not only the number and distribution of Paleoindian artifacts and components found and recorded to date but also the possible proxy measures of human demography and settlement range, which can be compared with figures from later periods or from different states or regions.