Reviewerly/exHarmony huggingface dataset:
ISHD: Intelligent Standing Human Detection of Video Surveillance for the Smart Examination Environment

In the environment of smart examination rooms, it is important to quickly and accurately detect abnormal behavior (human standing) for the construction of a smart campus. Based on deep learning, we propose an intelligent standing human detection (ISHD) method based on an improved single shot multibox detector to detect the target of standing human posture in the scene frame of exam room video surveillance at a specific examination stage. ISHD combines the MobileNet network in a single shot multibox detector network, improves the posture feature extractor of a standing person, merges prior knowledge, and introduces transfer learning in the training strategy, which greatly reduces the computation amount, improves the detection accuracy, and reduces the training difficulty. The experiment proves that the model proposed in this paper has a better detection ability for the small and medium-sized standing human body posture in video test scenes on the EMV-2 dataset.
Series editors' introduction: COVID, policy and the state of our institutions

COVID-19 and the Response of Central Banks analyses the reactions of central banks to the COVID-19 crisis in Sub-Saharan Africa. It focuses on how the pandemic has affected the economic performance of Sub-Saharan African countries, many of which were already struggling with growth and sustainability. The first part of the book covers countries within monetary unions such as Cameroon, Congo, Senegal, and Cote d'Ivoire. In the second half, countries with their own independent central banks, The Democratic Republic of Congo, Nigeria, Ghana, and Sierra Leone, are discussed. Chapters highlights the differences between Monetary Union membership and independent Central Banks in policymaking during health crises and explore the role of central banking in minimizing the deleterious effects.
Analyzing the Use of Blockchains for Challenges in Inter-organizational Business Processes

Managing business processes in a (semi)-automated manner remains challenging, especially when processes span across two or more companies. This area of interest, also named inter-organizational process management, faces challenges like correctness, traceability and trust of data objects and message flows. There are different solutions in existence which are aimed at tackling the aforementioned challenges. These range from electronic data interchange through the use of trusted third parties to solutions which use blockchains. However, a detailed comparison of currently employed solutions and suggested alternatives is still missing. We aim to fill this gap and furthermore provide a method that helps to choose the best-fitting architecture based on business needs.
Efficient Carrier Frequency Offset Estimation in Wireless Sensor Networks

The carrier frequency offset (CFO) estimation is a crucial problem in orthogonal frequency division multiplexing (OFDM) systems, especially for the enabling technologies in the Internet of Things, which demand stringent limitations of low cost, low power consumption, and wide ranges, like wireless sensor networks. In this letter, we propose an efficient CFO estimation method for OFDM systems with in- and quadrature-phase (IQ) imbalance, which combines the emerging multitask learning (MTL) with the channel residual energy (CRE) to reduce the average inferring time greatly. The MTL estimator can extract the correlated features between the CFO and the IQ imbalance so as to mitigate the intractable coupling effect. On the other hand, the CRE technique can guide the neural network and loss function design to improve the estimation accuracy and the training efficiency. Numerical results demonstrate that our proposal achieves high estimation accuracy while the inferring time is significantly reduced compared with other methods.
APPLICATION OF MORPHOLOGIC ANALYSIS TO QUANTIFY DIVERSION OF FLOOD WATERFLOW AND POTENTIAL ELECTRICAL ENERGY

As significant catchment of flood water, this study examined important morphological structures of Lake Buhi (LB) catchment area which is a part of Bicol River Basin Area (BRBA). The information taken was used to derive a mathematical model which was then used in analysing the hydrologic event in the area. Different scenarios were investigated to provide solutions and avoid major risks and damages in BRBA during extreme precipitation. The method used to gather the data are Geographical Information System (GIS), digital elevation modelling (DEM), hydrologic modelling, site visit, and remote sensing. It was found that the morphology is well structured to collect and impound vast amount of rainwater flow and form huge body of water known as the LB. The area has immense number of streams that directs surface runoff into the impounded body of water having low drainage density. Enormous amount is needed to discharge away and prevent high water level rise and stop flood in the nearby villages. The capacity of the outflow river is sufficient in most cases of the forecasted precipitation while during the time of typhoon event was found insufficient. However, there are three options to divert away the accumulated floodwater and become a potential source of electrical energy.
106 Affective dialogue systems

Human conversational partners usually not only focus on what is being said, but also try to interpret and respond to affective cues that have been conveyed in a dialogue. Several research projects started to simulate emotional behaviors through computer-based dialogue systems in order to enable more human-like styles of communication when humans communicate with machines. Especially in areas where machines can take on the role of assistants, consultants or even companions, the emulation of human affective behaviors might be desirable and necessary. This chapter provides a survey of dialogue systems that simulate certain aspects of human affective behaviors, focusing on the modeling, analysis and synthesis of affective behaviors. It discusses the following research questions: How can affective behaviors be modeled within a machine? To what extent is a machine capable of detecting human affective cues from facial expressions, gestures and speech? What possibilities does a dialogue system have to express affective behaviors as well? How do humans respond to a dialogue system that simulates certain aspects of affective behaviors? In addition to presenting affective computing technologies, we also provide representative examples of affective dialogue systems and discuss to what extent it is possible and desirable to simulate empathic behaviors by a machine.
Progressive Learning Hill Climbing Algorithm with Energy-Map-Based Initialization for Image Reconstruction

Image reconstruction is an interesting yet challenging optimization problem that has several potential applications. The task is to reconstruct an image using a fixed number of transparent polygons. Traditional gradient-based algorithms cannot be applied to the problem since the optimization objective has no explicit expression and cannot be represented by computational graphs. Metaheuristic search algorithms are powerful optimization techniques for solving complex optimization problems, especially in the context of incomplete information or limited computational capability. In this paper, we developed a novel metaheuristic search algorithm named progressive learning hill climbing (ProHC) for image reconstruction. Instead of placing all the polygons on a blank canvas at once, ProHC starts from one polygon and gradually adds new polygons to the canvas until reaching the number limit. Furthermore, an energy-map-based initialization operator was designed to facilitate the generation of new solutions. To assess the performance of the proposed algorithm, we constructed a benchmark problem set containing four different types of images. The experimental results demonstrated that ProHC was able to produce visually pleasing reconstructions of the benchmark images. Moreover, the time consumed by ProHC was much shorter than that of the existing approach.
Proposal for the Clustering of Characteristics to Identify Emotions in the Development of a Foreign Language Exam

Automatic emotion identification allows for obtaining information on emotions experienced by an individual during certain activities, which is essential for improving their performance or preparing for similar experiences. This document aims to establish the clusters of variables associated with the identification of emotions when a group of students takes a foreign language exam in Portuguese. Once the data clusters are determined, it is possible to establish the perception of emotions in the students with relevant variables and their respective decision thresholds. This study can later be used to build a model that relates the measured variables and the student’s performance so that strategies can be generated to help the student achieve better results on the test. The results indicate that the clusters and range values of the variables can be obtained to observe changes in the concentration of the students. This preliminary information can be used to design a fuzzy inference system to identify the student’s state of concentration.
Does Leader-Member Exchange and Organizational Commitment Affects Turnover Intention of Employees? A New Perspective in The Banking Sector

The success of an organization largely depends on how well the leadership process is executed. Now a days, relationship-based approaches of leadership are tapping the attentions of scholars. The study mainly focused on the effect of leader-member exchange and organizational commitment on employee turnover intention. Methodologically, the study was explanatory research in design. Besides, cross-sectional survey method was employed in an attempt to gather the relevant data at one point in time. A total of 399 respondents were participated in the survey. However, 371 respondents gave relevant information and their response score included in the final data analysis. Exploratory factor analysis via SPSS 23 and confirmatory factor analysis via AMOS 23 was conducted. Structural equation modeling using AMOS 23 was applied to examine the causal relationship between leader-member exchange, organizational commitment and turnover intention. The overall analysis result shows that, leader-member exchange has a positive and significant effect on employee organizational commitment level. In addition, organizational commitment significantly and negatively affects employee turnover intention. Moreover, organizational commitment fully mediates the causal link between leader-member exchange and turnover intention level of bank employees. The study result gives a clue that the level of relationship leaders and followers are reciprocating with each other significantly affects turnover intention of employees in the banking sector. The study result may broaden the understanding of leadership research in the context of the banking sector. Because of the study's use of a cross-sectional survey methodology, only less can be learned about causality. Future studies must be carried out in a manner that involves data collection using several approaches and longitudinal research designs.
A Comparative Analysis of Cloud Computing Simulation Tools: Understanding Features, Capabilities and Limitations

The growth of cloud computing has led to an increased demand for simulation tools that can model and analyze cloud computing environments.In this paper, we provide a comprehensive survey of the available cloud computing simulation tools, with a focus on their features, capabilities, and limitations.We begin by discussing the motivations for using simulation tools in cloud computing, and then provide an overview of the different types of simulation tools available.We categorize the simulation tools based on their approaches and models, such as discrete event simulation, agent-based simulation, and analytical modeling.We then describe the various features and capabilities of the simulation tools, including their support for different cloud architectures, scalability, and resource allocation strategies.Finally, we examine the limitations of the simulation tools, such as the accuracy of the models, the complexity of the simulation process, and the lack of standardization in cloud computing simulation.This survey provides a useful reference for researchers and practitioners who are interested in selecting the appropriate simulation tool for their cloud computing research or implementation.
Behavior of Observer Performing Discrete Gabor Transform

The theory of discrete Gabor transforms provides a constructive and interpretable way of creating biorthogonal transforms.These transforms can achieve a non-uniform frequency resolution and they can be implemented recursively by observers based on Hostetter's approach.The dead-beat property of the observers were previously shown for coherent signals with the help of an adequately chosen conceptual signal model.This paper investigates the properties of the signal modelobserver pair and derives the dead-beat property in the case of noncoherent signals based on a modified signal model.
Examining the Differential Effectiveness of Fear Appeals in Information Security Management Using Two-Stage Meta-Analysis

Most of the information security management research involving fear appeals is guided by either protection motivation theory or the extended parallel processing model. Over time, extant research has extended these theories, as well as their derivative theories, in a variety of ways, leading to several theoretical and empirical inconsistencies. The large body of fragmented, and sometimes conflicting, research has muddied the broader understanding of what drives protection- and defensive motivation. We provide guidance to the security discourse by offering the first study in the literature to employ two-stage meta-analytic structural equation modeling (TSSEM), which combines covariance-based structural equation modeling and meta-analysis. IS researchers have traditionally used meta-analysis for structural equation modeling for such purposes—an approach that has several serious statistical flaws. Using 341 systematically selected empirical security articles (representing 383 unique studies) and TSSEM, we pool a large series of five datasets to test six models, from which we examine the effects of constructs and paths in the security fear-appeals literature. We compare and test six versions of models inspired by issues in the broader fear-appeals literature. We confirm the importance of both the threat- and coping-appraisal processes; establish the central role of fear and that it has greater importance than threat; show that efficacy is a stronger predictor of protection motivation than is threat; demonstrate that response costs as currently measured are ineffective but that maladaptive rewards have a strong negative effect on protection motivation and a positive effect on defensive motivation; and provide evidence that dual models of danger control and fear control should be used.
The Complexity of Secure RAMs

In this invited lecture, I survey the recent results on the complexity of Oblivious RAMs and of related cryptographic data structures and highlight the proof techniques employed.
Literature image visualization processing and data analysis of the theoretical research on "middle school chorus" since the founding of New China from the perspective of big data

In this paper, we take "secondary school chorus" in CNKI as the data source, and use Citespace, a literature analysis software that will allow the visual analysis function of China Knowledge Network, to conduct a visual research analysis of secondary school chorus theory research literature, which focuses on the analysis of the number of articles published in secondary school chorus theory research since the founding of New China, the publishing institution The analysis will focus on the number of articles, authors, keywords, etc. The visualization and mapping of the current status and development trend of the research are combined with the data for research analysis.
Spring Data Access with NoSQL

Most applications use a relational database like Oracle, MySQL, or PostgreSQL; however, there is more to data storage than just SQL databases. There are
Accurate Context Extraction from Unstructured Text Based on Deep Learning

Information today is the pillar of the development and the economic growth. A reliable information comes from a flawless exploitation of the appropriate data. The semantic analysis of data is one of the key factors that have a direct impact on the quality of the information that is extracted from it. The subject matter of a given document is closely related to the context of the information conveyed by that document. The reliable identification of the context of a document is, in our opinion, an important added value for an efficient semantic exploitation of the data. In this paper we propose a new approach for the accurate identification of the context associated with a document, given a precise definition of the context. The system has been evaluated on corpora from the "New York Times" and on a context dataset, called "WikiContext", that we have built ourselves for this purpose. Our experiments have shown that our system outperforms the most advanced context extraction systems.
ATUAÇÃO DO ENFERMEIRO NA REALIZAÇÃO DO PRÉ-NATAL DE BAIXO RISCO NO CONTEXTO PANDÊMICO DA COVID-19

A recent discussion arose in Brazilian law with the creation of the COVID-19 vaccine, in which there was a collision of fundamental norms, on the one hand the collective interest in integral immunization and mitigation of the effects of the disease and on the other, the freedom of conscience of parents who refuse to vaccinate their children for reasons of philosophical conviction.Then, the problem reached the Federal Supreme Court in a regime of general repercussion in RE 1.267.879/SP,establishing the thesis of the illegitimacy of parents to vaccinate their children for philosophical reasons, considering that there is a prevalence of the interest of the community .Furthermore, the present study will unfold from the perspective of the doctrines of John Stuart Mill and Robert Nozick, verifying whether the Supreme Court's decision conforms to the thinking of these authors.In short, the aim is to promote collective well-being and completely dissipate COVID-19.
ATUAÇÃO DO ENFERMEIRO COMO LINHA DE FRENTE NO ENFRENTAMENTO DA PANDEMIA DA COVID-19

A recent discussion arose in Brazilian law with the creation of the COVID-19 vaccine, in which there was a collision of fundamental norms, on the one hand the collective interest in integral immunization and mitigation of the effects of the disease and on the other, the freedom of conscience of parents who refuse to vaccinate their children for reasons of philosophical conviction.Then, the problem reached the Federal Supreme Court in a regime of general repercussion in RE 1.267.879/SP,establishing the thesis of the illegitimacy of parents to vaccinate their children for philosophical reasons, considering that there is a prevalence of the interest of the community .Furthermore, the present study will unfold from the perspective of the doctrines of John Stuart Mill and Robert Nozick, verifying whether the Supreme Court's decision conforms to the thinking of these authors.In short, the aim is to promote collective well-being and completely dissipate COVID-19.
Multiple Linear Regression and Bagging-based Analysis and Modeling of Influence of Mother's Socio-economic Attributes on Anxiety of Online Education

Many mothers expressed varying degrees of anxiety about their children's online lessons at home. To investigate the related phenomena, data samples were obtained through questionnaires on mothers. The dependent variable of each sample was the anxiety degree towards online classes, and the independent variable was the various socioeconomic attributes of mothers. The results indicated that there was a strong correlation between the mother's anxiety about online classes and socioeconomic attributes such as age, educational background, income, and the elderly person at home accompanying children in online classes. The results of linear regression modeling indicate that it is difficult to fit a simple linear relationship between dependent and independent variables. The integrated learning model based on Bagging indicates that the dependent and independent variables can be fitted into a more complex numerical relationship. The experimental results show that the classification accuracy is 91.6%. The portrait characteristics of mothers with high anxiety about online classes include unaccompanied children at home during online classes, family income, and the mother's educational background. When there was only one child in the family, the age difference between mother and child was significantly larger than the average difference of all subjects.
IMPACTO DA PANDEMIA DO COVID-19 NA PREVALÊNCIA DE DOENÇAS DERMATOLÓGICAS NO AMBULATÓRIO DE UM CENTRO UNIVERSITÁRIO

A recent discussion arose in Brazilian law with the creation of the COVID-19 vaccine, in which there was a collision of fundamental norms, on the one hand the collective interest in integral immunization and mitigation of the effects of the disease and on the other, the freedom of conscience of parents who refuse to vaccinate their children for reasons of philosophical conviction.Then, the problem reached the Federal Supreme Court in a regime of general repercussion in RE 1.267.879/SP,establishing the thesis of the illegitimacy of parents to vaccinate their children for philosophical reasons, considering that there is a prevalence of the interest of the community .Furthermore, the present study will unfold from the perspective of the doctrines of John Stuart Mill and Robert Nozick, verifying whether the Supreme Court's decision conforms to the thinking of these authors.In short, the aim is to promote collective well-being and completely dissipate COVID-19.
Can higher-order structural features improve the performance of graph neural networks for graph classification?

Graph classification is a problem with applications in many different domains, which classifies a collection of graphs with categorical labels. One of the increasingly popular approaches to classify graphs is to use graph neural networks (GNNs), which capture the dependence of graph elements via message passing between the nodes. The key idea is to represent graphs in low-dimensional vectors by collectively aggregating node information guided by the graph structure. There are two types of information that can be used for graph classification. One is the textual features associated with each node in a graph, such as the keywords of a publication in a citation network. The other is the structural features that capture the higher-order dependencies between graph nodes. In this paper, we present a GNN-based graph classification framework that utilizes both textual and structural features, where the structural features of each node is calculated based on a set of small induced subgraphs (i.e., graphlets). We carry out experiments on several well-known graph-structured data sets, i.e., DD, MUTAG, NCI1, ENZYMES, and PROTEINS. By comparing with the state-of-the-art graph convolutional networks (GCNs), i.e., the spectral-based GCNs, the graph attention networks (GAT), and the transformer-based GCNs, we evaluate the effectiveness of involving graphlet-based structural features on the task of graph classification. The results also show that the transformer-based GCN, which integrates higher-order structural features as input, can significantly improve the accuracy of graph classification.
No pump required

Using a 3D printer, sports-equipment manufacturer Wilson has created a prototype basketball that never needs to be pumped up.
Philosophical ding-dong

A reply to an analysis by Robert P Crease and Gino Elia of the philosophical implications of the 2022 Nobel Prize for Physics, which was won by Alain Aspect, John Clauser and Anton Zeilinger for their work on the fundamentals of quantum information science.