Reviewerly/exHarmony huggingface dataset:
Statistical Modeling of Implicit Functional Relations

This study considers the statistical estimation of relations presented by implicit functions. Such structures define mutual interconnections of variables rather than outcome variable dependence by predictor variables considered in regular regression analysis. For a simple case of two variables, pairwise regression modeling produces two different lines of each variable dependence using another variable, but building an implicit relation yields one invertible model composed of two simple regressions. Modeling an implicit linear relation for multiple variables can be expressed as a generalized eigenproblem of the covariance matrix of the variables in the metric of the covariance matrix of their errors. For unknown errors, this work describes their estimation by the residual errors of each variable in its regression by the other predictors. Then, the generalized eigenproblem can be reduced to the diagonalization of a special matrix built from the variables’ covariance matrix and its inversion. Numerical examples demonstrate the eigenvector solution’s good properties for building a unique equation of the relations between all variables. The proposed approach can be useful in practical regression modeling with all variables containing unobserved errors, which is a common situation for the applied problems.
Deadline-constrained cost-aware workflow scheduling in hybrid cloud

Cloud computing offers resources to users for computing and storage needs on the policy pay-what-you-use model. A private cloud is an excellent cost-saving option because the user owns the resources and can be used free to execute workflow applications. However, when private cloud resources are insufficient to fulfill the needs of workflow applications, the public cloud is the only option. To this end, a hybrid cloud (HC) is an elegant way to combine public and private cloud resources to meet the requirements of workflow applications. Therefore, large enterprises prefer the HC, but task scheduling in the HC is complicated. Therefore, this study proposed a Deadline-constrained Cost-aware Workflow Scheduling (DCWS) algorithm to address task scheduling problems in the HC. Firstly, we will execute maximum workflow tasks on the private cloud owned by users under the deadline constraint. Secondly, send the unscheduled workflow tasks to the public cloud for execution. Lastly, we will ensure scheduling whole workflow tasks on the private cloud and the communication channels (for sending to the public cloud) to satisfy the task-precedence needs and the task deadline constraint while incurring a minimal monetary cost. Furthermore, we reduce task slack time with the help of the sub-deadline division technique and search for an ideal virtual machine (VM) to reduce further execution time and monetary cost. The experimental results prove that the DCWS algorithm outperformed existing algorithms.
Librarians’ Digital Competencies: Influence on their Utilization of Emerging Educational Technologies

Academic and instructional services in academic institutions, particularly in Higher Education Institutions (HEIs), have changed as a result of technological advancement. Library and information search services are being digitized, and librarians must gain digital skills in order to provide library users with more effective and efficient research services. The purpose of this study was to determine the influence of librarians’ digital competencies in their utilization of emerging educational technologies. This study used the descriptive correlational research design and analyzed using descriptive and inferential statistics. The participants were eighty (80) registered librarians and active Philippine Librarians Association, Inc. (PLAI) members who are employed in selected libraries in Northern Mindanao. Online survey questionnaires were used for data collection. Findings reveal that the librarians’ digital competencies in terms of managing and developing digital libraries were high. while the competencies in protecting digital content were rated to a moderate extent. Moreover, the librarians’ extent of utilization of emerging educational technologies in terms of cloud computing, web conferencing, and institutional repositories was rated as generally high. The librarians’ assessment of their digital competencies significantly influence their utilization of emerging educational technologies. It is concluded that the digital competencies of librarians are crucial and indispensable as it influences their utilization and application of emerging technologies in managing and developing digital library services. It is recommended that the librarians may acquire knowledge and skills, particularly on these topics related to protecting digital contents: computer and network security, security tools and techniques, digital copyright policy, copyright issues and security and privacy in digital libraries.&#x0D;
Automatically Finding the Right Probabilities in Bayesian Networks

This paper presents alternative techniques for inference on classical Bayesian networks in which all probabilities are fixed, and for synthesis problems when conditional probability tables (CPTs) in such networks contain symbolic parameters rather than concrete probabilities. The key idea is to exploit probabilistic model checking as well as its recent extension to parameter synthesis techniques for parametric Markov chains. To enable this, the Bayesian networks are transformed into Markov chains and their objectives are mapped onto probabilistic temporal logic formulas. For exact inference, we compare probabilistic model checking to weighted model counting on various Bayesian network benchmarks. We contrast symbolic model checking using multi-terminal binary (aka: algebraic) decision diagrams to symbolic inference using proba- bilistic sentential decision diagrams, symbolic data structures that are tailored to Bayesian networks. For the parametric setting, we describe how our techniques can be used for various synthesis problems such as computing sensitivity functions (and values), simple and difference parameter tuning and ratio parameter tuning. Our parameter synthesis techniques are applicable to arbitrarily many, possibly dependent, parameters that may occur in multiple CPTs. This lifts restrictions, e.g., on the number of parametrized CPTs, or on parameter dependencies between several CPTs, that exist in the literature. Experiments on several benchmarks show that our parameter synthesis techniques can treat parameter synthesis for Bayesian networks (with hundreds of unknown parameters) that are out of reach for existing techniques.
Examining the Mediating Effect of Knowledge Management on the Relationship Between Organizational Culture and Organizational Performance

Nowadays, organizations with international dimensions are surrounded by fierce competition and extreme, unstable environments. With these hostile conditions, only those organizations that demonstrate better performances in comparison to their rivals can survive. They can outperform their rivals regarding financial, procedural, and internal operations. To this aim, two variables, organizational culture, and knowledge management, play crucial roles. On the one hand, organizational performance hinges on the in-depth storage of experience, creation, and classification of knowledge. On the other hand, they are under the impression of social and cultural variables of the organization. Among these items, knowledge management is also under the influence of organizational culture and is capable of taking a mediatory role in organizational culture performance. Thus, this research is aimed at exploring the mediatory role of knowledge management in the relationship between organizational culture and performance. Simultaneously exploring these two factors can give new insight into organizational performance under the social and knowledge variables and extend the literature in this area. To this end, each of the three factors was investigated through library study and defined as factorial constructs. Afterward, their relationship was modeled, and the model was under effect in Etehad Rah Engineering Advisory Company as the research’s Statistical Society. A simple random method was used; 28 persons constitute the research’s participants. A questionnaire and PLS software were used to collect and analyze the data. The results reveal a positive and significant relationship between organizational performance and culture directly and through knowledge management. Moreover, a positive and significant correlation exists between organizational performance and knowledge management and organizational culture and knowledge management.
Quantum Random Access Memory for Dummies

Quantum Random Access Memory (QRAM) has the potential to revolutionize the area of quantum computing. QRAM uses quantum computing principles to store and modify quantum or classical data efficiently, greatly accelerating a wide range of computer processes. Despite its importance, there is a lack of comprehensive surveys that cover the entire spectrum of QRAM architectures. We fill this gap by providing a comprehensive review of QRAM, emphasizing its significance and viability in existing noisy quantum computers. By drawing comparisons with conventional RAM for ease of understanding, this survey clarifies the fundamental ideas and actions of QRAM. QRAM provides an exponential time advantage compared to its classical counterpart by reading and writing all data at once, which is achieved owing to storage of data in a superposition of states. Overall, we compare six different QRAM technologies in terms of their structure and workings, circuit width and depth, unique qualities, practical implementation, and drawbacks. In general, with the exception of trainable machine learning-based QRAMs, we observe that QRAM has exponential depth/width requirements in terms of the number of qubits/qudits and that most QRAM implementations are practical for superconducting and trapped-ion qubit systems.
Decentralized Finance Oracles

Financial markets have recently suffered the increased interest of the users for the cryptocurrencies and decentralized finance solutions.Although the Decentralized Finance (DeFi) has been designed based on smart contracts and leave out third party intermediaries, these platforms sometimes require information from the outside world, such as exchange rates or prices.The DeFi Oracles are the link solution between the on-chain world and the off-chain universe.This article describes the oracles, including taxonomy, governance and use cases.Afterwards, it considers their potential, and at the same time, addresses possible risk that they present, and that could impact the future DeFi space
Semantic Segmentation and Depth Estimation Based on Residual Attention Mechanism

Semantic segmentation and depth estimation are crucial components in the field of autonomous driving for scene understanding. Jointly learning these tasks can lead to a better understanding of scenarios. However, using task-specific networks to extract global features from task-shared networks can be inadequate. To address this issue, we propose a multi-task residual attention network (MTRAN) that consists of a global shared network and two attention networks dedicated to semantic segmentation and depth estimation. The convolutional block attention module is used to highlight the global feature map, and residual connections are added to prevent network degradation problems. To ensure manageable task loss and prevent specific tasks from dominating the training process, we introduce a random-weighted strategy into the impartial multi-task learning method. We conduct experiments to demonstrate the effectiveness of the proposed method.
Revolutionizing Worldwide Operations through Blockchain: A Marxian Perspective

&lt;p&gt;This study elucidates the implications of blockchain technology on the product and service offerings of a multinational corporation, examined through the lens of Karl Marx’s seminal work “Capital: A Critique of Political Economy”. Blockchain’s potential to decentralize control and distribute power counteracts Marx’s critique of worker alienation in capitalist economies. My investigation indicates that blockchain’s transparency can help demystify commodity fetishism by revealing production processes, and its decentralized structure can challenge the centralization of capital, offering solutions to traditional capitalist problems. This research contributes a unique perspective on the impact of blockchain technology in global companies, suggesting a revolutionary shift not only in their operations but also in their social relations of production. The study concludes by acknowledging the potential challenges in the practical implementation of these theoretical insights and suggests future research directions to overcome these obstacles. &lt;/p&gt;
The Effect of Expressive Writing Therapy in Reducing Anxiety Levels in Adolescent Bullying Victims

Introduction: Bullying is a social phenomenon that often occurs in society, especially in adolescents. There are many biological, psychological, and social changes that can cause conflict problems between themselves and the surrounding environment, causing bullying victims which have an impact on the emergence of anxiety in these adolescents. The purpose of this study was to determine the effectiveness of expressive writing therapy in reducing the level of anxiety among adolescent victims of bullying. Method: The research method used quasi-experimental research with pretest and posttest control group design approaches. The sampling technique with a total sampling of 44 respondents divided into 2, namely the experimental group and the control group. Results: the results of the study using the Paired Sample T-test showed that the value of p = 0.000 (p &lt;0.05) which means that there is an effect of expressive writing therapy in reducing anxiety levels in adolescent victims of bullying. This can be seen from the decrease in anxiety scores in adolescent victims of bullying from a severe anxiety level of 50.0% to 41.0% in mild anxiety after expressive writing therapy intervention. Conclusion: Therefore, it is suggested that expressive writing therapy can be used as an intervention in overcoming anxiety in adolescent victims of bullying both on campus and at home.
Assessing model performance for counterfactual predictions

Counterfactual prediction methods are required when a model will be deployed in a setting where treatment policies differ from the setting where the model was developed, or when the prediction question is explicitly counterfactual. However, estimating and evaluating counterfactual prediction models is challenging because one does not observe the full set of potential outcomes for all individuals. Here, we discuss how to tailor a model to a counterfactual estimand, how to assess the model's performance, and how to perform model and tuning parameter selection. We also provide identifiability results for measures of performance for a potentially misspecified counterfactual prediction model based on training and test data from the same (factual) source population. Last, we illustrate the methods using simulation and apply them to the task of developing a statin-na\"{i}ve risk prediction model for cardiovascular disease.
Development User Interface Hospital Management Information System Based on a Heuristic Evaluation Approach in Surabaya Hospital Medical Services

Surabaya health service hospital uses the Medical Management System (M applications). However, the application is rarely evaluated. Inputs or feedback obtained from user cases, especially This study, is written to report the improvement of the SIMRS application model based on a heuristic assessment method. The method used in this study was to distribute questionnaires to the research objects that were evaluated, calculate the results of the heuristic assessment using the severity rating method, and then improve the model using the usability theory. This research consists of five usability components that demand improvement: visibility of system status, a match between the system and the real world, user control and freedom, error prevention, and recognition rather than recall. The conclusion is that five usability aspects have been fixed by following the concepts in the heuristic evaluation theory developed in designing a user interface model for the SIMRS application. This research employed a heuristic evaluation approach, which could determine the number of usability problems to be repaired. From the research, the level of problems in the SIMRS application user interface was minor problems. These problem solutions were given low priority in a SIMRS application model adapted to the usability principle.
Explanation and the Right to Explanation

Abstract In response to widespread use of automated decision-making technology, some have considered a right to explanation . In this article, I draw on insights from philosophical work on explanation to present a series of challenges to this idea, showing that the normative motivations for access to such explanations ask for something difficult, if not impossible, to extract from automated systems. I consider an alternative, outcomes-focused approach to the normative evaluation of automated decision making and recommend it as a way to pursue the goods originally associated with explainability.
Dual-branch hybrid encoding embedded network for histopathology image classification

Objective.Learning-based histopathology image (HI) classification methods serve as important tools for auxiliary diagnosis in the prognosis stage. However, most existing methods are focus on a single target cancer due to inter-domain differences among different cancer types, limiting their applicability to different cancer types. To overcome these limitations, this paper presents a high-performance HI classification method that aims to address inter-domain differences and provide an improved solution for reliable and practical HI classification.Approach.Firstly, we collect a high-quality hepatocellular carcinoma (HCC) dataset with enough data to verify the stability and practicability of the method. Secondly, a novel dual-branch hybrid encoding embedded network is proposed, which integrates the feature extraction capabilities of convolutional neural network and Transformer. This well-designed structure enables the network to extract diverse features while minimizing redundancy from a single complex network. Lastly, we develop a salient area constraint loss function tailored to the unique characteristics of HIs to address inter-domain differences and enhance the robustness and universality of the methods.Main results.Extensive experiments have conducted on the proposed HCC dataset and two other publicly available datasets. The proposed method demonstrates outstanding performance with an impressive accuracy of 99.09% on the HCC dataset and achieves state-of-the-art results on the other two public datasets. These remarkable outcomes underscore the superior performance and versatility of our approach in multiple HI classification.Significance.The advancements presented in this study contribute to the field of HI analysis by providing a reliable and practical solution for multiple cancer classification, potentially improving diagnostic accuracy and patient outcomes. Our code is available athttps://github.com/lms-design/DHEE-net.
Intelligent Recommendation Algorithm of Consumer Electronics Products with Graph Embedding and Multi-Head Self-Attention in IoE

IoE connects and interacts with the widest range of people, data and things. With the progress of IoE and the popularity of mobile devices, the demand for consumer electronics is increasing. Various consumer electronic products emerge in an endless stream, and users often have no way to start when facing many products. Therefore, the product recommendation system came into being. This paper proposes an intelligent recommendation model for consumer electronics products (CEPR) in IoE. First, CEPR uses graph embedding to solve data sparsity and cold start. This introduces commodity auxiliary information, it uses random walk to model the commodities and obtain the embedding. Data sparsity can be addressed by transforming high-dimensional feature into low-dimensional feature. The auxiliary information can solve cold start. Secondly, CEPR introduces multi-head self-attention, which combines location coding to model the dependency relationship between users’ historical purchases of goods. AUGRU is embedded in CEPR to filter the evolution path of user interest and obtain accurate user interest representation. Finally, a comprehensive experiment is conducted for the CEPR. Experiments verify the effectiveness of applying this to consumer electronics products recommendation. CEPR achieves 77.2%/80.7% HR, 30.6%/32.1% Recall, and 10.6%/12.5% NDCG on two datasets, which surpass the compared methods.
Impact of cryptocurrencies on financial markets

Cryptocurrency is no longer an unfamiliar concept. With the development of the digital economy, cryptocurrencies have gradually replaced some functions of traditional currencies. This research aims to measure and evaluate the impact of cryptocurrencies on financial markets by considering their effects on exchange rates, gold prices, oil prices, and stock indices. Data for the analysis were collected on a weekly basis from 1 January 2014 to 28 February 2021. The multiple linear regression model was used to examine the relationships in the research model using the statistical analysis software SPSS 22. The research results indicate that cryptocurrencies have an impact on the financial market. Specifically, the research also identified the inverse effect of currency pairs on cryptocurrencies and the interaction between different cryptocurrencies. Consequently, financial market regulators, especially the agency responsible for monitoring the volatility of cryptocurrencies, exchange rates, gold prices, oil prices, and stock indices, have a basis for devising appropriate plans. From this research, managers can implement policies that enhance financial education and communication to help individuals understand the nature of virtual assets, especially cryptocurrencies, while creating motivation towards accepting cryptocurrencies in Vietnam.
Deep Learning Techniques for Television Broadcast Recognition

With the advent of the digital age, the amount of video content available has grown exponentially, especially in the context of television programs, Deep learning has proven to be an important technology capable of addressing the challenges associated with the classification of television programs. One of the main problems in video classification is the complexity of the data itself, moreover, video classification of TV programs by deep learning requires a large amount of training data. This paper proposes several deep learning techniques and the advancement of research in this area for TV program classification on long-form video.
Text embedding in image using an optimisation-based key generation method

Data hiding capacity and image quality of the cover object are important factors in text steganography, and data encryption is being widely employed to secure data in order to provide confidentiality and data privacy. A novel method for encryption and decryption message through optimisation using GAs is presented, which aims to maintain the security of the message via increasing the complexity of the key used in the encryption. The method is used to hide the ciphertext in colour and greyscale images of different sizes. The cover image was divided into several segments (2 * 2, 4 * 4) so that the ciphertext was hidden perfectly by GA. The results in this study using PSNR, MSE, STD, BER, entropy, histogram are more effective, robust, secure, and resistant to common attacks. The purpose of this study is to increase the amount of data to be hidden and to ensure high image quality.