Reviewerly/exHarmony huggingface dataset:
Selective image segmentation driven by region, edge and saliency functions

Present active contour methods often struggle with the segmentation of regions displaying variations in texture, color, or intensity a phenomenon referred to as inhomogeneities. These limitation impairs their ability to precisely distinguish and outline diverse components within an image. Further some of these methods employ intricate mathematical formulations for energy minimization. Such complexity introduces computational sluggishness, making these methods unsuitable for tasks requiring real-time processing or rapid segmentation. Moreover, these methods are susceptible to being trapped in energy configurations corresponding to local minimum points. Consequently, the segmentation process fails to converge to the desired outcome. Additionally, the efficacy of these methods diminishes when confronted with regions exhibiting weak or subtle boundaries. To address these limitations comprehensively, our proposed approach introduces a fresh paradigm for image segmentation through the synchronization of region-based, edge-based, and saliency-based segmentation techniques. Initially, we adapt an intensity edge term based on the zero crossing feature detector (ZCD), which is used to highlight significant edges of an image. Secondly, a saliency function is formulated to detect salient regions from an image. We have also included a globally tuned region based SPF (signed pressure force) term to move contour away and capture homogeneous regions. ZCD, saliency and global SPF are jointly incorporated with some scaled value for the level set evolution to develop an effective image segmentation model. In addition, proposed method is capable to perform selective object segmentation, which enables us to choose any single or multiple objects inside an image. Saliency function and ZCD detector are considered feature enhancement tools, which are used to get important features of an image, so this method has a solid capacity to segment nature images (homogeneous or inhomogeneous) precisely. Finally, the adaption of the Gaussian kernel removes the need of any penalization term for level set reinitialization. Experimental results will exhibit the efficiency of the proposed method.
FSR-SPD: an efficient chaotic multi-image encryption system based on flip-shift-rotate synchronous-permutation-diffusion operation

Abstract Images are a crucial component in contemporary data transmission. Numerous images are transmitted daily through the open-source network. This paper presents a multi-image encryption scheme that utilises flip-shift-rotate synchronous-permutation-diffusion (FSR-SPD) processes to ensure the security of multiple images in a single encryption operation. The proposed encryption technique distinguishes itself from current multi-image encryption methods by utilising SPD operation and rapid FSR-based pixel-shuffling and diffusion operation. The SPD is a cryptographic technique that involves the simultaneous application of permutation and diffusion methods. The FSR-based process involves the manipulation of pixels through three different operations, namely flipping, shifting, and rotating. In the process of encryption, the image components of red, green, and blue colours are merged into a single composite image. The large image is partitioned into non-overlapping blocks of uniform size. The SPD technique is employed to tackle each specific block. The encryption method is efficient and expeditious as it exhibits high performance with both FSR and SPD procedures. The method employs a single, fixed-type, one-dimensional, piecewise linear chaotic map (PWLCM) for both the permutation and diffusion phases, resulting in high efficiency in both software and hardware. The proposed method is assessed using key space, histogram variance, neighbouring pixel correlation, information entropy, and computational complexity. The proposed method has a much bigger key space than the comparative method. Compared to comparison approaches, the suggested solution reduces encrypted picture histogram variance by 6.22% and neighbouring pixel correlations by 77.78%. Compared to the comparison technique, the proposed scheme has a slightly higher information entropy of 0.0025%. Other multiple-color image encryption methods are more computationally intensive than the suggested method. Computer simulations, security analysis, and comparison analysis evaluated the proposed methodology. The results show it outperforms multiple images encrypting methods.
A Low-Cost Embedded System to Support Broadcasting Emergency Messages Through FM Radio Stations

Emergency Alert Systems (EAS) have been deployed in some countries in order to broadcast emergency alerts and warning messages to the public. In this letter, we describe an innovative electronic embedded solution, named Firetec Switch, intended to be placed in the FM radio stations. This switch will be supported by a server placed at the authorized officials, with the capability to generate the audio alerting messages. One interesting challenge is to deliver the messages only to the local public, in the catastrophe neighborhood, by using FM radio stations. This letter focuses on the description of the Firetec Switch hardware and software.
Application of Deep Belief Network for Sustainable Development via Deep Learning to Export Credit Risk Assessment Under the Belt and Road Strategy

The purpose of the study is to reduce the export credit risk of enterprises, and the export credit risk assessment of enterprises under the belt and road strategy based on deep learning is discussed. First, the research background and deep belief networks are introduced. Second, the contrast divergence algorithm based on the deep belief model is improved on the restricted Boltzmann machine. Finally, the deep belief network of classification and partition is constructed and simulated. The results show that the test accuracy of the classification and partition of the restricted Boltzmann machine (CPRBM) constructed is higher than that of the restricted Boltzmann machine (RBM). When the accuracy of the algorithm is verified under the condition of unbalanced two classification samples in a relatively small amount of datasets, the accuracy of the CPRBM algorithm is 93.71%, and the accuracy of the RBM algorithm is 89.86%. In the optimization stage of the deep belief networks, the convergence rate of the CPRBM is slower than that of the RBM. Since the optimized system increases the penalty term in the first training stage of the deep belief networks, the penalty term is canceled in the second stage of optimization. At three time points, the algorithm accuracy of the CPRBM is higher than that of the RBM. The simulation results are consistent with the previous experimental results. Although the accuracy is not high at the third time point, the CPRBM algorithm still has some advantages. Compared with the accuracy of the support vector machine (SVM) and the deep extreme learning machine (DELM), the CPRBM algorithm based on deep belief networks has the highest accuracy at any time point. The CPRBM algorithm constructed has obvious advantages compared with common models, and the overall performance of the algorithm is better. The conclusions provide the support for the sustainable development of the economy under the Belt and Road strategy.
Classifying Latent Classes in Beauty Specialized High School Students’ Career Preparation and The Exploration of Their Characteristics

The purpose of this study is to identify the various types of career preparation behavior of 3rd year students of the specialized beauty high school, and the influencing factors in each group’s categorization and the differences in career is as follows. First, the latent group for career preparation behavior was divided into three groups, and each group was named as follows: low group for career preparation behavior, the medium group for career preparation behavior, and the high group for career preparation behavior. All groups were low in that students visiting the counseling center to consult about their career problems. As a result of identifying the factors influencing the categorization of the latent group for career preparation behavior, all factors were significant. there was no statistically significant difference as a result of verifying the career plan after graduation according to the latent group for career preparation behavior. This study bears significance as it investigated the various types of career preparation behavior of 3rd year students of the specialized beauty high school and found that the influencing factors in each group’s categorization. planning after graduation according to latent classes.
PREVENTION OF RODENT ATTACK ON TERTIARY PACKAGE AT STORAGE LEVEL

This proposes to use some image processing methods as a data normalization method for machine learning. Conventionally, z-score normalization is widely used for pre-processing of data. In the proposed approach, in addition to z-score normalization, a number of histogram-based image processing methods such as histogram equalization are applied to training data and test data as a pre-processing method for machine learning. We evaluate the effectiveness of the proposed approach by using a support vector machine algorithm and a random forest one. In experiments, the proposed scheme is applied to a face-based authentication algorithm with SVM/random forest classifiers to confirm the effectiveness. For SVM classifiers, both z- score normalization and image enhancement work well as a pre-processing method for improving the accuracy. In contrast, for random forest classifiers, a number of image enhancement methods work well, although z- score normalization is unusual for improving the accuracy. The proposed pre-processing method focuses on leveraging histogram analysis to address challenges related to contrast variations, colour distributions, and noise levels in diverse image datasets. The key objectives include the development of an adaptive contrast enhancement algorithm, colour normalization techniques, and strategies for noise reduction.
Evaluating E-Commerce Engagement Factors In Saudi Arabia: Financial Loss, Identity Theft And Privacy Policies

Purpose: The purpose of this study is to investigate the influence of financial loss, identity theft, and privacy protection policies on the willingness of tourists in Saudi Arabia to engage in e-commerce. Theoretical framework: This study examines Saudi Arabian visitors' use of e-commerce, highlighting the consequences of financial loss, identity theft, and privacy laws. The study employs Smart PLS and structured questionnaires, underpinned by a theoretical framework of trust and security, to uncover heightened fears over online identity theft. Policies protecting privacy become essential, boosting customer confidence and promoting e-commerce. The study adds to the scant amount of information in this field by emphasising the role that regulations play in maintaining transaction security and promoting user confidence. Design/Methodology/Approach: This study used a quantitative research methodology to examine the relationship between financial loss, identity theft, privacy protection policies, and the willingness to engage in e-commerce in Saudi Arabia. Structured questionnaires were used to gather data, and Smart PLS was used to analyse the results using Structural Equation Modelling (SEM). Findings: The study reveals that Saudi Arabian tourists express more concern about online identity theft than financial loss. Privacy protection policies play a pivotal role in enhancing their trust and participation in e-commerce, underscoring the importance of regulatory measures for ensuring user confidence and online transaction security. Research, Practical &amp; Social implications: This study highlights the unique worries that Saudi Arabian internet customers have, prioritising identity theft over money loss. Policies protecting privacy become an important middleman that is essential to fostering trust and promoting e-commerce. The findings encourage more investigation into the fundamental elements influencing user choices in study. Practically speaking, companies may improve tactics by giving strong privacy safeguards top priority. In order to promote a safe and reliable e-commerce environment that benefits customers and the expansion of Saudi Arabia's online market, it is socially vital to advocate for explicit privacy legislation. Originality/value: This study is a pioneering attempt to investigate Saudi Arabian tourists' e-commerce behaviour. It adds to the scarce body of knowledge in this area by illuminating the importance of privacy protection policies in fostering consumer trust.
A fliped classroom experience to analyze language interaction in a group of students of English as a foreign language

This paper reports a research project carried out with eighth graders at the Pierre de Fermat School in Bogotá. The main objective was to analyze how this EFL group put into evidence language interaction through a flipped classroom experience in a blended learning environment. The type of research design was action research and the instruments used to collect data were video recordings, students’ production tasks in a virtual environment and a journal. Considering the data gathered, this research attempted to evidence how eighth graders, by participating in activities based on task-based instruction, interacted in the language classroom and beyond it within a virtual environment. The findings suggest that learners interacted in different and meaningful ways when exposing them to a blended-flipped scenario, unveiling signals and features that demonstrated students were able to sustain a conversation by using different resources with their interlocutors. Negotiation of meaning, social communication as well as learning strategies, real world contexts and materials, and collaborative work were the most remarkable patterns of interaction found in the context of this research project. The implementation of technology also played an important role which pretends to open up a path for further research on the implementation of ICTs on language teaching and learning.
Current Status and Application Analysis of Solar Photothermal and Optoelectronic Technology

With the development of society, energy issues have been increasingly valued. Solar energy, as an emerging energy source, has received widespread attention. This article conducts in-depth research on the application of solar technology in different fields. Firstly, the basic principles of solar photothermal technology and solar photothermal technology are analyzed. Secondly, the applications of photothermal and photovoltaic technology in different fields are introduced. Finally, the development status of solar photothermal and photovoltaic technology is discussed.
Efforts To Accelerate The Process Of Uterine Involution By Giving Complementary Therapy Of Oxytocin Massage In Postpartum Mothers

Efforts to prevent postpartum hemorrhage can be carried out since the 3rd and 4th stage of labor by administering oxytocin. The hormone oxytocin plays an important role in the process of uterine involution. The involution process will go well if the uterine contractions are strong so that action must be taken to improve uterine contractions. Efforts to control the occurrence of bleeding from the placenta by improving the strong contraction and retraction of myometrial fibers with oxytocin massage. Efforts to maintain uterine contractions through massage to stimulate the release of the hormone oxytocin is an important part of postpartum care. After direct practice, nursing mothers feel stomach contractions, so oxytocin really works and it is hoped that it will speed up the uterine involution process so that bleeding does not occur. Oxotocin massage is very important to speed up the process of uterine involution. So postpartum mothers really need to know the benefits of oxytocin massage for the mother's own health, so that postpartum mothers want to do oxytocin massage. In this community service activity, the lecturers and their students try to contribute their knowledge and experience about complementary techniques, especially "Oxytocin Massage" to 10 postpartum mothers. The participation of students of the Midwifery Study Program in the Undergraduate Program is intended to provide real experience in training cadres and baby mothers on complementary techniques: the oxytocin massage, which he saw, is expected to be a provision for students when they later work in providing midwifery services in the community
Beyond Negation and Excluded Middle: An exploration to Embrace the Otherness Beyond Classical Logic System and into Neutrosophic Logic

As part of our small contribution in dialogue toward better peace development and reconciliation studies, and following Toffler Toffler’s War and Antiwar (1993), the present article delves into a realm of logic beyond the traditional confines of negation and the excluded middle principle, exploring the nuances of Otherness that transcend classical and Nagatomo logics. Departing from the foundational premises of classical Aristotelian logic systems, this exploration ventures into alternative realms of reasoning, specifically examining Neutrosophic Logic and Klein bottle logic (cf. Smarandache, 2005). The study challenges conventional boundaries and explores the implications of embracing paradoxes and self-reference in logic systems, aiming to redefine approaches to understanding truth and reasoning. The paper investigates how these alternative logics open avenues for philosophical inquiry, redefining entropy, and potentially influencing innovative perspectives in free energy systems. Through this exploration, it seeks to expand the discourse on logic, welcoming a broader spectrum of thought beyond established frameworks; and we also discuss shortly a number of possible implementations including in risk management and also Klein bottle entropy redefinition (Tang et al, 2018).
Detecting Phishing Website Using Machine Learning

Abstract: The Internet has grown to be an essential aspect of our lives, but it has also given rise to instances of harmful activity, such as phishing, that may be carried out anonymously. Phishers attempt to trick their victims by using social engineering techniques or building dummy websites in order to get personal data from people and businesses, including usernames, account IDs, and passwords. Despite the fact that several techniques have been put out to identify phishing websites, scammers have developed ways to evade detection. Machine Learning is one of the best techniques for identifying these harmful actions. This is due to the fact that machine learning techniques can recognise some common traits shared by the majority of phishing assaults. In order to anticipate phishing websites, we compared the outcomes of many machine learning techniques in this article.
Emotional knowledge - the missing link of emotional intelligence

The paper is of a review nature. It revisits the existing definitions of emotional intelligence and emotional knowledge. While the constructs of emotional intelligence and emotional knowledge have existed in parallel in the literature for quite some time, researchers studying emotional intelligence seem to take little, if any, note of emotional knowledge, and those focusing on emotional knowledge rarely relate it to emotional intelligence. It should be noted that the definitions of both emotional intelligence and emotional knowledge refer to emotional information processing, and a comparison of the elements of the two constructs and their operationalizations reveals significant similarities. Indeed, different researchers have included the same elements in the scope of either emotional intelligence or emotional knowledge.&#x0D; The paper indicates the need to clarifying the relationship between the two constructs and determining the degree of their interdependence and autonomy. Based on the literature review, a new definition of emotional intelligence was proposed. Emotional intelligence has been defined as a set of abilities used to acquire emotional knowledge, that is, to assimilate and analyze emotional information, to incorporate it into one’s cognitive system, as well as to efficiently apply it in understanding and solving emotional problems. The proposed definition emphasizes the cognitive nature of emotional intelligence. It does not go beyond the generally accepted understanding of abilities, in contrast to some models. The definition also clarifies the relationship between emotional abilities and emotional knowledge.&#x0D; Adoption of the proposed definition of emotional intelligence has several major ramifications for an understanding of the development of emotional intelligence, its role in human functioning, as well as the operationalization of the construct. All these issues are discussed in the last part of the article.
Enhancing artificial intelligence-doctor collaboration for computer-aided diagnosis in colonoscopy through improved digital literacy

Establishing appropriate trust and maintaining a balanced reliance on digital resources are vital for accurate optical diagnoses and effective integration of computer-aided diagnosis (CADx) in colonoscopy. Active learning using diverse polyp image datasets can help in developing precise CADx systems. Enhancing doctors' digital literacy and interpreting their results is crucial. Explainable artificial intelligence (AI) addresses opacity, and textual descriptions, along with AI-generated content, deepen the interpretability of AI-based findings by doctors. AI conveying uncertainties and decision confidence aids doctors' acceptance of results. Optimal AI-doctor collaboration requires improving algorithm performance, transparency, addressing uncertainties, and enhancing doctors' optical diagnostic skills.
Impact of Strategic Leadership on Competitive Excellence: A Field Study of Banks in Yemen

This study aimed to identify the impact of strategic leadership on achieving competitive excellence at banks in Yemen. To achieve this, the descriptive, analytical method was adopted. In this study, the population was all leadership and supervisory staff members in the target banks which amounted to (1710) members, of which (313) members were selected as the study sample by the stratified sampling method. The questionnaire was used to collect data from the study participants. The data was coded, entered and processed by the SPSS 23. In addition, the (PLS- SEM) was used to test the study hierarchical model of two levels, using the program (Smart PLS 3) for structural equation modeling. The reflective model was used to analyze the first level, and the formative model for the second level. The study results revealed that the banks under study show great interest in achieving competitive excellence in all dimensions, as well as in practicing strategic leadership in all aspects. The results also showed that there is a direct, statistically significant effect of strategic leadership on achieving competitive excellence for the banks under study. The study recommended strengthening the practice of leadership, due to its impact on achieving competitive excellence in the banks under study, especially the dimensions of strategic and creative directions for their clear impact in achieving competitive excellence.
Study on the Cultivation Path of Translation Talents for Water Industry

China’s water industry’s increasing international cooperation calls for more translation talents in the field. This paper probes into cultivating translation talents for water industry, and puts forward some suggestions on the cultivation path, namely, optimizing the cultivation curriculum, enriching the courses, strengthening translation practice, improving the teaching staff, and establishing evaluation system. It is of great significance in strengthening international cooperation of China's water industry, and promoting China's water industry’s voice.