Reviewerly/exHarmony huggingface dataset:
Entrepreneurial Orientation and the Performance of the Agro-allied Enterprises in Akwa Ibom State, Nigeria

It is widely recognized that Small and Medium Enterprises (SMEs) are the major drivers of sustainable economic growth, sources of income generation and panacea for poverty reduction in developing countries. Unfortunately, they are faced with numerous challenges that threaten their existence and survival. This precarity of small entrepreneurial organizations in entrepreneurial ecosystem has prompted widespread attention by scholars and researchers. Hence, it is critical to take into consideration examining the performance of the enterprises. This study analyzed the effect of entrepreneurial orientation dimensions, personal and institutional characteristics on the performance of small and medium agro allied enterprises. Through the multistage sampling procedure, a total of 198 SMEs were selected for the study and data were collected with the aid of questionnaire. Data were analyzed using multiple regression analysis. Results revealed that among the entrepreneurial orientation dimensions, only risking taking and innovativeness significantly and positively affected the performance of SMEs agro allied enterprises. Furthermore, findings showed that except for size of business which was negatively related to the growth of SMEs, educational level of entrepreneurs, managerial and business experience positively and significantly impacted on the performance of SMEs. Policies to encourage SMEs invest in risky investments and create new product and services should be pursued.
How to Read a Kopla? Comparative Cognitive Analysis of the Basque Kopla Zaharrak and the Moroccan Ayyus

ABSTRACT In this article, the author continues to apply cognitive reading to oral traditional poetry, addressing the kopla zaharra—a short improvisational poetic genre from the Basque Country that is now extinct. In order to understand how this genre once functioned, the author draws her experience in working with ayyu—a living poetic tradition from Morocco, which has a strikingly similar structure. The analysis sets out from the historical evidence showing that the kopla zaharra was indeed once an improvisational genre. She then explains the notions of frames, or topics or themes, and scripts, or possible scenarios manifested at least partly in form of formulaic language, which are triggered by these, to analyze the typical cognitive structure of the kopla zaharra. Her analysis shows that on the cognitive level the kopla zaharra offers a structure similar to the Moroccan ayyu, where the first two lines work to activate a frame and the last two lines generally complete a script. This confirms the conclusion that although ayyu and kopla zaharrak are separated by a wide cultural and linguistic rift, the cognitive mechanisms behind the process of their creation are highly analogous.
Application of Pixel Drift Denoising Algorithm in Optimizing Gaussian Mixture Model

In real life, it is often necessary to detect moving targets in video streams captured by fixed video camera heads, and one of the key tasks is dynamic background modeling. Gaussian mixture model is a common modeling method. However, this modeling method is prone to random noise interference after extracting moving targets. How to eliminate or weaken such random noise is a research focus. Usually, there are two kinds of noise sources, that is, internal and external factors of video camera head imaging. The internal factors are related to manufacturing technology and materials of video camera heads, while the external factors are caused by micro-vibration, air disturbance and other factors. This paper focuses on how to eliminate or weaken interference of external factors. Firstly, a pixel drift denoising algorithm is proposed by analyzing formation mechanism of random noise caused by various external factors, that is, phenomenon of small scale drift of pixel position during imaging. Then, the pixel drift denoising algorithm is applied to Gaussian mixture model to determine foreground pixels, reduce noise impact, and improve integrity of moving targets. A comparative experiment is carried out in public data set CDnet 2014. The results show that in the same data set scene, the improved Gaussian mixture model algorithm integrating the pixel drift denoising algorithm can effectively reduce the noise in dynamic background, and the peak signal-to-noise ratio of experimental background and real background reaches 38. 2dB.
Pengembangan Media Pembelajaran Berbasis Video Animasi pada Materi Penyajian Data

This study aims to produce an Animation Video-based Learning Media product on data presentation material that is valid, practical and has good effectiveness on student learning outcomes. This study uses the Research and Development model with 10 stages, namely potential and problems, data collection, product design, design validation, design revision, product testing, product revision, usage trial, product revision and mass production. The object of research is an animated video-based learning media in class VII data presentation material. The results of this study are in the form of animated video-based learning media which have advantages such as presenting the material displayed using animated media so that in addition to being easy to read, it is also more interesting to help the online and face-to-face learning process. Based on the analysis of animated video-based learning media on the data presentation material for class VII, the results obtained from aspects of validity, aspects of practicality and aspects of effectiveness. a) aspects of validity obtained very valid criteria based on the assessment of the validator with a total average score of 93%; b) practical aspects obtained practical criteria based on student response questionnaires with an average score of 86%; c) aspects of effectiveness on student learning outcomes with good criteria based on learning outcomes tests with a percentage of completeness of 81.8%.
Kidney cancer diagnosis using deep learning

In this study, we trained a convolutional neural network (CNN) utilizing a mix of recent CNN architectural design strategies. Our goals are to leverage these modern techniques to improve the binary classification of kidney tumor images obtained using Multi-Photon Microscopy (MPM). We demonstrate that incorporating these newer model design elements, coupled with transfer learning, image standardization, and data augmentation, leads to significantly increased classification performance over previous results. Our best model averages over 90% sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUROC) in image-level classification across cross-validation folds, superior to the previous best in all four metrics.
Prediction of the high-cost normalised discounted cumulative gain (nDCG) measure in information retrieval evaluation

Introduction. Information retrieval systems are vital to meeting daily information needs of users. The effectiveness of these systems has often been evaluated using the test collections approach, despite the high evaluation costs of this approach. Recent methods have been proposed that reduce evaluation costs through the prediction of information retrieval performance measures at the higher cut-off depths using other measures computed at the lower cut-off depths. The purpose of this paper is to propose two methods that addresses the challenge of accurately predicting the normalised discounted cumulative gain (nDCG) measure. Method. Data from selected test collections of the Text REtrieval Conference was used. The proposed methods employ the gradient boosting and linear regression models trained with topic scores of measures partitioned by TREC Tracks. Analysis. To evaluate the proposed methods, the coefficient of determination, Kendall's tau and Spearman correlations were used. Results. The proposed methods provide better predictions of the nDCG measure at the higher cut-off depths while using other measures computed at the lower cut-off depths. Conclusions. These proposed methods have shown improvement in the predictions of the nDCG measure while reducing the evaluation costs.
Corporate learning and technology – opportunity and risk for the learning professional

Purpose The article aims to clarify the risks and opportunities available to corporate learning professionals with the advent of a tsunami of EdTech (educational technology) that promises to be the silver bullet of lifelong learning. It acknowledges that the urgency of up/reskilling adults provides learning professionals with a unique moment to make a difference by ensuring the right skills are available as needed. However, learning professionals are simultaneously under threat of irrelevance should we fail to use the avalanche of advances in technology combined with tried and tested pedagogical principles and a multi-channel approach. Design/methodology/approach The paper offers the author’s opinion on the future opportunities for learning professionals, using a summarized analysis of available technology and Educational Technology. In addition, it offers opportunities for further exploration by the reader. Findings The Future of Work has arrived and potentially signals the beginning of a golden age for learning professionals – or the beginning of the end, ultimately to be replaced by technology. The need is clear. What is not clear is whether the learning community – en masse – have the technological savvy to step up and maximize the use of technology, and specifically AI related services, to meet this need. Practical implications Learning professionals have a limited moment in time to pivot their existing skill set to a better understanding and use of technology. The paper offers some suggestions for action in this regard. Social implications Should the learning professional fail to utilize this unique moment in time to optimize the learning experiences of adults, the world faces a skills gap that will harm innovation, employment and social stability. Originality/value This paper fulfills an identified need for learning professionals to adjust their approach, skill set, and deliverables and offers guidelines for focus and action in a world saturated with information.
Conceptualization in ontology based on Peirce’s triadic model of the sign: a qualitative content analysis

Purpose This study aims to identify the effective components in object interpretation based on Pierce’s triadic sign model to determine how meaning is created and identify the components affecting conceptualization in ontology. Design/methodology/approach A qualitative approach was adopted, and 6320 comments posted on the Instagram pages of five groups forming the research population were analyzed by qualitative content analysis. Findings Concepts are dynamic and context-oriented. Over time, a person’s concepts and mental patterns about an object are altered or eliminated. Social, political and economic conditions, governance, technology, interactions, language, environment, culture and social habits contribute to object interpretation and meaning creation. Practical implications In designing ontologies, especially in a context-dependent field, attention should be paid to users’ mental preconceptions, mental patterns, context and personal characteristics. Instead of using words, researchers can use visual and multimedia signs. In addition to logic, statistics and computer science, personal and anthropological characteristics should be considered in the ontology design. Originality/value This study dealt with conceptualization, which is a principal component of ontology. Using Pierce’s triadic model sign and unstructured Instagram data, ontology was studied from a user and pragmatic viewpoint, independently of technical aspects.
LSL3D: A Run-Based Connected Component Labeling Algorithm for 3D Volumes

Connect Component Labeling (CCL) has been a fundamental operation in Computer Vision for decades. Most of the literature deals with 2D algorithms for applications like video surveillance or autonomous driving. Nonetheless, the need for 3D algorithms is rising, notably for medical imaging. While 2D CCL algorithms already generate large amounts of memory accesses and comparisons, 3D ones are even worse. This is the curse of dimensionality. Designing an efficient algorithm should address this problem. This paper introduces a segment-based algorithm for 3D labeling that uses a new strategy to accelerate label equivalence processing to mitigate the impact of higher dimensions. We claim that this new algorithm outperforms State-of-the-Art algorithms by a factor from $$\times $$ 1.5 up to $$\times $$ 3.1 for usual medical datasets and random images.
BA-LR: Binary-Attribute-based Likelihood Ratio estimation for forensic voice comparison

Likelihood ratio (LR) is a widely adopted paradigm in forensic science to represent the conclusion of a practitioner report. However, with existing estimation methods, the LR does not fully facilitate the decision making by judges and juries. With an explained decomposition of the LR value together with the case information, the judge can more easily take in hands the weight of evidence in the final decision. To that end, we propose the Binary-Attribute-based LR estimation approach (BA-LR) for Forensic Voice Comparison (FVC) where the LR is obtained as the composition of partial LRs, each dedicated to an attribute. An attribute is expressed by the presence or absence of a speaker voice characteristic. The partial LRs are directly computed following a formulation of prosecution and defence hypotheses using three probabilities that describe explicitly the behavior of the considered attribute. An implementation of our approach is evaluated on VoxCeleb1&2. The results show the effectiveness of our BA-LR approach and give hope on a better handling of LR-based FVC conclusions.
On some invariants under the action of an extension of <i>GA</i>(<i>n</i>, 2) on the set of Boolean functions

Abstract Let G be the extension of a general affine group by the group of affine functions. We study the action of G on the set of Boolean functions. The action consists in nondegenerate affine transformations of variables and addition of affine Boolean functions. We introduce and examine some parameters of Boolean functions which are invariant with respect to the action of G . These are the amplitude (which is closely related to the nonlinearity), the dimension of a function, and some others. The invariants, together with some additionally proposed notions, could be used to obtain new bounds on cryptographic parameters of Boolean functions, including the maximum nonlinearity of functions in an odd number of variables.
Embedded Librarianship in the Library:

The author describes three consecutive years of embedded librarianship with an interior design course in which students were asked to reimagine a selected space within Florida State University’s main university library as their final project. The narrative traces the evolution of this embedded partnership from its last in-person, pre-pandemic semester in 2019, through the fully remote 2020–2021 academic year, and back to in-person instruction in fall 2021. Besides discussing the agility and resiliency of this partnership, takeaways are offered for readers interested in strengthening similar embedded collaborations.
A Self-Occlusion Aware Lighting Model for Real-Time Dynamic Reconstruction

In real-time dynamic reconstruction, geometry and motion are the major focuses while appearance is not fully explored, leading to the low-quality appearance of the reconstructed surfaces. In this article, we propose a lightweight lighting model that considers spatially varying lighting conditions caused by self-occlusion. This model estimates per-vertex masks on top of a single Spherical Harmonic (SH) lighting to represent spatially varying lighting conditions without adding too much computation cost. The mask is estimated based on the local geometry of a vertex to model the self-occlusion effect, which is the major reason leading to the spatial variation of lighting. Furthermore, to use this model in dynamic reconstruction, we also improve the motion estimation quality by adding a real-time per-vertex displacement estimation step. Experiments demonstrate that both the reconstructed appearance and the motion are largely improved compared with the current state-of-the-art techniques.
Efficient web application vulnerabilities detection Model

The web application is gaining tremendous importance due to increased usage of the internet and with this, there is also an increase in attacks on the web applications. This leads to leakage of all the confidential data. For a web application to be completely secure it should not be vulnerable to any attack. Hence there is an emerging need to check the vulnerabilities of web applications. During the detection of vulnerabilities, each attack is carried out manually which requires the knowledge of security testing. This paper explores the characteristics of the proposed security tool. The proposed tool gives an efficient and fast scanning of any web application automatically. OWASP’s top 10 vulnerabilities include Injection, Broken authentication, broken access control, sensitive data exposure, XML, cross site scripting, insecure de-serialisation, security misconfiguration, making use of components with insufficient logging and monitoring and having known vulnerabilities. All these top 10 vulnerabilities of the open web application security project are automatically detected in the tool. Since the tool is automated, it reduces the manpower and the time required to check each vulnerability separately. Threading helps in improved processing and hence generates faster outputs. Automation reduces the entire time needed for the process with 100% efficiency.
Zenodo data repository: Providing practical solutions for data storage and data sharing

Properly managing, preserving, and sharing data can be a daunting task, especially for busy researchers who are constantly confronted with new tasks and requirements from funders and their institutions. Zenodo is a general-purpose data repository that enables researchers, scientists, project managers, and institutions to share, preserve, and showcase multidisciplinary research results (data, software, publications, and other research objects) that are outside the scope of existing institutional or subject-based repositories. Based in the trustworthy CERN data centre, Zenodo is a service provided by researchers to researchers contributing to open science by capturing research objects and making them FAIR (findable, accessible, interoperable, and reusable). This article addresses some of the challenges of data storage and data sharing, such as finding the right place to store data, citing data properly, and using hybrid data sharing solutions. It also demonstrates how using a data repository like Zenodo can help researchers address these challenges.
On a Solutonto the Statonary Problem of Two-Phase Filtration by the Monte Carlo Method

This article is devoted to solving the problems of applying Monte Carlo algorithms to filtration problems. The "sphere walk" and "boundary walk" algorithms of Monte Carlo methods are used to solve the stationary problem of filtering two immiscible inhomogeneous incompressible liquids in a porous medium. Estimates constructed using the "sphere walk" and "boundary walk" algorithms of Monte Carlo methods will be mostly ε-biased. Unbiased estimates are in most cases unrealizable on a computer, since with a probability of 1 they do not go to the boundary of the region, and therefore are of little use. In practice, they are usually limited to only the first two points of evaluation.
Access Control of Semantic Segmentation Models Using Encrypted Feature Maps

In this paper, we propose an access control method with a secret key for semantic segmentation models for the first time so that unauthorized users without a secret key cannot benefit from the performance of trained models. The method enables us not only to provide a high segmentation performance to authorized users but to also degrade the performance for unauthorized users. We first point out that, for the application of semantic segmentation, conventional access control methods which use encrypted images for classification tasks are not directly applicable due to performance degradation. Accordingly, in this paper, selected feature maps are encrypted with a secret key for training and testing models, instead of input images. In an experiment, the protected models allowed authorized users to obtain almost the same performance as that of non-protected models but also with robustness against unauthorized access without a key.
A compressible image encryption method based on nondegenerate hyperchaotic system and its implementation on ZYNQ

Abstract Digital image plays an important role of information carrier with the rapid development of information technology. In this paper, a compressible image encryption method based on nondegenerate hyperchaos is proposed. Firstly, a nondegenerate hyperchaotic system based on the nonlinear feedback controller is designed, and its dynamic characteristic is analyzed. The image information is compressed by integer discrete cosine transform (DCT). Then the image encryption algorithm is proposed by using the pseudo‐random sequence generated by hyperchaotic system. Finally, the proposed scheme is realized by using ZYNQ development platform. The security of proposed scheme is enhanced by the nondegenerate hyperchaotic system, and the data volume for encryption algorithm is reduced by DCT preprocessing without affecting the image visual quality. The hardware implementation based on ZYNQ platform shows the feasibility of the proposed scheme. Through the security analysis of the experimental results, it is verified that the image can be well encrypted by the proposed method with great performance.