Reviewerly/exHarmony huggingface dataset:
Real-Time Video based Human Suspicious Activity Recognition with Transfer Learning for Deep Learning

Nowadays, the primary concern of any society is providing safety to an individual.It is very hard to recognize the human behaviour and identify whether it is suspicious or normal.Deep learning approaches paved the way for the development of various machine learning and artificial intelligence.The proposed system detects real-time human activity using a convolutional neural network.The objective of the study is to develop a real-time application for Activity recognition using with and without transfer learning methods.The proposed system considers criminal, suspicious and normal categories of activities.Differentiate suspicious behaviour videos are collected from different peoples(men/women).This proposed system is used to detect suspicious activities of a person.The novel 2D-CNN, pretrained VGG-16 and ResNet50 is trained on video frames of human activities such as normal and suspicious behaviour.Similarly, the transfer learning in VGG16 and ResNet50 is trained using human suspicious activity datasets.The results show that the novel 2D-CNN, VGG16, and ResNet50 without transfer learning achieve accuracy of 98.96%, 97.84%, and 99.03%, respectively.In Kaggle/real-time video, the proposed system employing 2D-CNN outperforms the pretrained model VGG16.The trained model is used to classify the activity in the real-time captured video.The performance obtained on ResNet50 with transfer learning accuracy of 99.18% is higher than VGG16 transfer learning accuracy of 98.36%.
Improving Graph Neural Networks with Learnable Propagation Operators

Graph Neural Networks (GNNs) are limited in their propagation operators. In many cases, these operators often contain non-negative elements only and are shared across channels, limiting the expressiveness of GNNs. Moreover, some GNNs suffer from over-smoothing, limiting their depth. On the other hand, Convolutional Neural Networks (CNNs) can learn diverse propagation filters, and phenomena like over-smoothing are typically not apparent in CNNs. In this paper, we bridge these gaps by incorporating trainable channel-wise weighting factors $\omega$ to learn and mix multiple smoothing and sharpening propagation operators at each layer. Our generic method is called $\omega$GNN, and is easy to implement. We study two variants: $\omega$GCN and $\omega$GAT. For $\omega$GCN, we theoretically analyse its behaviour and the impact of $\omega$ on the obtained node features. Our experiments confirm these findings, demonstrating and explaining how both variants do not over-smooth. Additionally, we experiment with 15 real-world datasets on node- and graph-classification tasks, where our $\omega$GCN and $\omega$GAT perform on par with state-of-the-art methods.
A New Complexity Metric for UML Sequence Diagrams

Object-Oriented Programming (OOP) has been promoted as a way to produce high-quality software while increasing developer productivity through code reuse. Software systems and underlying designs get more extensive and more complicated while maintaining a high degree of quality. One of the widely accepted standards for describing software architectures is the UML Sequence Diagram. A sequence diagram depicts the interaction of two-dimensional chart players by showing messages delivered and received between them. This research aims to develop and validate a metric for complexity evaluation in software design architectures through UML Sequence diagrams. The study included design science, which included metric specification, the creation of a measurement tool, and conceptual and factual verification of the metrics. The metrics use diagram-centric complexity measurements shown to be meaningful when used to determine the difficulty of two example sequence diagrams. Furthermore, conceptual affirmation of the stated metrics was achieved through Weyuker's nine characteristics, which demonstrated that they are computationally efficient. The metric was empirically authenticated, and the findings show that measuring the complexity of sequence diagrams is expedient.
Education, Migration and Sustainability-Human Socioeconomic Development and Changing Technological Trends

Technological innovations earmark the trends witnessed today, driving various economic statuses. The current trends driving the education, migration, and sustainability of the human population include; cybersecurity, artificial intelligence, and blockchain technology. Artificial intelligence uses the data collected to predict future patterns in economic and climatic conditions, influencing migration as witnessed in Africa. The blockchain, as innovated, aims to establish a decentralized and stable financial economy in the current economy. Cybersecurity has arisen t curb the increased cyberattacks experienced in modern society hence the need for public education to curb the cases. The current trends depend on the various technological innovation driven by machine learning and algorithms.
Module and Lesson Unit Templates in the Course Book English in Mind: A Content Analysis

Using coursebooks in language learning is a crucial means of obtaining knowledge. This study focused on Indonesia, delves into examining the first edition of Cambridge University Press's English in Mind: Student's Starter Book through content analysis. The research objective is to uncover the book's module and lesson unit templates. The analysis concentrates on two of the four modules and their accompanying lesson units. Findings indicate that the module template is consistent and includes three significant components: topics and learning outcomes, lesson units, and assessment. The template for lesson units, however, displays a certain degree of variability regarding the number of sections and the content of learning tasks. This variance arises from the necessary scaffolding processes to achieve the unit's intended learning outcomes. Examining coursebook content through content analysis is crucial in understanding the language learning process and determining the effectiveness of instructional materials. In conclusion, the results of this study contribute to the advancement of language education by highlighting the significance of coursebook analysis in evaluating the quality and consistency of language instructional materials.
Open education: its pros and cons

The paper includes the definition of open education and its multiple means that allowed a large segment of society and teachers to join the ranks of higher education, and this university was gigantic and absorbed large numbers of students who graduated from multiple faculties. Multiple, and it is more popular as it helps to study in the student’s own time and in his own way and the place he determines, it breaks down the barriers of age, takes place without face-to-face meetings between the learner and the teacher, makes the student free to choose what to learn, when to learn and how to learn, open education is education without borders, Independent education, and home education, as well as education without walls, Self-education and multimedia education. There are many justifications for open education, such as the remoteness of geographical areas from the locations of universities and political problems in many countries and that women are no longer confined to the home and others. The method used: a historical and descriptive analytical method and a case study. Objectives: Providing educational opportunities for every citizen with a belief in the value of continuity of learning, providing freedom of study for the learner by liberating him from restrictions, providing professional growth for workers in work sites, providing new educational methods and media, providing opportunities for scientific, research and educational cooperation between educational institutions in the Arab world, preparing and developing Cadres in different areas of life according to the needs of society Case study: office theory and applied field Results: The researchers concluded that open education led to the education of a large segment of society who missed the education train and facilitated many ways of educational means for them, and that the book is the mainstay of open education, despite the existence of other means, but it has become inconsistent with development and other technologies
Pojok Baca sebagai Inisiasi Sentra Edukasi Menumbuhkan Minat Baca

Purpose: The purpose of community service, first: to deliver education about the importance of reading as a source of knowledge and a means of information for PAUD students. The second objective is to provide management, administrative and technical assistance in the management of reading corners for the Pokja II Team for PKK administrators and PAUD administrators so that they can become a center for community education. Method: The method of implementing the activities is participatory empowerment and mentoring. The results of community service are the Reading Corner which occupies space in the village office with a number of books as many as 205 titles and has been equipped with SOPs for the management of the Reading Corner. PAUD students participated in educational activities very enthusiastically. Results: As many as 77 percent of the participants in technical training for the management of the Reading Corner understand enough and become more skilled at managing the Reading Corner. The limitation of community service lies in the time of implementation because it is carried out in a COVID-19 situation so that community mobility is hampered. Contribution: The contribution of community service plays a major role in the education level of the people of Ulak Kembahang II Village.
Modeling the Machine Learning Multiverse

Amid mounting concern about the reliability and credibility of machine learning research, we present a principled framework for making robust and generalizable claims: the multiverse analysis. Our framework builds upon the multiverse analysis (Steegen et al., 2016) introduced in response to psychology's own reproducibility crisis. To efficiently explore high-dimensional and often continuous ML search spaces, we model the multiverse with a Gaussian Process surrogate and apply Bayesian experimental design. Our framework is designed to facilitate drawing robust scientific conclusions about model performance, and thus our approach focuses on exploration rather than conventional optimization. In the first of two case studies, we investigate disputed claims about the relative merit of adaptive optimizers. Second, we synthesize conflicting research on the effect of learning rate on the large batch training generalization gap. For the machine learning community, the multiverse analysis is a simple and effective technique for identifying robust claims, for increasing transparency, and a step toward improved reproducibility.
Interaction of Lherzolite Xenoliths with Basanite is not Isochemical: Evidence from Tumusun Volcano, Baikal Rift Zone

To investigate the process and chemistry of reaction zone formation, we conducted detailed petrographic observations and major-trace element analysis of rocks and minerals of lherzolite xenoliths from basanites of Tumusun volcano (Baikal Rift Zone). The reaction zones gradually disappear from contact toward center of xenoliths. This indicates their formation as a result of lherzolite-basanite interaction. The influence of basanite melt on major-trace element composition of secondary minerals of reaction zones is notable only at distance up to 100-200 &amp;mu;m from the contact. The major-trace element composition of secondary clinopyroxenes from orthopyroxene reaction zone indicates their formation from a melt formed by dissolution of orthopyroxene and influenced by element diffusion from basanite melt. Inside xenoliths the secondary minerals have Mg# values equal to or higher than Mg# of primary minerals. The secondary clinopyroxenes inherit their depleted or enriched REE pattern from primary pyroxenes. The major-trace element variations in secondary clinopyroxenes testify melt heterogeneity. Secondary clinopyroxene has slightly higher LILE and similar abundances of other trace elements compared to unreacted part of clinopyroxene grain. This is consistent with model developed from experimental studies: due to the interaction with basanite, incongruent dissolution of orthopyroxene occurs to form a melt which circulates in lherzolite and leads to pyroxenes and spinel dissolution. Non-dissolved centers of minerals are homogenous and similar in major-trace element composition to primary minerals. The process of peridotite-basanite interaction is not isochemical: even the centers of large xenoliths become enriched in LILE.
Hysteresis, endogenous growth, and monetary policy

Abstract I provide evidence of substantial hysteresis (i.e., a situation in which temporary shocks have long-run effects) from monetary shocks on two sources of endogenous growth; human capital and technological adoption. This contribution is the first to test for the presence of this phenomenon in direct measures of the supply-side potential of economies, instead of indirect measures, e.g., TFP. To estimate the effects of exogenous monetary policy shocks, I improve on the trilemma identification by incorporating a mean-unbiased instrumental variable estimator. Results show substantial hysteresis in both human capital and technological adoption. Importantly, these are found to be asymmetric, as only contractionary shocks result in long lasting responses. I evaluate the aggregate importance of monetary hysteresis with a growth accounting exercise. Across the 17 countries in sample, the accumulated average cost of monetary hysteresis ranges between 1.2 and 9.6% of TFP, for human capital and the adoption of electricity, respectively. JEL codes: E32, O40, O47.
Creating a Russian language corpora on the LingvoDoc. Part 1

The topic of this article is the creation of the Russian language corpus on the LingvoDoc platform. The article provides information about the features of its compilation and analysis, as well as briefly highlights further research prospects.
Hermeneutics of Virtual Legal Education in 360° Reality

The use of virtual reality in education and the professional field in the past two years.The so-called 360-degree virtual technology enables us to simulate space and conditions that are either rare or unexplorable in the real physical world.This research aims to know the limitations in current-day virtual learning is the main challenge for legal education.Students hope to develop their hard and soft skills, such as communication and how they react in real-life situations, and to know virtual reality can answer this challenge.The method of the study is descriptive-analytical.First, the researcher describes the current 'version' of virtual legal education, its challenge and adaptation so far, and the current use of virtual reality technology in simulations.Later, the researcher tries to analyze the problem by synthesizing the limits of virtual legal education and the possibility of the utilization of VR technology in it.The result shows that Developing new technology may present challenges.However, as an effort to provide social environment character in legal education, 360 virtual reality has many potentials.The pilot study was limited, but it helped us to understand how the simulation needed to be improved for future studies.However, our initial application still provides one-way information, in which the subject cannot interact with people in the environment and interaction between subjects in the same setting is still in process.
Unsupervised Deep Discriminant Analysis Based Clustering

This work presents an unsupervised deep discriminant analysis for clustering. The method is based on deep neural networks and aims to minimize the intra-cluster discrepancy and maximize the inter-cluster discrepancy in an unsupervised manner. The method is able to project the data into a nonlinear low-dimensional latent space with compact and distinct distribution patterns such that the data clusters can be effectively identified. We further provide an extension of the method such that available graph information can be effectively exploited to improve the clustering performance. Extensive numerical results on image and non-image data with or without graph information demonstrate the effectiveness of the proposed methods.
WITH THESE I KNOW IT WILL OR IT WILL NOT RAIN: INDIGENOUS INDICATORS OF RAINFALL IN MWANGA DISTRICT, TANZANIA

Weather forecasting is crucial to planning activities which should be undertaken in, and by, various earth systems. Forecasting the characteristics of rainfall, including the start and end of the rainy season, as well as the amount of rainfall to be received in the coming season, is very important in planning for rainfall-related activities and adapting to rainfall-related hazards. Through quantitative and qualitative techniques, this study sought to identify the indigenous indicators used in forecasting rainfall in Mwanga District, Tanzania. Being a semi-arid area and mostly relying on rain-fed agriculture, rainfall forecasting is crucial in planning agricultural activities and adapting to rainfall-related hazards. The identified indicators of the start of rainfall included the sun burning very intensely, the direction of wind, the appearance of clouds in the sky during the dry season, the budding and flowering of some trees, and the behavior of certain animals and birds. Regarding the end of the rainy season, the farmers observed the flowering of some vegetation, the appearance of mushrooms in the land, cold weather, and wind. Besides, the farmers observed wind, the intensity of the sun, high temperatures, as well as the appearance of some butterflies and ants to forecast the amount of rainfall that will be received in the coming season. Although traditional rainfall forecasting is very important, its accuracy has been decreasing due to increasing climate change and variability. Therefore, it is better to use a combination of indicators than relying on one or a few indicators to increase the accuracy of predictions. Further, indigenous knowledge of weather forecasting should be documented to increase the number of people who can use it effectively. It should also be integrated with modern technologies to improve its accuracy
ASSESSMENT OF RAINFALL VARIABILITY AND MAIZE CROP FARMING IN NYANDARUA COUNTY, KENYA

Crop farming is crucial to the livelihoods of communities living in the rural areas of Kenya. The sector largely depends on climatic variables such as rainfall for optimum crop output. This article presents the analysis, interpretation and discussion of the research findings based on the objective of assessing the climate variability of mean annual rainfall and its impact on crop farming (maize output) in Nyandarua County. To achieve this objective, the researcher sought to analyse rainfall amount and fluctuations in Nyandarua County for 21 years and correlate the variable with crop output (maize) over the same period. The variability of rainfall and maize output was analysed using descriptive statistics of mean, standard deviation and coefficient of variance. Simple line trends and scatter graphs were used to show this variation. The inferential statistics of regression and correlation analysis were used to establish the relationship between the rainfall factor and the selected crop output. The null hypothesis associated to this objective was tested using results of the correlation and regression analysis. Evidence of the study indicated that the annual average rainfall had increased from 1999 to 2006. However, the last fifteen years preceding the study, the mean annual rainfall had significantly fluctuated between the peaks of 117 mm and off peaks of 67 mm. The utmost variation in the rainfall amount was experienced between the years 2013 and 2007. Referring to the 21-year period of annual crop output records, the output of maize greatly fluctuated between 2019 and 1999 with an average tonnes of 29,145.76. Rainfall variability was concluded to have greatly influenced the changes in maize output (r=0.688). The regression line for rainfall variability and maize output produced a slope that was described by the equation y=463x - 11100 + έ. The regression value (R2 = 0.47189 showed that 47.19% of the fluctuation in maize output was as a result of the disparity in rainfall amount and distribution. In conclusion, results of the study implied that rainfall amount and distribution was highly erratic and unpredictable. Therefore this scenario exhibited much uncertainty to the small scale farmers in the region. For better planning of the effect of climate variability, the study recommended that policymakers and other relevant stakeholders should come up with awareness programs through the provision of useful information that assimilate the small holder farmer indigenous knowledge and perception and its effects on their livelihoods with the accurate meteorological scientific data.
Scattering entropies of quantum graphs with several channels

This work deals with the scattering entropy of quantum graphs in many different circumstances. We first consider the case of the Shannon entropy, and then the Rényi and Tsallis entropies, which are more adequate to study distinct quantitative behavior such as entanglement and nonextensive behavior, respectively. We describe many results, associated with different types of quantum graphs in the presence of several vertices, edges and leads. In particular, we emphasize that the results may be used as quantifiers of diversity or nonadditivity, related to the transport in quantum graphs.
Improving Adversarial Robustness by Penalizing Natural Accuracy

Current techniques in deep learning are still unable to train adversarially robust classifiers which perform as well as non-robust ones. In this work, we continue to study the space of loss functions, and show that the choice of loss can affect robustness in highly nonintuitive ways. Specifically, we demonstrate that a surprising choice of loss function can in fact improve adversarial robustness against some attacks. Our loss function encourages accuracy on adversarial examples, and explicitly penalizes accuracy on natural examples. This is inspired by the theoretical and empirical works suggesting a fundamental tradeoff between standard accuracy and adversarial robustness. Our method, NAturally Penalized (NAP) loss, achieves 61.5% robust accuracy on CIFAR-10 with $$\varepsilon =8/255$$ perturbations in $$\ell _\infty $$ (against a PGD-60 adversary with 20 random restarts). This improves over the standard PGD defense by over 3%, against other loss functions proposed in the literature. Although TRADES performs better on CIFAR-10 against Auto-Attack, our approach gets better results on CIFAR-100. Our results thus suggest that significant robustness gains are possible by revisiting training techniques, even without additional data.
Research on mobile robot path planning and tracking control

Autonomous navigation of a robot is a promising research domain due to its extensive applications in which planning and motion control are the most important and interesting parts.The proposed techniques are classified into two main categories: the first group focuses on the improvement of model free adaptive control (MFAC) to meet the extreme performances of the control system, and the second concentrates on the classic artificial potential field (APF) algorithm to deal with the limitations like falling into local minima and a non-reachable goal problem.This paper proposes a novel exponential feedforward-feedback control strategy based on iterative learning control (ILC) MFAC to the reference trajectory tracking, and then introduces a virtual target with exponential coordinated form to realise local risk collision avoidance for path planning.Compared to some traditional models, our proposed methods have a faster trajectory convergence rate, lower avoidable error, and higher safe performance.The simulation results verify that our work would bring meaningful insights to future intelligent navigation research.