Reviewerly/exHarmony huggingface dataset:
KOALA: A Kalman Optimization Algorithm with Loss Adaptivity

Optimization is often cast as a deterministic problem, where the solution is found through some iterative procedure such as gradient descent. However, when training neural networks the loss function changes over (iteration) time due to the randomized selection of a subset of the samples. This randomization turns the optimization problem into a stochastic one. We propose to consider the loss as a noisy observation with respect to some reference optimum. This interpretation of the loss allows us to adopt Kalman filtering as an optimizer, as its recursive formulation is designed to estimate unknown parameters from noisy measurements. Moreover, we show that the Kalman Filter dynamical model for the evolution of the unknown parameters can be used to capture the gradient dynamics of advanced methods such as Momentum and Adam. We call this stochastic optimization method KOALA, which is short for Kalman Optimization Algorithm with Loss Adaptivity. KOALA is an easy to implement, scalable, and efficient method to train neural networks. We provide convergence analysis and show experimentally that it yields parameter estimates that are on par with or better than existing state of the art optimization algorithms across several neural network architectures and machine learning tasks, such as computer vision and language modeling. The project page with the code and the supplementary materials is available at https://araachie.github.io/koala/.
Actionable Model-Centric Explanations (Student Abstract)

We recommend using a model-centric, Boolean Satisfiability (SAT) formalism to obtain useful explanations of trained model behavior, different and complementary to what can be gleaned from LIME and SHAP, popular data-centric explanation tools in Artificial Intelligence (AI).We compare and contrast these methods, and show that data-centric methods may yield brittle explanations of limited practical utility.The model-centric framework, however, can offer actionable insights into risks of using AI models in practice. For critical applications of AI, split-second decision making is best informed by robust explanations that are invariant to properties of data, the capability offered by model-centric frameworks.
ENHANCING EFL LEARNERS’ READING COMPREHENSION THROUGH SQ4R METHOD AT THE SECONDARY SCHOOLS

This study is intended to investigate how EFL secondary students’ reading comprehension can be enhanced through the SQ4R method. The design of this study was classroom action research which involved 31 students from class IX-A of one secondary school in Samarinda, Indonesia. Two instruments were used in this study, i.e., an observation sheet and a reading test. The observation data were qualitatively analyzed using the provided checklist, while the data from reading test was quantitatively analyzed by having percentage analysis and categorizing the results into score levels. The results revealed that the implementation of the SQ4R method could enhance the students’ reading comprehension achievement. Quantitatively, 18 students (58%) out of 31 passed the KKM (minimum completeness criteria) in cycle 1; meanwhile, in cycle 2, 27 students (87%) out of 31 students passed the KKM. In addition, problems faced by the collaborator-teacher can be solved by the dynamics happening in the two cycles, which results in the betterment of the teaching and learning process using the SQ4R method. The reflection from the two cycles showed that SQ4R can improve EFL students' reading comprehension. As the method's impacts develop, teaching and learning quality changes.
Towards Automating the Generation of Human-Robot Interaction Scenarios

My work studies the problem of generating scenarios to evaluate interaction between humans and robots. I expect these interactions to grow in complexity as robots become more intelligent and enter our daily lives. However, evaluating such interactions only through user studies, which are the de facto evaluation method in human-robot interaction, will quickly become infeasible as the number of possible scenarios grows exponentially with scenario complexity. Therefore, I propose automatically generating scenarios in simulation to explore the diverse possibility space of scenarios to better understand interaction and avoid costly failures in real world settings.
Analisis Sentimen Komentar Youtube Mengenai Vaksin Covid-19 Menggunakan Support Vector Machine

Vaccination is one of the policies taken by the Indonesia government to control the Covid-19 pandemic. Vaccination is expected to form community immunity to prevent the spread of Covid-19. This policy taken by the government has generated various responses from the public in the form of opinions posted on social media platforms, one of which is on the Youtube. In order to better understand public sentiment towards this vaccination policy, it is necessary to analyze the opinions given. The Support Vector Machine (SVM) algorithm used in this study to classify public sentiment on vaccination policy. The data sources used are comments posted on Youtube videos regarding Covid-19 vaccinations. Three scenarios were used in the test with variations in the ratio of the testing data and training data of 90:10, 80:20, and 70:30 with 10 tests on each variation of the data ratio. Based on the tests carried out, the results obtained accuracy of 86%, 85%, and 84%.
Graph-based Transform based on 3D Convolutional Neural Network for Intra-Prediction of Imaging Data

This paper presents a novel class of Graph-based Transform based on 3D convolutional neural networks (GBT-CNN) within the context of block-based predictive transform coding of imaging data. The proposed GBT-CNN uses a 3D convolutional neural network (3D-CNN) to predict the graph information needed to compute the transform and its inverse, thus reducing the signalling cost to reconstruct the data after transformation. The GBT-CNN outperforms the DCT and DCT /DST, which are commonly employed in current video codecs, in terms of the percentage of energy preserved by a subset of transform coefficients, the mean squared error of the reconstructed data, and the transform coding gain according to evaluations on several video frames and medical images.
Lightweight face mask detection and face recognition

The Covid-19 pandemic has abruptly changed our daily life: face mask and wearing a mask have become popular, and are occasionally obligatory for some public activities. As a result, several methods were proposed to address the problem of face mask detection and/or face recognition. While these methods showed promising results, they often require high computational resource due to sophisticated deep learning models. In this paper, we will propose lightweight methods to detect face masks and recognize human faces simultaneously. Our proposed methods are based mainly on Random Forest and MobileNetV2, and are trained and tested with our own dataset collected from Vietnamese faces. The experiment shows that Random Forest can address face mask detection and face recognition with high accuracy. Moreover, a combination of Random Forest and MobileNetV2 can still improve the performance of detection and recognition while keeping the method at relatively low computational complexity.
Tenant-Centric Attribute Semantic Access Control Policy Model for the Cloud Service Platform

In the open Internet environment, there is a cross-platform access control problem that when a tenant needs to access the business resources of other collaborative platforms through the cloud service platform, the cloud service platform only supports the tenant to access the business resources within the platform. When tenants need to access business resources through the cloud service platform, the authorization method of the cloud service platform is static and the authorization granularity is coarse-grained, so dynamic fine-grained authorization is not supported. To solve the above problems, based on ABAC, this paper proposes a tenant-centric attribute semantic access control policy model for cloud service platforms. The model and its application framework can automatically evaluate whether it has cloud service platform or cross-platform access control rights according to the change of the tenant’s attributes so as to determine whether it can obtain the corresponding business resources. Through a practical case analysis, we prove that the application of ASACPM and its application framework to the cloud service platform have good flexibility, scalability, and practicability. In addition, we design some experimental scenarios to verify that the performance of ASACPM and its application framework meet our expectations and have good reliability, validity, and rationality.
Globalization and Economic Growth in West Africa (1990 - 2019)

This study investigated the impact of overall and economic globalizations on economic growth in West Africa between 1990 and 2019.Data were sourced secondarily from the World Development Indicators (WDI) and Konjunkturforshungsstelle (KOF) data base.The study employed Pooled Ordinary Least Suare (OLS) and Fixed effect OLS for data analysis.The result obtained from sPooled OLS revealed that overall globalization had significant and positive impact on economic growth in West Africa while the result from Fixed effect showed otherwise.Further,the result from Pooled OLS also showed positive and significant effect of economic globalization on economic growth while the result from the Fixed effect showed no impact of economic globalization on economic growth in West Africa.Therefore, the study recommended that Government policy should adopt overall globalization as a major factor of economic growth.Also, Government should formulate policy to encourage trade openness,Foreign Direct Investment (FDI) and capital inflows which are the major platform upon which economic globalization rested.
Discussion on the Value of Integration Traditional Culture Into Kindergarten Curriculum ---Take Wuhu Iron Painting Technology as an Example

Traditional culture is the overall expression of various ideological, cultural and conceptual forms formed in the historical development of our country. Traditional culture breeds the precious spiritual character of the Chinese nation and has very important educational value. Integrating traditional culture into kindergarten curriculum can promote the development of children's character and sociality. Iron painting is a folk craft with a long history in China. Integrating iron painting technology into kindergarten curriculum is an effective way to inherit Chinese excellent culture, an inevitable requirement to promote children's physical and mental development, and a feasible path to promote kindergarten curriculum construction. At the same time, it is feasible to integrate traditional culture into kindergarten curriculum. On the one hand, the research on traditional culture has accumulated some early achievements, which provides a theoretical basis for the development of kindergarten based curriculum; On the other hand, kindergartens can develop game materials of iron painting technology, which reduces the operation difficulty of iron painting technology. It is suggested that kindergartens establish curriculum concepts around children's development and promote the construction of teachers of traditional culture.
First Ideas Matter: A Selection Loop Process for Business Model Adaptation

Experimentation plays a vital role in firms’ activities and strategies. Yet, we still have a scant understanding of the processes underpinning experimentation for business modelling in startups. Through a qualitative longitudinal case, we investigate how a startup reshapes its value proposition and engages with external market stakeholders – in this case end customers – when their heterogeneity could not be catered by the first business model designed by the entrepreneur. We look at cases where a business model, originally envisioned with a single value proposition, needs to cater different groups of customers (i.e., product users, buyers, and beneficiaries) with diverse expectations in terms of value and engagement. Our study identifies a ‘selection loop’ process formed of three different experimentation phases – environment-based stress, cognitive-driven adaptation, and stakeholder-led evolution. This process contributes to better understand how managerial cognition drives business model design and experimentation towards a “fit” between the entrepreneur’s cognitive model and the stakeholders in the external environment. We also advance the cognitive perspective on business models as we take a cognitive lens to explore experimentation as a process to identify and adapt a business model to emerging evidence about a startup’s customers.
Factors Influencing Performance of Applied Research: Evidence from the Higher Education Institutions in the Kurdistan Region of Iraq

Knowing what influences researchers on conducting applied research in developing countries is of interest to policymakers in higher education institutions. This study utilized a quantitative approach to investigate factors that drive academics in the higher education institutions in the Kurdistan Region of Iraq on conducting applied research. For this purpose, data were collected using a survey from researchers at different universities and fields and then were analyzed using the probit model. Based on the main findings of this study, the probability of conducting applied research increases as the years of experience increase when a researcher has external funding, while the probability decreases as the years of experience increase when a researcher has only the knowledge of applying to external funding without actually having any funding available. This study recommends some actions that help in promoting the research field in the higher education institutions in the Kurdistan Region of Iraq.
Effect of Waiting Time on Gap Acceptance Characteristics of Change Direction U-Turn Opening

This paper investigated the effect of some traffic factors such as waiting time and traffic volume increasing on gap acceptance characteristics i.e., driver's behavior, limits of gap acceptance, critical gap, and the facility capacity.The current study depends on real field data collected at selected change direction U-turn within Najaf city highways network (center city of the Najaf governorate located approximately 160 km south of Baghdad, the capital of Iraq).The study focused on the passenger cars and minibus drivers maneuvering at change direction U-turn opening as the primary case study.The statistical analysis approach depended on evaluating traffic factors selected as parameters and their degree of influence on U-turn capacity.The results of statistical analysis established that firstly, waiting time range between 21 -30 sec, lead driver to enforce opposing direction traffic flow to accept gap size less than that when waiting time fall in the range of 11 to 20 sec at a confidence interval of 95% for a passenger car.While, minibus shows results higher than passenger car by approximately 20%.Secondly, there is a slightly different mean gap acceptance between an interval of (1-10) and (11-20) sec) for both types of vehicles at the same confidence interval.On the other hand, results showed the studied U-turn change direction median critical gap equal to 3.75 sec.and follow-up time was 1.1 seconds.According to Siegloch's formula the maximum capacity of 3273 pcu/hr.Other results show that the highest wait time group interval (when taken as the mean value) is lower than the critical gap.The main recommendation obtained due to the hazard of studied change direction U-turn is the importance of control and management it.
PPAR: A Privacy-Preserving Adaptive Ranking Algorithm for Multi-Armed-Bandit Crowdsourcing

This paper studies the privacy-preserving adaptive ranking problem for multi-armed-bandit crowdsourcing, where according to the crowdsourced data, the arms are required to be ranked with a tunable granularity by the untrustworthy third-party platform. Any online worker can provide its data by arm pulls but requires its privacy preserved, which will increase the ranking cost greatly. To improve the quality of the ranking service, we propose a Privacy- Preserving Adaptive Ranking algorithm called PPAR, which can solve the problem with a high probability while differential privacy can be ensured. The total cost of the proposed algorithm is ${\mathcal{O}}(K\ln K)$, which is near optimal compared with the trivial lower bound Ω(K), where K is the number of arms. Our proposed algorithm can also be used to solve the well-studied fully ranking problem and the best arm identification problem, by proper setting the granularity parameter. For the fully ranking problem, PPAR attains the same order of computation complexity with the best-known results without privacy preservation. The efficacy of our algorithm is also verified by extensive experiments on public datasets.
Indexed Steep Descent Fish Optimization with Modified Certificateless Signcryption for Secured IoT Healthcare Data Transmission

IoMT is a healthcare strategy and utilization connected with online computer networks for IoT. During data communication from machine to machine, Security is one of essential barriers. In order to improve security, Jaccardized Czekanowski Indexive, Steepest Descent Fish Optimization Based Kupyna Schmidt-Samoa Certificateless Signcryption (JCISDFO-KSSCS) is introduced. JCISDFO-KSSCS is used for enhancing authentication and secure Data Transmission. JCISDFO-KSSCS comprises two major processes, namely authentication, and secured data transmission. The discussed results indicate that proposed JCISDFO-KSSCS increases the performance results than the conventional approaches.
Digital educational environment of the university as a means of promoting the values of physical culture

In the context of the digitalization of society, there is a crisis in the values of physical culture; the fact of falling down in individual health potential is alarming. The purpose is to determine the influence of digital didactic resources on formation of the values of physical culture in the educational environment in the medical university. In modern conditions of digitalization of society the values of physical culture are becoming increasingly important as a system-forming factor of the nation’s health. The purpose of the research is to reveal the educational potential of digital technologies in promoting the values of physical culture in the educational environment of the university. The study involved 1569 young men and women of the first and second years of full-time education of the State Medical University of Orenburg. The survey method and the in-depth interview method in online mode were used to collect statistical data in a pandemic. As a result of the study, respondents developed basic digital skills in using various fitness applications for mobile devices, physical education and sports programmes, digital tools, smart wearable devices (MHealth gadgets) that allow monitoring students’ physiological indicators. There was an increase in the level of motor activity in online classes using educational platforms. There is a positive dynamics in the orientation of the student’s personality to health as a value. At the end of the experimental work, the respondents improved their digital skills in using various fitness applications for mobile devices, physical education and sports programmes, digital tools, smart wearable devices (MHealth gadgets) that allow monitoring the students’ physiological indicators. The systematic introduction of digital technologies in the teaching of disciplines (modules) in physical culture and sports and understanding of their educational potential contributed to the personification of the learning process; visualization of environments for the training process; enlargement of forms of physical culture and recreation activities; introduction of platform solutions as a space for pedagogical innovations.
CREATION OF DIGITAL SECURITY COMPONENTS FROM ELECTROENCEPHALOGIC SIGNALS

In a technological environment such as the current one, in which all daily digital tools are within the reach of a digital device, facilitating immediate access to information, various security tools in the area of information are implemented day by day based on in algorithms of various kinds, including both hardware and software tools; specifically, the tools of daily use such as: mail, social networks, education, banking applications allow the interconnection of multiple devices and thus have the availability of information in an interconnected manner, which implies a good level of aspects such as authentication and security management, key factors required to guarantee the availability, security and fidelity of information in an interconnected environment, promoting the creation of new security methods and paradigms.
Web-Based General Affair Information System Using Prototyping Method

Operations are one of the main activities in the company; almost all companies have a General Affairs (GA) section that takes care of all household and operational matters. The bigger the company, the more complex the problems faced. To overcome these problems, the role of technology is needed. In today's digital era, many technologies can be used to assist companies in administrative activities, one of which is a website-based application. Website-based applications have advantages over desktop applications because users only need a browser to access the application. This research will build a web-based general affairs administration system using the prototype method. The prototype is a method that requires the developer's interaction with the client so that it can overcome the incompatibility between the system developer and the client. Tests are carried out using black-box testing techniques focusing on checking system functionality. The results of tests conducted by 26 respondents show that the system built is 100% feasible and meets expectations.
Agent-based modelling of electricty access in informal settlements in South Africa

In the latter half of the 20th century and the first decades of the 21st century, urbanisation in developing countries has increased dramatically.Across the developing world, urban populations have been rising rapidly, and the capacity of national and municipal governments to service these growing populations has not been able to keep pace with rising demand.This has led to a rise in informal access to services and the development of informal settlements.Defining informal habitation is complex, but some common features are a lack of access to the formal services of the city, be that water, sanitation, electricity, or housing services.Across the developing world, growth in informal settlements has accompanied urban population growth: 1.03 billion people in 2018 lived in informal settlements, according to the United Nations, 233 million of whom live in Sub-Saharan Africa.For the United Nations Sustainable Development Goal 11 (SDG11), to be achieved by 2030, access to urban services needs to be accelerated.South African electricity services in urban informal settlements have seen significant development over the past decade, with programs such as the Upgrading Informal Settlements Program (UISP) 'giving priority to the provision of basic services and functional tenure'.However, access to services is still a challenge in many informal settlement communities, and illegal access to basic services, particularly electricity, remains high.This research uses a novel agent-based modelling methodology to determine which factors affect most strongly the incidences of illegal access to electricity, as well as the main drivers and barriers to greater formal access to electricity in informal settlements in South Africa.Access to capital, political economy barriers, and technical challenges contribute to high incidences of illegal access, and addressing these barriers will lead to higher formal access rates and more reliable electricity services for residents.