Reviewerly/exHarmony huggingface dataset:
Identification of Geochemical Distribution of REEs Using Factor Analysis and Concentration-Number (C-N) Fractal Modeling in Granitoids, South of Varcheh 1:100000 Sheet, Central Iran

The purpose of the present research is delineating geochemical anomaly of REEs in granitoids in south of Varcheh 1:100,000 sheet by the use of C-N fractal model and classical statistical methods. We gathered and studied 59 rock samples for REEs by ICP-MS method in the laboratory of Iran Mineral Processing Research Center (IMPRC). The Concentration–Number (C-N) fractal model was used to delineate elemental thresholds. According to the results, the distribution of elemental concentration for Pr and Sm were divided to three classifications and Ce, La, Nd and Y had five geochemical populations in the area. The classical statistics methods were able to separate three geochemical populations. The results obtained by this study showed that the separation of geochemical anomalies for REEs using C-N fractal model and classical statistics methods yielded to the same results. Meanwhile, the high ratio of LREE to HREE in rock samples as well as high P content, assigns monazite, apatite, and sphene as a possible source of REEs in the study area.
Planning and Search

The typical computer program goes about the business of move choice by generating alternative moves, searching the ensuing lines of play, evaluating the endpoints thus reached, and finally selecting the best valued move. Planning is a process that seems to characterize most human chess play, although it is perhaps at a minimum during speed play. The direction given by plans can help enormously in cutting down the potential volume of forward search. Plans are conceived and executed in the context of forward search. However, opinions differ concerning the importance of search, or calculation, in human play. Selectivity in human search probably begins as soon as a player is faced with a novel position. Like computers, human problem-solvers often use heuristics, or general “rules of thumb,” in order to simplify and accelerate the search for a solution. It is reasonable to expect that the use of heuristics will differentiate between good and poor performers.
Agent based Modeling for Activation Function in Enhancement Logic Programming in Hopfield Neural Network

Artificial Neural Network (ANN) uses many activation functions to update the state on neuron. The research and engineering have been used activation functions in the artificial neural network as the transfer functions. The most common reasons for using this transfer function were its unit interval boundaries, the functions and quick computability of its derivative, and several useful mathematical properties in the approximation of theory realm. Aim of this study is to figure out the best robust activation functions to accelerate HornSAT logic in the Hopfield Neural Network's context. In this paper we had developed Agent-based Modelling (ABM) assessed the performance of the Zeng Martinez Activation Function (ZMAF) and the Hyperbolic Tangent Activation Function (HTAF) beside the Wan Abdullah method to do Logic Programming (LP) in Hopfield Neural Network (HNN). These assessments are carried out on the basis of hamming distance (HD), the global minima ratio (zM), and CPU time. NETLOGO 5.3.1 software has been used for developing Agent-based Modeling (ABM) to test the proposed comparison of the efficaecy of these two activation functions HTAF and ZMAF.
Distance education

This chapter investigates the development of distance education from its origins as humble correspondence courses focusing on the teaching of short hand writing in the early 18th century to the current globe-spanning industry of e-learning courses, programmes and degrees. It considers the future opportunities and threats against distance learning and discusses the various scholarly arguments regarding the broader usefulness of distance education in terms of providing high-value content, student retention, student experience and its comparison to face-to-face courses. The concept of distance education was further developed and formalised in the mid-19th century by the educator Sir Isaac Pitman. Distance learning, in particular online distance learning, is a teaching method in constant flux and development, buffeted by rapidly developing technologies and equally rapidly developing pedagogical theories and methodologies. The foundational literature on online distance learning may appear at once prescient and outdated.
Field-Guide-Inspired Zero-Shot Learning

Modern recognition systems require large amounts of supervision to achieve accuracy. Adapting to new domains requires significant data from experts, which is onerous and can become too expensive. Zero-shot learning requires an annotated set of attributes for a novel category. Annotating the full set of attributes for a novel category proves to be a tedious and expensive task in deployment. This is especially the case when the recognition domain is an expert domain. We introduce a new field-guide-inspired approach to zero-shot annotation where the learner model interactively asks for the most useful attributes that define a class. We evaluate our method on classification benchmarks with attribute annotations like CUB, SUN, and AWA2 and show that our model achieves the performance of a model with full annotations at the cost of a significantly fewer number of annotations. Since the time of experts is precious, decreasing annotation cost can be very valuable for real-world deployment.
A Study on Cloud Methodology

Cloud computing has received increasing interest from enterprises since its inception. With its innovative information technology (IT) services delivery model, cloud computing could add technical and strategic business value to enterprises. However, cloud computing poses highly concerning internal (e.g., Top management and experience) and external issues (e.g., regulations and standards). This paper presents a systematic literature review to explore the current key issues related to cloud computing adoption. This is achieved by reviewing 15 articles published about cloud computing adoption. Using the grounded theory approach, articles are classified into eight main categories: internal, external, evaluation, proof of concept, adoption decision, implementation and integration, IT governance, and confirmation. Then, the eight categories are divided into two abstract categories: cloud computing adoption factors and processes, where the former affects the latter. The results of this review indicate that enterprises face serious issues before they decide to adopt cloud computing. Based on the findings, the paper provides a future information system (research agenda to explore the previously under-investigated areas regarding cloud computing adoption factors and processes. This paper calls for further theoretical, methodological, and empirical contributions to the research area of cloud computing adoption by enterprise.
The student time-budget and their adaptation to educational activities

The article examines the general picture of the use of their time fund by university students, its structuring, as well as the dynamics of time indicators related to the main spheres of students' daily activities in 2002–20. Particular attention is paid to educational activities as status-forming for full-time students. A survey of university students in Moscow, Omsk and Pskov was conducted in March 2019 – January 2020 by the sector for studying the daily activities and time budget of the ISRAS within the framework of the monitoring complex research "Daily activities of the urban population: time budget, values, social adaptation." The real behaviorur of students was analyzed in combination with the analysis of motivation to obtain higher education, adaptive opportunities for participation in educational activities and the success of professional socialization of students. It was revealed that the dominant motive for obtaining higher education - “the need to have a higher education diploma in order to get a good job” - not only retained (over the period 2002–20) its dominance and a significant “gap” from the significance of other motives, but its importance even rose. In general, the configuration of educational motivation has not changed much. Changes in resource time possibilities least of all affected the segment of daily activities related to the total work load (study time, paid work, unpaid work). The study time of students decreased more significantly for women (much less for men). The amount of time spent on physical recovery of students has decreased, but the space for studying in their free time has significantly expanded. It is shown that the types of students' value orientation in relation to educational activities have a connection with the volume, structure of educational time and other indicators. Indicators of adaptive capabilities of students for the implementation of educational activities are associated with the degree of preparedness of students to study at the university. It was revealed that the time spent on all educational activities and its structure in 2019–20 differs by the worst characteristics both in comparison with 1920 and in comparison, with other periods (the period of 1920–2020 was considered in dynamics).
Quantum Parallelism and Computational Complexity

Quantum computers can use a superposition to run many states simultaneously through an algorithm, rather than sequentially as in a classical computer. This principle is called quantum parallelism. This chapter refines our notion of quantum parallelism and introduces computational complexity to quantify the power of quantum computers.
“Anthropological or Humanistic View of the World”: a Model of Scientific Cognition in Mikhailovsky&amp;apos;s Heritage

The article focuses on the approach to the study of human psyche and behavior, suggested by literary critic and ideologist of Narodnikism N. K. Mikhailovsky in the second half of the 19th century. Mikhaylovsky is proved to have developed an original model of scientific cognition, which was based on the synthesis of knowledge from different fields of science and built around an “anthropological or humane point of view”. Scientific search within the framework of this model is carried out with the help of the subjective method, based on the effort to understand another person, and the subject of cognition is a “profane” — a person complete with all of his social experience. The problems put forward by Mikhailovsky seem relevant in the light of the modern “cognitive turn” in the humanities, the desire for interdisciplinary approach and the creation of an integrative scientific picture of the world, fundamental rethinking of the classical objectivistic model of scientific knowledge.
STUDIES ON THE FACTORS ASSOCIATED WITH MALARIA INFECTION AMONG RESIDENTS OF GWADABAWA LOCAL GOVERNMENT AREA SOKOTO STATE, NIGERIA

Malaria is a major public health concern in Nigeria where it accounts for more morbidity and mortality than any other country in the world. This study investigates the factors associated with malaria infection among residents of Gwadabawa Local Government Area, Sokoto State Nigeria. A total of 350 individuals were randomly selected from 350 systematically selected households across five wards of the area. A structured questionnaire was developed and administered to gather information on factors influencing the infection in the area. The study revealed that, presence of stagnant water or pond around households is a significant factor influencing the prevalence of the disease in the area, (OR =1.981, 95%CI: 1.103 - 3.558). Hence improved health education, environmental modification and other vector control measures as well as prompt diagnosis and treatment of infected individuals should be carried out to prevent the spread of the infection in the area.
Enhanced Full-Resolution Residual Network for Image Super-Resolution

Convolutional neural network (CNN) has played a critical role in promoting image super-resolution (SR) performance, and researchers have proposed various models in recent years. Although these models can improve the resolution of the image, the sharpness is not ideal. In this paper, We modify a semantic segmentation network: Full-Resolution Residual Network (FRRN) and propose an Enhanced Full-Resolution Residual Network for Image Super-Resolution (EFRN) model to improve the image clarity. Our network mainly applies a long skip connection to the direct fusion of low-level and high-level features. Besides, we also introduce the attention mechanism into the model to enhance the network’s repair capability. Moreover, experimental results confirm that EFRN has achieved better accuracy and visual effects than state-of-the-art methods.
Flood Control in Pajang Stowage Tangerang City with Zero Run Off Concept

Changing the function of the land from green land to stowage land can increase rain runoff so that it has the potential to become a flood. Flood is a natural phenomenon that can threaten the existence of human life, especially during the rainy season. In the area of Pajang, Tangerang City, south of Soekarno-Hatta Airport, a stowage center with an area of ± 6 Ha will be built. Existing land is vacant land that has not been equipped with a drainage system. Based on the technical principles and policies of the local government related to the urban water system and flood control, it is necessary to find the right solution to minimize the impact that will occur due to the construction of these facilities without disrupting the existing water system. Therefore, a technical study is needed in the form of a Flood Control In Pajang Stowage Tangerang City With Zero Run Off Concept. Based on the analysis that has been done, with the application of the use of retention ponds in the stowage area, it can reduce the impact of flooding so as not to burden the river outside the stowage area.
Stakeholders' Readiness Towards Technology Integration for Out-of-School Time Primary School Learning

Successful integration of technology requires readiness of key stakeholders. This paper is an attempt to examine the readiness of stakeholders in education towards integration of technology for Out of School Time (OST) learning in the context of Tanzania’s primary schools. The paper is informed by a model for hastening technology integration in OST learning which was developed by Kiwango (2018). The research was a case study that involved Class Seven pupils (475) and parents (275) as well as subject teachers (77) from nine (9) primary schools in Arusha Region specifically Meru District. The findings show that most of the involved stakeholders demonstrated readiness to take part in assessment and validation of technology needs, along with partnership contracting. This also applied to technology acquisition, deployment and evaluation. Comparatively, teachers showed more inspiration, followed closely by pupils and parents. This indicates that the involved stakeholders could successfully advocate for technology integration in primary schools in the country. This requires similar support from The Government and other education stakeholders with a view to reinforcing the use of educational technology in school curriculum.
Image inpainting by low-rank tensor decomposition and multidirectional search

For a damaged image, the loss of pixel information can be roughly divided into two categories, random missing and non-random missing. The missing of an entire row or column of the image is a specific structural missing pattern that is extremely difficult to deal with. Although most of the existing methods have partially fixed this information missed problem, the diffusion-based methods tend to produce blur, the exemplar-based methods are prone to error filling, and the neural network-based methods are highly dependent on data, which cannot handle this special structural missing very well. Using the nonlocal self-similarity prior and the low-rank prior, we present multidirectional search and nonlocal low-rank tensor completion (MS-NLLRTC) algorithm based on the tensor ring (TR) decomposition and multidirectional search (MS). The MS method is a newly proposed method that can search similar patches much more diversified. Using MS method, we directly stack the similar patches into a three-dimensional similar tensor instead of pulling them into column vectors, then the similar tensor can be completed by TR decomposition. The optimization results can be obtained by leveraging the alternating direction method under the augmented Lagrangian multiplier framework. Moreover, we add a weighted nuclear norm to the tensor completion model (WNLLRTC), achieving a better inpainting performance. We also combine a noise removal method with WNLLRTC algorithm, which can handle image random missing and image noise removal simultaneously. Experimental results indicate that our proposed algorithms are competitive with some state-of-the-art inpainting algorithms in terms of both numerical evaluation and visual quality.
Mouse Movement through Finger by Image Grabbing

Touch less Interactive screen or system is an upcoming technology in the real life world, which will be another biggest realization followed by the technologies based on the monitors cove as PC monitor, TV Screens without a stroke or touch. The aim of this project is to develop a touch less interactive screen which helps to ease the controlling of any kind of monitors. Project is based on MATLAB platform. In this work, the finger gestures which are captured by a webcam and that will be converted into mouse movements to control the screen. Various Analysis result has included in the report to show the feasibility of the application of this development. A Graphical Interface User (GUI) is developed to let the user to choose perform calibration and number of times it’s required to perform calibration and to trigger the program upon user’s request.
INTUITION AND SYNERGETICS IN THE WORLD OF MATHEMATICS

Introduction.Today, the development of science, the need to develop a culture of philosophical thinking require further expansion and strengthening of human knowledge, a comprehensive study of events and phenomena taking place in the world, the formation of scientific knowledge about the future of humanity on this basis, as well as the formation of a new approach to the problem of scientific creativity. This, in turn, determines the need for a scientific and philosophical study of the mechanisms of scientific creativity, i.e. intuition, a theoretical justification of its place in scientific knowledge.
Model Extraction and Adversarial Attacks on Neural Networks Using Switching Power Information

Artificial neural networks (ANNs) have gained significant popularity in the last decade for solving narrow AI problems in domains such as healthcare, transportation, and defense. As ANNs become more ubiquitous, it is imperative to understand their associated safety, security, and privacy vulnerabilities. Recently, it has been shown that ANNs are susceptible to a number of adversarial evasion attacks - inputs that cause the ANN to make high-confidence misclassifications despite being almost indistinguishable from the data used to train and test the network. This work explores to what degree finding these examples may be aided by using side-channel information, specifically switching power consumption, of hardware implementations of ANNs. A black-box threat scenario is assumed, where an attacker has access to the ANN hardware’s input, outputs, and topology, but the trained model parameters are unknown. Then, a surrogate model is trained to have similar functional (i.e. input-output mapping) and switching power characteristics as the oracle (black-box) model. Our results indicate that the inclusion of power consumption data increases the fidelity of the model extraction by up to 30% based on a mean square error comparison of the oracle and surrogate weights. However, transferability of adversarial examples from the surrogate to the oracle model was not significantly affected.
Bridging the Digital Divide in the Digital Economy with Reference to Intellectual Property

Computer networks are widely used in digital work across the globe, and easy access to the world wide web has led to widespread information of all the digital form sectors. In digital form, such abundance of information has given so many benefits to the countries such as publishing, creating, using, distributing, and reusing has become so easy compared to the past decades. Therefore, the good thing is the information enrichment, which benefits the people as individuals and society as a whole. However, the bad news is it also to misuse and illegally copying, distributing, and using of information illegally. Therefore, the paper aims to address the international intellectual property treaties and agreements, which led to the digital divide in the digital economy. Qualitative data analysis is used in this paper as a research method to define the concept of the digital divide in the context of the digital economy and illustrate the role of international intellectual property in the digital world. The research also sheds light on stakeholders' perspectives in order to stem the digital economy. The researcher also explores this digital divide's reasons while looking at gaps between policies and their implementation in the digital world. The research comes to an end while concluding that all the international intellectual property agreements and policies have failed in their implementation, which has further widened this digital gap.
Kernel Machines in Time (Invited Talk)

Kernel machines is a powerful class of models in machine learning with solid foundations and many existing application fields. The scope of this talk is kernel machines in time with a main focus on least squares support vector machines, and other related methods such as kernel principal component analysis and kernel spectral clustering. For dynamical systems modelling different possible input-output and state space model structures will be discussed. Applications will be shown on electricity load forecasting and temperature prediction in weather forecasting. Approximate closed-form solutions can be given to ordinary and partial differential equations. Kernel spectral clustering applications to identifying customer profiles, pollution modelling and detecting topological changes in time-series of bridges will be shown. Finally, new synergies between kernel machines and deep learning will be presented, leading for example to generative kernel machines, with new insights on disentangled representations, explainability and latent space exploration. Application of these models will be illustrated on out-of-distribution detection of time-series data.
Mathematical Modeling of Human Learning and Decision Making

Author(s): Xia, Liyu | Advisor(s): Pitman, Jim; Collins, Anne GE | Abstract: Reinforcement learning (RL) has been widely used to study and model human, animal and artificial intelligence. In this thesis, we focus on modeling human learning and decision making, and exemplify two ways mathematical RL modeling can add to our existing knowledge of human cognition: (1) provide a quantitative tool for parametrizing individual differences in human behavior, and (2) augment existing models to account for complex multi-step human learning.
Point-cloud neural network predicts porous media permeability

Proposed network relaxes GPU memory limitations in convolutional neural networks.