Reviewerly/exHarmony huggingface dataset:
The Definitions of Learning Process (Types of Motivation and Theories)

Learning is one of the major items dealt with psychology. Learning is typically defined as modification of behavior through practice and training. The process of learning is affected by such factors as environment, motivation, health, emotions, maturation and aging. A number of theories are found operative for the process of learning among which the prominent are: Classical conditioning by I.P. Pavlov, Operant conditioning by B.F. Skinner, Trial and Error theory by E.L. Thorndike, Gestalt or Insightful theory by F. D. Kohler and Kurt Koffka, J. B. Watson’s theory of learning and etc. It is suggested that for the best transfer of learning the individual should discover interrelationships for himself. He should be exposed to the diverse and multifaceted interrelationships and principles that can help draw generalizations.
Social media analytics to connect system performability and quality of experience, with an application to Citibike

Historically performance of city services has mostly been evaluated from a performability perspective based on what is commonly understood as a quality of service (QoS) analysis. High values of QoS are generally perceived by systems owners as reflective of a high quality of experience (QoE) by the system users. Unfortunately, such a statement is many times untrue. There is a lack of connection between the user experience and the system performance. As such, and given the availability of social media data regarding the usage of services and experiences, this work presents a novel framework to bring together QoS and QoE analyses, to integrate performance metrics with user expectations of services. This framework is illustrated via the bicycle sharing program of New York City, Citibike, where the combination of both QoS and QoE provides insights into a variety of issues related to the city and the service itself.
Stereo matching using convolution neural network and LIDAR support point grid

This paper proposes a stereo matching method that uses a support point grid in order to compute the prior disparity. Convolutional neural networks are used to compute the matching cost between pixels in two pictures. The network architecture is described as well as teaching process. The method was evaluated on Middlebury benchmark images. The results of accuracy estimation in case of using data from a LIDAR as an input for the support points grid is described. This approach can be used in multi-sensor devices and can give an advantage in accuracy up to 15%.
New Online Kernel Ridge Regression via Incremental Predictive Sampling

Online kernel ridge regression via existing sampling approaches, which aim at approximating the kernel matrix as accurately as possible, is independent of learning and has a cubic time complexity with respect to the sampling size for updating hypothesis. In this paper, we propose a new online kernel ridge regression via an incremental predictive sampling approach, which has the nearly optimal accumulated loss and performs efficiently at each round. We use the estimated ridge leverage score of the labeled matrix, which depends on the accumulated loss at each round, to construct the predictive sampling distribution, and use this sampling probability for the Nyströ m approximation. To avoid calculating the inverse of the approximated kernel matrix directly, we use the Woodbury formula to accelerate the computation and adopt the truncated incremental singular value decomposition to update the generalized inverse of the intersection matrix. Our online kernel ridge regression has a time complexity of $O(tmk+k^3 )$ for updating hypothesis at round t, where k is the truncated rank of the intersection matrix, and enjoys a regret bound of order $O(\sqrtT )$, where T is the time horizon. Experimental results show that the proposed online kernel ridge regression via the incremental predictive sampling performs more stably and efficiently than the online kernel ridge regression via existing online sampling approaches that directly approximate the kernel matrix.
A Dynamic Evolutionary Scheduling Algorithm for Cloud Tasks with High Efficiency and High QoS Satisfactions

Efficient task scheduling is one of the main ways to increase cloud computing’s throughput. In cloud computing, many tasks need to be scheduled on different virtual machines to augment system throughput while satisfying QoS conditions. Task scheduling is an NP-Complete problem, especially dynamic task scheduling in heterogenous cloud environments. This paper presents a dynamic task scheduling algorithm based on heterogeneous cloud environment. The proposed algorithm uses the preponderance of Topological sort, Genetic Algorithm (GA) and NSGA-II. The experimental results show that the scheduling efficiency and QoS satisfaction of the algorithm are significantly better than GA, and the latter is one of the most commonly used heuristic optimization techniques in task scheduling problems.
Identifying Suitable Icon Button for Museum Application Interface Using Online Card Sorting Method

A good interface is made up of components that allow users to readily comprehend the conceptual model and know what interacting on an interface is like. There are many graphical components in an interface to help user interacts which include icon buttons. The countless number and range of icon buttons comes with a need to define the most appropriate set for the interface of museum implementation. This paper presents a card sorting method that uses feedback from users to obtain an efficient classification of possible navigation structure icon buttons. It interprets the exploratory analysis of a histogram matrix related to the final categories of cards used in the study. It focuses on the details of making a decision about the final categories and associated labels. It also helps in making an informed judgment when multiple interpretations of data are possible. The study findings suggest that “play, pause and stop”, “speaker”, and “video”, the icon representation on the buttons could be a set of potential signifier for a museum interface.
Reflections on Structured Common Sense in an Era of Machine Learning

Crowdsourcing common sense training data was born twenty years ago. It began with the idea to "harness the power of bored people on the Internet" to collect "what everyone knows but no one writes down". This was an era when we were all just starting to learn how to search the web, before people learned the dismal art of keywords, they tried typing their wants and needs. Search engines were woefully unequipped for these kinds of queries, and it was in that climate that we started ConceptNet, which we originally called OpenMind CommonSense (OMCS). Over the years, the effort and its methods evolved to address new applications. Today, we explore how structured common sense is becoming more relevant in NLP and the role it can play in helping solve problems of explainability, scalability, low-resourced languages, domain transfer, and AI bias. We look at how structured common sense and transformers are complementary and how we can combine them to keep the best of both worlds. Additionally, how does common sense need to evolve as we tackle larger and more complex and cross domain problems?
Fuzzy c-means and gath-geva methods in clustering districts based on human development index (hdi) in south sulawesi

Abstract District grouping in South Sulawesi based on the Human Development Index (HDI) indicators needs to be done as a material for planning and evaluating the targets of government work programs. This grouping is based on dominant indicators of the high and low HDI. The value of the HDI indicator needs to be considered so that the achievement of each indicator is known. Statistical analysis that can be used to group districts that have similarities is cluster analysis. The method that is currently developing is fuzzy clustering analysis, which classifies objects using certain membership degrees. Fuzzy clustering algorithm that can be used is Fuzzy C-means (FCM). Another method of fuzzy clustering analysis developed further is Gath Geva (GG), which is able to detect groups with different forms. In this study, the fuzzy clustering process on the FCM and GG methods with the same parameters and shows that the GG method is better than the FCM method. This conclusion is based on a total of 1000 iterations. The GG method gives an objective function value smaller than FCM, besides it gives a faster- conferencing iteration result.
Multimedia Use For Beginner Level of Teaching Languages For Deaf Children: Study in Special Schools in Surakarta

Abstract Education for children with special needs, in Indonesia language known as Anak Berkebutuhan Khusus or ABK, at an early age plays a significant role in triggering their abilities to communicate by using sign language. This paper discusses the results of research on the development of sign language learning media for ABK with age range 6-9 years. The stage of designing learning media includes the SDLC method (System Development Life Cycle). The result of the design is a Multimedia Application for Introduction to Sign Language for Deaf Children age 8-12 years in the form of CD-ROMs. The first is the CD-ROM for PC / computer which contains Indonesian language learning element, examples of simple sentences, practice questions and puzzles, and each item includes a sign-language video, the second is in the form of VCD containing Indonesian language learning material and in each element comprises sign language video.
Distribution Law for Constituent Minerals and Chemical Components in Rocks and Ores

Rocks and Ores comprising of multicomponent of minerals (Group I) contain chemical molecules (Group II) which further contain (Group III). Man has exploited these valuable constituents for industrial, economic, and social growth causing serious depletion of high-grade ores at surface and shallow depths. Sustainable growth now requires exploitation of low-grade ores and those occurring at depth which imply optimal and most efficient mining efforts based on spatial, temporal, and spatiotemporal distribution of these constituents. The optimal decisions resulting in profit maximization is possible by obtaining precise and accurate parameters of these distributions. Sample size required for such estimations must be at least representative elementary volume (REV) of the rocks/ores, but the data matrix is not full-rank for statistical analyses and sample mean (x; 0 &lt; x &lt; 1) must be transformed to Gaussian to apply standard univariate (UND)/multivariate (MND) statistical techniques. A log(x/(1−x)) or ln(x/(1−x)) transform is shown to be an appropriate pre-transformation that eliminates the twin problems of full-rank, and spurious negative correlations as well as makes the distribution Gaussian for major, minor, and trace components. Mining applications using the univariate and/or Multivariate Normal Theory of pre-transformed sample mean (x) in rocks/ores is optimal for anomaly detection, drilling site selection, global reserve and grade estimations, mine planning, ore mineral liberation, blending, sustainable mine developments and maximization of profits computed on net profit value (NPV) basis to present time.
Professional position as a scientific phenomenon

The article analyzes the scientific approaches to the treatment of a professional position as a scientific phenomenon. It is noted that the conceptual-categorical dimension of the study of the problem of formation of a professional position as a scientific phenomenon lies in the plane of the characteristics of such concepts as «position» («position of the person»), «pedagogical position» («position of the teacher»), «professional position» the need for clarification regarding their interpretation. It was found out that in the reference journals, the notion of «position» is regarded as developing, a multi-dimensional education as a point of view, attitude to something that determines the nature of behavior, action; the nature of the actions caused by this attitude. It is stated that the position in psychology means the official position of a person in one or another subsystem of relations, reflects the dominant and selective attitude of a person to what is essential to it. It is noted that the formation of a position goes through its expansion, awareness and differentiation, one of the manifestations of which is gradually crystallizing professional position. It is generalized that despite the differences in the definition of this definition by different authors, the general definition is a position as a specialist’s attitude to the profession and to itself within it, which is manifested in activity, and a professional position as a person-professional quality, which is formed in the process of learning and is based on development of value-semantic relations to the pedagogical profession, determines the individual style of professional activity of the teacher.
On College English Teaching Reform Strategies Based on Large Data Technology

Large data technology is the model based on cloud computing data processing and application, which employs integration and sharing of data to form intellectual resources and knowledge service capabilities. Hence, big data technology is capable of expanding the time and space of education, which has a profound impact on higher education and also puts forward new requirements for college English teaching. In the context of large data technology, the article focuses on such reform strategies as college English education concept, role of teacher, teaching mode, student learning needs, personalized self-learning and other factors for the purpose of improving the quality of college English teaching by virtue of large data technology.
A simultaneous perturbation weak derivative estimator for stochastic neural networks

In this paper we study gradient estimation for a network of nonlinear stochastic units known as the Little model. Many machine learning systems can be described as networks of homogeneous units, and the Little model is of a particularly general form, which includes as special cases several popular machine learning architectures. However, since a closed form solution for the stationary distribution is not known, gradient methods which work for similar models such as the Boltzmann machine or sigmoid belief network cannot be used. To address this we introduce a method to calculate derivatives for this system based on measure-valued differentiation and simultaneous perturbation. This extends previous works in which gradient estimation algorithm’s were presented for networks with restrictive features like symmetry or acyclic connectivity.
AFIrM

Languages commonly used to implement adaptive middleware systems are object-oriented with reflective capability, what entails in performance overhead, exposure of internal structure and also limits the abstraction level. In this paper, we propose an adaptive middleware, namely AFIrM, developed in Haskell, a purely functional language whose features like algebraic data types, no reflection, and control of side effects, provide a new approach for developing this kind of system. The evaluation of the proposed middleware concentrated on measure the impact caused by the use of functional features on its execution. We intend that developers of Haskell applications use the proposed middleware as a solution to deal with distribution and adaptation issues, and also to show that purely functional programming can facilitate the development of adaptive middleware systems, and hence should be considered as an alternative to traditional approaches.
Multiple Key Encryption (MKE) Technique: An Encryption-as-a-Service Delivery Model in Cloud Computing

Cloud Computing, a form of on-demand service offering resources such as platform, infrastructure, software, database etc., Cryptography, the art of altering the user defined plain data into inarticulate form, and that exists as a prime solution to protect the data on network transmission. This research work specifically focused on, an interesting, Encryption-as-a-service application delivery model, with a symmetric key algorithm based on the conventional encryption mode is developed. Key generation for the encryption is a most important phase of every cryptographic process. Multiple keys are involved for encryption of data to strengthen the encryption quality via the ICBC Process. It is tested with various inputs and the quality of the algorithm is investigated. The Motivation behind this application development is to minimize the execution time for the encryption within limited storage capacity. The proposed application model in this research work, avoids unauthorized attacks such as brute-force, chosen plaintext, CCA and CPA and the results are analyzed using the cryptanalysis process. The Performance of the proposed algorithm is analyzed with statistical measures such as frequency analysis. Comparative analysis is done to reveal the enhancement of the algorithm. The homogeneity of the data from the experimental results shows the efficiency of the data of how well it is encrypted. Evaluation reports state that the developed application service relic’s potential for authenticating text files with better satisfaction while out-sourced as an application in cloud computing environment.
Depth prediction from 2D images: A taxonomy and an evaluation study

Abstract Among the various cues that help us understand and interact with our surroundings, depth is of particular importance. It allows us to move in space and grab objects to complete different tasks. Therefore, depth prediction has been an active research field for decades and many algorithms have been proposed to retrieve depth. Some imitate human vision and compute depth through triangulation on correspondences found between pixels or handcrafted features in different views of the same scene. Others rely on simple assumptions and semantic knowledge of the structure of the scene to get the depth information. Recently, numerous algorithms based on deep learning have emerged from the computer vision community. They implement the same principles as the non-deep learning methods and leverage the ability of deep neural networks of automatically learning important features that help to solve the task. By doing so, they produce new state-of-the-art results and show encouraging prospects. In this article, we propose a taxonomy of deep learning methods for depth prediction from 2D images. We retained the training strategy as the sorting criterion. Indeed, some methods are trained in a supervised manner which means depth labels are needed during training while others are trained in an unsupervised manner. In that case, the models learn to perform a different task such as view synthesis and depth is only a by-product of this learning. In addition to this taxonomy, we also evaluate nine models on two similar datasets without retraining. Our analysis showed that (i) most models are sensitive to sharp discontinuities created by shadows or colour contrasts and (ii) the post processing applied to the results before computing the commonly used metrics can change the model ranking. Moreover, we showed that most metrics agree with each other and are thus redundant.
Intelligent Classroom Information System

Owing to the fact that people often walk lights, windows, fans and other equipment are not turned off in the classroom, and the time occupied by teachers in the classroom affects the classroom and can not report the attendance rate in time, which greatly reduces the work efficiency, so a mobile phone client is developed for the use of school teachers, students and classroom administrators to query the classroom environment parameters and classroom attendance in time. This paper will introduce the function analysis of mobile client and the specific steps of software function.
Comparison between stochastic support vector machine (stochastic SVM) and Fuzzy Kernel Robust C-Means (FKRCM) in breast cancer classification

Cancer is one of the most famous diseases in recent years. Cancer has various classifications, which are difficult to detect in patients. Most patients are diagnosed with cancer after being at an advanced stage. To analyze the disease and diagnose cancer in patient, we need gene expression data. Gene expression data contain so many genes, even thousands of genes, but not all of those genes contain information necessary to detect cancer for its classification. Machine learning can choose the important genes for the classification to reduce running time, cost, and increase classification accuracy. In this paper we use Stochastic SVM and Fuzzy Kernel Robust C-Means (FRKCM). Then we will compare the accuracy of the methods in breast cancer classification. Stochastic SVM achieves a high prediction accuracy by learning the optimal hyperplane from the training set, which greatly simplifies the classification and regression problems. Fuzzy Kernel Robust C-Means (FKRCM) will define the membership function, set specific data called prototype and use the learning rate on each iteration. Based on the experiment, we get 90.43 % accuracy for the Stochastic SVM and 95.65 % accuracy for Fuzzy Kernel Robust C-Means.
Integrating a Minerals Systems Approach with Machine Learning: A Case Study of ‘Modern Minerals Exploration’ in the Mt Woods Inlier – northern Gawler Craton, South Australia

SummaryThere has been extensive exploration in the Mt Woods inlier in the northern Gawler Craton, South Australia since the 1960s, particularly for iron oxide-copper-gold (IOCG) mineralisation since the 1975 super giant Olympic Dam IOCG-U discovery by Western Mining Corporation ~150 km to the SE.IOCG discoveries in the Gawler Craton have been largely made based on targeted discrete magnetic and/or gravity anomalies since the advent of regional geophysical datasets. Only one significant economic discovery has been made in the Mt Woods inlier – the Prominent Hill metasediment-hosted IOCG deposit in 2001 by Minotaur Resources, with no subsequent economic mineralisation encountered elsewhere in the inlier.In an effort to accelerate the discovery of a new economic deposit, OZ Minerals opened up more than 2TB of their private geoscience data to external participants as part of the ‘Explorer Challenge’ competition in early 2019. Once registered, participants had 3 months to use the data provided, along with what was already in the public domain, to attempt to predict where the next mineral deposit in the Mt Woods inlier might be.Integrating a minerals systems approach with machine learning (ML) has allowed the development of a series of conceptual models for targeting a range of deposit types and commodities. Exploration success based on this modern targeting approach will eventually lead to new suitable exploration strategies to discover the next generation of economic mineral deposits, particularly those concealed under cover.
Glossary of Selected Hiligaynon Words: Development and Evaluation

Abstract This study deals with the development of glossary of selected Hiligaynon words and the evaluation of the appropriateness of the glossary developed and its level of acceptability.Descriptive-evaluative was used as a methodology of this study. Researcher made questionnaires were utilized to measure the appropriateness of the developed glossary and the level of acceptability in which were evaluated by experts according to different aspects. The developed glossary contained five hundred sixteen entries which underwent into cross-checking. Moreover, the developed glossary was evaluated highly appropriate by panel of experts and highly acceptable as a whole and according to the evaluation made by: teachers in Literature and Hiligaynon language, teachers of Mother-Tongue, and writers and language practitioners. The development of the glossary showed that Hiligaynon language is very rich and that it represents the authenticity and identity of the culture.