Reviewerly/exHarmony huggingface dataset:
Development of stratocumulus cloud modeling in coastal California for solar forecasting

Author(s): Wu, Elynn | Advisor(s): Kleissl, Jan | Abstract: Solar energy integration into the power grid requires forecasting of solar power due to the natural variability of solar irradiance, primarily due to clouds. Recently, rooftop solar photovoltaic (PV) installations have grown dramatically in California, with a majority of these systems concentrated near the densely-populated coast. A common weather phenomena in coastal California are vast sheets of convective low clouds called Stratocumulus (Sc) clouds. The formation and dissipation of Sc clouds greatly impact solar power production. The challenges surrounding the modeling of Sc clouds come from the complex interplay among the governing physics-- namely, surface-driven convection, cloud-top triggered convection, microphysical processes, and entrainment across the inversion layer. This thesis uses tools including satellite images, statistical analysis, and numerical weather prediction (NWP) models to predict Sc clouds and generate solar irradiance forecast. First, a novel approach of tracking the evolution of stationary clouds to produce short-term solar forecast using satellite images is presented. The method predicts Sc dissipation time accurately by overcoming the limitation of the frozen cloud assumption in traditional cloud motion vector satellite solar forecasting techniques. Second, an observation-based analog ensemble forecast is investigated to better understand the meteorological variables contributing to Sc cloud lifetime. Intra-day analog ensemble solar forecasts suggest that boundary layer averaged heat, moisture, and height are key to capturing Sc dissipation time. Finally, Sc forecast beyond intra-day is investigated through the use of a numerical weather prediction model-- Weather Research and Forecasting (WRF). In WRF, the planetary boundary layer (PBL) scheme parameterizes mixing processes in the PBL that impact the heat and moisture profile in the PBL. An eddy-diffusivity/mass-flux (EDMF) framework is adopted to model realistic mixing in the PBL. The framework models the non-convective environment through eddy-diffusivity and the convective area using a mass-flux. Specifically, cloud-top triggered downdrafts are developed and integrated into the mass-flux model. Turbulent downdrafts help mix the heat and moisture in the top part of the PBL and result in better representation of thermodynamic profiles and cloud thickness.
Peculiarities and stages of Higher School teacher formation

The article discusses the issues of improving the professionalism of a higher education teacher; it also describes activities designed to improve the quality of the educational process, and the role of the leadership of an educational organization in the formation of a teacher. Initial knowledge, skills, abilities and qualities are formed by the teacher in the process of his or her professional activity and during the part of his or her life that precedes work in an educational institution. Professional qualities actively develop only with the beginning of teaching activities. The development of the initiative and the creative search in the work of a teacher is of particular relevance, because improving the quality of training cannot be achieved by simply increasing the working time of teachers and the study time of students. New teaching methods and new techniques for developing the students' creative activity are needed, which would allow them to achieve high results. Moreover, the state’s policy should include real material and moral support for a creatively working teacher of an educational institution, including teachers at higher schools.
A Reliable Workload Management Based on Predictive Analysis and Characterization of Workload Resources in HPC

Execution of Big Data workloads upon High Performance Computing (HPC) infrastructures has become an attractive way to improve their performances. In resource management, a large volume of multi-structured log data of Cloud Data Center (CDC) is generated regarding job arrival patterns, CPU memory consumption, task duration and many others. The system also provides the mechanism for CACO Cauchy matrix method for automatic data recovery from disk failure; it can also remove the lengthy process like replica management. In this paper we proposed a system for dynamic load rebalancing and resource allocation technique using machine learning algorithm. Q-Learning based on ML algorithm has used for validating the system. Experimental analysis illustrates that how proposed system eliminates the present approaches drawbacks. Our key is to identify the various workload patterns generation in heterogeneous storage environments using machine learning algorithm.
Structural properties of solutions, approximation and control for conservation laws with discontinuous flux and bioinspired PDE models

In this thesis we study theoretical and control type properties of three different classes of PDE: Scalar Conservation Laws with flux discontinuous, respectively, in the space and in the conserved quantity, and a Partial Integro-Differential Equation. In the first chapter we analyze the set of attainable profiles, at fixed time (with initial datum regarded as a control), by solutions to conservation laws with flux having a single discontinuity in the space and strictly convex or strictly concave behaviour outside the discontinuity. This analysis yields compactness properties of such a set which is instrumental to study many variational problems involving the profiles of the solutions. In the second chapter we examine a class of conservation laws with flux discontinuous in the conserved quantity that emerges in a model of industrial conveyor belt and in supply chains. We first introduce an appropriate notion of pair of entropic solution-flux, we provide existence of entropic solution-fluxes by front-tracking and we show a Kruzhkov’s type stability of such solutions. Next, we analyze the associated Hamilton-Jacobi equation, we derive an Hopf-Lax type representation formula of the solutions and we show how to recover the pair of entropic solution-flux of the conservation law from the gradient of the solution of the Hamilton-Jacobi equation. Finally, we consider the problem of a junction with a buffer (to store processed products) and with incoming and outgoing belts modeled by the class of conservation laws analyzed beforehand. Existence and uniqueness of the solution to the junction problem is established.The last chapter describes a mathematical model for a robotic root to be used in rescue technology. The root movement is described by two different Partial Integro-Differential Equations, one for the body and one for the tip. When the root encounters an obstacle in a good configuration, it moves around it by bending through a determined angular velocity minimizing the elastic deformation, the cost of moving sand and digging where the soil is dense. A restarting procedure is instead introduced to handle the crossing of obstacles in bad configuration. Some numeric simulation are also produced.
A new grid partitioning technology for location privacy protection

Nowadays, the location-based service (LBS) has become an essential part of convenient service in people's daily life.However, the untrusted LBS servers can store lots of information about the user, such as the user's identity, location, and destination.Then the information can be used as background knowledge and combined with the query frequency of the user to launch the inference attack to obtain user's privacy.In most of the existing schemes, the author considers the algorithm of virtual location selection from the historical location of the user.However, the LBS server can infer the user's location information on the historical data that has been counted for a long time.In order to ensure that the users' historical query data and query frequency will not be obtained by the attacker, we propose a privacy protection algorithm based on grid expansion.With the help of third-party agents, the combination of cooperative users and pseudonyms can resist the privacy disclosure caused by users requesting services during the mobile process.Extensive simulation experiments have been carried out on Gowalla dataset to evaluate the efficiency of the proposed algorithm.By comparing with other existing methods, the experimental verify the effectiveness of our algorithm in privacy protection.
A Novel Method based on the Cocomo model to increase the accuracy of software projects effort estimates

It is regarded as a crucial task in a software project to estimate the criteria, and effort estimation in the primary stages of software development is thus one of the most important challenges involved in management of software projects. Incorrect estimation can lead the project to failure. It is therefore a major task in efficient development of software projects to estimate software costs accurately. Therefore, two methods were presented in this research for effort estimation in software projects, where attempts were made to provide a way to increase accuracy through analysis of stimuli and application of metaheuristic algorithms in combination with neural networks. The first method examined the effect of the cuckoo search algorithm in optimization of the estimation coefficients in the COCOMO model, and the second method was presented as a combination of neural networks and the cuckoo search optimization algorithm to increase the accuracy of effort estimation in software development. The results obtained on two real-world datasets demonstrated the proper efficiency of the proposed methods as compared to that of similar methods.
LibGuides: Audio Visual Support: Cables

See what the Innovation Commons has available to borrow to support digital learning and teaching.
Synthesis of gold compositional data from petrography and detrital gold particles to characterize complex lode gold mineralization

Determining the contribution of individual episodes of mineralisation to the overall metal endowment of a gold deposit may be challenging, especially where veins are complex. We have applied a novel approach to address this problem which utilizes the variance of gold alloy compositions with environment of mineral precipitation. Studies at Curraghinalt (N. Ireland) and Cononish (Scotland) have linked gold compositions to paragenetic stage through classical petrography, and then correlated these compositional ranges to those observed in populations of detrital gold particles derived from erosion of the whole orebody. In this way the gold compositions which relate to the most important phase of mineralisation may be identified. The approach may, in some cases, be limited by the particle size ranges of gold, but can offer an efficient route to targeting the most important ore type and underpin further studies which develop deposit models.
Fixed-size video summarization over streaming data via non-monotone submodular maximization.

Video summarization which potentially fast browses a large amount of emerging video data as well as saves storage cost has attracted tremendous attentions in machine learning and information retrieval. Among existing efforts, determinantal point processes (DPPs) designed for selecting a subset of video frames to represent the whole video have shown great success in video summarization. However, existing methods have shown poor performance to generate fixed-size output summaries for video data, especially when video frames arrive in streaming manner. In this paper, we provide an efficient approach k-seqLS which summarizes streaming video data with a fixed-size k in vein of DPPs. Our k-seqLS approach can fully exploit the sequential nature of video frames by setting a time window and the frames outside the window have no influence on current video frame. Since the log-style of the DPP probability for each subset of frames is a non-monotone submodular function, local search as well as greedy techniques with cardinality constraints are adopted to make k-seqLS fixed-sized, efficient and with theoretical guarantee. Our experiments show that our proposed k-seqLS exhibits higher performance while maintaining practical running time.
Real-Time Signature Recognition Using Neural Network

The human signature is an important biometric feature that is used to identify human identity.It is essential in preventing falsification of documents in numerous financial, legal, and other commercial settings.The computerized system enters many aspects of our life, security is one of them, continues developing in computer vision and artificial network leads researcher to develop computerized signature recognition.This paper proposed a real-time algorithm for signature recognition.It is based on client and server operation.inwhich, client agent captures a signature and sends it to the server through the network.The server receives data and performs processing on it.Processing algorithm is based on weightless neural network.It is chosen for its simplicity and few numbers of sample required for training.The algorithm is tested and evaluated and show the ability to process 4.7 images per second.
Innovation Approach and Framework for HCI Research

The chapter presents the specific innovation approach to HCI research, including an illustration from the literature. The latter proposes how novel, emerging smell technology might be applied to develop smell-enhanced human–computer interactions. It then presents the specific innovation framework for HCI research, comprising innovation as discipline, general problem, particular scope, research, knowledge and practices. The specific innovation framework is followed by the innovation design research exemplar, as the innovation design cycle and the innovation design research cycle. The lower-level innovation framework comprises the innovation application, the innovation interactive system, and the innovation interactive system performance. Both the exemplar and the lower-level framework are applied to the same illustration of the innovation approach taken from the literature, which proposes how novel, emerging smell technology might be applied to develop smell-enhanced human–computer interactions.
On the symbol-pair distances of repeated-root constacyclic codes of length 2p s

© 2019 Elsevier B.V. Let p be an odd prime, and λ be a nonzero element of the finite field Fpm. The λ-constacyclic codes of length 2ps over Fpm are classified as the ideals of quotient ring [Formula presented] in terms of their generator polynomials. Based on these generator polynomials, the symbol-pair distances of all such λ-constacyclic codes of length 2ps are obtained in this paper. As an application, all MDS symbol-pair constacyclic codes of length 2ps over Fpm are established, which produce many new MDS symbol-pair codes with good parameters.
SORRY I AM STILL LEARNING - ACTIVE EXPECTATION MANAGEMENT OF CHATBOTS

Chatbots gained strong popularity in customer service, although users often experienced unsatisfactory interactionsregularly due to highly exaggerated performance expectations.As a viable option, firms might, therefore, think about active expectations management by describing chatbot limitations.However, the question remains whether this strategy has positive or negative impacts on customer satisfaction and re-use intentions.Building on the Expectancy Violation Theory and the CASA-Paradigm, this study plans to evaluate the effects of two different expectation management strategies.As work in progress, we describe the status and plan of this research.
LibGuides: Narrowing and Broadening Topic Specificity of Search Results: Home

This guide provides search strategies to retrieve information sources with varying levels of topic specificity. For example information could range from an article with an overview on the solar system planets to an article on just one planet.
On the Robustness of Neural Network: Attacks and Defenses

Author(s): Cheng, Minhao | Advisor(s): Hsieh, Cho-Jui | Abstract: Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples. That is, a slightly modified example could be easily generated and fool a well-trained image classifier based on deep neural networks (DNNs) with high confidence. This makes it difficult to apply neural networks in security-critical areas.To find such examples, we first introduce and define adversarial examples. In the first part, we then discuss how to build adversarial attacks in both image and discrete domains. For image classification, we introduce how to design an adversarial attacker in three different settings. Among them, we focus on the most practical setup for evaluating the adversarial robustness of a machine learning system with limited access: the hard-label black-box attack setting for generating adversarial examples, where limited model queries are allowed and only the decision is provided to a queried data input. For the discrete domain, we first talk about its difficulty and introduce how to conduct the adversarial attack on two applications. While crafting adversarial examples is an important technique to evaluate the robustness of DNNs, there is a huge need for improving the model robustness as well. Enhancing model robustness under new and even adversarial environments is a crucial milestone toward building trustworthy machine learning systems. In the second part, we talk about the methods to strengthen the model's adversarial robustness. We first discuss attack-dependent defense. Specifically, we first discuss one of the most effective methods for improving the robustness of neural networks: adversarial training and its limitations. We introduce a variant to overcome its problem. Then we take a different perspective and introduce attack-independent defense. We summarize the current methods and introduce a framework-based vicinal risk minimization. Inspired by the framework, we introduce self-progressing robust training. Furthermore, we discuss the robustness trade-off problem and introduce a hypothesis and propose a new method to alleviate it.
Meningkatkan Keterampilan Membuat Sulam Kalocsa Melalui Model Pembelajaran Direct Instruction Bagi Anak Tunarungu

This paper describes the problem of two students who experience problems in learning skills to make kalocsa embroidery. Observations show that children have good interests and talents in embroidery. But the ability of children is minimal in making Kalocsa embroidery neatly. One way to overcome this is to teach making kalocsa embroidery through classroom action research methods. Through training, making Kalocsa embroidery gives children the opportunity to practice independently step by step in a structured manner so that it helps children to understand it more easily. Direct instruction learning model can improve the skills to make kalocsa embroidery
Interactive re-ranking for cross-modal retrieval based on object-wise question answering.

Cross-modal retrieval methods retrieve desired images from a query text by learning relationships between texts and images. This retrieval approach is one of the most effective ways in the easiness of query preparation. Recent cross-modal retrieval is convenient and accurate when users input a query text that can uniquely identify the desired image. Meanwhile, users frequently input ambiguous query texts, and these ambiguous queries make it difficult to obtain the desired images. To alleviate these difficulties, in this paper, we propose a novel interactive cross-modal retrieval method based on question answering (QA) with users. The proposed method analyses candidate images and asks users about information that can narrow retrieval candidates effectively. By only answering the questions generated by the proposed method, users can reach their desired images even from an ambiguous query text. Experimental results show the effectiveness of the proposed method.
Explaining Machine Learning Decisions

The operations of deep networks are widely acknowledged to be inscrutable. The growing field of “Explainable AI” (XAI) has emerged in direct response to this problem. However, owing to the nature of the opacity in question, XAI has been forced to prioritise interpretability at the expense of completeness, and even realism, so that its explanations are frequently interpretable without being underpinned by more comprehensive explanations faithful to the way a network computes its predictions. While this has been taken to be a shortcoming of the field of XAI, I argue that it is broadly the right approach to the problem.
Factor Affecting the Growth and Continuity of Micro and Small Enterprise in Robe Town

Micro and Small Enterprises (MSEs) play an important economic role in many countries and recognized as an important vehicles of economic diversification, employment creation, income generation and distribution, and poverty alleviation.MSEs occupy a prominent position in the development agenda of many developing countries like Ethiopia.Currently, In Ethiopia, the Government has been promoting the development of MSEs through the formulation and implementation of Micro and Small Enterprises development strategy and the number of MSEs in the country is steadily growing.But, much more important than increase in their numbers, their current status, stage and pace of development is significant because most MSEs are hibernated from growing and faced with the threat of failure due to many factors.The purpose of this study is to assess the constraining factors related to marketing, management, finance and government supports hindering the growth of MSEs in Robe town.The study was employed through descriptive and explanatory research design in which stratified random sampling method was used to collect data from MSEs owners/ managers of five selected sectors according to the objective of the study with a total population of 949 and 281 samples.The samples were selected randomly from each stratum using proportionate allocation after stratification of the sectors.Questionnaires of data collection were used in the study.The data was analyzed using descriptive statistics and explanatory research design.The findings indicate that the most common factors constraining the growth of MSEs in Robe are: lack of working premises, lack of working capital and access to credit, lack of market access, inadequate availability of infrastructure, lack of managerial training and experience; and scanty of marketing information.Replication of this study using larger samples and a broader geographic base, longitudinal data collection and using more statistical tests is suggested for cross-validation purposes in future researches to identify the factors constraining the growth and survival of MSEs.
A new system to detect coronavirus social distance violation

In this paper, a novel solution to avoid new infections is presented. Instead of tracing users’ locations, the presence of individuals is detected by analysing the voices, and people’s faces are detected by the camera. To do this, two different Android applications were implemented. The first one uses the camera to detect people’s faces whenever the user answers or performs a phone call. Firebase Platform will be used to detect faces captured by the camera and determine its size and estimate their distance to the phone terminal. The second application uses voice biometrics to differentiate the users’ voice from unknown speakers and creates a neural network model based on 5 samples of the user’s voice. This feature will only be activated whenever the user is surfing the Internet or using other applications to prevent undesired contacts. Currently, the patient’s tracking is performed by geolocation or by using Bluetooth connection. Although face detection and voice recognition are existing methods, this paper aims to use them and integrate both in a single device. Our application cannot violate privacy since it does not save the data used to carry out the detection and does not associate this data to people.