AUTOMATE TASK’ DEPENDENCIES COMPLETION TRACKING IN SMARTSHEET

Smartsheet is the well-known project management application. It helps managers to develop project plans and track their execution. When project’s tasks have many dependencies, a manager often has to scroll the plan up and down for visual assessment of completion statuses each of them. It needs many efforts and time. The article proposes a solution to automate the dependencies completion tracking in Smartsheet for a specific task. The solution allows tracking different types of dependencies for a task: completing direct dependencies from other tasks and completing dependencies for all of its ancestors. The algorithm of the solution, Smartsheet’s functions for its implementation, and the screen forms of the obtained results are given.

Practitioner Perspectives on a Restorative Community: An Inductive Evaluative Study of Conceptual, Pedagogical, and Routine Practice

The purpose of this study is to explore restorative practice (RP) within education, highlighting practitioner views from an inductive evaluative perspective. This is in response to the suggested ambiguity over what constitutes RP in education. Practitioner perspectives are explored, providing an insight into an established restorative school. New data offer further clarity on RP in education by describing embedded practice and highlighting sites for further specific task analysis. Methods include semi-structured questionnaires in the form of employee practice statements, situated within insider research. Eleven practice statements were completed, which were then subject to in-depth thematic analysis. The main findings of this study suggest that RP is clearly understood in this educational setting and participants described a wholistic approach that is part of a wider culture, not just practice as an intervention. Analysis suggests that this can be categorised into the following three themes: Conceptual, Pedagogical and Routine Practice. When used alongside one another, it is suggested that these themes create a restorative paradigm, which is of relevance to the field of education. As such, this paper is designed to provide a useful resource for schools, policy makers and researchers alike.

OverFeat Network Algorithm for Fabric Defect Detection in Textile Industry

Automation of systems emerged since the beginning of 20th century. In the early days, the automation systems were developed with a fixed algorithm to perform some specific task in a repeated manner. Such fixed automation systems are revolutionized in recent days with an artificial intelligence program to take decisions on their own. The motive of the proposed work is to train a textile industry system to automatically detect the defects presence in the generated fabrics. The work utilizes an OverFeat network algorithm for such training process and compares its performances with its earlier version called AlexNet and VGG. The experimental work is conducted with a fabric defect dataset consisting of three class images categorised as horizontal, vertical and hole defects.

Compensation incentive contract of the subject librarians based on the H-M model

Based on the H-M model, the compensation incentive model is constructed under the inter-task cost function. The best compensation incentive contract is constructed by solving the incentive model, and the incentive characteristics are analysed. The results show that the best incentive intensity decreases as the subject service selectivity increases. The higher incentive intensity of university managers for specific tasks, the lower efforts of subject librarians for another specific task. Moreover, when the tasks are substituted for each other, the profit-sharing ratios corresponding to different tasks are complementary. Finally, we establish the econometric empirical models to test these results.

Detection Based on Crack Key Point and Deep Convolutional Neural Network

Based on the features of cracks, this research proposes the concept of a crack key point as a method for crack characterization and establishes a model of image crack detection based on the reference anchor points method, named KP-CraNet. Based on ResNet, the last three feature layers are repurposed for the specific task of crack key point feature extraction, named a feature filtration network. The accuracy of the model recognition is controllable and can meet both the pixel-level requirements and the efficiency needs of engineering. In order to verify the rationality and applicability of the image crack detection model in this study, we propose a distribution map of distance. The results for factors of a classical evaluation such as accuracy, recall rate, F1 score, and the distribution map of distance show that the method established in this research can improve crack detection quality and has a strong generalization ability. Our model provides a new method of crack detection based on computer vision technology.

Towards Human-Centered AI: Psychological concepts as foundation for empirical XAI research

Human-Centered AI is a widely requested goal for AI applications. To reach this is explainable AI promises to help humans to understand the inner workings and decisions of AI systems. While different XAI techniques have been developed to shed light on AI systems, it is still unclear how end-users with no experience in machine learning perceive these. Psychological concepts like trust, mental models, and self-efficacy can serve as instruments to evaluate XAI approaches in empirical studies with end-users. First results in applications for education, healthcare, and industry suggest that one XAI does not fit all. Instead, the design of XAI has to consider user needs, personal background, and the specific task of the AI system.

Detection of Cotton Plant Diseases Using Deep Transfer Learning

Agriculture is a vital part of every country’s economy, and India is regarded an agro-based nation. One of the main purposes of agriculture is to yield healthy crops without any disease. Cotton is a significant crop in India in relation to income. India is the world’s largest producer of cotton. Cotton crops are affected when leaves fall off early or become afflicted with diseases. Farmers and planting experts, on the other hand, have faced numerous concerns and ongoing agricultural obstacles for millennia, including much cotton disease. Because severe cotton disease can result in no grain harvest, a rapid, efficient, less expensive and reliable approach for detecting cotton illnesses is widely wanted in the agricultural information area. Deep learning method is used to solve the issue because it will perform exceptionally well in image processing and classification problems. The network was built using a combination of the benefits of both the ResNet pre-trained on ImageNet and the Xception component, and this technique outperforms other state-of-the-art techniques. Every convolution layer with in dense block is tiny, so each convolution kernel is still in charge of learning the tiniest details. The deep convolution neural networks for the detection of plant leaf diseases contemplate utilising a pre-trained model acquired from usual enormous datasets, and then applying it to a specific task educated with their own data. The experimental results show that for ResNet-50, a training accuracy of 0.95 and validation accuracy of 0.98 is obtained whereas training loss of 0.33 and validation loss of 0.5.

Turn taking is not restricted by task specialisation but does not facilitate equality in offspring provisioning

Sexual conflict arises when two individuals invest in their common offspring because both individuals benefit when their partner invests more. Conditional cooperation is a theoretical concept that could resolve this conflict. Here, parents are thought to motivate each other to contribute to provisioning visits by following the rules of turn taking, which results in equal and efficient investment. However, parents have other tasks besides provisioning, which might hinder taking turns. To investigate restrictions by other care tasks and whether turn taking can be used to match investment, we manipulated brooding duration in female blue tits (Cyanistes caeruleus) during the early nestling phase by changing nest box temperature. As expected, females subjected to cold conditions brooded longer than females under warm conditions. Yet, contrary to our prediction, females had similar visit rates in both treatments, which suggests that females in the cold treatment invested more overall. In addition, the females’ turn taking level was higher in the more demanding cold condition (and the calculated randomised turn taking levels of females did not differ), hence females don’t seem to be restricted in their turn taking strategy by other care tasks. However, males did not seem to match the females’ turn taking levels because they did not adjust their visit rates. Thus, level of turn taking was not restricted by an other sex-specific task in females and did not facilitate a greater investment by their male partners.

Determining the Composition of a Mixed Material with Synthetic Data

Determining the composition of a mixed material is an open problem that has attracted the interest of researchers in many fields. In our recent work, we proposed a novel approach to determine the composition of a mixed material using convolutional neural networks (CNNs). In machine learning, a model “learns” a specific task for which it is designed through data. Hence, obtaining a dataset of mixed materials is required to develop CNNs for the task of estimating the composition. However, the proposed method instead creates the synthetic data of mixed materials generated from using only images of pure materials present in those mixtures. Thus, it eliminates the prohibitive cost and tedious process of collecting images of mixed materials. The motivation for this study is to provide mathematical details of the proposed approach in addition to extensive experiments and analyses. We examine the approach on two datasets to demonstrate the ease of extending the proposed approach to any mixtures. We perform experiments to demonstrate that the proposed approach can accurately determine the presence of the materials, and sufficiently estimate the precise composition of a mixed material. Moreover, we provide analyses to strengthen the validation and benefits of the proposed approach.