Reviewerly/exHarmony huggingface dataset:
Image Segmentation to Identify Safe Landing Zones for Unmanned Aerial Vehicles

There is a marked increase in delivery services in urban areas, and with Jeff Bezos claiming that 86% of the orders that Amazon ships weigh less than 5 lbs, the time is ripe for investigation into economical methods of automating the final stage of the delivery process. With the advent of semi-autonomous drone delivery services, such as Irish startup `Manna', and Malta's `Skymax', the final step of the delivery journey remains the most difficult to automate. This paper investigates the use of simple images captured by a single RGB camera on a UAV to distinguish between safe and unsafe landing zones. We investigate semantic image segmentation frameworks as a way to identify safe landing zones and demonstrate the accuracy of lightweight models that minimise the number of sensors needed. By working with images rather than video we reduce the amount of energy needed to identify safe landing zones for a drone, without the need for human intervention.
Cost Optimization for Migration of Data in Cloud Data Centers

Cloud merchants can give intermediation and collection abilities to empower suppliers to send their virtual frameworks over various mists. In view of an incorporated examination, various creative stages have been produced for cloud merchants, for example: Repository, PCMONS, RightScale, and SpotCloud. Be that as it may, the vast majority of these stages do not give trust the board capacities to settling on trust choices. In this way, to expand the appropriation of cloud benefits, a cloud specialist ought to set up and give trust the executives ability to ease the concerns of their clients. Also, in real-world users of cloud services face vendor lock-in problem with cloud service providers. Our project aims to overcome this problem. In the proposed “Broker” scheme, we propose a cost-efficient data hosting scheme with high accessibility in heterogeneous multi-cloud with limited money-related expense and ensured accessibility.
LSB Steganography Strengthen Footprint Biometric Template

Steganography is the science of hiding secret data inside another data type as image and text. This data is known as carrier data; it lets people interconnect secretly. This suggested paper aims to design a Steganography Biometric Imaging System (SBIS). The system is constructed in a hybridization manner between image processing, steganography, and artificial intelligence techniques. During image processing techniques the system receives RGB foot-tip images and preprocesses the images to get foot-template images. Then a chain code is illustrated for personal information within the foot-template image by Least Significant Bit (LSB). Accurate recognition operation is performed by artificial bee colony optimization (ABC). The automated system was tested on a live-took about ninety RGB foot-tip images known as the cover image and clustered to nine clusters that authorized visual database. The Least Significant Bit method transforms the foot template to a stego image and is stored on a stego visual database for further use. Features database was constructed for each stego footprint template. This step converts the image to quantities data and stored in an Excel feature database file. The quantities data was used at the recognition stage to produce either a notification of rejection or acceptance. At the acceptance choice, the corresponding stego foot-tip template occurrence was retrieved, it is corresponding individual data were extracted and cluster position on the stego template visual database. Indeed, the foot-tip template is displayed. The suggested work consequence is affected by the optimum feature selection via the artificial bee colony optimization usage and clustering, which declined the complication and subsequently raised the recognition rate to 93.65 %. This rate competes out the technique over others’ techniques in the field of biometric recognition.
Peningkatan Kreatifitas dan Efektifitas Pembelajaran Daring Matematika Bagi Guru SMP di Kecamatan Pattalassang melalui ISpring dan GeoGebra

Daring mathematics study leaves pupils with troubles and anxiety after more than a year of daring learning. Furthermore, students often struggle to understand instructional materials that need graphic interpretation when learning mathematics. This activity aims to improve the competence of mathematics teachers with skills using the iSpring and GeoGebra so that they can create creative and effective learning media so that their virtual mathematics learning becomes more interesting and understandable for students. This activity is conducted daring via Zoom and live on YouTube, as well as offline meetings that are carried out with due observance of health protocols. Lectures, demonstrations on the use of iSpring and GeoGebra, as well as monitoring and evaluation are methods used in daring and offline activities. The target audience for this community service is Middle School Mathematics teachers in Pattalassang District. The results of the training showed an increase in the understanding and skills of mathematics teachers in utilizing iSpring and GeoGebra multimedia. This improvement in IT skills can contribute to increasing creativity and effectiveness in learning mathematics in the classroom.
Varia: When Trying to Resolve Aggressive Conflicts Online, is it more Effective to Employ Counter-Speech, or is it more Effective to Remove the Aggressive Content?

The terms and conditions for using social platforms and the Internet grow more complex as governments struggle to provide instruments that avoid employing censorship techniques to revoke free speech, but at the same time provide online users with a safe environment and shelter from experiencing abuse or aggressive behavior. This ethical dilemma has apparently split Europe into two contradicting schools of thought. In July 2017 a search to identify relevant publications on the topic of online moral disagreement was conducted on the “Communication and Mass Media Complete” electronic database. The objective of the search was to find articles related to the topic, published in English between 2015 and 2018 with access to the full text. This systematic literature review is the final product of the materials found.
INTEGRAÇÃO ENTRE DADOS TEXTUAIS DE PRONTUÁRIOS ELETRÔNICOS DO PACIENTE (PEPS) E TERMINOLOGIAS CLÍNICAS

Objective.Evaluate a platform for daily survey of COVID-19 signs and symptoms in health employees.The platform was developed to indicate the need of additional individual diagnostic procedures, assist institutional planning to prevent the spread of the virus and sustain the hospital operations during the pandemic.Methods.We used information from a recent meta-analysis to simulate datasets of patients with different signs, symptoms and comorbidities to evaluate machine-learning algorithms for each dataset classification.The best performing model identifying COVID-19 from other similar conditions including H1N1 and seasonal influenza was selected as the base model for developing a platform for risk assessment.Results and Conclusion.The platform was deployed for surveillance of 4,200 collaborators from a tertiary hospital on a voluntary basis, but it can be readily adapted for Coleção desafios das engenharias Engenharia de computação 3 Capítulo 1 2 other environments or populational surveillance to assist public authorities devising strategies to prevent the spread of the virus.The evaluation by laboratory data stratification showed that the platform detected a significantly higher risk among infected individuals.
Waste Segregation using CNN & IoT

Due to rapid urbanization and ever-increasing population, India requires a sophisticated waste management system. Only one-fifth of the generated waste is treated and the rest is dumped in landfill sites. Segregation of household waste reduces a lot of complexity in waste treatment plants. Usually, segregation of household waste into dry and wet categories is done manually by the workers collecting the waste. This paper describes a waste management system to segregate waste into dry waste and wet waste through automation. The automation process saves a lot of time and effort and makes it easier for waste treatment plants. The system includes a camera module, which captures the image of the waste based on input from a sensor. The image, that is stored on Raspberry Pi, is then classified used the deep learning classification model. The proposed model is a miniaturized waste management system uses IoT and Deep Learning which can be implemented in the waste treatment plants or large sized community garbage containers on a large scale. The project consists of sensors, Raspberry Pi board, Deep Learning model, model training tool—Lobe, IoT dashboard, camera module, deep learning libraries which include lobe, TensorFlow Lite and open CV and servo motor. The CNN learning model uses ResNet and MobileNet algorithms for speed, accuracy and compatibility. The model, trained using a total of 12000 image samples, is efficient in segregating waste into dry waste and wet waste.
Label Assistant: A Workflow for Assisted Data Annotation in Image Segmentation Tasks

Recent research in the field of computer vision strongly focuses on deep learning architectures to tackle image processing problems. Deep neural networks are often considered in complex image processing scenarios since traditional computer vision approaches are expensive to develop or reach their limits due to complex relations. However, a common criticism is the need for large annotated datasets to determine robust parameters. Annotating images by human experts is time-consuming, burdensome, and expensive. Thus, support is needed to simplify annotation, increase user efficiency, and annotation quality. In this paper, we propose a generic workflow to assist the annotation process and discuss methods on an abstract level. Thereby, we review the possibilities of focusing on promising samples, image pre-processing, pre-labeling, label inspection, or post-processing of annotations. In addition, we present an implementation of the proposal by means of a developed flexible and extendable software prototype nested in hybrid touchscreen/laptop device.
Data Curation in the Era of Research Infrastructures

Governments and funding bodies enact policies to set up Research Infrastructures (RIs) to integrate data from different environmental monitoring research sites. The goal of such policies is to publicly open access to research data to support research and innovation and to develop policies for sustaining RIs and the environment at large. However, little is known about the data curation practices of environmental monitoring research scientists at the origins of data. Informed by practice theories on the constitutive entanglement of the social and the material in everyday organisational work, the study zooms-in on the data curationpractices utilised by environmental monitoring research scientists to create data that is of good quality and reliable for sharing. Early findings are based on observations and semi-structured interviews of participants in environmental research sites, who collaborate on taking samples of animal and plant species in marine and terrestrial environments. We find that data curation at the origins of data are rife with challenges and opportunities in: data competence, data management and data quality practices. This poster submission concludes with implications of such practices for data governance of large-scale RIs.
SecHDFS-AWS: A Novel Approach to Design Efficient and Secure Data Storage Model Over HDFS Enabled Amazon Cloud

Big data is associated with the enormous and indistinct nature of the data generated at a tremendous rate. In this era, data gets generated from multiple sources in various formats like images, videos, audio, PDF, text documents, etc. Most organizations generate terabytes or petabytes of data. Acquiring data by any organization is merely an issue, but what these organizations do with a large amount of information is a decisive concern. The Hadoop-distributed file system (HDFS) is an open-source, reliable, and user-friendly system that plays a vital role in handling a massive volume of generated data with the least fault tolerance. The Apache Hadoop is well designed to procure and process colossal data but cannot guarantee data security when acted solely. Thus, to ensure the same, several encryption techniques have been developed and are in the developing phase that certifies the adequate security of consumer’s data claiming guaranteed protection of sensitive information. This paper deals with designing secure HDFS for efficient and secure data storage. The designed system inherits symmetric cryptographic algorithms, i.e., advanced encryption standard (AES), Rivest Cipher 6 (RC6), and Blowfish, which earns transparent end-to-end data encryption. These algorithms are tested individually, and the performance gained from all these algorithms is compared later in this paper.
Sistem Informasi Pariwisata Kabupaten Pemalang Selatan yang Berbasis Web

The web-based South Pemalang Regency tourism information system is used as a tool in delivering information on tourist and cultural areas in Pemalang Regency to people who want to know tourist areas in Pemalang Regency. The web-based Pemalang Regency tourism information system is one form of regional tourism promotion in Pemalang Regency, in order to attract tourists to visit Pemalang Regency. The research methods used include direct observation, interviews with related parties, and literature studies related to the issues raised. The application development methodology that the author uses is the waterfall model. The programming language used is Sublime Text using mysql as the database. This research is useful as a medium of information and promotion of tourism potential in Pemalang Regency, so that people will be more familiar with regional tourism places in Pemalang Regency. The more tourists who visit, the income of the area and the surrounding community will increase.
Interaction Between Human And Explanation in Explainable AI System For Cancer Detection and Preliminary Diagnosis

Nowadays, Artificial Intelligence (AI) systems are everywhere and AI helps to make decisions for us is our daily occurrence. AI provides for us from recommendations product on Amazon and video recommendations on YouTube, to tailored advertisements on Google search result pages. Even though they appear powerful in terms of results and predictions, AI algorithms suffer from transparency problem. Modern AI algorithms are complex and difficult to get the reasoning and the insight into AI algorithms’ work mechanism. However, in critical decisions that involves individual’s well-being such as disease diagnosis or prognosis, it is important to know the reasons behind such a critical decision. An emerging research area called Explainable AI (XAI) looks at how to solve this problem by providing a layer of explanation which helps end users to make sense of AI results. The overall assumption behind XAI research is that explicability can improve trust and social acceptability of AI assisted predictions. In our research, we specifically look at cancer detection and diagnosis and hypothesize that appropriately designed Explainable AI systems can improve trust in AI assisted medical predictions.
C4.5, K-Nearest Neighbor, Naïve Bayes, and Random Forest Algorithms Comparison to Predict Students’ On Time Graduation

Study program performance can be seen from the achievement of accreditation status, where one of the assessment instruments related to the graduate profile is the length of study. Graduation on time is one indicator of student’s success in obtaining a bachelor's degree and is an important attribute, because by being able to predict the period of study, universities can minimize student graduation failures by making more intensive planning, study escort, and guidance. Data mining classification techniques can be used to predict students graduation on time. Many data mining classification algorithms can be used, so it is necessary to make comparisons to determine the level of accuracy of each algorithm. The algorithms that will be compared in this study are C4.5, K-Nearest Neighbor, Naive Bayes, and Random Forest. The data used were 2,022 graduates from Informatics Engineering and Information System Study Program of STMIK Mikroskil Medan from 2011 to 2014, in which the attributes used include gender, regional origin, time of study, grade of Entrance Screening Examination, and Grade Point Average (GPA). The results of the classification process are evaluated using cross validation and confusion matrix to determine the most accurate data mining classification algorithm for predicting student graduation on time, where the K-Nearest Neighbor and Random Forest algorithms have the highest accuracy of 72,651%, followed by the C4.5 algorithm 72,453%, and the Naive Bayes algorithm 71,860%.
Bridging shock: Blackstock’s general solution for propagation of finite-amplitude sound waves

Reflections are offered on D. T. Blackstock [“Connection between the Fay and Fubini solutions for plane sound waves of finite amplitude,” J. Acoust. Soc. Am. 39, 1019–1026 (1966)]. This work treats the propagation of planar finite-amplitude sound waves. It nominally links the Fourier-series-based solution of Fubini for the pre-shock condition, and the distant, post-shock solution of Fay. However, addressing the transition region between these two required solving the entire problem, which Blackstock did with aplomb. This additionally enabled specification of undetermined factors in the Fay solution, statement of an explicit bound on the applicability of the solution, and generalization to cylindrical and spherical waves. Hallmarks of Blackstock’s work are evident: scholarly mindfulness of historical precedent, meticulous analysis that inevitably advanced understanding of nonlinear acoustic phenomena, and elegance in presentation, which was, moreover, pedagogically convincing.
Guides: 2021 First Year Writing 1120-03 Prof. Bogard: Web Sites

Library resources and services for student success with the annotated bibliography & research argument essay assignment.
Intention based Clustering of Relevant Reviews using Content Similarity

The proposed work deals with finding related reviews posted on various online Forums. Conventional methods for matching related documents compute the content similarity over the entire review instead of partitioning into segments revealing different intentions. In this work, intention-based similarity clustering is introduced to find the relatedness of two documents. This method forms the document clusters based on the similarity of the segments with similar intentions. The segmentation points are identified using a number of text features which can express when the segmentation should be done. Finally, the document clusters are formed by grouping the segments with similar intentions in same cluster and then the similarities among the segments with the same intention are computed. The proposed model is trained on TripAdvisor and Yelp Open Review datasets to evaluate the performance of the model, and the evaluation results show that the model produces more precise results in mining documents related to the user’s interest.
Simulating Turn-Taking in Conversations with Varying Interactivity

The impact of transmission delay on a telephone conversation dependsnot only on the severity of the delay, but also on the interactivity of the conversation.To model the perceived quality it is thus important to first model the interactivity ofa conversation. In this paper we propose the method of simulating a conversationwith different interactivity levels by simulating human turn-taking behavior. Wesimulate two types of conversations with different conversational interactivity withand without turn-taking. We then perform a parametric conversation analysis on theresulting conversations to show that adding a turn-taking mechanic to a simulationcreates differences in interactivity that can be seen in human conversation.
Machine learning-based analysis of COVID-19 pandemic impact on US research networks

This study explores how fallout from the changing public health policy around COVID-19 has changed how researchers access and process their science experiments. Using a combination of techniques from statistical analysis and machine learning, we conduct a retrospective analysis of historical network data for a period around the stay-at-home orders that took place in March 2020. Our analysis takes data from the entire ESnet infrastructure to explore DOE high-performance computing (HPC) resources at OLCF, ALCF, and NERSC, as well as User sites such as PNNL and JLAB. We look at detecting and quantifying changes in site activity using a combination of t-Distributed Stochastic Neighbor Embedding (t-SNE) and decision tree analysis. Our findings bring insights into the working patterns and impact on data volume movements, particularly during late-night hours and weekends.
Discussion on the Construction of Cloud Platform for Clarinet Teaching in Mobile Terminal

This paper makes a survey based on the clarinet education in music colleges. This paper analyzes the existing problems of clarinet education in music colleges, constructs a clarinet teaching mode based on the mobile terminal network, and considers the MOOC teaching mode and combining the knowledge characteristics of clarinet education. The platform will sort out the domestic clarinet education resources, complete the clarinet network education resources as much as possible, and meet the needs of College Students' artistic, personalized and diversified clarinet education.
Deep Learning-Based Real-Time Multiple-Person Action Recognition System

Recognition of human activity has attracted growing interest from researchers, mainly because of its potential in areas such as surveillance videos, robotics, interaction with human computers, user interface design, and multimedia. Since it includes details about a person's identity, personality, and psychological state, it is difficult to remove. The human ability to recognise another person's behavior is a primary subject of study in the scientific fields of computer vision and machine learning. People's actions are determined by their behaviors, which makes assessing the problem of determining the underlying action extremely difficult. The proposed system would identify and track a large number of people arriving on the scene before recognizing their behavior. Due to the improved resolution of the video frames, when persons in the scene become too far away from the camera, we build a zoom-in feature to produce more suitable action identification results.The work proposed is based on the behavior recognition model of Deep Belief Networks and Deep Boltzmann Machines.