Reviewerly/exHarmony huggingface dataset:
Sarcasm Identification and Detection in Conversion Context using BERT

Sarcasm analysis in user conversion text is automatic detection of any irony, insult, hurting, painful, caustic, humour, vulgarity that degrades an individual. It is helpful in the field of sentimental analysis and cyberbullying. As an immense growth of social media, sarcasm analysis helps to avoid insult, hurts and humour to affect someone. In this paper, we present traditional machine learning approaches, deep learning approach (LSTM -RNN) and BERT (Bidirectional Encoder Representations from Transformers) for identifying sarcasm. We have used the approaches to build the model, to identify and categorize how much conversion context or response is needed for sarcasm detection and evaluated on the two social media forums that is twitter conversation dataset and reddit conversion dataset. We compare the performance based on the approaches and obtained the best F1 scores as 0.722, 0.679 for the twitter forums and reddit forums respectively.
Water Utility Communications Can Build Trust During the <scp>COVID</scp>‐19 Pandemic

In April 2020, AWWA hosted a webinar on how utilities can be a trusted source of information during the COVID-19 pandemic. Following a communications plan and using a spokesperson, water utilities should focus on simple messages that use basic terminology. Utilities should express empathy, focus on facts, and be transparent to build and reinforce public trust during a crisis.
Calibration of the U.S. Geological Survey National Crustal Model

The U.S. Geological Survey National Crustal Model (NCM) is being developed to include spatially varying estimates of site response in seismic hazard assessments.Primary outputs of the NCM are continuous velocity and density profiles from the Earth's surface to the mantle transition zone at 410-kilometer (km) depth for each location on a 1-km grid across the conterminous United States.Datasets used to produce the NCM may have a resolution of better than 1 km near the Earth's surface in some regions, but, with increasing depth, NCM resolution decreases to tens to hundreds of kilometers in the mantle.Basic subsurface information is provided by the NCM geologic framework, thermal model, and petrologic and mineral physics database.In this report, the velocities and densities that can be extracted from the NCM are calibrated through the development of a porosity model based on Biot-Gassmann theory and more than 2,000 compressional-and (or) shear-wave velocity profiles less than 10 km deep from across the conterminous United States and southwestern Canada.Sediment and rock porosities are derived from shear-wave velocity and are found to depend on effective pressure, rock type, and age (for sedimentary and extrusive volcanic deposits).Porosity-effective pressure functions are then estimated for each rock type (and age for sedimentary and extrusive volcanic deposits).Unconsolidated sediments are found to have higher porosities than consolidated units, which have higher porosities than unweathered igneous units; young sedimentary units (for example, Quaternary age units) tend to have higher porosities than older sedimentary units (for example, pre-Cenozoic age units); porosity decreases with increasing effective pressure; and porosities can decrease quickly through the weathered layer of intrusive rocks.Comparing two Los Angeles area velocity models and the U.S. Geological Survey Bay Area velocity model with the NCM, the NCM does a better job on average of reproducing observed shear-wave velocities below 1 km per second because it has less bias and uncertainty.Approaching and above 1 km per second, the NCM tends to underpredict observed shear-wave velocity.Whereas several factors could contribute to this, the primary factor is probably bias in the NCM geologic framework.For example, the NCM will predict lower velocities in places where the depth to bedrock and basement appear shallower in the measured velocity profiles than specified in the NCM geologic framework.With regard to observed compressional-wave velocity and density, the NCM has significantly less bias than California models for the former, especially below 2 km per second, and all models tend to overpredict density for densities less than about 2,200 kilograms per cubic meter.
Examining Technology for Teaching and Learning

Much time, money, and attention has been given to the issue of technology in the K-12 classroom. In addition, there is an ever-expanding interest in preparing students for success in a transforming, technology-rich world. In the 21st century technology and education must work together to secure the future for students and society. Society in the 21st century is knowledge-based; learning is critical as knowledge resiliency continually becomes important for survival in the rapidly changing environment. The need for changing traditional approaches to education from the acquisition of short-term skills to proactive programs that infuses technology into the learning are of utmost importance as educators prepare students for the 21st century.
THE SURVEY OF HEIS PERFORMANCE AS A DATA SOURCE ON HIGHER EDUCATION IN RUSSIA

Annual Survey of Performance of Higher Education Institutions, conducted in Russiasince 2012, is the main source of open-access information on Russian universities. The discussion on the indicators of the Survey mainly focuses on their applicability for assessing higher education institutions (HEIs). The Survey, however, is not observed as a possible source of data for researchers in higher education. To remedy this deficiency, this paper evaluates the Survey data in terms of their quality and applicability for statistical analysis. The quality of the data is measured in four dimensions: accuracy, timeliness, completeness, and consistency. The technical convenience of the data is evaluated through the analysis of the variables distribution. The conclusion contains recommendations for researchers, who plan to use the Survey data for studying Russian higher education.
The 3D annotated scans method: a new approach to ship timber recording

Abstract Every year, maritime archaeologists around the globe are faced with the daunting task of recording hundreds, if not thousands, of individual ship timbers. The in-depth recording of ship timbers is a process which is both challenging and laborious, but it is an indispensable step in order to fully understand the construction of the ship the timbers formed a part of, and as archaeologists it is our duty to document all the information these complex artefacts hold to the best of our abilities. In this article, the authors first provide an overview of the methods currently in use for ship timber recording, namely 2D scaled drawing, 2D tracing, 3D contact digitising and 3D scanning. The respective advantages and limitations of these methods are then discussed in light of the various scientific and practical considerations that go into choosing a recording method for a project. Next, a new approach to ship timber recording, termed the “3D annotated scans method”, is introduced and discussed using a recent case study in northern Germany. At its core the method consists of two phases; a 3D scanning phase in which the timber is scanned in 3D, followed by a 3D annotation phase in which the recorder interprets the timber by tracing diagnostic features such as fasteners and toolmarks directly onto the timber’s digital model. The authors conclude that this new approach represents an improvement over current methods—both in terms of the quality of the scientific outputs and in terms of recording efficiency—and that, besides being implemented for the recording of ship timbers, the same method can also be used for a wide range of other heritage applications.
INNOVATIVE ACTIVITY CONCEPT “DESIGN UNIVERSITY” IN THE HIGHER EDUCATION SYSTEM

Иванова Татьяна Николаевна, доктор
System for monitoring parameters of functioning infrastructure objects and their external environment

The paper addresses relevant issues applying the concept of Industry 4.0 in related to modeling infrastructure objects at the Baikal natural territory that use environmentally friendly technologies. In particular, the use of heat pumps belongs to such technologies, since this enables us to reduce air emissions. Object models are designed on the basis of their digital twins. Digital twins are intended to reflect the structure and processes of object functioning. In addition, we plan to delegate them the decision-making in managing these objects in real-time. Such a digital twin has to become smarter over time. This virtual entity has to gain knowledge and skills to select optimal scenarios for controlling object and improving its functioning parameters. Therefore, the initial problem in its development is creating a monitoring system for the collection, unification, aggregation, storage, and transmission of subject-oriented data. Such data include information about the object operation and environmental state. The data must be promptly obtained from peripheral equipment (controlling and measuring devices). For the effective operation of a digital twin, we have to partially transfer functions of primary data processing, their intellectual analysis and decision-making to the controlling and measuring devices. To this end, we use agents that implement software on peripheral equipment.
E-Learning Utilizing Schoology: Teaching Theory of Relativity Using Online

Physics is not only learning through the presentation of concepts or formulas, but many events can be understood through various learning media. Evidently, students better understand the material if they can interact directly with the topic of the lesson. In addition, the rapid internet makes teachers are required to actively develop learning media. This research aims to develop physics learning using E-Learning learning media on the Special Theory of Relativity material for high school students in class XII. This study uses research and development methods that refer to the ADDIE model. This research is limited to making applications. Based on the results of research, this development has produced products that function well and present features that support student learning. Available features include quizzes, learning videos, materials and simulations that students can access. E-learning developed using Schoology can support learning through the online platform. E-learning using Schoology can display the stages of stimulus that can be determined by educators when using this e-learning. Therefore, e-learning learning that utilizes Schoology on relativity material can be designed and developed according to the needs of teachers and students in the learning process in class.
Classifying Limited Resource Data Using Semi-supervised SVM

The success of machine learning models is subjected to the availability of sufficient amounts of training data.Avail-ability of large amounts of labeled data is difficult especially in the domains of image, speech, video etc. Semi-supervised learning is an approach that uses additionally available unlabeled data to improve performance of a model with limited data.To gain better performance with limited training data we suggest semisupervised SVM models for EEG Signal classification and image classification tasks.We explore multiple approaches to semi-supervised learning based on Support vector machines: Semi-supervised Support vector machines (S 3 VM), S 3 VM light , SVM-light and label switching based Support Vector Machine (Lap-SVM) for different tasks.Our experiments show that semi-supervised approaches when trained with sufficient unlabeled data can significantly improve performance of the model when compared with its counterpart supervised model.The proposed models are verified on 3 different benchmark data sets.Proposed semi-supervised approach for image classification task show a remarkable 20% improvement over baseline SVM model.
The Implementation of an English Word Learning System Feedback System and Smartphone App

Considering the issue of Chinese college students that are not interested in learning English and have a lack of enthusiasm for learning overall, a feedback and motivation application based on cloud computing to teach English vocabulary to Chinese college students has been developed. The mobile application can collect and analyze the problems that users encounter in the process of learning English through the cloud server in real-time, and advise on how to resolve these problems. The feedback system also allows users to access this data. In the case of uploading to designated teachers, teachers can adjust their teaching methods and means through the cloud server in real-time to maximize the quality of teaching and achieve the purpose of monitoring teaching. The system uses a cloud service teaching assistant system;therefore the teaching feedback incentive system is not limited by space, time or place, and is also easy to use. During the development of the mobile application, the difficulty of keeping students interested in learning languages is taken into consideration. Attention must be paid to the learning of English words, assisting in the phonetic training system, and the use of animation in stimulating students’ interest in learning. The program can effectively improve students’ interest in learning, and the feedback obtained from this method of teaching can also help teachers and students in the future.
Pengembangan Sistem Informasi Seminar dan Skripsi Mahasiswa

Thesis is the final stage in the lecture process, but there is not yet an adequate system to record the implementation of the thesis at IKIP PGRI Pontianak. This study aims to develop, test the feasibility, and see the user's response from the Seminar and Thesis Information System. The development model used in the development of Seminar, and Thesis Information Systems is ADDIE with research instruments in the form of interviews and questionnaires. Data analysis techniques use descriptive statistics to define the process of system development, system feasibility, and user response to the system. Seminar and Thesis Information System were developed using HTML, PHP, MySQL and the Bootstrap framework. Users of Seminar and Thesis Information Systems consist of five actors, namely general users, students, examination staff, scheduling staff, and admins. The test results from Information system experts and response from users indicate that Seminar and Thesis Information Systems are in the very good category so that the information system that has been developed can be applied to record the thesis in a real situation in IKIP PGRI Pontianak.
A Novel Method of Radar Emitter Identification Based on the Coherent Feature

To deal with the problem of reliability degradation of radar emitter identification (REID) based on the traditional five parameters in a complex electromagnetic environment, a new feature extraction method based on the autocorrelation function of coherent signals, which makes full use of the coherent characteristic of modern radar emitters, is proposed in this paper. The main idea of this paper is utilizing the instantaneous autocorrelation function to obtain the correlation results of coherent and noncoherent signals. To this end, a new feature parameter, named the ratio of the secondary peak value to the main peak value (SMR), is defined to describe the difference of correlation results between coherent and noncoherent signals. Through simulation analysis, the feasibility of using SMR as the coherent feature for REID is verified. In order to evaluate the effectiveness of the coherent feature, an analytical hierarchy process (AHP) was introduced to compare the comprehensive performance of the coherent feature and the existing parameters, and then convolution neural network (CNN) and support vector machine (SVM) were selected as the classifier to check the recognition capability of the proposed feature. Simulation results show that the proposed feature can not only be used as a new feature for REID but can also be used as a supplement to existing feature parameters to improve the accuracy of REID as it is more insensitive to the signal-to-noise ratio (SNR) and signal modulation type changes.
Performance and Security Challenges Digital Rights Management (DRM) Approaches Using Fog Computing for Data Provenance: A Survey

Over the past few years, the cloud based storage providers such as Google drive, OneDrive, Box and Dropbox are increasingly being used to store users personal profile based data, which users could share over cloud storage devices. Immediately, the users data leaves the user premises and enters the cloud storage, the user has lost all the control over its data, and even when that data is further shared for the purposes of collaboration. Digital rights management (DRM) for Content hosted in Cloud based storage covers technologies related devices that protects the digital assets stored in cloud by enforcing the permission regulator, and when the data is in collaboration. This is important to protect healthcare (Fiaz, M., et al., 2019. Social media news classification in healthcare communication. Journal of Medical Imaging and Health Informatics, 9 (6), pp.1215–1223; Irfan, R., et al., 2019. Ontology learning in text mining for handling big data in healthcare systems. Journal of Medical Imaging and Health Informatics, 9 (4), pp.649–661; Ikram Ullah, L. and Ali Shariq, I., 2019. A special section on effectiveness of search engine and social networking data in healthcare systems. Journal of Medical Imaging and Health Informatics, 9 (4), pp.647–648; Preethi, P. and Asokan, R., 2019. A high secure medical image storing and sharing in cloud environment using hex code cryptography methodsecure genius. Journal of Medical Imaging and Health Informatics, 9 (7), pp.1337–1345; Shahzad, B., et al., 2017. Finding healthcare issues with search engine queries and social network data. International Journal of Semantic Web and Information System, 13 (1), pp.48–62; Saleem, K., et al., 2015. Human-oriented design of secure machine-to-machine communication system for e-Healthcare society. Computers in Human Behaviour, 51 , pp.977–985.) data from being misused by authorized and unauthorized entities. To implement DRM in cloud, these Authors have proposed encryption based solution. Due to the distributed nature of cloud it is suitable for the real time data transportation. However, DRM process requires performance that is close to real time. This paper summarized the current state of the research in Digital Rights Management and leveraged Fog computing in DRM. This is to meet the new performance requirements for next-generation distribution networks, in terms of performance metrics, such as energy consumption, traffic delay, and network throughput, as required by in large and heterogonous Cloud solutions (Preethi, P. and Asokan, R., 2019. A high secure medical image storing and sharing in cloud environment using hex code cryptography methodsecure genius. Journal of Medical Imaging and Health Informatics, 9 (7), pp.1337–1345.) as data storage system.
Visualization and virtual environment technologies in the tasks of cosmonaut training

This paper presents original methods and approaches to implement virtual environment system for one of the International Space Station module.Common structure of such system is described, based on using modern technologies of virtual reality (VR), including VR headset Oculus Rift CV1 and Kinect device.To provide human interaction with an environment synthesized by means of computer, a model of virtual observer is introduced into threedimensional scene of space module interior.A problem of combining such observer with the operator located in real world is considered in detail.Proposed solutions can be used as a part of cosmonaut training complexes in order to orient trainees in the internal space of the module as well as to develop their skills of onboard equipment control.
Can Social Media Anti-abuse Policies Work? A Quasi-experimental Study of Online Sexist and Racist Slurs

The authors use the timing of a change in Twitter’s rules regarding abusive content to test the effectiveness of organizational policies aimed at stemming online harassment. Institutionalist theories of social control suggest that such interventions can be efficacious if they are perceived as legitimate, whereas theories of psychological reactance suggest that users may instead ratchet up aggressive behavior in response to the sanctioning authority. In a sample of 3.6 million tweets spanning one month before and one month after Twitter’s policy change, the authors find evidence of a modest positive shift in the average sentiment of tweets with slurs targeting women and/or African Americans. The authors further illustrate this trend by tracking the network spread of specific tweets and individual users. Retweeted messages are more negative than those not forwarded. These patterns suggest that organizational “anti-abuse” policies can play a role in stemming hateful speech on social media without inflaming further abuse.
Pulse propagation in a 1D array of excitable semiconductor lasers

Nonlinear pulse propagation is a major feature in continuously extended excitable systems. The persistence of this phenomenon in coupled excitable systems is expected. Here, we investigate theoretically the propagation of nonlinear pulses in a 1D array of evanescently coupled excitable semiconductor lasers. We show that the propagation of pulses is characterized by a hopping dynamics. The average pulse speed and bifurcation diagram are characterized as a function of the coupling strength between the lasers. Several instabilities are analyzed such as the onset and disappearance of pulse propagation and a spontaneous breaking of the translation symmetry. The pulse propagation modes evidenced are specific to the discrete nature of the 1D array of excitable lasers.
A Data Encryption and Fast Transmission Algorithm Based on Surveillance Video

Video surveillance is an effective way to record current events. In view of the difficulty of efficient transmission of massive surveillance video and the risk of leakage in the transmission process, a new data encryption and fast transmission algorithm is proposed in this paper. From the perspective of events, the constraints of time and space dimension is broken. First, a background and moving object extraction model is built based on video composition. Then, a strong correlation data encryption and fast transmission model is constructed to achieve efficient data compression. Finally, a data mapping mechanism is established to realize the decoding of surveillance video. Our experimental results show that the compression ratio of the proposed algorithm is more than 60% under the premise of image confidentiality.
Tuning of Classifiers to Speed-Up Detection of Pedestrians in Infrared Images

This paper presents an experimental evaluation of real-time pedestrian detection algorithms and their tuning using the proposed universal performance index. With this index, the precise choice of various parameters is possible. Moreover, we determined the best resolution of the analysis window, which is much lower than the initial window. By such means, we can speed-up the processing (i.e., reduce the classification time by 74%). There are cases in which we increased both the processing speed and the classification accuracy. We made experiments with various baseline detectors and datasets in order to confirm versatility of the proposed ideas. The analyzed classifiers are those typically applied to detection of pedestrians, namely: aggregated channel feature (ACF), deep convolutional neural network (CNN), and support vector machine (SVM). We used a suite of five precisely chosen night (and day) IR vision datasets.