Reviewerly/exHarmony huggingface dataset:
Maintaining Topic Models for Growing Corpora

A reference library can be described as a corpus of an individual composition of documents. Over time, the corpus might grow because an agent decides to extend its corpus with additional documents, e.g., new publications, or new articles. Existing approaches use topic modelling techniques to compare documents with each other within the same corpus by the documents' topic distribution. However, for new documents, only the text, and no topic distribution is available. Thus, this paper describes three techniques for estimating topic distributions of new unseen documents considering the initial documents in a corpus. Additionally, we present an extensive evaluation about the performance and runtime of the three topic modelling techniques for various scenarios and different sized corpora.
Recommended Practices for Python Pedagogy in Graduate Data Science Courses

This Research to Practice Full Paper discusses experiences and recommendations highlighting best practices for teaching Python <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> in data science at higher education institutions. Python has become the language of choice for teaching data science at both graduate and undergraduate programs. From data preprocessing, pattern extraction, predictive modeling, to visualization, the language has important libraries and tools to achieve this seamlessly. In this paper, the pedagogical approaches and learning objectives achieved by a cohort of graduate students specific to using Python at all stages of the KDD (Knowledge Discovery in Databases) life cycle are highlighted. The need to do this is motivated by describing an undergraduate data mining course taught at a STEM program in Computer Science. Teaching and research experiences to build projects are also elaborated across these groups. The paper then proposes recommended practices for teaching Python at the graduate level in similar data science programs. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Python Software Foundation, [Online]. Available: https://www.python.org/
Realization of Technology to Create a Situation of Success for a Future Teacher by Means of Theatrical Pedagogy

The article justifies relevance of principles and techniques of pedagogy of success for modern teachers, emphasizes importance of such concepts as “success”, “situation of success”, “successful” and “efficient activity” in a teacher’s personal development. The authors reveal contribution of theatrical pedagogy to formation of artistic and esthetic values required for optimization of communication concerning successful pedagogical activity. Technology to create a situation of success for a future teacher is presented in the form of a master-class whose participants submerge into “atmosphere of success” by means of theatrical pedagogy.
Time Capsule Gift with Affective Awareness of Event Memories via Near Field Communication

In this work, we adopt the Near Field Communication (NFC) function on a smart phone, and affective awareness by the deep neural network to build the time capsule gift application in which a gift giver and a recipient are involved. The proposed application system consists of the cloud system at the server and the application (APP) software at the edge. The gift giver at the edge employs a smart phone to tap the NFC tag of a gift, and thus to download and activate the APP which guides the user to upload the photo or video accompanied with the greeting words to the cloud system. Our cloud system applies the deep neural network to identify the subject emotion of the uploaded photo or video, and provides the recognition result back to the APP for generating the recommended stickers and background music which the user may include to enrich the greeting message. The gift recipient can also use the smart phone to read the NFC tag of the present to obtain the URL, and then to open the web browser to watch the greeting message. Particularly, the proposed deep neural network for emotion recognition of subjects exhibits fairly good performance as compared to the conventional one. Therefore, the proposed time capsule gift application can considerately embed and effectively access the event memory in a gift to prosperously promote various commercial services.
Exploring Staff Perception of InfoSec Policy Compliance: Palestine Universities Empirical Study

There is worldwide recognition of the problems associated with insider threats, intentional and unintentional; One of the significant issues in mitigating InfoSec risks is Staff compliance intention of InfoSec policies (ISP`s). The study purpose seeks to understand and explore Staff compliance intention of ISP's based on how Staff perceive several factors adopted from multi-theories from ISP's research. Such as Sanction from General Deterrence Theory, Rewards as extrinsic motivation, Coping Appraisal from Protection Motivation Theory, and, Information Reinforcement that constitute the theoretical model. In the context of Palestine universities, the research Model first part constitutes the understanding of Staff awareness of ISP's based on four selected Policy-Focused Areas (PFA) from SANS 2014. Moreover, to assess this knowledge to reflect on the multi-theory perception factors effects on Staff compliance intention. Furthermore, to investigated Staff SETA to assess how universities value their Staff knowledge on ISP's. The research Methodology explores the problem empirically using descriptive analysis research design such as summaries and regression analysis. The study has 600 datasets collected from six universities by the developed research quantitative instrument. The findings show that Staff value the importance of the model factors in promoting Staff compliance intention. Staff have some awareness of PFA's and SETA needs extra attention. The Practical implications for creating ISP compliance among university Staff by drawing on their perception of factors adopted from multi-theories. Consequently, as for the organisation, effective SETA programs to sustain the effort of Staff compliance intention of ISP's. Meanwhile, this study seeks to assert that ISP's compliance is the responsibility of all academic and administrative Staff from all department and as a paradigm for other employees. The research covers ISP perception among Palestine Staff, and the expected outcome includes - a novel theoretical contribution model with six significantly contributing factors. Also, PFA in the context of Palestine contributes as a methodological contribution and Staff SETA as practical research contribution.
A Comparative Study of Spark Schedulers' Performance

Big data applications have become an integral part of many intelligent systems, enabling better business decision making, by extracting useful information from historical data. This involves a large amount of computational tasks and algorithms. In such scenarios, the time taken by the processing become an important concern, and needs to be optimized. Apache Spark is a high speed big data analytics engine, best known for its fast computation speeds, due to the use of in-memory data structures and its efficient schedulers. Further optimization to enhance the speed of Spark jobs will be valuable. In this paper, the efficiencies of the four standard schedulers of Apache Spark i.e., Standalone Scheduler, Mesos, YARN(Yet Another Resource Negotiator) and the recently introduced Kubernetes Scheduler are compared. The speeds of these schedulers are compared for jobs of different sizes, and the best alternative is identified.
Sentiment Analysis on Bengali Text using Lexicon Based Approach

In this modern era, we daily involve in the internet strongly. We express our opinion about products, services, books, movies, songs, politics, sports, organizations, etc. through the internet in social media, blogs, micro-blogging websites or any media. Public opinion with Bengali text in internet media is increasing very rapidly. Due to a few works in Bengali text sentiment analysis, it has become an important issue of extracting opinions, emotions from Bengali textual data through Sentiment Analysis (SA) for better knowledge extraction. Sentiment Analysis (SA) is effectively used for classifying the opinion expressed in a text according to its polarity (e.g., positive, negative or neutral). This paper represents a lexicon dictionary-based approach for polarity detection of Bengali text data. We compared our proposed model with machine learning classifiers such as Decision Tree (DT), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers and it works as a much better accurate model for Bengali text polarity detection.
Meeting Users Where They Are

Campus portals are one of the most visible and frequently used online spaces for students, offering one-stop access to key services for learning and academic self-management. This case study reports how instruction librarians at the University of Vermont collaborated with portal developers in the registrar’s office to develop high-impact, point-of-need content for a dedicated “Library” page. This content was then created in LibGuides and published using the Application Programming Interfaces (APIs) for LibGuides boxes. Initial usage data and analytics show that traffic to the libraries’ portal page has been substantially and consistently higher than expected. The next phase for the project will be the creation of customized library content that is responsive to the student’s user profile.
Changes in society and language

This study addresses how societal and linguistic changes can be detected using historical corpora, with the topics of poverty and industrial revolution as a case study, based on large historical corpora, in particular EEBO, and CLMET3.0. The results, based on a rich array of state-of-the art statistical approaches (such as kernel density estimation), show how poverty, industrial revolution, and urbanization are associated through, for instance, the associations of war, religion, family, poverty, and suffering. The study also discusses the importance of data size and cleanness, the temptations of distant reading, and the necessity for validating the discovered patterns in close reading and distant reading in interaction.
Network Traffic Outlier Detection Based on Full Convolutional Network

Network traffic flow can be represented as time series. Outliers of these time series is important for the computer network security. The existing methods for the time series anomaly detection mainly operate on the entire time series to detect the abnormal series, seldom find out the position of the abnormal point. To solve this defect, we propose a method to discover the location of the outliers in time series based on a full convolutional network. We first map the time series into two-dimensional grayscale images by sliding window approach, and then feed them into the full convolutional network. In the classified stage, the feature maps of the last convolution layer are restored to the same size as the input image by up-sampling. Finally, we predict the class for each pixel and calculate the loss on a pixel-by-pixel basis. We tested the model on Yahoo Webscope S5 anomaly benchmark dataset, and the results show that our proposed method can achieve a mean pixel accuracy of 99.4% and mean IOU of 97% on the test dataset.
Measures of Nonlinearity and non-Gaussianity in Orbital Uncertainty Propagation

Orbit uncertainty propagation (OUP) is an important tracking problem appearing in space situational awareness. The uncertainty, which is initially approximately Gaussian, is transformed by the time propagation and eventually becomes non-Gaussian. Gaussian representation of the uncertainty becomes gradually inaccurate, which is often addressed by splitting the Gaussian representation into a mixture of Gaussian densities (GM), which can describe the uncertainty with arbitrary accuracy. Measures of nonlinearity (MoNL) and non-Gaussianity (MoNG) assess the degree of the model nonlinearity around a working point and thus they pose a convenient means to indicate time instants suitable for the splitting. The paper provides an analysis of several MoNLs and MoNGs with a special focus on their behavior in the OUP from the numerical, theoretical, and practical points of view. Based on the analysis, measures eligible for governing the splitting in the OUP based on GM are recommended.
Massive Power Information Processing Scheme Based on MongoDB

Abstract In order to solve the problem that the power consumption data presents a trend of sea quantification, and the huge power consumption data brings tremendous pressure to the system, especially slows down the speed of data query and gives users poor experience in using it, this paper proposes to use one of the non-relational databases (NoSQL) to store power consumption data instead of the traditional relational databases. To solve the problem that the collected electricity data can not meet the demand of page display, Hadoop MapReduce is proposed to preprocess the electricity data. Distributed architecture is adopted for storage and processing. MongoDB cluster and Hadoop cluster are overlapped and deployed. Combined with MongoDB’s powerful storage capacity and Hadoop MapReduce’s analytical and computational capability, a set of high-availability and high-performance data storage and pre-processing scheme for power-consuming universities is constructed.
A Color Image Watermarking Scheme Employing the Features of Directive Contrast in the DWT-SVD Domain

With the massive expansion of image security for day to day purposes, image watermarking has become a critical factor regarding protection schemes. Here, we proposed a watermarking plan where we created a secondary image using directive contrast of the DWT (Discrete Wavelet Transform) subbands from the host image and used this secondary image for watermark embedding using DWT-SVD transformation. The horizontal subband was decomposed up to 2-levels during insertion of the watermark into the cover image to achieve better performance against noise attacks. The obtained results depict that our scheme can resist a number of signal processing attacks maintaining good perceptual quality with PSNR above 50 dB and NC more than 0.99. The evaluations of our process justify both of the imperceptibility to human visual system and robustness against noise attacks which are the prime aspects of image watermarking.
Teaching L1 and L2 communication skills with a robotic head

Robot assisted language learning (RALL) includes various challenges as well as potentials for teaching communication skills both in L1 and L2. Previous research shows still a dearth of studies in this new area and the existing studies tend to focus on L2 vocabulary teaching and the use of the NAO robots. In this paper we outline the approaches, and describe a proof of concept, of how the social robotic head Furhat might be used in assisting human teachers in improving communication skills in second language acquisition as well as in the native language with people of various communication handicaps.
Noise Removal of Depth Sensor Using Particle Filter

Recently, with the advent of depth sensors, various kinds of 3D object recognition tasks based on depth images and point clouds have been achieved. However, inexpensive sensors, such as the Kinect, often make accurate measurement difficult owing to the influence of noise. Moreover, in situations where high-accuracy measurements are required, expensive sensors must be selected, which restrict their practicality. Therefore, in this research, we propose a method to achieve high-precision depth measurements with information from a depth sensor mixed with multimodal noise distribution using a particle filter. Upon applying this method in a verification simulation with a real KinectV2 sensor, the accuracy could be improved to the resolution of a single pixel. The results proved the effectiveness of the proposed method into facilitating high-precision measurements with an inexpensive sensor.
Three-Dimensional Human Motion Simulation and Video Analysis System for Physical Training

Introducing digital graphics and image technology into sports training to develop a three-dimensional human movement simulation and video analysis system for sports training, the system provides scientific and technological guarantee for effective sports training. In this paper, firstly, we introduce the background and significance of the development system, and then from video analysis. The key functions of the system are summarized in two aspects, including 3D motion simulation. Finally, the key problems and solutions needed to achieve these functions are explained in detail, including video motion human body extraction and tracking, 3D human motion simulation and simulation.
Pooling Tweets by Fine-Grained Emotions to Uncover Topic Trends in Social Media

In this paper, we present a lexicon-based sentiment analysis method that is used as an annotation scheme for identifying fine-grained emotions in social media topics. This methodology is based on Plutchik&#x0027;s wheel of emotion and Latent Dirichlet Allocation (LDA). We firstly annotate a tweet based on eight basic emotions and secondly we compute further eight dyads as a product of the basic emotions. We demonstrate that this lexicon-based approach achieves up to 78.53&#x0025; ground truth accuracy when compared to human annotated data that is split into positive and negative polarities. Moreover, we investigate a novel means to identify trending topics in twitter data by utilizing LDA and focusing on fine-grained emotions associated with each tweet. We compare the most dominant emotions in social media as topics from an emotion-document pooling strategy and compare the results to an author-topic modeling strategy.
A 2020 perspective on “Client risk informedness in brokered cloud services: An experimental pricing study”

Cloud computing and the cloud services market have advanced in the past ten years. Cloud services now include most information technology (IT) services from fundamental computing services to more cutting- edge artificial intelligence (AI) services. Accordingly, opportunities have emerged for research on the design of new market features to improve the cloud services market to benefit providers and users. Based on our observation of the recent development of cloud services, in this short research commentary, we share our agenda for future studies of this important sector of IT services.
FDNN: Feature-based Deep Neural Network Model for Anomaly Detection of KPIs

Anomaly detection of KPIs (key performance indicators) has been widely applied to guarantee systems stability in real world. KPIs include response time of Web pages, CPU utilization, memory utilization, disk IO and so on. However, time series of different KPIs have different shapes, so that it is a great challenge to detect anomaly of KPIs by a simple statistical or machine learning model. In this paper, we design and implement FDNN (Feature-based Deep Neural Network) model for anomaly detection of KPIs. We present a novel feature engineering approach called MSWFeature (multiple sliding windows feature) which is more suitable to extract temporal feature for time series of KPIs. FDNN model with MSWFeature achieves good performance in F1-Score over other supervised models for anomaly detection on the studied KPIs dataset collected by the top global internet companies.
Delving into the Methods of Coverless Image Steganography

Conventional cover-based image steganography methods embed secret information by modifying the original state of a cover image. This type of algorithm leaves a trace of changes on output stego image and eventually leads to successful detection by common steganalysis tools. As a solution, a coverless image steganographic method is proposed, where no cover image is required for embedding secret information. In this paper, the conventional coverless image steganography methods are first reviewed and categorized into constructive and nonconstructive-based methods. Next, these methods are summarized and analyzed, followed by a discussion about their advantages and drawbacks. Finally, the performances of the proposed methods are discussed using the common steganography evaluation metrics, including resistance to attack, embedding capacity, and perceptual image quality.
The Application of Virtual Reality Technology in Sports Club Training and Teaching

Virtual reality technology (VR), as a representative of high-tech products in the Internet era, has become a hot emerging technology in recent years with the continuous breakthrough of its technology, especially with the development of computer and sensor technology. The application field is wide, and the combination of VR and sports training has also been studied. However, VR+ sports is still in the development stage, which makes the development of VR+ sports in China still full of challenges. One of the specific application directions of VR+ sports is to combine it with sports training and teaching, and to achieve the purpose of effective and repeated training by developing personalized training programs for trainers. This paper mainly USES the case analysis method to analyze the specific cases of VR+ sports, and obtains the teaching advantages of implementing VR+ sports from the analysis of the specific cases. At the same time, it combines the questionnaire survey method to analyze the application status of VR technology in sports. This paper believes that the combination of VR technology and sports training is necessary. In addition, according to the teaching advantages of VR and its application status, the problems of VR technology in sports club training and teaching were found, and the feasible Suggestions for developing VR+ sports were put forward to improve the development level of sports club training and teaching.
Research Publication Trends in Software Engineering

There has been outstanding growth in the field of Software Engineering. With this emergence, scholars of this area worked hard to produce researches that would really be effective in the progress of Software Engineering. New researchers always face number of problems while initiating any work. Lack of proper guideline and being progressing field, it becomes crucial to provide such detailed and up-to-date information about latest research publication trends in Software Engineering. This research has been conducted in order to address this problem and help the researcher community by providing them guidance. While conducting this study, about 57 research articles published only in 2018 have been considered from renowned Journal of Software Engineering. On the basis of title, abstract, content, contribution form, area, occurrences and citation, those articles are evaluated. Results show that software testing is on hype while comparing with development, maintenance, refactoring and management. MS scholars and PhD students surely get advantage after getting such information. This study is more focused on providing guidelines so that scholar community can emerge in a better way.