Reviewerly/exHarmony huggingface dataset:
Digital Wellbeing Educators:A compendium of innovative practices.

We live in a highly digitally connected Europe where people of all ages use the internet to link, communicate, explore and learn through digital media. However, a recent JISC study reported that educators in Higher Education institutions are concerned about the wellbeing of their students in digital settings. In addition, the Opening Up education report shows that 70% of educators in the EU understand the importance of digital literacy but only 20-25% students are taught by digitally confident educators (Opening Up Education, 20131). Hence, improving the digital literacy of educators is required, particularly in the area of digital wellbeing. Higher Education institutions are becoming increasingly aware of the consequences of digital distraction and overload on the mental health of individual students, the societal impact on relationships such as cyberbullying, technical issues such as cybersecurity and the addictive design of technology and the controversies surrounding fake news, radicalisation and challenges to democracy. The Digital Wellbeing Educators Project (DWE), funded by the ERASMUS+ programme of the European Union, aims to increase the capacity of educators in Higher Education institutions so that they can integrate digital education and promote the wellbeing of their students. The first step towards this has been to publish a Compendium of innovative practices and open educational resources that showcases how the challenges of the digital era are addressed by others so that teachers are inspired and can find solutions that can be transferred to their students. DWE has 6 partners: Lancaster University (UK), Letterkenny Institute of Technology (IE), Universidad de Alcala (ES), eucen (BE), European Institute of E-Learning (DK) and Momentum Marketing Services (IE). The project started in September 2018 and will finish in August 2020.
#metoo - now what? - En komparativ studie mellan svenska och amerikanska arbetsgivares ansvar vid sexuella trakasserier i arbetslivet

As the hashtag #metoo went viral around the world in fall 2017, the issue of sexual harassment at work was shed a light on. The employee protection against sexual harassment was questioned in terms of employer liability regarding preventive and repressive measures. This matter is underlying the following question at issue: how sexual harassment and employer liability is regulated in employment law. The comparison between the Swedish and American regulations examines the legal systems and the requirements imposed upon an employer. This study shows both countries define sexual harassment as type of discrimination consisting of unwelcome, sexual advances. Furthermore, each country has separate requirements that need to be met in order to have a case. In addition, both countries have extensive and explicit regulations on employer responsibility. The importance of preventive and repressive measures shows clearly in the examined cases. However, some findings of the study reveal a few shortcomings, especially in regard to the preventive measures applied. These measures are nonetheless fundamental in the battle against sexual harassment at work. As of today, there is a lack of satisfactory knowledge among employers, employees and supervisors. The need of increased knowledge about sexual harassment is shown in the statutory training and policy requirements in both Sweden and the United States. Lastly, through the adoption of several new bills in response to #metoo, California is in the lead of the #metoo reforms. Therefore, the hopes are that other states, and countries, will follow their lead. (Less)
20 UMP staff conquered Mount Kinabalu

September 6, 2019 was a historical and memorable date for 20 Universiti Malaysia Pahang (UMP) staff who successfully scaled the Mount Kinabalu, the highest peak in South East Asia, standing at 4,905.2 metres (13,455 feet) from sea level. According to d’Puncak Expedition Programme Director, Mohd Shahrul Azuar, the participants who were from various faculties underwent physical and mental training by having hiking trainings to ensure that they were fit for the climb.
Semi-automatic reconstruction and documentation of software development methods

Software development is a complex and creative process. In contrast to a typical business process it tends to be more dynamic and dependent on a number of circumstances. Empirical studies show that companies still don’t document their development practices, or if they do, these are not up-to-date and do not reflect how they really develop software. On the other hand, various supporting tools such as issue tracking system, revision control system, document management system, etc. are used by developers and project managers during their work, capturing a vast body of knowledge about how a software development process has been performed. The main objective of this dissertation is to propose an approach that can help companies in documenting their real development practice. Comparing to existing approaches that require substantial effort on the side of project members, our approach extracts information on development practice directly from software repositories. Five companies have been studied to identify information that can be retrieved from software repositories. Based on this, an approach to reconstruct development practice has been developed. The approach has been evaluated on a real software repository shared by an additional company. The results confirm that software repository information suffice for the reconstruction of various aspects of development process, i.e. disciplines, activities, user roles, and artifacts.
Used Eigenvalues and Eigenvectors to Compress the Image

This article is a detailed review and explanation of the field of linear algebra in which all mathematical concepts and structures concerned with linear algebra. The main objective of the paper is to highlight the significant applications of linear algebra in the field of medical engineering. Principal component analysis is one of the most important compression and feature extraction algorithms used in engineering (Santo, 2012). It mainly depends on the calculation and extraction of eigenvalues and eigenvectors which are then used to represent an input; whether it's the image or a simple matrix. In this article, the use of principal component analysis for medical image compression is an important and novel application of linear algebra (Rajendra, 1996).
Video Streaming Transport: Measurements and Advances

Video streaming data forms the largest portion of the current Internet and is expected to rise even further. Providing and maintaining a high-quality experience for video is in the interest of both consumers and providers. However, the nature of network requirements for video traffic is different from other types of web traffic, and hence the goal requires a constant effort to advance video protocols in line with the changing Internet. This consists of not only developing new protocols and techniques to optimise the use of the Internet for video traffic but also developing meaningful video measurements that reflect user experience. In this thesis, we propose extensions and mechanisms to existing Internet protocols that enhance video quality and experience. We present results from experimentation that show how slight modifications to current protocols can be done to significantly improve the video streaming experience: (1) by introducing multiple path awareness (e.g. 3G, WiFi, Ethernet) to RTP, and (2) by using out of order delivery over TCP to improve timeliness for adaptive streaming. We also present a framework for enhancing current edge network measurements to encompass video experience using active testing while keeping the tests computationally light and easily deployable for large-scale network measurements. This thesis contributes to an evolving Internet, where video traffic has gained an equal if not higher importance to other web traffic. The large-scale video tests provide a mechanism where subscribers and Internet providers can better gauge performance from a video perspective, whereas, the work on adaptive video streaming over partially reliable and out of order transport paves the way for newer Internet protocols, which are now widely being recognised as a need of the hour in the heavily ossified and somewhat inflexible Internet.
Versatile Anomaly Detection with Outlier Preserving Distribution Mapping Autoencoders

State-of-the-art deep learning methods for outlier detection make the assumption that outliers will appear far away from inlier data in the latent space produced by distribution mapping deep networks. However, this assumption fails in practice,because the divergence penalty adopted for this purpose encourages mapping outliers into the same high-probability regions as inliers. To overcome this shortcoming,we introduce a novel deep learning outlier detection method, called Outlier Preserving Distribution Mapping Autoencoder (OP-DMA), which succeeds to map outliers to low probability regions in the latent space of an autoencoder. For this we leverage the insight that outliers are likely to have a higher reconstruction error than inliers. We thus achieve outlier-preserving distribution mapping through weighting the reconstruction error of individual points by the value of a multivariate Gaussian probability density function evaluated at those points. This weighting implies that outliers will result in an overall penalty if they are mapped to low-probability regions. We show that if the global minimum of our newly proposed loss function is achieved,then our OP-DMA maps inliers to regions with a Mahalanobis distance less than \delta, and outliers to regions past this \delta, \delta being the inverse ChiSquared CDF evaluated at 1−\alpha with \alpha the percentage of outliers in the dataset. We evaluated OP-DMA on 11 benchmark real-world datasets and compared its performance against 7 different state-of-the-art outlier detection methods, including ALOCC and MO-GAAL. Our experiments show that OP-DMA outperforms the state-of-the-art methods on 7 of the datasets, and performs second best on 3 of the remaining 4 datasets, while no other method won on more than 1 dataset.
The Last Byte: Big Data, Big Faults

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">One of the keynote</b> addresses at the just concluded International Test Conference noted that even very high fault coverage (over 99%) in billion transistor circuits will still leave hundreds of thousands of untested faults. The speaker was concerned about the quality issues due to this, and also security issues. If all these untested faults represent a block of untested logic, how can we be sure that the untested logic wasn’t inserted by some third party?
Deep learning techniques applied to constituency parsing of German

Constituent parsing attempts to extract syntactic structure from a sentence. These parsing systems are helpful in many NLP applications such as grammar checking, question answering, and information extraction. This thesis work is about implementing a constituent parser for German language using neural networks. Over the past, recurrent neural networks have been used in building a parser and also many NLP applications. In this, self-attention neural network modules are used intensively to understand sentences effectively. With multilayered self-attention networks, constituent parsing achieves 93.68% F1 score. This is improved even further by using both character and word embeddings as a representation of the input. An F1 score of 94.10% was the best achieved by constituent parser using only the dataset provided. With the help of external datasets such as German Wikipedia, pre-trained ELMo models are used along with self-attention networks achieving 95.87% F1 score.
Verified Background-Aware Filters and Re-detection Tracking Algorithms

In order to overcome the problem of the the tracking accuracy of the correlation filter is reduced when the target is occluded. We propose a validated background-aware filters and re-detection tracking algorithm to improve the robustness of target tracking. Firstly, the maximum response value and the average peak-to correlation energy are applied to judge the occlusion degree of the predicted target with the background-aware filters. Then, we used the high-confidence target that matched by SiamFC to replace the occluded target, or discarded to update the model of the low-confidence targets. The TB-50 Benchmark is used to compare the experimental results. Our proposed algorithm can obtain the success rate of 55.7% and the accuracy of 78.0%. Compared with the background-aware correlation filters algorithm, the proposed algorithm improves 4.3% and 1.1% respectively. By updating the target model in terms of validation and re-detection, the background-aware filters algorithm can achieve higher tracking success rate and accuracy. In general, the proposed algorithm shows more stable and accurate tracking performance in the case of occlusion, fast moving, background clutter, etc.
Green Accelerated Hoeffding Tree

For the past years, the main concern in machine learning had been to create highly accurate models, without considering the high computational requirements involved. Stream mining algorithms are ab ...
Design of Real-time Data Safety Monitoring System for Small Sluice

In view of the complex process of information integration and untimely data sharing during data acquisition of small sluices, it is necessary to establish a real-time data security monitoring platform, which involves solving the data transmission problem of different types of sensors and multi-measurement points. Taking a small sluice as an example, a data acquisition scheme for multi-type communication interface and the solution of real-time data monitoring for multi-measurement points are introduced. The working principle of the sensor in the monitoring system is discussed. The software function of the monitoring system is discussed. The real-time data acquisition, monitoring and management in the operation of the sluice are realized. It effectively improves the efficiency of safety monitoring and management of sluices.
LibGuides: Microforms @ Pitt: About Microforms

This is a guide for finding and using the microforms collection in the University Library System. What are microforms? Why do we use microforms? Microforms location and hours.
Solving a Case of Sexual Assault by DNA Fingerprinting: A Case Study

To solve a case of sexual assault by DNA fingerprinting.
USING SOCIAL MEDIA IN LEARNING ENGLISH AS A FOREIGN LANGUAGE: A CASE STUDY OF ENGLISH STUDY PROGRAM AT JAMBI UNIVERSITY

ABSTRACTWulandari, J. 2019. Using Social Media in Learning English as a Foreign Language: A Case Study of English Study Program in Jambi University. Thesis. English Study Program. Faculty of Teacher Training and Education of Jambi University in Academic Year 2018/2019.The First Supervisor is Dedy Kurniawan, S.S, M.A. The Second Supervisor is Lilik Ulfiati, S.Pd., M.Pd.Key words: social media, language learningThis research is a qualitative research with a case study approach aimed at knowing the social media which are mostly used in learning English by the college students of English Study Program at Jambi University, describing the college students’ ways in using the social media in learning English at Jambi University and describing how the social media help the college students to improve their learning. The participants of this research are 6 students (3 male students and 3 female students) of the fifth semester of English study program in Jambi University. The participant at least two social media among Facebook, Twitter, Instagram and Youtube, and GPA is more than 3,00. From the data that the research gets, it is concluded that the social media which are mostly used in learning English by the college students of English Study Program at Jambi University is Instagram, the college students’ ways to use the social media in learning English at Jambi University when they have spare time at home, or when the lecturer let them to use it in the class especially for the group discussion and the social media help the college student to improve their learning from the content of the social media that is more various and this encourages students to study using social media.
Segmentation of White Blood Cells Using Deep Learning

The white blood cell count and differential is an important part of diagnosing a number of medical conditions. Instead of doing this by manual microscopy, CellaVision’s technology has automated the process of finding and classifying white blood cells. To support a diagnosis it is desired that the system can produce cytoplasm-to-nucleus-ratio. This ratio is calculated from a segmented image where the pixels are labelled as background, cytoplasm, or nucleus. The system used today, using active contours, does not always produce perfect segmentations for all cells, and it would therefore be beneficial to improve the segmentation. Using machine learning, we have constructed a network for segmenting white blood cell images. This model, with some small modifications, produces both binary (cell and background) and multi-class (cytoplasm, nucleus and background) segmentations. The model is a U-net inspired by work previously done on other similar segmentation tasks. The network reached an IoU of 93.9% in the binary case, and in the muli-class case 82.8% and 94.5% for the cytoplasm and nucleus respectively. The main challenges were to separate neighbouring cells and cells in a cluster. Over all the network performed better than the active contour method in difficult images, and in cases where neither were good, the network was usually better. If the network was trained more on images that are difficult to segment, the resulting segmentations of these images could be improved. (Less)
The Effects of Using Interactive Multimedia to Increase Students’ Vocabulary at VIII Grade of SMPN 2 Jambi City in Academic Year 2019

This research was aimed to find out the effects of using interactive multimedia at the eighth grade students of SMPN 2 Jambi City in the academic year of 2019. The subject research was class VIIIB and VIIID. The instrument used in this thesis was test. In collecting the data, the researcher used pre-test. Before conducting post-test, the researcher gave treatment. After getting enough data, the researcher calculated the data using t-test. The result of this research was found the mean score of pre-test of Experiment class is 45.02 and the mean of post-test 80.48. To see whether the hypothesis is accepted or rejected, the significance value in the table above was .306, which means that the significance value is greater than 0.05, based on decision in the statistical correlation test, there is no relationship between pre-test and post-test. Paired sample t-test is used to determine whether there are difference can be seen from the sig value (2-tailed), the value in the table above is 0.000, so it can be concluded that the results of the effects of using Interactive Multimedia to increase students’ vocabulary are real significant on the pre-test and post-test data. Based on the result finding, it can be concluded that the data presented is there is an effects of using interactive multimedia towards students’ vocabulary mastery.
Motivasi Pengelola dalam Mendirikan Taman Baca Masyarakat (TBM) di Kabupaten Bandung Barat

This study aims to determine the motivation of managers in establishing community reading parks. This research was conducted at the Community Reading Park in West Bandung Regency. The method used in the study uses a qualitative approach to the type of phenomenological research. The technique of collecting data is done through observation, interviews and through library research. Based on the results of the study, it was revealed that the motives of the manager's goal in establishing TBM were to facilitate the community through TBM, increase public interest in reading and to provide facilities that could benefit the community. As for the motives for the reason for the management to establish TBM is due to lack of information or knowledge of the community, to preserve the reading culture, increase children's knowledge, to limit uncontrolled child association, high rates of illiteracy, and still lack public awareness of the importance of education.
A Study on Comparison of Lung Cancer Prediction Using Ensemble Machine Learning

Lung cancer is a chronic disease which ranks fourth in cancer incidence with 11 percent of the total cancer incidence in Korea. To deal with such...
A serious game to make learning Japanese (Katakana) easier

Learning foreign language can be difficult at later age. Although, currently there are many different type of gaming apps and online material that can help people of all ages to learn foreign language. The problems are identified when user find it difficult to learn foreign language when the application is not engaging enough and user afraid of making mistakes when learning. Therefore, this project may solve those problems by designing and developing a serious game in order to increase learner’s ability to learn Japanese language and tests its system. There are two fields that needs to be considered in language learning and teaching using with video games. The two fields are Computer-assisted Language Learning (CALL) and Digital game-based Learning (DGBL). For this project, the fields that will be used is DGBL. It is not used as an assistance but to make the learning material more fun by adding few engaging game mechanics. This project uses Rapid Application Development methodology. In conclusion, this project has developed a useful game application that can be used by students and people interested in learning Japanese language.
LSC-North Harris Library Research Guides: North Harris Library Makerspace: Retired Tech

Information about the tools and events in the LSC-North Harris Library Makerspace The following is equipment that was once cutting edge but no longer supported or has been discontinued by their manufacturer. Students are still free to explore this equipment, but the library will provide limited support for it.
Image Deblurring Using Scale-recurrent Network for Mobile Devices

Image deblurring is a problem in computer vision that aims to restore blur images into sharp images. The blurring might be caused by the camera shaking or an object moving when the image is captured, resulting in an image with a non-uniform blur in a dynamic scene. One recent approach to restoring images with non-uniform blur is by using end-to-end deep neural networks. Continuing the deblur research using a scale-recurrent network, we modify the neural network architecture to be lighter to run on mobile devices. The proposed method achieves PSNR of 29.55 and SSIM of 0.8873 in a 16.9 MB sized model. The inference process on a mobile device only requires 1 GB of memory with 8.2 seconds in latency for deblurring a single 1280x720 pixel image.
The Optimization of Resources Within the Implementation of a Big Data Solution

Every day we notice and witness the creation of a significant volumes of data from user-data generated automatically on the internet. To control a large amount of data we need powerful tools that can give us the ability to store and process big data. Moreover, a problem will arise when we start the procedure on large amounts of data in parallel because clustered computing platforms are complex and difficult environments to manage. In this article, we propose a method based on machine learning algorithms that recommends a parameter of task parallelism in big data workloads. As a solution, we propose a method based on optimizing the parallelism of tasks in clustered applications and by borrowing methods of machine learning. In order to optimize the numbers of the clusters in the implementation of a solution Big Data.