Reviewerly/exHarmony huggingface dataset:
Distributed data analytics

The advertising and associated online industries in particular have fuelled a rapid rise in the deployment of personal data collection and analytics tools. Distributed data analytics, where code and models for training and inference are distributed to the places where data is collected, has been boosted by two recent and ongoing developments: (i) increased processing power and memory capacity available in user devices at the edge of the network such as smartphones and home assistants and (ii) increased sensitivity to the highly intrusive nature of many of these devices and services and the attendant demands for improved privacy. Indeed, the potential for increased privacy is not the only benefit of distributing data analytics to the edges of the network: reducing the movement of large volumes of data can also improve energy efficiency, helping to ameliorate the ever-increasing carbon footprint of our digital infrastructure, and enable much lower latency for service interactions than is possible when services are cloud-hosted. We begin by discussing the motivations for distributing analytics and outlining the different approaches that have been taken. We then expand on ways in which analytics can be distributed to the very edges of the network, before presenting the Databox, a platform for supporting distributed analytics. We continue by discussing personalising and scaling learning on such a platform, before concluding.
The News Angler Project: Exploring the Next Generation of Journalistic Knowledge Platforms

The News Angler project aims to support journalists in finding new and unexpected connections and angles in the news. The project therefore explores how recent artificial intelligence (AI) techniques — such as knowledge graphs, natural-language processing (NLP) and machine learning (ML) — can support high-quality journalism that exploits big and open data sources. A central contribution is News Hunter, a series of prototype journalistic knowledge platforms (JKPs).
Do We Still Need Automatic Speech Recognition for Spoken Language Understanding

Spoken language understanding (SLU) tasks are usually solved by first transcribing an utterance with automatic speech recognition (ASR) and then feeding the output to a text-based model. Recent advances in self-supervised representation learning for speech data have focused on improving the ASR component. We investigate whether representation learning for speech has matured enough to replace ASR in SLU. We compare learned speech features from wav2vec 2.0, state-of-the-art ASR transcripts, and the ground truth text as input for a novel speech-based named entity recognition task, a cardiac arrest detection task on real-world emergency calls and two existing SLU benchmarks. We show that learned speech features are superior to ASR transcripts on three classification tasks. For machine translation, ASR transcripts are still the better choice. We highlight the intrinsic robustness of wav2vec 2.0 representations to out-of-vocabulary words as key to better performance.
Construction of a Blended Teaching Model of English : Viwing, Listening and Speaking based on MOOC and Rain Classroom

The in-depth integration of the Internet and education is an inevitable trend of the development of the times. Only by firmly applying the Internet technology and promoting its organic integration with education, can the university education reform in the new era be effectively realized and the ways to improve the quality of college education be explored with the help of the Internet become possible. English teaching in colleges and universities should fully embody the three basic principles of higher education: being student-centered, being outcome-based and clutlivating interdisciplinary talents. “Rain Classroom” is a convenient, practical, intelligent, and flexible educational tool created by Tsinghua University and XuetangX based on the use of WeChat platform and PowerPoint. It provides a new way for the construction of a blended teaching model of English: Viewing,Listening and Speaking. This article is based on Rain Classroom and MOOC education platform to discuss the superiority and practical design of a blended teaching model of English:Viewing, Listening and Speaking.
Teaching Accounting Courses Via Distance Learning From The Point Of View Of Accounting Instructors During The Covid 19 Pandemic

There searchaims to evaluate distance learning strategies as well as university plans for the accounting department from the viewpoint of faculty members, and the materials they are studying, During the COVID 19 pandemic. The study tries to reach the preference of professors of accounting courses for teaching through the following methods (video conference, traditional lessons, Blackboard) in accounting. And that is by submitting a questionnaire that includes two main axes, the first includes a set of demographic information about the respondents who represent professors of courses, while the second axis includes a set of paragraphs that reflect their preferences for remote teaching methods, and the quality of their outputs. The questionnaire includes items that reflect the extent to which students are better absorbed through distance learning, and which methods are best for them. The questionnaire paragraphs also measure the extent to which the distance learning method has a good quality of communication between the teacher and students. And how easy the method used for distance learning is the ease and availability anytime and anywhere. It also measures the extent of the interaction between the teacher and students. During the COVID 19 pandemic. The study sample was withdrawn from the faculty members of the Accounting Department at the College of Applied Studies and Community Service, Imam Abdulrahman bin Faisal University, Dammam, Kingdom of Saudi Arabia. A set of statistical programs was used to analyze the results of the questionnaire and arrive at the results.
COLLABORATIVE TEACHING APPROACH AND PROBLEM-BASED LEARNING FOR THE AGILE BOOOTCAMP

Many companies have been using agile methods because it gives them more flexibility in their development process. Comparing agile with traditional management approaches, agile offers many benefits such as increasing team productivity, lean development and employee satisfaction. Design thinking belongs to the family of modern agile approaches to project management and development of new products. It is based on intensive collaboration of individually working agile team members. As such it needs to be taught accordingly.At MIAS, CTU in Prague, we offer a course called Agile BootCamp. As the CTU in Prague is newly participating in the EuroTeQ Teaching Fund, the Agile BootCamp was selected as one of the courses that will be offered and taught to other universities participating in the EuroTeQ Engineering University. It is not a coincidence that authors of this article are also teachers of the Agile BootCamp course. Our decision was to use innovative approaches to both the way of teaching and study materials and the way of collaboration between students and universities during the course. This paper introduces the methods and innovative approaches the authors used as preparation for teaching the course. Our approach is based on three pillars including:(i) educational videos, textbook and working sheets mutually interconnected and used for self-education of the students, (ii) an intensive 5 day course where students work on real problems and real life assignments while guided and mentored by the teachers, and (iii) international collaboration as students of the course come from several European universities participating in the EuroTeQ Engineering University. The paper closely describes the individual pillars while presenting the innovative approaches to teaching and learning Agile.
PERANCANGAN JARINGAN WIRELESS LAN DENGAN MENGGUNAKAN ROUTER OS MIKROTIK PADA SEKOLAH DASAR NEGERI PAHONJEAN 01

The background is a problem that occurs in al-Ittihad elementary school which causes the author to take the title of this thesis. . Around the Al-Ittihad Elementary School, the network signal from ISP providers, both GSM and CDMA, is not very good so that the data access received is not optimal. This is based on the author's own experience using GSM modems, surveys and interviews with teachers and school staff at Al-Ittihad Elementary School. Also faced with the fact that the high tariff for unlimited data access from the service provider PT. Telkom (speedy) made the author look for a solution, is there another way that is cheaper and simpler to be able to use the internet easily in the scope of this al-Ittihad elementary school comfortably, safely without having to worry about banned sites.&#x0D; The purpose and objective of the research is to first recognize and understand further computer network technology, especially wireless/wireless (wireless) networks. Both members make it easy for users to access the internet. Third, knowing the technique of making / building an infrastructure and implementation. Fourth, get to know information technology in the al-Ittihad elementary school environment.&#x0D; The research method is the method used by the author in the preparation of this thesis. The author uses methods and techniques to compile and present appropriate and accurate information. Therefore, appropriate methods and techniques are needed to obtain valid and accountable data. The method used by the author is as follows, the first research location. Second Collecting data and third Designing. Implementation is discussing how to install and configure the devices used in designing the WLAN network at the Al-Ittihad elementary school in Cilacap with Mikrotik.&#x0D; The output or results achieved in the design of this WLAN (Wireless Local Area Network) is to provide convenience for teachers, students and school visitors in accessing the internet, accessing the internet is not only in the school room but can also be accessed around the school environment via a wireless network. and does not require a data cable so that it is more efficient and effective.
A REVIEW ARTICLE ON THE INTERNET OF THINGS AND WASTE MANAGEMENT

Garbage has become a major source of concern on the planet at least 2.01 billion tons of municipal solid waste isgenerated globally with almost one-third of it left untreated and prone to causing permanent ecologicalmisbalanced on the planet. Kevin Ashton has aptly quoted “The Internet of Things has the potential to changethe world, just as the internet did. Maybe even more so”. Internet of things is a revolutionary technology thatcan be brought into play to manage the-waste smartly. Smart waste management is a key factor of future smartcities. The fact that the internet of things can come to the rescue of urban local bodies across the globe efficientlyremains a major challenge.
The English-majored Students’ Practices of Mind Maps in Writing Skills

Writing is one of the two productive and four vital skills in acquiring any foreign language. Therefore, it is necessary for language educators, teachers, linguists, and learners to make use of a wide range of different methods to enhance all four English skills for language learners. The researchers have the assumption that using the mind mapping technique positively influences the improvement of this productive skill for the first-year students at the Faculty of English, University of Foreign Language Studies – The University of Danang. The purposes of this article entitled “The English-majored Students’ Practices of Mind Maps in Writing Skills” are to investigate the practices of mind maps related to the rate of occurrence, the phases in writing, and the perceptions of first-year students when taking advantage of mind maps to boost their writing skill. This research is quantitatively and qualitatively carried out, which gathered information from three research instruments: classroom observation, interviews, and questionnaires for freshmen students. The assumption of the researchers was proved to be correct since the first-year students advocated the effectiveness of mind maps in writing skills at different rates and often used mind maps during the pre-writing stage.
Imaging framework: An interoperable and extendable connector for image-related Java frameworks

The number of computer vision and image processing tasks has increased during the last years. Although Python is most of the time the first choice in this area, there are situations, where the utilization of another programming language such as Java should be preferred. For this reason, multiple Java based frameworks as e.g. OpenIMAJ, ND4J or multiple OpenCV wrappers are available. Unfortunately, these frameworks are not interoperable at all. In this work, the open-source Imaging Framework is introduced to solve exactly this problem. The project features a concept for combining multiple frameworks and provides an interoperable and extendable foundation to 9 image-related projects with 10 different image representations in Java.
Identification Probability and Pseudo-Entropy Criterion to Locate Drilling Locations

An approximate model, based on Bayesian law, for calculating identification probability of classification problem is presented. An encoding procedure for existing variables, in which the geologist’s adventurous psychology and the credibility of the original data can be considered, is discussed in order to integrate spatial data. A pseudo-entropy criterion is proposed in order to select optimally drilling locations in oil-gas exploration. A case study based on the modified data of a gasfield is included.
Comparative Study on Sentiment Analytics Using Deep Learning Techniques

Sentiment analytics is the process of applying natural language processing and methods for text-based information to define and extract subjective knowledge of the text. Natural language processing and text classifications can deal with limited corpus data and more attention has been gained by semantic texts and word embedding methods. Deep learning is a powerful method that learns different layers of representations or qualities of information and produces state-of-the-art prediction results. In different applications of sentiment analytics, deep learning methods are used at the sentence, document, and aspect levels. This review paper is based on the main difficulties in the sentiment assessment stage that significantly affect sentiment score, pooling, and polarity detection. The most popular deep learning methods are a Convolution Neural Network, Recurrent Neural Network and Long Short Term Memory. Finally, a comparative study is made with a vast literature survey using deep learning models.
Set-Membership Estimation in Shared Situational Awareness for Automated Vehicles in Occluded Scenarios.

One of the main challenges in developing autonomous transport systems based on connected and automated vehicles is the comprehension and understanding of the environment around each vehicle. In many situations, the understanding is limited to the information gathered by the sensors mounted on the ego-vehicle, and it might be severely affected by occlusion caused by other vehicles or fixed obstacles along the road. Situational awareness is the ability to perceive and comprehend a traffic situation and to predict the intent of vehicles and road users in the surrounding of the ego-vehicle. The main objective of this paper is to propose a framework for how to automatically increase the situational awareness for an automatic bus in a realistic scenario when a pedestrian behind a parked truck might decide to walk across the road. Depending on the ego-vehicle's ability to fuse information from sensors in other vehicles or in the infrastructure, shared situational awareness is developed using a set-based estimation technique that provides robust guarantees for the location of the pedestrian. A two-level information fusion architecture is adopted, where sensor measurements are fused locally, and then the corresponding estimates are shared between vehicles and units in the infrastructure. Thanks to the provided safety guarantees, it is possible to adjust the ego-vehicle speed appropriately to maintain a proper safety margin. Three scenarios of growing information complexity are considered throughout the study. Simulations show how the increased situational awareness allows the ego-vehicle to maintain a reasonable speed without sacrificing safety.
A comparative analysis of hyper-parameter tuned supervised machine learning algorithms on breast cancer prediction

In recent years, breast cancer is one of the expansive diseases among various sorts of tumors in women. Around 20 million women are determined to have breast cancer globally. It is normal that out of 8 ladies, one lady would get affected by breast tumor. In this specific situation, early diagnosis of the disease can increase the survival rate of the patients. There are some different ways to diagnose. In this paper, author attempted to tackle this issue specifically by machine learning applications. It has been executed using several supervised learning algorithms, for example, Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF) and K-Nearest Neighbor (K-NN). These algorithms show a noteworthy accuracy in performance over the breast cancer dataset. Logistic Regression has an accuracy of 94.50%, SVM has that of 97.58%, K-NN has that of 96.70% and Random Forest has that of 92.39%. Hyper-parameter tuning is a way of finding best optimal parameter to learn the machine learning algorithms. However, after hyper-parameter tuning of each model, SVM has revealed the most noteworthy accuracy among other models. Author has decisively compared the proposed model with different past research models.
Temp-Frustum Net: 3D Object Detection with Temporal Fusion.

3D object detection is a core component of automated driving systems. State-of-the-art methods fuse RGB imagery and LiDAR point cloud data frame-by-frame for 3D bounding box regression. However, frame-by-frame 3D object detection suffers from noise, field-of-view obstruction, and sparsity. We propose a novel Temporal Fusion Module (TFM) to use information from previous time-steps to mitigate these problems. First, a state-of-the-art frustum network extracts point cloud features from raw RGB and LiDAR point cloud data frame-by-frame. Then, our TFM module fuses these features with a recurrent neural network. As a result, 3D object detection becomes robust against single frame failures and transient occlusions. Experiments on the KITTI object tracking dataset show the efficiency of the proposed TFM, where we obtain 6%, 4%, and 6% improvements on Car, Pedestrian, and Cyclist classes, respectively, compared to frame-by-frame baselines. Furthermore, ablation studies reinforce that the subject of improvement is temporal fusion and show the effects of different placements of TFM in the object detection pipeline. Our code is open-source and available at https://github.com/emecercelik/Temp-Frustum-Net.git.
LibGuides: Godzilla: Where to Start: Research and Resources

Excited about Godzilla King of the Monsters or the upcoming Godzilla vs. Kong?. Curious about the 65-year history of an iconic film monster? In the mood to see buildings get smashed up, but don't know where to start? This is the guide for you!
the PHENOMENON OF CODE MIXING ADN CODE SWITCHING IN FACEBOOK USER STATUS

The occurrence of code mixing and code switching in Indonesian is a result of bilingualism by people who do have their own regional languages, and the presence of foreign language interventions. This is interesting to study so that it can be described how mixed use and code switching in life is socializing. The collection of research data was taken by the authors of the Facebook status of students who were working in the academic world. From the results of the analysis, it can be concluded that the most frequently used code mix is ​​in the form of words, and all of them are spoken in a variety of non-standard languages. The use of this mixed code is motivated by the factors of mother tongue (Javanese) and the surrounding language environment. Therefore this code mix is ​​dominated by a mixture of Javanese. While the code switching that is often used comes from foreign languages ​​both Arabic and English. the negative impact of code mixing and code switching is the loss of the characteristics and identity of Indonesian as a national language. In addition, it also triggers the assimilation of language which will later result in the extinction of the language.
Photonic Neural Networks Applications

Integrated optical neural networks are much smaller (hundreds of neurons) than electronic implementations (tens of millions of neurons). However, the bandwidth and interconnect density in optics is significantly superior to that in electronics. This raises a question: what are the applications where sub-nanosecond latencies and energy efficiency trump the sheer size of processor? This talk will discuss of photonic neural networks in computing, communication, and signal processing.
Pengekalan Warisan Pakaian Pahlawan Masyarakat Iban Oleh Kelab Gagung Sarawak (KGS) Di Kapit Sarawak

This research on the clothing of Iban warriors is emphasized by preserving the aspectsof manufacturing and maintaining the heritage of traditional Iban warrior clothingmanaged by Kelab Gagung Sarawak. This study focuses on how to preserve anddefend the heritage of the Iban warriors' clothing. Apart from that, the researcheremphasized on the importance and suggestions in assisting the Kelab Gagung Sarawakin maintaining the heritage of the Iban warriors' culture tradition dress. This is because,the production or production of this garment is heavily influenced by it is difficult toget the real material in maintaining the originality of the garment.
Audio Podcast-based Learning Media in Improving Students’ Listening Comprehension and Pronunciation

The study is about to improve the students listening skill and pronunciation by using podcast as one of material sources taken from the internet. The objectives of this study are to identify some steps that are going to be implemented in teaching listening and speaking using podcast to improve the students’ listening skill and their pronunciation of English Department students of Dian Nusantara University, and to analyze the effectiveness of using podcast in their learning. The researchers apply the concept of ADDIE (Analysis, Design, Development, Implementation, and Evaluation) paradigm in this study because it is more effective and systematic and supported new learning models. The participants are the fourth- semester students of English Literature students. There are two methods of collecting data in this research. They are quantitative and qualitative data. Each week, quantitative data were gathered via a series of tests, including a listening comprehension and a pronunciation examination. For qualitative data, questionnaires were used to elicit student perspectives on podcasts' effectiveness in improving listening comprehension and pronunciation. Preliminary comparative data will be gathered through a pre-test. This study uses descriptive statistical methods to analyze the data. The result is then analyzed using descriptive statistics to know the difference of scores before and after the tests. The students' ability to pronounce words has improved. The majority of the pre-test pronunciation errors are no longer present in the post-test. Furthermore, the use of podcasts as a learning medium received positive responses in the student survey. Students are encouraged to improve their listening skills because podcasts appeal to them as a learning medium.