Reviewerly/exHarmony huggingface dataset:
Driving Subscriptions Through User Behavior Modeling and Prediction at Bloomberg Media

In May 2018, Bloomberg Media launched a digital subscriptions business with a paywall. This meant that users would now be blocked after reading a fixed number of articles per month on Bloomberg.com and the Bloomberg mobile application. For the launch, all users were allowed to read 10 articles for free per month and were offered the same introductory price for subscription. Although we were able to get a large number of subscribers immediately after launch with this “one size fits-all” approach, the conversion rate steadily dropped in the next few months (the initial spike in subscriptions can be attributed to the purchase of subscription by our loyal users). In order to overcome this problem and to keep our subscription conversion numbers high, our team leveraged user engagement data and built a subscription propensity machine learning model to intelligently target users with the right introductory offers. Furthermore, our team used the engagement data trends across months along with the subscription propensity scores to optimize the paywall height for each user, thus maximizing revenue. In this talk, we will present details about the challenges we faced and approaches we considered to implement the subscription propensity model. We will also talk about how we combined this model with user engagement trends to dynamically calculate the paywall height for each user for every month. Finally, we will share details on how we integrated and used the subscription propensity and dynamic paywall techniques in production, along with results that demonstrate the success of these approaches in increasing Bloomberg Media’s subscription revenue.
Teachers’ Readiness for Distance Education: From Theory to Practice

The paper has a two-fold purpose. The first purpose of the research is to clarify the concept of “readiness of teachers of higher education institutions to use distance learning technologies”, to determine the components and indicators of teachers’ readiness to use distance learning technologies. The other purpose is to announce the results of the conducted online survey on teachers’ readiness for distance learning; understand how distance learning methods are being implemented, which need to be modified or improved, establish the depth of complications associated with transitioning to online teaching due to the introduction of COVID-19 quarantine and provide the information to stakeholders to further improve the process of distance learning. The article is based on the survey of scientific literature on teachers’ readiness/preparedness to distance education and results of the authors’ COVID-19 Teachers’ Questionnaire with 485 faculty members of major higher education institutions of Cherkasy region (Ukraine). The author proves that the effectiveness of distance learning depends on subjective (teachers’ and students’ preparedness for distance education) and objective factors (material and technical base of higher education institutions). Components of teacher’s readiness for distance learning and their indicators are summarized: motivational, cognitive, operational, technological, creative, personal. The outcomes of the questionnaire have identified that most respondents lack theoretical knowledge and practical skills necessary for the implementation of distance learning and encounter difficulties in teaching during quarantine.
Enhancing Convolutional Neural Network using Hu’s Moments

Convolutional Neural Networks (CNN) is a powerful deep learning method which is mostly used in image classification and image recognition applications.It has achieved acceptable accuracy in these fields but it still suffers some limitations.One of these limitations of CNN is the lack of ability to be invariant to the input data due to some transformations such as rotation, scaling, skewness, etc.In this paper we present an approach to optimize CNN in order to enhance its performance regarding the invariant limitation by using Hu's moments.The Hu's moments of an image are weighted averages of the image's intensities of the pixels, which produce statistics about the image, and these moments are invariant to image transformations.This means that, even if some changes were made to the image, it will always produce almost the same moments values.The main idea behind the proposed approach is extracting Hu's moments of the image and concatenating them with the flatten vector then feeding the new vector to the fully connected layer.The experimental results show that an acceptable loss, accuracy, precision, recall and F1 score have been achieved on three benchmark datasets which are MNIST hand written digits dataset, MNIST fashion dataset and the CIFAR10 dataset.
Domain-aware Neural Language Models for Speech Recognition

As voice assistants become more ubiquitous, they are increasingly expected to support and perform well on a wide variety of use-cases across different domains. We present a domain-aware rescoring framework suitable for achieving domain-adaptation during second-pass rescoring in production settings. In our framework, we fine-tune a domain-general neural language model on several domains, and use an LSTM-based domain classification model to select the appropriate domain-adapted model to use for second-pass rescoring. This domain-aware rescoring improves the word error rate by up to 2.4% and slot word error rate by up to 4.1% on three individual domains -- shopping, navigation, and music -- compared to domain general rescoring. These improvements are obtained while maintaining accuracy for the general use case.
The Role of Social Media Companies in the Regulation of Online Hate Speech

This chapter is about online hate speech propagated via platforms operated by social media companies (SMCs). It examines the options open to states in forcing SMCs to take responsibility for the hateful content that appears on their sites. It examines the technological and legal context for imposing legal obligations on SMCs, and analyses initiatives in Germany, the United Kingdom, the European Union and elsewhere. It argues that while SMCs can play a role in controlling online hate speech, there are limitations to what they can achieve.
Relationship between Code Reading Speed and Programmers' Age

According to the aging society, it is getting more important for software industry to secure human resources including senior developers. To enhance the performance of senior developers, we should clarify the strengths and weaknesses of senior developers, and based on that, we should reconsider software engineering education and development support tools. To a greater or lesser extent, many cognitive abilities would be affected by aging, and we focus on the human memory as one of such abilities. We performed preliminary analysis based on the assumption. In the preliminary experiment, we prepared programs in which the influence of human memory performance (i.e., the number of variables remembered in the short-term memory) on reading speed is different, and measured time for subjects to understand the programs. As a result, we observed that the code reading speed of senior subjects was slow, when they read programs in which the influence of human memory performance is larger.
InferBench: Understanding Deep Learning Inference Serving with an Automatic Benchmarking System

Deep learning (DL) models have become core modules for many applications. However, deploying these models without careful performance benchmarking that considers both hardware and software's impact often leads to poor service and costly operational expenditure. To facilitate DL models' deployment, we implement an automatic and comprehensive benchmark system for DL developers. To accomplish benchmark-related tasks, the developers only need to prepare a configuration file consisting of a few lines of code. Our system, deployed to a leader server in DL clusters, will dispatch users' benchmark jobs to follower workers. Next, the corresponding requests, workload, and even models can be generated automatically by the system to conduct DL serving benchmarks. Finally, developers can leverage many analysis tools and models in our system to gain insights into the trade-offs of different system configurations. In addition, a two-tier scheduler is incorporated to avoid unnecessary interference and improve average job compilation time by up to 1.43x (equivalent of 30\% reduction). Our system design follows the best practice in DL clusters operations to expedite day-to-day DL service evaluation efforts by the developers. We conduct many benchmark experiments to provide in-depth and comprehensive evaluations. We believe these results are of great values as guidelines for DL service configuration and resource allocation.
Polynomial functions on rings of dual numbers over residue class rings of the integers

Abstract The ring of dual numbers over a ring R is R [ α ] = R [ x ]/( x 2 ), where α denotes x + ( x 2 ). For any finite commutative ring R , we characterize null polynomials and permutation polynomials on R [ α ] in terms of the functions induced by their coordinate polynomials ( f 1 , f 2 ∈ R [ x ], where f = f 1 + αf 2 ) and their formal derivatives on R . We derive explicit formulas for the number of polynomial functions and the number of polynomial permutations on ℤ p n [ α ] for n ≤ p ( p prime).
Robots in Retail Marketing: A Timely Opportunity

Change is indeed inevitable. In a world full of technological advancements there has been a change in retail marketing on how they conduct business. This has prompted a fast pace of automation and artificial intelligence (AI) as strategies that can be used to propel and reshape most business models. Machine intelligence is now more powerful, and it is changing the lives of people everywhere around the world. One such machine intelligence is the robot, which is almost being implemented on a daily basis to form part of the retail marketing solution. Hence, it is important to critically analyze the significance of robots in retail stores and how the same can be used to change the current status quo into a new business model. In answering the research questions, this research will adopt both secondary and primary data methods that will ensure that robust information is collected to meet the objectives of the study. Analysis of data revealed that change is inevitable in the retail industry, and it ought to be embraced in phases. The conclusion also revealed that robots have had both positive and negative impacts on the local stores. The idea of robots taking over various jobs in the industries may not be a good idea, but they have proved to be efficient and reliable.
Key Aspects of Open Data in Finnish RDI Cooperation between Higher Education and Businesses

The article highlights aspects that should be considered during an open Research, Development, and Innovation (RDI) process cycle to improve the utilization of research data and foster open cooperation between higher education and businesses. The viewpoint here is in publicly funded joint research projects of the universities of applied sciences (UAS), the concept is, however, applicable in other higher education and research organizations as well. There are various challenges related to research data management in general as well as to the openness and reuse of data and results. The findings of this article are based on the results of a two-day expert workshop, and these results are interlinked with five phases of an open RDI process cycle: planning, implementation, documentation, sharing, and commercialization. Various drivers and barriers can be identified in different stages of the process. On a general level, special attention must be paid to critical factors such as ownership and sharing of data and results, confidential information and business secrets as well as following the requirements of the Open Science (OS) policies of the participating organizations and funders. This article also highlights several best practices that should be considered in each phase of an open RDI process cycle with businesses.
The Perceptions of Students’ Experiential Learning in Relation to Theoretical Concept with Real Practice

By joining theoretical concept with real practice and experience, the students are able to develop their skills, knowledge and values.Thus, they are able to have the skill on problem solving, critical thinking and new experience to the new situations outside.The aim of this paper is to gauge the students' perception on the first-time experience having experiential learning in Basic Hydraulics subject.A total of 120 students were involved in this study and they were divided into two groups; conventional and experiential learning.As for experiential learning, students were requested to find solution according to the real case problem.They need to undergo the process of experiential learning in order to find the solution.Then, self-administrated questionnaires were disseminated among the students who went through the process of experiential learning to gauge their perceptions on the outcome.The results indicate that they have satisfactory results for having experience in experiential learning.They achieved meaningful learning experience through this method which required them to have full involvement along the process of problem solving.
Collaborative Filtering Movie Recommendation System

Recommendation System plays an important role in today’s era of e-commerce. From OTT platforms to the shopping application and music application everywhere we see that after watching a movie or buying an item, listening a song we are recommended with some other movie or item or song. Most of the time we select our next movie, item or song from the recommended one. In this paper I will give you a brief description of collaborative and user-based filtering. The data used in this research is taken from Movie Lens. The result obtained contains some movie recommendations.
OT-Feature Extraction on Scrambled Images with Instantaneous Clustering for CBIR Scheme in Cloud Computing

A novel feature extraction algorithm using Otsu’s Threshold (OT-features) on scrambled images and the Instantaneous Clustering (IC-CBIR) approach is proposed for Content-Based Image Retrieval in cloud computing. Images are stored in the cloud in an encrypted or scrambled form to preserve the privacy content of the images. The proposed method extracts the features from the scrambled images using the Otsu’s threshold. Initially, the Otsu’s threshold is estimated from the scrambled image and based on this threshold the image is divided into two classes in the first iteration. Again, the new threshold values are estimated from two classes. The difference between the new threshold and the previous threshold gives two features. This process is repeated for number of iteration to obtain the complete OT-features of the scrambled image. This paper also proposes an instantaneous clustering approach (IC-CBIR) where the image is moved into a cluster as soon as the image is uploaded by the image owner. Therefore while retrieving the images, the images near to a particular cluster are matched instead of matching with a complete set of image features in the dataset which reduces the search time. The performance of the proposed algorithm is being tested using four different types of the dataset such as Corel 10K, Misc, Oxford flower, and INRIA Holidays dataset. The experimental evaluation reveals that the proposed method outperforms better than the traditional CBIR algorithm on encrypted images in terms of precision, time of search and time of index construction.
Empirical model for estimation of global solar radiation at lowland region Biratnagar using satellite data

This study proposes to find the regression coefficient of the modified Angstrom type model for the estimation of global solar radiation (GSR) in lowland Biratnagar (Lat. 26.5º N, Long. 87.3º E and Alt. 72m) using relative sunshine duration and satellite data of GSR. Using the regression technique, the empirical constants 0.29 and 0.56 are found in the modified Angstrom model. Furthermore, Modified Angstrom model along with other linear models such as Glover and McCulloch model, Page model, Rietveld model, and Turton's model are statistically assessed to evaluate the significance of models. Statistical tests like MPE, MBE, RMSE, and CC reveal that all these models are statistically significant. These findings can be utilized for other locations with a high confidence level at the similar climatic locations of Nepal.&#x0D; BIBECHANA 18 (2021) 193-200
Towards Optimizing Cloud Computing Using Residue Number System

Abstract In the work methods for building systems of distributed data storage based on the system of residual classes are considered. The use of direct conversion of data from the positional system of calculation to the residue number system will have a large computational complexity, the use of modules of a particular type allows you to solve this problem. The operation of scaling and expansion of the base system, which is necessary to restore the number of stored parts in case of failure of one or more cloud servers, is considered.
A timed tally counter for microscopic examination of thick blood smears in malaria studies

Abstract Background Despite many technological advances for malaria parasite detection (e.g. high resolution image acquisition), microscopic reading of thick blood smear (TBS) remains the gold standard. Even though available in low technology environment, the microscopy of TBS is slow and time consuming. Moreover microscopy may induce errors at many levels and has no quality control. Methods A electronic extension of the mechanical tally counter is proposed. In addition to the counting process it includes the process of counting itself that relies on the time elapsed between two successive pressures of the counting button leading to a timed tally counter (TTC). The microscopist performs the reading with the specific instruction starting by counting, in each high power fields, leucocytes first and then parasites. The time-stamp of all pressures of counting buttons are recorded along with the nature of the count. The data are recorded internally in CSV format and are exportable. The detection of HPFs locations and leukocyte/parasite counts per HPFs is performed through a hidden semi-Markov model (with outliers) allowing both to take into account the known distribution of leukocyte per HPFs (using a negative binomial distribution) and the pauses and hesitation of the microscopist during the reading. Parameters are estimated via the expectation-maximization algorithm. Hyper-parameters are calibrated using expert annotations. Forward/backward recursions are used to obtain the HPFs locations. Results This approach provides richer data at no extra cost. It has been demonstrated that the method can derive parasites per HPF, leukocytes per HPF, and parasite/leukocyte ratio with robust non-parametric confidence intervals. Moreover a direct digital data entry leads to a less expensive process and decreased time-consuming and error-prone manual data entry. Lastly the TTC allows detecting possible protocol break during reading and prevents the risk of fraud. Discussion and conclusion Introducing a programmed digital device in the data acquisition of TBS reading gives the opportunity to develop easily new (possible adaptive) reading protocols that will be easily followed by the reader since they will be embedded directly in the device. With the TTC the reader only has to read HPFs, counting leukocytes first and parasites second, and the counter will beep when the protocol is completed.
Kriminaliteta v Ljubljani v času pandemije covida-19

At a time when the world is faced with exceptional conditions and governments are taking various precautions to prevent the spread of the COVID-19, in addition to social life, trends in crime are also changing. According to world experts, reported crime fluctuates, but most forms of crime are in decline. After reviewing police statistics for the period from 1. January to 31. August 2020, in Ljubljana and comparing them with the same period in 2019, we found that reported crime had decreased. A total number of offences and misdemeanours have declined, as have robberies, thefts, burglaries, and domestic violence. The Institute of Criminology, online media, and newspapers in Slovenia have reported an increase in domestic violence, while some also reported a rise in thefts and burglaries in the country. Cybercrime is expected to increase in the future as more people and businesses move online due to the newly formed conditions, using online services, which poses new risks and opens up new opportunities for perpetrators.
The library terminology: Theoretical and practical approaches

The author responds to the article by Elena I. Kozlova and Valentina A. Tsvetkova "Terminological aspects in the processes of standardization of library and information activities" (published in the journal "Scientific and technical libraries", № 3, 2020) devoted to the draft state standard of basic terms and definitions of library and information activities. The author objects to their interpretation of mathematical induction method in formal logic, by which they justify the attempt to standardize the concept of "library and information activities". Through this method, the opposite result would be achieved; redundancy or even artificiality of the proposed phrase is demonstrated consequentially. The author argues that, within the framework of the Federal Law "On librarianship", the term "library and information activity" as proposed for federal standardization along with the term "information and library activity" as it appears in the current standard, are both wrongful. The author also criticizes other theoretical and methodological provisions proposed by the state standard writers.The author suggests to improve the system of standards for information, library and publishing (SIBID) by segregating the competence areas of information, library and publishing activities, without mixing them in any proportion or combination. SIBID must comprise the concepts of "library" and "publishing", since these terms are embedded in the name of the system and, therefore, they make the original reference points. The terminology set must be based on the key terms of laws and other national regulatory and legal acts, though the definitions of these terms may differ, and even radically differ, from those fixed in legal acts where they convey juridical conceptions while the standard is to regulate their professional, scientific, and practical meaning, which indeed completely corresponds to the legal interpretation. The author introduces the following approach to these general terms: since the library system is essentially of document and communicative character, the key concepts of the system shall form its foundation: library, librarianship, document, library collection, reader (user), reader (user) group, material and technical resources of library, library re-source studies, library model, library collection model, and other general terms within the discipline.