Reviewerly/exHarmony huggingface dataset:
Information aggression—A Battlefield of Smartphones

Information—primarily digital—is an increasingly important commodity in the modern world and the ubiquitous use of smartphones has meant a change in the way people access information and communicate. The virtual information space has become the primary location for threats, aggression and even potentially foreign occupation. The number of electronic services with fluid lines between public and private, professional and amateur; an absence of physical borders; the 24-hour news cycle: this provides new opportunities for virtual attacks. The ancient tool of propaganda takes on a new importance. There is widespread agreement that Russia under Putin has lifted propaganda to new levels—or perhaps back to Soviet levels but with the ubiquity of modern media in addition. Through access to the information space via such a common device as the smartphone, everyone is affected by the new form of information aggression.
A Bivariate Poisson Time Series of Counts with Two Latent Autoregressive Processes

In this paper, we propose a Bivariate Poisson model with two different latent processes to analyze a bivariate time series of counts. The latent processes are introduced to accommodate the temporal correlations while the bivariate model incorporates the cross-correlations between the two time series. The maximum likelihood estimates of the model parameters are obtained by using data cloning approach. A simulation study is carried out to examine the performance of the estimators and the model is applied to a real data set on the asthma-related number of visits to Emergency Departments at hospitals across the Province of Ontario, Canada.
Initial empirical evidence for Agile Project Management in the Film Industry

In the course of continuous volatility like production stops due to COVID-19 pandemic, video-on-demand player monopolize the film industry, while filmmakers are stuck in traditional, linear content development processes. The traditional film industry has to become more agile in order to react quickly and easily to unexpected changes and competitive pressure. Despite the benefits of agility demonstrated in software development and successfully integrated agility into project management, the adaptation in creative industries such as content development for film production have largely been neglected. To determine the potential value for agility in the film industry, we build on theory about agile project management and examine in an online survey with 184 German filmmakers (producers, authors, directors, actors, film financiers), if the Agile Manifesto values and principles from the Software Development can be adapted to the film industry. Based on our data, we validated the agile manifesto for the film industry with content development specific four values related to nine own principles. Our results show that it is crucial to digitalize traditional content development to agile content development end-to-end, with digital tools, lean processes, new collaboration structures and holacracy to prepare for any volatility. Our findings help to get a better understanding of the agile manifesto beyond the Software Development as a guideline for implementing agility in the Film Industry.
Raspberry pi based secured cloud data

Abstract In the growing digital world, Cloud Storage plays a major role. Cloud Storage is preferred for storing enormous amounts of data that our system cannot hold. But the cost of using private Cloud Storage services will be costly and our data is under the control of others. The main objective of our idea is that our data will be within ourselves rather than sending the data to unknown Cloud Storage providers and which is also cost effective. We are using the hard disks as our own Cloud Storage which will be controlled by raspberry pi and it can be accessed from anywhere. It is also more secure of using the material cloud with ourselves rather than depending on a private Cloud Storage service which may or may not be secure but here our data is only with ourselves as we will implement our suitable security measures. The design of our project is also much compact and portable as it gives a major advantage, we can carry it anywhere. The main motto of our project is to give a user personal Cloud Storage of his own in a cost-efficient manner which will be much more reliable than any other material cloud services.
Desiderata for Explainable AI in Statistical Production Systems of the European Central Bank

Explainable AI constitutes a fundamental step towards establishing fairness and addressing bias in algorithmic decision-making. Despite the large body of work on the topic, the benefit of solutions is mostly evaluated from a conceptual or theoretical point of view and the usefulness for real-world use cases remains uncertain. In this work, we aim to state clear user-centric desiderata for explainable AI reflecting common explainability needs experienced in statistical production systems of the European Central Bank. We link the desiderata to archetypical user roles and give examples of techniques and methods which can be used to address the user’s needs. To this end, we provide two concrete use cases from the domain of statistical data production in central banks: the detection of outliers in the Centralised Securities Database and the data-driven identification of data quality checks for the Supervisory Banking data system.
How Secure Is Blockchain? Extending the Protection Motivation Theory with Trust

In light of the ongoing paradigm shift of blockchain technology from trusting people to trusting algorithms, we investigate the role of trust in blockchain security behaviors. In this research-in-progress paper, we draw on the protection motivation theory (PMT) and develop a conceptual definition of the construct of trust in blockchain. We situate PMT in a blockchain context to scrutinize security issues with the blockchain technology, such as cryptocurrency hacking risks or data loss, which hamper the widespread adoption of blockchain. We expect that our study will provide implications regarding the role of trust in blockchain, which will help to advance the understanding of blockchain security behaviors from a protection motivation perspective.
Implementasi pembelajaran online pada sekolah dasar negeri di daerah pinggiran perkotaan

This research is caused by online learning conducted by public elementary school in suburban areas. Online learning is carried out because the “COVID-19” virus outbreak has become a disaster in the world of education. Online learning requires several facilities such as an internet signal and a smartphone. This facility becomes a problem if an area experiences internet problem. This research is a qualitative descriptive study of phenomenology. The subjects in this study were teachers, parents, and fourth grade students. Data collection used is interviews, observation, and documentation. The data analysis used was data reduction, data presentation and conclusion drawing. The results of this study indicate that the implementation of online learning in grade IV public elementary school in suburban areas during the “COVID-19” pandemic was shown based on input, process, and output indicators. This can be seen from the input indicator, namely the application of teacher characteristics, namely motivation, fostering enthusiasm, and mastering technology, communication. The learning facilities used by students include cellphones/smartphones, internet networks, and the cost of buying internet data packages. Process indicators are related to the allocation of teacher time, namely managing time effectively. The application of time allocation for students is to take advantage of time tolerance in completing tasks. The output indicator is related to student learning outcomes. The application of changes in student attitudes is to be more motivating, disciplining, and timely in submitting assignments.
Augmented Reality-Based Dance Training System: A Study of Its Acceptance

Blending the user environment with digital information in a real-world scenario is characterizes as Augmented Reality (AR). In the field of learning and training, there are many state-of-art learning theories that focus on behaviorism, cognitivism and constructivism domains. Therefore, we aim to develop an AR based Dance Training System (ARDTS) for teaching psychomotor skills (voluntary dance movements) based on the category of constructivism learning theory. The dance training systems based on AR fall under the category of cognitivism learning theory, where the learner is provided with the feedback for a subsequent training session based upon the output of the previous session (history of performance). On the other hand, the proposed ARDTS is developed based upon the information flow model of constructivism learning theory where the feedback is based upon learner’s actions, self-learning and overall competence making it distinctive in comparison to the existing systems. ARDTS is investigated and validated for user acceptance based upon the Technology Acceptance Model (TAM). This multiplayer fun-filled dance training system with interactive feedback and guidance mechanism is the ultimate outcome that eventually is evaluated and validated for user acceptance.
An In-depth Analysis on the Use of Long Short-term Memory Networks to Predict Incidence and Prevalence of Covid-19 in Latin America

The use of machine learning techniques, especially deep learning, could improve the predictions of the currently used epidemiological models for predicting Covid-19 in the short term. This information is essential for better decision making and to reduce the impacts of the disease spread in different countries. We explored the use of support vector regression (SVR) and long short-term memory networks (LSTM), the state of the art neural network architecture for time series analysis, to predict the daily incidence and prevalence for nine countries in Latin America. Our methodology and the models used can be replicated in other countries. Our main findings were: (i) there is no single best model or best hyperparameters configuration for all countries and targets; (ii) the LSTM showed an average MAE that was around 50% lower for incidence and 20% lower for prevalence when considering all countries; (iii) the LSTM showed better results for predicting incidence for most countries (Argentina, Bolivia, Brazil, Guatemala, and Haiti); (iv) the SVR showed better results for predicting prevalence for most countries (Argentina, Bolivia, Colombia, Cuba, Guatemala, and Haiti); and (v) for Brazil, the LSTM provided better results for both targets, with an MAE that was 68% lower for incidence and 73% lower for prevalence.
Higher education as factor of sustainable development

The paper analyzes the system at higher education in the context of achieving the goals of sustainable development of modern society. From the point of view of a systemic approach, an attempt is made to identify and classify the factors that can measure the contribution of higher education institutions to achieving the Sustainable Development Goals at the regional level. These factors are correlated with the corresponding tasks, as well as possible problems that are posed by the present unstable situation, and hinder sustainable development of the society. The research uses system-wide methods of analysis and synthesis, generalization and analogy. The analysis of the tasks and problems presented in the article allows us to identify and investigate the cause-effect relationships between the factors that determine the achievement of the Sustainable Development Goals and the complexities that accompany this process, and also contributes to the holistic perception of the concept of sustainable development by university staff members and students. The results of the study can be used in event planning within the framework of implementing the strategies drawn up by universities in the context of the sustainable development paradigm.
Learing spatial associate temporal correlation filter for visual tracking

Discriminative Correlation Filters (DCF) have proved efficient and useful in the tracking community. The success of DCF trackers is attributed to their ability to efficiently generate large numbers of negative examples by shifting a single sample. However, when training the filter, large amounts of impractical samples are also acquired, which results in boundary effects. Traditional methods trained the filter with larger sets to suppress boundary effects, but sacrificed computational efficiency, which affected the real-time performance of the tracker. Recently, a Background-Aware Correlation Filter (BACF) was suggested to mitigate the boundary effect. It proved robust and efficient, but the BACF tracker suffers from drifting when the target rotates in the scene. This occurs because BACF ignores the temporal connection between the frames in the video. In this paper, we propose a Spatial Associate Temporal Correlation Filter (SATCF) by formulating spatial and temporal regularization terms in the BACF model. We trained the filter to recognize patches that belong to the target as positive, foreground samples and other patches from the background as the negative examples. This setup, promotes the discriminative ability of the filter. In addition, we also exploited a spatial term to measure the spatial difference between two consecutive frames - a technique that is capable of alleviating model over-fitting. Our tracker enables computational efficient and achieves superior performance in complex video sequences. We demonstrate the efficiency of the SATCF tracker by comparing with state-of-the-art trackers. Experimental results on the OTB-2013 dataset demonstrate the favorable performance of our tracker against other state-of-the-art methods.
Revisiting Fast and Slow Thinking in Case-Based Reasoning

A dichotomous Case-Based Reasoning (CBR) model is one in which two kinds of reasoning mechanisms are employed; these may be for realizing fast and slow problem-solving as demanded by the nature of the incoming query. Such dichotomous operation is inspired by Daniel Kahneman’s seminal work on the two modes of thinking observed in humans. In this paper, we present the following three directions of refinement for a dichotomous CBR model: selection of attributes for a fast thinking model based on parsimonious CBR, switching from fast to slow thinking based on constraints derived from domain knowledge and arriving at a complexity measure for evaluating dichotomous models. For all the three improvements identified, we discuss the results on real-world data sets and empirically analyse the effectiveness of the same.
Comparing fine-grained source code changes and code churn for bug prediction - A retrospective

More than two decades ago, researchers started to mine the data stored in software repositories to help software developers in making informed decisions for developing and testing software systems. Bug prediction was one of the most promising and popular research directions that uses the data stored in software repositories to predict the bug-proneness or number of bugs in source files. On that topic and as part of Emanuel's PhD studies, we submitted a paper with the title Comparing fine-grained source code changes and code churn for bug prediction [8] to the 8th Working Conference on Mining Software Engineering, held 2011 in beautiful Honolulu, Hawaii. Ten years later, it got selected as one of the finalists to receive the MSR 2021 Most Influential Paper Award. In the following, we provide a retrospective on our work, describing the road to publishing this paper, its impact in the field of bug prediction, and the road ahead.
Complexity-Configurable Learning-based Genome Compression

In this paper, we propose the complexity configurable learning-based genome data compression method, in an effort to achieve a good balance between coding complexity and performance in lossless DNA compression. In particular, we first introduce the concept of Group of Bases (GoB), which serves as the foundation and enables the parallel implementation of the learning-based genome data compression. Subsequently, the Markov model is introduced for modeling the initial content, and the learning-based inference is achieved for the remaining base data. The compression is finally achieved with efficient arithmetic coding, and based upon a set of configurations on compression ratios and inference speed, the proposed method is shown to be more efficient and provide more flexibility in real-world applications.
EXPERIMENTAL RESEARCH ON FORECASTING INDEX OF BIOLOGICAL IMAGE AEROBIC EXERCISE ANALYSIS OF ARTIFICIAL NEURAL NETWORK

ABSTRACT Objective: The paper uses artificial neural network images to explore the effects of aerobic exercise on the gamma rhythm of theta period in the awake hippocampal CA1 area of APP/PS1/tau mice and the low-frequency gamma rhythm of the sleep state hippocampal CA1 area SWR period. Methods: Clean grade 6-month-old APP/PS1/tau mice were randomly divided into quiet group (AS) and exercise group (AE), C57BL/6J control group mice were randomly divided into quiet group (CS) and exercise group (CE). The AE group and the CE group performed 12-week treadmill exercise, 5d/week, 60min/d, the first 10min exercise load was 12m/min, the last 50min was 15m/min treadmill slope was 0°. Eight-arm maze detection of behavioral changes in mice; multi-channel in vivo recording technology to record the electrical signals of the awake state and sleep state in the hippocampal CA1 area, MATLAB extracts the awake state theta period and sleep state SWR period, multi-window spectrum estimation method Perform time-frequency analysis and power spectral density analysis. Results: 12 weeks of aerobic exercise can significantly improve the working memory and reference memory of the AS group, increase the gamma energy in theta period of the awake hippocampus CA1 area and the low-frequency gamma energy in the sleep state CA1 area SWR period. Conclusions: Aerobic exercise can improve the neural network state of the AD model and increase the gamma energy in theta period of the hippocampus CA1 area, and the low-frequency gamma energy in the SWR period is one of the neural network mechanisms for its overall behavioral improvement. Level of evidence II; Therapeutic studies - investigation of treatment results.
Requirements Analysis on Emotional Preferences for Leisure Activities in Virtual Reality for Female Nursing Home Residents – A Mixed Method Approach

Insufficient tailoring of individual needs of nursing home residents can lead to standardized (leisure) activities. Virtual Reality (VR) provides the potential to offer a multimodal experience of individualized activities to residents. The aim of the VR4Care study is to explore attitudes, knowledge, and expectations regarding the use of VR glasses for (leisure) activities in female residents living in rural nursing homes. A mixed method approach in two phases (phase 1: interviews with a complementary standardized questionnaire; phase 2: measurement of affective states) was used. In total, 20 participants were interviewed regarding nine categories. The results show that the most described VR-scenarios for (leisure) activities include arts and crafts (e.g., sewing and knitting), followed by gardening (e.g., farmhouse garden), washing and ironing clothes and dancing (e.g., Austrian folk dances). This study serves as an important basis for scenario development and for testing the use of biosensor measurements in phase 2.
Transfer Learning in AI is a way forward for User Organizations

Artificial Intelligence applications deployment has always been a challenge for non-Tech firms, the firms whose core business is not technology. They need to depend on vendors to provide them necessary support or they need to contract one of the service providers. This paper discusses possible strategy that can be adopted by such firms. The research done in the implementation science discipline helps to conceptualize the methodology. This paper zeroes on to the image classification to focus on an important area of artificial intelligence. The experimentation done using transfer learning principles exhibit way forward for AI implementation.
Cloud Motion Estimation with ANN for Solar Radiation Forecasting

The most critical issue in integrating solar energy into the electricity grid is the variability of solar energy. Cloud cover and motions are the most fundamental factors in the formation of this variability. In the related study, a 167-degree camera is placed in the main campus area of Afyon Kocatepe University, and sky images are recorded at regular intervals. Using the obtained images, cloud motion estimations are made for a 10 second time horizon at a 1 second time scale. Firstly, within the scope of this purpose, the points to be tracked by the Shi-Tomasi algorithm were determined. Then, using the Lucas-Kanade optical flow algorithm, the points found are followed on sequential images. Finally, cloud motion estimations are obtained using the Feed Forward Backpropagation Artificial Neural Network. The results obtained showed that the approach could be used successfully in cloud motion estimation.
Multi-scale Haze Removal via Residual Network

The scattering of climatic particles significantly alters images captured under unfavorable weather situations. Although many traditional methods have efficiently committed to annihilating haze, they pose some limitation due to their hand-crafted features, e.g., dark channel, maximum contrast, and colour disparity. This paper presents an end-to-end algorithm to restore a hazy image using a residual-based deep CNN straightforwardly. The proposed algorithm is non-subject to the climatic dispersing model, yet it learns the mapping relationship within the hazy input image and their corresponding transmission map. The network architecture constitutes a convolution kernel and multi-scale fusion layers in extracting relevant features in predicting a holistic propagation map. Ultimately, we obtain the residual image and circumvent the loss of information using a residual network. Comprehensive empirical results demonstrate that the proposed technique outperforms multiple conventional algorithms.
A Parametric Logarithmic Image Processing Framework Based on Fuzzy Graylevel Accumulation by the Hamacher T-Conorm

It has been proven that Logarithmic Image Processing (LIP) models provide a suitable framework for visualizing and enhancing digital images acquired by various sources. The most visible (although simplified) result of using such a model is that LIP allows the computation of graylevel addition, subtraction and multiplication with scalars within a fixed graylevel range without the use of clipping. It is claimed that a generalized LIP framework (i.e., a parameterized family of LIP models) can be constructed on the basis of the fuzzy modelling of gray level addition as an accumulation process described by the Hamacher conorm. All the existing LIP and LIP-like models are obtained as particular cases of the proposed framework in the range corresponding to real-world digital images.