Reviewerly/exHarmony huggingface dataset:
Human Cognition and Data Journalism

ABSTRACT Data journalism storytelling has become an important aspect of journalism during the twenty-first century. This cross-disciplinary research draws the attention of data journalism stakeholders to the mental operations (conscious choices and nonconscious mental processing) of a person who experiences storytelling with data. It is argued that data journalists can learn from the availability heuristic, the cognitive bias, and other concepts of cognitive science in an effort to become more attentive to the mental meFchanisms of their audience. Research from other disciplines, such as law (guilt determination) and economics, suggests that taking nonconscious decision-making seriously would be very productive for the field. Evidence suggests that a better understanding of the human brain's decision-making as well as of cognitive control may provide important insights for the data storytellers. This study is undertaken with an initial focus on the reasons why the audience acts upon emotional stories rather than data and statistics. We begin by arguing that data collection methods, measurement, and quantification may not be the only ‘obscure’ and difficult part to control for a data journalist, but after the cleaning, the analysis, and the visualization, the workings of the brain of the receptor, play a crucial role on what the individual will decide to do. The acts of journalism do not enter a tabula rasa, but rather a terra incognita. The research also examines the role of the language use by the data journalist; and whether language can overshadow data and consequently influence the reader’s perception on the information from an article. The study uses theories from various disciplines (communication, psychology, neuroscience, and cognitive science) and gathers data by the use of qualitative research through interviews with cognitive scientists, neuroscientists, and psychologists. Keywords: Data Journalism, Cognitive Science, Data storytelling, Data, Bias, Language, perception.
Promotional activities at Bahagia Mendawai Library Bansir Laut Village Pontianak Tenggara District

Background of the study: Bahagia Mendawai Library wants to empower the community in the Kampung Wisata Caping Pontianak, which is on the outskirts of the Kapuas River. Purpose: To introduce the existence and benefits of libraries to the community, efforts are made through library promotional activities. Method: This study uses a qualitative descriptive method. Collecting data through observation, interviews and documentation. The number of informants consists of 4 (four) people. Findings: The results showed that the promotional activities carried out by the Bahagia Mendawai Library used publications using social media and brochures, participated in exhibitions and had various innovation programs such as Siberliterasi, JeLi, and Pojok Literasi. The media used are print media and the internet. The perceived obstacle are the lack of available resources in the form of inadequate facilities in carrying out promotional activities on social media and the weak knowledge of librarian related to promotions. Conclusion: The promotional activities carried out by the Bahagia Mendawai Library in terms of its existence are quite good because the library is already known by the community around the library and attracted the interest of people outside the Kampung Wisata Caping Pontianak. However, in terms of the usefulness of the promotion, it has not been maximized because not all people have used the library.
Privacy-preserving spatial and temporal data aggregation for smart metering

Smart metering is becoming a promising approach that enables the energy management through real time consumption measurements. However, these fine-grained energy consumption data collected by smart meters might reveal sensitive information about the smart grid users. In this paper, we propose a privacy-preserving scheme that supports both spatial and temporal data aggregation. Our proposed solution uses the Paillier homomorphic encryption scheme to perform the aggregation of data without revealing the energy measurements. Through security analysis, we show that our proposed solution provides various security requirements and guarantees the privacy preservation of the smart grid consumers. Additionally, we demonstrate its robustness and resistance against various security attacks.
A Novel Identification Method for Frequently Switched Encrypted Video Streaming in Complex Network Environment

Nowadays, to provide stable video streaming and smooth user experience among different scenarios, more and more online video sharing and social media platforms adopt the Dynamic Adaptive Streaming over HTTP (DASH), which can transmit and play videos of appropriate quality levels according to the user's network conditions. For sake of illegal videos spreading on the platforms, the identification of encrypted DASH video streaming is of great importance. However, related works on the identification of encrypted DASH video streaming are seldom concerned with the streaming under poor network conditions that the video quality levels are switched relatively frequently. Therefore, we propose a novel video fingerprint extraction method as well as a novel multi-layer fuzzy matching scheme. So far as we know, we are the first to solve the identification of encrypted DASH video streaming under poor network conditions. And to reduce the overhead we establish a local video fingerprint database using the fingerprint extraction method. Our scheme can identify the encrypted DASH video streaming of frequently switched quality levels using very short-time network traffic sniffing. Experiments show that the time required for traffic sniffing is just 6 seconds and the accuracy exceeds 98%, which is significantly better than state-of-art methods. Besides, our scheme performs well on the identification of streaming in multiple scenarios.
The effect of learning attitude on learning flow among students at beauty specialized high schools in South Korea

Background:The class form of beauty specialized high schools is mainly practical education rather than theoretical education, and the acquisition of national technical qualifications and employment skills are given priority.In the beauty industry, both theoretical basic knowledge and technical practical competence must be equipped.Learning in beauty education requires, above all, the attitude of the learner to constantly study the knowledge and skills delivered by the instructor.This can lead to lasting classroom effects.Objective: This study aimed to examine the effect of learning attitude on learning flow among students at beauty specialized schools to provide a foundation for improving the quality of beauty education and preparing a plan to increase the learning satisfaction of students.Methods: A total of 370 questionnaires were distributed to students at beauty specialized high schools in South Jeolla Province, of which 335 were analyzed after excluding 35 that lacked sincerity. Results:The results of this study showed statistically significant positive correlations between the sub-factors of learning attitude, namely attention factor, learning skill factor, and self-learning factor, and, the sub-factors of learning flow, namely the cognitive and affective domains. Conclusion:These findings can be used as a foundation for designing educational plans to improve the academic achievement and learning satisfaction of students at beauty specialized high schools.
Library anxiety and fulfillment information needs of collage students of the Faculty of Mathematics and Natural Sciences Bengkulu University

Background of the study: The development and knowledge, the library is used as a source to meet the information needs each student. However, ther are still many students who experience confusion in using the library so that library anxiety will occur and the fulfillment of their information needs will not be fulfilled. Purpose: This study aims to determine the level of library anxiety and the fulfilment of information needs for students of the faculty of Mathematics and Natural Sciences Bengkulu University batch 2019. Method: The method use is a descriptive quantitative research method. The sampling technique used is proportionate stratified random sampling, selected as many as 83 students Findings: the result showed that the level library anxiety in students of the Faculty of Mathematics and Natural Sciences Bengkulu University batch 2019 was 3,5 categorized with high level of anxiety. The level of fulfillment of information needs after being associated with library anxiety in students students of the Faculty of Mathematics and Natural Sciences Bengkulu University batch 2019 is 3,6 , categorized as a high level. Conclusion: The level of library anxiety that occurs in students of the Faculty of Mathematics and Natural Sciences Bengkulu University batch 2019 includes a high level of anxiety of fulfillment of their information needs after being associated with library anxiety is categorized with a high level so that the overal fulfillment of their information needs is not met
Information dissemination of local content through Muhi Corner in Muhammadiyah 1 Yogyakarta High School Library

Background of the study: The library of SMA Muhammadiyah 1 Yogyakarta has a unique and distinctive collection that is referred to as local content and organized in Muhi Corner. Libraries have a duty to collect and disseminate them to users Purpose: This study aims to analyze the local content information service of Muhi Corner, analyze the information dissemination process carried out, and evaluate the obstacles in the dissemination of information. Method: This study uses a qualitative approach with a descriptive method. Data were collected through observation, interviews, and documentation analysis. The data analysis was conducted by data reduction, data presentation, and conclusion. Findings: The results showed that Information dissemination activities for Muhi Corner's collections started with information dissemination for teachers, education staff, students, and researchers; then packaged in the form of print and digital papers. Information sources are presented in the form of local collections and gray literature. The information channels used are direct (socialization &amp; library tour) and social media (Instagram). Conclusion: Dissemination of information on the Muhi Corner collection has been carried out but is still limited, either directly or using the media. Obstacles in disseminating Muhi Corner information include, (1) limited human resources, (2) limited collection on the subject of literature, and (3) limited promotional activities.
Tendency and Student Satisfaction Using Printed and Electronic Information Resources at the University of North Sumatra Library

Background of the study: The presence of electronic Information Resources (SDI) in university libraries is especially attractive for users. The convenience and not having to come physically to the location make users prefer to use electronic SDI compared to printed SDI. Purpose: The main purpose of this research is to find out the trend of using SDI in the USU Library based on the field of science. Another objective is to find out the actual satisfaction of users with the electronic and printed SDI available at the USU Library. Method: This research is descriptive. The method used is a survey and data collection techniques are carried out by distributing questionnaires to respondents. Findings: The results showed that USU undergraduate students (S1) had different tendencies in using information resources. Students from non-exact study programs prefer to use the printed format, while students from the exact sciences study program more often use electronic SDI. Then in terms of the satisfaction that is desired and obtained, non-exact students have an average percentage value that is higher than exact. Conclusion: The results of this study can be used as evaluation material for the USU Library to continue to provide SDI in both printed and electronic formats following the information needs of its users.
TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification

Learning unbiased node representations under class-imbalanced graph data is challenging due to interactions between adjacent nodes. Existing studies have in common that they compensate the minor class nodes `as a group' according to their overall quantity (ignoring node connections in graph), which inevitably increase the false positive cases for major nodes. We hypothesize that the increase in these false positive cases is highly affected by the label distribution around each node and confirm it experimentally. In addition, in order to handle this issue, we propose Topology-Aware Margin (TAM) to reflect local topology on the learning objective. Our method compares the connectivity pattern of each node with the class-averaged counter-part and adaptively adjusts the margin accordingly based on that. Our method consistently exhibits superiority over the baselines on various node classification benchmark datasets with representative GNN architectures.
Peculiarities of legal assessment of subjective elements of offenses containing signs of special organized forms of extremist activity

The article draws attention to the special, legally significant characteristics of the subjective elements of crimes containing signs of special organized forms of extremist activity. The content of the forms of guilt is determined, with the allocation of general and special characteristics arising from the provisions of the Federal Law “On countering extremist activity” and the Decree of the Plenum of the Supreme Court of the Russian Federation “On judicial practice in criminal cases on crimes of extremist orientation”. Separately, the characteristics of general and special subjects committing acts are presented, with signs of special organized forms of extremist activity.
Innovative management of secondary educational institutions

Modern education is in the process of dynamic changes. The system of interaction of all participants in the educational process is changing, and this requires teachers, and first of all, leaders, to comprehend all innovations and transformations. As a result, a new vision of the management system of an educational institution is being formed in terms of its effectiveness, innovation and competitiveness. This article explores an innovative approach to the management of secondary educational institutions. The studies of domestic and foreign scientists on this issue have been researched, studies have been carried out in educational institutions, and the practical experience of the authors of the article has been used. Innovative pedagogical management is characterized as a set of principles, methods, organizational forms and technological methods for managing pedagogical systems, aimed at improving the efficiency of their functioning and development, and allows you to move from a vertical management system (subject-object) to a horizontal system of organizational and managerial cooperation (subject-subject ), providing creative development, both the personality of the pupil and the personality of the teacher. Management of innovative processes is multivariate; it involves a combination of standards and originality of combinations, flexibility and originality of methods of action, based on a specific situation. There are no ready-made recipes in innovation management and there cannot be. But he teaches how, knowing the techniques, methods, ways of solving certain problems, to achieve tangible success in the development of the organization.
When the Sun Goes Down: Repairing Photometric Losses for All-Day Depth Estimation

Self-supervised deep learning methods for joint depth and ego-motion estimation can yield accurate trajectories without needing ground-truth training data. However, as they typically use photometric losses, their performance can degrade significantly when the assumptions these losses make (e.g. temporal illumination consistency, a static scene, and the absence of noise and occlusions) are violated. This limits their use for e.g. nighttime sequences, which tend to contain many point light sources (including on dynamic objects) and low signal-to-noise ratio (SNR) in darker image regions. In this paper, we show how to use a combination of three techniques to allow the existing photometric losses to work for both day and nighttime images. First, we introduce a per-pixel neural intensity transformation to compensate for the light changes that occur between successive frames. Second, we predict a per-pixel residual flow map that we use to correct the reprojection correspondences induced by the estimated ego-motion and depth from the networks. And third, we denoise the training images to improve the robustness and accuracy of our approach. These changes allow us to train a single model for both day and nighttime images without needing separate encoders or extra feature networks like existing methods. We perform extensive experiments and ablation studies on the challenging Oxford RobotCar dataset to demonstrate the efficacy of our approach for both day and nighttime sequences.
Effective real-time face mask detection on NVIDIA edge devices

During these difficult times of COVID-19, people are struggling to return to their normal routines, including going back to schools and workspaces. To prevent the spread of the disease, wearing face masks is essential for everyone to protect themselves and the ones around them. However, challenges arise in regard to enforcement of wearing masks in large crowds such as at educational centres and public transportation. This paper proposes a robust automatic system for face mask detection using transfer learning kits from NVIDIA. Based on the backbone of Resnet-18, the model results in high accuracy in the distinguishing of persons who do and do not wear masks. Leveraged by the NVIDIA edge accelerator, the system can run in real-time environments, making it applicable in various venues. Its feasibility was demonstrated by deploying the approach in an education centre in Hong Kong.
C++ and Java performance on the Android platform

The article presents a comparative analysis of Java and C ++ technologies in terms of performance on the Android platform. The research was carried out on custom applications in both languages. The series of repetitions of each test were performed on three research units - Samsung and Xiaomi devices. The following criteria were used: test execution time, CPU load, and RAM usage. The performance, in most of the carried out tests, was in favor of the C++ language, and the main difference and with the greatest discrepancy between the technologies tested was the execution time, where C++ scored 18 points, and Java only 3 points. For the CPU usage, the result was the same, but value differences were much smaller. A nondiscerning parameter that was the use of RAM. C++ received 11 points and Java 10.
At-scale evaluation of weight clustering to enable energy-efficient object detection

Accelerators implementing Deep Neural Networks for image-based object detection operate on large volumes of data due to fetching images and neural network parameters, especially if they need to process video streams, hence with high power dissipation and bandwidth requirements to fetch all those data. While some solutions exist to mitigate power and bandwidth demands for data fetching, they are often assessed in the context of limited evaluations with a scale much smaller than that of the target application, which challenges finding the best tradeoff in practice. This paper sets up the infrastructure to assess at-scale a key power and bandwidth optimization - weight clustering - for You Only Look Once v3 (YOLOv3), a neural network-based object detection system, using videos of real driving conditions. Our assessment shows that accelerators such as systolic arrays with an Output Stationary architecture turn out to be a highly effective solution combined with weight clustering. In particular, applying weight clustering independently per neural network layer, and using between 32 (5-bit) and 256 (8-bit) weights allows achieving an accuracy close to that of the original YOLOv3 weights (32-bit weights). Such bit-count reduction of the weights allows shaving bandwidth requirements down to 30%-40% of the original requirements, and reduces energy consumption down to 45%. This is based on the fact that (i) energy due to multiply-and-accumulate operations is much smaller than DRAM data fetching, and (ii) designing accelerators appropriately may make that most of the data fetched corresponds to neural network weights, where clustering can be applied. Overall, our at-scale assessment provides key results to architect camera-based object detection accelerators by putting together a real-life application (YOLOv3), and real driving videos, in a unified setup so that trends observed are reliable.
Shallow Layer Convolutional Features with Correlation Filters for UAV Object Tracking

In this paper, convolutional shallow features are proposed for unmanned aerial vehicle (UAV) tracking. These convolutional shallow features are generated by pre-trained convolutional neural networks (CNN) and are used to represent the target objects. Furthermore, to estimate the location of the target objects, an adaptive correlation filter based on the Fourier transform is used. This filter is multiplied with the convolutional shallow features by using pixel-wise multiplication in the Fourier domain. Then, the inverse of Fourier is performed to estimate the location of the target object, where its location is represented by the maximum value of the response map. Unfortunately, the target object always changes its appearance during tracking. Therefore, we proposed an updated model to address this issue. The proposed method is evaluated by using the UAV123 10fps benchmark dataset. Based on the comprehensive experimental results, the proposed method performs favorably against state-of-the-art tracking algorithms
A View Independent Classification Framework for Yoga Postures

Yoga is a globally acclaimed and widely recommended practice for a healthy living. Maintaining correct posture while performing a Yogasana is of utmost importance. In this work, we employ transfer learning from Human Pose Estimation models for extracting 136 key-points spread all over the body to train a Random Forest classifier which is used for estimation of the Yogasanas. The results are evaluated on an in-house collected extensive yoga video database of 51 subjects recorded from 4 different camera angles. We propose a 3 step scheme for evaluating the generalizability of a Yoga classifier by testing it on 1) unseen frames, 2) unseen subjects, and 3) unseen camera angles. We argue that for most of the applications, validation accuracies on unseen subjects and unseen camera angles would be most important. We empirically analyze over three public datasets, the advantage of transfer learning and the possibilities of target leakage. We further demonstrate that the classification accuracies critically depend on the cross validation method employed and can often be misleading. To promote further research, we have made key-points dataset and code publicly available.