Reviewerly/exHarmony huggingface dataset:
Change Detection For Area Surveillance Using A Moving Camera

This paper tackles change detection for area surveillance with a moving device. None of the existing datasets for change detection meets a surveillance scenario where a camera is mounted on a moving platform and pointed in the direction of moving. Thus, this paper creates a new dataset including several challenging points. For this dataset, this paper employs a composable method and proposes some components. To evaluate the proposed components, some corresponding classic methods were also tested on the dataset. As a result, the proposals outperformed them. Moreover, this paper investigated the relationship between the parameters of the components and their performance.
OR-Net: Pointwise Relational Inference for Data Completion under Partial Observation

Contemporary data-driven methods are typically fed with full supervision on large-scale datasets which limits their applicability. However, in the actual systems with limitations such as measurement error and data acquisition problems, people usually obtain incomplete data. Although data completion has attracted wide attention, the underlying data pattern and relativity are still under-developed. Currently, the family of latent variable models allows learning deep latent variables over observed variables by fitting the marginal distribution. As far as we know, current methods fail to perceive the data relativity under partial observation. Aiming at modeling incomplete data, this work uses relational inference to fill in the incomplete data. Specifically, we expect to approximate the real joint distribution over the partial observation and latent variables, thus infer the unseen targets respectively. To this end, we propose Omni-Relational Network (OR-Net) to model the pointwise relativity in two aspects: (i) On one hand, the inner relationship is built among the context points in the partial observation; (ii) On the other hand, the unseen targets are inferred by learning the cross-relationship with the observed data points. It is further discovered that the proposed method can be generalized to different scenarios regardless of whether the physical structure can be observed or not. It is demonstrated that the proposed OR-Net can be well generalized for data completion tasks of various modalities, including function regression, image completion on MNIST and CelebA datasets, and also sequential motion generation conditioned on the observed poses.
Three arrows models in the developing of new digital learning experiences

The many challenges arising from COVID-19 have forced Japanese businesses and higher education to accelerate innovations and digital transformations. Japan is a well-known global leader in adopting innovative approaches with the use of advanced cutting-edge technologies, and education is not an exception. Digital learning and effective implementation of new pedagogies focused on distance learning, flexibility, and adaptation to innovative teaching practices are helping students and educators across Japan. As a result, Japan’s traditional face- to-face lecture style is being rapidly transformed into a new Education 5.0, involving Cloud tools, correct use of learning management systems (LMS), web 4.0/5.0, and a wide range of other educational tools. Additionally, in our research and course design, we have adopted a new collaborative agile approach, including academic, industry expertise and teaching assistance facilitators. We identify challenges and opportunities through a case study that examines the design of a new course that incorporates agile collaboration and student perceptions after the adoption of this rapid and unexpected digital learning approach.
Correcting Classification: A Bayesian Framework Using Explanation Feedback to Improve Classification Abilities

Neural networks (NNs) have shown high predictive performance, however, with shortcomings. Firstly, the reasons behind the classifications are not fully understood. Several explanation methods have been developed, but they do not provide mechanisms for users to interact with the explanations. Explanations are social, meaning they are a transfer of knowledge through interactions. Nonetheless, current explanation methods contribute only to one-way communication. Secondly, NNs tend to be overconfident, providing unreasonable uncertainty estimates on out-of-distribution observations. We overcome these difficulties by training a Bayesian convolutional neural network (CNN) that uses explanation feedback. After training, the model presents explanations of training sample classifications to an annotator. Based on the provided information, the annotator can accept or reject the explanations by providing feedback. Our proposed method utilizes this feedback for fine-tuning to correct the model such that the explanations and classifications improve. We use existing CNN architectures to demonstrate the method's effectiveness on one toy dataset (decoy MNIST) and two real-world datasets (Dogs vs. Cats and ISIC skin cancer). The experiments indicate that few annotated explanations and fine-tuning epochs are needed to improve the model and predictive performance, making the model more trustworthy and understandable.
Algoritmos de minería de datos en la industria sanitaria

In this paper, we review data mining approaches for health applications. Our focus is on hardware-centric approaches. Modern computers consist of multiple processors, each equipped with multiple cores, each with a set of arithmetic/logical units. Thus, a modern computer may be composed of several thousand units capable of doing arithmetic operations like addition and multiplication. Graphic processors, in addition may offer some thousand such units. In both cases, single instruction multiple data and multiple instruction multiple data parallelism must be exploited. We review the principles of algorithms which exploit this parallelism and focus also on the memory issues when multiple processing units access main memory through caches. This is important for many applications of health, such as ECG, EEG, CT, SPECT, fMRI, DTI, ultrasound, microscopy, dermascopy, etc.
A Privacy-Preserving Outsourcing Data Storage Scheme with Oruta Data Auditing

Cloud Computing has been envisioned as the next generation architecture of IT Enterprise. In contrast to traditional solutions, where the IT services are under proper physical, logical and personnel controls, Cloud Computing moves the application software and databases to the large data centers, where the management of the data and services may not be fully trustworthy. With cloud computing and storage, users are able to access and to share resources offered by cloud service providers at a lower marginal cost. With cloud computing and storage services, data is not only stored in the cloud, but routinely shared among a large number of users in a group. In this project, we propose Oruta, a privacy-preserving auditing scheme for shared data with large groups in the cloud. We utilize ring signatures to compute verification information on shared data, so that the TPA is able to audit the correctness of shared data, but cannot reveal the identity of the signer on each block. We can implement the batch auditing scheme to perform efficient public auditing to protect both identity and data privacy in cloud environments.
인체 바이오모니터링 지식 모델링 및 데이터베이스 구축

Human bio-monitoring (HBM) data is a very important resource for tracking total exposure and concentrations of a parent chemical or its metabolites in human biomarkers. However, until now, it was difficult to execute the integration of different types of HBM data due to incompatibility problems caused by gaps in study design, chemical description and coding system between different sources in Korea. In this study, we presented a standardized code system and HBM knowledge model (KM) based on relational database modeling methodology. For this purpose, we used 11 raw datasets collected from the Ministry of Food and Drug Safety (MFDS) between 2006 and 2018. We then constructed the HBM database (DB) using a total of 205,491 concentration-related data points for 18,870 participants and 86 chemicals. In addition, we developed a summary report-type statistical analysis program to verify the inputted HBM datasets. This study will contribute to promoting the sustainable creation and versatile utilization of big-data for HBM results at the MFDS.
USING PICTURE STRIP STORY IN TEACHING SPEAKING SKILL

This research is aimed to find out whether students’ speaking skill can be increased by using picture strip story or not. This research employed pre-experimental design. The variables of this research were the picture strip story as the independent variable and the students’ speaking skill as the dependent variable. The population of this research was the XI grade of SMK Daerah Luwuk which consists of thirteen students. The technique sampling was total sampling, which means that all population was taken as the sample of this research. The researcher use test as the main instrument in this research which is divided into pre-test and post-test. The data analysis of this research used was statistic calculation. The result of the research, the mean score of pre-test was 67.76 while the mean score in post-test was 75.73 and the testing hypothesis, the t-counted score was 5.533, while t-table for degree of freedom 12 and the level of significance = 0.05 was 1.782. It indicated that the hypothesis was accepted because picture strip story had increased the students’ speaking skill.
MULTIMODALITY AND DIGITAL SPACE: COGNITIVE AND SOCIO-EMOTIONAL FACTORS IN ONLINE COMMUNICATION

The expansion of digital technology is weaving social and professional actions on mental powerof the human life, while promoting not only decision-making but also socio-emotional management, which leads to human behavior modification. In the digital age, the implementation of new multi-modal, hybrid and open devices promotes distance learning/teaching while developing digital and pragmatic skills among training people. In fact, the digital space allows to diversify methodological approaches but also to enrich methods and techniques by the learning path's individualization. In an E-learning environment, pedagogical mediation is involved in the learning process. In fact, a transmission of knowledge and experience is established through interactive learning, while emphasizing the effective time management of the educational process, with a view to cultivating learning strategies. This is why, in online communication, it is essential to take into account the factors related to hot or cold cognition, because different kinds of memories including sensory, short-term and long-term depend on the encoding and storage of cognitive, socio-affective and metacognitive decision-making in any learning situation. Ultimately, Teacher Mentor's contribution is essential by implementing good educational practices that cultivate a know-how to act and also by adopting tactics to accomplish strategic experiential training through IT systems.
LibGuides: Education for COVID-19 Testing - Regulated Care Providers: COVID-19 Point-of-Care Testing for Peri-op and Birthing Units

This guide contains the mandatory education and other resources required for Regulated Care Providers to implement COVID-19 swab collection.
Analysis of Student Conformity Based on Gender Using the Rasch Model

This research is based on the phenomenon that occurs, namely there are still students who differentiate themselves based on the groups they associate with at school, causing some students to get into problems. This type of research is a comparative descriptive which aims to describe the differences in conformity based on the gender of the students. The population in this study were students of SMK class XI. The sampling technique in this research is to use random sampling technique. The instrument used was a Likert scale model and a questionnaire containing statements related to student conformity. Test the validity of the instrument using the Rasch Model. The data analysis technique used the Independent Sample t-Test. The results of the study found that there were differences in conformity between male and female students.
과학관 과학 논증 글쓰기 프로그램에 참여한 초등학생들의 글쓰기 변화에 미치는 영향

This study was conducted for 6 weeks for 5th grade elementary school students participating in the “Science Experiment Class” program at ‘S Science Museum’ in Seoul. Through this study, we analyzed how their argument elements and types of argument structures changed, and investigated their perception of this class. The results of the study are as follows. First, continuous argumentative writing activity and feedback varied the argument elements used by the students, and it can be seen that this has a positive effect on students'' argumentation activities. Second, the “claim type” and “reason type” decreased, and the “acknowledgment type” and “rebutted type” increased. This shows that the ability to actively utilize objective fact information is improved. Third, as a result of semi-structured interviews, students had positive perceptions of scientific argumentative writing education, continuous writing, and feedback. It was confirmed that the positive recognition of these students not only develops the type of argumentation structure but also enriches the argument elements and allows them to be used in various ways.
CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING

Credit card transactions have become common location these days and so are the frauds related to it. One of the most commonplaces to carry out fraud is to acquire the card facts illegally and use it to make on line purchases. For credit card businesses and merchants, it's far in-viable to detect these fraudulent transactions among heaps of regular transactions. If enough statistics is gathered and made available, device mastering algorithms may be implemented to remedy this problem. In this work, popular supervised and unsupervised device getting to know algorithms were implemented to discover credit card frauds in a enormously imbalanced dataset. It was found that unsupervised machine learning algorithms can cope with the skewness and give nice classification results
Rethinking Sustainability Requirements: Drivers, Barriers and Impacts of Digitalisation from the Viewpoint of Experts.

Requirements engineering (RE) is a key area to address sustainability concerns in system development. Approaches have been proposed to elicit sustainability requirements from interested stakeholders before system design. However, existing strategies lack the proper high-level view to deal with the societal and long-term impacts of the transformation entailed by the introduction of a new technological solution. This paper proposes to go beyond the concept of system requirements and stakeholders' goals, and raise the degree of abstraction by focusing on the notions of drivers, barriers and impacts that a system can have on the environment in which it is deployed. Furthermore, we suggest to narrow the perspective to a single domain, as the effect of a technology is context-dependent. To put this vision into practice, we interview 30 cross-disciplinary experts in the representative domain of rural areas, and we analyse the transcripts to identify common themes. As a result, we provide drivers, barriers and positive or negative impacts associated to the introduction of novel technical solutions in rural areas. This RE-relevant information could hardly be identified if interested stakeholders were interviewed before the development of a single specific system. This paper contributes to the literature with a fresh perspective on sustainability requirements, and with a domain-specific framework grounded on experts' opinions. The conceptual framework resulting from our analysis can be used as a reference baseline for requirements elicitation endeavours in rural areas that need to account for sustainability concerns.
Equivalence of Correlation Filter and Convolution Filter in Visual Tracking

(Discriminative) Correlation Filter has been successfully applied to visual tracking and has advanced the field significantly in recent years. Correlation filter-based trackers consider visual tracking as a problem of matching the feature template of the object and candidate regions in the detection sample, in which correlation filter provides the means to calculate the similarities. In contrast, convolution filter is usually used for blurring, sharpening, embossing, edge detection, etc in image processing. On the surface, correlation filter and convolution filter are usually used for different purposes. In this paper, however, we proves, for the first time, that correlation filter and convolution filter are equivalent in the sense that their minimum mean-square errors (MMSEs) in visual tracking are equal, under the condition that the optimal solutions exist and the ideal filter response is Gaussian and centrosymmetric. This result gives researchers the freedom to choose correlation or convolution in formulating their trackers. It also suggests that the explanation of the ideal response in terms of similarities is not essential.
Extracting Multiple Features for Dynamic Hand Gesture Recognition

In this work a framework based on histogram of orientation of optical flow (HOOF) and local binary pattern from three orthogonal planes (LBP_TOP) is proposed for recognizing dynamic hand gestures. HOOF algorithm extracts local shape and dynamic motion information of gestures from image sequences and local descriptor LBP is extended to three orthogonal planes to create an efficient motion descriptor. These features are invariant to scale, translation, illumination and direction of motion. The performance of the new framework is tested in two different ways. The first one is by fusing the global and local features as one descriptor and the other is using features separately to train the multi class support vector machine. Performance analysis shows that the proposed approach produces better results for recognizing dynamic hand gestures when compared with state of the art methods
Modulating Regularization Frequency for Efficient Compression-Aware Model Training

While model compression is increasingly important because of large neural network size, compression-aware training is challenging as it needs sophisticated model modifications and longer training time.In this paper, we introduce regularization frequency (i.e., how often compression is performed during training) as a new regularization technique for a practical and efficient compression-aware training method. For various regularization techniques, such as weight decay and dropout, optimizing the regularization strength is crucial to improve generalization in Deep Neural Networks (DNNs). While model compression also demands the right amount of regularization, the regularization strength incurred by model compression has been controlled only by compression ratio. Throughout various experiments, we show that regularization frequency critically affects the regularization strength of model compression. Combining regularization frequency and compression ratio, the amount of weight updates by model compression per mini-batch can be optimized to achieve the best model accuracy. Modulating regularization frequency is implemented by occasional model compression while conventional compression-aware training is usually performed for every mini-batch.
Variational Pedestrian Detection

Pedestrian detection in a crowd is a challenging task due to a high number of mutually-occluding human instances, which brings ambiguity and optimization difficulties to the current IoU-based ground truth assignment procedure in classical object detection methods. In this paper, we develop a unique perspective of pedestrian detection as a variational inference problem. We formulate a novel and efficient algorithm for pedestrian detection by modeling the dense proposals as a latent variable while proposing a customized Auto Encoding Variational Bayes (AEVB) algorithm. Through the optimization of our proposed algorithm, a classical detector can be fashioned into a variational pedestrian detector. Experiments conducted on CrowdHuman and CityPersons datasets show that the proposed algorithm serves as an efficient solution to handle the dense pedestrian detection problem for the case of single-stage detectors. Our method can also be flexibly applied to two-stage detectors, achieving notable performance enhancement.
Addressing the ethical principles of the Norwegian National Strategy for AI in a kindergarten allocation system

The Norwegian National Strategy for Artificial Intelligence (NNSAI) published in 2020 includes seven principles of ethical AI. This paper explores whether those seven principles are stated in a clear enough way and are feasible to be satisfied by a specific AI system. We build an implementation of the Gale-Shapley algorithm to allocate kindergarten places in Bergen, Norway. The presented solution is then evaluated against the ethical principles from the NNSAI. We argue that it is difficult to respect all the ethical principles when implementing a solution to a matching problem.
E-FIR System with Chatbot

In this project we are implementing the E-FIR (First Information Report) system with chatbot integration for the public use. Because number of cases are increasing day by day and looking at the current situation of COVID-19 we have decided to implement the ONLINE portal. It is also, designed to bridge the gap between the police and the common people. Generally, many crimes are seen by the citizens but most of the crime are not registered due to the tedious and hectic process. In this project we are implementing the easiest way so that people can register their compliant easily, for that we have introduced the chatbot for registering the complaint or we can also choose the option given in the chatbot to register the complaint. We can also, monitor the registered complaint that we have done. For this we are using the Cloud Computing which can help Government to speed up in rolling out of e-Governance projects and to reach out it to a larger number of people in a short period. Since, Government is putting a lot of effort towards Information Technology (IT) for cost shrinking and making it available for all the citizens equally in urban and rural areas. It entails use over the Internet of computing hardware, software and network infrastructure and the main thing is the applications are hosted remotely. By using this, the current system user’s information will be kept confidential and only users complain will be forwarded to the nearest police station. The main web application will be maintained by the admin from the police station.