Reviewerly/exHarmony huggingface dataset:
Creativity of Social Groups. The Method of Morphological Analysis and its Application in the Experiment “Metamorph. Chain of Changes”

The article is part of the project called “Metamorph. Chain of Changes.” It includes participants’ self-reflections, a self-reflexive analysis, and a theoretical analysis. In the multidimensional complex of problems considered, key points of reference and positions are outlined, among which are: the phenomenological path of discovery and critical thinking, functionalism and computationalism, identity, individuality and social environment, as well as stereotypes and norms in the field of creative practices.
photosav: A Tkinter Based Photo Editing Application

The human brain is more engaged by viewing images than by reading lists of facts. Whenever we try to view, edit, or create an image, we actually have our own interaction with the visual expression in the image. Although everyone has a unique editing style, it is a common practice in the photography industry. Photo editing enables users to enhance and stylize images in ways that would be impossible in-camera. It is much more difficult without image editing application. This paper presents an approach for the development of Windows application that can be a full-scale photo editing tool. Our application is easy to use and it also helps to store images via different extensions. The various image editing libraries used to develop this application are explained here. Different image editing features such as cropping, drawing, etc. help all type of users to use the application efficiently. The detailed explanation of these features are provided. This paper shows how image processing can be used to edit images by using various computer vision algorithms to perform several operations on an image. However, there are certain image editing applications available, but photosav is a simple and comprehensive windows application in which maximum features are clubbed together for the users
Digitization of the Education in Bulgaria: State and General Trends

The accelerated implementation of information and communication technologies in all areas of public development requires in-depth studies of its effects at both national and global level. As education is one of the sectors most influenced by these trends, the need for systematic research on the application of digital technologies in the educational process is becoming increasingly apparent nowadays. In this context, the study presents an attempt to compare the level of use of e-services in education and other public sectors, also identifying the main target groups of users. The results of an author's empirical study are presented, which analyzes the state of the educational environment in Bulgaria, the use of information and communication technologies and their impact on it. The challenges in introducing digital education as well as the factors for their sustainable implementation in Bulgaria is also outlined. Based on the analysis are derived the types of educational e-applications offered, their quality, trends in their use, as well as the most typical groups of users of this type of education in Bulgaria.
Single-photon-memory measurement-device-independent quantum secure direct communication

Quantum secure direct communication (QSDC) uses the quantum channel to transmit information reliably and securely. In order to eliminate the security loopholes resulting from practical detectors, the measurement-device-independent (MDI) QSDC protocol has been proposed. However, block-based transmission of quantum states is utilized in MDI-QSDC, which requires practical quantum memory that is still unavailable at the time of writing. For circumventing this impediment, we propose a single-photon-memory MDI QSDC protocol (SPMQC) for dispensing with high-performance quantum memory. The performance of the proposed protocol is characterized by simulations considering realistic experimental parameters, and the results show that it is feasible to implement SPMQC by relying on present-day technology.
News Headlines Dataset For Sarcasm Detection

Past studies in Sarcasm Detection mostly make use of Twitter datasets collected using hashtag-based supervision but such datasets are noisy in terms of labels and language. Furthermore, many tweets are replies to other tweets, and detecting sarcasm in these requires the availability of contextual tweets. To overcome the limitations related to noise in Twitter datasets, we curate News Headlines Dataset from two news websites: TheOnion aims at producing sarcastic versions of current events, whereas HuffPost publishes real news. The dataset contains about 28K headlines out of which 13K are sarcastic. To make it more useful, we have included the source links of the news articles so that more data can be extracted as needed. In this paper, we describe various details about the dataset and potential use cases apart from Sarcasm Detection.
Research on Application of artificial Neural Network in econometrics

As a fringe subject of artificial intelligence and systems engineering, artificial neuron network can not only discover more valuable data information, but also accelerate the calculation speed of data information in econometrics. With the rapid development of econometrics, the artificial neural network method has received wide attention of scholars research at home and abroad, compared with the traditional econometric method, artificial neural network algorithm with adjustable parameters and highly nonlinear simulation operation ability, self-learning and self-organization can function in dealing with practical problems, present a're likelier to carry a strong and adaptability. Therefore, on the basis of understanding the research status of econometrics and artificial neural networks, this paper mainly discusses how to apply artificial neural networks in econometrics, and makes clear the future application direction from the perspective of the development of the new era.
Case Studies

The following is a write-up by Won Jenn Lee, a family member and past intern who worked on some of the following case studies to gain some knowledge about Serverless. His insights should be useful to those new to this type of architecture.
Strategy

Serverless architecture is quite unlike the previous evolutions of cloud software. As such, existing cloud management strategies are often incompatible. This is especially a challenge in larger enterprises where for years, the focus has been on optimizing the security, delivery, and operation of server-based applications.
Freeform optics characterization with surface registration and fitting algorithms for optical point-based spatial path 3D topography metrology

Accurate characterization of the form error for freeform optics is critical for controlled manufacturing and evaluation of optical properties. To solve the difficulty of current surface registration and fitting algorithms, and improve characterization accuracy of the form error of freeform optics for optical point-based spatial path 3D topography metrology, in this paper, improved surface registration and fitting algorithms are proposed, including a B-spline surface description of freeform optics, point orthogonal projection, registration parameter optimization, and B-spline fitting. Limited by the angular characteristics of an optical point-based sensor, the slope and reference frame of freeform optics must be flexibly adjusted, and polynomial surfaces are described and fitted as B-spline surfaces based on B-spline geometric invariance. To efficiently determine corresponding points on B-spline surfaces, the point orthogonal projection algorithm without Bezier subdivision is proposed using the first-order Newton method. Then, the iteration procedure of coordinate adjustment in sphere space using Lie algebra registration parameters is proposed to solve the difficulty of the current registration parameter optimization procedure. To fit a spatial path form error, the least squares B-spline fitting method is proposed to improve the Zernike method. Through simulation experiments, the proposed registration algorithm with good convergence can improve accuracy by one to two orders of magnitude compared with current registration algorithms. Through repeated experiments of a freeform prism, the proposed method can significantly improve the peak-to-valley and RMS accuracies compared with the stylus method, and characterize the mid-high frequency form error (about 200 nm) of a freeform prism.
Curve Fitting with MATLAB®

Curve fitting is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points.
Analisis kualitas search engine sebagai media pembelajaran online di perpustakaan perguruan tinggi di Bali

Introduction. Previous research on the quality of search engines through a combination of three supporting theories is limited. This research which used the three theories aims to measure the quality of search engines based on user perceptions, utilization, and recall and precision values. The three supporting theories include recall and precision analysis, TAM (Technology Acceptance Model) and WebQual.&#x0D; Research methods. This study used a quantitative inferential method with questionnaires, observations, and online search.&#x0D; Data analysis. This study used inferential statistical data analysis techniques which used to determine the relationships among research variables.&#x0D; Results and discussion. Search engine Yahoo shows the highest recall value, while Google shows the highest precision value. The two search engine are well accepted and used frequently by users. The usability quality dimension has a significant effect on the utilization of Google and Yahoo, while the interaction quality dimension has no significant effect on those two search engines. Meanwhile, the dimension of information quality has a significant effect on Yahoo, however, it has no a significant effect on Google. &#x0D; Conclusion and suggestions. The two search engines are accepted by user. They used as online learning media in universities to provide ease of use and support user interaction.
Tinjauan literatur sistematis pemanfaatan electronic document management system bagi organisasi dalam menunjang manajemen pengetahuan

Introduction. Organizations need information systems to facilitate document management in support of their business processes that can be managed through EMDS. This study aims to find out the literature on the benefits of EDMS in supporting knowledge management.&#x0D; Data Collection Methods. This study uses the Systematic Literature Review (SLR) method on the results of journal articles regarding the use of EDMS in supporting knowledge management during 2015-2020. The stages of SLR research use three stages, namely planning, collecting and reporting.&#x0D; Data Analysis. Research data using descriptive analysis presented with simple statistical tables..&#x0D; Results and Discussion. There are ten articles showing that EDMS used in various organizations can assist in the management of KM information assets such as storage, distribution, security that can improve organizational performance that needs to be supported by good human resources.&#x0D; Conclusion. HR plays an important role in information management through the use of EDMS to support KM. This research provides awareness of the importance of utilizing EDMS and competent human resources in KM management in organizations. The collection of articles uses two databases so that the data obtained is still limited.
Systematic literature review and bibliometric analysis of archivist competencies in digital curation

Introduction. Digital curation tasks and objectives align with the task of archivists who must digitally plan to ensure safe long-term preservation. As information professionals, archivists must master competencies to become digital curators in the future. This study aims to determine what competencies an archivist must have in digital curation.&#x0D; Data Collection Methods. This study uses a systematic literature review (SLR) available in SCOPUS from 2018-2022, considering 11 selected articles. This study also applied bibliometric analysis using VOSViewer to identify the main characteristics, discuss, and provide research opportunities in this field.&#x0D; Data Analysis. In the first phase, SLR consists of three steps: planning, conducting, and reporting. In the second phase, the bibliometric using co-occurrence analysis to visualize the keyword network, density, and overlay visualization. &#x0D; Results and Discussion. The results of the bibliometric analysis indicate that there are still several topics regarding digital curation that have not been widely studied, including those related to competence, cultural heritages, data management, career, and learning. Based on the systematic literature review there are 11 selected articles that are relevant to research topics and used for data analysis. &#x0D; Conclusion. From 11 selected articles, it was found that archivist competencies in digital curation can be divided into four categories: cognitive, functional, social, and meta competence. The competencies found have implications for archivists as future digital curators by combining technical skills, including expertise in metadata standards and information organization, as well as personal skills. Based on the findings, it also can be an opportunity for further research to examine digital curation competency seen in the topics that are still rarely studied.
Source-attention features fusion network for person re-identification

Person re-identification (Re-ID) shows huge application scenarios in video retrieval scenarios. However, there are many challenges that hinder the development of person Re-ID technology such as variable recognition background, pedestrian misalignment and occlusion. Attention mechanism is proposed to strengthen the ability of the basic network in extracting discriminative features, but it ignores some useful detailed information in the original features. In order to solve this problem, we propose a Source-Attention Feature Fusion network (SAFF-Net) to extract pedestrian representations combining attentional features and original features. Specifically, SAFF-Net first extracts the saliency discriminative pedestrian features of pedestrians by embedding the channel attention module in the backbone network. And then by cross-fusing saliency discriminatory features and original features, these fused features can direct the model to focus on both saliency discriminatory features and original features, thus increasing the completeness and richness of pedestrian representations. Experiments show that our method is competitive with other state-of-the-art person Re-ID methods.
Remove Blur Image Using Bi-Directional Akamatsu Transform and Discrete Wavelet Transform

Purpose: Image is an imitation of everything that can be materialized, and digital images are taken using a machine. Although digital image capture uses machines, digital images are not free from interference. Image restoration is needed to restore the quality of the damaged image.Methods: Bi-directional Akamatsu Transform is proven to have an effective performance in reducing blur in images. Meanwhile, Discrete Wavelet Transform has been widely used in digital image processing research. We had been investigated the image restoration method by combining Bi-directional Akamatsu Transform and Discrete Wavelet Transform. Bi-directional Akamatsu Transform applied in Low-Low (LL) sub-band is the Discrete Wavelet Transform decomposition image most similar to the original image before decomposing. In this study, there are still shortcomings, including the determination of the values of N, up_enh, and down_enh, which are still manual. Manually setting the three values makes the Bi-directional Akamatsu Transform method not get the best results. With the use of machine learning methods can get better restoration results. Further testing is also needed for a more diverse and robust blur. The image data has a resolution of 256x256, 512x512, and 1024x1024. The image will be directly converted to a grey-scale image. The converted image will be given an attack model: average blur, gaussian blur, and motion blur. The image that has been attacked will apply two restoration methods: the proposed method and the Bi-direction Akatamatsu Transform. These two restoration images will then be compared using PSNR.Result: The average PSNR value from the restoration of the proposed method is 0.1446 higher than the average PSNR value from the restoration of the Bi-directional Akamatsu Transform method. When we compare it with the average PSNR value of the Akamatsu Transform restoration method, the average PSNR of the proposed method is 0.2084.Value: The combination of DWT and akamatsu transform results produce good PSNR values even though they have gone through the blurring method in image restoration.
The Development of Chicken Coop Automatic Remote Visual Monitoring System

Purpose: A remote visual monitoring system will be very helpful for chicken farmers to monitor their cages, that usually located away from their houses. This system needs adequate bandwidth in transmitting the video over the internet, which is usually very limited in urban areas. The main goal of this research is to develop an automatic chicken coop remote monitoring system and define the optimum video resolution to be transmitted. Methods: We used an 8 MP Raspberry Pi camera V2 to record the video and send the results to Google Drive by utilizing the GDrive API. Furthermore, a live streaming video from the chicken coop is accessible through a simple HTTP web page utilizing ngrok as a tunneling software so that the live streaming video can be publicly accessed from anywhere using a web browser. Three video resolutions of 640x480, 800x600, 1024x768 with 15 and 30 framerates were used in our experiments. Each scenario has a duration of five minutes and takes 12 times.Result: The experiment results showed, resolutions that provide a stable video recording and streaming are 640x480 and 800x600. The resulting system succeeded in performing live streaming along with the process of data acquisition. Value: The Google Drive infrastructure is used because of its popularity and convenience by people with limited digital literacy such as smallholder chicken farmers. Furthermore, the video produced by this system can be used in supporting research of chicken behavior pattern identification to build a system notification of an emergency situation in the cage.
CausalEGM: a general causal inference framework by encoding generative modeling

Although understanding and characterizing causal effects have become essential in observational studies, it is challenging when the confounders are high-dimensional. In this article, we develop a general framework $\textit{CausalEGM}$ for estimating causal effects by encoding generative modeling, which can be applied in both binary and continuous treatment settings. Under the potential outcome framework with unconfoundedness, we establish a bidirectional transformation between the high-dimensional confounders space and a low-dimensional latent space where the density is known (e.g., multivariate normal distribution). Through this, CausalEGM simultaneously decouples the dependencies of confounders on both treatment and outcome and maps the confounders to the low-dimensional latent space. By conditioning on the low-dimensional latent features, CausalEGM can estimate the causal effect for each individual or the average causal effect within a population. Our theoretical analysis shows that the excess risk for CausalEGM can be bounded through empirical process theory. Under an assumption on encoder-decoder networks, the consistency of the estimate can be guaranteed. In a series of experiments, CausalEGM demonstrates superior performance over existing methods for both binary and continuous treatments. Specifically, we find CausalEGM to be substantially more powerful than competing methods in the presence of large sample sizes and high dimensional confounders. The software of CausalEGM is freely available at https://github.com/SUwonglab/CausalEGM.
Gaze Grammars - Is there an invariant hierarchical sequential structure of human visual attention in natural tasks?

Human visual attention is highly structured around gathering relevant information on the underlying goals or sub-goals one wishes to accomplish. Typically, this has been modelled using either qualitative top-down saliency models, or through the use of highly reductionist psychophysical experiments. Modelling an information-gathering process in these ways however often ignores the rich and complex repertoire of behaviour that make up ecologically-valid gaze. We propose a new way of analysing natural data, suitable for analysing temporal structure of visual attention in complex, freely moving environments. To achieve this, we capture visual information from subjects performing an unconstrained task in the real world - in this case, cooking in a kitchen. We use eye-tracking glasses with a built-in scene camera (SMI ETG 2W @ 120Hz) to record n=15 subjects, setting up, cooking breakfast and eating in a real-world kitchen. We process the visual data using a deep-learning based pipeline (Auepanwiriyakul et al., 2018, ETRA), to obtain the stream of objects in the field of view. Eye-tracking gives us a sequence of objects people are focussing their overt attention on throughout the task. We resolve ambiguities using pixel-level object segmentation and classification techniques. We analyse these sequences using HMM and context-free grammar induction models (IGGI), revealing a potential hierarchical structure which is invariant across subjects. We compare this grammatical structure against a “ground-truth”, the WordNet lexical database of semantic relationships between objects (Miller et al, 1995), and find some surprising similarities and counterintuitive differences between attention-derived and textual base structure of objects, suggesting that the differences between how we look and how we verbally reason about tasks is an open question of cognition and attention.
Blockchain-based Privacy Protection Credential Model for Zero- Knowledge Proof over Distributed Systems

The bulk of blockchain-based identity authentication solutions are developed on public blockchain platforms, which still use centralized identity management and verification methodologies, making it difficult for microgrids to satisfy their demands for trusted access and fine-grained access control. As a result, a distributed identity authentication system capable of supporting multi-center operations is created using the FISCO BCOS alliance’s blockchain technology. To enable autonomous control of user identification, DID-based identity management protocol is developed; the distribution of terminal nodes in the micronetwork is also explored. It creates a zero-knowledge proof-based privacy-protecting credential. It establishes an open, transparent, and credible underlying identity architecture for users and upper-layer apps, ensuring that users’ trustworthy and verifiable identities are protected in a variety of privacy and security circumstances. It enables autonomous identity management, fine-grained access control, and secure data interchange. System trials and performance analysis demonstrate the intended system’s usability and effectiveness.