Reviewerly/exHarmony huggingface dataset:
Crypto Trading and Bitcoin Prices: Evidence from a New Database of Retail Adoption

Prices for cryptocurrencies have undergone multiple boom-bust cycles, together with ongoing entry by retail investors. To investigate the drivers of crypto adoption, we assemble a novel database (made available with this paper) on retail use of crypto exchange apps at daily frequency for 95 countries over 2015–22. We show that a rising Bitcoin price is followed by the entry of new users. About 40% of these new users are men under 35, commonly identified as the most “risk-seeking” segment of the population. We confirm these findings by exploiting two exogenous price shocks: the crackdown of Chinese authorities on crypto mining in mid-2021 and the social unrest in Kazakhstan in early 2022. Moreover, we find that when prices rise retail investors buy, while the largest holders sell — making a return at the smaller users’ expense. Overall, back of the envelope calculations suggest that around three-quarters of users have lost money on their Bitcoin investments.
Generating synthetic mixed-type longitudinal electronic health records for artificial intelligent applications

The recent availability of electronic health records (EHRs) have provided enormous opportunities to develop artificial intelligence (AI) algorithms. However, patient privacy has become a major concern that limits data sharing across hospital settings and subsequently hinders the advances in AI. Synthetic data, which benefits from the development and proliferation of generative models, has served as a promising substitute for real patient EHR data. However, the current generative models are limited as they only generate single type of clinical data for a synthetic patient, i.e., either continuous-valued or discrete-valued. To mimic the nature of clinical decision-making which encompasses various data types/sources, in this study, we propose a generative adversarial network (GAN) entitled EHR-M-GAN that simultaneously synthesizes mixed-type timeseries EHR data. EHR-M-GAN is capable of capturing the multidimensional, heterogeneous, and correlated temporal dynamics in patient trajectories. We have validated EHR-M-GAN on three publicly-available intensive care unit databases with records from a total of 141,488 unique patients, and performed privacy risk evaluation of the proposed model. EHR-M-GAN has demonstrated its superiority over state-of-the-art benchmarks for synthesizing clinical timeseries with high fidelity, while addressing the limitations regarding data types and dimensionality in the current generative models. Notably, prediction models for outcomes of intensive care performed significantly better when training data was augmented with the addition of EHR-M-GAN-generated timeseries. EHR-M-GAN may have use in developing AI algorithms in resource-limited settings, lowering the barrier for data acquisition while preserving patient privacy.
Who is making the decisions? How retail managers can use the power of ChatGPT

Purpose This paper aims to explore ChatGPT’s (generative pre-trained transformers) potential as a tool for retailers to improve customer experience and boost sales. While it provides benefits like personalized recommendations and 24/7 assistance, there are limitations, like difficulty in understanding unconventional language. The paper stresses careful integration to overcome these limitations and create a better customer experience. Additionally, it discusses the potential for further development and integration of ChatGPT in retail, such as generating product descriptions and virtual try-on experiences. Finally, the paper encourages retailers to embrace ChatGPT to meet their customer needs. Design/methodology/approach Case-based methodology involves using specific cases or examples to explore a broader issue or phenomenon. Researchers have analysed real-world cases to identify patterns, themes and insights that can be applied to other contexts or situations. This was useful for understanding complex and multifaceted issues as it allowed us to delve deeper into specific examples and explore the nuances of the situation. Findings While ChatGPT is a powerful tool for retailers, limitations such as difficulty in understanding non-standard accents and unconventional language can arise, causing customer frustration. Retail managers must integrate ChatGPT in a way that enhances customer experience. In the future, ChatGPT has the potential to generate product descriptions, provide virtual try-on experiences and integrate with augmented or virtual reality technology to offer more immersive experiences. Careful consideration and integration can help retailers overcome these limitations and offer personalized recommendations, round-the-clock assistance and an engaging customer experience that improves sales. Originality/value The case topic is very much in a novel stage of research and writing.
Perspectives on Participation

This workshop brings together a cross-disciplinary community of researchers and practitioners interested in participative practice and interactive systems design. The workshop addresses growing fascination with participation across interaction design, community informatics, the arts, science and social science, and asks workshop participants to reflect on the ethics and efficacy of the tools and methods used in these diverse practices. The aim of the workshop will be to map out a critical framework exploring the qualities of participation from multiple disciplines. The workshop outcomes will outline how cross-disciplinary perspectives on participation can contribute to participatory and user-centred interaction design.
Understanding “Rules”: When is Behavior Rule-Guided?

The extent to which human cognition can be understood as rule-based is a classic issue in Cognitive Science and one which continues to provoke heated debate in a wide variety of areas, ranging from Implicit Learning through Inflectional Morphology to the acquisition of reading skills. Despite its centrality, the central notion of “rule” is far from well-defined. This paper examines a central feature of rulebased models, the concept of rule-following, and clarifies its role, its content, and some of the typical fallacies associated with its use.
Language Understanding by Computer: Developments on the Theoretical Side

This chapter discusses the notion of language understanding and how it relates to Artificial Intelligence. It reviews some of the more important work on the theoretical side in the design of computer systems intended to understand natural language. The chapter presents a view on directions in the computational modelling of language understanding that seem most important for the near future. Language understanding systems are designed so as to produce symbolic structures supposed to represent meaning, so-called &quot;meaning representations&quot;. R. Montague's work has popularized the use of expressions in a formal, logical language as semantic representations. Understanding a natural-language expression would change the state of the system; for instance, understanding the question should bring the system into a state where it knows that the speaker wants to know the value of the corresponding semantic representation. Speech act theory distinguishes a perlocutionary dimension of language use, which concerns the effects that a linguistic act altogether achieves.
Graduated Fidelity Motion Planning

This paper presents an approach to differentially constrained robot motion planning and efficient re-planning. Satisfaction of differential constraints is guaranteed by the search space which consists of motions that satisfy the constraints by construction. Any systematic replanning algorithm, e.g. D*, can be utilized to search the state lattice to find a motion plan that satisfies the differential constraints, and to repair it efficiently in the event of a change in the environment. Further efficiency is obtained by varying the fidelity of representation of the planning problem. High fidelity is utilized where it matters most, while it is lowered in the areas that do not affect the quality of the plan significantly. The paper presents a method of modifying the fidelity between replans, thereby enabling dynamic flexibility of the search space, while maintaining its compatibility with replanning algorithms. The approach is especially suited for mobile robotics applications in unknown challenging environments. We successfully applied the motion planner on a real robot: the planner featured 10Hz average replan rate on minimal computing hardware, while satisfying the car-like differential constraints.
Concept And Practices In Inclusive Education

The philosophy of inclusion is not new to India. It was very much there in the ancient times when all children were sent to ‘gurukuls’ for education. In today's context also, the emphasis is on providing education to all children in an inclusive environment. Inclusive education means education of all children, with and without disabilities together in regular schools. It is an approach, which takes into account unique characteristics, interests, abilities and learning needs of all children. It is an attempt to meet the unique needs of every child in a regular school setting where all children, including those with disability, try to participate in all facets of school life. The goal is to provide accommodating and personalized education for all students within the context of a general educational classroom. The need of the hour is to develop a universal design of learning which makes learning joyful for all children.
Chapter 12. Looking Forward

The current version of Techniques for Electronic Resource Management (TERMS), as represented through the TERMS wiki, outlines some of the best practices for electronic resource management today. Chapter 8 of Library Technology Reports (vol. 49, no. 2) “Techniques for Electronic Resource Management” explores how the resources to be managed continue to evolve and develop into new models of scholarship and new systems for management. This situation sometimes requires workflows to be rewritten and redesigned as the scholarship models change and the systems used to manage the resources are upgraded.
Assessment of Agricultural Water Use in Uzbekistan using Radiation Balance Method with Radiometric Data MODIS

The spatial images of agricultural water use in the irrigated lands of Khorezm region, located in the lower reaches of the Amudarya River, were mapped and monitored using the data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). Land use categories at the case study areas were classified using a recursive partitioning and regression tree with 250 m MODIS NDVI time series. A seasonal existing evapotranspiration (ET) rate was obtained through application of the Surface Energy Balance Algorithm for Land to 1 km daily in MODIS data. In the Khorezm region, a seasonal maximum values for ET was estimated at 1,000 and 1,200 mm for cotton and rice fields, respectively.
Micro Service Based System for Cost Based Selection of Cloud Provider Services

The increase in the amount of data generated on a daily basis coupled with the need to store and manage this data has encouraged the organizations to adopt cloud computing. In order to ensure better availability and reliability of their data as well as resources, most of the organizations make use of one or more cloud service providers .But the use of cloud resources puts forth some challenges as well. One of the challenges is its detailed monitoring. As the number of services utilized by the cloud consumers goes on increasing, the number of logs and metrics generated by them also scales rapidly.The dynamic nature of cloud infrastructure and the variety of services offered by several cloud vendors demands a sophisticated mechanism to calculate and analyze the cost of using different services. The billing reports by the cloud service providers deliver statistics about the usage of resources and the costs associated with them. It contains large amount of data which needs to be processed in order to gain useful information. In this paper, we propose a micro service based architectural framework which gathers the data from two different cloud service providers. This data is not only stored but processed to generate reports to enable optimal use of cloud infrastructure. The use of microservices framework provides benefits and is a preferred framework for the development of cloud applications. The main aim of this work is to provide an integrated mechanism to enable the comparison of cost for using similar cloud services.
Motion judgment algorithm based on joint angular velocity with variable viscoelastic assistive suit

In recent years, the burden for labor is increasing due to an aging society.To solve this issue, assistive suits are proposed as one of the solutions.In the previous study, an assistive suit has been developed that is based on variable viscoelasticity.However, the motion judgment interface for the variable viscoelastic based actuating device has not been studied.In this study, joint angle and joint angular velocity are used to judge the motion of the wearer, because variable viscoelastic based actuating device has a high backdrivability.In addition, with a suit of high backdrivability, the intention of a wearer can be shown as the joint angle.This study reports the motion judgment algorithm that is based on the wearer's joint angle, and joint angular velocity, and experiments have been conducted to evaluate the utility of the algorithm.
Critical Succes Factor Information System Model

The successful application of knowledge management as information system is determined by many factors where these factors are a component or part of knowledge management. The success of developing knowledge management will depend on these important factors. So far the knowledge system model that was built is only based on the main part without looking at the important parts of the model. This research aims to build a information system model based on factors of critical success. The identification of the critical success factors was sourced from 51 articles with a time span between 2006 and 2019. The results of this paper are a collection of several factors that determine the success of building a information management system. The most critical factor for information management systems is management support and organizational culture. Future research will continue research on strategies for implementing information management systems.
Detection, Localization of Text in Images by Mser and Enhanced Swt

The number digital images and digital videos has raised highly. The text shows in the images is major thing for fully understanding the images. So it is very signifigant to recognize the text. First step of text recognization system is known as text detection. But detecting text from images is very challenging and it is receiving high amount of concentration. Text detection is improved by pre-procssing techniques like noise removal, contrast changes and etc. Recently MSER (Maximally Stable Edge Region) and SWT (Stroke Width Transformation) methods are used seperately or combined with each other for detecting and localizing text. This paper analyzes and compares technical challenges and performance of above methods and take in new and fast method for text detection and localization. The proposed method comprises an enhanced technique based on SWT which combined with MSER. The proposed method detect wording which is located in differnt directions, different angle and combined letters. Performance of exisisting and proposed methods on road signal images depicted in experimental results.
Development of rough terrain mobile robot with non-circular wheel

Robot using wheels with special shape have been studied as a kind of robot aiming to search rough terrain. Wheels with the special shapes can leach step by higher than the wheel radius by hooking on steps and irregularities. However, the robot using the wheels with the special shape has a problem that he vehicle body moves up and down while traveling on a flat road. The purpose of this research is to develop a robot that can run on irregular ground and can travel without moving the body up and down during running on flat ground using wheels with special shape. In this paper, we developed a new prototype that is more useful for exploring indoor environments more freely, and investigated its operation method and conducted operation experiments.
Facebook and Michigan Uni fight deepfakes

Facebook has partnered with researchers from Michigan State University (MSU) to find a way to track and trace ‘deepfake’ images that have been created for malicious or criminal attacks.
Enhanced Approach For Handwritten Text Recognition Using Neural Network

The off-line character recognition is a process which is used to recognition the pattern. The problem for recognition is to segment the character into isolated word. In this paper, we use a new method which calculates the approximation width of the word for the segmentation. After using this algorithm characters are segmented which may have some extra stokes, braked image. To remove these errors some pre-processing steps are performed to make the character smoother. A new feature extraction algorithm is applied to this segmented character, in this algorithm we used to calculate the pixels range and also exact value of a image. The range value is used for the classification. The classifier has been used to train the neural network. Recognition of a character image we used to calculate the approximation value of this image and the value of this image has tested on the trained neural networks, which shows the recognized character. This system is developed by using JAVA. This system converts the handwritten document into structural text form.
Learning Collaboration in Home Care Service

Since the 1990s, the municipalities in Sweden have been more restrictive in their allocation of relief in elderly care, for example cutting the times granted for home care.Elderly care hasbeen streamlined in ways that force the staff to take care of increasing numbers of clients with extensive and complex needs.To compensate for the restrictions a value system has been implemented to improve care quality.The purpose of this project was to study the implementation of the value system and to promote knowledge among home care staff, in order to allow the structures and conditions for care provision to be questioned and developed.After an introduction to qualitative research methods staff interviewed elderly people receiving home care.The results were analyzed through dialogue and interaction between researchers and practitioners.Staff awareness of their own skills and the complexity of their work increased significantly.They concluded overall that increased resources are needed in order to achieve the national value system goals and to comply with the rehabilitative and independent-living approach of the Social Services Act.While the value system itself is an expression of the human rights of older people, enforcing it may conflict with the provisions of the Work Environment Act concerning working conditions.The work-integrated learning (WIL) profile at UW focuses on the practical integration of learning into the world of work and the interface between learning and application.This project, as an example of learning within an organization, fits the profile.
Data Visualization

Visualization is a powerful method to gain more insight about data and underlying processes. It is extensively used during the exploration phase to attain a better sense of how data is structured. Visual presentation of results is also an effective way to convey the key findings; people usually grasp nice and informative diagrams much easier than they grasp facts embedded in convoluted tabular form. Of course, this doesn’t mean that visualization should replace other approaches; the best effect is achieved by judiciously combining multiple ways to describe data and outcomes. The topic of visualization itself is very broad, so this chapter focuses on two aspects that are not commonly discussed elsewhere: how visualization may help in optimizing applications, and how to create dynamic dashboards for high-velocity data. You can find examples of other uses throughout this book.
A Chronological Method of Detecting Image Based Email

In this paper we present a Visual feature extraction using improvised SVM and KNN classifiers. The proposed method is an automatic, stable, quick response automatic segmentation, followed by feature extraction and classification to detect spam from the images and the text. The KNN classifier is used to extract features by predicting nearest neighbour while SVM, analyze the data for classification and regression. The hybrid-based Visual feature extraction and classification is elaborated wherein this work discuss the proposed approach which incorporated using improvised SVM and KNN classifier. Moreover, identified patterns via feature extraction method by means of a minimum number of features that are effective in discriminating pattern classes. With all the aforementioned concepts elaborated, the experimental set-up was elaborated with the experimental task, and the results of the character recognition component are further elucidated.
Actor Information Crawling System Based On Python

With the progress of the times and the development of society, the quantity of information on the network has increased explosively, but the quality of information is mixed. Therefore, this paper obtains the information needed by the system from Baidu Encyclopedia, one of the most authoritative network encyclopedias in China. A large amount of information in this information is useless or repetitive. If all of them are stored, they will occupy a lot of space. Therefore, it is the most important thing to filter out useful information. Nowadays, actors have gradually become a hot topic during one’s leisure time. Manual screening of information is outdated and inefficient. Therefore, in order to comply with the trend of the times and improve the efficiency of screening, this paper designs and develops an actor information crawling system based on Python combined with popular technologies and actual needs. The user interface and web crawler module of the system are written in Python language, the data display module uses HTML + jQuery to realize the front-end page, the Java language to realize the back-end business, and the data is stored in MySQL database. The project system flexibly uses computer technology to complete the actor information crawling system, conforms to the trend of the times and improves the screening efficiency, which can effectively prevent the current situation of high consumption and low efficiency of manually collecting actor information.
Enhanced Framework for Ensuring Privacy Preserving Image Retrieval in Cloud

Large scale of images data sets are being produced every day by various digital devices. Due to huge computational jobs make people seizure to cloud platforms for their efficient &amp; economical reckoning resources. These computing platforms in which assets are provided as services of the internet. Sensitive information stored in cloud makes more challenging in data security and access control. Once data is uploaded to cloud-platform, the privacy and security of image-data fully depend and believe upon cloud service provider honesty. Our proposed work deals with securing image where high protections are applied on multimedia contents. This paper deals with studies security challenges algorithms lies in image at the time of constructing cloud platform. In this a new enhanced security technique investigated, includes secure by using computation and encryption, act as a security information guard for high secrecy in cloud platform data storage areas. In our research work, cipher-text image is created and performing encryption-decryption at User level. Data hiding and ECC (Elliptic curve cryptosystem) based watermarking technique at cloud computing platform.