Reviewerly/exHarmony huggingface dataset:
Cloud and Mobile Systems as a Means of Forming Professional Competencies in Economics and Engineering Students

The study is relevant since there is a contradiction between growing requirements for the informational and communicative competence of different students, including the use of cloud-based computer modeling systems, and the insufficient use of cloud technologies in teaching bachelors of economics and technology. The study aims at determining cloud-based systems that are advisable to use as a means of forming the professional competencies of future specialists in economics and technology and developing guidelines for their use. Based on the analysis of various programs, the authors have proved that the most appropriate is the use of specific software in the professional training of future specialists. Scattered software and cloud services need integration based on unified access that a Google account can provide. They also developed a method of using cloud technologies as a means of forming the informational and communicative competence of bachelors of economics and technology. The use of cloud-based information and communication technologies ensures an increase in the level of professional competence of future specialists.
Socially Assistive Robots for Exercising Scenarios. Studies on group effects, feedback, embodiment and adaption

Even though positive effects of being physically active are commonlyknown, only a few parts of the world population are sufficiently ac-tive. The World Health Organization (WHO) states that this problemaffects 31% of the adult’s world population and 80% of the adolescentpopulation. Appropriate levels of physical activity (PA) are essentialto prevent obesity in childhood and to keep a Quality of Life (QOL)in old age but are also essential to prevent other NoncommunicableDiseases (NCDs). Thus, physical inactivity is growing into a severeproblem globally, and there is a growing need to motivate peopleto become more physically active during their lifetime. One primarycause that raises PA levels is having a peer or help from professionals.However, having assistance is not possible in every situation. It mightbe challenging to find and schedule with a partner or to commute toother places. Roboticist introduced Socially Assistive Robot (SAR) asan assistive tool for exercising, cognitive or rehabilitation tasks. Thisthesis explores SAR in the context of exercising along four featuresthat have been partly targeted but not yet thoroughly investigated.These features are a) the social role of the robot, b) encouragementc) embodiment and d) adaptation. First, this thesis looks at the mo-tivational effects of exercising with SAR concerning features a) - c).Second, this thesis questions how a system can adapt to the user, andwhether adaptivity or adaptability is enough to close the gap betweenuser needs and system behavior. I conducted studies that test the dif-ferent features by assessing subjective ratings of the robot as well asmeasurable motivational variables (e.g., time spent exercising withthe robot) in a bodyweight workout scenario.The results show that features a) - c) have a positive influence onuser’s exercising time. Additionally, users perceive a robot compan-ion as more likable than a robot instructor or a human partner. Fur-thermore, an adaptive robot increases the associated competence andquality of relationship compared to an adaptable robot. However, theresults also show that the robot does not always have to exercise alongwith the user. In situations where it is not possible, the robot couldalso only give encouraging feedback. This thesis backs up earlier find-ings of using SAR by replicating motivational group exercising ef-fects found in Human-Human Interaction (HHI). Thus, the evidencethat SARs are a suitable tool for rehabilitative interventions increaseswhich may convince health experts to consider SAR as a useful thera-peutic tool. Nevertheless, this thesis evaluated the effects only duringshort-term interactions. Thus, proving that the found effects are long-lasting is essential for future studies.
Comparison Between Types of Solar Panels for Meteorological Stations in Different Areas of Ecuador

Throughout the history of the human being, it has been tried to understand how the effects of the climate can alter the daily life, and how a prediction can be established that allows to understand how the atmospheric phenomena will behave in a region that is governed by physical laws, which affect the environment, and create climatic changes. Through a weather station, a database can be established, with information from sensors specialized in the measurement of different parameters, which make up a climate study and their representation. In Ecuador and in the world, the use of photovoltaic panels has a very important role in the generation of energy since its use does not contribute to the emission of greenhouse gases and can provide energy to remote places, the energy generated by photovoltaic panels is clean and does not generate damage to the environment, and its maintenance is simple and low cost, which is why this type of energy is growing in several developed countries worldwide.
A Study of Internet of Medical Things (IoMT) Used in Pandemic Covid-19 For Healthcare Monitoring Services

Before Internet of things, visit or meet a doctor is based on the appointments, by tele and text communication and also interaction with patient and doctors are limited. IoMT enables medical devices remote monitoring, unleash the possibility for patients to keep safe and healthy, also made easy for physicians to deliver excellent care for patients. The capability of IoT or IoMT in infectious disease control a network of interconnected systems and Artificial intelligence, Data analytics and using omnipresent connectivity in all these networks based upon real time data can help to provide an early warning system to restraint the spread of Pandemic like situation (Covid-19 corona virus, Ebola virus, Hanta Virus etc.) and it also help in healthcare monitoring and treatment services.
A Hybrid Capsule Network with Attention and BiLSTM for Opinion Mining in Text

Examining the product review or review on web service facilitate to increase the product quality or web service. By means, comments from online shopping sites such as Amazon, Flipkart, EBay etc will not only assist the users to purchase the product however besides be able to guide the producer/supplier to identify the advantages and disadvantages of the goods. Mining online shopping websites with their information will becomes a major important task. Web Mining plays a major important role to mine the details of online websites efficiently. Web mining is the process of data mining that learns without human intervention and mine information obtained from the documents in web and also services. Sentiment categorization and web mining has become a truly significant task recently, with profound business and research impact. Machine learning algorithms and soon after deep learning methods have been the market leaders in sentiment analysis. The advent of capsule networks has been a landmark event in deep learning. It has been truly proficient in image processing. In case of text classification, standalone capsule networks are not optimally suitable. Here, a hybrid BiLSTM-Capsule framework is introduced for sentiment analysis of web texts of reviews from various datasets. In the model beginning, there is a bidirectional LSTM layer after which is an attention layer and a final capsule layer. This review analysis will helps to improve the products from amazon, increase the movie quality. The analysis of outcome depending on MR, IMDB, SST and Amazon datasets indicated the introduced framework performs better than some benchmark deep learning models. Significantly, the BiLSTM-Capsule can put its words in sentimental trend showing the capsules’ attributes without utilizing the linguistic knowledge.
Online Anomaly Detection for Streaming Data Implemented on Top of Kafka, Scikit-Multiflow and River

Identification of rare events is an important component in intrusion and fraud detection, system monitoring and event detection. Anomalies can represent problematic situations where early, accurate and actionable insights are critical to make situational assessments in the event of unexpected conditions. For many problems, the state of the art in machine learning is batch learning. However, training models in a batch context can represent an opportunity cost. For instance, in applications such as fraud detection, the time and investment spent on training batch models, with increasingly larger data sets and complex infrastructure, could be spent detecting aberrant behavior. Online anomaly detection algorithms offer rapid access to useful insights with fewer computing capacity requirements and often need to be integrated with existing or legacy data streams in the enterprise. This work introduces a proof of concept integration of two online streaming anomaly detection algorithms available in the scikit-multiflow and River frameworks with the popular Kafka event streaming platform.
CUI@CSCW: Inclusive and Collaborative Child-facing Voice Technologies

Conversational User Interfaces (CUIs), from online chatbots to smart speakers, are increasingly being used to support children and family activities. This virtual workshop seeks to bring together researchers from academia and practitioners from industry who are interested in the design, development, application, and study of CUIs with a focus on children and family based interactions. We aim to examine the challenges involved in designing and developing CUIs that are capable of supporting everyday activities of children and families. Furthermore, we will also discuss the insights CSCW research can provide into understanding how CUIs can participate in collaborative activities of children and families. By bringing together existing researchers and new ideas in this space, we intend to map new areas of work including addressing the technical, social, and ethical challenges that lay ahead. Additionally, this workshop seeks to foster a strong community and enable potential future collaborations on this important topic.
Efficiency Evaluation of China's Regional Innovation Process Based on a Network Data Envelopment Analysis Model with a Mixed Structure: Enlightenment from the Regional Tobacco Industry

Accurate measurement of regional efficiency is a prerequisite for effective management. Prior studies have expanded on the overall "black box" evaluation with two stages of research and development (R&amp;D) and commercialization, opening up the internal structure of the regional innovation process, but ignoring the independent innovation activities of universities, research institutes, and firms in the R&amp;D stage. We construct a mixed structure with two stages, three actors, and four subsystems, and conduct an empirical analysis of China's provincial samples from 2017 to 2019 by using the network data envelopment analysis (DEA) model. Results show that the efficiency of the R&amp;D stage at the provincial level is generally higher than that of the commercialization stage. However, the three subsystems of the R&amp;D stage perform poorly. Spearman’s rank correlation coefficient suggests that there is a significantly positive correlation between total regional efficiency and commercialization. In addition, we use the k-means method to divide 27 provinces into three clusters, setting a more appropriate improvement benchmark for inefficient provinces. Based on enlightenment of regional tobacco industry, we put forward some proposals for specific stage and specific subsystem.
Evaluation of User Interfaces Description Languages to Augmented Reality

Augmented reality is a rapidly advancing technology as it is widely used in several applications and domains, from the aerospace industry to medical-surgical applications, particularly in learning and training. A fundamental part of the educational use is the graphical user interface as a vehicle to deliver information to students. Within the augmented experiences development process, it is necessary to identify the most appropriate way to describe the user interface. This article evaluates languages describing augmented reality user interfaces. In this paper, several languages are analyzed, and new criteria are proposed to extend the evaluation framework. Finally, the results show the current limitations and the slowdown of development and adaptation of new languages for augmented reality platforms.
Analysis of Human Behavior as a Condition for Creative Artificial Storytelling

On the one hand, there is currently no convincing example of AI writing successful fiction stories. For computers to write good stories, they need to understand human behavior. But most contemporary systems, designed for automatic extracting storylines from stories and producing new plots, ignore knowledge about human behavior and its circumstances. On the other hand, theoretical understanding of human behavior requires empirical material, and stories analyzed by computers can be such material. If the problem of theoretical explanation of behavior and the problem of computational story understanding and storytelling are considered from a single point of view, then there is a possibility of significant progress in solving the both problems. A method to analyze descriptions of human behavior in fiction stories, that is based on Lewinian formula of behavior, as well as some ideas about possible ways to implement this method of text analysis into computing systems are suggested. If computers can automatically analyze stories not only at the level of linguistic grammar, but also at the level of behavioral grammar, they will reach a deeper understanding of human behavior and the stories people tell. This will allow computers to create meaningful new stories that are truly interesting to people.
Contextuality-based quantum conferencing

Nonlocality inequalities for multi-party systems act as contextuality inequalities for single qudit systems of suitable dimensions. In this paper, we propose the procedure for adaptation of nonlocality-based quantum conferencing protocols (QCPs) to contextuality-based QCPs. Unlike the nonlocality-based protocols, the proposed protocols do not involve nonlocal states. These protocols can also be implemented in a class of noisy channels without any compromise in the key generation rate. As an illustration of the procedure, we present a QCP based on Mermin's contextuality inequality. As a significant improvement, we propose a QCP based on CHSH contextuality inequality involving only four-dimensional states irrespective of the number of parties sharing the key. The key generation rate of the latter is twice that of the former. Although these QCPs allow for an eavesdropping attack which has no analog in nonlocality-based QCPs, a way out of this attack is demonstrated. Finally, we show the feasibility of experimental implementation of these protocols with orbital angular momentum (OAM) states.
Uncovering Retail Trading in Bitcoin: The Impact of COVID-19 Stimulus Checks

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RETRACTED ARTICLE: Recognition method of radar intra-pulse modulation type based on signal square spectrum bandwidth ratio

Abstract The twenty-first century is the era of electronic warfare and information warfare. The focus is of the battle between all parties. CEEMD can link the time domain and frequency domain, describe the two-dimensional time–frequency characteristics of the signal, and draw the time–frequency diagram of the signal, so as to reduce the noise signal and improve the signal-to-noise ratio of the signal. The purpose of this paper was to study how to adjust the signal square spectrum bandwidth ratio in the subject of identifying the intra-pulse modulation of radar, so as to solve the problem of identifying the type of radar intra-pulse modulation. The experimental results in this paper show that the decomposition result of EEMD is incomplete and the signal reconstruction error is larger. Compared with the previous two methods, not only the CEEMD method can effectively suppress modal aliasing, but also the decomposition result is complete; the signal reconstruction error is very small, and the decomposition results close to ideal value. The interleaving filter with a bandwidth ratio of 1:2 can divide the 100 GHz channel spacing into asymmetric output spectra with bandwidths greater than 60 GHz and 30 GHz, which effectively improves the current mix of 10 Gb/s and 40 Gb/s The bandwidth utilization of the system illustrates the success of the simulation experiment.
OPTIMAL KEY GENERATION FOR PRIVACY PRESERVATION USING BLOCKCHAIN TECHNOLOGY

Supply chain management is an important area of logistics.It is a process involving many stakeholders with multiple areas and even more moving parts.The proposed technology aims to make the supply chain more efficient, investing in the wrong technology complicates productivity further while hampering profits, leading to multiple issues in supply chain.Blockchain technology is the solution to the problem of structuring data with built-in security features.Blockchain technology enables decentralization with the participation of members on distributed networks.There is not a single point of failure because of the distributed structure and the transaction record cannot be change by single user.In this paper for making data more secure while sharing information in supply chain a key generation process is done using data sanitization and restoration process and new improved Fittest Self Adaptive sea lion algorithm is discussed using only the authorize person will be able to use shared data and it will be compared with existing model using different parameters.
Accessible or Illusory Transparency? COVID-19 and Philippine Open Government Data

Abstract A little over a month after the coronavirus ( COVID -19) was declared as a pandemic, the World Health Organization expressed the need for open data to support policies that seek to address COVID -19, even as governments were called upon to facilitate access to data and information. Adopting the transparency framework developed by Fung, Graham and Weil (2007), this study examines ‘ laginghanda.gov.ph ,’ the official Philippine government website for COVID -19 data, to determine if it serves the open government data ( OGD ) goals as outlined and identified by the Organization for Economic Cooperation and Development ( OECD ) and as agreed under the Open Government Partnership. Using an exploratory research approach, a documentary analysis and content audit of the laginghanda.gov.ph showed that the website did not provide timely data even as the website contained mostly static, general information. Health statistical data could be accessed but it was neither machine readable nor timely; while, financial and economic data were lacking. In other words, the website does not contribute to government transparency and cannot be used to determine the accountability of government agencies. Thus, the author concludes that it fails as a tool for democratization.
Doubly Strongly First Order Dependencies

Team Semantics is a generalization of Tarskian Semantics that can be used to add to First Order Logic atoms and connectives expressing dependencies between the possible values of variables. Some of the resulting logics are more expressive than First Order Logic, while others are not. I characterize the (relativizable) atoms and families of atoms that do not increase the expressive power of First Order Logic when they and their complements are added to it, separately or jointly.
Automatic Chinese Meme Generation using Deep Neural Networks

Internet memes have become widely used by people for online communication and interaction, particularly through social media. Interest in meme-generation research has been increasing rapidly. In this study, we address the problem of meme generation as an image captioning task, which uses an encoder–decoder architecture to generate Chinese meme texts that match image content. First, to train the model on the characteristics of Chinese memes, we collected a dataset of 3,000 meme images with 30,000 corresponding humorous Chinese meme texts. Second, we introduced a Chinese meme generation system that can generate humorous and relevant texts from any given image. Our system used a pre-trained ResNet-50 for image feature extraction and a state-of-the-art transformer-based GPT-2 model to generate Chinese meme texts. Finally, we combined the generated text and images to form common image memes. We performed qualitative evaluations of the generated Chinese meme texts through different user studies. The evaluation results revealed that the Chinese memes generated by our model were indistinguishable from real ones.
Neuromuscular Control of the Face-Head-Neck Biomechanical Complex with Learning-Based Expression Transfer from Images and Videos

The transfer of facial expressions from people to 3D face models is a classic computer graphics problem. In this paper, we present a novel, learning-based approach to transferring facial expressions and head movements from images and videos to a biomechanical model of the face-head-neck complex. Leveraging the Facial Action Coding System (FACS) as an intermediate representation of the expression space, we train a deep neural network to take in FACS Action Units (AUs) and output suitable facial muscle and jaw activation signals for the musculoskeletal model. Through biomechanical simulation, the activations deform the facial soft tissues, thereby transferring the expression to the model. Our approach has advantages over previous approaches. First, the facial expressions are anatomically consistent as our biomechanical model emulates the relevant anatomy of the face, head, and neck. Second, by training the neural network using data generated from the biomechanical model itself, we eliminate the manual effort of data collection for expression transfer. The success of our approach is demonstrated through experiments involving the transfer onto our face-head-neck model of facial expressions and head poses from a range of facial images and videos.
Digital Curation and Information Design in digital environments: women’s museums panorama

Abstract Women’s museums take on significant functions for social discussions which, through preservation and access to the memory and culture of their collections, provide new habits and perspectives on objects constituted by structures of multiple languages on the Web. In this perspective, this study aims to understand how Digital Curation and Information Design in Information Science contribute to information presentation, access and sharing in these digital environments on the Web. The general objective was to contextualize the concepts and definitions of Digital Curation and Information Design to identify possible articulations for information organization and its presentation of women’s museums digital collections. The study is qualitative in nature and descriptive-exploratory. It consisted of a bibliographic review of the themes correlated with data collection in the scope of collections and digital environments of women’s museums, through information from the International Association of Women’s Museums and Patrimonio en Femenino. The results showed a low number of digital collections available and also the need for reflection on the methodology of planning and creating digital environments as a laboratorial process, continuous, in perpetual beta, iterative and in successive improvements. The study concluded that other dimensions of women’s memory preservation should be constituted to facilitate information access and sharing with the implementation of Digital Curation and Information Design resources for the construction of environments and, in them, their collections.