Reviewerly/exHarmony huggingface dataset:
HANDWRITING SYNTHESIS WITH DEEP LEARNING

In the past few years, there have been massive developments in the field of Deep Learning and Neural Networks, this work intends to leverage these recent developments for the use of Handwriting Generation. Input for handwriting data can be taken in two forms, off-line and on-line. Off-line format of handwriting data consists of input in the form of images usually in gray-scale. Whereas on-line format consists of time series data having coordinates and a timestamp associated with multiple input points which together constitute a character and ultimately assimilate to form a word. Generally Recurrent Neural Network (RNN), Long Short Term Memory (LSTM) and Mixture density networks (MDN) as used to train a model to probabilistically predict the direction of the next stroke from the string input and the previous data point coordinates. This method, though effective, has a greatly reduced scalability as the input collected is restricted to only the handwriting collected via digital writing boards. There have been many advances in the field of Deep Learning and Generative Neural Networks. Our proposed work utilizes Deep Convolutional Neural Networks in the form of conditional Deep Convolutional Generative Adversarial Networks, to overcome the shortcomings, and expand the scalability of our model to accept documents and pictures as handwriting input, greatly increasing the scalability of our system.
Exploring the adoption of Artificial Intelligence in the Finance Industry: The case of Chatbots in the Kenyan Finance Industry

This study was a review of Chatbot AI technologies in the financial sector in Kenya whichincluded Banks and Insurance organizations. A total of 42 Banks and 41 Insurance companieswere reviewed firstly to confirm if chatbot technologies had been adopted in the organizations.The study was further elaborated by asking customers of the institutions that had deployedchatbots to review and provide feedback on their perceptions and opinions regarding them. Thisresearch was an extension of the recommendations for future studies by Araujo (2018), whoprovided input into how chatbots that were human-like were perceived by users. Since hisresearch was in a controlled environment, there was a need to review the actual perceptions ofcustomers towards chatbots. The result of the study showed that only 7% of Kenyan financialinstitutions in the study (which consisted of most Banks and Insurance Companies in thecountry) had chatbots. The study also showed that the majority of users and customers thatinteracted with chatbots had a positive experience. These conclusions confirm the study byHertzfeld (2019) who found that 80% of consumers that were exposed to chatbots reported apositive experience. The low penetration of chatbot technologies in financial institutions inKenya may indirectly be due to the findings by the same researcher that though perceptions ofchatbots were positive, financial institution customers preferred interacting with human beings, therefore, causing low adoption (Hertzfeld, 2019). It is recommended that futurestudies review this assumption in detail.
High-Capacity Complex Convolutional Neural Networks For I/Q Modulation Classification.

I/Q modulation classification is a unique pattern recognition problem as the data for each class varies in quality, quantified by signal to noise ratio (SNR), and has structure in the complex-plane. Previous work shows treating these samples as complex-valued signals and computing complex-valued convolutions within deep learning frameworks significantly increases the performance over comparable shallow CNN architectures. In this work, we claim state of the art performance by enabling high-capacity architectures containing residual and/or dense connections to compute complex-valued convolutions, with peak classification accuracy of 92.4% on a benchmark classification problem, the RadioML 2016.10a dataset. We show statistically significant improvements in all networks with complex convolutions for I/Q modulation classification. Complexity and inference speed analyses show models with complex convolutions substantially outperform architectures with a comparable number of parameters and comparable speed by over 10% in each case.
LB Scalability: Achieving the Right Balance Between Being Stateful and Stateless

A high performance Layer-4 load balancer (LB) is one of the most important components of a cloud service infrastructure. Such an LB uses network and transport layer information for deciding how to distribute client requests across a group of servers. A crucial requirement for a stateful LB is per connection consistency (PCC); namely, that all the packets of the same connection will be forwarded to the same server, as long as the server is alive, even if the pool of servers or the assignment function changes. The challenge is in designing a high throughput, low latency solution that is also scalable. This paper proposes a highly scalable LB, called Prism, implemented using a programmable switch ASIC. As far as we know, Prism is the first reported LB that can process millions of connections per second and hundreds of millions connections in total, while ensuring PCC. This is due to the fact that Prism forwards all the packets in hardware, even during server pool changes, while avoiding the need to maintain a hardware state per every active connection. We implemented a prototype of the proposed architecture and showed that Prism can scale to 100 million simultaneous connections, and can accommodate more than one pool update per second.
Weaving Text into Tables

In this paper, we showcase JIGSAW, a system that is able to shape unstructured text into structured tables for user-defined schemas. In short, to structure text into tables, JIGSAW leverages the lexico-syntactic structure imposed by linguistic annotations (e.g., part-of-speech, named entities, temporal and numerical expressions) on natural language text. We describe how challenging knowledge-centric tasks such as question answering, summarization, and analytics can be greatly simplified with the help of JIGSAW.
Surveillance, Privacy, and App Tracking

Over the last several months, global innovators have developed a heterogenous array of “smart” technology protocols and applications aimed at tracking, tracing, and containing the spread of the novel coronavirus, SARS-CoV-2, which causes the disease COVID-19. The United States, which has left it to the states to acquire or build their own automated track and trace platforms, currently lags behind other countries. However, technology companies Apple and Google have announced co-production of a digital tracing platform for their phones. As this Chapter details, the United States lacks a comprehensive federal health data privacy law that protects the privacy of sensitive information collected and stored by digital contact tracking applications. The Chapter also explains how digital COVID-19 surveillance applications work, assesses their effectiveness from a public health perspective, and enumerates the legal and ethical issues they implicate. It concludes with proposals aimed at maximizing the public health benefits of COVID-19 surveillance technology while minimizing its inherent and conceivable threats to privacy, civil liberties, and vulnerable populations.This paper was prepared as part of Assessing Legal Responses to COVID-19, a comprehensive report published by Public Health Law Watch in partnership with the de Beaumont Foundation and the American Public Health Association.
Development of EQ teacher as one of selfregulation components in professional activity

The relevance of the study is that the phenomenon of emotional intelligence at the modern stage of science development is already generally recognized among psychologists, sociologists, philosophers, etc. The whole scientific community today recognizes the need and importance of developing the characteristics and capabilities of emotional intelligence, as they contribute to the professional and personal growth of a person, influence his success in life. The problem of the study is the study of selfregulation as a multi-component system where emotional intelligence is of particular importance. This problem is reflected in the pedagogical sphere. The effectiveness of the professional activity of a specialist engaged in the field of «man-man» is largely conditioned by the ability to self-regulate and successfully develop the personal qualities necessary for the profession, because the teacher as a subject of pedagogical activity is a combination of individual, personal, subjective qualities, the adequacy of which the requirements of the profession ensure the efficiency of his work. The aim of the study is to determine the place of EQ in the system of self-regulation components as one of the personal qualities of the teacher, to identify the main provisions of EQ development and its impact on self-regulation. The main methods of study are theoretical analysis method and empirical methods (observation, testing). The result of the study can be considered the determination of the place of EQ in the system of self-regulation components as one of the personal qualities of the teacher, as well as the identification of the basic provisions of EQ development in the conditions of pedagogical activity. The development of emotional competence and emotional intelligence of the teacher is a necessary condition for the effectiveness of his professional activity, as EQ occupies a special place in the self-regulation of the teacher, which is due to the specificity of the content of professional activity.
A Study on the Construction Method of HS Item Classification Decision System Based on Artificial Intelligence

Industrial Revolution means the improvement of productivity through technological innovation and has been a driving force of the whole change of economic system and social structure as the characteristic of technology as the tool of this productivity has changed. Since the first industrial revolution of the 18th century, productivity efficiency has been advanced through three industrial revolutions so far, and this fourth industrial revolution is expected to bring about another revolution of production. In this study, the demand for the introduction of artificial intelligence(AI) technology has been increasing in various business fields due to the rapid development of ICT technology, and the classification of HS(harmonized commodity description and coding system) items has been decided using artificial intelligence technology, which is the core of the fourth industrial revolution. And it is enough to construct HS classification system based on AI technology using inference and deep learning. Performing the HS item classification is not an easy task. Implementation of item classification system using artificial intelligence technology to analyze information of HS item classification which is performed manually by the current person more accurately and without any mistake, And the customs administrations, customs offices, and customs agencies, it is expected to be highly utilized in the innovation of trade practice and the customs administration innovation FTA origin agent.
베트남전쟁기 한국 종군기자의 파견과 활동 고찰

The previous studies did not deal with the activities of Korean war correspondent who were dispatched to the Vietnam War. The purpose of this study is to examine the activities of Korean war correspondents who were dispatched during the Vietnam War. Korean government dispatched a war correspondent to Vietnam from the time of the dispatch of the Bi-doul-gi unit(Pigeon unit), which was the second Korean troops in March 1965, and they were the first war correspondent dispatched to the first overseas war in Korean media history. The overall status of the dispatched reporters in Vietnam is not confirmed precisely due to a lack of data, but it is estimated to be at least 200 people. Some of them were journalists who died during the coverage. At that time, the Korean government wanted to inform the domestic community of the Korean militarys activities in Vietnam through dispatch of reporters and to gain public support for troops dispatching to South Vietnam. The reporters faithfully carried out the purpose of this government in the constraints of the risk of life, traffic, communication fee, and coverage. However, criticism is also raised that the media has become a war propaganda tool for the government. Through this study, we could find out the dispatch activities of Korean war correspondent during the Vietnam War, which was not well known until now. The case of the activities of the reporters dispatched during the Vietnam War can be used as a useful experience for the ongoing overseas war and the dispatch of the Korean troops overseas.
SUREMap: Predicting Uncertainty in CNN-based Image Reconstruction Using Stein's Unbiased Risk Estimate

Convolutional neural networks (CNN) have emerged as a powerful tool for solving computational imaging reconstruction problems. However, CNNs are generally difficult-to-understand black-boxes. Accordingly, it is challenging to know when they will work and, more importantly, when they will fail. This limitation is a major barrier to their use in safety-critical applications like medical imaging: Is that blob in the reconstruction an artifact or a tumor? In this work we use Stein's unbiased risk estimate (SURE) to develop per-pixel confidence intervals, in the form of heatmaps, for compressive sensing reconstruction using the approximate message passing (AMP) framework with CNN-based denoisers. These heatmaps tell end-users how much to trust an image formed by a CNN, which could greatly improve the utility of CNNs in various computational imaging applications.
Gestação em tempos de COVID-19: como garantir a segurança e preservar os avanços conquistados?

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Entangled and correlated photon mixed strategy for social decision making

Collective decision making is important for maximizing total benefits while preserving equality among individuals in the competitive multi-armed bandit (CMAB) problem, wherein multiple players try to gain higher rewards from multiple slot machines. The CMAB problem represents an essential aspect of applications such as resource management in social infrastructure. In a previous study, we theoretically and experimentally demonstrated that entangled photons can physically resolve the difficulty of the CMAB problem. This decision-making strategy completely avoids decision conflicts while ensuring equality. However, decision conflicts can sometimes be beneficial if they yield greater rewards than non-conflicting decisions, indicating that greedy actions may provide positive effects depending on the given environment. In this study, we demonstrate a mixed strategy of entangled- and correlated-photon-based decision-making so that total rewards can be enhanced when compared to the entangled-photon-only decision strategy. We show that an optimal mixture of entangled- and correlated-photon-based strategies exists depending on the dynamics of the reward environment as well as the difficulty of the given problem. This study paves the way for utilizing both quantum and classical aspects of photons in a mixed manner for decision making and provides yet another example of the supremacy of mixed strategies known in game theory, especially in evolutionary game theory.
Short Video-based Advertisements Evaluation System: Self-Organizing Learning Approach

With the rising of short video apps, such as TikTok, Snapchat and Kwai, advertisement in short-term user-generated videos (UGVs) has become a trending form of advertising. Prediction of user behavior without specific user profile is required by advertisers, as they expect to acquire advertisement performance in advance in the scenario of cold start. Current recommender system do not take raw videos as input; additionally, most previous work of Multi-Modal Machine Learning may not deal with unconstrained videos like UGVs. In this paper, we proposed a novel end-to-end self-organizing framework for user behavior prediction. Our model is able to learn the optimal topology of neural network architecture, as well as optimal weights, through training data. We evaluate our proposed method on our in-house dataset. The experimental results reveal that our model achieves the best performance in all our experiments.
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data

In this paper, we consider the problem of designing Differentially Private (DP) algorithms for Stochastic Convex Optimization (SCO) on heavy-tailed data. The irregularity of such data violates some key assumptions used in almost all existing DP-SCO and DP-ERM methods, resulting in failure to provide the DP guarantees. To better understand this type of challenges, we provide in this paper a comprehensive study of DP-SCO under various settings. First, we consider the case where the loss function is strongly convex and smooth. For this case, we propose a method based on the sample-and-aggregate framework, which has an excess population risk of $\tilde{O}(\frac{d^3}{n\epsilon^4})$ (after omitting other factors), where $n$ is the sample size and $d$ is the dimensionality of the data. Then, we show that with some additional assumptions on the loss functions, it is possible to reduce the \textit{expected} excess population risk to $\tilde{O}(\frac{ d^2}{ n\epsilon^2 })$. To lift these additional conditions, we also provide a gradient smoothing and trimming based scheme to achieve excess population risks of $\tilde{O}(\frac{ d^2}{n\epsilon^2})$ and $\tilde{O}(\frac{d^\frac{2}{3}}{(n\epsilon^2)^\frac{1}{3}})$ for strongly convex and general convex loss functions, respectively, \textit{with high probability}. Experiments suggest that our algorithms can effectively deal with the challenges caused by data irregularity.
A Review on Fraud Research : A Study of Vote Count

This study was aimed to conduct a literature study on research related to fraud. The research method used is to use the literature study method by using the vote counting model. This research would identify papers relating to fraud that have been obtained from various existing databases. Next, the researcher classified the paper by classifying the focus of research at the paper stage in fraud prevention, detection, investigation or remediation. The results of this study were recommendations for researchers, in terms of what is still open as material in research related to fraud
Coroboration of magnetotelluric investigations with other geophysical anomalies for a case study located in North-West to central part of Romania

&amp;lt;p&amp;gt;The results of the magnetoteluric investigations carried out along the profiles are presented in the form of sections in which the variations of the different parameters, the 2-D modeling, as well as the inversions.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The results of the geophysical researches (magnetotellurics, gravity, geomagnetics) obtained were aimed at obtaining a unitary image on the deep geological structure in the investigated area. A number of information was obtained regarding such as:&amp;lt;/p&amp;gt;&amp;lt;ul&amp;gt;&amp;lt;li&amp;gt;Determination of the thickness of the package of formations belonging to the post-tectogenetic sedimentary cover of the Transylvanian Depression; sedimentary sedimentary cover, conductive, with a maximum thickness of approximately 4000 m in the Pannonian Depression;&amp;lt;/li&amp;gt; &amp;lt;li&amp;gt;Contouring of the Tethysian Major Suture (near the town of Alba Iulia in the Transylvanian Basin), represented by the Transylvanian nappes system (ofiolitic complex and sedimentary formations), with resistivities of about 500 Ohm*m, which separates two blocks with continental crust of different thicknesses (22- 27 km for Internal Dacids and 32-36 km for Median Dacids);&amp;lt;/li&amp;gt; &amp;lt;li&amp;gt;Highlighting the change of nappes systems belonging to the Transylvanians, with a wide development both to the north (C&amp;amp;#259;p&amp;amp;#226;lnas-Techereu nappes and the nappes of Gro&amp;amp;#351;i and Cri&amp;amp;#351;), as well as to the east (the ophiolite complex and sedimentary cover), over the Biharia nappes system, respectively Central-Eastern Carpathian nappes; extension of the Codru and Biharia - Arie&amp;amp;#351;eni nappes, the last with higher resistivities (200 Ohm*m);&amp;lt;/li&amp;gt; &amp;lt;li&amp;gt;Highlighting the transcrustal fault that marks the contact between the Inner Dacides and the Median ones;&amp;lt;/li&amp;gt; &amp;lt;li&amp;gt;Individualization at the level of the lower crust of a transition zone; significant decrease of resistivity, as a consequence of the presence of the fluids in the transition zone, from the pressure in the pores from lithostatic type to the hydrostatic type (occurs at depths of 22 - 30 km and at temperatures of 350&amp;amp;#186; - 400&amp;amp;#186; C).&amp;lt;/li&amp;gt; &amp;lt;/ul&amp;gt;&amp;lt;p&amp;gt;The electromagnetic data reflect the anomalies of electrical conductivity in a sensitive way, but due to the many causes that can generate them, a careful analysis of the particularities existing for each case, especially the superficial ones, was necessary.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The correlation of the all the information provided in sections (resistivity, phases, density, magnetic susceptibility), inversions, modeling, lead to the validation of the model.&amp;lt;/p&amp;gt;
Image Fog Density Recognition Method Based on Multi-Feature Model and S-DAGSVM

Aiming at the demand of intelligent image defogging systems for automatic recognition of fog density, this paper proposes an image fog density recognition method based on multi-feature model and S-DAGSVM. By analyzing the image characteristics of different fog densities, a multi-feature model based on the combination of four features of color, dark channel, information and contrast is constructed to characterize the image fog density, and the features are represented in the form of a histogram. Then, the S-DAGSVM algorithm is proposed to performs supervised learning on the combined feature vectors to realize automatic classification of the image fog density. The experimental results show that the proposed multi-feature model can characterize the fog density more efficiently. Compared with existing multi-class SVM algorithms, the S-DAGSVM algorithm has a higher classification accuracy of up to 96.19%, which has a good reference value for the realization of intelligent image defogging systems.
PEMBERDAYAAN KOMPETENSI PEDAGOGIK BERBASIS KEMAMPUAN REFLEKTIF UNTUK PENINGKATAN KUALITAS INTERAKSI PEMBELAJARAN

The achievement of community service resultsreaches a good level of effectiveness as a solution to partner problems, with the results: (1) increasingpedagogical literacy knowledge was 80%, (2) 100% of teachers have pedagogical competencies ingood classification (27%) and sufficient (73%) ); (3). 100% of teachers have reflective abilities with agood classification was 20% and a sufficient classification was 80%; and (4) Ability to manage thequality of learning interactions with a good classification was 39%, and a sufficient classification was61%.
Laplacian pretty good fractional revival

We develop the theory of pretty good fractional revival in quantum walks on graphs using their Laplacian matrices as the Hamiltonian. We classify the paths and the double stars that have Laplacian pretty good fractional revival.