Reviewerly/exHarmony huggingface dataset:
Multilevel polarization for quantum Channels

Recently, a purely quantum version of polar codes has been proposed in~\cite{DGMS19} based on a quantum channel combining and splitting procedure, where a randomly chosen two-qubit Clifford unitary acts as a channel combining operation. Here, we consider the quantum polar code construction using the same channel combining and splitting procedure as in~\cite{DGMS19}, but with a fixed two-qubit Clifford unitary. For the family of Pauli channels, we show that polarization happens in multi-levels, where synthesized quantum virtual channels tend to become completely noisy, half-noisy, or noiseless. Further, we present a quantum polar code exploiting the multilevel nature of polarization, and provide an efficient decoding for this code. We show that half-noisy channels can be frozen by fixing their inputs in either the amplitude or the phase basis, which allows reducing the number of preshared EPR pairs compared to the construction in~\cite{DGMS19}. We provide an upper bound on the number of preshared EPR pairs, which is an equality in the case of the quantum erasure channel. To improve the speed of polarization, we propose an alternative construction, which again polarizes in multi-levels, and the previous upper bound on the number of preshared EPR pairs also holds. For a quantum erasure channel, we confirm by numerical analysis that the multilevel polarization happens relatively faster for the alternative construction.
Non-dominated Sorting Social Group Optimization algorithm for multiobjective optimization

In this paper, a posteriormultiobjective optimization algorithm named as Non-dominated Sorting Social Group Optimization (NSSGO), has been proposed for multiobjective optimization. The algorithm acquires the combined concept of nondominated sorting and crowding distance computation mechanism. The proposed algorithm was simulated on a set of multi-objective CEC 2009 functions and competitive results were obtained.
Design and Implementation of Convolutional Neural Network Accelerator Based on RISCV

Abstract Internet of Things devices are faced with ever-increasing amounts of data, and they can no longer only do data collection as they did in the past, and hand over computing tasks to servers on the cloud. The growing computing requirements in the field of Internet of Things and the diversity of its scenarios put forward configurable and flexible customization requirements for customized processors. This paper conducts in-depth analysis and research on configurable customized processors, and analyzes related work in the field of RISC-V chips and deep convolutional neural networks in the field of device-side optimization at home and abroad. Based on this, this paper proposes an instruction and hardware design of a customizable deep convolutional neural network accelerator attached to Rocket-Chip Generator based on the RISC-V modular instruction set.
Foundational Patents in Artificial Intelligence

Abstract Despite advocating openness, technology leaders have been patenting artificial intelligence (AI) inventions at an exponentially increasing rate. Patents on foundational techniques with broad applications have the potential to deter innovation in the field, should the privatization of key components of artificial intelligence be used to exclude third-party innovators. This chapter studies patent applications in the US and demonstrates the extent to which the field has grown over the last twenty years. It then seeks to explore foundational patents by focusing on triadic patents: patents filed jointly with the United States Patent and Trademark Office, the European Patent Office, and the Japanese Patent Office. The results of this chapter indicate an increasingly globalized AI field. A proverbial land grab seems to be occurring; an increase in AI patenting, particularly the protection of triadic patents, illustrates industry players aggressively attempting to own the building blocks of a rapidly emerging market. A number of policy levers are presented, highlighting how the patent offices and courts can counter the patenting of foundational patents by relying on strict and narrow patentability standards.
Implementation paper on analyzing covid-19 vaccines on twitter dataset using tweepy and text blob

This paper depends on utilizing twitter for sentiment analysis of the views of people on COVID-19 vaccines and eagerness to have the jab An ever increasing number of individuals have begun posting on the web about whether they are in favor of these vaccines or not Many mediums are there which give these analysis among which one such medium is Twitter which has gotten very mainstream as of late Twitter is a famous microblogging website where clients are permitted a constraint of 280 characters;this sort of limitation causes the clients to be compact just as expressive simultaneously Thus, it turns into a rich hotspot for opinion examination and belief mining In this examination, we will investigate the eagerness of having different vaccine by people and their recommendation to others after having the same In the wake of applying different models and machine learning algorithms to tweets information, we have discovered that it is for sure conceivable to anticipate the vaccine is being favored or not on a large scale © 2021, Universitatea de Vest Vasile Goldis din Arad All rights reserved
Minimal enumeration of all possible total effects in a Markov equivalence class

In observational studies, when a total causal effect of interest is not identified, the set of all possible effects can be reported instead. This typically occurs when the underlying causal DAG is only known up to a Markov equivalence class, or a refinement thereof due to background knowledge. As such, the class of possible causal DAGs is represented by a maximally oriented partially directed acyclic graph (MPDAG), which contains both directed and undirected edges. We characterize the minimal additional edge orientations required to identify a given total effect. A recursive algorithm is then developed to enumerate subclasses of DAGs, such that the total effect in each subclass is identified as a distinct functional of the observed distribution. This resolves an issue with existing methods, which often report possible total effects with duplicates, namely those that are numerically distinct due to sampling variability but are in fact causally identical.
A Survey of Audiovisual Deepfake Detection Techniques

Deep learning has been widely used in fields such as natural language processing, computer vision, and driverless vehicles, leading a new wave of artificial intelligence. Deep learning advances however have also been used to create technologies that pose potential threats to national security, social stability, and personal privacy. For example, deepfakes that have recently attracted widespread attention worldwide, which could generate seemingly realistic fake images, audio and video content. This article introduces the background of deepfakes and the principles of deepfakes creation, and then outlines and analyzes the detection methods and datasets for different types of deepfakes, including images, videos, audios, etc. Finally, the article discusses potential research directions and challenges of deepfakes detection and prevention.
Applications of Machine Learning and Deep Learning for maintaining Electronic Health Records

Due to the advances in machine learning and deep learning models, their use in medical decision frameworks is still minimal. However, the rapid digitization of health information has created an excellent forum for evaluating the effectiveness of such strategies in healthcare. H s a result, an increasing number of research studies are appearing in the fields that use deep learning on Electronic health Records (EHR) for customized threat and health trajectory prediction. Predictive analytics in machine learning is primarily used in the healthcare sector for disease diagnosis. Machine learning approaches aid in the prediction of relationships in EHR results. The health system recommendations in the corpus could be one of the sources, and different healthcare criteria could be element sized, resulting in a structure that has a meaningful effect on healthcare decision-making. Although this is a consistent growth, the field's potential to analyse and differentiate such frameworks for a given scenario has been hindered by considerable paper-to-paper heterogeneity (from sources of data and frameworks used to the research situation they seek to respond).As a result, the objective of this study is to include Machine Learning and Deep Learning Applications for preserving electronic health records. We also hope to: (1) develop and utilize one of the globe's greatest and more important associated primary healthcare EHR datasets as an unique resource for educating such information models; (2) include a guidelines for dealing with EHR data for machine learning; (3) provide a few of the quality standards for evaluating the awesomeness of deep learning models in improved clinical forecasting; and (4) suggest future studies for making deep learning models more suitable for EHR data. Our findings identify the importance of interacting with extremely imbalanced records and suggest that concurrent deep learning frameworks like RNN might be better suited to dealing with EHR's spatial design.
Statistical levelling of multi-element geochemical data

Regional compilations of multi-element geochemistry can show shifts of level and dynamic range between component surveys, for example when analyses have been made using different laboratories or procedures or at different times. To create a unified composite picture of the geochemistry over a region, some form of relevelling of individual surveys may be needed. Existing treatments have focused on individual elements, independently, across individual pairs of map sheets. Such approaches, however, may fail to preserve the important covariance structure between elements of interest, and may risk dependency of the final blend on the order of levelling of sheets. The paper proposes a method for levelling all elements, and their covariances, simultaneously across all component sheets, taking full account of the compositional nature of the data. The method is shown to be computationally feasible, requiring manageable execution time even for large numbers of elements and large numbers of component surveys.
Ecospiritual Pedagogy: A Learning Centric Educational Practice

This article discusses a twofold educational system, i.e. ecospiritualism that intertwines ecopedagogy and spiritual intelligence. It explores a discourse on ecological and spiritual dimensions from secondary resources referring to the East and West practices. The discursive thematic analysis constitutes ecospiritual pedagogy since the future world needs an integration between human and non-human facts of existence. It incorporates eastern spiritual values; concepts of ecopedagogy; western practice in spiritual intelligence; Leopold's idea on integration of land and people; and spiritual basis of science of neuroplasticity. After getting through thematic analysis, it will conclude with the three possible ecospiritual pedagogic practices. At first it ensures teacher's spiritual rigor with a higher order of tripartite consciousness, i.e. tamas, rajas and sattva. Secondly, it focuses on pedagogic procedure leading through the three yogic practices, i.e. karma, bhakti and jnana. At last, it ensures supreme affinity between human and non-human existence by bringing contents and activities in amalgamation. Moreover, it proposes necessary research to concretize educational resources by suggesting adopting local spiritual and ecological practices and values from their own traditions.
Credit Risk Analysis and Evaluation of Internet Supply Chain Finance Listed Companies -- Based on Structural Entropy Weight TOPSIS Method

The emergence of supply chain finance has effectively solved the problem of financing difficulty and high cost for small and medium-sized enterprises.The rapid development of the Internet has magnified this advantage.However, based on its diversified main body and complex business process, the risk of Internet supply chain is also increasing, resulting in economic instability and hindering economic development.Scientific and reasonable analysis of Internet supply chain financial credit risk is of great significance to promote the development of supply chain finance and expand financial services.In view of the limitation of single evaluation method and imperfect index system, this paper introduces Baidu search index and stock bar comprehensive score to construct the credit risk evaluation system of Internet supply chain finance from six aspects: operation ability, debt paying ability, profitability, development potential, mortgage factor and condition factor.It also selects 24 Internet supply chain finance related companies in 2019 The empirical results show that: profitability, operating capacity, solvency can provide strong explanatory power, and Structural Entropy TOPSIS method can analyze the credit risk of these enterprises to a certain extent.
Application of Robust Digital Watermarking Technology in Image Copyright Disputes

Abstract In recent years, with the increasing application of computer technology in daily life, the phenomenon of data transmission or transaction on the network is becoming more and more common. On the one hand, it brings great convenience to our life, on the other hand, there are also great hidden dangers, such as information replication, forwarding, and even tampering. Based on the research of DWT dual color image digital watermarking algorithm based on HVS, this paper introduces Arnold transform to improve the robustness of the algorithm, and studies the application of the improved algorithm in e-commerce image disputes.
A recommendation system for scientific water data

Abstract We describe a recommendation system for HydroShare, a platform for scientific water data sharing. We discuss similarities, differences and challenges for implementing recommendation systems for scientific water data sharing. We discuss and analyze the behaviors that scientists exhibit in using HydroShare as documented by users’ activity logs. Unlike entertainment system users, users on HydroShare tend to be task-oriented, where the set of tasks of interest can change over time, and older interests are sometimes no longer relevant. By validating recommendation approaches against user behavior as expressed in activity logs, we conclude that a combination of content-based filtering and a latent Dirichlet allocation (LDA) topic modeling of user behavior—rather than and instead of LDA classification of dataset topics—provides a workable solution for HydroShare and compares this approach to existing recommendation methods.
Database Construction in Computer Audit under Big Data Environment

Abstract In the information age, the explosive growth of data forces users to deliver the data to the database service provider to manage. This paper mainly studies the database construction of computer audit under the environment of big data. This paper proposes a queryable database encryption storage scheme and a searchable multi-keyword encryption scheme under distributed environment. In this paper, the non-relational database is used in distributed storage in the form of key-value to improve the query performance. For distributed database, this paper proposes a queryable database encryption scheme. In this scheme, there are two encryption query modes: exact matching mode and range matching mode. Users encrypt and store data to the corresponding server nodes in these two ways, which can support rich queries. In the process of query, the user generates the corresponding query password for each server node, and then the parallel query can be realized. While protecting data privacy, the performance of the scheme is also well guaranteed.
A survey on analyzing covid-19 vaccines on twitter dataset using tweepy and text blob

This paper depends on utilizing twitter for sentiment analysis of the views of people on COVID-19 vaccines and eagerness to have the jab. An ever increasing number of individuals have begun posting on the web about whether they are in favor of these vaccines or not. Many mediums are there which give these analysis among which one such medium is Twitter which has gotten very mainstream as of late. Twitter is a famous microblogging website where clients are permitted a constraint of 280 characters; this sort of limitation causes the clients to be compact just as expressive simultaneously. Thus, it turns into a rich hotspot for opinion examination and belief mining. In this examination, we will investigate the eagerness of having different vaccine by people and their recommendation to others after having the same. In the wake of applying different models and machine learning algorithms to tweets information, we have discovered that it is for sure conceivable to anticipate the vaccine is being favored or not on a large scale.
Patent Examination of Artificial Intelligence-related Inventions

Abstract To date, artificial intelligence (AI) has caused fundamental changes in inventing processes, and many issues are arising therefrom, especially whether and in what way inventions implemented by AI are patent-eligible under the current patent regime. To deal with the issue of patentability, some countries have adjusted their patent regime to AI-related inventions. As a major economy with a world-leading AI industry, China has accumulated abundant experience in examining applications with respect to AI-related inventions. This chapter focuses on China’s experience in the examination of AI-related inventions and its latest regulatory movements in this regard. More specifically, it revolves around China’s practice of modifying its Guidelines for Patent Examination (‘Guidelines’) to clear the way for AI-generated inventions in April 2017, reaching consensus on applying the Patent Law, Implementing Rules of the Patent Law, and the Guidelines to examining the applications of AI-generated inventions, and further revising the Guidelines in December 2019 to make them more suitable for AI-related inventions. This chapter argues that these experiences can be a useful benchmark for other jurisdictions to grant patent rights to AI-related inventions.
Analysis of algorithms and implementation of real time speaker identification system

The article describes an implementing a real time speaker identification system by voice for embedded and general purpose computers. A review and analysis of existing speaker identification algorithms are made. The speaker's input speech is recorded in the system, go through the preprocessing stage, extract features and voice parameters for further identification. To recognize the speaker by voice parameters, the Vector quantization and Hidden Markov model algorithms are used. The VQ and HMM algorithms showed recognition accuracy of 96% and 98%, respectively.
Ridge Regression with Over-parametrized Two-Layer Networks Converge to Ridgelet Spectrum.

Characterization of local minima draws much attention in theoretical studies of deep learning. In this study, we investigate the distribution of parameters in an over-parametrized finite neural network trained by ridge regularized empirical square risk minimization (RERM). We develop a new theory of ridgelet transform, a wavelet-like integral transform that provides a powerful and general framework for the theoretical study of neural networks involving not only the ReLU but general activation functions. We show that the distribution of the parameters converges to a spectrum of the ridgelet transform. This result provides a new insight into the characterization of the local minima of neural networks, and the theoretical background of an inductive bias theory based on lazy regimes. We confirm the visual resemblance between the parameter distribution trained by SGD, and the ridgelet spectrum calculated by numerical integration through numerical experiments with finite models.
Mobile Applications as Tools for Virtual Education in Indigenous Communities During the COVID-19 Pandemic in the Peruvian Amazon

The present work includes the development and validation of two bilingual mobile applications initially created as basic learning tools in Educational Institutions in Amazonian indigenous communities of the Shipibo-Konibo and Taushiro languages, both with weakening and extinction problems. The applications seek to overcome or adapt to the conditions and limitations of indigenous communities, caused by a series of bureaucratic administrative processes, and also seek to be complementary tools or support to the Virtual Education services that the Peruvian government has been promoting in the context of the COVID-19 Pandemic, and although classes continue to develop virtually through television, radio, software messaging services and electronic platforms, these media have been receiving a series of criticisms by parents and part of the education sector regarding the quality of service. Through the realization of two case studies, favorable results were achieved on the use and potentialities of supporting the educational social inclusion of indigenous children. Finally, a comparative table was made on the frequent problems, the new problems that have arisen in the context of COVID-19, and the scope and advantages of each of the mobile applications. At the same time, it is suggested that government strategies incorporate the use of interactive mobile self-learning applications as complementary mechanisms to current educational services, which should be contextualized to the geographic, cultural and linguistic environment of students, taking into account the limited telecommunications and electricity coverage of indigenous communities in Peru.
Processing the Textual Information Using Open Natural Language Processing (NLP)

NLP is the application of computational technique and component of AI helps to analysis and synthesis of Standard English Language. It draws from many disciplines like computer science and computational linguistics to process and analyze large amount of natural language data. To identify the phrases in language that refers to specific types of entities and relations in text. The problem identified in N-gram approach is, balance weight is placed between in-frequent and frequent grams. It is efficient only for small amount of textual data. Thus, it is difficult to discover the named entities available in the corpus. The main aim of this paper is to overcome the limitations available in n-gram approach and facilitates to find desired pieces of entities by using Open NLP tool kit. It helps to store the information in a XML format that provides an easy way of querying and processing. Hence the information extracted from the web is an unstructured form. This approach is a promising way for processing the text in an efficient manner. To enhance the effectiveness of text mining, the researcher focused on the task of the NLP to discover knowledge from many unstructured text documents that leads to the largest available source of knowledge. The experiments and results shown in this paper with accuracy as 0.95 prove the confidence level is better than the N-grams approach.