Reviewerly/exHarmony huggingface dataset:
Multidigraph representation of analogies from oneiric stories

Learning relationships between nodes in a directed graph is a task that has been widely studied and it has been applied to a large number of topics and research areas. We establish a definition of particular kind of relationship, called analogy in a directed multigraph. An analogy can be defined for a certain pair of concepts, and the paths connecting them are called explanation of this analogy. We experiment with a structure built from real oneiric stories obtained from psychoanalytic descriptions (e.g. mother is represented as a bull; book represents power). Analogies found by the analysts are automatically identified by means of linguistically motivated patterns. Analogies have degrees of similarity based on the words used to describe them: represents, is a, is like a, can be a, refers to, etc. Once they are identified and graded, they are represented in the multidigraph, allowing us to provide different hypotheses in how these analogies can be explained. In order to enrich the concept graph, we added information from ConceptNet and WordNet. In addition, we propose a learning method for association rules that, given the degree of the analogy and a starting concept, allow reaching a destination concept. For example, starting from “dream”, we obtain the path &lt;dream, psychic, neurosis, symptom&gt;, being "dream is a symptom" a description previously given by a psychoanalyst, that was not included when training the algorithm. We evaluated 100 analogies on 171 concepts with 8,034 properties using Leave One Out cross validation, and found that the correct analogy was found within the all the possible paths for 94% of the analogies, restricted to 85% if only the top 20% possible paths are considered. This implies that, by using our method, it is possible to learn analogies between two concepts by reconstructing paths of different lengths based on local decisions considering concept, property and degree of analogy.
Why neural networks in the first place: a theoretical explanation

Neural networks – specifically, deep neural networks – are, at present, the most effective machine learning techniques. There are reasonable explanations of why deep neural networks work better than traditional “shallow” ones, but the question remains: why neural networks in the first place? why not networks consisting of non-linear functions from some other family of functions? In this paper, we provide a possible theoretical answer to this question: namely, we show that of all families with the smallest possible number of parameters, families corresponding to neurons are indeed optimal – for all optimality criteria that satisfy some reasonable requirements: namely, for all optimality criteria which are final and invariant with respect to coordinate changes, changes of measuring units, and similar linear transformations.
A fast clustering algorithm for evolving fuzzy classifier based on samples mean

Evolving models have shown great success in processing non-stationary data that change their characteristics over time. Motivated by elaborating a high-performance model for data classification, the present work proposes a new evolving fuzzy classifier. The proposed model, named evolving Fuzzy Mean Classifier (eFMC), has a low computational cost and is autonomous, i.e., no has user-defined parameters. The eFMC is based on fuzzy clustering structures, where the membership degree between the samples and the clusters is used to obtain the output. In the proposed approach, each class is represented by a cluster, and new clusters are created whenever a new class is discovered. The centers of the clusters are updated through the sample’s means calculated incrementally. Computational experiments were carried out to evaluate and compare the performance of the eFMC in terms of accuracy and processing time. Experimental results and comparisons against alternative state-of-the-art evolving classifiers show that the eFMC is accurate and fast, characteristics essential for adaptive classifiers, especially in online and real-time environments.
IMAGE PROCESSING USING K MEANS CLUSTERING AND EUCLIDEAN DISTANCE METHOD

- Image processing refers to the processing of images, which includes many different techniques that are used till we reach our goal. The processing output can either be in the form of an image, or a feature of the given image. This can be used for decision making and a more comprehensive image analysis. The Image compression process is a variation of data compression that is used on images without affecting their quality. Reduced size of the file makes sure that a lot of images can be put in a certain amount of memory / disk space. Processing also decreases the required time for the images to travel over the Internet or to be downloaded from different web pages. K - means clustering using Euclidean distance method includes vector quantization, which is originally a part of signal processing. It is very popular in cluster analysis for mining data. In this Project, we are using Image compression and segmentation algorithms using K – means clustering and Euclidean Distance method and many more algorithms used to process images.
CYBER SMART: PROTECT THE PATIENT; PROTECT THE DATA ON ELECTRONIC MEDICAL RECORD

Cyber security is the practice of defending the computers, servers, electronic devices and data stored in online cloud from mislenious attacks. It is divided in some part like network security and computer security. Network security is to make the network from the attackers from hacking. In a health care organization where nowadays all work done by using electronic medical record and storing the information in online cloud, there is the higher risk of getting affected by the unwanted attackers and get the information from third party sources. Electronic medical records are at higher risk of getting affected by different method like pissing method or also can be effected by the malware and ransom ware that can be more dangerous to the system. To make the proper safety, data encryption is very useful where the thirdparty source cannot access the information and the information gets limited the sender and the receiver. And also there is different ways to give the safeguard the patient information in hospital RMR is browsing the internet by using VPN, and not to open any suspicious site.
Classification Weighted Deep Neural Network Based Channel Equalization for Massive MIMO-OFDM Systems

Massive multi-input multi-output (MIMO) has attracted significant interest in academia and industry, which can efficiently increase the transmission rate.However, the error rate of conventional channel equalizations in massive MIMO systems may be high owing to the dynamic channel states in practical conditions.To solve this problem, in this paper, we propose an improved channel equalization framework based on the deep neural network (DNN).Based on the analyzed relationship between the input and output of the DNN, the data can be recovered without the channel state information.Furthermore, aiming at reducing the convergence time and enhancing the learning ability of the DNN, a classification weighted algorithm is proposed to optimize the cost function of the DNN, which is named as classification weighted deep neural network (CW-DNN).Simulation results demonstrate that compared to conventional counterparts, the proposed CW-DNN based equalizer can achieve a better normalized mean square error (NMSE).Upon approximating the optimal neural network parameters with the significantly improved convergence speed and reduced training time of the network, under the condition of the fixed learning rate.
Can You Hear Me Now? [Microwave Surfing]

The following excerpt is from a U.S. government press release <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> :
Physiological Feature Recognition Algorithm of Human Exercise-Induced Fatigue Based on Parameter Analysis of Nanomachine Simulation for a Future Smart World

Abstract Traditional methods cannot fully reflect the fatigue degree of human exercise through a single parameter, which has the problems of long recognition time, high recognition coincidence, and poor anti-interference. Therefore, a physiological feature recognition algorithm of human exercise-induced fatigue based on parameter analysis of nanomachine simulation is proposed. The output value of body weight and a three-axis acceleration sensor are used to calculate the energy consumption parameters. By judging the time series period of fatigue physiological features data, the physiological features of human exercise-induced fatigue are extracted, and the feature extraction results are fused. According to the fusion results, the feature recognition is realized by analyzing the simulation parameters of nanomachine. The experimental results show that the shortest feature recognition time of the proposed method is 2.35 s, which is significantly lower than that of the traditional method, the feature recognition coincidence degree of the proposed method is lower, and the anti-interference performance is better, which fully shows that the method can accurately judge the degree of human exercise-induced fatigue.
An Evaluation of Techniques for Classification of Conditional Sentences and Their Structural Components

Conditional statements are an important part of procedural knowledge as they determine the decision points in the control flow. In order to bootstrap conversation bots and automation tools automatically from natural language procedure documents, it is important to be able to classify the conditional statements accurately and separate the condition and effect correctly. This paper aims at exploring three different techniques to classify and analyze conditional statements and discusses the advantages and drawbacks of each of them. This paper also aims at understanding the drawbacks of the three techniques and overcoming them by building models with better performance.
Implementing Butterfly Key Expansion Using Post-Quantum Algorithms

Vehicular ad-hoc networks are important components in intelligent transportation systems which provide a reliable means of communication. Given that a security breach in such networks can result in fatal situation, the security of these networks is as important as their reliability. However, implementing security is not always a straightforward task especially when there are different devices with different capabilities. Additionally, current public-key infrastructure makes use of public-key algorithms that are susceptible to quantum-computation attacks making them not usable if quantum computers become a practical reality. In this paper, we present a public-key infrastructure implementation which makes use of a post-quantum algorithm providing an extra layer of protection to it. We compare the results of our implementation to implement which make the use of classical algorithms and show that they are very comparable in terms of speed, power, and energy consumption.
Data Checks in <i>ACS Organic &amp; Inorganic Au</i>

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTData Checks in ACS Organic & Inorganic AuAmelia Newman*Amelia NewmanACS International Ltd., Begbroke House, Wallbrook Court, North Hinksey Lane, Oxford OX2 0QS, U.K.Institut de Chimie des Substances Naturelles, Université Paris Saclay, CNRS, UPR2301, 1 Avenue de la Terrasse, Gif-sur-Yvette 91198 Cedex, France*Email: [email protected]More by Amelia Newman and Géraldine MassonGéraldine MassonACS International Ltd., Begbroke House, Wallbrook Court, North Hinksey Lane, Oxford OX2 0QS, U.K.Institut de Chimie des Substances Naturelles, Université Paris Saclay, CNRS, UPR2301, 1 Avenue de la Terrasse, Gif-sur-Yvette 91198 Cedex, FranceMore by Géraldine Massonhttps://orcid.org/0000-0003-2333-7047Cite this: ACS Org. Inorg. Au 2022, 2, 4, 261Publication Date (Web):August 3, 2022Publication History Received13 July 2022Published online3 August 2022Published inissue 3 August 2022https://doi.org/10.1021/acsorginorgau.2c00039Copyright © Published 2022 by American Chemical SocietyRIGHTS & PERMISSIONSACS AuthorChoiceCC: Creative CommonsBY: Credit must be given to the creatorNC: Only noncommercial uses of the work are permittedND: No derivatives or adaptations of the work are permittedArticle Views269Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (775 KB) Get e-AlertsSUBJECTS:Energy,Gold,Materials science,Nanoscience,Organic chemistry Get e-Alerts
Looping Through Color Space: A Simple Augmentation Method to Improve Biased Object Detection

In this work, we address the challenging problem of color-dependent and imbalanced datasets. For many use cases, the training of models based on such data will not generalize well enough and fail even on slight domain variations. This issue is usually addressed by artificially extending the data by manipulating input data or using synthetic data. In this context, we introduce a new augmentation method for extended color mapping from single-channel depth images that reduce color dependency and decrease the amount of annotated data needed for comparable model performance. We found that this method improves the generalization of models for depth-based hand detection on our dataset captured at a manual assembly workspace. Additionally, we validated our results on a publicly available dataset.
Electronic Health Record’s Security and Access Control Using Blockchain and IPFS

An electronic health record (EHR) typically contains sensitive medical records, personal information, doctors’ provided prescription, and other physical histories of a patient. This digital approach remodeled the health sector while increasing privacy concerns and possibility of security breaches. This paper proposes an EHR system based on blockchain, interplanetary file system (IPFS), and cryptographic functions and includes features like secure access control having accountability, transparency, immutability of data in a cost-efficient patient-centered architecture which is free from third-party interruption. Here, we divided data into two categories and three types of participants who are verified with digital certificates are granted permission by the patients and then they can access data. Finally, we build and investigate a simple implementation to analyze the cost of the system and propose some approaches to optimize it.
Moroccan Sign Language Video Recognition with Deep Learning

Sign language is the native form of expression used by deaf people in the world. With the recognition techniques applied to sign language, a significant need for developing tools to facilitate the accessibility of information to the deaf public has arisen. Little work deals with recognizing Moroccan sign language (MoSL) for the Moroccan deaf community. In this paper, a deep learning architecture is presented to be used to recognize MoSL signs. The proposed system uses 3D convolution neural networks to describe effectively video sequences containing Moroccan signs. Experiments showed that the system is able reliably to recognize Moroccan word signs, with 99.60% of accuracy.
ImposeSVD: Incrementing PureSVD For Top-N Recommendations for Cold-Start Problems and Sparse Datasets

Abstract In this paper, we introduced two novel collaborative filtering techniques for recommendation systems in cases of various cold-start situations and incomplete datasets. The first model establishes an asymmetric weight matrix between items without using item meta-data and eradicates the disadvantages of neighborhood approaches by automatic determination of threshold values. Our first model, z-scoREC, is also regarded as a pure deep-learning model because it performs like a vanilla auto-encoder in transforming column vectors with z-score normalization similar to batch normalization. With the second model, ImposeSVD, we aimed to enhance the shortcomings of the PureSVD in cases of cold-start and incomplete data by preserving its straightforward implementation and non-parametric form. The ImposeSVD model relies on the z-scoREC, produces synthetic new predictions for the users by decomposing the latent factors from the imposed matrix. We evaluated our method on the well-known datasets and found out that our method was outperforming similar approaches in the specific scenarios including recommendations for cold-start users, strength in cold-start systems, and diversification of long-tail item recommendations in lists. Our z-scoREC model also outperformed familiar neighbor-based approaches when operated as a recommender system and gave a closer appearance to the decomposition methods despite its simple and rigid cost framework.
Why were the budget management and library acquisition module in ILS so important?

The library acquisition module is one of the important components of ILS (integrated library system, abbreviated as ILS as follows). However, some small, even medium sized libraries refused or hesitated to use this module for several reasons. This article focused on the discussions why the library acquisition module in ILS was so important for the library budget and purchase management, and what the benefits of using the library acquisition module in ILS were.
Application of Artificial Intelligence and Virtual Reality Technology in the Rehabilitation Training of Track and Field Athletes

The rapid improvement of the economic level has led to the prosperity of sports, and the rehabilitation training of athletes has also developed rapidly and has attracted widespread attention from all over the world. How to make track and field athletes quickly relieve the negative impact of sports injuries on the body during rehabilitation training is the most concerned issue in the field of sports medicine today. The traditional rehabilitation training cycle is long, and the rehabilitation effect is very small, which cannot fundamentally eliminate the injury and restore the athletes’ various physical functions and sports qualities to the original level. This article combines the latest artificial intelligence and virtual reality technology to analyze the causes and control strategies of athletes’ injuries and to understand the reasons for the injuries of track and field athletes in daily training and the various factors that affect their rehabilitation. And it was used in rehabilitation training practice and analyzed from four aspects: body shape, physical function, sports quality, and rehabilitation effect. The experimental results show that the rehabilitation training using artificial intelligence and virtual reality technology can make the athletes’ physical function recover more than 96%, and the overall average rehabilitation effect can reach 93.79 points, which is feasible to a certain extent. The application of artificial intelligence and virtual reality technology to the current stage of rehabilitation training can effectively improve the rehabilitation effect of track and field athletes.
Classification of English Translation Teaching Models based on Multiple Intelligence Theory

In order to improve the quality of English translation teaching, this paper combines the theory of multiple intelligence to classify the English translation teaching process. Moreover, this paper adopts Fisher’s discriminant method and Bayesian discriminant method to classify the English translation teaching samples. In order to improve the discrimination accuracy of the extreme learning machine algorithm, this paper applies the particle swarm optimization extreme learning machine algorithm to the research on the classification of English translation teaching samples and proposes an intelligent English classification teaching model based on the actual situation of English translation teaching. In addition, this paper verifies the system model proposed in this paper by evaluating the teaching method. The research shows that the classification model of English translation teaching mode based on the theory of multiple intelligence proposed in this paper has a certain effect, which can promote the effect of English translation teaching.
Social ties and home bias in mergers and acquisitions

Abstract This paper explores whether social ties, proxied by Facebook friendship links, can explain why the number and value of mergers and acquisitions (M&amp;As) are greater within countries than between countries. We find that social ties are positively correlated with the number and value of M&amp;As. We also demonstrate that the home bias in M&amp;As is greatly reduced once we control for the differences in social ties between and within countries. We further find that social ties particularly facilitate M&amp;As when the level of corruption is high, press freedom is limited in the target country, and there are more cultural differences between the acquirer and target countries.
Introduction

This introduction presents an overview of the key concepts discussed in the subsequent chapters of this book. The book argues how programmers should explore new musical ideas. It focuses on the commonalities between music theory and computer programming. The book discusses how artificial intelligence (AI) continues to inform the authors’ work and considers its impact on the music industry and creative practice. It presents a ‘human-first deep learning design’ for embodied AI music robotics. The book examines approaches for AI and music within immersive cross-reality environments such as virtual and augmented realities. It reviews the adoption of AI with music from the standpoint of data. The book also presents the legal challenges of AI to music copyright and considers the application of global AI ethics reports for a fair and sustainable music ecosystem. It builds the review of AI ethics into a genuinely global perspective through a series of interviews with AI music practitioners in Asia and India.
Automated categorization of student's query

Students face many problems like mental stress, peer pressure, and health issues in college, but they do not report these due to lack of awareness, shy nature, or fear. This article proposes a platform that will unite students and the authority responsible for resolving these problems. The platform's core has a query classifier that uses text classification for categorizing the query. Text classification has already been used widely in many different problems like the chatbot, query categorization, intent, and detection. This is the first attempt to use text classification for categorizing students' queries. The query and category data have been collected from more than 400 students. Data pre-processing is done to convert textual data into numerical data. Dimensionality reduction has been applied in order to minimize the number of features. Five different machine learning (ML) approaches are applied to evaluate the performance of query categorization. K-fold cross-validation has been applied, and the ML approaches have been compared using accuracy, precision, recall, and F1-measure. The results show that Multi-Layer Perception with Back Propagation (MLP with BP) performs better than other classifiers in terms of all the metrics.