Reviewerly/exHarmony huggingface dataset:
Research on White-Box Counter-Attack Method based on Convolution Neural Network Face Recognition

In recent years, deep neural network has been widely used in face recognition, in which the model of a convolution neural network for face recognition is mostly black box model. Because the model structure and related parameters cannot be obtained, the attack effect of the counter sample is poor. In order to better realize the attack effect of the black box attack, this paper uses the white box attack to realize the black box attack. Aiming at the convolution neural network face recognition model, this paper proposes an improved FGMS counterattack algorithm, which uses the cosine similarity between the clean sample and the antagonistic sample as the loss function. The threshold is set to 0.8 as the condition for the success of the attack. In order to avoid excessive changes in the image, the threshold super-parameters is set to limit the range and size of the disturbance fluctuation, so that the countermeasure samples are not easy to be detected and improve the visual quality. Countermeasure samples are detected by black box attack on the VGG16 model, and a good attack effect is obtained.
Location Based Recommendation System with machine learning.

Machine learning allows us to give vast volumes of data to computer algorithms and have them assess and make data-driven judgments and recommendations entirely based on the incoming data. This project will employ machine learning to analyze geolocation data and user preferences in order to provide the user with intelligent recommendations. In today's fast-paced and hectic world, it's common to be too exhausted to prepare a home-cooked supper. Even if you eat home-cooked meals every day, it's not uncommon to desire to treat yourself to a decent dinner now and then for social or recreational reasons. Consider the instance of someone who has recently relocated to a new location. They already have particular tastes and interests. . If a person lives near his favorite outlet, it will save him and his food providers a lot of time and effort. This project involves categorizing popular snack destinations for incoming students based on their mood and preferences for amenities, affordability, and proximity to the place using K-Means Clustering to find the easiest food location for students in Greater Noida (or any other city of your choice) by using K-Means Clustering to find the easiest food location for students in Greater Noida (or any other city of your choice) by using K-Means Clustering to find the easiest food location for students in Greater Noida.
FUNDING HYPERLEDGER BLOCKCHAIN DAPP FOR COVID-19 PANDEMIC

Initial Crypto-Token Offering (ICO) strategy has worked so far to fund any project. In this scheme, the ICO launches a token playing the role of a stockholder’s share; the more the people buy tokens, the more the funding for the projects. The stakeholders also earn money if the project gains more reliability and more people buy this token. In other words, an ICO strategy works based on either selling or buying of tokens in the areas of security, utility, equity, etc. However, this paper’s focus is only on using oracles and Hyperledger, which means that there is no need to launch a token, instead it uses the network blockchain benefits, particularly oracles and the Ethereum virtual machine, i.e. not the Ethereum blockchain platform but its virtual machine. Since this is a proposal for governmental or corporate usage, the Hyperledger and oracles strategy fits better for this application. Funds for this use-case are gathered in the healthcare field, specifically to reduce the COVID-19 pandemic. The system’s reliability is the core for attracting investors and donors to fund the system if it guarantees that resources will get to the right destiny. DAPP is based on a smart contract aligned with the smart societies concept to ensure system sustainability.
Approaches to Study the Categorial System

Introduction. The current state of scientific knowledge has new specific features that facilitate generalizations, including when working with categorical systems. The research problem lies in the need of science and practice in the presence of scientifically grounded categorical systems. The purpose of the article is to describe the possibilities of scientific approaches in the formation of a scientifically grounded categorical system. Materials and Methods. In the development of categorical systems, it is necessary to search at the level of a general methodology where there is reliance on philosophical scientific approaches to research. Results. The categorical, systemic and holistic approaches proposed by the author for the study of categorical systems are presented. In research, the system and integrity must be considered as characteristics, fundamental properties and attributes of categories. At the same time, an integral object should be characterized in spatial and temporal organization. The categorical system is understood as a specially developed form of organization of scientific knowledge, which allows to represent objects in a holistic way through given meaningful relations in the totality of their interaction. Discussion and Conclusion. The methodological foundations of the study have been determined, which serve as guidelines for further research of the categorical system: categorical, systemic and holistic approaches. To describe the categorical system of social education, a classification of categories is proposed, based on two criteria: the appointment of a category and the mechanism of formation of a category. The system-forming features of the description of the categorical system have been determined.
Sign Language Recognition Application using Python and OpenCV

Deep learning (DL) is a machine learning method that allows computers to mimic the human brain, usually to complete classification tasks on images or non-visual data sets. Deep learning has recently become an Industry-defining tool for it’s to advances in GPU technology. Deep learning is now used in self-driving cars, fraud detection, artificial intelligence programs, and beyond. These technologies are in high demand, so deep learning data scientists and ML engineers are being hired every day. Sign language recognition has gained focus over the last few years, it is a difficult task for normal human beings to interpret sign language. The project deals with the real time input which is given by the user , recognition of gesture is done by application of support vector machine(SVM), Convolutional neural network (CNN) and other required algorithms, after this step the hand gesture is recognized and its meaning is predicted and message.
Relations

There are many forms of relations. Relations from many basic aspects are discussed in this chapter. First, relations on sets and their properties are discussed. Tables, arrow diagrams, digraphs, and zero-one matrices as methods to represent relations are introduced. Operations on relations, including union, intersection, difference, and composition, are detailed. Closure properties, equivalence relations, equivalence class, and representative are discussed. The focus then turns toward partial orderings and linear order relations. Finally, some elementary features of relational databases are discussed.
Sentiments Analyzer

Individual internet usage has expanded significantly in today's globe when compared to previous years. The evolution of AI has occurred as technology has advanced dramatically. Covid-19 is a global pandemic that has been underway since 2019, and the WHO has advised governments to take the appropriate steps to contain the problem. To safeguard citizens from this hazardous illness, the Indian government has planned a state-by-state lockdown beginning March 24, 2020. People were not allowed to work outside their homes during the curfew. All modes of transportation were restricted, with the exception of critical commodities transit and fire, police, and emergency services. Lockdowns were also imposed on educational institutions, industry, and hospitality services. On social media sites like Twitter and Koo, millions of people around the world began to express their views on the imposition of lockdown during the outbreak. Sentiment analysis, a subset of machine learning, has emerged as one of the most important areas of natural language processing (NLP). The skills of sentiment analysis can be used to examine people's reactions to an event. This required data extraction in order to understand people's emotions and play a key role in making changes to keep the problem under control. The purpose of this study is to find out what Indian citizens think about the nationwide lockdown imposed by the Indian government in order to reduce the spread of Coronavirus. In this study, NLP and machine learning classifiers were used to analyse the sentiment of tweets. Information was obtained from Twitter, annotated with TextBlob, and preprocessed with the Python's natural language tool package. RNN is used to classify sentiment in this study. The majority of Indian inhabitants accept the Indian government's decision to enforce a lockdown during the corona outburst, according to the results of this survey.
Risks and Benefits of i-Voting in Public Opinion: Evidence from Poland

Apart from benefits, internet voting security is most frequently discussed by opponents and supporters of this form of electoral participation. Although this voting method is not available in Poland, the debate on the implementation of i-voting appears in each election. The COVID-19 pandemic showed a great need to implement additional remote voting methods in the elections, and increasingly more countries are considering the introduction of i-vote in the future. Although there is no plan for the nearest future in Poland, citizens' opinions suggest that this voting procedure would meet many of its users. The paper's main aim is to analyse Poles' opinions about the risks and benefits of internet voting. The results of our own studies are based on a survey among Poles regarding their views on internet voting. The main goal is to verify if Poles more often highlight the benefits than risks of i-voting, and if the opinions about risks and benefits of i-voting differ depending on respondents’ sex, age, education and domicile.
A multi-level information extraction model for end-to-end aspect-based sentiment analysis

Aspect-based sentiment analysis mainly processes natural language and generates its aspect term and corresponding sentiment. Previous studies focused on individual subtasks, did not make full use of large-scale corpus and dig out the semantic information of sentences, and did not consider the different contributions of different words in aspect-based sentiment analysis. In this paper, a unified sequence labeling BIO model is used, and the two subtasks of aspect word extraction and sentiment analysis are fused. By sharing a unified BERT model, and using the auxiliary training of domain-related documents, the attention is focused on aspect words and emotional words. Through experiments, a better effect was finally achieved.
A design method of system simulation electronic sand table software

This paper presents a design method of system simulation electronic sand table software. The software consists of display effect and touch action control module, interface communication module, entity state information processing module, data filtering and secondary development interface module. Based on the software, the electronic sand table can realize the relevant functions of system simulation, accept external simulation information and support users to generate typical scenarios.
How I stopped worrying about training data bugs and started complaining

There is an increasing awareness of the gap between machine learning research and production. The research community has largely focused on developing a model that performs well on a validation set, but the production environment needs to make sure the model also performs well in a downstream application. The latter is more challenging because the test/inference-time data used in the application could be quite different from the training data. To address this challenge, we advocate for "complaint-driven" data debugging, which allows the user to complain about the unexpected behaviors of the model in the downstream application, and proposes interventions for training data errors that likely led to the complaints. This new debugging paradigm helps solve a range of training data quality problems such as labeling error, fairness, and data drift. We present our long-term vision, highlight achieved milestones, and outline a research roadmap including a number of open problems.
Updating Probability Intervals with Uncertain Inputs

Probability intervals provide an intuitive, powerful and unifying setting for encoding and reasoning with imprecise beliefs. This paper addresses the problem of updating uncertain information specified in the form of probability intervals with new uncertain inputs also expressed as probability intervals. We place ourselves in the framework of Jeffrey's rule of conditioning and propose extensions of this conditioning for the interval-based setting. More precisely, we first extend Jeffrey's rule to credal sets then propose extensions of Jeffrey's rule to three common conditioning rules for probability intervals (robust, Dempster and geometric conditionings). While the first extension is based on conditioning the extreme points of the credal sets induced by the probability intervals, the other methods directly revise the interval bounds of the distributions to be updated. Finally, the paper discusses related issues and relates the proposed methods with respect to the state-of-the-art.
Human Behaviour Detection by Reading Facial Expression

Abstract: There are many way to by which human being communicate with each other verbal, sign language or facial expression are one of them. Here we propose artificial intelligence based facial expression detection system by which we can detect behaviour of human being by computer system. Facial expression detection have lot of real life applications including computer to human interaction, Human behaviour reorganisation, cognitive study, emotion analysis, personality development etc. Here we set a study which uses a new technique for facial expressions using single frame uses combination of vector and geometrical feature based classification method Generally to study human face the image of face is divided into small grid of pixels . In this Study we have divided whole face into sub region of local regions. This technique acquire most important sub regions of the domain and start collecting most important sub regions incrementally which enhance accuracy and speed. The consequences of looks acknowledgment utilizing highlights from space explicit locations , likewise contrasted and the outcomes acquired utilizing all encompassing portrayal. The presentation of the proposed look acknowledgment framework has been approved on openly accessible expanded Cohn-Kanade (CK+) look informational collections.
The family as a source of the youth education system: expectations, reality, prospects

The paper examines the history of parenting styles in the "parent-child" system, the evolution of the Russian family and its impact on intergenerational relations, and the importance of intra-family and subsequent out-of-family education for the younger generation. It is shown that the family is a cell of society in which new generations grow up and the inherent positive qualities of a person are strengthened, while flaws and vices are smoothed out. Love and respect of spouses for each other, honoring parents, caring for children, friendship of brothers and sisters represent a school of normal relations with all people around.
Security issues in blockchain as access control in electronic health records

Recent solutions of storing and distributing patient data have several confines that limit users’ access to their patient health records, decrease accessibility of critical information to care providers, and eventually extant a boundary in the transfer of traditional healthcare into a digital healthcare approach. In order to remediate such shortcomings, a blockchain-based solution is the best tool to provide storage and trust. In digital healthcare data allocation, numerous cloud-based approaches have been suggested, but it would be untrustworthy to rely on a third party. In recent times, blockchain has been implemented in digital healthcare record sharing, which skip trusting a third party. Although current methods only focus on the clinical-related records received from a medical diagnosis, they are not considered resourceful regarding their data sharing which is always generated from biomedical and monitoring devices. In this chapter, we have proposed blockchain as a novel approach to secure patient-related data access, implementation obstacles, and a strategy for transitioning gradually from current technology to a blockchain-based solution.
Prediction Performance Comparison of Risk Management and Control Mode in Regional Sites Based on Decision Tree and Neural Network

The traditional risk management and control mode (RMCM) in regional sites has the defects of low efficiency, high cost, and lack of systematism. Trying to resolve these defects and explore the application possibility of machine learning, a characteristic dataset for RMCM in regional sites was established. Three decision tree (DT) algorithms (CHAID, EXHAUSTIVE CHAID, and CART) and two artificial neural network (ANN) algorithms [back propagation (BP) and radial basis function (RBF)] were implemented to predict RMCM in regional sites. The results showed that in the aspects of accuracy (ACC), precision (PRE), recall ratio (REC), and F 1 value, CART–DT was superior to CHAID–DT and EXHAUSTIVE CHAID–DT (E-CHAID–DT); and BP–ANN was superior to RBF–ANN. However, CART–DT was inferior to BP–ANN in ACC, PRE, REC, and F 1 value. BP–ANN model is good at non-linear mapping, and it has a flexible network structure and a low risk of over-fitting. The case study of a typical county demonstration area confirmed the extensibility of the method, and the method has great potential in RMCM prediction in regional sites in the future.
Deep neural network goes lighter: a case study of deep compression techniques on automatic RF modulation recognition for Beyond 5G networks

Automatic RF modulation recognition is a primary signal intelligence (SIGINT) technique that serves as a physical layer authentication enabler and automated signal processing scheme for the beyond 5G and military networks. Most existing works rely on adopting deep neural network architectures to enable RF modulation recognition. The application of deep compression for the wireless domain, especially automatic RF modulation classification, is still in its infancy. Lightweight neural networks are key to sustain edge computation capability on resource-constrained platforms. In this letter, we provide an in-depth view of the state-of-the-art deep compression and acceleration techniques with an emphasis on edge deployment for beyond 5G networks. Finally, we present an extensive analysis of the representative acceleration approaches as a case study on automatic radar modulation classification and evaluate them in terms of the computational metrics.
A Parallelism-Based Earliest Finish Time (PBEFT) Algorithm for Workflow Scheduling in Clouds

For those public-cloud-based application providers, workflow scheduling in clouds must not only meet traditional performance optimization goals, but also minimize financial costs. This paper focuses on budget-constrained workflow scheduling issue on heterogeneous cloud resources, and tries to minimize the end-to-end delay of the workflow while meeting the given budget. A heuristic algorithm is proposed, which introduces a resource leasing mechanism to maximize the usage of the budget. This paper also defines a scheduling factor parameter consisting of two factors (parallelism and urgency) to determine the scheduled tasks. A scheduling algorithm based on task duplication is designed, which reduces the finish time of workflow tasks by using the idle time slots in existing servers. Finally, compared with contrast algorithms, the experimental results show that the proposed algorithm is superior in both meeting budget constraints and minimizing end-to-end delay.
Online conflict resolution strategies for human activity recognition in smart homes

DCR-OL is a Distributed Collaborative Reasoning multi-agent model with an Online Learning that aims to identify human activities in smart homes from distributed, heterogeneous and dynamic sensor data. In this model, distributed learning agents with diverse classifiers, detect sensor stream data, make local predictions, communicate and collaborate to identify current activities. Then, they learn from their collaborations to improve their own performance in activity recognition. Conflict resolution strategies are applied to generate one final predicted activity when the local predicted activity of an agent is different from received predicted activities of other agents. In this paper, two conflict resolution strategies using online learning, w-max-trust and w-max-freq, are proposed. We experimentally test these strategies by performing an evaluation study on the Aruba dataset. The obtained results indicate an enhancement in terms of accuracy and F-measure metrics compared to the offline strategies max-trust and max-freq and also to the online existing one max-wPerf .
Pengaruh Kebiasaan Membaca dan Penguasaan Kosakata Terhadap Kemampuan Membaca Bahasa Inggris

The purpose of this research is to know whether there are the effects of reading habit and vocabulary mastery towards student’s reading skill. The method used was survey. The sample taken was 30 students of Grade XI of Vocational High Schools in Depok using simple random technique. The instrument used to collect the data was a questionnaire. The data analysis used was descriptive statistics such as finding the mean, median, mode, standard deviation, and inferential statistics which was used for finding the simple and multiple correlation coefficients, followed by significance test of correlation coefficient with t test. This research was conducted on April 1 to May 30, 2015. The results showed: 1). There is a significant influence of the reading habit and vocabulary mastery towards student’s reading skill, it was proved by the value of F = 79.250 and f table = 3,35. 2). There is a significant influence between the reading habit towards the student’s reading skill, it was proved by the value of t = 2.319 and Sig. 0.028 &lt; 0.05. 3). There is a significant influence of vocabulary mastery towards student’s reading skill, it was proved by the value of t = 2.608 and Sig. 0.015 &lt; 0.05.