Reviewerly/exHarmony huggingface dataset:
Visual Question Answering based on Formal Logic

Visual question answering (VQA) has been gaining a lot of traction in the machine learning community in the recent years due to the challenges posed in understanding information coming from multiple modalities (i.e., images, language). In VQA, a series of questions are posed based on a set of images and the task at hand is to arrive at the answer. To achieve this, we take a symbolic reasoning based approach using the framework of formal logic. The image and the questions are converted into symbolic representations on which explicit reasoning is performed. We propose a formal logic framework where (i) images are converted to logical background facts with the help of scene graphs, (ii) the questions are translated to first-order predicate logic clauses using a transformer based deep learning model, and (iii) perform satisfiability checks, by using the background knowledge and the grounding of predicate clauses, to obtain the answer. Our proposed method is highly interpretable and each step in the pipeline can be easily analyzed by a human. We validate our approach on the CLEVR and the GQA dataset. We achieve near perfect accuracy of 99.6% on the CLEVR dataset comparable to the state of art models, showcasing that formal logic is a viable tool to tackle visual question answering. Our model is also data efficient, achieving 99.1% accuracy on CLEVR dataset when trained on just 10% of the training data.
A VAE Conversion Method for Private Data Linkage

Data linkage plays a crucial role in realizing big data's value but is often regarded as a threat to personal privacy. Regulations like GDPR requires users' consent on each specific use of data, which is not practical for data analyzers. In this study, we propose a way to address the problem by having a trustworthy third party collect data from two or more parties, then use the data to train one or more variational autoencoder (VAE) models to remove privacy and send them to the data providers. Using this model, the users express their consent to share data with a trustworthy party. The third party links data from various datasets together to build a variational autoencoder model that allows all parties to generate datasets with full attributes without revealing sensitive personal data. System architectures and machine learning accuracy of generated data sets are measured in this study.
Multiview Classification with Missing-Views Through Adversarial Representation and Inductive Transfer Learning

The importance of Multiview learning in classification task cannot be further stressed. Over the years, there is an increasing interest among researchers to enhance Multiview classification models. However, very little attention has been paid to handling the missing-view(s) problem in existing systems. Missing-view can be caused by various reasons. It can be a technical fault during data measurement which can occur in real-world applications. Handling such occurrences is paramount to determine the success of the classification model on real-world data. In this paper, a Multiview deep classification model is proposed. The model implements an adversarial network that learns a latent space of semantic knowledge of the Multiview data. Further, the model used an inductive transfer style to train a neural classifier network from the learned latent space. Therefore, understanding the semantics is required to recreate any corresponding alternate view from a given view. The proposed model is trained and evaluated on MNIST and Noisy MNIST datasets. Its performance was compared with existing systems in terms of accuracy and normalized mutual information (NMI). The proposed model achieved better performance. The model is evaluated across different missing rates which further demonstrated that it is capable of handling missing-view(s) effectively.
Security Issues and Solutions for Healthcare Informatics

Online healthcare system allows patients to remotely access the required medical history whenever required. Accessing the patient records as well as medical history associated with the diagnosis is beneficial not only for patients but also for healthcare personnel. But this practice may lead to major security and privacy concerns of patient’s private data, e.g., no patient would like to reveal the health-related data which may cause trouble in his/her personal life as well as the professional life. For electronic health record (EHR) adaptation, the major elements are laws and regulations, monetary inducement and hurdles, technology state, and corporation effect. Emerging technologies like blockchain, cloud computing, and IoT have a vital role in healthcare information systems. In this chapter, I have discussed several privacy and security issues and solutions for healthcare informatics. .
Efficient-way of Data Storage on Decentralized Cloud using Blockchain Technology

Recent years, cloud computing has introduced a number of emerging techniques for efficient ways of storing and sharing the land registry data on cloud. In this paper we have done a survey on how we can provide security to land registry data on cloud, we found one of the emerging techniques that is blockchain. The invention of decentralized network increases computational demands and it&#x0027;s proven to be more secure using lightweight cryptographic based on this we have design the architecture and Records storing and access protocol (RSAP) This advanced model helps to improve the service with high security, high flexibility, availability of data and improve the performance of the network. However, this new idea also raises concerns about land registry data security and privacy on ethereum private blockchain networks.
Joint Patch Weighting and Moment Matching for Unsupervised Domain Adaptation in Micro-Expression Recognition

Unsupervised domain adaptation (DA) is a challenging machine learning problem since the labeled training (source) and unlabeled testing (target) sets belong to different domains and then have different feature distributions, which has recently attracted wide attention in micro-expression recognition (MER). Although some well-performing unsupervised DA methods have been proposed, these methods cannot well solve the problem of unsupervised DA in MER, a. k. a., cross-domain MER. To deal with such a challenging problem, in this letter we propose a novel unsupervised DA method called Joint Patch weighting and Moment Matching (JPMM). JPMM bridges the source and target micro-expression feature sets by minimizing their probability distribution divergence with a multi-order moment matching operation. Meanwhile, it takes advantage of the contributive facial patches by the weight learning such that a domain-invariant feature representation involving micro-expression distinguishable information can be learned. Finally, we carry out extensive experiments to evaluate the proposed JPMM method is superior to recent state-of-the-art unsupervised DA methods in dealing with cross-domain MER.
Metode Bercakap-cakap: Pengaruhnya terhadap Keterampilan Berbicara Anak Usia 4-5 Tahun

Language is the ability to communicate with other people. One of the language skills that can be developed first is speaking skill. Speaking skills need to be optimized because they can affect the next child's life. There are various kinds of problems related to speaking skills including some children who have not been able to communicate the ideas in their minds, express their opinions, and have not dared to speak. In addition, the teaching methods used by teachers have an influence on children's interest in expressing their opinions. This study aims to determine the effect of the conversing method on the speaking skills of children aged 4-5 years. The quantitative approach was carried out using an experimental method of pre-experimental design, one group pretest, and post-test design. Subjects in this study as many as 17 children, data collection techniques using the method of observation and documentation. Furthermore, the data analysis technique used the Wilcoxon Matched Pairs Test formula. The results showed that the method of speaking had an effect on children's speaking skills.
The Power Spectrum

This chapter introduces the power spectrum. The power spectrum is the Fourier Transform of the autocovariance function, and the autocovariance function is the (inverse) Fourier Transform of the power spectrum. As such, the power spectrum and autocovariance function offer two complementary but mathematically equivalent descriptions of a stochastic process. The power spectrum quantifies how variance is distributed over frequencies and is useful for identifying periodic behavior in time series. The discrete Fourier transform of a time series can be summarized in a periodogram, which provides a starting point for estimating power spectra. Estimation of the power spectrum can be counterintuitive because the uncertainty in periodogram elements does not decrease with increasing sample size. To reduce uncertainty, periodogram estimates are averaged over a frequency interval called the bandwidth. Trends and discontinuities in time series can lead to similar low-frequency structure despite very different temporal characteristics. Spectral analysis provides a particularly insightful way to understand the behavior of linear filters.
Unprotected Processing by Default vs Data Protection by Design and by Default Under the GDPR for Schrems II and GDPR

This memorandum was submitted in response to a European Data Protection Board (EDPB) public consultation to clarify:(i) The requirements for GDPR-compliant pseudonymisation.(ii) The benefits of pseudonymisation for Schrems II and GDPR compliance.(iii) Whether it is true that EU companies can no longer use US cloud services under any situation.
A Physical ground for the scaling of Peak Ground Acceleration (PGA) with the integral of squared velocity ( I<sup>2</sup> <sub>Vp</sub> and its potential for Earthquake Early Warning

Abstract One of the main goals of an Earthquake Early Warning System (EEWS) is to estimate the expected peak ground motion of the destructive S-waves using the first few seconds of P-waves, thus becoming an operational tool for real-time seismic risk management in a short timescale. EEWSs are based on the use of scaling relations between parameters measured on the initial portion of the seismic signal, after the arrival of the P-wave. Scope of the present work is to study the physical basis of the scaling laws observed between the peak ground acceleration (PGA) and the integral of the squared velocity ( I 2 vp ). Based on Brune’s model, which is one of the most widely adopted earthquake source models, we explore the physical principles of the scaling relations between the root mean square (rms) of the velocity acceleration recorded in the first few seconds after P-wave arrival and acceleration (recorded in S-waves) and the II 2 vp . Assuming a relation between the PGA and the rms values estimated, the scaling of PGA with the integral of the squared velocity (I 2 vp ) which is calculated directly from the first few seconds-long signal window ( T ) after the P-wave arrival obtained. The latter formulation opens the possibility of using such laws for on-site and inter-site earthquake early warning.
Reflection in Teaching Psychological and Pedagogical Disciplines in Multi-level Engineering and Science Education

The relevance of the paper is due to the need to form the students’ conscious learning, planning an individual educational trajectory, ability to analyze and reflect at various stages of multi-level higher education. Such competencies can be formed in course of studying integrative psychological and pedagogical disciplines at each level of higher education, including practical tasks that require reflection on the learning process and a detailed response. The paper provides a case study of such a task offered to undergraduate, graduate and postgraduate students of natural science and technical profiles. They were asked to analyze their own experience of undergraduate studies and share their views on what a modern bachelor's degree program should be in the relevant areas. The results of this task have been summarized and analyzed. A look at the condition of undergraduate studies “from the inside” can be useful to improve it. The article is intended for educators, heads of educational institutions, and researchers.
The Application of Multimedia Technology in Video after Effects Production

The development of science and technology has stimulated people to be more and more critical of film and television works, and have higher requirements for the fineness of the film pictures and the quality of the audio-visual effect. The development of computer multimedia technology affects the development of video after effects production technology. Therefore, it has a far-reaching impact on the development of the video special effects to explore the application of the media technology in the video after effects production.
Disadvantages of Relational DBMS for Big Data Processing

In this paper, we are analyzing relational DBMS while working with big data. All experiments and performance tests are implemented in MS SQL Server 2019 Developer Edition and MongoDB. In the beginning, we start by creating a database that contains non-linked tables (flat tables) with maximum memory consummation. The performance test starts with filling (Insert) the database with millions of rows. Then we continue with data modification operations (Update). Finally, we launch the data cleaning (Delete) test. When all tests pass by and execution time is picked up for further analysis, we continue our experiment with relational tables and NoSQL collections. That’s why in the second part of this paper we take the same database structure where we add primary and foreign key constraints with cascade update and delete options. When repeating the performance test, we notice that the table size and execution time have significantly increased.
Use of VR in Engineers Certification at Hazardous Production Facilities in Petrochemical Industry

The study was aimed at developing a scenario for using VR for industrial safety certification in further professional education of managers and specialists in the field of operating pressure vessels at hazardous production facilities at the KNRTU Institute for Continuing Professional Education. A scenario expertise was held involving seven experts. The results of the expert evaluation were taken into account while developing the “Industrial Safety for the Petrochemical Industry” computer-training complex based on the scenario. We concluded as a result of the study that the stages of developing VR applications for the use in certification of specialists may be following: (1) development of a rubricator of assessment elements; (2) development of tasks and situations based on the rubricator of assessment elements; (3) combining assignments and situations into a scenario; (4) expertise of the rubricator and the scenario; (5) adjustment of the rubricator and scenario based on the results of the expert assessment; (6) development of software implementing the scenario.
Confidence Intervals

A goal in statistics is to make inferences about a population. Typically, such inferences are in the form of estimates of population parameters; for instance, the mean and variance of a normal distribution. Estimates of population parameters are imperfect because they are based on a finite amount of data. The uncertainty in a parameter estimate may be quantified using a confidence interval. A confidence interval is a random interval that encloses the population value with a specified probability. Confidence intervals are related to hypothesis tests about population parameters. Specifically, for a given hypothesis about the value of a parameter, a test at the 5% significance level would reject that value if the 95% confidence interval contained that hypothesized value. This chapter explains how to construct a confidence interval for a difference in means, a ratio of variances, and a correlation coefficient. These confidence intervals assume the samples come from normal distributions. If the distribution is not Gaussian, or the quantity being inferred is complicated, then bootstrap methods offer an important alternative approach, as discussed at the end of this chapter.
Accelerated Edge Detection Algorithm for High-Speed Applications

Digital Image Processing (DIP) is a growing field for various applications, such as autonomous vehicles and video surveillance. To improve the performance of DIP systems, image processing algorithms are implemented in hardware rather than software. The idea here is primarily to get a faster system than software imaging or other alternative hardware. Field-programmable gate arrays (FPGAs) have the advantages of parallel processing, low cost, and low power consumption. These semiconductor devices contain many logic blocks that can be programmed to perform everything from basic digital gate-level technology to complex image processing algorithms. This paper provides an enhancement pipeline system architecture using AXI interface to implement image processing algorithms such as Sobel edge detection and mean filters on the Zybo z7 Zynq 7010 board using Verilog HDL language. The system is implemented in a 512×512 image that takes 0.009ms in the processing system.
Enhancing Siamese Visual Tracking With Background Relations

Existing Siamese network-based trackers rely on stable appearance features extracted from the target object. However, such features might not be available during tracking due to non-digit appearance deformation and severe occlusion, which result in drift problems. In this paper, we propose a background-augmented tracking network that incorporates background information surrounding the target to make up for missing or deformed target features during the matching process. A novel Background Relation Network (BRNet) is designed to effectively encode and match the background information surrounding candidate objects in the search region to help identify the correct target, and thus avoid tracking error. BRNet can complement the base tracker when reliable target features cannot be obtained. Experiments on the OTB, VOT, and UAV123 datasets demonstrate that the proposed method achieves superior performance over existing state-of-the-art methods while maintaining reasonable real-time speed.
Research on Key Technologies of Robot for Visual Detection of Foreign Matter in Liquid Medicine Based on Machine Learning Algorithm

Abstract At present, in the pharmaceutical industry, people pay more and more attention to the quality of drugs, and therefore put forward higher requirements for the detection of drugs. In this paper, based on a large number of theories and experiments, an intelligent on-line detection system for visible foreign bodies in medicated liquor solution is designed based on machine learning algorithm. From the angle of mechanics and kinematics, the force analysis of moving objects in liquid after high-speed rotation and sudden stop is discussed, which provides an important reference standard for distinguishing visible foreign objects from random noise. The experimental results show that the algorithm can effectively eliminate the vibration interference of electromechanical system, and make the foreign body detection equipment of liquid medicine successfully migrate from single-station detection equipment to automatic online detection robot, which is of great significance to the research and development of the whole equipment.
Convolutional Neural Networks

We have introduced the basic theory of neural networks, the use of TensorFlow, and the basic fully connected network model and have a more comprehensive and in-depth understanding of neural networks. But for deep learning, we still have a little doubt. The depth of deep learning refers to the deeper layers of the network, generally more than five layers, and most of the neural network layers introduced so far are implemented within five layers. So what is the difference and connection between deep learning and neural networks?
Twitter Social Media Sentiment Analysis Of Telecommunications Service Provider Using Long Short-Term Memory Method

As one of the telecommunication companies in Indonesia, Provider X needs to pay attention to the services that their products provide for their users. This needs to be done so that their consumers don't switch to a different provider. To get the answers or information regarding the improvement of services, companies can process or analyze the users' sentiment toward their products through the Twitter social media. The purpose of this research is to analyze the sentiments of Provider X's tweet data of 5.375 on December 24, 2019 to February 24, 2020. The collected data is classified into two classes, negative and positive. This research uses the Long Short-Term Memory method. Hyperparameter testing is done to get the most optimal model. The test produced Word2vec hyperparameter including dimensions of 50, window size 5, epoch 50 and learning rate 0.025. In LSTM hyperparameter, optimal conditions are obtained when the number of LSTM layers is one, five LSTM units, the activation function is sigmoid, the optimizer is Nadam, the recurrent dropout size is 0.2 and the batch size is 32. The LSTM model is evaluated using K- fold cross validation and produces an average accuracy of 97.53%, f1score 97.39% and a loss of 10.36%.
Audio Zero-Watermarking Algorithm based on Beat Tracking

In order to protect the copyright of digital products effectively, digital watermarking technology is proposed. At present, synchronization attack is still a challenging problem in audio watermarking technology. In this paper, a robust zero watermarking scheme is proposed. Firstly, the beat activation function is obtained based on the bidirectional Long Short-Term Memory(BLSTM) recurrent neural network, and the beats of music are extracted by peak detection. Then the audio is segmented according to beat points. After framing for each segmentation, the mean value of each frame&#x2019;s normalized sampling values is calculated to obtain the mean value sequence. By encoding, the mean value sequence is converted into a binary sequence as the audio feature, which is xor with the watermarking image to generate the final zero-watermarking. Experiments show that the proposed scheme can effectively resist some synchronization attacks, such as TSM and jittering.
Analysis of Effective Application of Information Extraction Method for Online Product Reviews Based on User Experience

Aiming at product design improvement, a collection and analysis method of user experience information in online product reviews was proposed.First, an user experience element model was constructed.Then, according to the element model, the user emotion, product feature and usage context in a single review sentence were extracted step by step. Finally, the user experience elements extracted from a single review sentence were classified and integrated, and through quantitative analysis of the review data, the product features and use contexts that have a greater impact on user experience were identified. The method was verified by taking the online reviews of a smart phone on Jingdong-Mall as the example. The experimental results show that the method can effectively extract the user experience elements from online product reviews, identify the product features and use contexts that have a great impact on the user experience, and assist designers to improve the product design.