Reviewerly/exHarmony huggingface dataset:
Annotation quality vs. quantity for deep-learned medical image segmentation

For medical image segmentation, deep learning approaches using convolutional neural networks (CNNs) are currently superseding classical methods. For good accuracy, large annotated training data sets are required. As expert annotations are costly to acquire, crowdsourcing–obtaining several annotations from a large group of non-experts–has been proposed. Medical applications, however, require a high accuracy of the segmented regions. It is agreed that a larger training set yields increased CNN performance. However, it is unclear, to which quality standards the annotations need to comply to for sufficient accuracy. In case of crowdsourcing, this translates to the question on how many annotations per image need to be obtained. In this work, we investigate the effect of the annotation quality used for model training on the predicted results of a CNN. Several annotation sets with different quality levels were generated using the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm on crowdsourced segmentations. CNN models were trained using these annotations and the results were compared to a ground-truth. It was found that increasing annotation quality results in a better performance of the CNN in a logarithmic way. Furthermore, we evaluated whether a higher number of annotations can compensate lower annotation quality by comparing CNN predictions from models trained on differently sized training data sets. We found that when a minimum quality of at least 3 annotations per image can be acquired, it is more efficient to then distribute crowdsourced annotations over as many images as possible. The results can serve as a guideline for the image assignment mechanism of future crowdsourcing applications. The usage of gamification, i.e., getting users to segment as many images of a data set as possible for fun, is motivated.
Willingness to information security as a function of personality characteristics and threat assessment among adolescents

Purpose The study focuses on adolescents and the influence the big five great personality traits – extroversion, agreeableness, neuroticism, openness to experiences and conscientiousness – on self-disclosure. These personality traits, combined with the ability to cope with stress, determine the degree of threat felt by an individual towards their information, their evaluation of their personal ability to keep their information secure, and their willingness to secure information. Design/methodology/approach Five questionnaires relating to the big five personality traits, self-disclosure, cognitive assessment, self-efficacy and IS awareness were distributed among 157 adolescents. Findings Readiness for IS. Furthermore, the study showed that the more ostentatiousness, agreeable, goal oriented and open the subjects are, the lower they will evaluate the threat to their information. A relationship was also revealed between the subjects' agreeableness, goal orientation and their information threat assessment. It was also found that the more extroverted, agreeable, conscientious and the more inclined to self-disclosure, the higher they evaluate their self-ability to handle threats to their information. Originality/value For IS behavior to become second nature to adolescents they must first be educated and trained to do so. Knowing what motivates them and, on the other hand, what hinders them, to practice IS can help build training models for teachers which may be adapted according to their personal traits, thus getting the most out of such programs.
A Novel Trajectory Optimization for Affine Systems: Beyond Convex-Concave Procedure

Trajectory optimization problems under affine motion model and convex cost function are often solved through the convex-concave procedure (CCP), wherein the non-convex collision avoidance constraints are replaced with its affine approximation. Although mathematically rigorous, CCP has some critical limitations. First, it requires a collision-free initial guess of solution trajectory which is difficult to obtain, especially in dynamic environments. Second, at each iteration, CCP involves solving a convex constrained optimization problem which becomes prohibitive for real-time computation even with a moderate number of obstacles, if long planning horizons are used.In this paper, we propose a novel trajectory optimizer which like CCP involves solving convex optimization problems but can work with an arbitrary initial guess. Moreover, the proposed optimizer can be computationally upto a few orders of magnitude faster than CCP while achieving similar or better optimal cost. The reduced computation time, in turn, stems from some interesting mathematical structures in the optimizer which allows for distributed computation and obtaining solutions in symbolic form. We validate our claims on difficult benchmarks consisting of static and dynamic obstacles.
INTERPRETATION OF THE CONCEPT OF “PROFESSIONAL READINESS” IN THE CONTEXT OF TRAINING UNIVERSITY STUDENTSFOR FUTURE PROFESSIONAL ACTIVITIES

The authors consider the main approaches to the essence of the concept of “professional readiness” based on the analysis of scientific literature. In the context of new realities that require understanding, an attempt is made to come to a common understanding of terms, which will make it possible to correctly interpretand use them. The synthesis of the presented points of view allowed the authors to give their own interpretation of the definition of “professional readiness” which will become the basis for staff training in the future.
Study on Speech Recognition Method of Artificial Intelligence Deep Learning

Abstract This paper briefly describes the basic principles and related theories of speech recognition system, points out the existing problems of speech recognition technology of artificial intelligence deep learning, analyzes the speech recognition methods of artificial intelligence deep learning, puts forward the optimization methods of speech recognition technology of artificial intelligence deep learning from the aspects of strengthening targeted feature recognition of speech system, repeatedly carrying out simulation training of speech recognition and combining acoustic features with sports features, and forecasts the future prospects of speech recognition methods of artificial intelligence deep learning.
Multilingual Speech Synthesis for Voice Cloning

Speech synthesis technology is evolving from the concept of converting texts into voices to directly learning and imitating user voices. In the past, the voice was generated through several stages of recording frequently used sentences and synthesizing text into sounds that wanted to be converted. However, due to the limitations of naturalness and accuracy, research has been conducted to overcome this in various fields such as phonetics, linguistics, and statistics, and now the development of big data, AI technology, and parallel processing technology has increased to reflect human tone and timbre. If the voice information, which is one of the biometric information that can be used as personally identifiable information, can be made so natural that it is difficult for a machine to distinguish it from a human, personal information may be invaded by a security threat that may exist at any time. Therefore, in this paper, we explain representative speech synthesis models and introduce voice generation models, which are applications, to find out how to convert and synthesize Korean into other languages, and to look at what may become a security issue in the future.
Data-driven tool construction in exploratory programming environments

This work presents a new design for programming environments that promote the exploration of domain-specific software artifacts and the construction of graphical tools for such program comprehension tasks. In complex software projects, tool building is essential because domain- or task-specific tools can support decision making by representing concerns concisely with low cognitive effort. In contrast, generic tools can only support anticipated scenarios, which usually align with programming language concepts or well-known project domains.However, the creation and modification of interactive tools is expensive because the glue that connects data to graphics is hard to find, change, and test. Even if valuable data is available in a common format and even if promising visualizations could be populated, programmers have to invest many resources to make changes in the programming environment. Consequently, only ideas of predictably high value will be implemented. In the non-graphical, command-line world, the situation looks different and inspiring: programmers can easily build their own tools as shell scripts by configuring and combining filter programs to process data.We propose a new perspective on graphical tools and provide a concept to build and modify such tools with a focus on high quality, low effort, and continuous adaptability. That is, (1) we propose an object-oriented, data-driven, declarative scripting language that reduces the amount of and governs the effects of glue code for view-model specifications, and (2) we propose a scalable UI-design language that promotes short feedback loops in an interactive, graphical environment such as Morphic known from Self or Squeak/Smalltalk systems.We implemented our concept as a tool building environment, which we call VIVIDE, on top of Squeak/Smalltalk and Morphic. We replaced existing code browsing and debugging tools to iterate within our solution more quickly. In several case studies with undergraduate and graduate students, we observed that VIVIDE can be applied to many domains such as live language development, source-code versioning, modular code browsing, and multi-language debugging. Then, we designed a controlled experiment to measure the effect on the time to build tools. Several pilot runs showed that training is crucial and, presumably, takes days or weeks, which implies a need for further research.As a result, programmers as users can directly work with tangible representations of their software artifacts in the VIVIDE environment. Tool builders can write domain-specific scripts to populate views to approach comprehension tasks from different angles. Our novel perspective on graphical tools can inspire the creation of new trade-offs in modularity for both data providers and view designers.
The One-Way Communication Complexity of Dynamic Time Warping Distance.

We resolve the randomized one-way communication complexity of Dynamic Time Warping (DTW) distance. We show that there is an efficient one-way communication protocol using $\widetilde{O}(n/\alpha)$ bits for the problem of computing an $\alpha$-approximation for DTW between strings $x$ and $y$ of length $n$, and we prove a lower bound of $\Omega(n / \alpha)$ bits for the same problem. Our communication protocol works for strings over an arbitrary metric of polynomial size and aspect ratio, and we optimize the logarithmic factors depending on properties of the underlying metric, such as when the points are low-dimensional integer vectors equipped with various metrics or have bounded doubling dimension. We also consider linear sketches of DTW, showing that such sketches must have size $\Omega(n)$.
Pengaruh Model Pembelajaran Contextual Teaching And Learning Terhadap Hasil Belajar Matematika Ditinjau Dari Berpikir Kritis

The aim of this study is to investigate The Effect of Contextual Teaching and Learning Model on Mathematic Achievement in terms of Critical Thinking. The type of research used a semi- experimental quantitative research method in which applying two experimental classes with the same sample characteristics. The population was all the students in Kotapinang district and the sample was 62 of the fifth grade elementary students of SDN 112224 Kotapinang. In the experiment class (CTL Model), the sample was 32 students. While in the control class (Conventional Model), the sample was 30 students. Research instrument used Achievement test of 10 questions from sturffing that conducted validity and reliability tests and questionnaire Critical Thinking of 16 statements. Based on the treatment, In the experiment class got Achievement 81,88 While in the control class got Achievement 73,11. And for the result, there was a significant effect on Mathematics Achievement. Applying CTL model in learning on the fifth grade elementary students who had high critical thinking showed that their mathematics achievement is better than that applying conventional model. So, in conclusion, applying CTL model gave better effect on mathematics achievement of the fifth elementary students in terms of critical thinking in Kotapinang district.
A Traceable Firearm Management System Based on Blockchain and IoT Technology

In some liberal democracies, governments allow people to legally own guns to protect their lives and property. However, gun crime has been increasing in recent years. Taking the United States, for example, a report pointed out that Americans are 10 times more likely to be shot than citizens of other countries. Murder using guns in the United States is 25 times more than that in the other 22 high-income countries. Most of these guns came from other cities. These illegally circulating guns are directly linked to deadly street violence. This means that effective firearm management can reduce deadly violence. In the past few years, Radio Frequency IDentification (RFID) technology was often used to track the supply chain. However, in the traditional supply chain, only the participants of the supply chain can query information. Furthermore, only the participants can verify the correctness of the data. This result causes information to be not transparent. On the other hand, blockchain technology, with a unique combination of features such as distributed notes, decentralized structure, consensus algorithm, storage mechanism, asymmetric encryption, and smart contract, ensures network visibility, transparency, and security. Therefore, we combine blockchain and RFID technology to propose a traceable firearm management system based on blockchain and Internet of Things (IoT) technology. The proposed method achieves several goals. First of all, the characteristic data through the blockchain can be publicly verified and the information will not be modified. The traceability of the data and the application of RFID can effectively manage the production chain. In addition, we used Burrows–Abadi–Needham logic (BAN logic) logic to prove mutual authentication, and the nonrepudiation and integrity method can also be achieved in our proposed scheme.
GAMA: A General Automated Machine Learning Assistant

The General Automated Machine learning Assistant (GAMA) is a modular AutoML system developed to empower users to track and control how AutoML algorithms search for optimal machine learning pipelines, and facilitate AutoML research itself. In contrast to current, often black-box systems, GAMA allows users to plug in different AutoML and post-processing techniques, logs and visualizes the search process, and supports easy benchmarking. It currently features three AutoML search algorithms, two model post-processing steps, and is designed to allow for more components to be added.
Hardware Design of Image Encryption and Decryption Using CORDIC Based Chaotic Generator

This work proposes a Red Green Blue (RGB) image encryption and decryption digital system which uses random number sequence generators as core and also provides its internal architectural layout. SC-CNN (State Controlled Cellular Neural Network) based chaotic system which stands optimal in generating multi-scroll chaotic generators is inculcated in this system. The hyperbolic tangent function used in state equations of the chaotic generator is implemented via CORDIC (Coordinate Rotational Digital Computer) algorithm, which is an efficient algorithm to compute various trigonometric and hyperbolic functions. The above techniques are combined together to give hardware credibility to the scheme described. The randomness and the level of encryption are analyzed and validated with the help of multiple test inputs and corresponding encrypted outputs. The complete encryption and decryption flows are simulated using Xilinx Vivado 2019.1 and realized on FPGA (Field programmable gate array), Zynq 7 board, as the chosen device.
Soft Pseudo-labeling Semi-Supervised Learning Applied to Fine-Grained Visual Classification

Pseudo-labeling is a simple and well known strategy in Semi-Supervised Learning with neural networks. The method is equivalent to entropy minimization as the overlap of class probability distribution can be reduced minimizing the entropy for unlabeled data. In this paper we review the relationship between the two methods and evaluate their performance on Fine-Grained Visual Classification datasets. We include also the recent released iNaturalist-Aves that is specifically designed for Semi-Supervised Learning. Experimental results show that although in some cases supervised learning may still have better performance than the semi-supervised methods, Semi Supervised Learning shows effective results. Specifically, we observed that entropy-minimization slightly outperforms a recent proposed method based on pseudo-labeling.
The development and evaluation of an augmented reality learning system for Japanese compound verbs using learning analytics

Advances in mobile technology are providing new opportunities for mobile learning. Augmented reality (AR) technologies may be used on mobile devices and have the potential to facilitate improvements in learning performance. Meanwhile, the amount of data available about learners and their learning activities is rapidly increasing. Using learning analytics allows the collection of learning data to effectively analyze learning behaviors and processes. In this study, we designed and developed an AR learning system based on the temporal and spatial continuity principles of the multimedia learning theory to aid Japanese compound verb acquisition. We also employed the learner-to-learning materials interaction to analyze the relationship between learning behaviors and learning performance. The results showed that the use of an AR learning system assisted learners in improving their learning and retention of compound verbs, whereas repeated trials of the learning behavior enhanced the learning of compound verbs.
Big Data Privacy Breach Prevention Strategies

Over the length of time, there has been an exponential increase in the number of netizens throughout the world. The surge in internet users has been especially prominent in times of the Covid-19 pandemic. Consequently, there has been a rise in social data collection from social media sites and apps. In addition to it, there is machine data generated by the sensors and other industrial and medical equipment and finally, transactional data generated from transactions performed online as well as offline. The big data collected from these principal sources provides invaluable insights to various small as well as leading organizations. Hence, these organizations and researchers direct considerable attention and efforts towards the high volume, velocity and variety (referred to as the “3V”) challenges. However, due to the sheer volume of the big data, high computing power and substantial storage are needed. This feat is achieved by the use of a network of distributed systems. Since multiple systems are involved in this process, the risk of privacy breach is increased manifold. The prevention of such breaches is crucial as it may result in leakage of highly sensitive data and impose a severe threat to the privacy of individuals. The primary objective of this paper is to provide a detailed synopsis of countermeasures that can be adopted against possible data breaches at the several stages of the big data life cycle (i.e., data generation, data storage and data processing) to ensure better security of the exabytes of big data generated each day.
WITHDRAWN: Evaluating business Yelp’s star ratings using sentiment analysis

Yelp has collected enormous amount of data comprising of user's reviews and rating for businesses over many years. Businesses organize their own listings while users' rate any of these businesses from 1 5 stars and write text reviews. Users can also vote on other reviews indicating if they were helpful or not. Our aim for this project is to predict the ratings for a business based on the text-reviews. We are focusing on 'Restaurant' reviews, as they constitute more than 60% of the total reviews. We perform sentiment analysis using combinations of different features like unigrams, bigrams and Latent Semantic Indexing. The features are trained on Supervised Learning algorithms like Linear Regression, SVM and Random Forest to predict the star rating. Random Forests outperform the other models for all feature combinations with the best MSE value of 0.825.
Web service classification based on information gain theory and bidirectional long short‐term memory with attention mechanism

Summary With the increasing number of Web services, Web service discovery for service‐oriented application development has become more important. Clustering or classifying Web services according to their functionalities is an effective way for Web service discovery. Extracting latent topic features from service description by exploiting topic model can improve the accuracy of service classification. However, most of them simply treat the description document as a set of flat word features without considering the varying importance of different features as well as sequential relations between features. In this article, we proposed a Web service classification approach based on information gain theory and bidirectional long short‐term memory with attention mechanism for accuracy Web service classification by considering fine‐grained factors implicit in Web service description. The comparative experiments are performed on ProgrammableWeb dataset, and show that the proposed method achieves a significant improvement compared with baseline methods.
A content-sensitive citation representation approach for citation recommendation

Citation recommendation systems mainly help researchers find the lists of references that related to their interests effectively and automatically. The existing approaches face the issues of data sparsity and high-dimensional in large-scale bibliographic network representation, which hinder the citation recommendation performance. To address these problems, we proposed a Content-Sensitive citation representation approach for Citation Recommendation, named CSCR. Firstly, the Doc2vec model is used to generate a paper embedding according to paper content. Then, utilizing the similarity between the paper content embeddings to select the assumed neighbours of the target paper, append the auxiliary links between target paper and its new neighbours in the bibliographic network. Thirdly, distributed network representation method is implemented on appended bibliographic network to obtain the paper node embedding, which can learn interpretable lower dimension embedding for paper nodes. Finally, the embedding vectors of these papers can be used to conduct citation recommendation. Experimental results show that the proposed approach significantly outperforms other benchmark methods in Normalized Discounted Cumulative Gain (NDCG) and the positive rate (Recall).