Reviewerly/exHarmony huggingface dataset:
Conditional Generation of Medical Images via Disentangled Adversarial Inference

We propose DRAI—a dual adversarial inference framework with augmented disentanglement constraints—to learn from the image itself, disentangled representations of style and content, and use this information to impose control over conditional generation process. We undergo two novel regularization steps to ensure content-style disentanglement. First, we minimize the shared information between content and style by introducing a novel application of the gradient reverse layer (GRL); second, we introduce a self-supervised regularization method to further separate information in the content and style variables. We conduct extensive qualitative and quantitative assessments on two publicly available medical imaging datasets (LIDC and HAM10000) and test for conditional image generation and style-content disentanglement. We also show that our proposed model (DRAI) achieves the best disentanglement score and has the best overall performance.
Tyre Pattern Classification Based on Multi-scale GCN Model

Tyre pattern image classification plays an important role in traffic accidents and criminal scene investigation, and it contains rich texture structure information. Classic deep learning models, such as VGG, are often not targeted to represent the texture structure of tyre pattern images, and often cause over-fitting training due to large-scale parameters and insufficient training samples. To improve classification performance of tyre pattern image and solve the model overfitting problem, an efficient tyre pattern image classification model based on multi-scale Gabor convolutional neural network (MS-GCN) is proposed. First, a bank of large-scale directional Gabor filters are used to modulate the convolution kernel to extract more accurate texture features for large-size tyre pattern images, which greatly reduces the number of the training parameters and makes the model more streamlined. Secondly, due to the multi-scale texture similarity of the tyre pattern image, the multi-scale features from different convolutional layers are fused to produce the precise feature representation of the image, following by the optimal feature dimension selection. A large number of experiments were carried out on the real tyre pattern image data set. The results showed that the classification accuracy of the proposed algorithm is 95.9%, which is greatly improved compared with the handcrafted feature extraction algorithm and increased by 17.3% compared with deep learning-based model VGG16. In addition, the classification accuracy of the proposed algorithm on the GHIM-10K data set is 92%, which is also significantly improved compared to other methods. Overall, it shows the effectiveness and superiority of the proposed algorithm.
Cross-Project Dynamic Defect Prediction Model for Crowdsourced test

By comparing the predicted number of defects with the number found in crowdsourced test in real time, people can dynamically assess the progress of crowdsourced test tasks. In this paper, we propose a cross-project dynamic defect prediction model (CPDDPM) for crowdsourced test to predict the number of defects in real time. In the construction of training dataset, we use density-based clustering method to select instances from the multiple source project datasets and build the initial training dataset. In the dynamic correction, CPDDPM iteratively corrects the prediction model using crowdsourced test reports and ability attributes of the crowdsourced testers until the predicted results converge. We collected project defect datasets on the crowdsourced test platform, and evaluated prediction accuracy of CPDDPM by using relative error and prediction at level l. The results show that CPDDPM can greatly improve the prediction performance of defect number.
RETRACTED: Artificial intelligence hospital hardware system and clinical observation of molecular heparin sodium in hemodialysis patients

Mobile technology is changing the way the patient is undergoing medical treatment. The patient is integrated to manage their health, smartphone, and tablet. The mobile device, such as a patient monitoring system, provides easy-to-doctor real-time patient information. It is easier to communicate with patients and doctors. Mobile health care offers several advantages; however, there are also disadvantages to using these mobile technologies. Necessary to solve privacy is one of the few problems. The patient will not pay more attention. It will focus on mobile medical privacy issues. Based on the mobile medical privacy protection laws, will show how to protect the patient of intelligence Privacy-Preserving Approach for Mobile Healthcare (PAMH). Based on the smartphone's application and access control mechanism, the solution is designed to allow patients to develop their privacy settings, the disclosure of confidential medical information. Various aspects of solution are to use the proposed method; it is defined by whether the patient's privacy policy is shown in the actual scenario. Low Molecular Weight Heparin (LMWH) has been extensively studied; it has become the preferred treatment for several indications, including pulmonary embolism. Effectiveness of their blood dialysis, but is believed to be similar to that of Unfractionated Heparin (UFH), there is a discussion that the risk of their safety is still renal disorder in a patient bio-enrichment. Compared to unfractionated heparin for Extracorporeal Circulation (ECC) anticoagulant, Sodium has been introduced as a viable diagnostic tool for assessing the Na + content of the tissue in kidney disease patients.
Managing Security-Risks for Improving Security-Durability of Institutional Web-Applications: Design Perspective

The advanced technological need, exacerbated by the flexible time constraints, leads to several more design level unexplored vulnerabilities. Security is an extremely vital component in software development; we must take charge of security and therefore analysis of software security risk assumes utmost significance. In order to handle the cyber-security risk of the web application and protect individuals, information and properties effectively, one must consider what needs to be secured, what are the perceived threats and the protection of assets. Security preparation plans, implements, tracks, updates and consistently develops safety risk management activities. Risk management must be interpreted as the major component for tackling security efficiently. In particular, during application development, security is considered as an add-on but not the main issue. It is important for the researchers to stress on the consideration of protection right from the earlier developmental stages of the software. This approach will help in designing software which can itself combat threats and does not depend on external security programs. Therefore, it is essential to evaluate the impact of security risks during software design. In this paper the researchers have used the hybrid Fuzzy AHP-TOPSIS method to evaluate the risks for improving security durability of different Institutional Web Applications. In addition, the e-component of security risk is measured on software durability, and vice versa. The paper’s findings will prove to be valuable for enhancing the security durability of different web applications.
Educom.ml: A Modeling Approach for Mobile Educational Content

This full research paper is concerned with structuring educational content for mobile learning. Mobile devices are present in various areas of society and, in the educational context, the available resources of mobility and connectivity have contributed to their growing adoption. However, the generation of educational content for mobile devices requires special attention to issues such as: (i) restrictions on mobile devices; and (ii) complexity of developing mobile applications, related to the variety of existing platforms. This paper presents Educom.ml, which is a modeling approach for mobile educational content supported by MDE (Model-Driven Engineering) and IMA-CID (Integrated Modeling Approach - Conceptual, Instructional, Di-dactic). Educom.ml proposes a modeling approach that involves creating models that structure educational content and models that model mobile platforms. In this context, MDE minimizes the complexity of developing cross-platform applications and the automatic generation of educational content. Meanwhile, IMA-CID is responsible for structuring and organizing educational content. To illustrate our ideas, a mobile app was generated based on modeling educational content in the field of elementary mathematics.
Variational posterior approximation using stochastic gradient ascent with adaptive stepsize

Scalable algorithms of variational posterior approximation allow Bayesian nonparametrics such as Dirichlet process mixture to scale up to larger dataset at fractional cost. Recent algorithms, notably the stochastic variational inference performs local learning from minibatch. The main problem with stochastic variational inference is that it relies on closed form solution. Stochastic gradient ascent is a modern approach to machine learning and is widely deployed in the training of deep neural networks. In this work, we explore using stochastic gradient ascent as a fast algorithm for the posterior approximation of Dirichlet process mixture. However, stochastic gradient ascent alone is not optimal for learning. In order to achieve both speed and performance, we turn our focus to stepsize optimization in stochastic gradient ascent. As as intermediate approach, we first optimize stepsize using the momentum method. Finally, we introduce Fisher information to allow adaptive stepsize in our posterior approximation. In the experiments, we justify that our approach using stochastic gradient ascent do not sacrifice performance for speed when compared to closed form coordinate ascent learning on these datasets. Lastly, our approach is also compatible with deep ConvNet features as well as scalable to large class datasets such as Caltech256 and SUN397.
Low-rank embedded orthogonal subspace learning for zero-shot classification

Zero-shot classification methods have attracted considerable attention in recent years. Existing ZSC methods encounter domain shift, hubness and visual–semantic gap problems. To address these problems, we propose a low-rank embedded orthogonal subspace learning method (LEOSL) for ZSC. Many previous works project visual features to the semantic space. However, they often suffer from the visual–semantic gap problem. To handle this problem, we project the visual representations and semantic representations to the common subspace. To address the domain shift problem, we restrict the mapping functions with a low-rank constraint. To handle the hubness problem, we introduce the class similarity term so that samples of the same class are located near each other, while samples of different classes are located far away. Furthermore, we restrict the shared representations in the subspace with an orthogonal constraint to remove the correlation between samples. The results show the superiority of LEOSL compared to many state-of-the-art methods.
MLP4ML: Machine Learning Service Recommendation System using MLP

In this work, we propose a unique approach for Machine Learning (ML) service recommendation using multilayer perceptron architecture. A service is recommended based on its predicted performance on the input dataset. We take Quality of Services (QoS) as the performance indicator. Depending on the application domain and user requirements, the importance level of different QoS attributes could be different. For ML services, their QoS values are affected by both the input dataset and the service. It would be helpful if we can include their features into the recommendation model. In this work, we consider two types of side information: features of the services and of the user (in our case the dataset given by the user). In the experiment, we take OpenML as our data source and extract QoS values of multiple classification services running on 390 datasets. The result shows that dataset-service interactions can be used to predict the performance of a service on a given dataset. When we integrate all the side information, the performance is better than using the interaction data alone in terms of both prediction and recommendation accuracy.
Path Planning for Non-Playable Characters in Arcade Video Games using the Wavefront Algorithm

Search and planning is a common Artificial Intelligence task on videogames development. This exercise covers the design of Non-player character (NPC) behavior, content generation, and narrative's construction. This work focus on NPC behavior, specifically on path planning. This paper presents an implementation of an arcade videogame (Ms. Pacman type) with Unity, where the NPC's behavior (Ghosts) applies the Wavefront algorithm to find the shortest path to the Player Character (Mr. Pacman). This algorithm is an alternative to traditional algorithms the Djikstra algorithm and A-star used to solve this problem.
Automated Discussion Analysis - Framework for Knowledge Analysis from Class Discussions

This research full paper, describes knowledge management of class discussions using an analytics based framework. Discussions, either live classroom or through online forums, when used as a teaching method can help stimulate critical thinking. It allows the teacher to explore in-depth the key concepts covered in the course, motivates students to articulate their ideas clearly and challenge the students to think more deeply. Analysing the discussions helps instructors gain better insights on the personal and collaborative learning behaviour of students. However, knowledge from in-class discussions and online forums is not effectively captured and mined due to lack of appropriate automated tools. In this paper, the authors propose an automated discussion analysis (ADA) framework that provides a starting point providing guidance on the development of automated tools for performing different analysis on live classroom or online discussions. AI technology plays a key role in developing tools for knowledge representation and analysis. We propose software systems based on ADA framework and AI technology. The paper then describes a case study where one model of the ADA framework, individual behaviour analysis model, has been applied to automate the analysis of the online discussion forum used in a postgraduate course.
Novel Architectures for Contractive Autoencoders with Embedded Learning

A Contractive Autoencoder (CAE) is an unsupervised Artificial Neural Network (ANN) with a regularization term controlling the internal representations. During its operation, the autoencoder extracts dominant features of the input, which can be either used for communication bandwidth reduction or as an input to another neural network. While hardware implementations for the forward pass of various ANNs can be found in literature, this paper proposes three novel architectures for Field Programmable Gate Array (FPGA) based implementations of CAE, for the first time with embedded learning: (i) topography-affine inter-layer communication via crossbar; (ii) efficient carousel-type communication scheme; (iii) optimized carousel-type architecture. All architectures use node-level parallel calculation scheme and have been implemented on a Xilinx Zynq 7020 System On Chip (SoC).
Temporal-spatial analysis of alkaline rocks based on GEOROC

Alkaline rocks account for a small proportion of magmatic rocks with characteristics of the linear distribution, and can reveal the tectonic setting and in source mantle compositions. Based on the GEOROC global geochemical database, we studied the temporal-spatial evolution of alkaline rocks in the world through GIS spatial analysis and data statistical analysis techniques. The results show an abrupt increase of alkaline volcanic magmatism in the Cenozoic from the Mesozoic and Paleozoic, with the volcanic intensity in the Cenozoic being close to 100 times higher than the Paleozoic. The basalts fall mainly in the basanite and phonolite fields in the Total Alkali–Silica (TAS) discrimination diagram. Spatial analysis suggested that the Cenozoic basalts were usually produced along plate boundary or correlated with mantle-plume-related hot spots.
RETRACTED: Analysis of language features of English corpus based on Java Web

Corpus is relative to other similar language has many language users. The impact of this situation on corpus-based language research and corpus. In a few cases, the corpus is processed This paper briefly introduces an overview of Corpus processing tools and recently provided free of charge, and examines some of the corpus linguist may prevent the same problem. These questions reveal a new language resources, rich and necessity of corpus-based promotion. Therefore, the introduction of a new Java Web corpus, which includes all modern standards-based design from Corpus varieties include more than two million words of the newspaper, the translation is collected from undergraduate university students, in order to ensure a strong knowledge of the language. Java a parallel corpus of English language feature will help in comparing these languages. By rendering all the packages, which will be set as an initial data packet all prepared for further analysis based on baseline corpus. The obtained results are shown for a better listening when awakened into classes, price than when the corpus is divided into categories. Language Category achieve recognition rate of emotion mixed corpus of 91%. Overall, it's better than language classification; however, when we only use two classes, the performance results are roughly the same.
Predicting Students’ State Examination Results based on Previous Grades and Demographics

All Estonia's upper-secondary school students have three mandatory final state examinations - in mathematics, Estonian language and foreign language. We used data obtained from one school in order to predict student's final examination scores based on previous grades and demographic data. Our aim was to find most important factors that contribute positively to final results and vice versa. Machine learning package Weka was used to build predictive models and with all tested attribute sets we got accuracy over 80% on classification. On continuous models we got mean absolute error close or less to 10, when range of test result was 0-100 points. Most of our attributes were subject grades on scale 1-5, and therefore, as we limited our testing sample with one school only, it gave interesting insights into how some subjects and teachers do contribute to final test results. And, surprisingly, it turned out, that for some test result, most significant predictor was negatively correlated to the result. It means that having bad grades in some subjects was a good predictor of success in final test. Besides being a useful tool for students who want to estimate possible test results beforehand, it is also a useful tool for measuring teachers' contributions and relationships between different subjects.
Organization of distance learning on the base of information and digital technologies

The need for digital transformation of education in Russian Federation presupposes the development of new teaching methods, including online methods, information and digital interactive technologies and communication tools. All universities in Russia switched to distance learning due to the quarantine in the spring of 2020 and that was a kind of testing ground for testing effectiveness of these methods. The purpose of the article is to summarize the experience of distance learning of all types of classes in the discipline “Physics”. Analysis of this experience and the results of approbation of various teaching methods and interactive technologies allows to identify the optimal methods for conducting classes of various types online and the technical solutions for their implementation. This article provides systemic information on the arranging the distance learning in the discipline "Physics” at the Don State Technical University and gives specific recommendations on the methodology for all types of classes and on the organization of control over the assimilation of the material by students. The effectiveness of these methods is confirmed by the results of the exam session of full-time and part-time students.
The Application of Adaptive A-star Algorithm in Layout of Spatial Pipeline

In order to settle the complex problem of layout of spatial pipeline, the diameter of pipeline is taken as the distance between adjacent scattered points, and the spatial layout including obstacles is approximated as a three-dimensional diagram of scattered points. A-star algorithm is used to search an optimal pipelines path from the three-dimensional diagram of scattered points. However, when traditional A-star algorithm addresses pre-selected points that are the same cost, it selects pre-selected points randomly. In view of the disadvantages, an adaptive A-star algorithm is proposed based on the traditional A-star algorithm, in this paper. The adaptive A-star algorithm selects more reasonable pre-selected point according to the surrounding environment and situation of pre-selected point , so as to achieve the requirements of the shortest pipeline and the fewest inflection point. Finally, the experimental simulation shows that the adaptive A-star algorithm has better performance than the traditional A-star algorithm.
Control and readout of a superconducting qubit using a cryogenic photonic link

We perform the control and readout of a superconducting qubit using microwave signals generated by a high-speed photodiode operating a 20mK. We demonstrate fast single-qubit gates and high-fidelity single-shot readout while maintaining high qubit coherence.
The Combination of Natural Language Processing and Entity Extraction for Academic Chatbot

At ISB Atma Luhur, there is a web application specifically for students in accessing academic information in which the usage is good enough, but felt less practical and effective. After logged in, a student must provide some input, such as clicking on buttons and menus to display academic information. This web application interface is felt too small if opened by using a mobile browser that causes some students to experience difficulty in tapping buttons or menus. The solution that can be offered to overcome this problem is to use an Android-based chatbot. The chatbot can simplify the process of accessing information, i.e., by chatting on chatbot application and its interface size is adjusted according to the user device. The used model, method, and software development tools, respectively, are prototype models, object-oriented methods, and UML (Unified Modeling Language). The created chatbot apply NLP (Natural Language Processing) algorithm and entity extraction method, so that chatbot can respond appropriately and according to requests from students. The created chatbot makes the academic information at ISB Atma Luhur can be accessed easily, quickly, and efficiently by students without having to zoom in and zoom out.
A Study of Using Language Models to Detect Sarcasm

Organizations that provide online services solicit users to provide feedback to the services that their users receive. This feedback enables the organization to gauge user-satisfaction. To automatically process users' feedback, many organizations have used natural language processing and machine learning techniques to analyze users' sentiment from their feedback. In recent years, many language models have been developed for carrying out sentiment analysis. In this paper, we evaluate the effectiveness of several recently developed language models for carrying out sentiment analysis. The result of this research should help organizations in choosing an effective language model for analyzing feedback to monitor user-satisfaction rate.