Reviewerly/exHarmony huggingface dataset:
Gradient Feature-Based Classification of Patterned Images

Image classification is the task of assigning a class to an image. It has a wide range of applications: image and video retrieval, object tracking, object recognition, Web content analysis, number plate recognition, OCR in banking systems, etc. Color, texture, gradient, shape, keypoint descriptors, etc. are the various features being used for the image classification. A patterned image is an image in which selected pattern is repeated, for example, horizontal stripes, vertical stripes, polka dots, geometric shapes, etc. Gradient feature plays a vital role in distinguishing the different patterns. Therefore, in the proposed approach, gradient features are used for the classification of patterned images like cloth patterns (vertical stripes, horizontal stripes, polka dots, etc.), English characters (capital and small alphabets) and numerals (0–9) and geometric shapes (square, triangle, etc.). The different patterns recognized in the present paper show the versatility of the approach. It can be applied to many of the real-time applications like number plate recognition, cloth pattern recognition and retrieval. The proposed approach achieves the accuracies of 95.4, 93.5, 91.4 and 92% on standard datasets describable texture dataset (vertical stripes, polka dots), EnglishImg dataset (small and capital English alphabets), numerals dataset (0–9) and geometric shapes (triangle, square) dataset, respectively.
Deep boundary‐aware semantic image segmentation

Abstract While extensive research efforts have been made in semantic image segmentation, the state‐of‐the‐art methods still suffer from blurry boundaries and mismatched objects due to the insufficient multiscale adaptability. In this paper, we propose a two‐branch convolutional neural network (CNN) approach to capture the multiscale context and the boundary information with the two branches, respectively. To capture the multiscale context, we propose to embed self‐attention mechanism to the atrous spatial pyramid pooling network. To capture the boundary information, we propose to fuse the low‐level features in boundary feature extraction for refining the extracted boundaries via a feature fusion layer (FFL). With FFL, our method can improve the segmentation result with clearer boundaries. A new loss function is proposed which contains a segmentation loss and a boundary loss. Experiments show that our method can predict the boundaries of objects more clearly and have better performance for small‐scale objects.
An Chaotic Pseduo Inverse Polynomial Perceptron Network for Short Term Solar Power Prediction

High precision prediction of solar power generation is very much necessary with the continuous increase of grid connected solar electricity. The accurate power prediction is extremely important for the optimal scheduling and safe operation of the grid. In this paper, an Chaotic Water Cycle Algorithm (CWCA) based Pseudo Inverse Polynomial Perceptron Network (PIPPN) is proposed to accurately predict the solar power for different weather condition and for different time horizon. The random input layer weights of the PIPPN are optimized using the CWCA. Here, a sinusoidal chaotic map is applied to diversify the populations to improvise the performance of the basic PIPPN. The chaos in proposed Chaotic PIPPN (CPIPPN) helps to predict the future solar power very efficiently. The performance of the proposed CPIPPN model is verified through various performance measures. The dominance and diversity of the proposed CPIPPN method is verified against the basic Polynomial Perceptron Network (PPN) and PIPPN for 5 minute and 1 hour ahead time horizon.
A Location-based Blockchain Evidence Preservation Wireless Communitication Scheme for HuaTaiYiMei v Tongdao Technology Development Case

HuaTaiYiMei v Tongdao Technology Development case was heard by Hangzhou Internet Court on March 15th, 2018. The court ruled in favour of HuaTai and awarded monetary damages of 4,000 RMB. This is seen as one of the vanguard cases for the use of Blockchain as a form of digital evidence. In this case, a location-based and certified equipment with pre-shared secret wireless communication scheme is needed to meet legal requirement, which ensure that digital evidence is securely stored on the blockchain and the uploading device is authenticated by relevant legal department.
Assessing Problem-Based Learning satisfaction using Net Promoter Score in a virtual learning environment

The COVID-19 pandemic has forced us to adapt our teaching/learning strategies to a synchronous/asynchronous online environment, while maintaining a focus on outcomes in vocational training courses. Continuous improvement in education involves the measurement and feedback of teaching/learning processes. In this sense, the Problem-Based Learning strategy with the Net Promoter Score methodology was evaluated due to its simplicity and reliability of the methodology. For the application of Problem-Based Learning a problematic situation was enunciated for students to solve by applying an 8-step method and at the end of the course a questionnaire was applied with 15 items classified in three subscales (knowledge, skills and attitudes) to measure the Net Promoter Score. The result in the three courses: Data Structure, Artificial Intelligence and Emerging Technologies; were higher than 75%, which reveals a higher than average level of satisfaction in education/training whose value is 71%.
An artificial intelligence–based decision support and resource management system for COVID-19 pandemic

COVID-19 crisis has shown that the World is not ready for such a rapid spread of a virus resulting in a catastrophic pandemic. Effective use of information technologies is one of the key aspects in reducing the adverse effects of any epidemic or pandemic. Existing management systems have failed to fulfill requirements for curbing the rapid spread of the virus. This chapter firstly describes the current solutions by giving real-world examples. Then, we propose an epidemic management system (EMS) that relies on unimpeded and timely information flow between nations and organizations to ensure resources are distributed effectively. This system will use mobile technology, blockchain, epidemic modeling, and artificial intelligence technologies. We used the Multiplatform Interoperable Scalable Architecture (MPISA) model that allows the integration of multiple platforms and provides a solution for scalability and interoperability problems. Open data repositories and the MiPasa blockchain are also described. These relevant data can be used to predict the potential future spread of the epidemic. Selecting the correct methods for epidemic modeling is discussed as well. Another challenge is deciding on allocating resources where they are most necessary; we propose deploying automated machine learning and stochastic epidemic model-based decision support systems for such purposes. Citizens should not have privacy concerns about the information systems. These trust issues and privacy concerns can be solved by using decentralized identity and zero-knowledge proof-based mechanisms. These mechanisms will ensure that users are in control of their data. In this chapter, we also discuss choosing the right machine learning method, privacy measures, and how the performance challenges can be addressed. This chapter concludes on a discussion of how we can design and deploy better EMSs and possible future studies.
Strongly trapped space-inhomogeneous quantum walks in one dimension

Localization is a characteristic phenomenon of space-inhomogeneous quantum walks in one dimension, where particles remain localized around their initial position. The existence of eigenvalues of time evolution operators is a necessary and sufficient condition for the occurrence of localization, and their associated eigenvectors are deeply related to the amount of localization, i.e., the probability that the walker stays around the starting position in the long-time limit. In a previous study by authors, the eigenvalues of two-phase quantum walks with one defect were studied using a transfer matrix, which focused on the occurrence of localization (Quantum Inf. Process 20(5), 2021). In this paper, we introduce the analytical method to calculate eigenvectors using the transfer matrix and also extend our results to characterize eigenvalues not only for two-phase quantum walks with one defect but also for a more general space-inhomogeneous model. With these results, we quantitatively evaluate localization and study the strong trapping property by deriving the time-averaged limit distributions of five models studied previously.
Introduction to Healthcare Information Management and Machine Learning

Health information management deals with the collection, storing, analysis, and management of health data. It consists of various fields such as computer science, information science, information management, medicinal, business and data analytics. Machine Learning (ML) in healthcare evolving as an emerging field for healthcare industry. They help in analyzing the health data more effectively and timely. ML is one of the vital regions in the field of software engineering. It gives a streamlined answer for this present reality issues by utilizing past learning or past experience information. There are distinctive kinds of machine learning calculations present in software engineering. This chapter gives the outline of some chosen machine learning methods, for direct relapse, straight discriminant examination, bolster vector machine, innocent Bayes classifier, neural systems, and choice trees. Every one of these techniques is described in detail, which thus helps the reader to create our own answers for the given issues.
Reversed Lempel–Ziv Factorization with Suffix Trees

We present linear-time algorithms computing the reversed Lempel–Ziv factorization [Kolpakov and Kucherov, TCS’09] within the space bounds of two different suffix tree representations. We can adapt these algorithms to compute the longest previous non-overlapping reverse factor table [Crochemore et al., JDA’12] within the same space but pay a multiplicative logarithmic time penalty.
Introducing General Argumentation Frameworks and Their Use

In its original definition, the Abstract Argumentation framework considers atomic claims and a binary attack relationship among them, based on which different semantics would select subsets of claims consistently supporting the same position in a dispute or debate. While attack is obviously the core relationship in this setting, in more complex (and in many real-world) situations additional information may help, or might even be crucial, in determining such positions, and especially those that are going to win the debate. Different kinds of additional features have often been considered separately in the literature, yielding disjoint models for argumentation frameworks. In this paper we propose a model, called General Argumentation Framework (or GAF), aimed at unifying the different perspectives, and at further extending them by allowing to express contextual information associated to the arguments, in addition to their relationships. We also show how to express a number of existing frameworks in the literature as GAFs.
User-oriented Natural Human-Robot Control with Thin-Plate Splines and LRCN

We propose a real-time vision-based teleoperation approach for robotic arms that employs a single depth-based camera, exempting the user from the need for any wearable devices. By employing a natural user interface, this novel approach leverages the conventional fine-tuning control, turning it into a direct body pose capture process. The proposed approach is comprised of two main parts. The first is a nonlinear customizable pose mapping based on Thin-Plate Splines (TPS), to directly transfer human body motion to robotic arm motion in a nonlinear fashion, thus allowing matching dissimilar bodies with different workspace shapes and kinematic constraints. The second is a Deep Neural Network hand-state classifier based on Long-term Recurrent Convolutional Networks (LRCNs) that exploits the temporal coherence of the acquired depth data. We validate, evaluate and compare our approach through both classical cross-validation experiments of the proposed hand state classifier; and user studies over a set of practical experiments involving variants of pick-and-place and manufacturing tasks. Results revealed that LRCNs outperform single image Convolutional Neural Networks; and that users’ learning curves were steep, thus allowing the successful completion of the proposed tasks. When compared to a previous approach, the TPS approach revealed no increase in task complexity and similar times of completion, while providing more precise operation in regions closer to workspace boundaries.
Labeling Cost Sensitive Batch Active Learning For Brain Tumor Segmentation

Over the last decade, deep learning methods have achieved state-of-the-art for medical image segmentation tasks. However, the difficulty of obtaining sufficient labeled data can be a bottleneck. To this end, we design a novel active learning framework specially adapted to the brain tumor segmentation. Our approach includes a novel labeling cost designed to capture radiologists' practical labeling costs. This is combined with two acquisition functions to incorporate uncertainty and representation information, ensuring that the active learning selects informative and diverse data. The resulting procedure is a constrained combinatorial optimization problem. We propose an efficient algorithm for this task and demonstrate the proposed method's advantages for segmenting brain MRI data.
Mining Stack Overflow for API class recommendation using DOC2VEC and LDA

To address the lexical gaps between natural language (NL) queries and Application Programming Interface (API) documentations, and between NL queries and programme code, this study developed a novel approach for recommending Java API classes that are relevant to the program​ming tasks described in NL queries. A Doc2Vec model was trained using question titles mined from Stack Overflow. The model was used to find question titles that are semantically similar to a query. Latent Dirichlet Allocation (LDA) topic modelling was applied on the Java API classes (extracted from code snippets found in the accepted answers of these similar questions) to extract a single topic comprising of the Top-10 Java API classes that are relevant to the query. The benchmarking of the proposed approach against state-of-the-art approaches, RACK and NLP2API, by using four performance metrics show that it is possible to produce comparable API recommendation results using a less complex approach that makes use of some basic machine learning models, in particular, Doc2Vec and LDA. The approach was implemented in a Java API class recommender with an Eclipse IDE's plug-in serving as the front-end.
Implementing the Hammock-EFL Methodology for Project Management and Parallel Programming

short-paper Share on Implementing the Hammock-EFL Methodology for Project Management and Parallel Programming: (research in progress) Authors: Moshe Goldstein Jerusalem College of Technology, Israel Jerusalem College of Technology, IsraelView Profile , Oren Eliezer Jerusalem College of Technology, Israel Jerusalem College of Technology, IsraelView Profile , David Dayan Jerusalem College of Technology, Israel Jerusalem College of Technology, IsraelView Profile Authors Info & Claims ECBS 2021: 7th Conference on the Engineering of Computer Based SystemsMay 2021 Article No.: 19Pages 1–5https://doi.org/10.1145/3459960.3459967Online:27 May 2021Publication History 0citation12DownloadsMetricsTotal Citations0Total Downloads12Last 12 Months8Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Contextual Caption Generation Using Attribute Model

Image captioning has been a big field of research and gained many enterprises' attention in the past few years. Unfortunately, there are still many issues preventing the general usage of image captioning. One of the pitfalls is lack of context. The common approach to solve this issue is to retrain the model with a suitable dataset for every purpose, but this approach is expensive and time-consuming. We propose a method to shift words' distribution and guide the captioning process towards the desired result using a gradient vector acquired from attribute models. This method requires neither retraining captioning models nor customized datasets. In conclusion, this approach shows positive results even though the usage is still limited and highly dependent on the added context and parameter used.
Higher education in Russia: problems of environmental personnel training

In the course of the study, it was revealed that the criteria for the effectiveness of training engineers is the compliance of their competencies formed in the process of training with the requirements of the production in General and the employer in particular. Aims of the present study: to identify the main global trends in engineering education, determine the criteria for the effectiveness of training engineering personnel and analyze the compliance of graduate training with these requirements. Methodology, methods. The study was conducted among graduates; teachers of engineering areas of training of the Crimean engineering and pedagogical University named after Fevzi Yakubov, as well as among managers of enterprises of the Republic of Crimea, potentially being employers for the studied special-ties during 2019 A total of 412 respondents were interviewed. Of these, there are 248 graduates of engineering training areas (final courses), 92 teachers of engineering training areas, and 72 employers. The experimental object of the study was engineering education in higher education. Results: Engineering education is a continuous process of accumulating General cultural and professional competencies in the direction of secondary education – secondary vocational education – technical University. The criterion for the effectiveness of training engineers is the compliance of their competencies formed in the process of training with the requirements of the production in General and the employer in particular.
Design of projection optical system for 3D defect detection on PCB

Abstract According to the practical experience of PCB defect detection in semiconductor industry, a projection optical system suitable for structured light detection device is designed by using a DMD chip. In order to realize the lateral projection of structured light stripe and improve the quality of system projection, the design of tilt back group of projection lens is adopted and the projection size is 57×32mm.The simulation results show that the illumination uniformity of the projection system on the measured surface is more than 85%, the fringe contrast is larger than 0.5, and the maximum height of the measuring device is 5mm, which provides a guarantee for practical application of structured light fringe for 3D defect detection on PCB.
Automated Detection of Coronary Artery Disease using Machine Learning Algorithm

Abstract Data mining, an excellent development technology for discovering and gathering essential knowledge from vast data collection that can help analyze and draw up trends for decision-making in the industry. Talking about the medical sphere, data mining can be used to uncover and withdraw useful data and trends that can be helpful in clinical diagnostic results. The research focuses on the diagnosis of heart disease, taking past evidence and information into account. To achieve this SHDP, non-linear SVC with RBF kernel algorithms is designed to perfect this SHDP (Smart Heart Disease Prediction). The final is a useful algorithm to look for the right combination of hyper parameters to increase the precision of the algorithm (C, α). The requisite data was arranged in a structured way. The following features are derived from medical profiles for the estimation of the risks of heart failure in a patient: BP, age, sex, cholesterol, blood sugar, etc. The collected characteristics serve as an input to the Navies Bayesian heart disease prediction classification. The data collection used is divided into two parts, 80% of the data are used for preparation, and 20% are used for research. The method suggested includes data collection, user authentication, and log in (based on application), classification through Navies Bayesian, prediction, and safe data transmission via the AES application (Advanced Encryption Standard). An average accuracy, specificity, sensitivity, precision, 93.53% f-score, 89.22%, 91.24% and 86.98%, respectively. This method is also possible in clinical settings to help clinicians predict cardiac arrest.
Development of Publication App by Problem-Based Learning

PBL(Problem-Based Learning) is a style of a class that aims to study social problems. Its educational goal is the cultivation of problem-solving thinking of students. Thus, there are few precedents for the publication of the outcome. There is a likelihood that it fades the educational effect. Here we show the PBL team which developed the app for half a year and published it on Google Play Store. Its contents were developed to help the National Institute of Technology(KOSEN) students with their school life based on the questionnaire by the team. To clarify the quality of the publication, we analyzed the questionnaire data statistically. The result is different from the team's opinion. We interpret the difference as the existence of know-how of KOSEN students. This indicates that they developed the app that publishes their know-how.
BL_SMOTE Ensemble Method for Prediction of Thyroid Disease on Imbalanced Classification Problem

The imbalanced classification problem is one of the most challenging problems in various domains such as in machine learning and data mining. In this state of an imbalanced dataset, each class associated with a given dataset is distributed unevenly. This case arises when the positive class is smaller than the negative class. To overcome this problem, oversampling and undersampling techniques are used. Undersampling leads to the problem of information loss. In this paper, borderline_synthetic minority oversampling technique (BL_SMOTE) ensemble method is used for the prediction of thyroid disease to solve imbalanced classification problems using the oversampling technique. For the ensemble, we have used decision tree and random forest classifier. The proposed method for detection of the thyroid has achieved 98.88% accuracy, 99.12% specificity, 98.93% F-measure, and 98.66% sensitivity on thyroid UCI repository dataset. The proposed method is competitive to the other methods proposed in the literature for prediction of thyroid disease on an imbalanced classification problem.