Reviewerly/exHarmony huggingface dataset:
Boosting Synthetic Data Generation with Effective Nonlinear Causal Discovery

Synthetic data generation has been widely adopted in software testing, data privacy, imbalanced learning, artificial intelligence explanation, etc. In all such contexts, it is important to generate plausible data samples. A common assumption of approaches widely used for data generation is the independence of the features. However, typically, the variables of a dataset de-pend on one another, and these dependencies are not considered in data generation leading to the creation of implausible records. The main problem is that dependencies among variables are typically unknown. In this paper, we design a synthetic dataset generator for tabular data that is able to discover nonlinear causalities among the variables and use them at generation time. State-of-the-art methods for nonlinear causal discovery are typically inefficient. We boost them by restricting the causal discovery among the features appearing in the frequent patterns efficiently retrieved by a pattern mining algorithm. To validate our proposal, we design a framework for generating synthetic datasets with known causalities. Wide experimentation on many synthetic datasets and real datasets with known causalities shows the effectiveness of the proposed method.
Application of different feature selection and machine learning techniques in web page classification

It is obvious that search engines are in rampant use in today's web world for tracing Web documents) through particular keynotes which ultimately projects both the relevant and irrelevant ones, which contains the same keywords. Of course, web information is exponentially increasing now and then. While so, manual classifications of Web documents are redundant, inaccurate, complex and also a time consuming process. Though several researchers do sustained research for classifying the Web documents, they are not able to produce the accurate results. Hence, automatic classification of Web documents is the need of the hour to solve all such issues. At this juncture, this paper here presents different feature selection, machine learning techniques and its methods, performance and dataset applied in the classification of Web pages
Introduction to Quantum Error Correction and Fault Tolerance

These lecture notes from the 2019 Les Houches Summer School on 'Quantum Information Machines' are intended to provide an introduction to classical and quantum error correction with bits and qubits, and with continuous variable systems (harmonic oscillators). The focus on the latter will be on practical examples that can be realized today or in the near future with a modular architecture based on superconducting electrical circuits and microwave photons. The goal and vision is 'hardware-efficient' quantum error correction that does not require exponentially large hardware overhead in order to achieve practical and useful levels of fault tolerance and circuit depth.
SATS: Self-attention transfer for continual semantic segmentation

Continually learning to segment more and more types of image regions is a desired capability for many intelligent systems. However, such continual semantic segmentation exhibits catastrophic forgetting issues similar to those of continual classification learning. Unlike the existing knowledge distillation strategies for alleviating this problem, transferring a new type of information, namely, the relationships between elements (e.g., pixels) within each image that can capture both within-class and between-class knowledge, is proposed in this study. Such information can be effectively obtained from self-attention maps in a Transformer-style segmentation model. Considering that pixels belonging to the same class in each image typically share similar visual properties, a class-specific region pooling operator is novelly applied to provide reliable relationship information for knowledge transfer. Extensive evaluations on multiple public benchmarks reveal that the proposed self-attention transfer method can effectively alleviate the catastrophic forgetting issue. Furthermore, flexible combinations of the proposed method with widely adopted strategies considerably outperform state-of-the-art solutions.
Temporally Guided Articulated Hand Pose Tracking in Surgical Videos

Articulated hand pose tracking is an under-explored problem that carries the potential for use in an extensive number of applications, especially in the medical domain. With a robust and accurate tracking system on in-vivo surgical videos, the motion dynamics and movement patterns of the hands can be captured and analyzed for many rich tasks. In this work, we propose a novel hand pose estimation model, Res152-CondPose, which improves detection and tracking accuracy by incorporating a hand pose prior into its pose prediction. We show improvements over state-of-the art methods which provide frame-wise independent predictions, by following a temporally guided approach that effectively leverages past predictions. Additionally, we collect the first dataset, Surgical Hands, that provides multi-instance articulated hand pose annotations for in-vivo videos. Our dataset contains 76 video clips from 28 publicly available surgical videos and over 8.1k annotated hand pose instances. We provide bounding boxes, articulated hand pose annotations, and tracking IDs to enable multi-instance area-based and articulated tracking. When evaluated on Surgical Hands, we show our method outperforms the state-of-the-art method using mean Average Precision (mAP), to measure pose estimation accuracy, and Multiple Object Tracking Accuracy (MOTA), to assess pose tracking performance. Both the code and dataset are available at https://github.com/MichiganCOG/Surgical_Hands_RELEASE.
Teaching Python for Data Science: Collaborative development of a modular interactive curriculum

We are bioinformatics trainees at the University of Michigan who started a local chapter of Girls Who Code to provide a fun and supportive environment for high school women to learn the power of coding. Our goal was to cover basic coding topics and data science concepts through live coding and hands-on practice. However, we could not find a resource that exactly met our needs. Therefore, over the past three years, we have developed a curriculum and instructional format using Jupyter notebooks to effectively teach introductory Python for data science. This method, inspired by The Carpentries organization, uses bite-sized lessons followed by independent practice time to reinforce coding concepts, and culminates in a data science capstone project using real-world data. We believe our open curriculum is a valuable resource to the wider education community and hope that educators will use and improve our lessons, practice problems, and teaching best practices. Anyone can contribute to our Open Educational Resources on GitHub.
Ant Colony Optimization-based Light Weight Container (ACO-LWC) Algorithm for Efficient Load Balancing

Container technology is the latest lightweight virtualization technology which is an alternate solution for virtual machines.Docker is the most popular container technology for creating and managing Linux containers.Containers appear to be the most suitable medium for use in dynamic development, packaging, shipping and many other information technology environments.The portability of the software through the movement of containers is appreciated by businesses and IT professionals.In the docker container, one or more processes may run simultaneously.The main objective of this work is to propose a new algorithm called Ant Colony Optimization-based Light Weight Container (ACO-LWC) load balancing scheduling algorithm for scheduling various process requests.This algorithm is designed such that it shows best performance in terms of load balancing.The proposed algorithm is validated by comparison with two existing load balancing scheduling algorithms namely, least connection algorithm and round robin algorithm.The proposed algorithm is validated using metrics like response time (ms), mean square error (MSE), node load, largest Transactions Per Second (TPS) of cluster (fetches/sec), average response time for each request (ms) and run time (s).Quantitative analysis show that the proposed ACO-LWC scheme achieves best performance in terms of all the metrics compared to the existing algorithms.In particular, the response time for least connection, round robin and the proposed ACO-LWC algorithm are 58, 60 and 48 ms respectively when 95% requests are finished.Similarly, the error for scheduling 120 requests using least connection, round robin and the proposed ACO-LWC algorithm are 0.15, 0.11 and 0.06 respectively.
Research on aerobics training posture motion capture based on mathematical similarity matching statistical analysis

Abstract Aiming at the freely editable characteristics of human motion posture, a human skeleton model is extracted, and a human motion posture model library is established. The application of motion capture system in dance training is analysed, and a method based on similarity matching between feature planes is proposed to calculate each model. Parameters of motion data between parts are obtained. After verification, the method has high accuracy and robustness for the analysis of human poses so that dancers can accurately compare the differences with standard dance movements and provide theoretical support for dancers to perform scientific dance training.
EABlock

Despite encoding enormous amount of rich and valuable data, existing data sources are mostly created independently, being a significant challenge to their integration. Mapping languages, e.g., RML and R2RML, facilitate declarative specification of the process of applying meta-data and integrating data into a knowledge graph. Mapping rules can also include knowledge extraction functions in addition to expressing correspondences among data sources and a unified schema. Combining mapping rules and functions represents a powerful formalism to specify pipelines for integrating data into a knowledge graph transparently. Surprisingly, these formalisms are not fully adapted, and many knowledge graphs are created by executing ad-hoc programs to pre-process and integrate data. In this paper, we present EABlock, an approach integrating Entity Alignment (EA) as part of RML mapping rules. EABlock includes a block of functions performing entity recognition from textual attributes and link the recognized entities to the corresponding resources in Wikidata, DBpedia, and domain specific thesaurus, e.g., UMLS. EABlock provides agnostic and efficient techniques to evaluate the functions and transfer the mappings to facilitate its application in any RML-compliant engine. We have empirically evaluated EABlock performance, and results indicate that EABlock speeds up knowledge graph creation pipelines that require entity recognition and linking in state-of-the-art RML-compliant engines. EABlock is also publicly available as a tool through a GitHub repository and a DOI.
Quiz Design Task: Helping Teachers Create Quizzes with Automated Question Generation

Question generation (QGen) models are often evaluated with standardized NLG metrics that are based on n-gram overlap.In this paper, we measure whether these metric improvements translate to gains in a practical setting, focusing on the use case of helping teachers automate the generation of reading comprehension quizzes.In our study, teachers building a quiz receive question suggestions, which they can either accept or refuse with a reason.Even though we find that recent progress in QGen leads to a significant increase in question acceptance rates, there is still large room for improvement, with the best model having only 68.4% of its questions accepted by the ten teachers who participated in our study.We then leverage the annotations we collected to analyze standard NLG metrics and find that model performance has reached projected upper-bounds, suggesting new automatic metrics are needed to guide QGen research forward.Which questions would you include in a quiz about
Analytical study of two feature extraction methods in comparison with deep learning methods for classification of small metal objects

Abstract This paper addresses the efficiency of two feature extraction methods for classifying small metal objects including screws, nuts, keys, and coins: the histogram of oriented gradients (HOG) and local binary pattern (LBP). The desired features for the labeled images are first extracted and saved in the form of a feature matrix. Using three different classification methods (non-parametric K-nearest neighbors algorithm, support vector machine, and naïve Bayesian method), the images are classified into four different classes. Then, by examining the resulting confusion matrix, the performances of the HOG and LBP approaches are compared for these four classes. The effectiveness of these two methods is also compared with the “You Only Look Once” and faster region-based convolutional neural network approaches, which are based on deep learning. The collected image set in this paper includes 800 labeled training images and 180 test images. The results show that the use of the HOG is more efficient than the use of the LBP. Moreover, a combination of the HOG and LBP provides better results than either alone.
REET: robustness evaluation and enhancement toolbox for computational pathology

Digitization of pathology laboratories through digital slide scanners and advances in deep learning approaches for objective histological assessment have resulted in rapid progress in the field of computational pathology (CPath) with wide-ranging applications in medical and pharmaceutical research as well as clinical workflows. However, the estimation of robustness of CPath models to variations in input images is an open problem with a significant impact on the downstream practical applicability, deployment and acceptability of these approaches. Furthermore, development of domain-specific strategies for enhancement of robustness of such models is of prime importance as well.In this work, we propose the first domain-specific Robustness Evaluation and Enhancement Toolbox (REET) for computational pathology applications. It provides a suite of algorithmic strategies for enabling robustness assessment of predictive models with respect to specialized image transformations such as staining, compression, focusing, blurring, changes in spatial resolution, brightness variations, geometric changes as well as pixel-level adversarial perturbations. Furthermore, REET also enables efficient and robust training of deep learning pipelines in computational pathology. Python implementation of REET is available at https://github.com/alexjfoote/reetoolbox.Supplementary data are available at Bioinformatics online.
Interactive Task Learning

Experts from a range of disciplines explore how humans and artificial agents can quickly learn completely new tasks through natural interactions with each other. Humans are not limited to a fixed set of innate or preprogrammed tasks. We learn quickly through language and other forms of natural interaction, and we improve our performance and teach others what we have learned. Understanding the mechanisms that underlie the acquisition of new tasks through natural interaction is an ongoing challenge. Advances in artificial intelligence, cognitive science, and robotics are leading us to future systems with human-like capabilities. A huge gap exists, however, between the highly specialized niche capabilities of current machine learning systems and the generality, flexibility, and in situ robustness of human instruction and learning. Drawing on expertise from multiple disciplines, this Strüngmann Forum Report explores how humans and artificial agents can quickly learn completely new tasks through natural interactions with each other. The contributors consider functional knowledge requirements, the ontology of interactive task learning, and the representation of task knowledge at multiple levels of abstraction. They explore natural forms of interactions among humans as well as the use of interaction to teach robots and software agents new tasks in complex, dynamic environments. They discuss research challenges and opportunities, including ethical considerations, and make proposals to further understanding of interactive task learning and create new capabilities in assistive robotics, healthcare, education, training, and gaming. ContributorsTony Belpaeme, Katrien Beuls, Maya Cakmak, Joyce Y. Chai, Franklin Chang, Ropafadzo Denga, Marc Destefano, Mark d'Inverno, Kenneth D. Forbus, Simon Garrod, Kevin A. Gluck, Wayne D. Gray, James Kirk, Kenneth R. Koedinger, Parisa Kordjamshidi, John E. Laird, Christian Lebiere, Stephen C. Levinson, Elena Lieven, John K. Lindstedt, Aaron Mininger, Tom Mitchell, Shiwali Mohan, Ana Paiva, Katerina Pastra, Peter Pirolli, Roussell Rahman, Charles Rich, Katharina J. Rohlfing, Paul S. Rosenbloom, Nele Russwinkel, Dario D. Salvucci, Matthew-Donald D. Sangster, Matthias Scheutz, Julie A. Shah, Candace L. Sidner, Catherine Sibert, Michael Spranger, Luc Steels, Suzanne Stevenson, Terrence C. Stewart, Arthur Still, Andrea Stocco, Niels Taatgen, Andrea L. Thomaz, J. Gregory Trafton, Han L. J. van der Maas, Paul Van Eecke, Kurt VanLehn, Anna-Lisa Vollmer, Janet Wiles, Robert E. Wray III, Matthew Yee-King
BayesVL Package for Bayesian Statistical Analyses in R

This short book serves to be an introduction to the bayesvl R package, produced based on the official R documentation upon the official publication of package, replacing the previous (short) version.
A Hybrid Method for Payload Enhancement in Image Steganography Based on Edge Area Detection

Abstract In this research a new method for increasing the embedding capacity in images based on the edge area is proposed. The new approach combines Canny and Prewitt edge detection techniques using OR binary operation. The secret message is concealed using the Least Significant Bit (LSB) method. Embedding capacity, PSNR, SSIM, and MSE values are used as evaluation metrics. Based on the resulted values, the proposed method showed higher embedding capacity while keeping the PSNR, SSIM, MSE values without major changes of other methods which means keeping the imperceptibility quality of the stego image.
M-Learning for Technical and Vocational Education Training (TVET)

Technical and Vocational Education Training (TVET) plays an important role in shaping human workforce to meet the requirements set by the job market. As to equip the manpower with necessary skills to perform in the job market, the education and training delivery system must be improved. Therefore, TVET institutions take up the challenge by changing the curriculum content and the delivery system to produce students who have both the knowledge and skills. Teaching and learning are not only practiced traditionally where teachers focus on activities in the classroom solely, but it must in accordance with the latest technologies. Therefore, teachers’ must be up-to-date with the latest technologies such as the use of mobile devices for the delivery of instruction and they also need to have the necessary knowledge to plan and deliver the content. Specifically, this paper discusses the concepts of mobile learning in technical and vocational contexts, devices used in Mobile Learning, benefits of Mobile Learning, and challenges that exist in the implementation of Mobile Learning. This paper is technically reviewed using document analysis. The documents reviewed are from journal article, conference proceedings, concept paper; accessed via Universiti Teknologi Malaysia Library web portal (www.psz.utm.my). From the review, the researchers provide insights to the need of implementing Mobile Learning in the context of TVET and at the same time improving the quality of education in the vocational context.
EPPR: blockchain for educational record sharing and recommendation using the Ethereum platform

There has been a great deal of discussion of the challenges on privacy, data interoperability and quality of Educational Professional Personal Record (EPPR).Therefore, there is a need to reassess the current models, in which various parties generate, exchange and observe a huge amount of personal data with regard to EPPR.Ethereum blockchain has shown that trusted, auditable transactions is detectible using a decentralized network of nodes accompanied by a general ledger.Thus, due to the fast-moving development of educational and professional data generators such as online universities and distance learning, requires learners to engage in detail into their EPPR as well as the educational and professional data generators.In this paper, we propose a novel decentralized framework to manage EPPR using Ethereum blockchain technology.The framework provides the owner of the EPPR a comprehensive immutable log and ease of access to their educational records across the educational record editors and consumers.Furthermore, it provides a recommender engine to endorse skills and competencies to the education record owners and similar candidates for educational records editors and consumers.Ethereum blockchain can provide solutions in terms of exchanging of data among parties by ensuring privacy, accountability and data interoperability.The aim of the proposed framework is to enable educational stakeholders (universities and employing agencies) to participate in the network as blockchain miners rewarded by pseudonymized data in compliance with General Data Protection Rules in United Arab Emirates.
A Decisive Object Detection using Deep Learning Techniques

Object detection is one of the essential features of computer vision and image processing techniques. In today's world, the computer can replicate or outperform the operation that a human can do. One such thing is object detection, and In the case of it, the machines must be trained in such a way that it can recognize the object equivalent to the human does with maximum accuracy. Several object detection techniques are used to train the machine to detect the objects. Some of the most common object detection techniques are R-CNN, Fast R-CNN, Faster R-CNN) Single Shot MultiBox Detector (SSD), and You Only Look Once(YOLO),. Each of these techniques has a different way of approach and accuracy of detecting the objects in real-time. These techniques are differentiated based on their performances, i.e., speed and accuracy. Some techniques may be very accurate in detecting the objects but may lack in the time taken for detecting the objects, whereas, on the other hand, some techniques may be very fast in figuring out the objects but not with greater accuracy. We have trained an object detection model based on the YOLO technique which gave the best performance out of all other existing techniques, though the accuracy of the model is less, the speed of detection is extremely high. So based on our research we have figured out the best performance object detection techniques and also the most accurate technique. A well-trained object detection model must be very optimistic in terms of their speed and accuracy.