Reviewerly/exHarmony huggingface dataset:
Manifold learning to address catastrophic forgetting

A major challenge that deep learning systems face is the Catastrophic Forgetting (CF) phenomenon that is observed when fine-tuning is used to try and adapt a system to a new task or a sequence of datasets with different distributions. CF refers to the significant degradation in performance on the old task/dataset. In this paper, a novel approach is proposed to address CF in computer aided diagnosis (CAD) system design in the medical domain. CAD systems often need to handle a sequence of datasets collected over time from different sites with different imaging parameters/populations. The solution we propose is to move samples from all the datasets closer to a common manifold via a reformer at the front end of a CAD system. The utility of this approach is demonstrated on two common tasks, namely segmentation and classification, using publicly available datasets. Results of extensive experiments show that manifold learning can yield about 74% improvement on an average in the reduction of CF over the baseline fine-tuning process and the state-of-the-art regularization based methods. The results also indicate that a Reformer when used in conjunction with the state-of-the-art regularization methods, has the potential to yield further improvement in CF reduction.
Emancipatory action of teaching praxis in critical pedagogy

The aim to this study is to show how critical pedagogy as an emancipatory action of educational praxis from teachers&#x2019; experience. Extensive interviews with 10 key members of the School of Communication at a private university in Peru were conducted. For the investigation, the interview technique was applied and the in-depth interview guide served as an instrument. This data was analyzed using ATLAS.ti software, employing a process of author analogies. As a result, this study considers that it is fundamental to train teachers how to apply critical pedagogy as an emancipatory action. This action transforms the realities and generates a new dynamic in the university context that should mobilize the formative dynamics of the academia context.
Performance Comparisons in Association Rule Mining Over Public Datasets

Association rule mining techniques are widely employed in a variety of applications, including stock analysis, log mining, medical diagnostics, consumer market analysis, bioinformatics, and many more. When it comes to uncovering interesting relationships between variables in huge databases, association rule learning is a rule-based machine learning method that can be used. With the help of some metrics of interestingness, it is intended to find powerful rules that have been identified in databases. Discovery and validation are two subprocesses of association rule mining, which is a data processing technique that is divided into two parts. The first method is known as finding frequent itemsets, and the second method is finding association rules from these frequent itemsets. The ideas associated with the use of frequently used itemsets are retrieved from the data during this subprocess. Numerous techniques and procedures for locating frequently occurring itemsets and association rules have been devised by academics throughout the years. This study presents the results of a comprehensive performance analysis of ten distinct association rule mining methods, which was carried out on five different datasets.
A New Maneuvering Frequency and the Variance Adaptive Filtering Algorithm

The standard adaptive Kalman filter algorithm based on the “current” statistical model (AF) has the problem of selecting Maneuvering frequency and maximum acceleration based on experience, and the problem of low accuracy in tracking non-maneuvering or weak maneuvering target. By analyzing the physical meaning of maneuvering frequency and relationship with acceleration, the maneuvering frequency adaptive algorithm are obtained. By analyzing the relationship between Kalman filtering innovation and acceleration variance, the acceleration variance adaptive algorithm are obtained. The contrasting simulation results of AF (Adaptive Filtering Algorithm) and MAF (Mending Adaptive Filtering algorithm) have showed MAF’s validity. MAF algorithm also obtains a better tracking accuracy, especially for non-maneuvering or weak maneuvering target, and makes “current” statistical model more conveniently for engineering application.
Smart Campus Implementation Effects towards Student Interest in Higher Education: A Systematic Literature Review

The campus is one of the educational facilities provided by institutions to maximize knowledge in theory and practice. A smart campus optimizes the contribution variety of technologies. The usage of technology helps all activities become more efficient and effective. Thus, the implementation of a smart campus has many positive effects. This study follows the Kitchenham protocol to systematically explore the impact of a smart campus as a tool to help students find out their interest based on the literature review, then reviewed the techniques and technologies, which can be used in all universities in Indonesia. We got 38 final documents to analyze the result. The smart campus can affect student's interest to enroll or learn. The smart campus can be smart absence, e-learning, or smart map. All of component smart campus makes the business process easier. This study recommends applying a smart campus to virtual technology to increase student's attractiveness and enthusiasm to learn.
Analysis of breast lesions segmentation by watershed with variation of structuring elements

Mammography is the main exam used to perform early detection of breast cancer. Using digital mammographic images processing, it’s possible to improve visualization of structures and facilitate diagnosis, using noise-reduction filters, contrast enhancement and segmentation. Segmentation divides the image in regions of interest (lesions) and background. Watershed segmentation performs a topographic analysis of the image and object’s boundaries extraction. Watershed demands using markers, that limit the regions where processing will be applied, avoiding issues such as over-segmentation. The markers can be defined using morphological operators, which use structuring elements to identify interest pixels. Therefore, the aim of this work is to evaluate watershed segmentation of breast lesions, by changing the pre-processing and structuring elements. There were applied disk and diamond-shaped structuring elements, with 25, 50 and 100 pixels as size, in addition to noise-reduction Wiener filter and contrast enhancement with Wavelet transform and contrast limited adaptive histogram equalization (CLAHE). The results were evaluated by using accuracy, ROC curve and visual analysis of the images, allowing to more easily compare the efficiency of each processing. For the disk format obtained AUC equal to 0.891, while the diamond format has 0.909, it was possible to identify the relationship between size and pre-processing with the number of detected regions. Moreover, small structuring elements are more effective in watershed segmentation, requiring research with different denoising methods and marker definition algorithms. Finally, the results allowed us to establish how breast density and shape, size and type of lesion interfere in watershed segmentation results.
Multimodal Feedback Pen Shaped Interface and MR Application with Spatial Reality Display

Multimodal interface is essential to enrich the reality of drawing in the virtual 3D environment. We propose the pen shaped interface capable of providing the following multimodal feedbacks: (1) Linear motion force feedback to express contact pressure of virtual object, (2) Rotational force feedback to simulate friction of rubbing virtual surface and tangential contact force with virtual object, (3) Vibrotactile feedback, and (4) auditory feedback to express contact information and texture of virtual object. We developed Mixed Reality (MR) interaction system with pen shaped interface and Spatial Reality Display. This system will display virtual pen tip extended from the actual pen shaped interface, and user can use it to draw into virtual workspace as well as interact with virtual objects. This demonstrates the advantage of our proposal by improving the reality of virtual space interaction.
Bitmap Image Processing and Edge Detection Using Sobel Algorithm and Multi-Threaded Parallel Techniques

Image processing and edge detection have become some of the most essential problems that are solved by parallel computing techniques. One of the fundamental issues with image processing is the increased complexity and required time for processing which has a direct relationship to the size of the image data. This study aims to develop a multi-threaded parallel Sobel edge detection algorithm for image processing.
IoT-Based Small Scale Anomaly Detection Using Dixon’s Q Test for e-Health Data

Anomaly detection in the smart application domain can significantly improve the quality of data processing, especially when the size of a dataset is too small. Internet of Things (IoT) enables the development of numerous applications where sensor-data-aware anomalies can affect the decision making of the underlying system. In this paper, we propose a scheme: IoTDixon, which works on the Dixon’s Q test to identify point anomalies from a simulated normally distributed dataset. The proposed technique involves Q statistics, Kolmogorov–Smirnov test, and partitioning of a given dataset into a specific data packet. The proposed techniques use Q-test to detect point anomalies. We find that value 76.37 is statistically significant where P=0.012&lt;α=0.05, thus rejecting the null hypothesis for a test data packet. In other data packets, no such significance is observed; thus, no outlier is statistically detected. The proposed approach of IoTDixon can help to improve small-scale point anomaly detection for a small-size dataset as shown in the conducted experiments.
TraFoo: An Android Application for Food Delivery in Train

The popularity of food delivery increasing day by day. This work focuses on how to food delivery of the easiest way in the train journey. In the modern-day, almost everyone trying this online food. Otherwise, in this pandemic, the demand for online food delivery is sky scraping. So, how it see when everyone is going to travel in long-distance by train, then if they can be trying healthy and delicious food from a restaurant? In train journeys, travelers cannot get good quality food or the food price is high. By using this android application, travelers can fulfill their food demand. This thesis focuses on the best way to supply food during the trip by rail. Almost all of us try this internet food today. In this research work, there have three types of modules like one application for traveler means customer, one application for riders’ means deliveryman and another one is for restaurant authority. In this system customer can get good food, deliveryman gets a job and restaurant can increase their profit. The application needs to be responsive as the application can be accessed through any device with different sizes of screens. In this work, the firebase database and native android framework are used. Android studio application is used for writing code for the main body of this application. The food delivery apps have simple and easy features. So, it is easy to use for everyone. The new take-way system can make it easier and ensure that maintaining quality.
Gamification: Serious Games to promote multicultural attributes at PrepaTec Morelia HighSchool

research-article Gamification: Serious Games to promote multicultural attributes at PrepaTec Morelia HighSchool Share on Author: Arlette Audiffred Hinojosa PrepaTec Morelia, Mexico PrepaTec Morelia, MexicoView Profile Authors Info & Claims TEEM'21: Ninth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM'21)October 2021 Pages 140–144https://doi.org/10.1145/3486011.3486435Online:20 December 2021Publication History 0citation15DownloadsMetricsTotal Citations0Total Downloads15Last 12 Months15Last 6 weeks5 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 AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Aggregate Function Generalization to Temporal Data

In this article, we define an approximate generalization of aggregate functions for relational data with temporal attributes. This generalization is parametrized to allow simulation of a range of common aggregate functions and optionally take into account time. The parameters are not optimized, but we rather rely on repeated stochastic sampling of the parameters. We then apply a common regularized linear model to train a model on this high-dimensional space. Experimental results on 11 datasets suggest that there are datasets where incorporating time dimension into the model leads to an improvement in the predictive accuracy of the trained models.
ELIχR: Eliminating Computation Redundancy in CNN-Based Video Processing

Video processing frequently relies on applying convolutional neural networks (CNNs) for various tasks, including object tracking, real-time action classification, and image recognition. Due to complicated network design, processing even a single frame requires many operations, leading to low throughput and high latency. This process can be parallelized, but since consecutive images have similar content, most of these operations produce identical results, leading to inefficient usage of parallel hardware accelerators. In this paper, we present ELIχR, a software system that systematically addresses this computation redundancy problem in an architecture-independent way, using two key techniques. First, ELIχR implements a lightweight change propagation algorithm to automatically determine which data to recompute for each new frame based on changes in the input. Second, ELIχR implements a dynamic check to further reduce needed computations by leveraging special operators in the model (e.g., ReLU), and trading off accuracy for performance. We evaluate ELIχR on two real-world models, Inception V3 and Resnet-50, and two video streams. We show that ELIχR running on the CPU produces up to 3.49X speedup (1.76X on average) compared with frame sampling, given the same accuracy and real-time processing requirements, and we describe how our approach can be applied in an architecture-independent way to improve CNN performance in heterogeneous systems.
Roadmap for the future of global education project: designers' objectives and potential risks

The article carries out a teleological analysis of the structure and content of the map-scheme of the project of global education “The Future of Global Education”, which formed the basis of the National Project “Education”, the implementation of which was approved by the Ministry of Education in 2020 as a model of the desired future of global education and Russian education as an integral part of it until 2035. The analysis revealed the Roadmap (hereinafter referred to as “RM”) of the “Future of Global Education” project developed by ASI (Agency for Strategic Initiatives under the President of the Russian Federation), NRU HSE, Moscow School of Management Skolkovo, Sberinvest Management Company, Cisco, etc.; revealed its dystopian nature, and generalised the critical remarks of the pedagogic, educational and scientific communities regarding the structure, content and risks associated with the implementation of important events reflected by the designers in the “RM”.
Object detection based on deep learning

Object detection is a hot topic in the field of computer vision and pattern recognition. The task of object detection is to accurately and efficiently identify and locate many object instances of predefined categories from images. With the wide application of deep learning, the accuracy and efficiency of object detection have been greatly improved. However, object detection based on deep learning still faces challenges such as improving the performance of mainstream object detection algorithms and the detection accuracy of small target objects. In this paper, based on extensive literature research, we survey the mainstream algorithms of object detection from the angle of improving and optimizing the two-stage and onestage object detection algorithms. We also analyze the promotion method of small object detection accuracy combined with the backbone network, the visual receptive field, and the model's training. In addition, the common data sets of object detection are introduced in detail, while the performance of representative algorithms is compared from two aspects. The problems to be solved in object detection and the future research direction are predicted and prospected. More high precision and efficient algorithms are proposed, and more research directions will be developed in the future.
Architecture‐oriented security strategy determination for cyber‐physical systems

Abstract Cyber‐physical systems (CPSs) usually have a huge number of nodes, and it is very expensive to deploy security defenses at all nodes. Therefore, selecting the most appropriate nodes for protection to ensure system security is an essential issue in CPS. However, how to achieve this in the system design phase remains to be an open challenge. In this paper, we propose a security strategy determination method for CPS design models. In particular, we utilize SysML to model a CPS. To choose the best nodes for security protection, we realize an automatic construction of an attack‐defense tree (ADTree) model from the SysML model by considering the known attacks and defenses. Further, we transform the ADTree model into an atom attack‐defense tree (A2DTree) model and infer the optimal security strategy under a given cost constraint. We implement our method as an open‐source tool and test it with a pump station attack. The experimental results show that the proposed method is reasonable and feasible and can provide theoretical guidance for the formulation of CPS security policies.
FF-RRT: A Sampling Based Path Planner for Flexible Multi-Robot Formations

In this paper we present a centralised path planning approach, called flexible formation rapidly exploring randomized trees (FF-RRT) algorithm for navigation of formations of multiple homogeneous robots in obstacle-ridden environments. The path for the multiple robots, in a flexible formation, is planned in a configuration space, defined by its translation, rotation and scaling. The path of individual robots are then found using affine transformations of configurations in the planned path. The flexible formation was able to overcome obstacles that were impossible for rigid formations successfully by changing its size and orientation along the path. For large obstacles split and merge type behaviour was observed for the flexible formation, which would not be possible for rigid formations.
Using Think-Ahead and Write Strategy to Improve Student’s Reading Ability in English Learning

This study aims to determine which strategy is the most effective in improving students' reading skills in English Learning. This study used two comparison classes, namely the experimental class and the control class at MTs Khazanah Kebajikan. In the experimental class, class VIII A uses the Think-Ahead and Write strategy. While the control class uses class VIII B with conventional methods. Based on the data obtained by the researcher, the results of the t-test of this study were 3.92007, while for the t-table with levels of 0.05 and 28 degrees of freedom obtained 1.70113. The calculation results show that the t-observation is 3.92007 &gt; tt (t-table) 1.70113. The alternative hypothesis (Ha) is accepted and the null hypothesis (H0) is rejected. The results of the class research above show that the implementation of Think-Ahead and Write strategies as an effort to improve students' reading skills is effective and efficient.
Features of the content of teaching test tasks in mathematics for students of secondary vocational education institutions

The paper discusses the possibilities of using educational mathematical content in the implementation of educational testing. Distance learning requires a significant revision of educational content. This research is based on the analysis of the accumulated experience in the implementation of training testing. An analysis was carried out in the field of improving educational content and the main directions were determined. Variants of designing the content of test tasks for students of secondary vocational education institutions are proposed, taking into account the specifics of educational activities in the specialty 15.02.07 Automation of technological processes and production (by industry). A new methodological approach to the development and testing process is proposed. The test tasks and their sequence, designed adequately to the logical structure of the educational material, contribute to the formation of systemic knowledge and ways of working with them. It is advisable to include in the content of test materials tasks that focus on building logical conclusions. These techniques and tests are carried out at the Nizhnevartovsk Oil College.
Engineers Training for Digital Transformation of Space System Production Based on Information and Communication Technologies

Modern space technology manufacturing involves complex computer-aided design and production systems and the cooperation between many design and production enterprises. Instant information exchange between these enterprises at all stages of space technology development and production is a significant aspect of engineering collaboration. Such activities involve digital technologies of machinery design and information and communication technologies. Thus, the training of space engineers requires full-scale mastery of design and information and communication technologies. The article studies the initial experience of this task implementation through the collaboration of South Ural State University with enterprises to organize cooperative training of schoolchildren, students, scientists, teachers, and engineers. This collaboration involves universities, design, and production enterprises united under Ural Interregional Scientific and Educational Center in 2020. The analysis of this way of training confirms that the suggested approaches lead to encouraging results.