Reviewerly/exHarmony huggingface dataset:
Geometry and Complexity

Further than the framework of paired interactions, higher order networks can characterise data as diverse as functioning brain network, interacting proteins networks, and social networks. Higher - level networks include simplicial complexity, which are made up of nodes and links as well as polygons, octahedra, and other shapes. Elevated services can be defined as cell complexes built by glueing curved polytopes together along their faces. Human social networks, surprisingly, have a natural tools available to help and hence provide a natural technique to investigate the continuous technology solution of communication systems. This concept, which was originally proposed to explain the evolution of simplicial complexes, is now extended to cell-complexes generated by glueing multiple copies of any regular polytope. Connection science has aided in the understanding of the actual design of dynamic structures, and it has far-reaching ramifications in domains ranging from regenerative medicine to networking medicine to national economy. These include multilayer systems made up of many interaction networks, as well as higher order networks that go far beyond contact center. When studying brain networks, gene regulatory media platforms, or social media networks, higher cognitive networks can be very useful.
Practice of Cloud Class Combined with Hybrid Teaching Method in Biochemistry Classroom of Traditional Chinese Medicine Colleges and Universities

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
AUTOMATIC QUESTION GENERATION MODEL BASED ON DEEP LEARNING APPROACH

Nowadays, students face many difficulties to practice for exams. Professors and teachers spend a lot of time and effort to make exams. Automatic Question Generation Model proposes a solution to save time, effort, and student’s learning process which helps in educational purposes. AQGM is user-friendly which is implemented as a GUI-based system that generates Wh- questions which mean” WH” (” What”,” Who”, and” Where”) and formatted into two types of templates, Question Bank template, and Exam template. Exams have different difficulty levels (Easy, Medium, and Hard). Therefore, students can measure their level and teachers will know to what extent the students understand the course. Researches have shown that this method is helpful and successful for educational purposes. AQGM generates questions automatically by using its model that generated by using sequence-to-sequence approach specially encoder-decoder technique with copy mechanism and attention decoder. AQGM model uses SQuAD as a training dataset which helps to get more accurate results.
The Concept of Data Mining

Data mining is a technique for identifying patterns in large amounts of data and information. Databases, data centers, the internet, and other data storage formats; or data that is dynamically streaming into the network are examples of data sources. This paper provides an overview of the data mining process, as well as its benefits and drawbacks, as well as data mining methodologies and tasks. This study also discusses data mining techniques in terms of their features, benefits, drawbacks, and application areas.
A SURVEY OF PRIVACY PRESERVING DATA MINING ALGORITHMS

By Abou el ela Abdou Hussein, Nermin Hamza & 2 more. A wide survey of different privacy preserving data mining (PPDM) algorithms and analyses of the representative techniques for PPDM, and points out their merits and demerits.
Scientific evidence for the use of "serious games" or therapeutic games in people with Alzheimer's Disease and other dementias

The progressive ageing of the population, together with the increase in the prevalence of neurodegenerative diseases and the lack of effective pharmacological treatments for Alzheimer's disease (AD) raises the need to seek new perspectives. It has been proposed that technology can provide integrated solutions in different areas related to the health and well-being of the patients. In recent years there has been growing interest in the so-called or therapeutic aimed at diagnosis or cognitive training in people with Mild Cognitive Impairment (MCI) or AD. The aim of this work is to review the scientific evidence that supports the application of in the field of dementia. It presents the concept of as well as the objectives and main recommendations in the development of this type of games aimed at people with MCI or dementia. It also describes some examples of the implementation of serious games through Information and Communication Technologies. The main benefits and limitations arising from the application of these games in people with dementia are also exposed. We conclude future directions of research aiming to better interventions.
The Ethics of Data Science

In 2017, Ali Rahimi and Benjamin Recht received the “Test of Time Award” at the Neural Information Processing Systems (NIPS) conference—one of the top research conferences for machine learning and artificial intelligence.
Using self-assessment instruction to develop primary school students’ self-esteem in inclusive practice in Kazakhstan

The article presents an analysis of an experimental work. The purpose of the article is to analyze the effect of special self-assessment instruction on the self-esteem level of primary school students with and without special needs. The study’s participants were the students of inclusive classes of two state schools in Almaty (Kazakhstan). A total of 283 students participated in the study, 17 of whom were students with special educational needs (SEN), and 266 – students without SEN. The study was conducted in the form of a quasi-experimental study. For the data analysis, the quantitative method of analysis was used. In analyzing the data, descriptive statistics (average value, standard deviation) as well as the non-parametric paired samples t-test and McNemar criterion tests were used. The study results showed a positive effect of self-assessment instruction on the students’ level of self-esteem with and without SEN.&#x0D; &#x0D; Keywords: self-assessment, students, primary school, special needs, inclusive education
Extracting Prerequisite Relations among Concepts from the Introduction of Online Courses(SEKEEO) (S)

Affected by the COVID-19 pandemic, teaching tasks have gradually shifted from offline to online, which expanded online education resources unprecedentedly."Concept" is a professional vocabulary in the curriculum.Exploring the prerequisite relations among concepts is of great significance to educational planning.This research extracts concepts from online course introduction and proposes a mixed method for extracting concept prerequisite relations.Experiments on public data set show that this method outperforms existing ones.Tests were also carried out on the datasets of eleven schools, which proves that this model has good scalability.
FedPS: Model Aggregation with Pseudo Samples

Federated learning (FL) is an emerging machine learning task that allows many clients to train a global model collaboratively while keeping their respective data nondisclosure. The regular method of federated learning is to average the parameters of client models, which may only work in the cases of few client models with same architectures and initialized parameters. Moreover, this strategy has a heavy communication burden as it requires a lot of communication rounds to reach acceptable performance. In this work, we propose a novel model aggregation method for federated learning (FedPS), which generate pseudo samples from each client model to train the global model on the server. The proposed method could support the aggregation of more than twenty heterogeneous models simultaneously, only requires a single round of communication and can achieve an improved aggregation performance than state-of-the-art federated learning methods.
An Image Encryption Algorithm Based on Four Dimensional Hyperchaotic System

Aiming at the problem that the encryption structure of some image encryption algorithms had nothing to do with the plaintext and cannot guarantee the security of the ciphertext image, this paper proposed an image encryption algorithm based on a four-dimensional hyperchaotic system. The initial value of the chaotic system is determined by the SHA256 hash value of the plaintext image and the input parameters, making the algorithm capable of resisting known plaintext attacks and selected plaintext attacks. At the same time, the hash value and input parameters are used to determine the moving step size of the Zigzag scrambling algorithm. The algorithm uses the CFB mode in block ciphers, and updates the chaotic sequence according to the ciphertext, which makes the subscript selection of the Knuth algorithm more random to improve the encryption performance of this algorithm. The experimental results demonstrate the excellent robustness and security of the proposed scheme. It can resist common attack methods such as statistical analysis and differential attacks.
Brain Alpha Oscillations as a Neurobiological Marker of Postural Control

Although parallel corpora are essential language resources for many natural language processing tasks, they are rare or even not available for many language pairs. Instead, comparable corpora are widely available and contain parallel fragments of information that can be used in applications like statistical machine translation systems. In this research, we propose a generative latent Dirichlet allocation based model for extracting parallel fragments from comparable documents without using any initial parallel data or bilingual lexicon. The experimental results show significant improvement if the extracted fragments generated by the proposed method are used for augmenting an existing parallel corpus in an statistical machine translation system. According to the human judgment, the accuracy of the proposed method for an English-Persian task is about 59.7%. Also, the out of vocabulary error rate for the same task is reduced by 28%.
Transfer Learning Methods for Training Person Detector in Drone Imagery

Deep neural networks achieve excellent results on various computer vision tasks, but learning models require large amounts of tagged images and often unavailable data. An alternative solution of using a large amount of data to achieve better results and greater generalization of the model is to use previously learned models and adapt them to the task at hand, known as transfer learning. The aim of this paper is to improve the results of detecting people in search and rescue scenes using YOLOv4 detectors. Since the original SARD data set for training human detectors in search and rescue scenes are modest, different transfer learning approaches are analyzed. Additionally, the VisDrone data set containing drone images in urban areas is used to increase training data in order to improve person detection results.
VARGAN: variance enforcing network enhanced GAN

Generative adversarial networks (GANs) are one of the most widely used generative models. GANs can learn complex multi-modal distributions, and generate real-like samples. Despite the major success of GANs in generating synthetic data, they might suffer from unstable training process, and mode collapse. In this paper, we propose a new GAN architecture called variance enforcing GAN (VARGAN), which incorporates a third network to introduce diversity in the generated samples. The third network measures the diversity of the generated samples, which is used to penalize the generator’s loss for low diversity samples. The network is trained on the available training data and undesired distributions with limited modality. On a set of synthetic and real-world image data, VARGAN generates a more diverse set of samples compared to the recent state-of-the-art models. High diversity and low computational complexity, as well as fast convergence, make VARGAN a promising model to alleviate mode collapse.
KAJIAN STRATEGI PEMBELAJARAN MATEMATIKA BERBASIS ONLINE (Study Of Online Mathematics Learning Strategy)

The purpose of this study was to determine the online-based learning strategy in the form of variables to consider in online learning, the role of parents in online learning, government policies in online learning during the new normal period, and evaluation of online learning in the form of student attitudes towards online learning in the new normal period. The research method is literature study and survey by distributing questionnaires. The population of students at two Senior High Schools in Bandung Regency, the sample of students who filled out the google form amounted to 740 students at two Senior High Schools in Bandung Regency. The research instrument was in the form of a questionnaire distributed via the google form application. The results show that several important points are needed in online-based learning as follows: it is necessary to pay attention to variables in online learning, the role of parents, government policies, and the results of the evaluation of online mathematics learning from students.
Implementing and Managing a Data Curation Workflow in the Cloud

Objective: To increase data quality and ensure compliance with appropriate policies, many institutional data repositories curate data that is deposited into their systems. Here, we present our experience as an academic library implementing and managing a semi-automated, cloud-based data curation workflow for a recently launched institutional data repository. Based on our experiences we then present management observations intended for data repository managers and technical staff looking to move some or all of their curation services to the cloud. Methods: We implemented tooling for our curation workflow in a service-oriented manner, making significant use of our data repository platform’s application programming interface (API). With an eye towards sustainability, a guiding development philosophy has been to automate processes following industry best practices while avoiding solutions with high resource needs (e.g., maintenance), and minimizing the risk of becoming locked-in to specific tooling. Results: The initial barrier for implementing a data curation workflow in the cloud was high in comparison to on-premises curation, mainly due to the need to develop in-house cloud expertise. However, compared to the cost for on-premises servers and storage, infrastructure costs have been substantially lower. Furthermore, in our particular case, once the foundation had been established, a cloud approach resulted in increased agility allowing us to quickly automate our workflow as needed. Conclusions: Workflow automation has put us on a path toward scaling the service and a cloud based-approach has helped with reduced initial costs. However, because cloud-based workflows and automation come with a maintenance overhead, it is important to build tooling that follows software development best practices and can be decoupled from curation workflows to avoid lock-in.
Valentine in action

Capturing relationships among heterogeneous datasets in large data lakes - traditionally termed schema matching - is one of the most challenging problems that corporations and institutions face nowadays. Discovering and integrating datasets heavily relies on the effectiveness of the schema matching methods in use. However, despite the wealth of research, evaluation of schema matching methods is still a daunting task: there is a lack of openly-available datasets with ground truth, reference method implementations, and comprehensible GUIs that would facilitate development of both novel state-of-the-art schema matching techniques and novel data discovery methods. Our recently proposed Valentine is the first system to offer an open-source experiment suite to organize, execute and orchestrate large-scale matching experiments. In this demonstration we present its functionalities and enhancements: i) a scalable system, with a user-centric GUI, that enables the fabrication of datasets and the evaluation of matching methods on schema matching scenarios tailored to the scope of tabular dataset discovery, ii) a scalable holistic matching system that can receive tabular datasets from heterogeneous sources and provide with similarity scores among their columns, in order to facilitate modern procedures in data lakes, such as dataset discovery.
Dense Incremental Extreme Learning Machine with Accelerating Amount and Proportional Integral Differential

Incremental Extreme Learning Machine (I-ELM) has been widely concerned in recent years because of its ability to automatically find the best number of hidden layer nodes and non-iterative training mechanism. However, in big data scenarios, I-ELM and its variants face great challenges because the least squares method is used to calculate their output weights in the training process, which is time-consuming and unstable. To alleviate this problem, we propose a novel Dense I-ELM based on the Accelerating Amount and the Proportional Integral Differential techniques (AA-PID-DELM) in this paper. For AA-PID-DELM, the dense connection architecture can exert the maximum utility of each hidden layer node, and the accelerating amount and PID techniques can make the model achieve better generalization ability and stability in big data scenarios. Extensive experimental results on the approximation of 2D nonlinear function and several UCI data-sets have proved the effectiveness of AA-PID-DELM.
DISTORTIONS IN A PERSON'S BODY PERCEPTION AFTER PERFORMING DYNAMIC TASKS IN VIRTUAL REALITY

The article presents a study in the peculiarities of a person’s perception of the size of his own bodydistortion after a short-term immersion in a virtual environment and performing mobile purposeful tasks in it on behalf of a virtual character. The characteristic distortions in the perception of the sizes of the body parts most involved in the virtual activity in the direction of their increase were revealed. Thus, the study participants demonstrated characteristic statistically significant distortions in the perception of such parameters as neck length, shoulder length, hand length, body length, elbow joint width and head width. Distortions in the perception of the size of the upper half of the body (above the waist) correspond to those parts of the subjects’ bodies, the movement of which in space during the performance of the intra-environment task had the greatest functional significance. Immersion in virtual reality, mediated by a VR headset, leads to the formation of a specialvirtual environment perception in the recipient’s psyche. In this case, the perception of oneself also turns out to be tied to the perception of the controlled virtual character. Due to the uncertainty of its size and the blurring of the visual outlines, the image of one’s own body temporarily merges with the operatively formed image of the character, as a result of which an instrumental interiorization of its dimensions occurs. Thus, it is clearly demonstrated that the perception of the person’s body during the immersion in a virtual environment is connected to the nature of the performed movements.
Information Security Policy Compliance Culture

Information security policy (ISP) noncompliance is a growing problem that accounts for a significant number of security breaches in organizations. Existing strategies for changing employees' behavior intentions towards compliance have not been effective. It is therefore imperative to identify other effective strategies to address the problem. This article investigates the effect accountability constructs on employees' attitudes and behavior intentions towards establishing ISP compliance as a culture. In addition, the authors validate a testable research model for predicting employees' compliance behavior intentions in a field survey involving 313 employees from selected Ghanaian companies. The overall effect showed that measures of accountability significantly influenced employees' attitudes and behavior intentions to ISP compliance while the establishment of ISP compliance culture largely depended on the existence of a conducive information security culture and positive employee behavior intentions.
Overview of DETOXIS at IberLEF 2021: DEtection of TOXicity in comments In Spanish

In this paper we present the DETOXIS task, DEtection of TOxicity in comments In Spanish, which took place as part of the IberLEF 2021 Workshop on Iberian Languages Evaluation Forum at the SEPLN 2021 Conference. We describe the NewsCom-TOX dataset used for training and testing the systems, the metrics applied for their evaluation and the results obtained by the submitted approaches. We also provide an error analysis of the results of these systems.
Efficient Transmission of Watermarked Images with SC-FDMA

This paper studies the transmission of watermarked images on wireless SC-FDMA systems. Different watermarking schemes are provided in this frame work for security and authentication purposes.SC-FDMA is accomplished for broadband wireless communications, for its advantages of low PAPR and low sensitivity to carrier frequency offsets. The SC-FDMA system is used for communication as it is the uplink standard in coefficient mobile communication system. In this paper, two types of equalizers(MMSE and ZF) is used to mitigate the effect of channel fading and noise at different subcarriers mapping schemes(localized and distributed).Watermark extraction is performed at the receiver and an evaluation process for the quality of extracted watermarks is performed. Simulation experiments were established in MATLAB program R2014a version. Simulation results approved that the interleaved system provides better performance than that of the localized systems. In addition the system performance has better enhancement when using the ZF equalizer than the MMSE equalizer. The quality of the received of extracted watermarks is investigated with different metrics like PSNR, correlation coefficient (Cr) , SSIM and the histogram . Simulation results also demonstrate an efficient of image clarity at extraction process.
Deep Learning Approach for Traffic Sign Recognition on Embedded Systems

This research focuses on creating an affordable and effective warning system for drivers that is able to detect the warning signboards and speed limits in front of the moving vehicle and prompt the driver to lower to safer speeds if required. The software internally works on a deep learning-based modern neural network YOLO (You Only Look Once) with certain modifications that allow it to detect the road signs really quickly and accurately on low-powered Advanced RISC Machines Central Processing Units (ARM CPUs). Along with YOLO, the system uses a darknet classifier for second-stage detection. The system is also capable of noting the traffic violations made by the driver like driving over the speed limits prescribed by the Indian Roads Congress (IRC). The system works on the Raspberry Pi board and the Raspberry Pi camera that allows the system to be budget-friendly yet very effective. The dataset for Indian roads is not available in the public domain; hence, majority of the dataset has been created by us, while a small part of the dataset has been salvaged from the Belgium and German traffic sign datasets. Creating the dataset on our own means that images need to be taken of multiple signboards from multiple angles and varying distances. This is a very tedious and long task that is very time-consuming. The first stage involves using a tiny version of YOLO to detect the signboard in a natural scene, in case a speed limit sign is detected by the first-stage detector, the extracted region of interest (ROI) is passed on to the darknet classifier which then accurately classifies the speed limit value on that board.