Reviewerly/exHarmony huggingface dataset:
“Green” Trend of Developing Culture of Future

The Anthropocene, as a transitional geological era, is characterized by growing human activity that changes and destroys Earth's ecosystems. The ecological conception of a dialogue between humans and nature is ripening, where nature is an equal actor of coevolution of humankind and the living on our planet. For more than 50 years, we have gone from a green-house concept to plant-powered light bulbs, a tire concept using moss to purify urban air, and digital devices that “communicate” with plants. The connection of biology and technologies leads to the new bio-disciplines’ formation, which plays a remarkable role in our understanding of life, its bonds, targeting and meaning, and the develop­ment of sustainable ecosystems with various communicative connections of dif­ferent kinds. There is an influential developing movement whose task is to transform the relationship between humans and animals. New results in plant biology let us raise plants to a new level as agents. New ethical and legal issues emerge plant forests-sanctuaries are being created. Some issues of interdiscipli­nary communication and human communication with living systems are consid­ered as well as possible ways of their solution with the help of TRIZ and biosemiotics. An issue of distinguishing progress and sustainability in eco-tech­nological context is set up.
Keystroke Recognition With the Tapping Sound Recorded by Mobile Phone Microphones

Mobile phones nowadays are equipped with at least dual microphones. We find when a user is typing on a phone, the sounds generated from the vibration caused by finger’s tapping on the screen surface can be captured by both microphones, and these recorded sounds alone are informative enough to localize the user’s keystrokes. This ability can be leveraged to enable useful application designs, while it also raises a crucial privacy risk that the private information typed by users on mobile phones has a great potential to be leaked through such a recognition ability. In this paper, we address two key design issues and demonstrate, more importantly alarm people, that this risk is possible, which could be related to many of us when we use our mobile phones. We implement our proposed techniques in a prototype system and conduct extensive experiments. The evaluation results indicate promising successful rates for more than 4000 keystrokes from different users on various types of mobile phones.
A Special Design of Job Dropout Faces Detection and Recognition System (JDFDRs)

In today’s era of the technology-supported world, Computer Vision and Deep Learning are gaining immense importance in modern life and the contemporary world. The emergence of these techniques has led to the design of detection and recognition systems biometric for faces, allowing verification and identification of people. there is a correlation between these technologies, which represented in (face detection and recognition) and job dropout. Now, if we are talking about job discipline at the times of attendance and departure, then we are talking about a dangerous. “Job Dropout” is an intractable problem that must be placed on the list of priorities of all institutions even those that put fingerprint devices, and Eyes-print devices for entry and exit. With a lot of methods for circumvention, idleness, and escape from the job. This study provides a summary of to build a model for (JDFDRs) (Job Dropout Face Detection and Recognition system). with using the following categories (Location, Lighting, wearing glasses, Chin, and Masks) specified for the image, and with the help of a convolutional neural network (CNN), to classify such images with high resolution. It was implemented in Python using the (OpenCV) library in which the JDFDRs Dataset, which was created for the study privately. and excluding a few of those images with high noise and missing features. The results were classified were computed and the accuracy and error were calculated for the proposed model. and the model achieved a high accuracy of (99.4 percent) and an error rate (−6). This study suggests must the number of database photos increases, with as the increase in the image group increases, the accuracy of detection and recognition of those faces increases, and also work to link our proposed system to the Android system for phones.
DeepNFT: Towards Precise Neurofibrillary Tangle Detection via Improving Multi-scale Feature Fusion and Adversary

Detecting neurofibrillary tangles is an important procedure in the assessment of the intensity and distribution pattern of hippocampal tau pathology, which are the principal clinical phenotypes associated with Alzheimer’s disease. Existing deep learning based detectors still face a critical obstacle: the difficulty in detecting extremely small objects in high resolution images. In this paper, we propose a deep learning framework, named DeepNFT, which combines the multilevel feature aggregation pyramid network (MFAPN) and the adversarial feature generation module (AFGM) to acquire precise detection results with significantly reduced false positives. To prove its universality and robustness, DeepNFT has been validated on two datasets. Experiments show the significant performance gain of our proposed approach over state-of-the-art detectors. Ablation study shows our network components improve the performance of various backbones and detectors.
FHKG: A Framework to Harvest Knowledge from Groupware Raw Data for AI

In the era of textual data explosion, including due to a rising remote work culture, a system to harvest on-the-job knowledge of experts from groupware for AI enrichment has become one of the crucial technologies sought after in the field of knowledge technology. Most existing systems for knowledge harvesting are developed based on text corpora from the web, social media, newspapers and textbooks with little or no changeable modules and ontological representations. In this paper, we propose a deeper framework with changeable modules to acquire and represent knowledge from raw data in groupware discussions for AI. Such a framework can be implemented on any platform of choice using existing or newly designed modules that can be continually improved upon with higher sophistication or by added-value extensions. The framework is a formalisation of a semi-automated structure with reusable and incremental modules. The overall architecture of the framework is presented with evaluation results. The paper concludes by highlighting the proposed future developments within the framework.
Implementation of Backpropagation Artificial Network Methods for Early Children’s Intelligence Prediction

Intelligence is the ability to process certain types of information derived from human biological and psychological factors. This study aims to implement a Backpropagation artificial neural network for prediction of early childhood intelligence and how to calculate system accuracy on children's intelligence using the backpropagation artificial neural network method. The Backpropagation Neural Network method is one of the best methods in dealing with the problem of recognizing complex patterns. Backpropagation Neural Networks have advantages because the learning is done repeatedly so that it can create a system that is resistant to damage and consistently works well. The application of the Backpropagation Neural Network method is able to predict the intelligence of early childhood. The results of the calculation of the Backpropagation Artificial Neural Network method from 42 children's intelligence data being tested, with 27 training data and 15 test data, the results obtained 100% accuracy percentage results.
Digital Literacy in the Networked Educational Space

The modern digital world dictates its own trends and laws, against the background of which there is a change in the ways of thinking, highly qualified personnel are in demand, the preparation of which seems to be an urgent pedagogical task. Education is actively discussing the problem of the formation and development of digital literacy. Particular attention is paid to its components, aimed at developing the potential of the student on the basis of network interaction. In this regard, the theoretical foundations of digital literacy deserve detailed consideration, revealing the content of the concept of digital competence, as well as highlighting the criteria for achieving it.
Probing Andreev Levels with cQED

In this chapter, we first present the basic ideas of circuit quantum electrodynamics. We then describe the operating principles of the device used in this work, before demonstrating how we used it to investigate pair transitions in Josephson nanowires.
Software Defect Prediction Method based on Hybrid Sampling

Software defect prediction is an essential technology to provide guidance and assistance for software testers and developers. However, the problem of imbalanced data sets limits the effect and application of the software defect prediction. To address this issue, this paper proposes a software defect prediction method based on hybrid sampling, which combines the strategies of over-sampling with under-sampling. For minority class, over-sampling uses k-means to cluster samples, then adopts SMOTE to generate artificial data based on safe areas of the clustering outcome. For majority class, under-sampling uses logistic regression classifier to get the misclassification probability of each sample and its instance hardness value. Then the samples, whose instance hardness values are lower than the threshold, are removed from the datasets. The experimental results show that our method is superior to the previous methods. Compared with SMOTE-kNN, SMOTE-Tomek, SMOTE and DBSMOTE, the accuracy of our method is improved by 17.60%, 6.99%, 8.66% and 26.18% on average respectively.
Managing Individual Online Learning Experience: The Roles of Perceived Engagement and Perceived Performance

The pandemic of Novel Coronavirus 2020 has shifted and transformed several industries: including educational institutions. Universities and schools were forced to turn into online education to continue the learning process. Several information systems were adopted such as a learning management system, video conferencing, google classroom, etc. However, sudden changes from face-to-face education to online distance learning raise user engagement towards the usage of the online platform, vice versa its impact on individual performance. Therefore, the purpose of this study is twofold; first, to investigate the individual’s learning experience from the context of perceived engagement with the LMS and second, to investigate the impact of perceived engagement (user involvement and user participation) on perceived performance in the context of adopting a learning management system among the students from one of the local universities in Kelantan. A quantitative research instrument was developed, and data was collected. The study’s findings were described in terms of descriptive and inferential analysis using Statistical Package for Social Science (SPSS) version 26. The results indicated that user involvement and user participation have a positive and significant relationship with the perceived performance.
Semi-supervised Learning with Conditional GANs for Blind Generated Image Quality Assessment

Evaluating the quality of images generated by generative adversarial networks (GANs) is still an open problem. Metrics such as Inception Score(IS) and Fréchet Inception Distance (FID) are limited in evaluating a single image, making trouble for researchers' results presentation and practical application. In this context, an end-to-end image quality assessment (IQA) neural network shows excellent promise for a single generated image quality evaluation. However, generated image datasets with quality labels are too rare to train an efficient model. To handle this problem, this paper proposes a semi-supervised learning strategy to evaluate the quality of a single generated image. Firstly, a conditional GAN (CGAN) is employed to produce large numbers of generated-image samples, while the input conditions are regarded as the quality label. Secondly, these samples are fed into an image quality regression neural network to train a raw quality assessment model. Finally, a small number of labeled samples are used to fine-tune the model. In the experiments, this paper utilizes FID to prove our method's efficiency indirectly. The value of FID decreased by 3.32 on average after we removed 40% of low-quality images. It shows that our method can not only reasonably evaluate the result of the overall generated image but also accurately evaluate the single generated image.
The Study Of Voice Bots And Its Impact On Human Welfare

This paper concentrates upon a recent technological development called voice bots. In the past decade voice bots have been a very major part of various industries. Here the concentration will be on understanding the working of voice bots initially. Furthermore it includes a discussion on how these voice bots can be integrated in sectors which indulge in human welfare. Moving on to human welfare, there are existing projects that have tried to utilize voice bot technology for the betterment of the society like in healthcare and farming sectors. In this paper we will have a look on these projects and understand their functioning. The major concentration will be upon how this technology can be used for human welfare. This paper will provide a better understanding towards working of voice bots along with its association with human welfare.
Pargan: Parallel Gan for Single Image-to-Image Translation

Unsupervised image-to-image translation (UI2I) tasks aim to find a mapping between the source and the target domains from unpaired training data. Previous methods can not effectively capture the differences between the source and the target domain on different scales and often leads to poor quality of the generated images, \eg noise, distortion, and other conditions that do not match human vision perception, and has high time complexity. To address this problem, we propose a multi-scale training structure and a progressive growth generator method to solve UI2I task. Our method refines the generated images from global structures to local details by adding new convolution blocks continuously and shares the network parameters in different scales and also in the same scale of network. Experiments on our collected Opera Face, and other open datasets Horse$\leftrightarrow$Zebra, Monet$\leftrightarrow$Photo, Labels$\leftrightarrow$ Facade, Map$\leftrightarrow$Aerial Photo show that the proposed algorithm is superior to other state-of-art algorithms and the training speed of the model is increased by four times compared to TuiGAN.
Real-Time COVID-19 Infection Risk Assessment And Alert System Using Mask Detection

The onset of COVID-19 pandemic has led to many life-style changes, among them is the usage of Face masks to protect themselves from the deadly virus. It is recommended to use face masks, but it is a common practice for people to forget wearing it. Sometimes both the nose and mouth are not covered leaving them at a risk of infection. The purpose of this paper is to alert shopkeepers or property owners about the risk of spread of Covid virus due to the non-usage of face masks. By monitoring a place for number of people wearing and not wearing face masks, one is able to be aware of potential risks people carry. The owner is alerted about the count of non-masked people when it reaches a certain threshold through a siren and also through an alert message sent on a popular messaging platform. This way, one can persuade people to wear masks or to leave the premises, thereby protecting himself and others. This paper uses VGG Face model, OpenCV and TensorFlow and a custom trained mask detection model to achieve the same.
A Lightweight Multidimensional Self-attention Network for Fine-Grained Action Recognition

Fine-grained action recognition is a challengeable task due to its background independence and complex semantics over time and space. Multidimensional attention is essential for this task to capture discriminative spatial details, temporal and channel features. However, multidimensional attention has challenge in keeping the balance between adaptive feature perception and computational overhead. To address this issue, this paper proposes a Lightweight Multidimensional Self-Attention Network (LMSA-Net) which can adaptively capture the discriminative features over multiple dimensions in an efficient manner. It is worth remarking that the contextual relationship between time and channel is established in temporal stream, which is complementary for spatial attention in spatial stream. Compared with the RGB based models, LMSA-Net achieves state-of-the-art performance in two fine-grained action recognition datasets, i.e. FSD-10 and Diving48-V2. In addition, it can be found that the streams of LMSA-Net are end-to-end trainable to reduce the overhead of computation and storage, and the recognition accuracy can reach the level of two-stage two-stream models.
Multi-Target Detection and Tracking in a Heterogeneous Environment with Multiple Resource-Constrained Sensors

We consider the multiple-sensor multiple-target detection and tracking problem in a sensor network consisting of resource-constrained multi-function radars. Tracking and detection takes place in a known heterogeneous environment that causes signal degradation. The proposed formulation includes the effects of the environment in a multi-function radar model for searching, detection, tracking, and resource usage. The sensor-target assignment problem is optimized to maximize information gain subject to the radar resource constraints. Simulations demonstrate the effects of including the environment in the radar modeling and show performance improvement compared to performing assignment without considering the environment.
Basic principles of wavelet filtering

In the world, the development of modern information and communication technologies and their implementation in various industries are important. Digital filtering of noisy signals and images is important in solving a wide range of scientific and technical problems. Such problems arise, in particular, in communication technology to improve the quality of reception of transmitted messages.
Spin weighted wavelets on the sphere—Frames

A new construction of spin weighted wavelets on the sphere is presented in the paper. The wavelets are similar to those derived from approximate identities but the constraints of the definition are slightly weaker. They allow for quite a big freedom in wavelet construction so that even spin weighted wavelets with explicit representations exist. The price to be paid is that no direct reconstruction is given and the wavelet transform must be inverted with frame methods. The set of wavelet coefficients is a priori continuous but it is shown how it can be discretized so that no information about the analyzed signal get lost.
Object Detection using Statistical and textual Model

Optical system for humans has the ability to get to know a very large number of objects or classes of object from two-dimensional visual information or three dimensions. Solving this task of detection or recognition by humans without any major effort is due to the ability of humans for very fast parallel processing and their ability for self development, as well as the ability to learn from experience due to the exceptional structure of the human brain. Computer vision systems exist that are able to detecting an object in image, but usually these systems need to be instructed what the object to detect is. And the main difficulty comes from a very large number of objects, each of these objects may appear in an infinite number of several aspects: dimension, shape, color, position, lighting, and shade. In this paper, we present a robust method attempts to deal with the above challenges by using Local Binary Pattern (LBP) and Random Forest techniques (RF). Results show that using the suggested method 95.39% classification rate in the car detection, as well as 94.9%, for cat detection, and flower detection.
YABKO-Yet Another Big Knowledge Organization

Knowledge graph and its processing techniques have got wide spread attention from the AI and knowledge engineering society. However, the knowledge graph supporting platforms have gained much less concern. This paper emphasizes the role of knowledge graph platforms as an independent product of knowledge engineering. Starting from the introduction of HAPE - a programmable universal big knowledge graph platform, which is a predecessor of YABKO, we introduce the idea and technique of Web-based resource sharing public knowledge graph laboratory and its implementation YABKO, which has a threefold target. Firstly, it is an open source platform for researchers doing experimental research on knowledge graphs supported by YABKO's own resources. Secondly, it supports research on big knowledge engineering, in particular in the knowledge graph area. Thirdly, it supports a full life cycle research on big knowledge graphs. Further we introduce YABKOS, a constellation of YABKOs on the Web, which is a decentralized research lab for large scale knowledge graph experiments. Also the wide area programming language Knorc for knowledge graphs' operation orchestration is introduced.
A multidimensional-multilayered anomaly detection in RFID-sensor integrated internet of things network

Outlier detection in a single dimension is not enough to protect the network from known and unknown attacks. There is a strong need to apply multiple steps at multiple stages for combating these attacks. There are various approaches to protect the network from malicious activities and it is an ongoing process to fight against them from a multilayer perspective parallel to networking layering models. This work has considered a multilayered and multi-dimensional approach to protect the hierarchical mobile ad hoc network (MANET) from known and unknown attacks. The proposed multilayered approach consists of ultra-light, light and heavy computational overhead-based outlier detection approaches to combat the attacks. It has been observed that these approaches apply threshold as well as earning processes-based mechanisms for fighting against the attacks. Further, hardware constraint is considered to identify these schemes as ultra-light, light, or heavy. Simulation results show that variations of nodes from 10 to 5,000 vary the cluster formation from 5 to 53 with an error rate of 0.4%.
Semi-Supervised Domain Adaption Classifier via Broad Learning System

Broad Learning System (BLS) is effective and efficient in dealing with various machine learning problems. When constructing a BLS based classifier with domain adaption capability, the mapped features and enhancement nodes are determined by random parameters. Thus, there will be discrepancy between the hidden layer features of the source domain and target domain. Therefore, we propose a new semi-supervised classifier with domain adaption capability based on BLS. Firstly, in order to reduce the discrepancy between the hidden layer features of domains, our method aligns the second-order statistics of mapped features due to the fact that enhancement nodes are generated by mapped features. Further, when learning the classifier through the aligned features, we embed balanced distribution adaptation to improve domain adaption capability of the classifier. Experiments on benchmark datasets demonstrate our method has better classification accuracy than some existences.