Reviewerly/exHarmony huggingface dataset:
The Novel Three-Stage Outlier Disclosure Scheme For Fix Magnitude Impulsive Noise (FMIN)

For outlier eradication analysis society, in the outlier disclosure scheme, the disclosure depicted element (which is a midpoint) and its adjacency likeness (which are taken account of the disclosure depicted element and its adjacency depicted elements) are mathematically clarified as outlier likeness. This dissection paper proposes the novel outlier eradication scheme placed on a three-stage outlier disclosure scheme, which is derived from the two-stage scheme that is one of the leading-edge and most remarkable of outlier eradication scheme in order to deal with the drawback of the preceding outlier disclosure schemes for fix magnitude impulsive noise (FMIN) at pervasive diffusion of outlier repetition (5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> −90 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ). The outlier pattern indication, which is taken account of each disclosure depicted element and its adjacency depicted elements, is compared to the flexible threshold for disclosing whether it is the original element or outlier element. In the analytical investigation, capacious impaired pictures, which are integrated of Baboon, F16, Girl, Lena and Pepper with pervasive diffusion of outlier repetition, are investigated for analyzing the three-stage outlier disclosure scheme. The first augmentation is the mathematical dispersion of outlier/non-outlier depicted elements of three-stage disclosure scheme (such as norm value and predictable error), which is contrasted with the original 2-stage outlier disclosure scheme. The second augmentation is the outlier disclosure truthfulness of three-stage discovery scheme, which is compared with the original 2-stage outlier disclosure scheme.
Japanese Daily Vlogger Content as A Cultural Learning Medium for College Students: Study Case of Diponegoro University’s Student

The writing of this paper is motivated by the rising amount of YouTube vloggers and its viewers about learning and introducing one country’s culture virtually. The increasing spread of these cultures is supported by a simple way of seeking out inspiration and things people are interested about, which mainly seek out on YouTube for clearly image. This also includes vlogs where people film their daily life and so their viewers are able to notice and perceive the country or cultural activities they convey. We’re seen that YouTube is prospectively new media for learning in culture and in some way also fosters its vlogger nation culture. The distinction of this research led by Silent Vlogger who unintentionally introduced one country culture. More specifically, the research is analysing Choki’s channel, a Japanese woman who has more than 1 million subscribers. We disclose from her videos, bringing out to the viewers about Japanese culture in interesting and lifeful approach. We also conducted a questionnaire survey to college students in order to maximize the results. The results show that her videos on YouTube is acceptable to be used as new media for promoting as well as learning media of Japanese cultures.
CHENet: Image to Image Chinese Handwriting Eraser

Erasing Chinese handwritten words in images has many applications in our daily life. It can hide the private information in the archive images (home address, identity information), delete the signature information in the contract images, and clear the handwriting in the test paper to reuse it. However, the existing research mainly focuses on scene text removal. To fill this gap and facilitate this research direction, we propose a novel model called CHENet (Chinese Handwriting Eraser Network) that can automatically remove Chinese handwriting located on the images. In particular, we design two modules: CAB (Convolution Attention Block) and CAP (Chain Attention Pooling) that can improve the ability of the CHENet. Also, for information protection, we collect images with Chinese handwritten words from the archive, contract, and exam papers, and build the CH-dataset that consists of 1623 scrupulously annotated images. Qualitative and quantitative evaluations show that our CHENet can solve this task well.
Federated Learning at Mobile Edge Networks: A Tutorial

In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning, this opens up countless possibilities for meaningful applications to be developed. Traditional cloud-based Machine Learning approaches require the data to be aggregated in a cloud server or data center. However, this results in critical issues related to data privacy. In light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises the challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this chapter, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review cutting-edge solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL.
Application and Evaluation of a Taxonomy in the Context of Software Requirements Management

The requirements of a software are the characteristics of the system, being identified based on information provided by the users or by experts in the business; and the effective management of this information is essential to ensure that the system meets the needs of those who will use it. According to research, one of the problems that negatively impacts the software development process is related to the conduction of the project requirements management activity. And in order to alleviate this problem, we carried out a research to verify if taxonomies, as tools used by the area of information management, can be applied in the context of requirements management. For this, we developed a taxonomy to meet a real software project and the results were evaluated in relation to its complexity, satisfaction, resources involved and adaptability. The present research is characterized in terms of nature as an applied research, aiming at solving real-world problems; as for the objectives, as exploratory and descriptive; and as for the approach, mixed methods are used, since it combines qualitative and quantitative forms of research in the same study. As for the methodological procedures, the bibliographic research was chosen to understand the concepts related to information management, taxonomies and requirements management. The evaluation results showed that taxonomies can contribute to the activities of the requirements management process and consequently increase the chances of project success.
The Scope of Energy Efficient Techniques in Cloud Computing

There are many reasons for cloud computing to be used in the market today. It is a great alternative to traditional computing, which helps businesses stay competitive. Cloud computing is a new way of operating, storing, and distributing data. It is a huge trend in today's technology world. There are a lot of people that are still not familiar with this technology. I will also provide a list of cloud computing services and what to expect from each. Our aim is to find out the most optimized and efficient azure cloud computing techniques. This will help businesses to save costs. After making the changes in the database algorithm we noticed the efficiency and the QoS factor of our services has shown significant improvement. This can help companies to save cost and increase their efficiency.
Solving Multi-class Imbalance Problems Using Improved Tabular GANs

Multi-class imbalance problems are non-standard derivative data science problems. These problems are associated with the skewness in the data underlying distribution, which, in turn, raises numerous issues for conventional machine learning techniques. To address the lack of data in imbalance problems, we can either collect new data or oversample the underrepresented classes by synthesizing artificial data from original instances. This paper focuses on the latter and introduces two novel tabular GAN variants to handle multi-class imbalance problems. Empirical results on three datasets from the UCI repository demonstrated that the suggested approaches that use our proposed filtering algorithm based on neighboring rules improved the ability of the decision tree classification model to recognize underrepresented class instances, decreased the bias toward the majority class, and enhanced its generalization ability.
Global Update Guided Federated Learning

Federated learning protects data privacy and security by exchanging models instead of data. However, unbalanced data distributions among participating clients compromise the accuracy and convergence speed of federated learning algorithms. To alleviate this problem, unlike previous studies that limit the distance of updates for local models, we propose global-update-guided federated learning (FedGG), which introduces a model-cosine loss into local objective functions, so that local models can fit local data distributions under the guidance of update directions of global models. Furthermore, considering that the update direction of a global model is informative in the early stage of training, we propose adaptive loss weights based on the update distances of local models. Numerical simulations show that, compared with other advanced algorithms, FedGG has a significant improvement on model convergence accuracies and speeds. Additionally, compared with traditional fixed loss weights, adaptive loss weights enable our algorithm to be more stable and easier to implement in practice.
A Two-Stage Incentive Mechanism Design for Quality Optimization of Hierarchical Federated Learning

In this paper, we investigate the aggregated model quality maximization problem in hierarchical federated learning, the decision problem of which is proved NP-complete.We develop the mechanism MaxQ to maximize the sum of local model quality, which consists of two stages.In the first stage, an algorithm based on matching game theory is proposed to associate mobile devices with edge servers, which is proved able to achieve the stability and 1 2 -approximation ratio.In the second stage, we design an incentive mechanism based on contract theory to maximize the quality of models submitted by mobile devices to edge servers.Through thorough experiments, we analyse the performance of MaxQ and compare it with the existing mechanisms FAIR and EHFL, under different deep learning models ResNet18, ResNet50 and AlexNet, individually.It is found that the model quality can be improved by 8.20% and 7.81%, 10.47% and 11.87%, 10.98% and 11.97% under different models, respectively.
Connecting Compression Spaces with Transformer for Approximate Nearest Neighbor Search

We propose a generic feature compression method for Approximate Nearest Neighbor Search (ANNS) problems, which speeds up existing ANNS methods in a plug-and-play manner. Specifically, based on transformer, we propose a new network structure to compress the feature into a low dimensional space, and an inhomogeneous neighborhood relationship preserving (INRP) loss that aims to maintain high search accuracy. Specifically, we use multiple compression projections to cast the feature into many low dimensional spaces, and then use transformer to globally optimize these projections such that the features are well compressed following the guidance from our loss function. The loss function is designed to assign high weights on point pairs that are close in original feature space, and keep their distances in projected space. Keeping these distances helps maintain the eventual top-k retrieval accuracy, and down weighting others creates room for feature compression. In experiments, we run our compression method on public datasets, and use the compressed features in graph based, product quantization and scalar quantization based ANNS solutions. Experimental results show that our compression method can significantly improve the efficiency of these methods while preserves or even improves search accuracy, suggesting its broad potential impact on real world applications. Source code is available at https://github.com/hkzhang91/CCST .
Statistical Analysis of threatening IP in Universities Based Automated Script

The operation of cyber security has gradually become a significant part of informatization in the process of smart campus construction. However, universities rely too much on physical security devices in the current network security operation process, and lack of self-defined statistical analysis automation program. The current network logs have the following problems: difficult to gather data-sets of different application systems, difficult to unify attribute names of different data-sets, difficult to count the number of records with attacker and victim as the only items, and besieged to do repetitive work daily. In order to solve the above problems, this paper proposes an automatic statistical threatening IP algorithm, which can uniquely locate the attacker and victim and process other data through a programmatic way. Finally, it is used to report statistical data to departments related to cyber security. The results of experiment show that the proposed algorithm can effectively complete the statistical work of network attack threatening IP details.
Edge Augmentation for Large-Scale Sketch Recognition without Sketches

This work addresses scaling up the sketch classification task into a large number of categories. Collecting sketches for training is a slow and tedious process that has so far precluded any attempts to large-scale sketch recognition. We overcome the lack of training sketch data by exploiting labeled collections of natural images that are easier to obtain. To bridge the domain gap we present a novel augmentation technique that is tailored to the task of learning sketch recognition from a training set of natural images. Randomization is introduced in the parameters of edge detection and edge selection. Natural images are translated to a pseudo-novel domain called "randomized Binary Thin Edges" (rBTE), which is used as a training domain instead of natural images. The ability to scale up is demonstrated by training CNN-based sketch recognition of more than 2.5 times larger number of categories than used previously. For this purpose, a dataset of natural images from 874 categories is constructed by combining a number of popular computer vision datasets. The categories are selected to be suitable for sketch recognition. To estimate the performance, a subset of 393 categories with sketches is also collected.
Actor-Oriented Self-Organizing Maps

This paper explores the implementation of a distributed model of the Self-Organizing Map (SOM) and its subsequent validation through the implementation of a proof-of-concept prototype using the Typed-Akka tool kit. This work is a part of an on-going study into the intersection of the actor-model and SOMs following on from previous work on lattice initialization. A comprehensive software architecture model is given using various UML diagrams, specifically the behavioral and finite state-oriented aspects of the design are given using activity and state diagrams respectively. A parameter-study was conducted that focused specifically on the neighborhood definition function in the context of an actor-oriented SOM.
RFNet: A Refinement Network for Semantic Segmentation

As one of the basic tasks of computer vision, semantic segmentation is widely used in many fields, e.g., medical images parsing, scene parsing, autonomous driving, etc. In the current mainstream approaches, downsampling or patching operation is required to ensure that the GPU memory is not overloaded for dealing with the high-resolution input images. However, the corresponding cost is the lack of details in the final segmentation map. In this work, we proposed RFNet, a refinement network which resolves the lack of detailed information in coarse predictions by fusing the coarse predictions and the fine predictions gained by fine input image patches. There are three key characteristics: (i) designing a spatial information extraction module which can efficiently process information in coarse and fine feature maps at spatial level. (ii) proposing an auxiliary-fusion information branch calculated from the prediction maps, which contribute to refine predictions. (iii) designing a boundary auxiliary loss function in the training process, which makes the model pay more attention to those pixels belonging to the boundary of objects. We show the superiority of the proposed RFNet on the Cityscapes dataset, the experimental results illustrate that ours RFNet performance outperforms other state-of-the-art approaches with low computation consumption. The codes will be available at: https://github.com/zhu-gl-ux/RFNet.
Real-time based Violence Detection from CCTV Camera using Machine Learning Method

Based on deep-learning approaches, we developed a real-time violence detector for surveillance video systems. In the model given here (overall generality-accuracy-fast response time), CNN serves as a space feature extractor, while LSTM is used to learn time-related relationships. Due to the large number of devices that can record video from camera systems, like those used in surveillance systems, body-worn webcams, and phones, it has become hard to keep track of video footage from many surveillance devices. Using crowd-based video footage, we analyzes and alerts possible those who are affected by violent material is found in the clip. Keeping an eye on huge crowds during social gatherings, especially those where there is a possibility of violence becomes very difficult. The speed, accuracy, and generality of violent event detectors across a variety of video sources and formats are all factors that go into determining their usefulness. Intelligent monitoring technology has been extensively deployed in the nation in recent years to continually support the development of a safe city. Behavioral intelligence analysis is becoming more popular in the realm of intelligent image analysis. Currently, complex activities such as fighting or violence are rarely studied in behavior analysis techniques; instead, they focus on basic movements such as running or leaping. To preserve social order and safeguard people's lives and property, competent and intelligent analysis of violence through video surveillance is vital. To that end, we've put up an overview of the most recent techniques for spotting violent scenes in recorded video.
An Instance Segmentation Model to Categorize Clothes from Wild Fashion Images

Categorizing of clothes from wild fashion images involves identifying the type of clothes a person wears from non-studio images such as a shirt, trousers, and so on. Identifying the fashion clothes from wild images that are often grainy, unfocused, with people in different poses is a challenge. This research proposes a comparison between object detection and instance segmentation based models to categorise clothes from wild fashion images. The Object detection model is implemented using Faster Region-Based Convolutional Neural Network (RCNN). Mask RCNN is used to implement an instance segmentation model. We have trained the models on standard benchmark dataset namely deepfashion2. Results demonstrate that Instance Segmentation models such as Mask RCNN outperforms Object Detection models by 20%. Mask RCNN achieved 21.05% average precision, 73% recall across the different IoU (Intersection over Union). These results show promise for using Instance Segmentation models for faster image retrieval based e-commerce applications.
A QoE Model for Mulsemedia TV in a Smart Home Environment

The provision to the users of realistic media contents is one of the main goals of future media services. The sense of reality perceived by the user can be enhanced by adding various sensorial effects to the conventional audio-visual content, through the stimulation of the five senses stimulation (sight, hearing, touch, smell and taste), the so-called multi-sensorial media (mulsemedia). To deliver the additional effects within a smart home (SH) environment, custom devices (e.g., air conditioning, lights) providing opportune smart features, are preferred to ad-hoc devices, often deployed in a specific context such as for example in gaming consoles. In the present study, a prototype for a mulsemedia TV application, implemented in a real smart home scenario, allowed the authors to assess the user’s Quality of Experience (QoE) through test measurement campaign. The impact of specific sensory effects (i.e., light, airflow, vibration) on the user experience regarding the enhancement of sense of reality, annoyance, and intensity of the effects was investigated through subjective assessment. The need for multi sensorial QoE models is an important challenge for future research in this field, considering the time and cost of subjective quality assessments. Therefore, based on the subjective assessment results, this paper instantiates and validates a parametric QoE model for multi-sensorial TV in a SH scenario which indicates the relationship between the quality of audiovisual contents and user-perceived QoE for sensory effects applications.
Generation of Multiple-Choice Questions From Textbook Contents of School-Level Subjects

Multiple-choice question (MCQ) plays a significant role in educational assessment. Automatic MCQ generation has been an active research area for years, and many systems have been developed for MCQ generation. Still, we could not find any system that generates accurate MCQs from school-level textbook contents that are useful in real examinations. This observation motivated us to develop a system that generates MCQs to assess the student's recall of factual information. Also, the available systems are often domain, subject, or application-specific in nature. Although the MCQ generation task demands a specific setup, we expect a level of generalization can be achieved. In this development, we also focus on this issue. We propose a pipeline for automatic generation of MCQs from textbooks of middle-school level subjects, and the pipeline is partially subject-independent. The proposed pipeline comprises four core modules: preprocessing, sentence selection, key selection, and distractor generation. Several techniques have been employed to implement individual modules. These include sentence simplification, syntactic and semantic processing of the sentences, entity recognition, semantic relationship extraction among entities, WordNet, neural word embedding, neural sentence embedding, and computation of intersentence similarity. The system is evaluated using the National Council of Educational Research and Training (NCERT), India, textbooks for three subjects. The quality of system-generated questions is assessed by human experts using various system-level and individual module-level metrics. The experimental results demonstrate that the proposed system is capable of generating quality questions that could be useful in a real examination.