Reviewerly/exHarmony huggingface dataset:
Cryptography Professional Rival (CPR): A Game Designing Model to Learn Cryptography

Nowadays information technology and particularly artificial intelligence is a very vital tool in different aspects of human life. One inseparable part of human life is learning which has diverse methods. From the very first days of our life we start to learn by playing games in different ways. In recent years with growth of artificial intelligence we see widespread usage of it in game designing. On the other hand, one of the most important parts of information technology to be learned is cryptography, there is a need to find a good method of learning that. In our work we propose a model which is a game, based on lesson planning and utilizes artificial intelligence. Firstly, we explain about lesson planning then describe our game design and how artificial intelligence is used.
Panoptic-DLA: Document Layout Analysis of Historical Newspapers Based on Proposal-Free Panoptic Segmentation Model

In this paper, we introduce a novel historical newspaper layout analysis model named Panoptic-DLA. Different from the previous works regarding layout analysis as a separate object detection or semantic segmentation problem, we define the layout analysis task as the proposal-free panoptic segmentation to assign a unique value to each pixel in the document image, encoding both semantic label and instance id. The model consists of two branches: the semantic segmentation branch and the instance segmentation branch. Firstly, the pixels are separated to “things” and “stuff” by semantic classification taking the background as “stuff”, and content objects such as images, paragraphs, etc., as “things”. Then the predicted “things” are grouped further to their instance ids by instance segmentation. The semantic segmentation branch adopted DeepLabV3+ to predict pixel-wise class labels. In order to split adjacent regions well, the instance segmentation branch produce a mountain-like soft score-map and a center-direction map to represent content objects. The method is trained and tested on a dataset of historical European newspapers with complex content layout. The experiment shows that the proposed method achieves the competitive results against popular layout analysis methods. We also demonstrate the effectiveness and superiority of the methods compared to the previous methods.
Strengthening the Quality Education of College Students in the Internet Age

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.
Methods for Direct Representation of Asymmetry

After a brief recall to the main aspects of the graphical representation of a rectangular matrix, two classes of methods to represent proximity matrices are reviewed: bilinear and distance-like methods. The main steps for the bilinear model estimation are presented by studying the properties of the Singular Value Decomposition of a proximity matrix. The principal concepts concerning the distance models for the analysis of symmetric proximity matrices are recalled, then some distance-like methods dealing with the direct representation of the asymmetric proximities are presented. The chapter ends with two applications to real data and a software section to present some programs available for models estimation.
Design thinking approach to simplify monetary transactions for the people with visual impairment

Design thinking, having applications in many fields, is emerging to be a pragmatic technique for facilitating Human Centred Design (HCD), a modern-day necessity, through a well-defined systematic process. This paper presents a case study detailing the application of design thinking principles to approach and develop solutions for the visually challenged to recognize the denomination of Indian currencies. Throughout the paper, specific implementations of design thinking principles have been detailed with emphasis on the key takeaways from the various stages. A comprehensive review of our approach compared to existing and unconventional applications has been provided to underline the importance of incorporating design thinking into emergent consumer-oriented solutions. A lot of research has been done in developing denomination classification models, however, there seems to be a lack of solutions designed through the perspective of a blind person who plays the critical factor in determining effectiveness once deployed. The design procedure helped us realize the importance of developing a simple user-friendly interface with a single input button and audio relays to guide the blind user during operation. Establishing a good platform for interaction and feedback was found to be critical in helping us modify and prioritize aspects such as achieving a low false-positive percentage and the addition of a high threshold Softmax function which made the solution design effective.
Single Image Restoration with Generative Priors

Generative models can be used, as an alternative to conventional probability densities, to capture the statistical behavior of complicated datasets. Unlike probability densities with which the generation of realizations may become a challenging task, generative models have an inherent ability to easily produce realizations, which, in the case of natural images can be extremely realistic. In many image restoration problems, such as deblurring, colorization, inpainting, super-resolution, etc., probability densities are used as priors, one may therefore wonder whether we can, instead, adopt generative models. Indeed such methods have appeared in the literature, but they require exact knowledge of the transformations responsible for the data distortion and involve regularizer terms with weights that require adjustment. Our approach, by combining maximum a-posteriori probability with maximum likelihood estimation, can successfully restore images in both blind and non-blind modes without the need to fine-tune any regularization parameters. Simulations on deblurring, colorization, and image separation problems with exact knowledge of the transformation demonstrate improved image quality, reduced computational cost compared to existing methods. Comparable results are also enjoyed when the distortion models contain unknown parameters.
Research on the Improvement of College Physical Education Teaching Based on Computer-aided Teaching

Abstract Computer has been used as a new teaching means in various major teaching fields. Compared with traditional culture courses, physical education is not closely related to computer. The application of computer-aided technology in many teaching zone is quite different from that in physical education. In order to make the combination of the two produce the best practical effect, to make the students have interesting in learning and to make their learning and practice sports more efficient, this paper puts forward some special ideas for improvement by analyzing the application of CAT in physical education teaching.
Analysis of Professional Skills Using Growing Hierarchical Self-Organizing Map

With the increase of competitiveness among companies, knowledge management has become a strategic factor to ensure the sustainability of organizations. One of the most challenging problems within knowledge management is the representation of tacit knowledge, the subjective specialty, and the skills of a professional team. Many approaches have been proposed to store, represent, and retrieve tacit knowledge, but most fail on providing a sustainable and adaptable solution. In this paper, this problem was addressed by proposing a Growing Hierarchical Self-Organizing Map neural network to map the tacit knowledge from an organization in an unsupervised and hierarchical manner. It was introduced a novel relevance gain function, to process symbolic data, and find that our neural network can model the tacit knowledge of a real business.
Multi-semantic granularity graphical model with ensemble BERTs and multi-staged training method for text classification

Text classification is a classic problem in Natural Language Processing (NLP). The task is to assign predefined categories to a given text sequence. In this paper we present a new Multi-semantic granularity graphical model with ensemble BERTs and multi-staged training method, which is proposed EBG model (Ensemble BERTs with GNN), two kinds of language pre-training models like BERT and XLNet using extra side information acquired via GNN (Graph Neural Networks) in raw sentences. This new approach better captures the complex semantic characteristics and utilizes the different advantages of BERTs model to deal with the various classification demands. The experimentations text classification tasks show our proposed methods can significantly improve the performance than the recent novel methods and models.
Implementation of School Quality Management in Indonesia

Every educational institution which is engaged in services and society, must be entitled to the goals in accordance with the institutions that can achieve progress and success of other educational institutions. Therefore educational institutions must be able to create schools that are innovative, innovative and of high quality. This is done competing with other educational institutions, and creating quality schools. This research tries to. Through this research, it is expected to be a reference for schools. "Because school is a place for human resources to become superior human beings in the future and in the future. Quality in education in the form of services which require help and expectations of parties related to students. The type of research used in research is library research or research libraries. What is discussed with library research (Library Research) At this stage, researchers conduct what is called literature study, namely reference book research.
Proposal for a Classifier for Public Tenders for Software Based on Standard IEEE830

Nowadays, governments and private sectors request industry software solutions through public tenders that use websites for mass distribution. Not only is demand organized, but a large number of software tenders is produced. This study focuses on the analysis of texts from these documents to characterize them efficiently in order to find a specific solution to the general problem of how to make a bid and how not to make a bid. An automatic classifier is proposed for the public tender process for software based on IEEE standard 830-1998, which categorizes text from a pragmatic point of view. Development phases and classification success rates are shown for each algorithm used in the different experiments. This system may be an alternative for the early analysis of public tenders for software with fuzzy requirements.
A Comprehensive Review of Adversarial Learning and Impact of Unsharing Weights Across Classes

It is evident from recent developments that deep learning has the potential to be a fundamental part in almost every new technology that comes in the future. Deep learning models have performed exceedingly well on standard image classification problems, but their performance drops drastically when presented with adversarial inputs that are created by adding specific small perturbations to the original image. This paper will be divided into two sections. The first section will be a complete review of the existing research in this field. We will provide the reader with the basic concepts of adversarial learning and a broad classification of various adversarial attacks and defenses. In the second section, we propose an ensemble model with max voting and test the impact of adversarial attacks by converting the 10-class problem over MNIST images into 10 binary classification problems. Each weight in the middle layers of a multi-class neural network is shared across all the output classes of the model. We call them the shared weights of the network. Our proposed model consists of no shared weights, shows a slight improve in accuracy against adversarial samples and can detect out-of-domain inputs.
A Unified Density-Driven Framework For Effective Data Denoising And Robust Abstention

The success of Deep Neural Networks (DNNs) highly depends on data quality. Moreover, predictive uncertainty reduces reliability of DNNs for real-world applications. In this paper, we aim to address these two issues by proposing a unified filtering framework leveraging underlying data density, that effectively denoises training data as well as avoids predicting confusing samples. Our proposed framework differentiates noise from clean data samples without modifying existing DNN architectures or loss functions. Extensive experiments on multiple benchmark datasets and recent COVIDx dataset demonstrate the effectiveness of our framework over state-of-the-art (SOTA) methods in denoising training data and abstaining uncertain test data.
Financial Sharing of SMEs Based on Cloud Service SaaS Outsourcing Model

According to the framework of "necessity-feasibility-implementation path-problem", this paper puts forward the idea that SMEs can use the SaaS outsourcing service model to build a financial sharing center. Firstly, from the perspective of adapting to the development of the information age and the financial dilemma, the necessity of implementing financial sharing services for SMEs is expounded. Secondly, the feasibility of the core technology is analyzed as well as its implementation path. Business-finance-management Trinity framework and IT system bi-level architecture are built to achieve financial sharing for SMEs. Finally, the problems that may exist in the process of constructing the financial sharing service of SMEs are stated.
Preparing for Planned Events: National Security Special Events

Creating collaborations that endure throughout a national security event is an important issue for public safety organizations. What are the key factors that foster an environment in which collaborations can be sustained? Based on research and numerous interviews with law enforcement and public safety leaders involved in the 2002 Winter Olympics, several factors were identified that impact the effectiveness and endurance of collaborations. The enablers for effective collaborations before, during, and after the 2002 Winter Olympics included leadership, trust, social capital, and felt need.
Checking Robustness of Web Services based on Solving Automata Equations

In this paper, we consider checking the robustness of web services using the model of finite state automata and analyzing the solvability of an automaton equation over the concatenation operator. The paper contains the procedure for constructing the largest solution for a solvable automata equation, since a solution to the equation describes a malicious input. Experimental results have shown that this approach can be effective when checking the robustness of web services.
Vehicle re-identification method based on global and local feature fusion

Abstract Vehicle re-identification is still an issue worth discussing due to change of view, light and angle changes. At present, the key of vehicle reidentification research is to solve the problems that the difference between the same class is big and the difference between different classes is small. For the study, a vehicle re-identification method ground on view classification is proposed.The vehicle images are divided into four views, namely, front, rear, top and side, and local features are extracted by the segmented view information. Using the CNN feature extractor, the global feature representation with car ID attribute is learned. We have done experiments with the VERI-776 data sets. The mean average accuracy of 78.13% is obtained, which proves the effectiveness of the method.
The Design and Evaluation of English Class Equipped with the iClass Platform Incorporating Motivational Strategies

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.
Characterizing Phishing attacks using Natural Language Processing

Currently, there are many ways that phishing attacks placed people and businesses at risk. In the economic causes losses the money; in the social aspect, the perception of trust in users decreases; and on the psychological level, fear can avoid the use of digital tools and resources. This study aims to increase efficiency in detecting Phishing attacks using Natural Language Processing (NLP) to explore the mental model people use to detect whether an email is legitimate or not. Specifically, it is based on feedback vectorization and the movement of the mouse, which was obtained when the participants interacted in a test to detect phishing. The results obtained allow us to identify that people based their decision on the URL analysis in the mental model of legitimation and phishing decision. However, in the phishing model, the number of characteristics in each indicator would be more diverse and broader, which produces new challenges and future directions in this solution.
Akiba's logic of indeterminacy

A standard approach to indeterminacy treats ‘determinately’ and ‘indeterminately’ as modal operators. Determinacy behaves like necessity; indeterminacy like contingency. This raises two questions. What is the appropriate modal system for these operators? And how should we interpret that system? Ken Akiba has developed an account of ontic indeterminacy that interprets possible worlds as worldly precisifications. He argues that this account vindicates S4 as the logic of indeterminacy. In this paper I explore one significant and surprising consequence of this view. I do this in two stages. First, I prove a technical result, which I call the Infinite Indeterminacy Theorem. Roughly put, this theorem states that, at any given precisification w, either every instance of indeterminacy admits of never-ending higher-order indeterminacy, or else there's indeterminacy in an infinite amount of distinct atomic formulas. In slogan form: either all indeterminacy is infinitely ascending or else there's infinitely wide indeterminacy. Second, I unpack the metaphysical consequences of this technical result (under its intended interpretation) and assess their plausibility. I conclude that these consequences commit Akiba's theory to highly contentious – and arguably untenable – views about the metaphysics of indeterminacy and related matters.
Algorithms for Triggering General Regression Neural Network

In this article, three algorithms for training a general regression neural network (GRNN) will be presented. The first uses a nature-inspired optimization approach known as particle swarm optimization (PSO), while the latter two, i.e. the plug-in and the cross-validation, are based on classical mathematical methods, including the theory of kernel density estimators. The aforementioned algorithms will be applied to determining network smoothing parameters—which is the main task in GRNN learning. The trained GRNN will undergo benchmarking on repository data sets.
Impact of Cloud Attacks on Service Level Agreement

Cloud computing has taken center stage in the current business due to reduced cost, high performance and zero infrastructure. Cloud computing paradigm is yet unable to provide quality of service (QoS) by complying service level agreement (SLA) because of lack of analysis on the unaddressed security issues, challenges, threats which has paved the path for researchers to overcome the lapses in order to improve the QoS. In this paper, we present a survey on the cloud service model attacks and threat analysis. It is been observed after thorough analysis of attacks on all the service models that there is a need to have stronger security for infrastructure as a service level attack. Trust can be ensured among the cloud users with the introduction of security metrics in the service level agreement. Cloud service providers can mention the security metrics in SLA only if it can confidently address these attacks by adding security for infrastructure as a service.
3S-Net: Arbitrary Semantic-Aware Style Transfer With Controllable ROI Choice

A cluster of stunning style transfer approaches evolve to include single style transfer and multi-style transfer these years. However, few studies consider the style consistency between identical semantic objects in style images and the content image respectively. Especially for multi-style transfer, the merged style is obtained through a simple linear combination with given weights. So, the textures of each source style are mixed, which makes the result lack aesthetic value. To overcome this problem, we propose a 3S-Net to achieve semantic-aware style transfer mainly by Two-Step Semantic Instance Normalization (2SSIN) and Semantic Style Swap. 3S-Net can automatically accomplish the procedure of matching semantic objects between style images and content image. Besides, our method is adaptive and flexible. It succeeds in single style transfer and multi-style transfer from a semantic perspective, even ROI (Region of Interest) style transfer with user’s preference.
A Neuronavigation Toolkit for 3D Visualization, Spatial Registration and Segmentation of Brain Vessels from MR Angiography Images

Neuronavigations are real-time approaches implemented to help neurosurgeons precisely localize different intracerebral pathologies by using multiple neuroimaging modalities. However, current systems suffer from several shortcomings including time-consuming, 3D registration and segmentation accuracies as well as difficulties transferring 3D vessel imaging to the neuronavigation systems. In this work, we introduce a standalone platform for image-fusion, and semantic segmentation of MR angiography images supported for neurovascular interventions. The full-stack toolkit consists of two parts: back-end and front-end. At back-end, Tensorflow library and Ajax for application programming interface server request-response interactions are embedded. As to front-end, we implement C++_based Qt platform for GUI (Graphic User Interface) in Visual Studio integrated development environment. The toolkit provides 3D volume and slice-based visualizations of brain multimodal images. All medical images are archived in DICOM standard, then converted into NIfTI formats. Visualization Toolkit was used to render 3D MR images. The implemented volume-rendering techniques allow the direct visualization of vascular structures, thus reveal vessel abnormalities more faithfully. We provide GUI interface of the modality brain image registration and segmentation functionality. There are two machine-learning based automatic registration categories: (1) intensity-based, (2) geometry-based. The registration error in mm is computed by using Hausdorff Distance metric. As for cerebrovascular segmentation, a method that utilizes an enhanced vesselness filter which bases on multiscale Hessian eigenvalues is performed to extract neurovascular trees. The introduced toolkit is expected to be a validated platform that allows researchers to apply insightful results into the operating room for clinical evaluations.