Reviewerly/exHarmony huggingface dataset:
Image Quality Enhancement with Machine Learning Based Multi-Step Super-Resolution

Machine Learning based Super-Resolution techniques have recently been able to achieve good qualitative results. Convolutional Neural Networks have thus become the basis for recent studies on SR techniques. In this paper, we propose a novel Multi-Step Super-Resolution technique that applies previous methods that achieve good results. We also demonstrate that this proposed method achieves a significant improvement in both subjective visual quality and in reconstruction accuracy, relative to conventional methods.
Discovering Your Target Audience

The more a brand knows about its target audience, the easier it is to communicate value and build long-lasting, profitable relationships. Donald Trump, Oprah Winfrey, and Elon Musk all have very different brands, but the one thing they have in common is a strong ability to identify and connect with their target audiences. Learning information about a brand’s audience such as demographics, psychographics, and pain points can lead to the creation of a successful personal marketing strategy. Understanding how consumers make decisions can also prevent personal brands from wasting resources on inefficacious marketing tactics.
Research of the Inspection Optimization Strategy for Distribution Network Based on Comprehensive Information Model

Abstract To improve the quality and efficiency of inspection tasks, this paper studies the path planning optimization strategy for distribution equipment inspection of distribution networks. The comprehensive information model of each type of distribution equipment to be inspected is established by integrating the real-time monitoring information, geographic information and historic information effectively. The inspection path optimization model with an admissible cost function is established and solved by using the heuristic search A* algorithm. Comparison of the results of several inspection path planning strategies are provided to verify the effectiveness of the proposed strategy.
Visualizing data: A lived experience

Researching data visualization as a lived experience provides a perspective from which to explore its social life. Borrowing elements from feminist autobiographical research and critical making, this chapter uses the personal story of the design and circulation of a hand-drawn, small-data visualization depicting the author’s experience of Obsessive Compulsive Disorder. By critically reflecting on the visualization’s design and circulation, this chapter engages with wider academic debates about data visualizations’ subjectivities. Furthermore, by interrogating notions of authenticity and honesty associated with hand drawing, it introduces the idea of a politics of hand-drawn visual representations of data.
Research on the Influence of Fear Appeal on APP Users’ Privacy Protection Behavior: An Empirical Study

Abstract Under the background of Big Data Era, this paper explores the influencing factors of APP Users’ Privacy Protection Behavior based on the theory of fear appeals. We used scene-based questionnaire to collect the needed data. We found that selfefficacy, fear and response efficacy have a positive impact on APP users to adopt privacy security measures wishes. We did not include other influencing factors such as threat control, fear control and social factors in this study. This paper has some reference and reference significance for APP operators and service providers who provide third-party network security.
Early Shakespeare and the Authorship of <i>The Taming of the Shrew</i>

Along with 1, 2, 3 Henry VI and Titus Andronicus – plays that have been at the centre of debates surrounding Shakespeare’s canon and chronology since Malone – scholars have acknowledged, or suspected, the presence of someone other than Shakespeare in The Taming of The Shrew since the 18th century. There is thus a historical consensus regarding these other collaborations, but what about The Shrew? Unlike the Henry VI plays and Titus Andronicus (and more recently Arden and Edward III) there has been no recent attempt to validate or discredit claims for collaboration in this play. This designates The Shrew as the only early play recognized by early commentators as potentially collaborative that has not yet been systematically investigated for the presence of someone other than Shakespeare.
Visibility Tagging of nouns in Text-to-Scene Conversion

Abstract The recognition and extraction of visual text information is the basis of the realization of Text-to-Scene Conversion, while nouns are the main expression form of entity, event, orientation, action and other information in text, but not all nouns are suitable for visualization, so how to recognize the visibility of nouns is the key problem of Text-to-Scene Conversion. Based on the definition and classification of noun visualization, this paper establishes a keyword visibility dictionary. Furthermore, in view of the static limitations of the dictionary, a dynamic expansion method based on semantic similarity is studied, and an optimization method for the visibility annotation of nouns is proposed in combination with unsupervised clustering. The experimental results show that the proposed method is effective and feasible for nominal visibility annotation of common texts, and the accuracy of the optimized method is 16% higher than that of the simple semantic similarity method.
Comparison of Gradient Descent Methods in Online Fuzzy Co-clustering

Fuzzy co-clustering schemes including Fuzzy Co-Clustering induced by Multinomial Mixture models (FCCMM) are promising approaches for analyzing object-item cooccur-renee information such as document-keyword frequencies and customer-product purchase history transactions. However, such cooccurrence datasets are generally maintained as very large matrices and cannot be dealt with conventional batch algorithms. In order to deal with such problems, online FCCMM (OFC-CMM) that sequentially loads a single object has been proposed. Conventional OFCCMM uses stochastic gradient descent (SGD) to update parameters. SGD generally has drawbacks that convergence is slow and it is susceptible to vibration state and a saddle point. Many improvements on SGD have been proposed such as Momentum SGD, Nesterov's accelerated gradient method, AdaGrad, and Adam. In this study, we introduce various gradient descent methods into OFCCMM and observe their characteristics and performance through numerical experiments.
Local Search Trajectories over S-box space

A local search method is developed and applied to create trajectories over S-box spaces in this work. These trajectories show a strong linear correlation between confusion coefficient variance, transparency order, modified transparency order, and revised transparency order under the Hamming weight model. When the confusion coefficient variance values increase, the values of transparency order, modified transparency order beta zero, and revised transparency order beta zero decrease, reflecting the same theoretical resistance against side-channel attacks by power consumption. From the best of our knowledge, it is the first time that Local Search trajectories are used to discover relations between cryptography properties. The experimental results and comparisons provide evidence of the capabilities of the proposal to handle the cryptography problems.
A Mobile Platform for Social Media Filters

In the past few years, the quality of smartphone cameras has evolved, allowing users to take high-resolution images. Consequently, many smartphone applications, such as Snapchat and Instagram, emerged offering new forms of mobile imaging services that permit users to capture important moments or share their daily life with other people. However, these applications provide similar effects or filters for all users, not taking into account their custom requirements. To tackle this problem, this paper proposes a mobile platform for sharing image filters through social media. It allows mobile users to order special filters that match their requirements and allows designers to sell their designed filters as a service. The platform provides a filter gallery for every occasion from different designers and the user can request that filter that matches their need or order a custom filter from listed designers. Users and designers can communicate with each other to negotiate prices and designs via the platform’s private messages service. Additionally, a user can evaluate a design, rate a designer and pay through the application.
Some Improvements of the Parallel Karatsuba Algorithm: Student paper

Multiplying long integers is one of the basic mathematical operations used mostly in cryptography. The topic of this paper is experimental testing of different implementations of the Karatsuba algorithm for multiplying long integers using shared memory parallel programming. Different ways of using OpenMP were considered for optimizing implementations of the algorithm and the experiment results were analysed and compared. The parallel implementation has achieved a significant speedup compared to the sequential code, with the maximum achieved speedup of 9 on a 12 core CPU.
John Lyly and Shakespeare’s Early Career

When he wrote Gallathea, Lyly combined certain elements which might be said to be at the heart of Shakespearean comedy: cross-dressed lovers, woods as spaces for personal discovery, supernatural imps with power over human interaction, a narrative moving towards marriage against seemingly irreconcilable odds. Though Shakespeare’s earlier plays, such as The Two Gentlemen of Verona and Love’s Labour’s Lost, are most often described as Lylian, every one of his comedies engages with some combination of these elements, and towards the end of his career, in the second scene of The Tempest Shakespeare can be seen rewriting the first scene of Gallathea. This chapter will give this side of Shakespeare’s authorship a new emphasis by juxtaposing it with different elements of Lyly’s influence over his early career, charting the use of Lyly’s prose fiction in Two Gentlemen of Verona and his play The Woman in the Moon in Titus Andronicus. By looking at various kinds of influence – generic, structural, performative, tonal – this chapter will present a new understanding of Shakespeare’s early use of Lyly’s work.
Characterizing strongly admissible sets

The concept of strong admissibility plays an important role in dialectical proof procedures for grounded semantics allowing, as it does, concise proofs that an argument belongs to the grounded extension without having necessarily to construct this extension in full. One consequence of this property is that strong admissibility (in contrast to grounded semantics) ceases to be a unique status semantics. In fact it is straightforward to construct examples for which the number of distinct strongly admissible sets is exponential in the number of arguments. We are interested in characterizing properties of collections of strongly admissible sets in the sense that any system describing the strongly admissible sets of an argument framework must satisfy particular criteria. In terms of previous studies, our concern is the signature and with conditions ensuring realizability. The principal result is to demonstrate that a system of sets describes the strongly admissible sets of some framework if and only if that system has the property of being decomposable.
Multi-scale Deep CNN Network for Unsupervised Monocular Depth Estimation

Depth estimation problem is one of the most critical issues for robot SLAM.In this paper, the current unsupervised depth estimation method is mainly focused on single-scale features in CNN convolutional network architecture, and does not make full use of multi-scale features along the network.In addition, the design of the loss function for depth estimation is also adapted to the single scale.In this paper, we also propose a multi-scale unsupervised depth estimation for the above problems, and introduce a matching loss function to adapt to the training of multi-scale networks. Experiments were carried out on the KITTI dataset. The experimental results show that the proposed method improves the accuracy and efficiency of depth estimation.
Performance Analysis and Comparison of Parallel Computing Frameworks: Sips and Openmp

Parallel execution of jobs progresses towards becoming need of great importance as holding up time required to be decreased. To this perspective a few stages have been worked upon by the specialists. Jobs started by the clients are sequential in nature yet execution of jobs required parallel stage for better execution regarding complete time and holding up time. This paper proposed the execution correlation of two of the most recent stages utilized for parallel employment execution. First stage considered for assessment is SIPS and second stage is OpenMP. Sequential calculation in parallel framework (SIPS) stage decreases the system overhead and its execution should be possible both in Windows as well as UNIX condition. The proposed work is completed in Windows condition, Matrix and Pie calculation parallel jobs are displayed. In the second case OpenMP stage is utilized to execute Matrix and dart programs. The parameters considered for assessment incorporates organize overhead, execution time and scheduling overhead. Correlation result recommends SIPS/OpenMP performs essentially better as far as recorded parameters.
Bits to Color Image Cryptography for Secure Data Transmission in Cloud Databases

Digitized signatures empower the "validation" and non-repudiation of superior messages, guaranteeing the beneficiary of an advanced message for every persona of the sender and the uprightness of the message. As Attribute based total encryption is superior to anything, we have selected ABE for Encryption. To keep away from an outsider, we have actually selected quantum Mechanics for key age and dispersion to improve protection in the cloud. The difficulty is that after cloud professional co-ops deliver administration that time the programmer may hack the username and Password. To prevent this problem we have executed the idea of advanced Signature. Our proposed plan relies upon on generating the photograph from the content that we want to ship thoroughly and turning the content based on the quantum QU bit approach as a base. This implies that, no extra information is delivered to the underlying content material where we will be able to ship the information in a secured manner. This is probably completed by making use of various photograph companies beginning with grayscale images to 24 bits. The maximum crucial favorable function in our proposed cryptographic method is that it would not require an additional image to conceal the content material under it, apart from that it has a reduced size and extremely brief in an exam with unique structures. This is seen in disbursed storage devices with information mining that utilizes avaricious spread tree technique.
Exploratory Study on Correlations between Students' Characteristics and Effects in the Case of Online Learning on University Students

The failure to ensure spontaneous e-learning may not, in fact, be due to structural problems in the e-learning system itself, but may rather be attributable to a gap between the instructional design of materials provided by e-learning and the learning characteristics (approaches to learning) of students. Learning of “Low completion rate” has been brought up as one of the teething problems of e-learning. When one does e-learning is drawing great attention anew as a problem yet to be solved. In this research, we propose, put forward, test...the hypothesis. There could be students who are not suitable to e-learning because the learning effect of e-learning varies depending on the approaches to learning as a cause of poor completion rate of students and conducted verification of this hypothesis. Verification revealed that the task fulfillment rate of e-learning varies depending on the approaches to learning, which is divided into seven categories. Outcomes of our study suggest that e-learning can produce almost no learning effect when the approaches to learning do not match the suitable e-learning instructional design. This issue from results also seems to be relevant for highlighting: “high placement test scores are relevant to the respective constant learning characteristics over the entire period of e-learning”.
An Energy Optimal Endpoint Trajectory for Docking Mechanism On Mobile Robots

For a mobile robot driven by electric system which are mostly powered by limited-capacity lithium batteries, the optimal usage of energy plays an important role in its working process. To accurately analyse the energy consumption of the robot during its motion, it is necessary to model the dynamics and kinematics of the robot and analyse the model parameters in depth. Considering mechanical power and motor's copper loss, this paper identified an energy optimal endpoint trajectory by the comparative study between traditional reference trajectory and approximate Fourier expansion with the help of computer simulation. The result shows that the optimal trajectory could reduce the energy consumption effectively.
ReViVD: Exploration and Filtering of Trajectories in an Immersive Environment using 3D Shapes

We present ReViVD, a tool for exploring and filtering large trajectory-based datasets using virtual reality. ReViVD’s novelty lies in using simple 3D shapes—such as cuboids, spheres and cylinders—as queries for users to select and filter groups of trajectories. Building on this simple paradigm, more complex queries can be created by combining previously made selection groups through a system of user-created Boolean operations. We demonstrate the use of ReViVD in different application domains, from GPS position tracking to simulated data (e. g., turbulent particle flows and traffic simulation). Our results show the ease of use and expressiveness of the 3D geometric shapes in a broad range of exploratory tasks. Re- ViVD was found to be particularly useful for progressively refining selections to isolate outlying behaviors. It also acts as a powerful communication tool for conveying the structure of normally abstract datasets to an audience.
Interpretable Recommendation System Based on Knowledge Map Features

Abstract At present, it can be explained that the recommendation system is mostly designed for a specific recommendation model, and its scalability is weak. It is not enough for the emerging recommendation models, such as complex and mixed models with deep neural networks.Knowledge maps as a highly readable external knowledge carrier provide great possibilities for improving the ability of algorithms to interpret.In essence, the knowledge map is intended to describe the various entities or concepts and their relationships that exist in the real world, which constitute a huge semantic network diagram, nodes represent entities or concepts, and edges are composed of attributes or relationships.Based on the current research status, this paper analyzes and studies the concept of knowledge map, the combination of knowledge map and recommendation system, the object of interpretability and the application of knowledge map in interpretability model, combined with relevant recommendation models. The knowledge map, open up the relationship between the media, flexibly choose the most suitable medium according to the specific situation to recommend and explain the user.
Trajectory Tracking Control of an Underactuated Autonomous Surface Craft in the Presence of Environmental Disturbances

This paper presents a nonlinear trajectory tracking controller for an autonomous surface craft under the presence of external time-varying environmental disturbances generated by wind, wave and ocean current. The proposed controller is able to drive the craft towards an arbitrarily small neighborhood of a smooth desired trajectory, obtaining global practical stability as the vehicle eventually remains within that neighborhood for all time. To demonstrate the efficacy of the designed control strategy, we give and analyze simulation results.
A Hybrid Kernel Support Vector Machine with Feature Selection for the Diagnosis of Diseases

Feature selection (FS) is very important to speed up learning, generalization ability and computational efficiency to improve concept quality. In this paper, hybrid kernel Support Vector Machine (SVM) with feature selection is proposed to increase the performance of classification. The modified hybrid kernel SVM feature selection approach is used to increase the classification accuracy and generalization ability of filter methods by adding a particular learning algorithm in the selection procedure and narrowing the searching space to increase the efficiency of wrapper approaches. Moreover, a data preprocessing technique that improves classification accuracy and generalization ability by reducing noisy data. To verify the feasibility and efficiency of this proposed method, we used two different nonlinear breast cancer and heart disease datasets with different characteristics. The simulation results illustrate that the hybrid kernel SVM with feature selection is promising and effective while comparing with existing methods.
Convolution Neural Networks for Arabic Font Recognition

Designers have a large number of fonts to choose from. But what if they see the perfect font but do not know its name? In this paper we present a smart font recognition system, that takes a picture of the desired font, and helps with font identification. In this paper, we explore the usage of convolution neural networks to find the name of a font, given its picture. To achieve this task, a new Arabic font dataset is created. The dataset consists of 2500 pictures of single words covering 50 Arabic fonts. This dataset is then used to train different deep neural networks such as AlexNet, ResNet and other architectures to recognize single word fonts. Finally the results of the different models are compared and the best model was implemented using the sliding window technique in order to classify a full paper containing a whole text.
BEHAVIORAL PROBLEMS AND EDUCATIONAL APPROACHES

Quite significant is the proportion of students who have behavioral problems and attend all school levels. The purpose of this work is to highlight, through recent bibliographical data, the basic framework of educational approaches with an emphasis on effective teaching within the classroom and on effective management of behavioral problems in the educational community. In order to successfully address the behavioral problems of students, it is necessary that teachers try to integrate them, use appropriate educational approaches and cooperate effectively between the school and the families of these students. The aim is to offer a brief overview of literature related to the key role of educational techniques in behavioral problems. Article visualizations: