Reviewerly/exHarmony huggingface dataset:
Image Processing Using OpenCV Technique for Real World Data

Picture handling is a significant segment in many picture examination and PC vision assignments. A methodological report on importance of picture preparing and its applications in the field of PC vision is done here. During a picture preparing activity the information given is a picture and its yield is an upgraded excellent picture according to the procedures utilized. Picture preparing is normally alluded to as advanced image handling, however optical and simple image/s preparing likewise are conceivable. This a research work gives the data to the plan and usage of picture preparing utilizing OpenCV and information regarding image processing for study and research. Actualized by the python programming language, the last system gives another way and reference to the Customizable improvement of distant detecting picture preparing calculation. Picture handling is the utilization of a lot of methods and calculations to an advanced picture to examine, upgrade, or streamline picture attributes.
PyDGGA: Distributed GGA for Automatic Configuration

We present PyDGGA, a Python tool that implements a distributed version of the automatic algorithm configurator GGA, which is a specialized genetic algorithm to find high quality parameters for solvers and algorithms. PyDGGA implements GGA using an event-driven architecture and runs a simulation of future generations of the genetic algorithm to maximize the usage of the available computing resources. Overall, PyDGGA offers a friendly interface to deploy elastic distributed AC scenarios on shared high-performance computing clusters.
Mobile Voting – Still Too Risky?

This paper studies the challenges of creating a mobile device based voting client. We discuss the issues related to standalone and mobile browser based voting applications. In both cases we discuss the problems of vote privacy, integrity and voting channel availability. We conclude that neither of the options can currently achieve the level of security PC-based voting clients can provide, with the attack surface being larger in the case of mobile browser based voting application.
Universal access: user needs for immersive captioning

This article focuses on building a prototyping for immersive captioning following a user-centric approach. This methodology is characterised by following a bottom-up approach, where usability and user needs are at the heart of the development. Recent research on user requirements for captioning in immersive environments has shown that there is both a need for improvement and a wealth of research opportunities. The final aim is to identify how to display captions for an optimal viewing experience. This work began four years ago with some partial findings. We build from the lessons learnt, focussing on the user-centric design requirements cornerstone: prototyping. Our prototype framework integrates methods used in existing solutions aiming at instant contrast-and-compare functionalities. The first part of the article presents the state of the art for user requirements identifying the reasons behind the development of the prototyping framework. The second part of the article describes the two-stage framework development. The initial framework concept answered to the challenges resulting from the previous research. As soon as the first framework was developed, it became obvious that a second improved solution was required, almost as a showcase on how ideas can quickly be implemented for user testing, and for users to elicit requirements and creative solutions. The article finishes with a list of functionalities, resulting in new caption modes, and the opportunity of becoming a comprehensive immersive captions testbed, where tools such as eye-tracking, or physiological testing devices could be testing captions across any device with a web browser.
Convention Emergence with Congested Resources

Norms and conventions enable coordination in populations of agents by establishing patterns of behaviour, which can emerge as agents interact with their environment and each other. Previous research on norm emergence typically considers pairwise interactions, where agents’ rewards are endogenously determined. In many real-life domains, however, individuals do not interact with one other directly, but with their environment, and the resources associated with actions are often congested. Thus, agents’ rewards are exogenously determined as a function of others’ actions and the environment. In this paper, we propose a framework to represent this setting by: (i) introducing congested actions; and (ii) adding a central authority, that is able to manipulate agents’ rewards. Agents are heterogeneous in terms of their reward functions, and learn over time, enabling norms to emerge. We illustrate the framework using transport modality choice as a simple scenario, and investigate the effect of representative manipulations on the emergent norms.
Hybrid Cryptography Approach to Secure The Data in Computing Environment

Computer Network is associate degree interconnected cluster of computing nodes that uses completely different protocols to move with each other and permits resource allotment ideally in a very inevitable and governable fashion. In today's world, communication is crucial, and it is desirable to transmit data in a secure manner. Most defense attacks degrade security during data transfer, so a variety of private and public cryptography algorithms are expected to accomplish a variety of security resources like authentication, secrecy, integrity, availability and non-repudiation. Now days, a variety of cryptography techniques provide high safety for info on wellordered networks. These techniques are used to supply data protection and user credibility. A replacement encryption rule for online transactions combines mistreatment of each private key with public key cryptography techniques to optimize the strength of such authentication algorithms. This rule includes three cryptologic primitives: reliability, privacy, and validation. With the help of AES, Dual-RSA law, ECDH, ECDSA, and Secure Hash code SHA-1, a new security protocol is introduced in this paper to achieve these three primitives. Primarily AES is employed for message encryption, Dual-RSA rule is employed for authentication, ECDSA is employed to supply the digital signature and SHA-1 is employed for integrity. ECDH is employed for public key generation. Using a combination of unequal cryptological strategies for each personal key cruciate and Public Key, this new security rule is meant to provide higher security with integrity and authentication.
Educational Technologies for Advanced Training in FPGA Design

The article considers the issues of building advanced training courses for specialists using FPGAs to build digital devices and systems on a chip. The types of projects that require training materials of different levels of complexity are considered and approaches to the construction of training courses for such projects are proposed. Attention is drawn to the “problems of the real world” associated with the difference between real signals coming from external devices from their idealized representation used in simulators. This factor complicates the application of distance learning technologies based on the use of only software tools and requires the use of remote access to real equipment, which implies the organization of the educational process based on laboratory stands with FPGA and connection of real signal sources.
A Proposed Developments of Pollards Rho Method for Attacking the ECDLP

In cryptography, an attack is a method of solving a problem related to cryptosystem. Specifically, the aim of an attack is to find suitable fast method of solving a problem on which an encryption algorithm depends, or to find the secret key for encryption algorithm. This paper presents a proposed method for solving Elliptic Curve Discrete Logarithm Problem (ECDLP). Then present Developments that greatly reduce the Pollard's Rho method operations for solving ECDLP. Three developed approaches are tested on the Pollards Rho method in order to reduce the total number of mathematical operations achieved within the method applied to solve the ECDLP. One of the proposed developing method provided a reduction in mathematical operation more than half of the total operations.
The Classification of Electrooculogram (EOG) Through the Application of Linear Discriminant Analysis (LDA) of Selected Time-Domain Signals

Recently, Human Computer Interface (HCI) has been studied extensively to handle electromechanical rehabilitation aids using different bio-signals. Among various bio-signals, electrooculogram (EOG) signal have been studied in depth due to its significant signal pattern stability. The primary goal of EOG based HCI is to control assistive devices using eye movement which can be utilized to rehabilitate the disabled people. In this paper, a novel approach of four classes EOG has been proposed to investigate the possibility of real-life HCI application. A variety of time-domain based EOG features including mean, root mean square (RMS), maximum, variance, minimum, medium, skewness and standard deviation have been explored. The extracted features have been classified by the linear discriminant analysis (LDA) with the classification accuracy of training accuracy (90.43%) and testing accuracy (88.89%). The obtained accuracy is very encouraging to be utilized in HCI technology in the purpose of assisting physically disabled patients. Total 10 participants have been contributed to record EOG data and the range between 21 and 29 years old.
Weighted Histogram Block Detection Algorithm for Digital Trunking Terminal

The video transmission service of the digital trunking terminal requires a video scene detection algorithm with high precision and high recall, but the current video scene detection algorithm cannot meet the QoS requirements of the digital trunking terminal. This paper proposes a weighted histogram block detection algorithm. The algorithm uses block detection to ensure the accuracy of the algorithm's detection and switching in different network environments, and uses weighted histogram detection to effectively reduce the terminal's false detection in the brightness change scene and the video gray distribution before and after the switch. The precision and the recall are improved. The simulation results show that the algorithm meets the requirements of high precision and high recall of the terminal's video transmission service.
Fundamentals of Cryptography

The basic information security services are known to include confidentiality, integrity and availability, which are know as the CIA triad. This chapter introduces some basic concepts and techniques in cryptography, particularly some typical cryptographic methods to the CIA triad, i.e., encryption algorithms, message authentication codes, and digital signature algorithms. Encryption algorithms can further be categorized into symmetric key encryption algorithms and asymmetric key encryption algorithms, where the asymmetric key encryption algorithms are also called public key encryption algorithms. Some fundamental authentication protocols are introduced, which described how the authenticity of a remote party can be validated, e.g., how to verify an identity that a remote party claims to be is genuine.
Influencer Engagement Rate Under Scalable Machine Learning Approaches

Digital advertising is used to leverage Internet technologies to deliver advertisements to consumers. For an advertisement to reach a large number of audience, business owners usually relay on social media influencers to deliver advertisements messages. However, there are a large number of influencers and it is critical step for business owners to select influencers to be hired. One important measure that has been used to assist the process of selecting an influencer is the influencer engagement rate. Engagement rate measure aims to evaluate how well an influencer can attract potential customers. The current engagement rate measures depend on a simple information such as number of followers, posts, or comments on an influencer account. In this paper we propose a more sophisticated engagement rate measure based on a carful analysis of potential customers reaction to an influencer advertisements posts. The new measure works only on advertisements posts. Also, it take into account the polarity of the comments on these posts, not only their count. To efficiently compute the new engagement rate measure over large size of data, we propose machine learning (ML) approaches to generate necessarily information to compute the new engagement rate measure. We use ML approaches, in particular classifications, in two stages. First, we use classification to efficiently classify a post to an advertisement and none advertisement post. Next, we use ML based sentiments analysis approach to determine the polarity of the comments on an advertisement post. The new measure could be used to measure users engagement to any post and in a wide range of social media platforms. We tested the new engagement rate measure using Instagram influencer accounts, in specific Instafamous accounts. Compared to the current measures, our results show that this new ML based engagement rate measure suggests significantly different ranked list of potential influencers. This ranked list of influencers are more aligned with the business needs and the accepted practices in measuring successful advertisers.
Research on the Construction Method of Knowledge Ontology Facing the Field of Substation Maintenance

Abstract Starting from the definition and classification of knowledge, this paper gives the definition and classification of substation maintenance knowledge. Based on the “seven-step method”, it proposes a method of constructing knowledge ontology for narrow fields, including 9 steps, and establishes substation maintenance knowledge Ontology, including 13 concepts and 60 attributes, lists the concepts and attribute descriptions of the ontology, laying the foundation for the next step of building a knowledge navigation map.
A Designer Embedded Book Space Experiment

The relationship between books and their readers are intimacy and personal. The texts are firsthand resources for readers experience personal journey while reading, and their own unparalleled inspiration. The reflection after reading could be represented in diverse fashions, most of them were written using words as reviews. Visualization of the book experience is another presentation are used as the interpretation by designers that are often belonged to designers' own collection. With these reflections, a single story allowed widening imaginaries and extended values. Sharing such unique presentation requires a unique setting. How to create and what are such interactive reading spaces, called Muse Space in a co-existing fashion in both physical bookshop and the collective drawing space associated with it is the problem this project wants to explore. Muse Space is comprised of three layers: physical environment, collective drawing space for visualization of book-inspired drawing and association and interactive tools for creating the book-inspired drawing are examined and analyzed in three research steps, namely (1) persona and journey map for contextual inquiry and requirement for physical and virtual spaces; (2) scenario planning for interactive behaviors of user centered installation design; and (3) information visualization for visual cue and feedback of collective drawing. Part of Muse Space's system had displayed and been test by several kinds of target users, with their feedback we could identify that Muse Space creates opportunities for readers to learn more knowledges from not only books but also other readers feedbacks and how to emphasize independent bookstores' superiorities with interactive tools.
IB-DRR - Incremental Learning with Information-Back Discrete Representation Replay

Incremental learning aims to enable machine learning models to continuously acquire new knowledge given new classes, while maintaining the knowledge already learned for old classes. Saving a subset of training samples of previously seen classes in the memory and replaying them during new training phases is proven to be an efficient and effective way to fulfil this aim. It is evident that the larger number of exemplars the model inherits the better performance it can achieve. However, finding a trade-off between the model performance and the number of samples to save for each class is still an open problem for replay-based incremental learning and is increasingly desirable for real-life applications. In this paper, we approach this open problem by tapping into a two-step compression approach. The first step is a lossy compression, we propose to encode input images and save their discrete latent representations in the form of ‘codes’ that are learned using a hierarchical Vector Quantised Variational Autoencoder (VQ-VAE). In the second step, we further compress ‘codes’ losslessly by learning a hierarchical latent variable model with bits-back asymmetric numeral systems (BB-ANS). To compensate for the information lost in the first step compression, we introduce an Information Back (IB) mechanism that uti-lizes raw exemplars for a contrastive learning loss to regularise the training of a classifier. By maintaining all seen exemplars’ representations in the format of ‘codes’, Discrete Representation Replay (DRR) outperforms the state-of-art method on CIFAR-100 by a margin of 4% average accuracy with a much less memory cost required for saving samples. Incorporated with IB and saving a small set of old raw exemplars as well, the average accuracy of DRR can be further improved by 2%.
AI Writers in Language Learning

Artificial intelligence (AI) is fast becoming a key instrument in several fields and is also transforming the way we communicate. Consequently, we need to understand the role of AI and augmented writing (AW) in language learning (L2 text production) and to understand how AI writers and language learners work together. The objective of this paper is to examine selected AI writers and discuss theoretical considerations on how AI writers can enhance language learning. The paper draws on data from a structured test and analysis of 39 different AI writers [7], [8], [9] and small experiments in different classes at a Danish university. One aspect from the data was that students found that AI writers were useful, but that they need to learn how to work with an AI writer when producing L2 texts. In conclusion, AI writing technologies seem to be highly relevant in language classes just as the calculator was in math classes 50 years ago.
High-Quality Stereo Image Restoration from Double Refraction

Single-shot monocular birefractive stereo methods have been used for estimating sparse depth from double refraction over edges. They also obtain an ordinary-ray (oray) image concurrently or subsequently through additional post-processing of depth densification and deconvolution. However, when an extraordinary-ray (e-ray) image is restored to acquire stereo images, the existing methods suffer from very severe restoration artifacts due to a low signal-to-noise ratio of input e-ray image or depth/deconvolution errors. In this work, we present a novel stereo image restoration network that can restore stereo images directly from a double-refraction image. First, we built a physically faithful birefractive stereo imaging dataset by simulating the double refraction phenomenon with existing RGB-D datasets. Second, we formulated a joint stereo restoration problem that accounts for not only geometric relation between o/e-ray images but also joint optimization of restoring both stereo images. We trained our model with our birefractive image dataset in an end-to-end manner. Our model restores high-quality stereo images directly from double refraction in real-time, enabling high-quality stereo video using a monocular camera. Our method also allows us to estimate dense depth maps from stereo images using a conventional stereo method. We evaluate the performance of our method experimentally and synthetically with the ground truth. Results validate that our stereo image restoration network outperforms the existing methods with high accuracy. We demonstrate several image-editing applications using our high-quality stereo images and dense depth maps.
Validation of a Questionnaire to Evaluate the Usability in the Peruvian Context

Usability is considered one of the most significant aspects to contemplate during the software development process. For this reason, it is necessary to have tools that allow development teams and specialists to evaluate whether the interface design proposals are usable, understandable, and easy to use. One of the widely used instruments to determine if an interface design is usable are questionnaires. There are some proposals aimed at capturing the perception of end-users. Still, very few approaches are focused on covering all aspects of E-Commerce applications and are aimed at specialists in Human-Computer Interaction. In this study, we have used a proposal of a new questionnaire that was developed considering an in-depth analysis of the literature and the information collected from two consulting firms. This new assessment tool intended to provide specialists a way to perform more accurate usability evaluations that cover all aspects of a software in the E-Commerce domain and at the same time allows to quantify the level of user experience. The questionnaire was used to evaluate the usability of the three main E-Commerce websites in Peru. The results show that, despite being the most relevant platforms in the country, they must improve many aspects of usability. There is little concern on the part of companies in offering websites that are intuitive and that generate satisfaction to end users.
Always on Voting: A Framework for Repetitive Voting on the Blockchain

Elections repeat commonly after a fixed time interval, ranging from months to years. This results in limitations on governance since elected candidates or policies are difficult to remove before the next elections, if needed, and allowed by the corresponding law. Participants may decide (through a public deliberation) to change their choices but have no opportunity to vote for these choices before the next elections. Another issue is the peak-end effect, where the judgment of voters is based on how they felt a short time before the elections. To address these issues, we propose Always on Voting (AoV) – a repetitive voting framework that allows participants to vote and change elected candidates or policies without waiting for the next elections. Participants are permitted to privately change their vote at any point in time, while the effect of their change is manifested at the end of each epoch, whose duration is shorter than the time between two main elections. To thwart the problem of peak-end effect in epochs, the ends of epochs are randomized and made unpredictable, while preserved within soft bounds. These goals are achieved using the synergy between a Bitcoin puzzle oracle, verifiable delay function, and smart contracts.