Reviewerly/exHarmony huggingface dataset:
The Legal Consequences Of Digital Communication Language: Nigerian Law In Focus

The use of pictorial as well as character representations in online communications has become the acceptable norm. As some of these representations, referred to as emoticons, emoji, GIFs, Stickers, memes, pictures and such like are gaining recognition, so is the contention as to their legal consequences. Although, no certain and defined field of law has emerged to analyse or define in a systemic manner and with adequacy the legal implication of each form of such communication, emerging juristic principles have signified the readiness of the courts to treat them as creating legal consequences. Utilising the doctrinal approach, this paper considers the legal issues that may arise in their use within the context of the Nigerian legal system. By assessing judicial pronouncements in other jurisdictions, the paper makes suggestions on how to effectively deal with legal issues that may arise in relation to their use in Nigeria.
Towards User-Centred Framework for Reconfiguring Library Spaces in the Global South: Case of Kenyan Universities

In the global sphere, there is a trend that is gaining momentum in university libraries, and that is the shifting nature of the physical library spaces. The transition from print to electronic resources is among the issues influencing the shifting of library spaces. The change in pedagogy, where learning activities now lay emphasis on collaboration and group projects, necessitates new requirements in physical library spaces to rhyme with the new purposes and learning styles that better meet users’ needs. Unfortunately, the available library space reconfiguring frameworks are inclined to the global north, which may not be suitable for the global south countries such as Kenya. It is on this strength that this paper seeks to explore how to attain user-centred university library spaces in the global south that meet the needs and wants of the users. The main objective of this paper is to propose a framework for reconfiguring university library spaces. The paper intended to achieve this by exploring suggestions on additional spaces and facilities to be included in the library, identifying attractive spaces and features of a library, exploring projections of the future outlook of the library, and examining elements of a desirable model space for university libraries in Kenya. The methodology employed a pragmatic research philosophy and multiple case studies. Data was collected from students, academic staff, librarians and university librarians. The qualitative data was thematically analysed, while the quantitative data used descriptive statistics. Based on the findings, the study proposed a user-centred framework for reconfiguring university library spaces in Kenya.
Mendidik Toleransi Beragama Siswa; Analisis Tentang Efektivitas Layanan Informasi Menggunakan Media Film

This study aims to analyze teacher activities in educating religious students on religious tolerance, which is packaged through information services using film media. In this research, the film used is The Santri film by director Livi Zheng, released in 2019. The research location is in MTs. Insan Cita Medan, with research informants, focused on class VIII. This study uses quantitative research methods with a quasi-experimental approach. The research sample was 30 students, determined by purposive sampling. Data collection using questionnaires and interviews. Questionnaire to obtain data about information services and consultations to determine student responses to these services. Test the hypothesis made with the Product Moment formulation and for its significance using the t-test. The study results show a significant positive effect of information services using film media on religious tolerance in students VIII MTs. Insan Cita Medan Learning Year 2021/2022 with a correlation index of r-count = 0.7155 &gt; r-table = 0.334 and a significant level of 51.19%. Thus the better the information services provided, the better the changes in the attitude toward religious tolerance of students.
The Implementation of the Simple Multi Attribute Rating Technique Method for Evaluating the Guidance Process for the Final Project of the Applied Software Engineering Technology Students

Simple Multi Attribute Rating Technique (SMART) is a method that is able to solve problems with multi criteria. In the evaluation of the final project guidance process students of applied software engineering technology are intended to identify supporting factors as well as obstacles for students in completing the final project. Used several criteria such as liveliness of guidance, guidance response, how to communicate, mastery of materials, problem solving and report writing. The problem in determining the cause of the hampered completion of the final project is the lack of tools that can help in processing the criteria data. The solution is to utilize the SMART method with multi criteria problem solving as a tool in decision making. With the utilization of this method is expected to provide solutions in evaluating the guidance process so that the results can be accepted by all students objectively. With the decision support system model, it is expected to be able to know the problem factor in completing the final project so as to help in the implementation of the final project in the next period.
Implementing Laravel Framework for E-Commerce: Case Study at Indonesian Farmer Shop Center

This paper aims to implement e-commerce using the laravel framework. The implementation of e-commerce is applied to the Indonesian Farmer Shop Center (TTIC) in the city of Padang, especially in the sales section precisely sales and payment transactions. This e-commerce can make it easier for consumers to purchase products in TTIC. For making e-commerce using the laravel framework. For e-commerce creation use the laravel framework. Its data storage uses a MySQL database.This paper aims to implement e-commerce using the laravel framework. The implementation of e-commerce is applied to the Indonesian Farmer Shop Center (TTIC) in the city of Padang, especially in the sales section precisely sales and payment transactions. This e-commerce can make it easier for consumers to purchase products in TTIC. For making e-commerce using the laravel framework. For e-commerce creation use the laravel framework. Its data storage uses a MySQL database.
RETRACTED: Multimedia technology-assisted stereo geometry teaching

Multimedia technology makes our life more stereoscopic and intuitive. Graphic images and animations are the most important part of multimedia information expression. It makes our visual effects of three-dimensional representation of things more vivid, like the plane and three-dimensional we usually see. As with the picture, modern multimedia technology transforms our lives into three dimensions, integration, control, interaction and interaction. For the vivid three-dimensional representation of three-dimensional geometric figures in teaching, not only is it limited to the geometric composition of a piece of chalk, but more with the wide application of multimedia technology, multimedia-assisted teaching, especially multimedia-assisted three-dimensional geometric figure teaching is playing more and more The important role, through the aid of multimedia, has cultivated students’ spatial abstract thinking ability and ability to understand graphics and theorems. As multimedia becomes more and more concerned by the education community, this paper is about the question of why multimedia technology should be used to assist the teaching and application of solid geometry applications, and how to apply multimedia technology to achieve the maximum value of the teaching effect of stereo geometry. A lot of experimental research has been carried out. Through experiments, we have built a more complete multimedia assistant teaching system, which is beneficial for students to acquire new knowledge on the Internet. The multimedia technology-assisted three-dimensional geometric method is more conducive to mobilizing the enthusiasm and initiative of students than the traditional method. The biggest innovation of this paper is to use the indispensable advantages of traditional teaching to discount the media to the network platform. To some extent, the research in this paper enhances the guiding significance of interactive research to the actual development work.
Meta-learning with negative learning rates

Deep models require a large amount of data perform well. When data is scarce for a target task, we can transfer the knowledge gained by training on similar tasks quickly the target. A successful approach is meta-learning, or learn a distribution of tasks, where learning is represented by an outer loop, and to learn by an inner loop of gradient descent. However, a number of recent empirical studies argue that the inner loop is unnecessary and more simple models work equally well or even better. We study the performance of MAML as a function of the rate of the inner loop, where zero rate implies that there is no inner loop. Using random matrix theory and exact solutions of linear models, we calculate an algebraic expression for the test loss of MAML applied mixed linear regression and nonlinear regression with overparameterized models. Surprisingly, while the optimal rate for adaptation is positive, we find that the optimal rate for training is always negative, a setting that has never been considered before. Therefore, not only does the performance increase by decreasing the rate zero, as suggested by recent work, but it can be increased even further by decreasing the rate negative values. These results help clarify under what circumstances meta-learning performs best.
Real-Time Recognition and Parameters Estimation of Linear Frequency Modulation Microwave Signal Based on Reservoir Computing

Real-time waveform recognition and parameter evaluation for linear frequency modulated (LFM) pulse waveform is crucially important while challenging in microwave detection systems. To address this issue, in this work, we proposed a new artificial intelligence enabled classification method based on reservoir computing (RC). A sampled sequence, generated by random concatenation of LFM signals with different chirp rates and initial frequencies, is used to training the designed reservoir with 200 nodes. The testing result shows that the RC can recognize individual LFM signals in the sequence, and estimate the instantaneous frequency of an LFM signal within the sequence. Compared to conventional computing methods for instantaneous frequency identification such as Hilbert transform or short-time Fourier transform, RC-based approach features faster speed and great potential for hardware implementation using photonic devices.
Collaborative filtering based on multiple attribute decision making

To address the sparsity problem, a novel collaborative filtering approach based on multiple attribute decision making (MADM-CF) is proposed. In MADM-CF, users in collaborative filtering are treated as decision alternatives, items are treated as attributes. The weight of each item is determined, and the preference similarities between the active user and other users are computed. The preference similarity means that how the users’ preferences are similar on positive ratings and negative ratings. According to the preference similarities, the candidate neighbourhood of the active user is determined. A method to compute overall assessment value is designed, the overall assessment value of each user in the candidate neighbourhood is computed, and users with the smallest overall assessment values are selected as the active user’s nearest neighbours. Finally, the most frequent item recommendation method (MFIR) is used to provide top-N recommendations to the active user. Experimental results based on MovieLens and Netflix datasets show that the proposed approach is superior to existing alternatives.
Performance Evaluation of Face Recognition system by Concatenation of Spatial and Transformation Domain Features

Face biometric system is one of the successful applications of image processing. Person recognition using face is the challenging task since it involves identifying the 3D object from 2D object. The feature extraction plays a very important role in face recognition. Extraction of features both in spatial as well as frequency domain has more advantages than the features obtained from single domain alone. The proposed work achieves spatial domain feature extraction using Asymmetric Region Local Binary Pattern (ARLBP) and frequency domain feature extraction using Fast Discrete Curvelet Transform (FDCT). The obtained features are fused by concatenation and compared with trained set of features using different distance metrics and Support Vector Machine (SVM) classifier. The experiment is conducted for different face databases. It is shown that the proposed work yields 95.48% accuracy for FERET, 92.18% for L-space k, 76.55% for JAFFE and 81.44% for NIR database using SVM classifier. The results show that the proposed system provides better recognition rate for SVM classifier when compare to the other distance matrices. Further, the work is also compared with existing work for performance evaluation.
TwoChain: Leveraging Blockchain and Smart Contract for Two Factor Authentication

User identity and personal information remain to be hot targets for attackers. From recent surveys, we can categorize that 65.5% of all cyberattacks in 2018 target user information. Sadly, most of the time, the system's security depends on how secure it is the implementation from the provider-side. One defense technique that the user can take part in is applying a two-factor authentication (2FA) system for their account. However, we observe that state-of-the-art 2FAs have several weaknesses and limitations. In this paper, we propose TwoChain, a blockchain-based 2FA system for web services to overcome those issues. Our implementation facilitates an alternative 2FA system that is more secure, disposable, and decentralized. Finally, we release TwoChain for public use.
Financial Development and Income Inequality Relationship: Analyzing Financial Kuznets Curve with ARDL Boundary Test in Turkey

In this study, the relationship between the financial development and the income inequality in Turkey is investigated by testing the validity of Financial Kuznets Curve Hypothesis.To examine this relationship for the period of 1987-2017, the variables of the public final consumption expenditures, the modern sector share, the inflation and the human capitalare also added to the model setted as control variables, and the long-term relationships among the variables are analyzed by using the ARDL (The Autoregressive Distributed Lag) Boundary Test approach.Empirical evidence refers to the validity of the Financial Kuznets Curve Hypothesis in Turkey, in other words, there is an inverse-U shaped relationship between the financial development and the income inequality.Accordingly, while the financial development raises the inequality in income distribution to a certain threshold level, it reduces the inequality after this threshold level in Turkey.Moreover, in the long run increases in the modern sector share, the public final consumption expenditures and in the trade openness help to reducing income inequality but increases in the inflation and in the human capital make worse income inequality.
Distributed Generative Adversarial Networks for Anomaly Detection

Cognitive radio networks can be used to detect anomalous and adversarial communications to achieve situational awareness on the radio frequency spectrum. This paper proposes a distributed anomaly detection scheme based on adversarially-trained data models. While many anomaly detection methods typically depend on a central decision-making server, our distributed approach makes better use of decentralized resources, and decreases reliance on a single point of failure. Using a novel combination of generative adversarial network (GAN) elements, participating cognitive radio devices learn a representation of local network activity data through a non-cooperative (strategic) game. Deviations from this expected network activity are flagged as anomalies and treated as possible network security threats, improving situational awareness. Tested on a range of time series datasets, the performance of the proposed distributed scheme matches that of state-of-the-art, centralized anomaly detection methods.
Application of Computational Linguistics to Predict Language Proficiency Level of Persian Learners’ Textbooks

One subfield of assessment of language proficiency is predicting language proficiency level. This research aims at proposing a computational linguistic model to predict language proficiency level and to explore the general properties of the levels. To this end, we collect the data from Persian learners' textbooks and extract statistical and linguistic features from this text corpus to train 3 classifiers as learners. The performance of the model varies based on the learning algorithm and the feature set(s) used for training the model. For evaluating the models, four standard metrics, namely accuracy, precision, recall, and F-measure are used. Based on the results, the model created by the Random Forest classifier performed the best when statistical features extracted from raw text is used. The Support Vector Machine classifier performed the best by using linguistic features extracted from the corpus annotated automatically. This determines that enriching the model and providing various kinds of information do not guarantee that a classifier (learner) performs the best. To discover the latent teaching methodology of the textbooks, we studied the general performance of the classifiers with respect to the language level and the linguistic knowledge used for creating the model. Based on the obtained results, the amount of extracted features plays an important role during to training a classifier. Furthermore, the average best performance of the classifiers is extending the linguistic knowledge from syntactic patterns at level A to all linguistic information at levels B and C.
Development of the Method, Algorithm and Software of a Modern Non-invasive Biopotential Meter System

In this article discusses the possibilities of creating hardware and software for a non-invasive computer bio-meter. The main goal of this work is to create a computer bio-meter. To achieve this goal proposed a new approach to the development of adequate mathematical models and algorithms for an automated system for processing health diagnostics data. It has also shown that the effective functioning of a non-invasive hardware becomes needs are primarily being utilized to control relatively simple computer interfaces, with the goal of evolving these applications to more complex and adaptable devices, including small sensors.
UPHDR-GAN: Generative Adversarial Network for High Dynamic Range Imaging With Unpaired Data

The paper proposes a method to effectively fuse multi-exposure inputs and generate high-quality high dynamic range (HDR) images with unpaired datasets. Deep learning-based HDR image generation methods rely heavily on paired datasets. The ground truth images play a leading role in generating reasonable HDR images. Datasets without ground truth are hard to be applied to train deep neural networks. Recently, Generative Adversarial Networks (GAN) have demonstrated their potentials of translating images from source domain <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> to target domain <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula> in the absence of paired examples. In this paper, we propose a GAN-based network for solving such problems while generating enjoyable HDR results, named UPHDR-GAN. The proposed method relaxes the constraint of the paired dataset and learns the mapping from the LDR domain to the HDR domain. Although the pair data are missing, UPHDR-GAN can properly handle the ghosting artifacts caused by moving objects or misalignments with the help of the modified GAN loss, the improved discriminator network and the useful initialization phase. The proposed method preserves the details of important regions and improves the total image perceptual quality. Qualitative and quantitative comparisons against the representative methods demonstrate the superiority of the proposed UPHDR-GAN.
Bernstein-von Mises theorem for the Pitman-Yor process of nonnegative type

The Pitman-Yor process is a random probability distribution, that can be used as a prior distribution in a nonparametric Bayesian analysis. The process is of species sampling type and generates discrete distributions, which yield of the order nσ different values (“species”) in a random sample of size n, if the type σ is positive. Thus this type parameter can be set to target true distributions of various levels of discreteness, making the Pitman-Yor process an interesting prior in this case. It was previously shown that the resulting posterior distribution is consistent if and only if the true distribution of the data is discrete. In this paper we derive the distributional limit of the posterior distribution, in the form of a (corrected) Bernstein-von Mises theorem, which previously was known only in the continuous, inconsistent case. It turns out that the Pitman-Yor posterior distribution has good behaviour if the true distribution of the data is discrete with atoms that decrease not too slowly. Credible sets derived from the posterior distribution provide valid frequentist confidence sets in this case. For a general discrete distribution, the posterior distribution, although consistent, may contain a bias which does not converge to zero at the n rate and invalidates posterior inference. We propose a bias correction that solves this problem. We also consider the effect of estimating the type parameter from the data, both by empirical Bayes and full Bayes methods. In a small simulation study we illustrate that without bias correction the coverage of credible sets can be arbitrarily low, also for some discrete distributions.
Underwater Image Enhancement with the Low-Rank Nonnegative Matrix Factorization Method

Due to the scattering and absorption effects in the undersea environment, underwater image enhancement is a challenging problem. To obtain the ground-truth data for training is also an open problem. So, the learning process is unavailable. In this paper, we propose a Low-Rank Nonnegative Matrix Factorization (LR-NMF) method, which only uses the degraded underwater image as input to generate the more clear and realistic image. According to the underwater image formation model, the degraded underwater image could be separated into three parts, the directed component, the back and forward scattering components. The latter two parts can be considered as scattering. The directed component is constrained to have a low rank. After that, the restored underwater image is obtained. The quantitative and qualitative analyses illustrate that the proposed method performed equivalent or better than the state-of-the-art methods. Yet, it’s simple to implement without the training process.