Reviewerly/exHarmony huggingface dataset:
From characters to words: the turning point of BPE merges

The distributions of orthographic word types are very different across languages due to typological characteristics, different writing traditions, and other factors.The wide range of cross-linguistic diversity is still a major challenge for NLP, and for the study of language more generally.We use BPE and informationtheoretic measures to investigate if distributions become more similar under specific levels of subword tokenization.We perform a cross-linguistic comparison, following incremental BPE merges (we go from characters to words) for 47 diverse languages.We show that text entropy values (a feature of probability distributions) converge at specific subword levels: relatively few BPE merges (around 200 for our corpus) lead to the most similar distributions across languages.Additionally, we analyze the interaction between subword and word-level distributions and show that our findings can be interpreted in light of the ongoing discussion about different morphological complexity types. 1
Metrics to Measure Code Complexity Based on Software Design: Practical Evaluation

Software complexity is an indicator to the quality of software. It shows that a software or a component of software has a design or implementation that is difficult to understand, modify and maintain. Many complexity metrics have been developed by researchers to identify and measure the degree of complexity. Unfortunately, Current complexity metrics are based on the number of model elements only, regardless of the complexity of those elements themselves. In this paper, we propose new code complexity metrics that are derived from its design. This approach ensures that the derived metrics are uniquely tailored for the software under development, and the derived metrics are unique for each software module. In this study, we aim to attempt an empirical evaluation study in order to have a clear understanding of the proposed complexity metrics. We investigated whether there is a correlation between the proposed UML Class Diagram complexity and the associated code, specially code size, code smells, and technical debt. The result shows that there is a high correlation between the design driven code complexity metrics and the associated code.
Broad Linguistic Complexity Analysis for Greek Readability Classification.

This paper explores the linguistic complexity of Greek textbooks as a readability classification task. We analyze textbook corpora for different school subjects and textbooks for Greek as a Second Language, covering a very wide spectrum of school age groups and proficiency levels. A broad range of quantifiable linguistic complexity features (lexical, morphological and syntactic) are extracted and calculated. Conducting experiments with different feature subsets, we show that the different linguistic dimensions contribute orthogonal information, each contributing towards the highest result achieved using all linguistic feature subsets. A readability classifier trained on this basis reaches a classification accuracy of 88.16% for the Greek as a Second Language corpus. To investigate the generalizability of the classification models, we also perform cross-corpus evaluations. We show that the model trained on the most varied text collection (for Greek as a school subject) generalizes best. In addition to advancing the state of the art for Greek readability analysis, the paper also contributes insights on the role of different feature sets and training setups for generalizable readability classification.
The anthropology of mobile phones

The mobile phone is one of the most ubiquitous objects around the world. When mobile phones were introduced in 1979 to mass markets, they were an expensive technology available largely to wealthy business people living and working in industrialized contexts. The cost and capabilities of a mobile phone and the quality of the mobile network and associated services to operate varies considerably across countries and contexts. When mobiles entered the mass consumer market, voice was the primary functionality. Phones were often large and cumbersome and, especially in Europe and North America, often doubled as ‘car phones’ that enabled business persons and others to make calls while driving from point to point. Mobile phones have played – and are perceived to have played – an important role in intimate relationships. Mobile phones have become part of the everyday communication landscape of people around the world.
Meta-tuning Language Models to Answer Prompts Better.

Large pretrained language models like GPT-3 have acquired a surprising ability to perform zero-shot classification (ZSC). For example, to classify review sentiments, we can prompt the language model with the review and the question Is the review positive? as the context, and ask it to predict whether the next word is Yes or No. However, these models are not specialized for answering these prompts. To address this weakness, we propose meta-tuning, which trains the model to specialize in answering prompts but still generalize to unseen tasks. To create the training data, we aggregated 43 existing datasets, annotated 441 label descriptions in total, and unified them into the above question answering (QA) format. After meta-tuning, our model outperforms a same-sized QA model for most labels on unseen tasks, and we forecast that the performance would improve for even larger models. Therefore, measuring ZSC performance on non-specialized language models might underestimate their true capability, and community-wide efforts on aggregating datasets and unifying their formats can help build models that understand prompts better.
INJUSTICE EMBEDDED IN GOOGLE CLASSROOM AND GOOGLE MEET: A TECHNO-ETHICAL AUDIT OF REMOTE EDUCATIONAL TECHNOLOGIES

Forcing pre-COVID-19 structures of school onto the realities of the global pandemic ignores the systemic structures embedded in public education which made pre-pandemic school places of harm for students marginalized by racism and neoliberalism. Informed by critical theories of educational technology, this study investigated how the design of technologies central to emergency remote education carry powerful perspectives about the nature and dimensions of learning. We used the conceptual framework of a techno-ethical audit which asks scholars and practitioners to analyze technologies in order to uncover assumptions of pedagogy, implications for democracy, and complicities in injustice inherent to the design of the technology. The audit found a system that limited meaningful interaction, envisioned students as technology users with little agency or control, and predisposed students to unnecessary practices of surveillance and monitoring, all while subjecting them to regimes of data collection and sharing for corporate profit. Applied at scale and in marginalized communities, the current system denies justice for millions of students subjected to harmful educational practices. Through this study, we present three recommendations to improve online learning: teach the crisis; implement project-based learning; and investigate experiences with technology.
Multi-Purpose Dataset of Webpages and Its Content Blocks: Design and Structure Validation

The need for automated data extraction is continuously growing due to the constant addition of information to the worldwide web. Researchers are developing new data extraction methods to achieve increased performance compared to existing methods. Comparing algorithms to evaluate their performance is vital when developing new solutions. Different algorithms require different datasets to test their performance due to the various data extraction approaches. Currently, most datasets tend to focus on a specific data extraction approach. Thus, they generally lack the data that may be useful for other extraction methods. That leads to difficulties when comparing the performance of algorithms that are vastly different in their approach. We propose a dataset of web page content blocks that includes various data points to counter this. We also validate its design and structure by performing block labeling experiments. Web developers of varying experience levels labeled multiple websites presented to them. Their labeling results were stored in the newly proposed dataset structure. The experiment proved the need for proposed data points and validated dataset structure suitability for multi-purpose dataset design.
Minimum Energy Control of a Unicycle Model Robot

Abstract Point-to-point path planning for a kinematic model of a differential-drive wheeled mobile robot (WMR) with the goal of minimizing input energy is the focus of this work. An optimal control problem is formulated to determine the necessary conditions for optimality and the resulting two point boundary value problem is solved in closed form using Jacobi elliptic functions. The resulting nonlinear programming problem is solved for two variables and the results are compared to the traditional shooting method to illustrate that the Jacobi elliptic functions parameterize the exact profile of the optimal trajectory. A set of terminal constraints which lie on a circle in the first quadrant are used to generate a set of optimal solutions. It is noted that for maneuvers where the angle of the vector connecting the initial and terminal point is greater than a threshold, which is a function of the radius of the terminal constraint circle, the robot initially moves into the third quadrant before terminating in the first quadrant. The minimum energy solution is compared to two other optimal control formulations: (1) an extension of the Dubins vehicle model where the constant linear velocity of the robot is optimized for and (2) a simple turn and move solution, both of whose optimal paths lie entirely in the first quadrant. Experimental results are used to validate the optimal trajectories of the differential-drive robot.
A global optima search field division method for evolutionary algorithms

This study aims to investigate the deployment of a proposed search field division method (SFDM) within evolutionary algorithms (EAs) to enhance the capability of searching for the global optima in nonlinear problems. The proposed technique is benchmarked against the following eight widely-used single-modal, multi-modal, and unimodal benchmark functions: Sphere, Rosenbrock, Rastringin, Griewank, Ackley, Fletcher, Quartic, and Schwefel functions, and the outcome is compared to their standard EAs counterparts to validate the effectiveness of the deployed approach in EAs. In the proposed method, we apply three low, medium, high field divisions (1, 2, and 5) dimensions on nine different EAs simultaneously with two different scenarios, 10 and 100 variables, to reach the optimal solution. Then for the validity of our proposed SFDM technique, we examined the exploration-exploitation search space rates and diversity behavior. The results of the implementation of SFDM on eight benchmark test functions show that the consideration of dimensions using SFDM for EAs improves the outcomes of all nine tested EAs. In our proposed method, we find better compatibility with the integration of SFDM in the Particle Swarm Optimization Algorithm concerning searching for the optimum solution relative to the other EAs.HighlightsA novel Search Field Division Method (SFDM) for evolutionary algorithms.Applying three field divisions (1, 2, and 5) dimensions on nine different Evolutionary Algorithms (EAs) simultaneously to reach the optimal global solution.The implementation of SFDM on eight benchmark functions shows significant improvement in all nine tested EAs.In our proposed method, we find the Particle Swarm Optimization Algorithm has better compatibility with respect to the other EAs.
Eye Gaze Detection Based on Computational Visual Perception and Facial Landmarks

The pandemic situation in 2020 brought about a ‘digitized new normal’ and created various issues within the current education systems. One of the issues is the monitoring of students during online examination situations. A system to determine the student’s eye gazes during an examination can help to eradicate malpractices. In this work, we track the users’ eye gazes by incorporating twelve facial landmarks around both eyes in conjunction with computer vision and the HAAR classifier. We aim to implement eye gaze detection by considering facial landmarks with two different Convolutional Neural Network (CNN) models, namely the AlexNet model and the VGG16 model. The proposed system outperforms the traditional eye gaze detection system which only uses computer vision and the HAAR classifier in several evaluation metric scores. The proposed system is accurate without the need for complex hardware. Therefore, it can be implemented in educational institutes for the fair conduct of examinations, as well as in other instances where eye gaze detection is required.
Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors

Naively trained neural networks tend to experience catastrophic forgetting in sequential task settings, where data from previous tasks are unavailable. A number of methods, using various model expansion strategies, have been proposed recently as possible solutions. However, determining how much to expand the model is left to the practitioner, and often a constant schedule is chosen for simplicity, regardless of how complex the incoming task is. Instead, we propose a principled Bayesian nonparametric approach based on the Indian Buffet Process (IBP) prior, letting the data determine how much to expand the model complexity. We pair this with a factorization of the neural network's weight matrices. Such an approach allows the number of factors of each weight matrix to scale with the complexity of the task, while the IBP prior encourages sparse weight factor selection and factor reuse, promoting positive knowledge transfer between tasks. We demonstrate the effectiveness of our method on a number of continual learning benchmarks and analyze how weight factors are allocated and reused throughout the training.
A novel multiple color image encryption scheme based on algebra M(2, F2[u]/〈u8〉) and chaotic map

The substance security of computerized images ends up being a huge issue for researchers and architects. Thus, it is important to obscure and conceal image data before an impart over the web for security. Due to the operational strength of the matrix algebra M(2,F2[u]〈u8〉) and a unique attempt on a 4D chaotic map, this study recommends novel multiple colored image encryption techniques. This technique is applied in two stages. Initially, a double permutation model is built to permute a group of images, and hence row and column permutations are incorporated. Finally, chaotic sequences are produced by the 4D chaotic system to perform the exclusive OR operation on permuted images and chaotic successions. The proposed cryptosystem is assessed by using different analyses. The outcomes of our investigation support the fact that the proposed technique is satisfying in terms of high security and better adequacy when contrasted with the present algorithms.
Optical and SAR Image Registration Using Complexity Analysis and Binary Descriptor in Suburban Areas

Optical and synthetic aperture radar (SAR) image registration is a challenging task due to significant geometric and radiometric differences. In particular, the strong scattering phenomenon in SAR images can seriously affect the registration results. Accordingly, to solve the low repeatability of the key points in optical and SAR images, a complexity analysis scheme is proposed. In the first phase, the complexity distribution diagram is calculated by the threshold sliding window in the edge images obtained from the maximum moment of the phase congruency. Then morphological operation and connected regions are used to obtain the high-complexity regions and mask them to avoid the extraction of the interference points. Next to solve the limitations of the local self-similarity (LSS) descriptor in the optical and SAR image registration, such as poor discrimination, expensive computational complexity, and sensitivity to large geometric and radiometric difference, we propose a binary LSS descriptor (BLSS). We replace the correlation surface of the LSS with the local gradient orientation histogram. Furthermore, we construct descriptors based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">XY</i> -coordinate system and convert the correlation of the regions to binary descriptors. Finally, the fast sample consensus (FSC) is used to remove false correspondences. The experiments conducted on several optical and SAR image pairs verify the effectiveness of the proposed algorithm.
A Study on the Influence of Internet Cross-border E-Commerce on China's Import and Export Trade and Development Strategy

With the rapid development of Internet technology, cross-border e-commerce industry has entered the fast lane of development and become a new growth point of China's import and export trade. In order to better study the influence of Internet cross-border e-commerce on international trade, this paper deeply analyzes the development status of China's cross-border e-commerce, studies the influence mechanism of cross-border e-commerce on international trade. In order to verify the relationship between cross-border e-commerce and international trade, vector auto regression model is used for empirical research. According to the empirical results, this paper analyzes the challenges faced by cross-border e-commerce in the current international trade environment, and puts forward some suggestions on the development strategy of cross-border e-commerce.
Cluster Based Intelligent Load Balancing Algorithm Applied in Cloud Computing Using KNN

Cloud computing is now a day, a widely used computing paradigm. Cloud computing provides services in virtualized, distributed mode, which is basically an intellectual extension of client server architecture. The main expectation of the client from the service provider here is a scalable, uninterruptable service 24X7. Load balancing in cloud computing plays a major role for providing this above said featured services. In this paper we have proposed a cluster based load balancing algorithm, where according to the response time, priority, the processes are grouped into HIGH, MEDIUM, and LOW using K Nearest Neighbour Algorithm .Then the cluster would be formed with combinations of HIGH, MEDIUM and LOW in equals, so that the different servers are facing almost same amount of load.
Linearly Constrained Gaussian Processes with Boundary Conditions.

One goal in Bayesian machine learning is to encode prior knowledge into prior distributions, to model data efficiently. We consider prior knowledge from systems of linear partial differential equations together with their boundary conditions. We construct multi-output Gaussian process priors with realizations in the solution set of such systems, in particular only such solutions can be represented by Gaussian process regression. The construction is fully algorithmic via Grobner bases and it does not employ any approximation. It builds these priors combining two parametrizations via a pullback: the first parametrizes the solutions for the system of differential equations and the second parametrizes all functions adhering to the boundary conditions.
Improving prediction efficiency by revolutionary machine learning models

Deep learning is the extension of machine learning technique which attracted the researchers in the recent past and hence the literature is also limited. This research focused on the need for deep learning for effective prediction process, when compared with few other machine learning models. As a proof of concept, a dataset of students’ performance of a school is analysed by various machine learning models. The performance measurements by all such models are compared with deep learning. These are assessed by two familiar statistical measures, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The error rates are low for deep learning when compared with other models. It is been evident that the result obtained by the deep learning model is more effective than the other machine learning models. The results are improved by reducing the error rates at the rates of 35% by mean absolute error and 48% by root mean squared error.
Classification Between Pedestrians and Motorcycles using FasterRCNN Inception and SSD MobileNetv2

Philippines is on the list of the top ten fastest growing economy in the world. One of its developments is in its traffic law enforcement. Today, the government is continuously finding ways on how to alleviate the problem on its roads with the use of technology. The Metro Manila Development Authority (MMDA) has offered a solution which is the No Contact Traffic Apprehension Policy, that utilizes Closed-Circuit Television (CCTV) Monitoring to apprehend vehicles violating traffic laws, rules and regulations by capturing videos and images. To further enhance this, the government has partnered with the De La Salle University to use artificial intelligence in the system with the project “CATCH-ALL”. With the use of CCTVs and artificial intelligence system, it can help the system detect traffic violations using an automated process. But some tasks are not that easy to execute by the computer like differentiating a pedestrian and a motorcycle. In this study, a novel approach to classifying a pedestrian and a motorcycle with the use of object detection will be developed. It will be demonstrated using deep machine learning, specifically convolutional neural network and by utilizing different pre-trained models to a gathered dataset.
Technology for Filling Subject Ontologies of the Scientific Knowledge Space

Subject ontology in the context of this article is understood as a set of key concepts related to a certain field of science, with their semantic connections, supplemented by indexes of various classification systems describing this scientific field. Subject ontologies are a necessary component of each subspace that is part of the Unified digital space of scientific knowledge (DSSK). This article presents the results of research related to the construction of subject ontologies based on the created automated system for supporting terminological dictionaries and suggests a methodology for identifying new key terms in a particular field of science. The proposed methodology is based on the use of existing classification systems in conjunction with citation databases, such as Web of Science and Scopus for English–language publications and the Russian citation index for Russian-language publications. The methodology involves dividing the scientific field into a number of sections in accordance with the selected classification system, extracting from the CSB the core of articles related to each section, and from the articles - new author's keywords, which should constitute, in combination with the corresponding sections of classification systems, the basis of the subject ontologies of this scientific field.