Reviewerly/exHarmony huggingface dataset:
Breaking the Gap: Collaborative Environment as a Meeting Point to Provide and Receive Help to Overcome the Digital Gap

As a result of the COVID-19 the teaching staff has been required to use technological tools that allow them to follow their activities under confinement. Teachers have highlighted the problem of the digital gap, updating and inequality that they suffer in Mexico as one of the historical problems to be addressed in this sector. This paper presents a platform designed to work as a meeting point between teachers with the digital gap and people willing to help them. The process was based on the User Centered Design approach in order to find a relevant solution.
Modeling with The Projection Method Considering The Impact State

This paper proposes a hybrid system based on the collision-aware Projection Method. To derive a model of collision-aware objects, a hybrid system, in which system modes change according to discrete events, is proposed in the context of the Projection Method. As an example, we investigate modeling, strategy, and simulation of two boards in two-dimensional space to rotate a cylinder.
LibGuides: Knowledge Organization and Metadata Services: Description and Organization

Outlines the mission and responsibilities of the department and provides access to the tools used to accomplish this work. Provides summary and links to the basic tools used for describing and organizing library resources
Evolutionary synthesis of nonlinear models based on metaheuristic programming and templates

Abstract The application of a metaheuristic programming (MP) algorithm based on various bio-inspired algorithms for the evolutionary (metaheuristic) synthesis of nonlinear models is considered. The described approach of evolutionary synthesis uses a sequential operator structure of the chromosome, templates (with some undefined functions and parameters) of the models and the given sets of pairs of input and output data. The influence of the degree of specialization of the template on the characteristics of the search algorithm in the metaheuristic synthesis of nonlinear models defined by ordinary differential equations with undetermined functions and parameters is studied. Estimates of the complexity of this algorithm are obtained, and a significant reduction in the search time when using templates in the process of the evolutionary synthesis is shown.
Advanced Quantization Methods for CNNs

In this article, methods are presented that improve a quantization procedure for the inference of convolutional neural networks with respect to the resulting accuracy and resource requirements. The existing procedure is extended by pre-training, fine tuning and a suitable compression method. With the presented methods we get better convergence properties for deep quantized networks, we increase the accuracy by a several percentage points and we reduce the number of parameters by up to 90% depending on the model used.
Eradication Of Thalassemia By X-ray Photoelectronspectroscopy&amp;DNA Spectral Analysis.

Chromosome no 11 &amp; 16 of human embryo consist of the defective genetic sequence of alpha &amp; beta thalassemia trait respectively.Here we want to eradicate the thalassemia by systematic method of analysing the defective genetic sequence of the chromosome no 11 &amp; 16 if the conceiving couple are found to be carriers. This is further done amniocentesis by X-rayphotoelectronspectroscopy&amp;D.N.A spectral analysis(that is done by decoding of the graph obtained from spectral analysis using computer algorithms, digital signal processing).
Three Strategies for Improving Shortest Vector Enumeration Using GPUs

Hard Lattice problems are assumed to be one of the most promising problems for generating cryptosystems that are secure in quantum computing. The shortest vector problem (SVP) is one of the most famous lattice problems. In this paper, we present three improvements on GPU-based parallel algorithms for solving SVP using the classical enumeration and pruned enumeration. There are two improvements for preprocessing: we use a combination of randomization and the Gaussian heuristic to expect a better basis that leads rapidly to a shortest vector and we expect the level on which the exchanging data between CPU and GPU is optimized. In the third improvement, we improve GPU-based implementation by generating some points in GPU rather than in CPU. We used NVIDIA GeForce GPUs of type GTX 1060 6G. We achieved a significant improvement upon Hermans’s improvement. The improvements speed up the pruned enumeration by a factor of almost 2.5 using a single GPU. Additionally, we provided an implementation for multi-GPUs by using two GPUs. The results showed that our algorithm of enumeration is scalable since the speedups achieved using two GPUs are almost faster than Hermans’s improvement by a factor of almost 5. The improvements also provided a high speedup for the classical enumeration. The speedup achieved using our improvements and two GPUs on a challenge of dimension 60 is almost faster by factor 2 than Correia’s parallel implementation using a dual-socket machine with 16 physical cores and simultaneous multithreading technology.
A New Hybrid Shape Moment Invariant Techniques for Face Identification in Thermal and Visible Visions

Presently, the extraction of robust facial features is becoming very effective for accurate face recognition especially for smart security surveillance systems. This paper investigates three different moment invariants techniques for robust facial features extraction and then determine how each one of these moments is affected by whether the face image was thermal or on a greyscale with the proposal of a hybrid technique that deals with the robust descriptors of each method. This hybrid technique has improved the results and gave robust facial features for face identification. A feed-forward neural network is trained with these moments' features where the recognized faces are classified to one of the basic faces of IRIS and CARL face datasets which achieved high accuracy reaching 98.1% for thermal images and 81.2% for grey images.
Extending use-case point-based software effort estimation for Open Source freelance software development

Accurately predicting the software development effort is very crucial when delivering the software systems on time, within the budget and with the required functionality. overestimation of the software development effort can lead to losing the projects whereas underestimation can cause budget and schedule overruns. The development effort of a software project depends on various factors and these effort factors associated with the freelance software development are different from those of traditional software development. Software development companies employ various proprietary tools in their projects for their planning, development, testing, etc. However, freelance software developers functioning under tight budgetary constraints are not in a position to afford them. As a result, they tend to use free and open-source tools for their software developments. There are various types of software effort estimation models proposed, published and practiced in the industry. However, there is no such software effort estimation model specifically proposed to estimate the effort of freelance software development. The main objective of this paper is to extend Use Case Point-based software effort estimation for the open-source freelance software development. Initially, details of open source software projects were collected from several freelance software developers. Based on the use case diagrams, Use Case Points counts are then calculated for each project. Taking other effort drivers associated with open source freelance software development also into account, we then estimate the effort of each software development. Our aim is to explore the viability of using Use Case Points as the main effort driver in estimating the effort of open source freelance software development.
Deblurring of Motion Blurred Images Using GLCM and Elastic Net Regularization

An image deburring algorithm consists of rich edge area mining with a gray-level co-occurring matrix and elastic net regularisation is proposed in this paper. First, the luminance channel of an image is removed from the blurred image. The frequency layer is highthat can be derived from the blurred image by converting the 2D haar wavelet in the luminance channel.By the way, measurements were made using area and the richest edge region information is then collected. Finally, the extracted rich edge field, instead full motion blurred image, approximate the blur kernel elastic net regularisation and the image is returned. A measurement of image mechanism and running time measures the proposed system. Result suggestedto recommended strategy would improve efficiency and ensure continuity in recovery.
Diversity of Metaphoric Terms in the Sphere of English It Communication

The article is devoted to the consideration of the language used by IT-specialists which is enriched with new words, terms, and metaphors every day, as this area is a subject of constant changes, due to which the language does not stand still and develops and expands daily. Undoubtedly, there are words in this field that are narrower and will only be understood by specialists in this field. These words include metaphors, terms, and phraseological expressions that are of the greatest interest, and our work will focus on the analysis of metaphorical units that are found in the field of communication in IT. The authors analyze the metaphorical terms in the field of IT communication and explore the various basic images of metaphors that explain the meaning of these metaphorical units. As it was found out, the word-formation of English terms in the field of IT is a complex process that is undergoing various changes and is in constant development due to the significant progress of the sphere itself.
On the Use of Predation to Shape Evolutionary Computation

Classic Evolutionary Algorithms often use elitist approaches, such as fitness functions, to select individuals for new generations. In this work, we consider an alternative strategy to simulate the selection process that relies on exploiting ecological interactions between individuals instead of explicitly using a fitness based in the search progress. To demonstrate this strategy, we present an Artificial Life system which simulates an ecosystem where different species are different bio-inspired meta-heuristics, and the main ecological relationship is the predation. Specifically, individuals from a Particle Swarm Optimization (PSO), with movement rules defined by Genetic Programming, survive by predating on individuals from an Artificial Bee Colony (ABC) system that operates on traditional optimization rules. This ecology is investigated on optimization benchmarks, and we observed the development of interesting ecological dynamics between the two species.
Building Chinese Word Knowledge Base for Children’s Leveled Reading

With the development of great Chinese education, the domestic leveled reading of Chinese has attracted more and more attention. Both schools and parents urgently need a reading system that meets the development of children's reading ability. The hierarchical construction of words as the carrier of reading materials is even more important. The difficulty level of words has a direct and significant impact on the text complexity of reading materials. This paper focuses on the construction of the Chinese Character-word grading of the Chinese reading system, and attempts to establish the Chinese characters knowledge base with Character ranks in line with the characteristics of Chinese characters themselves. In terms of the Chinese character knowledge base, this paper absorbs the research results of exegetical studies, and determines the hierarchical attributes of Chinese characters including shape, meaning, and word formation ability of Chinese characters, builds the Chinese character knowledge base for leveled reading containing 3350 Chinese characters with features. As for the word knowledge base, this paper describes the attributes of part of speech, word meaning, context, etc., especially the use of Hierarchical Network of Concepts theory to define the level of difficulty about the cognitive attributes of semantic categories, and finally builds a Chinese reading leveled word knowledge base containing 18300 words with features covering shape, meaning and context. Based on it, the content of words, the word density, the proportion of super-class words, the number of class symbols, IOG and other attributes are described to guide the automatic grading of Chinese texts which got a better result.
The Usage of Artificial Intelligence in Marketing Automation : Potentials and Pitfalls

Marketing automation, neuro marketing, and users’ personalization have been booming with the rise of Internet revolution and digital marketing. Due to the accumulation of rich consumer data generated from social engines, the AI-based marketing application is now taking a wide leap. This article attempts to address the prominent use of Artificial Intelligence (AI) in marketing in term of its potentials in becoming a pervasive part of today’s competitive world. The basic concept of AI is presented so that thorough understanding on its application in marketing can be grasped. Dealing with the dynamic nature of marketing and people’s sensitive data, despite of the opportunies offered, the pitfalls of AI in marketing automation should be taken into consideration. The security issues concerning the users’ privacy and possible malicious activities are highlighted in this article.
Printed Popular Science Literature as a Way to Popularize Technical Science

The article is devoted to the analysis of tools for popularization of technical sciences. The levels of public funding in the leading countries, as well as the number of popular science editions in the different countries of the world are considered. The objective of this article is to identify the factors of science development, taking into account the number of popular science periodicals, the level of English language proficiency as well as the level of research funding in these countries; to analyze the number of popular science editions in general and to single out the periodicals of exactly technical nature in order to have a broader understanding of the ways of science popularization among young audiences; to trace the relationship between promotion of science and science level in general. To achieve this objective, the methods of analysis of documentary information, as well as the method of generalization were used. The dependence of the level of development of science on the level of its state funding and the number of popular science editions has been proved. Popularization is necessary for further development of science in Ukraine and involvement of young people to enter technical educational institutions, which will increase the level of science and economy in the country.
Style Transfer by Rigid Alignment in Neural Net Feature Space

Arbitrary style transfer is an important problem in computer vision that aims to transfer style patterns from an arbitrary style image to a given content image. However, current methods either rely on slow iterative optimization or fast pre-determined feature transformation, but at the cost of compromised visual quality of the styled image; especially, distorted content structure. In this work, we present an effective and efficient approach for arbitrary style transfer that seamlessly transfers style patterns as well as keep content structure intact in the styled image. We achieve this by aligning style features to content features using rigid alignment; thus modifying style features, unlike the existing methods that do the opposite. We demonstrate the effectiveness of the proposed approach by generating high-quality stylized images and compare the results with the current state-of-the-art techniques for arbitrary style transfer.
Searching for High Accuracy Mix-Bitwidth Network Architecture

The quantization of deep neural networks has drawn great research interest as well as industrial support for its tremendous advantages on boosting the inference speed of convolutional neural network models, especially on resource-constrained devices. Emerging research work and software framework for low-bit quantization, i.e., quantization under 8 bits, also shows strong potential. However, extreme low-bit neural network, e.g., binary neural network (BNN), suffers from significant accuracy loss. A previous work tried to improve the performance of extreme low-bit neural networks by adjusting channel numbers but achieves a poor balance between accuracy and model size. Alternatively, adjusting bitwidth for each layer gains a higher return on accuracy with an acceptable increase in model size. In this work, we employ a genetic algorithm to find the optimal bitwidth assignation for layers in VGG-Small and ResNet18. Both searched mix-bitwidth network architectures achieve higher accuracy with lower time and memory consumption, compared to their fix-bitwidth counterparts. A further investigation on the combination of BNN and the intermediate layer of higher bitwidth is also conducted.
Improving funding and publishing workflows with PIDs

Tracking and understanding the impact of research is important to funders, publishers, and researchers. But the process of doing so is full of pain points – from the time it takes to apply for a grant and report on it, to the often poor data quality of the information being collected, and the challenge of keeping track of researchers and their research over time. The ORCID Funder Working Group recently analyzed these pain points and published their recommendations for using persistent identifiers to help address these challenges. In this article, you will learn more about persistent identifiers for researchers, publications, and grants and about what each stakeholder group can do to reduce the burden and improve the transparency of the process – from applying for a grant to publishing the research results.
Towards a proof theory for Henkin quantifiers

Abstract This paper presents a methodology to construct globally sound but possibly locally unsound analytic calculi for partial theories of Henkin quantifiers. It is demonstrated that usual locally sound analytic calculi do not exist for any reasonable fragment of the full theory of Henkin quantifiers. This is due to the combination of strong and weak quantifier inferences in one quantifier rule.
Integral Estimate of the Tourist Potential by Knowledge Compilation Based on Expert and Consumer Preferences

The concept of a combined system for assessing and ranking of the regions relative to the levels of development of their tourism potentials is proposed. This concept is based on the compilation of knowledge presented in the form of expert and consumer assessments of heterogeneous natural, cultural and historic, and socio-economic indicators. Within the framework of this system, it is proposed to compile the expert-consumer assessments in the fuzzy information environment using the fuzzy inference system and fuzzy maxmin convolution method. The assessment results obtained for various scenarios of the tourist potential of hypothetical regions in the form of acquired knowledge can be compiled using a three-layer feedforward neural network. After the necessary training on various scenarios of the tourist potential, the feedforward neural network turns into a universal assessment mechanism and it is able to induce an integral estimation of the tourism potential for any region given by itself indicators.
Construction of MRA and non-MRA wavelet sets on Cantor dyadic group

W. C. Lang determined wavelets on Cantor dyadic group by using Multiresolution analysis method. In this paper we have given characterization of wavelet sets on Cantor dyadic group which provides another method for the construction of wavelets. All the wavelets originating from wavelet sets are not necessarily associated with a multiresolution analysis. We have also established relation between multiresolution analysis and wavelets determined from wavelet sets.
A-VQA: Attention Based Visual Question Answering Technique for Handling Improper Count of Occluded Object

Recently, visual question answering (VQA) system is being used in various research disciplines for extracting meaningful data from images and communicating this meaningful data to humans. Thus, to implement VQA system, computer vision and natural language processing fields are combined. Existing VQA system contains many open research issues such as improper count of occluded object problem, single word answer, time-specific answer problem and many more problems because of its wide assortment of utilizations and its more extensive region of research. In this paper, we present attention-based visual question answering (A-VQA) method for handling improper count of occluded object. A-VQA systems generate the textual answer by extracting image and textual features and applying multi-layer attention mechanism on these images and textual features. A-VQA system handle object recognition, counting, color and activity recognition types of visual questions. For training and evaluating A-VQA method, visual genome dataset is used.
Three-class association schemes from partial geometric designs

In this paper, we show that three-class association schemes can be constructed from certain partial geometric designs. First, we show that partial geometric designs with exactly three indices: r−n, λ1 and λ2 give rise to three-class association schemes, using the method of Beker and Haemers (1980). Second, we describe parameter sets of certain partial geometric designs that also give rise to three-class association schemes.
Integrating Contents with Language through LOTUS Model Impacting Teaching &amp; Learning in English Language Classrooms

English has departed from just being a ‘lingua-franca’ for global citizens. It has become an entry point to the dynamic repository of global knowledge base. Concepts of “Industry 4.0” and “Society 5.0” will effectively alter the gamut of education in general and technical education in specific. These industrial advancements should be supported by integrating technological advancements and creating ‘sustainable intelligence’ within an employee. This paper attempts to highlight the ‘role of communication’ and integrating content with language classrooms in transition from one society to another. These amendments to the curriculum, methodology and assessment for accomplishing the requirements of learning a language in general and English in particular. Integrating content with classroom calls for a professional interaction between the planners, developers and teachers of the institution. The author with the “LOTUS Model” demonstrates how to structure, orient and re-orient components for language teaching-learning for the purpose of establishing provisions for such interactions.