Reviewerly/exHarmony huggingface dataset:
Functional Literacy of a Primary Schoolchildren: Ways to Overcome Difficulties in Its Formation

The article discusses the problem of the formation of functional literacy of primary schoolchildren as the main learning outcome that meets the requirements of the Federal State Educational Standard of Primary General Education. The article reveals the characteristics of the concept of “functional literacy”, which includes two groups of achievements of a younger student - subject and metasubject. The reader gets acquainted with the widespread pedagogical “mistakes” of the teacher, as well as the difficulties that children face in the learning process. The main types of educational activity are determined, which to the greatest extent contribute to the formation of functional literacy of primary schoolchildren, which primarily include search and research activities.
Application of Survey Technology in Geotechnical Engineering Investigation

With the continuous development of the construction engineering industry, geotechnical engineering construction has attracted more and more attention from the society. In the construction process of geotechnical engineering, scientific and reasonable engineering investigation is the key to ensuring construction quality and improving engineering safety. Based on this, this article analyzes the application of measurement technology in the process of geotechnical engineering investigation. It is hoped that through this analysis, the application effect of measurement technology in geotechnical engineering survey can be effectively improved, and the quality of geotechnical engineering survey can be improved, so as to promote the overall construction quality and safety of geotechnical engineering.
Good and bad luck of a computer project

A computer project in the world and in Russia is now usually viewed as an economic, technological or military one. At the same time, it is a global social project. From the theory of the humanitarian and technological revolution, it follows that at the current point of bifurcation, its results can determine the path of humanity to the future. I show that the results of this project reflect the readiness of civilizations for socio-technological change. The COVID-19 pandemic turned out to be a test for social imperatives and had a huge impact on computer reality in the formation of a new sociality. Book by K. Schwab, T. Mallerert “COVID-19: The great reset” represents a variant of such sociality. The analysis presented in this work shows the unacceptability of the proposed changes for the world of Russia. A study of large-scale computer projects in Russia and Belarus shows the need to bring them to a new sociocultural level, much higher than the current one. The unsuccessful experience of mass e-education in the Union State confirmed the imperative of Norbert Wiener: “Render unto man the things which are man’s and unto the computer the things which are the computer’s”. This should, apparently, be a reasonable line of behavior in organizing joint actions of people and machines.
Image Splicing Detection Using Hybrid Feature Extraction

Digital images are widely used powerful medium for information. Due to advancements in image processing methods, image forgery techniques are improving by the day. Splicing is one of the popular image forgery techniques, and image splicing detection is a desired thing. Most existing keypoints-based detection techniques may fail to precisely locate spliced regions in the spliced image because of noisy data or a carefully done splicing. This paper presents an improved local and global features-based technique which can be exploited efficiently to classify images as authentic and spliced. First, a robust interest detector SIFT is applied which is tweaked with center of mass algorithm which localizes the spliced object, and only nearest points are used concentrically with respect to coordinates of center of mass of given image. Global features like Zernike and Haralick are also computed on the image. Final feature vector by applying KPCA which reduces dimension to a fixed component and final feature vector is used to train SVM classifier for training model and N-fold cross-validation is used to get minimally overfitted and accurate model. Experimental results demonstrate that the proposed model can efficiently detect spliced and authentic images even after applying transformation such as scaling, rotation, and JPEG compression and noise.
Building a Domain Ontology for the Construction Industry: Towards Knowledge Sharing

As the construction industry has crucial interplays with many related fields, all in a constant state of flux, the use of a domain ontology to capture various relevant entries from related subdomains has become of paramount importance. The purpose of this article is to conceptualize the utility of ontologies in the construction industry as a solution for inter-sectorial transactions. Thereafter, upon presenting the process of ontology development, a real-life company was taken as a case study to implement the developed ontology. The purpose behind this study is to fully automate the generation of a domain ontology from Excel to OWL files. The methodology and process followed are thoroughly explained in the paper. The process is meant to be generic in such a way it could be reused in other projects for other industries, provided that the data is hierarchical and presented under Excel.
Adaptive Multi-Scale Semantic Fusion Network For Zero-Shot Learning

Zero-shot learning aims at accurately recognizing unseen objects by learning matrices that bridge the gap between visual information and semantic attributes. Existing approaches predominantly focus on learning the proper mapping function for visual-semantic embedding while neglecting the effect of learning discriminative semantic features, which leads to severe semantic ambiguity. We propose a practical Adaptive Multi-scale Semantic Fusion (AMSF) framework to perform object-based multi-scale attribute attention for semantic disambiguation. Considering both low-level visual information and global class-level features that relate to this ambiguity, the proposed method jointly learns cooperative global and local semantic attributes from different scales. Moreover, with the joint supervision of embedding softmax loss and class-center triplet loss, the model is encouraged to learn high discriminative semantic features and visual features with high interclass dispersion and intra-class compactness. The method is evaluated on CUB, AwA2, and SUN datasets, and the experimental results indicate the method achieves state-of-the-art performance.
Benefit and Cost Periodized: Stretching Your Points

Abstract When you estimate the life cycle cost and benefit of your software product, your stakeholders should not only be assured that you will deliver value, but also be informed when that value is expected to manifest itself. Periodization is a common method for showing when a return of investment is expected, and one is often careful to express the present value of future cash (net present value) in such deliberations. This chapter shows how to carry out periodization using points. Periodized points then amount to plan templates that can be instantiated with monetary values according to most likely, bad-case, and good-case uncertainty assessments.
Pedagogical factors of formation of improvisational acting skills in amateur theatre

We consider some issues of modern society related to the growing trend of social and emotional isolation of the individual, changes in their worldview, leading to the degradation of thinking and intellectual abilities. There is need to search for psychological and pedagogical ways, means and methods to activate the intellectual, emotional and creative development of the individual. A possible way to solve the identified social issues is the possibility of forming a person's skills for improvisation. We consider the implementation of this process in educational and creative activities of an amateur theater group as a sphere that provides ample opportunities for creative self-realization of the individual. We define the term “improvisation” as a universal ability of the individual, which is manifested in various creative processes, as well as in many other aspects of human life. An actor forms improvisational skills in an amateur theater group due to the organization and implementation of a number of pedagogical conditions, such as: creating a climate of psychological comfort that promotes the self-disclosure of individual and creative abilities of participants in an amateur theater group; using the etude method in the process of mastering the elements of acting improvisation by an amateur actor; mastering the basics of “effective thinking” through specific training exercises. The content component of the implementation of these pedagogical conditions, according to the authors, activates the course of the described process.
La storia finzione del re di Girgenti di Andrea Camilleri

Published in 2001 and Mondello prize winner in 2002, the novel Il re di Girgenti represents one of the best proofs of Camilleri as narrator. Firmly believing in the historical novel, Camilleri builds on a real fact, a biography based on invented documentation, first sign of his experimental will. But, above all, the language is the field on which Camilleri focuses his attention. To the numerous Sicilian terms are added Italian and Spanish ones; great space is given to intertextuality. The result is a stratification not without meaning. The “parlata stramma” corresponds to the strangeness, to the distortions of society, in which the popular classes always have the worst, as some sentences attest. In the foreground, then, is Camilleri’s civil commitment.
Digital Design and Innovation of Objects after COVID-19

With COVID-19, while questioning the coverage areas, duties, and functionality of objects that are indispensable for our lives, we see that objects destroy the weak and the design itself eliminates the non-immune and the weak in some cases. In this process, it is of great importance to reconsider design for the reason of existence of humanity and to develop new design concepts from a holistic perspective. The decolonisation of design, social innovation, and transformations of production and consumption forms in relation to the crisis are possible with speculative design approaches. In the new world order, designing forward-looking nanotechnologies with measureless and extraordinary scenarios will be the beginning of new alternatives. Emergency situations overcome the fluid modernity in our lives and the new developing normality is possible with design projects covering emergency situations. While each crisis creates its opportunities in itself, transition design needs to be planned by adopting an interdisciplinary understanding of how to initiate and direct change in social and natural systems through design. In this study, the reconstruction of COVID-19 social distance alerts on objects in the light of science and technology will be examined.
More than it Seams: Garment Stitching in Wearable e-Textiles

Abstract: We consider how seams in clothing can be designed for interactive sensing of body movement. Conductive yarn is activated at low voltages which fluctuate as body movement varies tension across the seams. Traditional garment construction describes how fabrics, blocks and seam types are configured according to mechanical performance and historical changes in fashion. Bodice blocks span the human torso, anchoring and reflecting core body movement. We explore the stability and calibration of seam voltage in a bodice block, while performing a yoga-inspired movement sequence. Our exploratory studies test signal orthogonality between seam stitch types and seam placements for supporting reliable sensing. We use our exploratory work to consider design alternatives which balance seam requirements against sensing opportunities, including back seams for torso garments. Our findings contrast optimal seam placement and design in wearable e-textiles with those in traditional clothing.
Business Value Disadvantaged

Abstract Despite the current emphasis on benefit in stakeholders’ minds, there is still a focus on cost management when it comes down to the day-to-day work in modern software development. This works counter to underlying assumptions in modern development methodology. We motivate a more deliberate approach to benefits management during development, but it is the combination of cost and benefits management that saves the day.
Cartpole Problem with PDL and GP Using Multi-objective Fitness Functions Differing in a Priori Knowledge

We present a study looking at the effect of a priori domain knowledge on an EA fitness function. Our experiment has two aims: (1) applying an existing NSGA-II framework for GP with PDL to the cartpole problem—applying GP & PDL to cartpole and a purely behavioral problem for the first time—and (2) contrasting two multi-objective fitness functions: one with high and the other with low a priori domain knowledge. In our experiment we created two populations with an EA, varying in the number of objectives use for the fitness function, 2 objective criteria to represent low a priori knowledge and 3 to represent high. With fitness functions tailored to find specifically prescribed solutions we expect greater discriminating power and more feedback to an evolutionary process. This comes at the cost of excluding some unexpected solutions from the evolutionary process and placing a greater burden on the designer. We address the question: how large is the disadvantage for the low a priori fitness function in a worst-case scenario, where innovative solutions will not enhance performance. This question is interesting because we would prefer to guide EA with simple, easy to create and understand, objective criteria rather than complex and highly specific criteria. Understanding any associated penalty for using simple, easy to create fitness functions, is crucial in assessing how much effort and should be put into designing objective criteria.
Evaluating a mobile-based short virtual course on family planning for frontline health workers in India

One of the ways to address the critical gap of trained human resources in health is to train frontline workers (FLW) on health prevention and health promotion. The FLW play an important role in community mobilization, outreach activities, and service delivery at the last mile. In rural India, the number of internet and smartphone users is rapidly increasing, presenting an opportunity to reach and virtually train FLW. The current evidence base shows progress towards creating and using mobile-based training, but significant work is needed to improve the quality of mobile-based training programs. This study showcases Project Samvad ’s experience in creating and rolling out a virtual training course for FLW, highlighting the feasibility of virtual training at scale and the quality of engagement of the participants with the course content. The method for this descriptive study is divided into two broad categories: (i) course development, promotion, and rollout processes, and (ii) course evaluation. Both qualitative and quantitative approaches were used to collect and analyze data. We found that the overall average course completion rate was 82% (992 out of 1211 registrants). The average increase in the endline question accuracy was around 15 percentage points from the baseline. We conclude that mobile-based virtual training of FLW is feasible and program managers and policymakers should consider creating hyperlocal course content, ensure participation of partners at various stages of development and rollout of the course and use a peer-to-peer and community-led system to support the learners.
Solving Cryptarithmetic Puzzles by Logic Programming

As a personal interest of study, I tried a logic programming approach towards the problem solving of cryptarithmetic puzzles that are commonly discussed as a subcategory of constraint satisfaction problems in the literature of artificial intelligence. While there are possibly several methods capable of solving constraint satisfaction problems, I took into consideration the efficiency as well as the completeness that will identify all possible solutions under the specified constraints and exclude trivial and useless solutions from the perspective of real-life practice. In this paper, I demonstrated an approach that can be adapted to solve most of the constraint satisfaction problems especially within the context of cryptarithmatic puzzles. This method will also perform forward checking to have early backtracking and prevent searching the entire search tree exhaustively.
An Automatic Clustering Algorithm Leveraging Reinforcement Learning for Model Compression

Although the deep network model has been widely used in industry, it cannot be applied well to devices with limited memory or limited computing resources, such as mobile phones and satellites. Model compression technology can reduce the size of the model and runs better on devices with memory limitations. In this paper, we proposed a learning-based strategy leveraging reinforcement learning with compression ratio and accuracy exceeding those of current the rule-based policy. The reason why we achieved the great progress of significant performance is that we leverage DDPG of reinforcement learning to provide the model compression strategy based on the pruned model. The method has a higher compression ratio, better retains accuracy and freeing human labor. The proposed method shows that the model achieved more than 3.1% accuracy and more than 6.46X compression ratio compared with the hand-crafted model compression policy for ResNet20 on Ciar10.
Improved Image Semantic Segmentation Based on Cascade Data Augmentation

A large portion of the research for the task of image semantic segmentation focuses on optimizing deep learning networks to improve the state-of-the-art results. In this work, however, we take a different approach in order to enhance the performance of state-of-the-art methods for image semantic segmentation. Benefiting from the internal learning process of deep learning models we propose a Cascade Data Augmentation (CDA) technique that can effectively increases the size of data based on the characteristics of the dataset in hand. CDA utilizes basic image transformation techniques such as geometric and photometric transformations to augment the size of data. In particular, CDA blends these transformations by assigning two adjustable parameters that are 1) the probability of applying the transformation operation and 2) the magnitude of the operation. We evaluate the effectiveness of our method on the validation set of Pascal Context benchmark dataset for image semantic segmentation. Experimental results prove that incorporating CDA with baseline deep learning models can increase their performance. In particular we attain a average gain of ≈ 4.72 pixel accuracy and ≈ 1.9 mean intersection over union Pascal-Context dataset. Moreover, CDA is a generic method that can be integrated with any deep learning model and attains improved performance while adding trivial extra training time of the network.
MLAF: Multi-Level Attention Fused Model for Sentence Similarity Measurement

Abstract Semantic textual similarity measurement is an essential task in text mining and natural language processing, which has profound research value. Recent LSTM-based methods have achieved attractive success by modelling sentences as fixed-length vectors. However, since the LSTM network can only extract the semantic information of sentences sequentially, the longterm dependency of sentences cannot be efficiently captured when the sequence length is long. Moreover, current models treat each syntactic component in the sentence equally, while the effects of different syntactic elements to the sentence meaning expression are different. Generally, nouns, verbs, and adjectives contribute more than prepositions and conjunctions to the semantic meaning of the sentence. In this paper, we propose a multi-level attention fused (MLAF) model, which correlates self-attention and word attention with sentence representations, extracting the semantic information of sentences from different levels. The self-attention supplements sequential operations to model long-term dependency of sentences. The word attention built upon raw sentence highlights the vital syntactic components of sentences. Experiments on different datasets demonstrate that the proposed MLAF model has achieved significant improvements over baselines.
Predictive Control of a Robot Manipulator with Deep Reinforcement Learning

This paper tackles the problem of trajectory following of a two-link rigid robot manipulator. The proposed controller bases its operation on the idea behind preview control in which the control law is divided in two parts: a feedback component that depends only on the present state of the system, and a predictive component that only uses future values of the reference trajectory. In this sense, the designed controller uses for training and control both present and future states of the system. Simulation results when following a test trajectory are presented to validate the proposed method and to show that the proposed controller exhibits better performance with respect to a neurocontroller that does not use a predictive component neither for training nor control.
Efficiency of the Experimental Method of Positioning a Teacher in the Modern Communication Space

The results of the study allow us to identify the need and usefulness of the lecture material developed by us on the discipline "Image-science in the practical activity of a teacher" for students in the following areas of training: "Psychological and pedagogical education "and" Personnel Management". Also, to compare the existing and received level of knowledge on the discipline "Image-science in pedagogical activity" among students in the areas of training: "Psychological and pedagogical education" and to identify the need for the introduction of this discipline in the curriculum.
Application of big data in bauxite provenance: A case study of Yanlong area in Western Henan, China

Abstract The application of big data is of great significance to the quantitative study on provenance. In this paper, taking the bauxite in Yanlong area, western Henan, as an example, the detrital zircon U-Pb age big data obtained by Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) are used to study the bauxite provenance. The results show that the Caledonian and Sibao Jinning zircons are from the North Qinling Orogenic Belt (NQOB). In contrast, the Lvliang and Wutai zircons may be derived from karst insoluble of Majiagou Formation limestone.
Application Analysis of User Portrait in Library Field

This paper takes the literature of user portrait application in the field of library as the research object and analyzes the collected literature through the method of visualization analysis of knowledge graph. The research shows that the current research topics of user portrait in the field of library mainly include personalization, knowledge discovery, reading promotion, reading therapy, etc., and accurate recommendation has always been a research hotspot in the field of library. The research results will help to provide reference and reference for the promotion and expansion of user portrait in the field of library and other fields in the future.