Reviewerly/exHarmony huggingface dataset:
Role of Software Agent in Big Data Analytics

The concept of software agent and big data plays a crucial responsibility in several fields. With new multiple dynamic parameters of software agent with the concept of big data analytic is growing rapidly in few years. The emergent figure of applications helping millions of users and dealing with terabytes of data require to a faster processing prototype. The concept of software agent in Object-Oriented techniques describes an advance and stimulating way of analyzing, developing, and modelling advanced and useful data analysis. This paper intends to identify and describing how such a concept of software agent might be useful to big data in the appearance of an adaptive software agent System based analytics and upcoming rising challenges. Proposed critique focuses at inquire about new attributes and recitation that how such a technology be able to useful to big data in the appearance of an adaptive software-agent system as long as vibrant analytics capabilities. This ongoing review of research has capable outcomes but will be require for discussing and validating.
Genetic programming-based symbolic regression for goal-oriented dimension reduction

The majority of dimension reduction techniques are built upon the optimization of an objective function aiming to retain certain characteristics of the projected datapoints: the variance of the original dataset, the distance between the datapoints or their neighbourhood characteristics, etc. Building upon the optimization-based formalization of dimension reduction techniques, the goal-oriented formulation of projection cost functions is proposed. For the optimization of the application-oriented data visualization cost function, a Multi-gene genetic programming (GP)-based algorithm is introduced to optimize the structures of the equations used for mapping high-dimensional data into a two-dimensional space and to select variables that are needed to explore the internal structure of the data for data-driven software sensor development or classifier design. The main benefit of the approach is that the evolved equations are interpretable and can be utilized in surrogate models. The applicability of the approach is demonstrated in the benchmark wine dataset and in the estimation of the product quality in a diesel oil blending technology based on an online near-infrared (NIR) analyzer. The results illustrate that the algorithm is capable to generate goal-oriented and interpretable features, and the resultant simple algebraic equations can be directly implemented into applications when there is a need for computationally cost-effective projections of high-dimensional data as the resultant algebraic equations are computationally simpler than other solutions as neural networks.
Increased intensity of interference fringes in Digital Holography

Digital holographic interferometry is a full field optical technique, used in the measurement of dynamic and non-contact events, this technique works with arrays where the initial acquisition of interference fringes is not necessary, but rather involves the superposition of two wave fronts. Where from the holograms and by means of a Fourier window-based processing it is feasible to recover the information of both intensity and phase. However, the interference fringes resulting from the demodulation of the holograms may have low intensity, affecting their subsequent analysis. In this work, the combination of the holographic techniques, filters and the superposition principle is proposed to increase the intensity of the fringes, the results show that using the Fourier method in combination with the superposition theorem is possible to obtain greater intensity between the minimum and maximum of the fringes.
Intelligent Cartoon Image Generation Based on Text Analysis and MCMC Algorithm

Intelligent cartoon image generation is to generate an image corresponding to a given text through a certain pattern. However, the absent of proportion and position information of each objects directly affects the quality of output image. This paper focuses on the optimization of the layout based on MCMC method and Metropolis-Hastings Algorithm. The proportion and position of each cartoon characters compose into a state and optimized with Metropolis-Hasting method iteratively. Poisson image fusion method is used to make the images fusion area transition smooth to reduce synthetic trace. Experimental results show that the algorithm in this paper can generate cartoons images intelligently.
Centralised IT Structure and Cyber Risk Management

Against the backdrop of organisational needs to derive value from IT Organisations through agility, efficiencies and cost effectiveness, many organisations have adopted a decentralised IT organisational structure, enabling individual business units the autonomy to implement, operate and govern technology. The increase risk that poses organisations through cyber-attacks, raises the question of how IT security could effectively provide the level of organisations governance to counter cyber threats in a decentralised organisational model. In exploring the challenges in the decentralization of IT security, we highlighted that the accountability of such activities would become diluted, with each business unit managing security in their own methods and practices or lack of, while unable to take full accountability due to the complex independencies of modern system architectures, often resulting in a lack of ownership, accountability and reporting of security at an organisational group level. This ultimately increases the overall security risk to the organization. We further highlighted that while centralization of IT security at a group level would be more effective, a hybrid model of IT security at two-levels with strategy and policy at the central governance level and a degree of autonomy and decision at the IT Operational level could also be considered.
Artificial Intelligence based Temporal Material Identification for Improving Qulaity of Service in Communication

Abstract The artificial intelligence based learning model helps in identifying temporal material by processing linguistic terms that express time duration in code mixed data. The data available on social media contents are written in mixed script format and from this content temporal material content identification is a challenging task. The retrieval of temporal information and its corresponding time duration expression terms can be identified using artificial intelligence technique based neural learning model. The temporal retrieval and its time duration representation are widely used to present the opinions over the social media. The work described in the paper gives the comparative view of different techniques used in the area of transliteration. The rule framed approach is presented which accepts the roman form text as input and as per the defined rules the system is developed to give the temporal details words available in the sentence. The evaluation measures used here to validate the hypothesis is based on statistical measures along with HLSTM learning model. Further the result is validated using the voting technique that can choose appropriate Temporal label which are not identifies by the learning model. The applied neural learning approach increases the precision value.
Descriptive Indic Answer Script Evaluation Using Deep Learning

The evaluation of descriptive answer scripts is a monotonous, time-intensive process and is prone to human errors and sometimes bias. As a result, a student’s career can be at stake. Hence, in order to mitigate the errors and efforts during answer script evaluation, an automated evaluation system is proposed to assign marks to the answer scripts correctly. Our proposed system, ‘Descriptive Indic Answer Script Evaluation using Deep Learning’ will provide the marks to an answer based on its content and correctness depending upon the model trained using natural language processing and deep learning techniques on the dataset containing some of the manually evaluated sample answers. In the proposed sequential model, the learning is done by the convolutional and LSTM cells. The input to the system is the extracted text from the answer script which after preprocessing was fed to the model for training. As there was no significant system available for Indic languages, the system will be very helpful for the education boards conducting examinations in Indic languages. In this paper, among the different Indic languages, we have focussed on Odia language as very minuscule amount of work has been done in this language with regard to any NLP task.
Improved Particle Swarm Optimization By Means of Manipulation of the Inertia Weighting Factor Based on Albert Einstein Theory of Photoelectric Effect

Particle Swarm Optimization (PSO) has been praised by many researchers in the field of Engineering and computer science since its introduction in 1995.This is due to its fast convergence and ability to reach optimal solutions during problem optimization. However, like any other Evolutionary algorithms it has its own drawbacks. PSO suffers premature convergence and getting stuck on local minima sometimes. This paper proposes an improved PSO based on the theory of photoelectric effect by Albert Einstein. The constrained and unconstrained benchmark functions have been used to validate the optimization performance of the proposed method. The statistical results showed that the proposed method is able to explore best solutions faster and effective during optimization for both constrained and unconstrained problems compared to the traditional method.
Critical Co-Reflection on Artifact Use

ABSTRACT Current design approaches mainly focus on co-design activities for creating (novel) digital artifacts. Less attention is paid on implications of new technologies on people and their experiences. This paper proposes a conceptual framework aimed at describing and supporting systematic and sustained critical co-reflection on artifact use. It employs and expands ideas from design research and user-oriented design approaches to describe sources and nature of critical co-reflection and to discuss appropriate analytical lenses. Experience spaces are introduced as a complementary concept to design spaces. An experience space is a conceptual space emerging from the collaborative creation, manipulation, merging, and discarding of experience-related descriptions of technology appropriation. From a critical reflective perspective, designers not only care about rich design spaces with diverse design solutions but also about rich experience spaces displaying diverse appropriation processes and a broad spectrum of users’ perspectives on effects of artifact use.
Blind image steganalysis using features extraction and machine learning

The main theme of this work is the steganalysis of image steganography techniques by classification and feature extraction methods. Our contribution is to improve a steganalysis algorithm and implement it to extract the characteristics of the Cover and Stego images. Thus the use of a modern Machine Learning “Ensemble Classifier” is presented here as a powerful development tool that allows the rapid construction of steganographic detectors with significantly improved detection accuracy in a wide range of integration methods. The power of the proposed framework is demonstrated by a steganographic method nsF5 which masks one or more messages in JPEG images.
Use of complementary feature to enhance the performance of speaker identification system

Nowadays, Bio-metric recognition systems are becoming popular due to cost-effectiveness. The Speaker identification (SI) system uses an audio signal of the speaker to identify the unknown speaker. There are various approaches to increase the performance of the SI system. That includes the use of the popular cepstral feature, MFCC (Mel frequency cepstral coefficient). In this paper, along with MFCC, the complementary features of MFCC, i.e., IMFCC (Inverse mel frequency cepstral coefficient) is used. Along with that, a recently used feature speech-signal-based frequency cepstral coefficient (SFCC) and its complementary feature inverse SFCC (ISFCC) are also used. The experiments are carried on the POLYCOST database. The performance of the SI system due to ISFCC is better than IMFCC. To enhance the SI system's accuracy, a score-level fusion of MFCC with IMFCC and SFCC with ISFCC is done. The relative improvement due to the fusion of SFCC with ISFCC is up to 4% and 7% over MFCC and IMFCC, respectively.
FOUP Contamination and Limitation of Cleaning Procedure : Topic/category CFM, Contamination Free Manufacturing

The Front Opening Unified Pod (FOUP) is used to protect wafers from the Fab environment when they are not being processed. During Gate stack etch, a process known to be a potential source of etch residuals, wafers are separated in a different FOUP to avoid cross contamination. We show that the FOUP in which the wafers are placed can itself be a source of contamination. The contamination is found through inspection and it can also be shown to have a detrimental impact on product yield. We argue that outgassing of the FOUP is the source of the contamination and that an established cleaning process is insufficient to completely remove the contamination. The issue is eliminated by a change of type of FOUP used to contain the wafers.
Intensive teaching technology of imported rabbit meat in Uzbekistan

One of the urgent problems is the development of modern technologies for the care and feeding of rabbits imported to the country on farms and households. This article provides scientific recommendations for feeding imported rabbits on farms and households on the basis of rations enriched with valuable macro and micro elements and vitamins. Feeding rabbits on the basis of complete rations, taking into account the nutritional norms, is important in the emergence of the genetic potential of the New Zealand rabbit breed.
The influence of information and communication technologies in the educational process of the university

This article summarizes the results of research aimed at studying the impact of the transition to online learning using information and communication technologies (ICT) in the educational process on the psychological comfort of students at the Adyghe State University in the direction of “Psychological and pedagogical education”. During the study, a set of complementary research methods was used: theoretical, empirical, statistical, as well as private methods aimed at assessing the level of anxiety, sociability, motivation for learning, and the questionnaire aimed at identifying the level of psychological comfort of students in online learning. We suspected that the transition to online learning can affect the psycho-emotional state and psychological comfort of students: trait and state anxiety will increase; the level of motivation for learning, and the level of students’ communicative activity will decrease. Analysis and interpretation of the data showed that in the process of online learning, the majority of the tested respondents were dominated by average and high values of situational and personal anxiety (43.9% and 47.9%, respectively). 59% of respondents faced psychological discomfort because of the necessary to sharply adapt to the new format of education. There were also difficulties with self-organization of learning process outside the university - 21.7%, as well as difficulties associated with the material and technical base, created an uncomfortable psycho-emotional state in the subjects of the educational process - 3.5%. Therefore, in introducing online learning, it is necessary to take into account several possible risks that affect both the quality of the learning process and the psycho-emotional state of students. Moreover, a necessary condition, in our opinion, is considering the individual and personal characteristics of students in the process of integrating ICT into the educational system to improve health and create conditions for the psychological comfort of students.
Design, Build and Play - Online Robotics Classes

In the last few years, Robotics has been one of the most sought after activities for teens who were looking to engage as a complement to their school classes. Many clubs and associations provided simple activities focused on entertaining young minds after-school but as those projects were mostly conducted face-to-face, the students were left without alternatives since the pandemic started. Design, Build and Play is an online-based program in which introductory robotics classes are conducted remotely to introduce key iSTEME concepts.
L’italiano con i graffiti Patrimonio artistico-culturale: percorso per l’italiano L2

Over time, many models have been conceived to teach and evaluate the Italian language and Art culture for foreign learners and more recently for sectorial language learning. This paper is the result of research on specialist vocabulary and the acquisition of pragmatic skills through innovative, motivating teaching strategies, also useful for the enhancement of intrinsic skills of each learner. The corpus of the research includes: i. the implementation of an Italian L2 Art syllabus inspired by established models; ii. the results of experimentation based on a didactic module, in which graffiti were used as functional input for the learning; iii. the initial assessment of the relationship between iconic and linguistic codes aimed to improve the acquisition of skills in L2.
Design of interactive agricultural extension media for student in the material of family medicinal plant utilization

Abstract This program aims (1) to produce interactive extension media design in pandemic time, (2) to produce agricultural extension media in helping community service, particularly about the utilization of family medicinal plants to students. Data were analyzed using the triangulation technique. The result of the research video-based extension design about the introduction of the family medicinal plant using combined text (letter), image, sound, and color in the form of animation video supported with direct delivery using zoom and google meeting applications.The evaluation result show level of interest for text very interesting 16,67%, interesting 73,33%, quite interesting 6,67%, less interesting 3,33%; level of interest for image very interesting 23,33%, interesting 76,67%; level of interest for sound very interesting 10%, interesting 80%, quite interesting 10%; and level of interest for color very interesting 6,67%, interesting 63,33%, quite interesting 30%.
Search for an optimal model for using online education in higher education in the context of sustainable digitalization

The relevance of the presented article is determined by the widespread use of online distance learning technologies in the educational space of higher education in Russia. This process is supported and initiated, among other things, by the modern Russian authorities, whose actions are based on the presumption of the ability of online education to provide the same quality of the educational process that takes place in the classroom-contact format. The authors ‘ hypothesis is based on the fact that the widespread introduction of online education cannot be evaluated positively, since its quality has not been tested empirically. Institutionalization should be carried out only after it is scientifically confirmed “where”,” for whom”,” by whom “ online education can be implemented. For this purpose, the authors study the types of online education, its content, and assess the readiness of students to study online. The goal is formulated as determining the segments of the educational process in higher education in which online education will improve the quality of training of qualified specialists.
Adopting Agile Software Development Combined with User-Centered Design and Lean Startup: A Systematic Literature Review on Maturity Models

The use of Agile in the software development industry in the past two decades revealed that it is lackluster in some aspects, such as in guaranteeing user involvement and assuring that the right software is being built. There are reports that combining Agile with Lean Startup and User-Centered Design (UCD) helps in overcoming these shortcomings while also yielding several other benefits. However, there is not much documentation on how to use this “combined approach” and adapting existing organizations to use it is a challenge in of itself, in which the use of an instrument to guide or assess such transformations is typically pivotal to their success. As such, in this paper we seek to identify maturity models that assess the use of Agile, Lean Startup, and UCD. We conducted a systematic literature review of maturity models for these three methods published between 2001 and 2020. We characterized the maturity models and determined how they see maturity, how they are applied, and how they were evaluated. As an extended version of a previous paper, we augmented our analysis criteria and further classified the models in how they interpret maturity and what strategy they suggest when undergoing an improvement process, in addition to providing new insight on various aspects of the models. We found 35 maturity models, of which 23 were for Agile, 5 for Lean thinking, 5 for UCD, and 2 for Agile and UCD combined. No models for the combination of the three methods were found (nor for Lean Startup), as expected due to the novelty of the approach. Existing models mostly focus on practice adoption and acquiring continuous improvement capabilities, and are typically developed with a specific context in mind. We also note a lack of proper evaluation procedures being conducted on the majority of models, which could be due to the lack of well-established maturity model development methods and guidelines.
Preliminary Sensors Selection for Reconfigurable Continuous Track Robot Obstacle Detection

Nowadays, the topic of autonomous vehicles is attracting attention. Such technologies and innovative approaches can help to facilitate people's life in different. However, practical implementation of autonomous vehicles requires innovation in different spheres such as regulations, cybersecurity, and ethics. The demand for applying autonomous vehicles in rugged terrain conditions is growing, bringing new challenges for both obstacle overcoming and detection. This paper describes a process of preliminary sensors selection obstacle detection in an autonomous continuous track robot. Different sensors are studied and chosen for a particular case. The initial algorithm for robot navigation is proposed.
Human activity recognition by wearable sensors in the smart home control problem

Abstract In this work we analyze and compare machine learning methods for recognizing human activity in the context of smart home by using data obtained from an optical heartbeat sensor and an accelerometer embedded in a smart watch, and find a number of activity classes that can be predicted. The conclusion is made that for such type of problems the random forest method with about 10 classes shows the best results.