Kinds Of Tasks Research Articles

Методический фреймворк как средство проектирования образовательных продуктов

Frameworks are one of the most relevant tools for designing educational products today. Despite the various sources devoted to the definition of the framework, it should be noted that there is no unified understanding of this concept. In the Russian educational field, the framework is used as a tool for solving various kinds of tasks. The working concept of a "Methodological framework" is to define it as a ready-made methodological tool that allows you to systematically solve highly specialized tasks in the design of educational products. The paper describes the stages of designing a methodological framework, which includes the following steps: preparatory, basic and final. Research objectives to be solved: to formulate the concept of a methodological framework for use in the design of an educational product; to give a comparative characteristic between a methodological framework and a methodological recommendation; to describe the main elements of a methodological framework taking into account a systematic approach; to demonstrate the results and advantages of using a methodological framework. The article describes the experience of implementing a methodological framework within the framework of the "Creative Workshop Framlab" in the format of a telegram group with more than 455 participants. The conclusions made make it possible to contribute to the practice of using a methodological framework for designing educational products, as well as to the development of domestic pedagogical design.

Contrastive Clustering Learning for Multi-Behavior Recommendation

Increasing multiple behavior recommendation models have achieved great successes. However, many models do not consider commonalities and differences between behaviors and data sparsity of the target behavior. This paper proposes a novel Multi-behavior Recommendation Model (MBRCC) based on Contrastive Clustering Learning. Specifically, the graph convolutional network (GCN) is employed to obtain the embeddings of users and items, respectively. Then, three kinds of tasks (including behavior-level embedding, instance-level embedding and cluster-level embedding) are designed to optimize the embeddings of users and items. In behavior-level embedding, we design an adaptive parameter learning strategy to analyze the impact of auxiliary behaviors on the target behavior. Then, the embeddings of users for each behavior are weighted to obtain the final embeddings of users. In instance-level embedding, we employ contrastive learning to analyze the instances of user and item for mitigating the issue of data sparsity. In cluster-level embedding, we design a new cluster contrastive learning method to capture the similarity between groups of user and item. Finally, we combine these three tasks to improve the quality of the embeddings of users and items. We conduct extensive experiments on three real-world datasets and experimental results indicate that the MBRCC remarkably outperforms numerous existing recommendation models.

CHALLENGES OF USING ARTIFICIAL INTELLIGENCE AT THE UNIVERSITY LEVEL

The capacity of robots to mimic human thinking processes, especially in huge data systems for certain kinds of tasks, is known as artificial intelligence. The purpose of the study was to identify the barriers to artificial intelligence adoption at the university level. Research was quantitative in nature and a survey method was used to collect data. The population of this research is limited to all students enrolled in two public and two private universities located in the Lahore district. The 487 students were selected using a convenient sampling technique from two public and two private Universities in Lahore. Data was gathered by using a self-developed instrument. The data were analyzed through SPSS (Statistical Packages for Social Sciences). inferential statistics were used to calculate the data. Regarding the challenges posed by AI in higher education, male and female students' opinions did not significantly vary from one another. Universities should employ artificial intelligence, with explicit ethical principles for AI development that cover bias mitigation, student data protection, and responsible development. Keywords: Artificial Intelligence, Higher Education, Machine Learning

Sentence Repetition as a Diagnostic Tool for Developmental Language Disorder: A Systematic Review and Meta-Analysis.

This systematic review and multilevel meta-analysis examines the accuracy of sentence repetition (SR) tasks in distinguishing between typically developing (TD) children and children with developmental language disorder (DLD). It explores variation in the way that SR tasks are administered and/or evaluated and examines whether variability in the reported ability of SR to detect DLD is related to these differences. Four databases were searched to identify studies that had used an SR task on groups of monolingual children with DLD and TD children. Searches produced 3,459 articles, of which, after screening, 66 were included in the systematic review. A multilevel meta-analysis was then conducted using 46 of these studies. Multiple preregistered subgroup analyses were conducted in order to explore the sources of heterogeneity. The systematic review found a great deal of methodological variation, with studies spanning 19 languages, 39 SR tasks, and four main methods of production scoring. There was also variation in study design, with different sampling (clinical and population sampling) and matching (age and language matching) methods. The overall meta-analysis found that, on average, TD children outperformed children with DLD on the SR tasks by 2.08 SDs. Subgroup analyses found that effect size only varied as a function of the matching method and language of the task. Our results indicate that SR tasks can distinguish children with DLD from both age- and language-matched samples of TD children. The usefulness of SR appears robust to most kinds of task and study variation. https://doi.org/10.23641/asha.25864405.

A Critical Review of Human Jaw Biomechanical Modeling

The human jaw is a complex biomechanical system involving different anatomical components and an articulated muscular system devoted to its dynamical activation. The numerous actions exerted by the mandible, such as talking, eating or chewing, make its biomechanical comprehension absolutely indispensable. To date, even if research on this topic has achieved interesting outcomes using in vitro testing, thanks to the development of new apparatus and methods capable of performing more and more realistic experiments, theoretical modeling is still worthy of investigation. In light of this, nowadays, the Finite Element Method (FEM) approach constitutes certainly the most common tool adopted to investigate particular issues concerning stress–strain characterization of the human jaw. In addition, kinematics analyses, both direct and inverse, are also diffuse and reported in the literature. This manuscript aimed to propose a critical review of the most recurrent biomechanical models of the human mandible to give readers a comprehensive overview on the topic. In light of this, the numerical approaches, providing interesting outcomes, such as muscular activation profiles, condylar forces and stress–strain fields for the human oral cavity, are mainly differentiated between according to the joint degrees of freedom, the analytical descriptions of the muscular forces, the boundary conditions imposed, the kind of task and mandible anatomical structure modeling.

False belief understanding in deaf children: what are the difficulties?

Children with cochlear implants (CIs) demonstrate proficiency in verbal-story elicited-response (VS-ER) false-belief tasks, such as the Sally & Ann task, at a similar age as typically developing hearing children. However, they face challenges in non-verbal spontaneous-response (NV-SR) false-belief tasks, measured via looking times, which hearing infants typically pass by around 2 years of age, or earlier. The purpose of the present study was to examine whether these difficulties remain in a non-verbal-story elicited-response (NVS-ER) false-belief task, in which children are offered the opportunity to provide an elicited response to a non-verbal-story task. A total of thirty 4- to 8-year-old children with CI-s and hearing children completed three different kinds of false-belief tasks. The results showed that children with CI-s performed above chance level on the verbal task (i.e., VS-ER task), but not on the two non-verbal tasks, (i.e., NVS-ER and NV-SR tasks). The control group of typically developing hearing children performed above chance on all three kinds of tasks (one-tailed significance level). Our findings highlight the importance of external narrative support for children with CIs in tasks that involve mental perspective-taking, and specifically predicting actions based on false beliefs.

ANALYSIS OF THE SUCCESSFUL EXPERIENCE OF CREATING A BRAND IN THE MANICURE INDUSTRY

The article identifies and analyzes the prerequisites for the success of creating a brand in the manicure industry. Theoretical research methods are applied: analysis, synthesis, generalization. Analytical research methods: segmentation of the target audience, segmentation of goods and services, audit of the branded salon. A financial model has been compiled for the salon. When a person decides to open his own business, a huge number of questions arise in front of him. Will his project be successful? What is a business plan and how to make it? Which tax system should I choose? And all these issues turn into a kind of tasks that become the main starting point for successful entrepreneurial activity. The purpose of the work is to solve the entrepreneurial tasks of creating a brand for the startup “NaiLis”. The subject of the study is the study of the practice of solving entrepreneurial tasks to create a brand in the field of manicure services.

The study for word prediction based on Skip-gram and CBOW model

In the modern world, when people use their texting apps to communicate with each other, they find their phones are trying to predict what they want to type. The process of this word prediction is actually not a new topic, previous researchers have involved the Natural Language Processing (NLP) word prediction algorithms in many studies. Thus, in this article, a project implementing the Skip-gram model and the Continuous Bags of Words (CBOW) model is created in this study to find out whichever of the two newest and reversed word prediction models would be better to utilize. By comparing the efficiency items including total training time, total effective words, training time per epoch, and effective words per epoch in the experimental results, the project discovered that the CBOW model could be better utilized than the Skip-gram model, which means that the CBOW model can be considered more under this kind of task.

Мethod for determining the level of similarity of images by color component based on correlation analysis and color space quantization

The color component of an image is the basic information about the image. Color determines the mood of an image, its concept, and can also serve as a semantic accent. Obtaining information about the color component of an image is necessary when solving various kinds of tasks. For example, the task of finding similar images according to the content, as information about the color component can be combined with other metadata of the image and more fully describe its concept. In this paper authors describe the physical representation of color, the most popular digital representations of color, ways of extracting color components of an image, such as color histograms, quantization, coherent vectors and K -means, and ways of comparing color components of images, such as Euclidean distance, cosine distance, Manhattan distance, Chebyshev distance, Jaccard index, correlation analysis, Pearson's agreement test and Bhattacharya distance. It is suggested that the use of histogram comparison and vector comparison methods is acceptable for comparing the color components of images, since color histograms can be written in vector form. The authors conduct an experiment in which a human expert reviews combinations of color component acquisition and comparison methods and completes a test protocol based on the criteria highlighted by the authors. The criteria were based on the human interpretability of the results of the methods of determining and comparing the color component. On the basis of the experiment it has been determined that the best of the methods of comparison of color components of the definition images considered in the article is the correlation analysis of color histograms of images, which have been extracted in a quantized color space, because this approach meets all the criteria for testing and keeps the greatest amount of information about the color component.

JUSTIFICATION OF APPROACHES BEFORE PLACEMENT OF PASSENGER VESSELS IN THE DIRECTIONS OF THEIR WORK AND IMPROVING THE WORK PLANNING OF THE SHIPPING COMPANY’S FLEET

Abstract. The purpose of the article is to solve the task of arranging the passenger fleet and improving the planning of the fleet of the shipping company. To achieve the goal, the possible operating conditions of the passenger fleet were studied; developed approaches that will allow, taking into account different types of sailing, different directions of work, sizes of ships, as well as the possibility of replenishment of the fleet, to obtain the maximum value of the financial result when deploying the passenger fleet of the shipping company, which made it possible to improve the planning of the passenger fleet of the shipping company. The process of managing a shipping enterprise is a continuous development of management decisions, which largely depends on the final result of the work of any company. This is especially important for shipping, which functions in conditions of significant dynamics of both the external environment and the direct production of transport processes. The proposed solution will allow the shipping company to effectively locate its fleet, obtain the maximum financial result, taking into account the conditions of transportation and operation of vessels of various types of navigation, forms of organization of transportation, various types of vessels, routes, and also takes into account the possibility of replenishing the fleet of the shipping company in the event that its potential freight capacity does not exceed the amount of transport work. One of the most important areas of the shipping company’s work is planning and optimizing the use of the company’s resources. The shipping company strives for the maximum financial result from the work performed. If we consider the "external environment" as a management subject that also makes decisions corresponding to the options of the possible structure of passenger flows, then this kind of task can be formalized and presented in the form of a matrix game. A characteristic condition is that each party has no information about the decision of the other. Thus, the maximin (minimax) principle is implemented in a conflict situation. The value of the game determines the mathematical expectation of winning the first player, that is, the value of the financial result, which should be taken as planned. As a result, the forecast of the results of the shipping company will reflect the production capabilities of the "external environment" (the second player) and the conditions in which it will have to work. The proposed approach can also be used to determine the values of the planned indicators of other maritime transport enterprises - ports, ship repair plants, as well as standards of transport work; development of the replenishment (write-off) program of the shipping company’s fleet; determining the tariff policy related to the transportation of passengers in the shipping company’s region.

Reinforcement Learning-based approach for dynamic vehicle routing problem with stochastic demand

This paper studies a dynamic vehicle routing problem under stochastic demands, drawn from a real-world situation. Specifically, a single courier must accomplish two kinds of tasks: deliveries known at the beginning of the operation and pickups that appear throughout the daily operation with specific patterns. The objective is to maximise the rewards obtained from serving both types of customers during a limited period. Our contribution lies in using the neural network and historical couriers’ decisions to learn a base policy that captures human experience for better decision making. The reinforcement learning framework is then used to make the base policy explore new scenarios through simulations and further train the base policy with newly generated data. We show that our approach allows the serving of an average of 12% and 8% more customers under some conditions than the nearest-neighbour policy in high density area and low density area, respectively.

Comparative Analysis of CNN, LSTM And Random Forest for Multivariate Agricultural Price Forecasting

Time series forecasting is an important research topic among agriculture economics. Especially, multivariate, multi-step and multiple output prediction tasks pose a challenge in research as their nature requires the investigation of intra- and inter-series correlation. The common statistical methods like ARIMA and SARIMA fall short in this kind of tasks. Deep learning architectures like Convolutional Neural Networks and Long Short-Term Memory networks are quite good at modelling the structures of complex data relations. In this study, a new dataset is composed through manual collection of data from the Ministry of Commerce of Turkish Republic. The dataset contains daily trade volumes and prices of potato, onion and garlic, which are most commonly consumed products in Turkish cuisine. The data pertains to the period between January 1, 2018 and November 26, 2022 (1791 days). A simple CNN and LSTM architectures as well Random Forest machine learning method are used to predict the next 10-day prices of the products. Accordingly, three models provided acceptable results in the prediction tasks, while CNN yielded by far the best result (MAE: 0.047, RMSE: 0.070).

Representation and decoding of bilateral arm motor imagery using unilateral cerebral LFP signals.

In the field of upper limb brain computer interfaces (BCIs), the research focusing on bilateral decoding mostly based on the neural signals from two cerebral hemispheres. In addition, most studies used spikes for decoding. Here we examined the representation and decoding of different laterality and regions arm motor imagery in unilateral motor cortex based on local field potentials (LFPs). The LFP signals were recorded from a 96-channel Utah microelectrode array implanted in the left primary motor cortex of a paralyzed participant. There were 7 kinds of tasks: rest, left, right and bilateral elbow and wrist flexion. We performed time-frequency analysis on the LFP signals and analyzed the representation and decoding of different tasks using the power and energy of different frequency bands. The frequency range of <8 Hz and >38 Hz showed power enhancement, whereas 8-38 Hz showed power suppression in spectrograms while performing motor imagery. There were significant differences in average energy between tasks. What's more, the movement region and laterality were represented in two dimensions by demixed principal component analysis. The 135-300 Hz band signal had the highest decoding accuracy among all frequency bands and the contralateral and bilateral signals had more similar single-channel power activation patterns and larger signal correlation than contralateral and ipsilateral signals, bilateral and ipsilateral signals. The results showed that unilateral LFP signals had different representations for bilateral motor imagery on the average energy of the full array and single-channel power levels, and different tasks could be decoded. These proved the feasibility of multilateral BCI based on the unilateral LFP signal to broaden the application of BCI technology. https://www.chictr.org.cn/showproj.aspx?proj=130829, identifier ChiCTR2100050705.

Heatmap-Guided Selective Feature Attention for Robust Cascaded Face Alignment

Face alignment methods have been actively studied using coordinate and heatmap regression tasks. Although these regression tasks have the same objective for facial landmark detection, each task requires different valid feature maps. Therefore, it is not easy to simultaneously train two kinds of tasks with a multi-task learning network structure. Some studies have proposed multi-task learning networks with two kinds of tasks, but they do not suggest an efficient network that can train them simultaneously because of the shared noisy feature maps. In this paper, we propose a heatmap-guided selective feature attention for robust cascaded face alignment based on multi-task learning, which improves the performance of face alignment by efficiently training coordinate regression and heatmap regression. The proposed network improves the performance of face alignment by selecting valid feature maps for heatmap and coordinate regression and using the background propagation connection for tasks. This study also uses a refinement strategy that detects global landmarks through a heatmap regression task and then localizes landmarks through cascaded coordinate regression tasks. To evaluate the proposed network, we tested it on the 300W, AFLW, COFW, and WFLW datasets and obtained results that outperformed other state-of-the-art networks.

Radio astronomical images object detection and segmentation: a benchmark on deep learning methods

In recent years, deep learning has been successfully applied in various scientific domains. Following these promising results and performances, it has recently also started being evaluated in the domain of radio astronomy. In particular, since radio astronomy is entering the Big Data era, with the advent of the largest telescope in the world - the Square Kilometre Array (SKA), the task of automatic object detection and instance segmentation is crucial for source finding and analysis. In this work, we explore the performance of the most affirmed deep learning approaches, applied to astronomical images obtained by radio interferometric instrumentation, to solve the task of automatic source detection. This is carried out by applying models designed to accomplish two different kinds of tasks: object detection and semantic segmentation. The goal is to provide an overview of existing techniques, in terms of prediction performance and computational efficiency, to scientists in the astrophysics community who would like to employ machine learning in their research.

The spectral dynamics of visual awareness: An interplay of different frequencies?

The goal of this study was to investigate the neurophysiological correlates of visual awareness, with a specific focus on its event-related spectral perturbation (ERSP) features. To this aim, we tried to disentangle the proper neural correlates of consciousness (NCC) from other prerequisite and post-perceptual processing. To do so, we administered an orientation discrimination task, inducing a response bias through task instructions. EEG results showed that different frequency bands are involved in this kind of task, with different spectral and temporal dynamics. In particular, alpha and beta bands seem to be particularly engaged, especially in the aware-unaware contrast, showing a main power suppression for aware trials and replicating previous literature. Moreover, we demonstrated that the process of visual awareness is orchestrated by a complex interaction of different frequencies (i.e., theta, alpha, beta and gamma) being involved as prerequisites and post-perceptual processes.

Studies of Satellite Position Measurements of LEO CubeSat to Identify the Motion Mode Relative to Its Center of Mass

This paper addresses the possibility of reconstructing motion relative to the center of mass of a low Earth orbit (LEO) nanosatellite of the CubeSat 3U standard using satellite position measurements (Two-Line Element Set (TLE)). This kind of task needs to be performed in the case where it is not possible to establish radio communication with the nanosatellite after it is launched into orbit. Therefore, it is important for the nanosatellite developers to develop some understanding of what is going on with the nanosatellite in order to be able to analyze the current situation after deployment. The study was carried out on the example of the aerodynamically stabilized SamSat-218D nanosatellite developed by the professors and students of Samara National Research University. SamSat-218D was launched into a near-circular orbit with an average altitude of 486 km on April 2016 during the first launch campaign from the Vostochny cosmodrome. Knowledge of CubeSat aerodynamics allows estimating the nature of its possible motion relative to the CubeSat center of mass by ballistic coefficient changes, evaluated with the use of satellite position measurements. The analysis showed that SamSat-218D performed spatial rotation with an angular velocity of more than two degree per second and had not stabilized aerodynamically by 2 March 2022, when it entered the atmosphere and was destroyed.

A Successful Crowdsourcing Approach for Bird Sound Classification

Automated recorders are increasingly used in remote sensing of wildlife, yet automated methods of processing the audio remains challenging. Identifying animal sounds with machine learning provides a solution, but optimizing the models requires annotated training data. Producing such data can require much manual effort, which could be alleviated by engaging masses to contribute to research and share the workload. Birdwatchers are experts on identifying bird vocalizations and form an ideal focal audience for a citizen science project aiming for the required multitudes of annotated avian audio data. For this purpose, we launched a web portal that was targeted and advertised to Finnish birdwatchers. The users were asked to complete two kinds of tasks: 1) classify if a given bird sound belonged to the focal species and 2) classify all the bird species vocalizing in 10-second audio clips. In less than a year, the portal achieved annotations for 244,300 bird sounds and 5,358 clips, and attracted, on average, 70 visitors on daily basis. More than 200 birdwatchers took part in the classification tasks, of which 17 and 4 most dedicated users produced over half of the sound and clip classifications, respectively. As expected of birder experts, the classifications among users were highly consistent (mean agreement scores between 0.85–0.95, depending on the audio type) and resulted in high-quality training data for parameterizing machine learning models. Feedback about the web portal suggested that additional functionality such as increased freedom of choice would increase user motivation and dedication.

Adaptive Task Scheduling Algorithm for Multifunction Integrated System with Joint Radar–Communications Waveform

Joint radar–communications (JRC) waveform could enable simultaneously radar sensing and communication functions in a multifunction integrated system (MFIS). Because of the special JRC waveform characteristics of MFIS, the traditional task scheduling algorithm for multifunction phased array radar cannot be applied to the MFIS to release its full potential. Therefore, a novel adaptive task scheduling algorithm for JRC waveform enabled MFIS is proposed in the paper. Firstly, the modified JRC waveform scheduling criteria for MFIS are remodeled after establishing the multi-task models. Secondly, the task scheduling optimization problem, which can reflect the scheduling capability of the JRC waveform, is reformulated, and the proposed novel adaptive task scheduling algorithm based on the JRC waveform effectively schedules different kinds of tasks. Finally, the simulation results show that the proposed algorithm not only has a high JRC waveform scheduling capability but also provides better performance compared with the traditional ones.

ARTIFICIAL INTELLIGENCE AS A TOOL FOR ANALYZING LITERARY TEXTS

To date, the issue of integrating artificial intelligence technology in solving various kinds of tasks is being updated. One of the most promising areas of use of intelligent systems is text analysis. This article is devoted to an actual topic related to the integration of artificial intelligence as a tool for analyzing literary texts. The author uses theoretical and empirical research methods. Scientific materials of domestic and foreign authorship are used in the work in order to obtain the most relevant and objective information. The predominant part of the work is devoted specifically to the use of artificial intelligence technology in text analysis.