Reviewerly/exHarmony huggingface dataset:
Psychological and pedagogical support for students in the context of digitalization of the educational environment: analysis of the current state

Digitalization of education has led to the need to develop new means of psychological and pedagogical support for modern schoolchildren, focused on the formation of key competencies in demand in a rapidly changing world. In order to identify teachers&amp;apos; ideas about the problems of schoolchildren and the means used to support primary school students (grades 5-8), a survey of teachers was conducted at the first stage of the study. The total sample consisted of 788 respondents and is represented by teachers with different work experience in the general education system; with different levels of education; teaching subjects that cover all subject areas. The collected empirical material is systematized using the methods of factor analysis and discourse analysis. As a result of the study, the state of the real practice of psychological and pedagogical support of students in modern schools of megalopolis and rural areas with different levels of digitalization of the educational environment is characterized; groups of problems of adolescents are systematized, which, according to the respondents, have a significant impact on their development in the conditions of digitalization of general education; it is found that teachers identify problem-solving tools and means of supporting schoolchildren in the process of solving them.The results obtained confirm the importance of the implementation of psychological and pedagogical support as the most important pedagogical means of professional activity of a teacher in the conditions of digitalization of education; they are of practical importance for the actualization of humanistic interaction between a teacher and a student in the process of joint search for solutions to problems significant for a student in a digital educational environment.
Analyzing Computational Components of Standard Block Encryption Schemes

Encryption is used to secure sensitive computer data which may be at rest or in motion.There are several standard encryption algorithms that have been used to encrypt and protect blocks of sensitive data to ensure confidentiality.The most popular standard block encryption schemes are the Advanced Encryption Standard (AES), Triple Data Encryption Standard (3DES), and the first standardized encryption scheme, which is no longer the standard scheme now, namely the Data Encryption Standard (DES).AES is the current standard for block encryption used worldwide and is implemented on many processors.In this work, we compare the hardware performance of these three encryption schemes.First, we identified the underlying computational components for these three encryption schemes, and then we analyzed to what extent these computational components were being used in these block encryption schemes to encrypt and decrypt a given message.In this paper, we compared the contribution of these computational components to evaluate the overall encryption efficiency in terms of speed and computational delays for encrypting a given block of data for a given hardware platform.AES was found to be the faster scheme in terms of hardware computation speed in accomplishing the same encryption task compared to the other two block encryption schemes, namely, the DES and 3DES schemes.
Correction to: Sequence Analysis and Modern C++

The original version of the book was inadvertently published without a volume number. This has now been amended in the book with volume number 33.
Research on Anti-noise Activation Function Based on LIF Biological Neuron Model

In the environment of Internet of vehicles ranging, the image sensor works for a long time, and the brightness is not enough or uneven when shooting, which will produce noise, resulting in poor image quality. In this paper, based on the LIF (Leaky Integrate-And-Fire) biological neuron model with noise immunity, NST (Noisy Softplus and Tanh) activation function is proposed and applied to PSM-Net to effectively overcome the influence of noise on image quality. The NST function is used to fit the response mechanism of LIF model, and the unknown $$\mathrm{\alpha }$$ are added to make the NST function curve change with the change of input noise, so it has anti-noise performance. On this basis, the non monotonic property is added to the negative half axis to improve the processing accuracy of the function in the deep network. Experiments show that NST function is stable in the presence of noise input, and the error value is 13.504 lower than ReLU function in the presence of large noise input with variance value of 0.2, which effectively improves the processing accuracy of binocular stereo disparity prediction network.
Social Media Integration Into Contemporary Human Resource Management: A Qualitative Approach

This study explored the theme “social media integration into contemporary human resource management: a qualitative approach.” The study was guided by three research objectives which are;to determine whether human resource managers integrate social media in their practices; to ascertain the benefit of integrating social media into human resource management and to determine challenges associated with the integration of social media into human resource management. The study was built on the Theory of Creative Destruction and the population of the study comprised of human resource managers and academicians within Yola metropolis. The study adopted dual research methods which are the in-depth interview and integrative literature review design. The researcher interviewed human resources managers and academicians that specialized in human resource management until the results reachedsaturation point. Findings revealed that human resource managers have incorporated social media into contemporary human resource management. It was also found that social media platforms offer lots of benefits to human resource managers in their professional practices. However, it was unraveled that there are quit a several disadvantages associated with the integration of social media into contemporary human resource management even though the advantages outweigh the disadvantages. Therefore, it was recommended among other things that there is the need for managers of human resources to be aware of the right limits and boundaries when it comes to using social media for their professional practices.
Conversational Search with Mixed-Initiative - Asking Good Clarification Questions backed-up by Passage Retrieval

We deal with the scenario of conversational search, where user queries are under-specified or ambiguous.This calls for a mixed-initiative setup.User-asks (queries) and system-answers, as well as system-asks (clarification questions) and user response, in order to clarify her information needs.We focus on the task of selecting the next clarification question, given the conversation context.Our method leverages passage retrieval from a background content to fine-tune two deep-learning models for ranking candidate clarification questions.We evaluated our method on two different use-cases.The first is an open domain conversational search in a large web collection.The second is a taskoriented customer-support setup.We show that our method performs well on both use-cases.
Influence of the Vection Effect on VR Motion Sickness

In this paper we studied the influence of the combination of linear velocity and rotational velocity on vection and visually induced motion sickness. The experiment was performed with subjects wearing a head-mounted display. While the stimuli videos were shown, strength of vection, the time in which vection was experienced and the level of motion sickness were evaluated on 5-level scale by subjects. We found that as linear velocity increases, vection induced by rotational motion decreases. Furthermore, in the presence of rotational motion, the sense of vection experienced was greater as the rotational velocity increased at low linear velocities, accompanied by a longer duration of vection and a higher level of motion sickness. In contrast, it is possible to make the level of motion sickness the same as a video without rotational motion by limiting the rotational velocity to 20°/s, and it is possible to suppress motion sickness to an extent without impairing immersion by limiting rotational velocity to within a certain range.
Selection of suitable concentrated solar power farm’s locations A Case study: Fez-Meknes Region, Morocco

Nowadays, the transition to green energies becomes a necessity in the worldwide. In order to have an optimal yield, the selection of the adequate site for a given technology is required. In this study, the geographical environment of Fez-Meknes region, Morocco, is explored to select suitable location for concentrated solar power plants (CSP). For this purpose, a combination of a geographic information system (GIS) and the multi-criterion decision-making (MCDM) method is used for this aim. Various factors are taken into consideration, namely climate conditions, environment, water resources and location. These criteria are calculated and weighted using the Analytical Hierarchy Process (AHP) approach. The findings indicate that the Fez-Meknes region is a viable location for the implementation of CSP plants. Thus, the most suitable site represents 12% of the total region area. These suitable areas are located in the southern and southeastern part of the region with an irradiation value of about 6.57 kWh/m 2 /yr.
Cree Corpus: A Collection of nêhiyawêwin Resources

Plains Cree (nêhiyawêwin) is an Indigenous language that is spoken in Canada and the USA.It is the most widely spoken dialect of Cree and a morphologically complex language that is polysynthetic, highly inflective, and agglutinative.It is an extremely low resource language, with no existing corpus that is both available and prepared for supporting the development of language technologies.To support nêhiyawêwin revitalization and preservation, we developed a corpus covering diverse genres, time periods, and texts for a variety of intended audiences.The data has been verified and cleaned; it is ready for use in developing language technologies for nêhiyawêwin.The corpus includes the corresponding English phrases or audio files where available.We demonstrate the utility of the corpus through its community use and its use to build language technologies that can provide the types of support that community members have expressed are desirable.The corpus is available for public use 1 .
On the Effect of Isotropy on VAE Representations of Text

Injecting desired geometric properties into text representations has attracted a lot of attention.A property that has been argued for, due to its better utilisation of representation space, is isotropy.In parallel, VAEs have been successful in areas of NLP, but are known for their suboptimal utilisation of the representation space.To address an aspect of this, we investigate the impact of injecting isotropy during training of VAEs.We achieve this by using an isotropic Gaussian posterior (IGP) instead of the ellipsoidal Gaussian posterior.We illustrate that IGP effectively encourages isotropy in the representations, inducing a more discriminative latent space.Compared to vanilla VAE, this translates into a much better classification performance, robustness to input perturbation, and generative behavior.Additionally, we offer insights about the representational properties encouraged by IGP. 1
Technical, Legal, and Economic Risks

Abstract In the following chapter the author traces the technical improvements in vehicle safety over recent decades, including new sensor technologies with image recognition and Artificial Intelligence, factoring in growing consumer expectations. Through Federal Court of Justice rulings on product liability and economic risks, he depicts requirements that car manufacturers must meet. For proceedings from the first idea until development to sign-off, he recommends interdisciplinary, harmonized safety and testing procedures. He argues for further development of current internationally agreed-upon standards including tools, methodological descriptions, simulations, and guiding principles with checklists. These will represent and document the practiced state of science and technology, which has to be implemented technically suited and economically reasonable. Dilemma situations have always served to clarify ethical and legal principles, such as in the famous example of the so-called “trolley case”. The answer of the law is clear: the killing of a human being with the intention of saving others from certain death may be excused in a concrete case, but it remains illegal in any case. The solution is to avoid accidents at any rate by adapting and forward-looking driving. Relevant maneuvers of driving robots have to be defined and assessed for example using accident data and virtual methods. Further investigation of real driving situations in comparison with system specifications with tests on proving grounds, car clinics, field tests, human driver training or special vehicle studies are recommended. For the required exchange of information, storage of vehicle data and possible criminal attacks protective technical measures are necessary.
Tracing Changes in Shape of Historical Artefacts Across Time Using 3D Scans: A New Computational Approach

This paper pioneers a new computational approach for the study of changes in shape of objects across time. Previously, such a study was undertaken by scholars using a purely visual approach and relied on images of objects or in-person observations. This paper’s approach is based on 3D scans of historical artefacts. Sample points are extracted from these 3D scans and the distance between analogous points across different objects is computed using an approximation of the Wasserstein metric, namely the Sinkhorn distance. In this paper, the approach is demonstrated on a small set of ancient Greek vessels of the Krater, Pelike, and Kylix types, as the variation in their shapes across time is well known to archaeologists. Results offer, for the first time, a way of quantifying differences between objects. Benefits of this approach lie in its ability to quantify change, to study complex 3D material, and to analyse large datasets of objects, opening the possibility of constructing new large-scale studies of object shape across time and geographical regions. These have a range of applications in art history, archaeology, digital humanities, museology and extended reality studies.
Analysis, evaluation and comparison of knowledge extraction tools in the Environmental and Health domain. A holistic approach

Knowledge extraction in the Environment and Health domains is certainly an important asset for both scientific research and decision support.However, these strategic domains are characterized by a significant heterogeneity of structured and unstructured documents that do not allow a complete transfer of knowledge.Not infrequently, this critical point emerges during the implementation of research projects as an obstacle to the capitalization of information useful for the management of territories or in more recent times to the fight against the pandemic.This paper aims to achieve an analysis of the different forms of knowledge that characterize the scientific production in these specific fields trying to take a holistic approach to text management, tables and graphs through a multidisciplinary logic with the aim of 1 Authors contribution statement.Anna Rovella wrote and is responsible for the sections 1.0, 2.0, 3.0, 4.0 and 4.1.Francesca M.C.Messiniti carried out the experiments and is responsible for the statistical data of part 4.1.Alexander Murzaku wrote and is responsible for the section 4.2, Eugenio Cesario wrote and is responsible of section 4.3.Armando Bartucci wrote and performed the experiments of part 4.4 in collaboration with Francesca M.C.Messiniti.
Genetic Disorder Prediction Using ANN with Fog Computing

Distinguishing the source of a hereditary malady may be a principal challenge in science. The issue of machine learning in recognizing potential connections of innate disarrangement is challenging due to the need of known affiliations and the requirement of "negative" affiliations. Fog Computing organizes and analyzes information to supply and collect the information required for expectation, whereas lessening idleness and brief buffer periods for malady forecasts. This paper proposes an proficient AI-based hereditary clutter expectation strategy utilizing counterfeit neural organize with Fog Computing. Expectations are based on hereditary infections. At first, quiet wellbeing information is accumulated through numerous datasets and put away on Fog Hubs. Profound Learning, a shape of AI time called Fake Neural systems, is utilized to accept hereditary issues. As a portion of ANN, the connections between the convolution layers in a feedforward neural organize calculation don't create a circle. To begin with, the methodology is utilized to cluster the tireless prosperity records. At long last, a feedforward neural organize is utilized to figure hereditary clutters. A point by point test and ponder on genuine healthcare information was performed to assess the execution of the proposed works. The test appears that the proposed work viablely predicts infection with a tall degree of exactness in distinguishing hereditary anomalies, as well as extraordinary soundness and execution.
Deep Learning Techniques For Improving Breast Cancer Detection And Diagnosis

In this paper, we aim to introduce a survey on the applications of deep learning for breast cancer detection and diagnosis to provide an overview of the progress in this field. In the survey, we firstly provide an overview on deep learning and the popular architectures used for breast cancer detection and diagnosis. Especially we present four popular deep learning architectures, including convolutional neural networks, fully convolutional networks, auto encoders, and deep belief networks in the survey. Secondly, we provide a survey on the studies exploiting deep learning for breast cancer detection and diagnosis.
Detecting Various Types of Noise for Neural Machine Translation

The filtering and/or selection of training data is one of the core aspects to be considered when building a strong machine translation system.In their influential work, Khayrallah and Koehn (2018) investigated the impact of different types of noise on the performance of machine translation systems.In the same year the WMT introduced a shared task on parallel corpus filtering, which went on to be repeated in the following years, and resulted in many different filtering approaches being proposed.In this work we aim to combine the recent achievements in data filtering with the original analysis of Khayrallah and Koehn (2018) and investigate whether state-of-the-art filtering systems are capable of removing all the suggested noise types.We observe that most of these types of noise can be detected with an accuracy of over 90% by modern filtering systems when operating in a well studied high resource setting.However, we also find that when confronted with more refined noise categories or when working with a less common language pair, the performance of the filtering systems is far from optimal, showing that there is still room for improvement in this area of research.
Philosophy

Abstract In many respects, machine learning’s current concerns are reminiscent of those which heralded the rejection of GOFAI.
A High-Speed Low-Cost Hardware Implementation for Depth Estimation Using Disparity Fusion Method

Depth estimation using stereo images can be achieved by calculating the disparity values between the left and the right images captured by two parallel cameras. Reconstructing depth information from 2D images is crucial in many applications, such as self-driving vehicles and robot navigation. Furthermore, most of these applications are employed with resource-constrained devices and have real-time requirements. In this paper, a high-speed, low-cost hardware implementation for disparity estimation is proposed. We adopted the novel disparity fusion method in our architecture, which can significantly reduce the number of calculations in the overall process. A refinement method is also designed to reduce the error rate of the resulting depth map and improve the tolerance to light noise. The proposed algorithm was implemented with the Kintex-7 field-programmable gate array. Its performance was tested by using the Middlebury-Version 2 and -Version 3 datasets. The proposed algorithm provides an operating speed of 118 fps with an error rate of only 6.36%. Compared with other state-of-the-art designs used for similar applications, the proposed method can achieve a 34.6% reduction in the error rate while providing the highest speed with competitive hardware cost.
IMPLEMENTATION OF A PERSONALLY-ORIENTED APPROACH IN THE PROCESS OF PROFESSIONAL TRAINING OF FUTURE PRIMARY SCHOOL TEACHERS IN REMOTE CONDITIONS

The article deals with the problems of professional and pedagogical education, in particular the question of implementation of the personality-oriented approach in the context of distance education psychological and pedagogical preparation of future primary school teachers. The author describes the scientific approaches of using the virtual educational technologies, which emphasize the development of the personality of future primary school teachers. The relevance of the article is due to the need to understand the problems of implementation and effectiveness of distance learning, when it becomes almost the only, albeit temporary, option for quality of vocational education. The article presents the results of a pilot experiment on a detailed study of the problem of implementing the personality-oriented paradigm of professional education of future teachers in the context of virtual learning. For this purpose was developed a three-level online questionnaire (using Google Forms), which contained the main markers of successful implementation of this phenomenon: comfort of educational interaction during virtual learning, psychological climate in distance learning and interest in the chosen profession network communication. The practical optimization of a certain process was implemented by identifying the main ways to optimize the problem: adherence to the etiquette of virtual educational cooperation (using online services YouTube, Zoom, Google Meet, Google Chat, Telegram, Viber, Canva, Easel.ly, Wordle, etc.); conducting psychological and pedagogical diagnostics of emotional and psychological state of student youth (determination of professional preferences (Yovaishi method), research of leading professional motives (Shane's "Anchor Careers" method), diagnostics of communicative and organizational tendencies (V. Sinyavsky, B. Fedorishin), checking the levels of emotional intelligence (N. Hall), interpersonal research (T. Leary), etc., with using the online applications such as Google Forms, Google Classroom, MOODLE, LearningApps, Quizizz, etc.); activation of the facilitation and educational function of a teacher of pedagogical university (with the help of Miro, AWWApp, Edmodo, Skype, Google Meet, Google Docs, Google Chats, Canva, iMindMap, educational platforms Prometheus and EdEra, etc.).