Reviewerly/exHarmony huggingface dataset:
Forms and methods of inclusive work in mathematics lessons in primary school

The article clarifies the content of the concepts "person with special educational needs", describes the features of the organization of education in an inclusive classroom in mathematics lessons; identified special educational needs of primary school children with disabilities on the basis of a secondary school, explored effective ways of inclusive work in mathematics lessons in modern primary school. Innovative technologies of multilevel support of participants of educational process are offered: technology of adaptation of the child to a new educational level; teacher support technology; technology to help the child in the learning process; family interaction technology; technology of personality education. The following methods of inclusive learning in mathematics lessons in primary school are described: supportive learning, parallel learning, alternative learning, learning in small groups, alternate learning, team learning. Innovative technologies are considered, which realize the complex multilevel character of support of participants of pedagogical process and by means of which support of participants of educational process at lessons of mathematics is carried out: technology of adaptation of the child to new educational level; teacher support technology; technology to help the child in the learning process; family interaction technology; technology of personality education. The aspects that belong to the category of methodological and educational requirements and which must be performed by the teacher in mathematics lessons in an inclusive classroom are indicated: the topic of the lesson should correspond to long-term planning; pedagogical communication should have clearly defined goals, adaptation of the structure of the mathematics lesson. Options for solving the problem of planning a math lesson and creating a lesson outline in an inclusive classroom, focusing on children with special educational needs. Planning a math lesson in an inclusive classroom should include both general education tasks to meet the educational needs within the state standard and correctional and developmental tasks.
Research Guides: Maroon Edition 2021: The 7 Habits of Highly Effective People: For Faculty: Using the Maroon Edition in Your Class

An introduction to resources related to the 2021/2022 Maroon Edition Common Book, The 7 Habits of Highly Effective People.
Global Perspective: Cyberlaw, Regulations and Compliance

Cyber security provides protection to the internet connected networks and system from the cyber-attacks. To stop attacks everyone must know and aware of all cyber law, regulations and compliance to secure the cyber. Cyber security is all about to stop cyber-crime. Cyber security is must and we have to know about all safety measures required to stop cyber-crime. This paper give details information about cyber security and its safety measure. Also we will discuss about the activities related to it and how actually cyber-crime happens and all steps taken by the various organization and Government to have cyber ethics everywhere. Cyber security provides protection against the cyber-crime and teach us what essential safety measures one need to follow from all cyber-crimes. Securing online information is priority where everyone is involved with technology. Whenever anyone talked about cyber security, straight one thing comes in mind that is cyber-crime and what safety measures need to take to be safe from it.
Spectral Graph Convolutional Networks WithLifting-based Adaptive Graph Wavelets

Spectral graph convolutional networks (SGCNs) have been attracting increasing attention in graph representation learning partly due to their interpretability through the prism of the established graph signal processing framework. However, existing SGCNs are limited in implementing graph convolutions with rigid transforms that could not adapt to signals residing on graphs and tasks at hand. In this paper, we propose a novel class of spectral graph convolutional networks that implement graph convolutions with adaptive graph wavelets. Specifically, the adaptive graph wavelets are learned with neural network-parameterized lifting structures, where structure-aware attention-based lifting operations are developed to jointly consider graph structures and node features. We propose to lift based on diffusion wavelets to alleviate the structural information loss induced by partitioning non-bipartite graphs. By design, the locality and sparsity of the resulting wavelet transform as well as the scalability of the lifting structure for large and varying-size graphs are guaranteed. We further derive a soft-thresholding filtering operation by learning sparse graph representations in terms of the learned wavelets, which improves the scalability and interpretablity, and yield a localized, efficient and scalable spectral graph convolution. To ensure that the learned graph representations are invariant to node permutations, a layer is employed at the input of the networks to reorder the nodes according to their local topology information. We evaluate the proposed networks in both node-level and graph-level representation learning tasks on benchmark citation and bioinformatics graph datasets. Extensive experiments demonstrate the superiority of the proposed networks over existing SGCNs in terms of accuracy, efficiency and scalability.
Analisis praktik belajar dari rumah kelas VI SD Gayamsari 02 Semarang di masa pandemi COVID-19

The aim of this study was to find out how the practice learning from home for class VI SDN Gayamsari 02 Semarang during COVID-19 pandemic could be implemented. The type of research used is a qualitative method with descriptive approach. This study uses several methods to collect data, including: interviews, observations, questionnaires and documentation. The data analysis technique used is triangulation technique. Results of this study indicate that Class VI students SDN Gayamsari 02 carry out learning at home accompanied by parents/family members/tutors.  In one week students study from home 5-6 days with an average of 1-2 hours every day. Implementing learning from home that is taught by teachers to students is by communicating via WhatsApp, providing learning videos, assignments, making projects/creativity practices, and online learning through zoom/google meet application. Social media that are most often used to help by students during learning are whatsapp and youtube. Obstacles experienced by students are lack of concentration, difficulty understanding lesson, students feel bored and cannot ask questions directly to teacher. Support provided by the school while studying from home is by providing internet data packages and lending books. Internet facilities and student devices are also available and can be used properly.
Cultura digital y el desarrollo de competencias matemáticas en la educación universitaria

Esta investigacion tuvo como objetivo identificar el nivel de cultura digital y su relacion con el desarrollo de competencias matematicas en los estudiantes al inicio de la carrera universitaria. El diseno de investigacion fue mixto, la muestra estuvo constituido por 43 estudiantes del segundo ciclo de la carrera profesional de economia, para la recoleccion de datos y produccion de informacion se tuvo en cuenta la observacion directa, cuestionario, entrevista y aplicacion de rubrica de evaluacion de logro de capacidades. Tras la realizacion del trabajo de campo se obtuvo como resultado, que el 85% de los estudiantes tienen acceso y son usuarios natos de la tecnologia digital, mas del 50% en primera instancia no utilizaban la tecnologia con fines educativos, la insercion de la tecnologia en el proceso didactico influyo de manera significativa durante las actividades educativas, habituandose al uso pertinente de su conocimiento y mentalidad digital, mostrando agilidad y mentalidad innovadora en trabajos colaborativos, como parte de su cultura digital; mostrando conformidad sobre las actividades realizadas mas del 84% de los participantes. Evidenciandose, que la cultura digital de los estudiantes expresado en el uso eficiente de los recursos que brinda tecnologia, tiene relacion directa y favorece de manera significativa en el desarrollo de las competencias matematicas de los estudiantes universitarios.Palabras clave: Cultura digital, recurso digital, competencias matematicas, estudiante universitario. ABSTRACTThis research aimed to identify the level of digital culture and its relationship with the development of mathematical competencies in students at the beginning of the university career. The research design was mixed, the sample consisted of 43 students from the second cycle of the professional career in economics, for data collection and information production, direct observation, and questionnaire, interview and application of the evaluation rubric were taken into account ability achievement. After carrying out the field work, it was obtained as a result that 85% of the students have access and are natural users of digital technology, more than 50% in the first instance did not use technology for educational purposes, the insertion of technology in the didactic process he had a significant influence during educational activities, getting used to the pertinent use of his knowledge and digital mentality, showing agility and innovative mentality in collaborative work, as part of his digital culture; showing agreement on the activities carried out, plus 84% of the participants. Evidenced that the digital culture of students expressed in the efficient use of resources provided by technology, has a direct relationship and significantly favors the development of mathematical competencies of university students.Keywords: Digital culture, digital resource, mathematical skills, university student.
IMPACT OF SOCIAL MEDIA INFLUENCERS ON CUSTOMER ENGAGEMENT AND LOYALTY: A STUDY OF SELECTED HEALTHCARE PRODUCTS IN CHENNAI

Customer is a king and plays vital role in determining the success of an organization. In order to survive and grow, it is important to boost the engagement of the customers by employing various marketing strategies with the latest trends and follow innovative methods in increasing their loyalty towards the organization. Many previous researches from around the world pointed out that enhancing customer engagement is one of the important goals and strategies of the success of the organization. Not only does customer engagement have the potential to significantly affect customer retention, it is also a key link to customer loyalty. The present study identifies and analyses the various Social Media Influencer (SMI) factors that affecting customer engagement and its impact on the level of customer engagement and customer loyalty towards Healthcare Products in Chennai. Based on a sample of 160 customers who are buying healthcare products with the influence of Social Media Influencers with respect to Healthcare Products in Chennai, descriptive and inferential analysis are carried out. Independent Sample “t” test, Correlation Analysis and Multiple Regression are used to test the hypotheses. The research reveals that there is a strong relationship between the SMI factors (Content Quality, Interaction, Attractiveness and Expertise) that affecting Customer Engagement and the level of Customer Engagement and Customer Engagement has strongly influenced the Customer Loyalty towards Healthcare Products. The study revealed that the female customers have more engagement with respect to healthcare products than the male customers. Out of five SMI factors that affecting the level of Customer Engagement, ‘Attractiveness’ is the strongest factor and ‘Trustworthiness’ is the weakest factor in determining the level of Customer Engagement and Customer Loyalty towards Healthcare Products. The study suggests that the management of Healthcare Products should design and implement effective customer engagement practices with the help of Social Media and take appropriate steps to improve the customer loyalty.
Hierarchical expert networks for meta-learning

The goal of meta-learning is to train a model on a variety of learning tasks, such that it can adapt to new problems within only a few iterations. Here we propose a principled information-theoretic model that optimally partitions the underlying problem space such that specialized expert decision-makers solve the resulting sub-problems. To drive this specialization we impose the same kind of information processing constraints both on the partitioning and the expert decision-makers. We argue that this specialization leads to efficient adaptation to new tasks. To demonstrate the generality of our approach we evaluate three meta-learning domains: image classification, regression, and reinforcement learning.
Stability and Generalization for Randomized Coordinate Descent

Randomized coordinate descent (RCD) is a popular optimization algorithm with wide applications in various machine learning problems, which motivates a lot of theoretical analysis on its convergence behavior. As a comparison, there is no work studying how the models trained by RCD would generalize to test examples. In this paper, we initialize the generalization analysis of RCD by leveraging the powerful tool of algorithmic stability. We establish argument stability bounds of RCD for both convex and strongly convex objectives, from which we develop optimal generalization bounds by showing how to early-stop the algorithm to tradeoff the estimation and optimization. Our analysis shows that RCD enjoys better stability as compared to stochastic gradient descent.
RANCANG BANGUN SISTEM INFORMASI PRESENSI KARYAWAN PADA PT INFINITE BUSINESS SYNERGY DI JAKARTA

The recording of employees' daily attendance still uses manual input. With these conditions, HRD staff find  it  difficult  to control employees  when the hours are in, whether the employee is really in the office. The next obstacle is recording attendance written  on  paper,  it  will  be  vulnerable  to  lost  data  damaged  and  difficulty  in recapitulating it. This will lead to uncontrolled employees. In the research phase carried out is collecting  field  data by observation,  and  analyzing  system requirements.  By looking at the needs, the system is made web-based, the next stage is to design, build a database with MYSQL, design the user interface, build a system using the Laravel framework, and create and test programs. Based on the results of the system test, the design of the employee attendance information system at PT Infinite Business Synergy in Jakarta can facilitate the parties with an interest in this final project (in terms of authors, companies, and in education) to obtain the information and benefits needed, to become a solution in increasing discipline for employees as well as making it easier to record, control and report because it is easily accessible via web media. Keywords : Framework, Design, Employees, Presence
Management system to minimize loss of data and time wastage in library

Manually managing library operations wastes time and adds needless tension to the situation. In light of the above, the computerized framework for managing library activities allows the best possible use of available library manpower and resources for the benefit of users. The aim of this design is to provide solutions to the challenges that manual library systems face. The Library Management System and the Online Library System are also included in this comprehensive design. It's a website that makes it simple for students and staff to use the library while also automating library processes by keeping track of library resources and automatically allocating them.
Study of the Distribution and Modification of Writing in Proto-Chinese Language Communities: Correct Identification of Hieroglyphic Signs in the Jiaguwen Inscriptions

The Chinese language uses the ideographic writing. The outer form of Chinese characters in 3rd century ВС and later underwent strong changes, which were caused mainly by change in the technique of writing. In the first century AD the writing style that has survived to the present time. We recall some of main results of the spread and change of linguistic information in certain Chinese community obtained with the help of a dynamic nonlinear model. Undoubtedly, Oracle script writings Jiaguwen (甲骨文) played an important role in this process. We consider it quite legitimate to discuss rather about the written language of the Chinese language, as main link between ancient Chinese, Middle Chinese, and modern Chinese languages Putonghua. In the study of the oldest examples of art, culture and science, for example, such as Jiaguwen, there is a need to study ancient hieroglyphs depicted on the surface of a tortoise shell, copper vessel or stone. New method for correct solving of an inverse problem for pattern recognition is presented. Our method is based on photometry of the studied surface. The obtained data are digitally processed in order to determine the characteristics and parameters characterizing the properties of the investigated surface. The possibilities and limitations of the method, as well as the mathematical model of the groove in the optical range, are given. The main attention is paid to the development of methods for more accurate calculation of the number of solitary grooves and structural elements such as hieroglyphs or ancient Chinese radicals (部首 / bùshǒu). This task is part of the complex inverse problem of pattern recognition, namely: identification of various structural elements, for example, ancient hieroglyphs depicted on the surface of a tortoise shell. To solve this important problem, two main methods were proposed, which are based on the study of both the initial photometric data and the study of their Fourier spectra. The first method allows you to visually identify the location of the characters along the photometric line and determine their approximate number. The second method allows us to get a stable and unambiguous solution to this problem: to determine accurately the number of characters on the photometric line.
Design of Network Management System for Access Network of Power Dispatching Data Network under Information Security Protec-tion

Ubiquitous power Internet of Things puts forward the architecture of application layer, platform layer, network layer and perception layer, in which the communication attribute of network layer makes it very important, and power dispatching data network is the key link in the network layer. With the accelerated construction of the dual-plane access network of power dispatching data network, the information security protection system covers the plant-station side and the service side in an all-round way, which not only strengthens the security of service information access, but also leads to the blind area and fragmentation of management in the original data network. Based on the existing dual-plane architecture of power dispatching data network access network, this paper analyzes the influence of network information security protection deployment on network management, deeply studies the business characteristics and access modes of production control areas, and puts forward the network management system configuration and communication mode design under the information security protection framework, so as to realize the efficient and convenient management of the access network of dual-plane dispatching data network.
Wireless Motes Outlier Detection Taxonomy Using ML-Based Techniques

WSNs have enthused resurgence in research on machine learning-based approaches with the intent of overcoming the physical restraints of sensors. Although resource constrained in nature, WSNs domain has tremendous potential for building powerful applications, each with its own individual characteristics and requirements. This fascinating field of WSNs although comprises of various research issues and challenges, viz. energy efficiency, localization, etc., which needs to rectified. One such prominent challenge is detection of outliers whose chore is to preclude any kind of malicious attacks in the network or lessen the noisy error prone data in millions of wireless sensor networks. To do so, the methodologies developed needs to take care of inherent limits of sensor networks so that the energy intake of motes is minimum and lifespan of the motes is maximized. Consequently, the quality of data must be thoroughly patterned as any kind of outlier in the sensed network may degrade the quality of the data and hence affect the final decision. Thus, it becomes imperious to retain the quality of the data. Numerous ML-based methodologies have been used by several researchers over the time to detect any form of outliers or anomaly present in the network. In this paper, some machine learning methodologies have been discussed which have proved their mettle in outlier detection for sensor networks. This paper presents a brief survey on outlier detection in WSNs data using various ML-based techniques.
Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks

Despite the great successes achieved by deep neural networks (DNNs), recent studies show that they are vulnerable against adversarial examples, which aim to mislead DNNs by adding small adversarial perturbations. Several defenses have been proposed against such attacks, while many of them have been adaptively attacked. In this work, we aim to enhance the ML robustness from a different perspective by leveraging domain knowledge: We propose a Knowledge Enhanced Machine Learning Pipeline (KEMLP) to integrate domain knowledge (i.e., logic relationships among different predictions) into a probabilistic graphical model via first-order logic rules. In particular, we develop KEMLP by integrating a diverse set of weak auxiliary models based on their logical relationships to the main DNN model that performs the target task. Theoretically, we provide convergence results and prove that, under mild conditions, the prediction of KEMLP is more robust than that of the main DNN model. Empirically, we take road sign recognition as an example and leverage the relationships between road signs and their shapes and contents as domain knowledge. We show that compared with adversarial training and other baselines, KEMLP achieves higher robustness against physical attacks, $\mathcal{L}_p$ bounded attacks, unforeseen attacks, and natural corruptions under both whitebox and blackbox settings, while still maintaining high clean accuracy.