Reviewerly/exHarmony huggingface dataset:
MODERN APPROACHES TO THE FORMATION OF INTERDISCIPLINARY COMPETENCIES OF TEACHERS-DEFECTOLOGISTS

Рассматриваются тенденции подготовки дефектологов с учетом исторического аспекта и анализа современного состояния проблемы. Определены основные направления, определяющие тенденции формирования междисциплинарных компетенций, обозначены причины и проанализирован имеющийся исторический опыт подготовки полифункциональных специалистов.Различные виды профессиональной деятельности педагога-дефектолога в условиях инклюзивного образования предполагают наличие профессиональных и личностных компетенций, что диктует необходимость расширения междисциплинарного подхода за счет пересмотра методик проведения лекционных занятий через использование поисковой, исследовательской деятельности студентов, решение различных профессиональных задач в рамках содержания учебной программы на основе использования принципа трансдисциплинарности. Проанализированы различные формы проведения лекционных и практических занятий, определены организационные параметры. Trends in the training of defectologists are considered, taking into account the historical aspect and analysis of the current state of the problem. The main directions that determine the trends in the formation of interdisciplinary competencies are determined, the reasons are indicated and the existing historical experience of training interdisciplinary specialists is analyzed. The reasons for the need to include interdisciplinary technologies and forms of interaction in the training of specialists are highlighted. Various types of professional activities of teacher-defectologists in the conditions of inclusive education presuppose the presence of professional and personal competencies, which dictates the need to expand the interdisciplinary approach by revising the methods of conducting lectures through the use of search, research activities of students, solving various professional tasks within the content of the curriculum based on the use of the principle of interdisciplinarity. Various forms of conducting lectures and practical classes are analyzed, organizational parameters are determined. The emphasis is made on the application of the methods of conducting lectures, using the search, research activities of students, which makes it possible to enrich literacy in related areas of defectological science. The authors indicated the need to introduce the content of professional disciplines for solving defectological problems through the establishment of interdisciplinary connections, using interactive test tasks of a different nature. An innovative form of control in the form of a practice-oriented exam has been determined, which allows to determine the readiness of a future specialist to solve professional problems.
How (Not) to Deploy Cryptography on the Internet

The core protocols in the Internet infrastructure play a central role in delivering packets to their destination. The inter-domain routing with BGP (Border Gateway Protocol) computes the correct paths in the global Internet, and DNS (Domain Name System) looks up the destination addresses. Due to their critical function they are often attacked: the adversaries redirect victims to malicious servers or networks by making them traverse incorrect routes or reach incorrect destinations, e.g., for cyber-espionage, for spam distribution, for theft of crypto-currency, for censorship [1, 4-6]. This results in relatively stealthy attacks which cannot be immediately detected and prevented [2, 3]. By the time the attacks are detected, damage was already done.
Efficient Architecture Search for Diverse Tasks

While neural architecture search (NAS) has enabled automated machine learning (AutoML) for well-researched areas, its application to tasks beyond computer vision is still under-explored. As less-studied domains are precisely those where we expect AutoML to have the greatest impact, in this work we study NAS for efficiently solving diverse problems. Seeking an approach that is fast, simple, and broadly applicable, we fix a standard convolutional network (CNN) topology and propose to search for the right kernel sizes and dilations its operations should take on. This dramatically expands the model's capacity to extract features at multiple resolutions for different types of data while only requiring search over the operation space. To overcome the efficiency challenges of naive weight-sharing in this search space, we introduce DASH, a differentiable NAS algorithm that computes the mixture-of-operations using the Fourier diagonalization of convolution, achieving both a better asymptotic complexity and an up-to-10x search time speedup in practice. We evaluate DASH on ten tasks spanning a variety of application domains such as PDE solving, protein folding, and heart disease detection. DASH outperforms state-of-the-art AutoML methods in aggregate, attaining the best-known automated performance on seven tasks. Meanwhile, on six of the ten tasks, the combined search and retraining time is less than 2x slower than simply training a CNN backbone that is far less accurate.
A Survey on Auto-Parallelism of Neural Networks Training

The datas in this survey were collected from reference papers.
Evaluating the Effectiveness of Handling Abusive Domain Names by Internet Entities

A large number of domains are abused every day for cybercrime. At the same time, the fight against abusive domains is not the fight of one person or organization but a battle that requires the cooperation of the entire community. However, very little research has been done to quantify the positive benefits of this strategy for dealing with abusive domains. As a result, using pornography and gambling domain names as examples, we present the first empirical study evaluating the usability and effectiveness of all Internet entities (e.g., registrars and hosting providers) in the DNS ecosystem for receiving and handling abusive domain reports. First, the paper thoroughly demonstrates the mechanisms for receiving and handling abusive domain reports at various Internet entities in China. Second, we select and report the appropriate 2433 abusive domains to 43 service providers across six categories of Internet entities. Finally, we discover the methods and response time used by each Internet entity to handle abuse reports based on the changes in reported domains. Based on the above data, we analyze and evaluate the effectiveness of Internet entities in dealing with abusive domains. Moreover, we indicate the scope of protection and disadvantages of each method, i.e., whether the abusive domain can escape handling. The paper aims to provide a more detailed overview and reference for the security communities, service providers, and Internet entities concerned with dealing with abusive domains.
Exploiting Embodied Simulation to Detect Novel Object Classes Through Interaction

In this paper we present a novel method for a naive agent to detect novel objects it encounters in an interaction. We train a reinforcement learning policy on a stacking task given a known object type, and then observe the results of the agent attempting to stack various other objects based on the same trained policy. By extracting embedding vectors from a convolutional neural net trained over the results of the aforementioned stacking play, we can determine the similarity of a given object to known object types, and determine if the given object is likely dissimilar enough to the known types to be considered a novel class of object. We present the results of this method on two datasets gathered using two different policies and demonstrate what information the agent needs to extract from its environment to make these novelty judgments.
Spatiotemporal Matching Cost Function Based on Differential Evolutionary Algorithm for Random Speckle 3D Reconstruction

Random speckle structured light can increase the texture information of the object surface, so it is added in the binocular stereo vision system to solve the matching ambiguity problem caused by the surface with repetitive pattern or no texture. To improve the reconstruction quality, many current researches utilize multiple speckle patterns for projection and use stereo matching methods based on spatiotemporal correlation. This paper presents a novel random speckle 3D reconstruction scheme, in which multiple speckle patterns are used and a weighted-fusion-based spatiotemporal matching cost function (STMCF) is proposed to find the corresponding points in speckle stereo image pairs. Furthermore, a parameter optimization method based on differential evolutionary (DE) algorithm is designed for automatically determining the values of all parameters included in STMCF. In this method, since there is no suitable training data with ground truth, we explore a training strategy where a passive stereo vision dataset with ground truth is used as training data and then apply the learned parameter value to the stereo matching of speckle stereo image pairs. Various experimental results verify that our scheme can realize accurate and high-quality 3D reconstruction efficiently and the proposed STMCF exhibits superior performance in terms of accuracy, computation time and reconstruction quality than the state-of-the-art method based on spatiotemporal correlation.
A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond

Non-autoregressive (NAR) generation, which is first proposed in neural machine translation (NMT) to speed up inference, has attracted much attention in both machine learning and natural language processing communities. While NAR generation can significantly accelerate inference speed for machine translation, the speedup comes at the cost of sacrificed translation accuracy compared to its counterpart, autoregressive (AR) generation. In recent years, many new models and algorithms have been designed/proposed to bridge the accuracy gap between NAR generation and AR generation. In this paper, we conduct a systematic survey with comparisons and discussions of various non-autoregressive translation (NAT) models from different aspects. Specifically, we categorize the efforts of NAT into several groups, including data manipulation, modeling methods, training criterion, decoding algorithms, and the benefit from pre-trained models. Furthermore, we briefly review other applications of NAR models beyond machine translation, such as grammatical error correction, text summarization, text style transfer, dialogue, semantic parsing, automatic speech recognition, and so on. In addition, we also discuss potential directions for future exploration, including releasing the dependency of KD, reasonable training objectives, pre-training for NAR, and wider applications, etc. We hope this survey can help researchers capture the latest progress in NAR generation, inspire the design of advanced NAR models and algorithms, and enable industry practitioners to choose appropriate solutions for their applications. The web page of this survey is at \url{https://github.com/LitterBrother-Xiao/Overview-of-Non-autoregressive-Applications}.
IOT Based Deep Learning framework to Diagnose Breast Cancer over Pathological Clinical Data

Metastasis of breast cancer cells is a critical element in determining a patient&#x0027;s prognosis. A sentinel lymph node biopsy may be used to determine metastases. The standard pathologist examination procedure, on the other hand, is redundant and time consuming, and it is easy to overlook micro metastatic lesions. At the moment, the findings of employing a convolutional neural network to research breast cancer sentinel lymph node metastases have been obtained. Nonetheless, the accuracy rate is low, and the micro metastasis detection impact is poor. A multichannel convolutional neural network model was constructed and suggested in answer to the aforesaid challenges using the sentinel lymph node pathological imaging dataset of breast cancer (PCam). The model employs stacked multichannel convolutional units and IOT based CNN modules, as well as skip cross-layer connections, a mix of conventional and depth wise separable convolutions, and a combination of sum and concatenation operations. Iteratively train 50&#x0025; of the photos 35 times to produce the model weights. Then, using the accuracy and area under the curve (AUC) values, evaluate the test pictures. Accuracy is 97.32 percent and AUC is 98.05 percent. When compared to the findings of previous research and mainstream convolutional network models, the model scores first in AUC values for 49 percent, 51 percent, and 100&#x0025; test sets. The findings indicate that the model is very accurate at recognizing lymph node metastasis and performs well at detecting micro metastases.
A Fisher Information-Based Incompatibility Criterion for Quantum Channels

We introduce a new incompatibility criterion for quantum channels, based on the notion of (quantum) Fisher information. Our construction is based on a similar criterion for quantum measurements put forward by H.~Zhu. We then study the power of the incompatibility criterion in different scenarios. Firstly, we prove the first analytical conditions for the incompatibility of two Schur channels. Then, we study the incompatibility structure of a tuple of depolarizing channels, comparing the newly introduced criterion with the known results from asymmetric quantum cloning.
Prospects of standardization of professional education

The authors consider the problem of standardization in the system of professional education under the modern conditions. In the Soviet time unification in the system of professional education was comprehensive and based on the model curricula approved by the corresponding sectoral ministries. In 1992 the Law on Education of the Russian Federation introduced educational standards to preserve the unity of professional education in the country. At the same time the formation of the higher education structure of our country was taking place. In 2000 educational and methodical associations developed uniform standards for each area of training of graduates - the second generation standards. After Russia entered the European Educational Area the standards were developed for each direction (specialty). The fundamental difference of the third generation standards involved new approaches to the assessment of the results of higher educational training: transition from the traditional orientation on formation of knowledge, skills and abilities to the assessment of social, personal and professional competences of the graduates. Introduction of competencies as the results of the training into standards implied transformation of the system of students' education and training in order to obtain these competences. However, the universities preserved traditional system of lectures and seminars, which involves the transfer of information and its memorization by students. After numerous modernizations the third generation standards have become prescription in nature and have lost their relevance The authors suggest developing standards for organizing educational process for each category of educational programs (Bachelor, Specialist, Master) to represent general requirements for the term and form of training, the program structure and the conditions of their implementation. The educational and methodical associations together with representatives of potential employers ought to develop qualification characteristics for the graduates of the training directions (specialties). The characteristics ought to be systematically updated according to the transformations of the labour market. Educational programs developed by the universities ought to become the main documents within the professional education system. The education system will be constructed on the basis of academic freedoms and autonomy of educational institutions responsible for the quality of the students' training.
College English Intercultural Teaching Strategies Based on Network Construction under the Concept of Teaching to the Future

With the global integration and the development of science and technology, higher requirements have been put forward for English teaching in China. Society urgently needs English talents with high English cross-cultural communication ability and strong self-learning ability. The concept of “Teaching for the Future” (TTF) advocates the use of technology as a tool for teaching and learning. In the context of the “big data” era, the rapid development of network information technology has spawned a series of new educational models. Based on the SPOC teaching model, this paper constructs a new cross-cultural teaching model for college English and conducts experiments on two selected classes of the same grade in a university. The experimental results show that students increase their interest in learning English culture and enhance their ability to communicate and collaborate. Therefore, the integration of future education and network technology represented by SPOC will play a certain role in promoting the cross-cultural teaching of English in colleges and universities.
Preparation of future teachers for organizational activities

The essence of organizational activity is determined, the relevance of training teachers for organizational activity is justified on the basis of the requirements of professional standards “Teacher (pedagogical activity in the field of preschool, primary general, basic general, secondary general education)ˮ, “Specialist involved in organizing the activities of a children's and youth team (counselor)ˮ, “Teacher of additional education for children and adultsˮ. The purpose, tasks, approaches (reflexive-activity, subject-oriented, practice-oriented), binary principles of preparation are considered. The content of the module “Educational activityˮ is revealed, consisting of three interrelated disciplines and practices, the content of which provides for the study of theoretical aspects of organizational activity, the use of modern technologies for the development of organizational abilities of future teachers, the formation of the need for self-organization and self-development. The possibilities of educational classes, extracurricular activities for preparing students for organizational activities are shown. A subject-oriented technology is proposed, including self-diagnosis, self-analysis, self-determination, self-realization, self-esteem and self-affirmation, which can be the basis for the development of students' self-organization and the formation of their subjective position. The relevance of students' involvement in the design of individual and collective activities is emphasized, and the importance of organizing collective creative activities, project activities, and the development of student self-government is also noted. In conclusion, conclusions are drawn about the conditions for preparing students for organizational activities.
Interoperability in AECO and the oil &amp; gas sectors: object-based standards and systems

This research proposes recommendations that could improve interoperability in the architecture, engineering, construction and operations (AECO) sector, by connecting domains, building lifecycles, and software systems with each other and the web. The objective has been to identify methods that promote evolution from file-based formats by advancing object-based data exchange solutions. The research design is a mapping of standards and systems that have affected the nature of object-based data exchanges, and which have either been proposed or implemented in AECO and the Oil &amp; Gas sector. This is an approach which allows for the range and diversity of information to be examined. A review of the Oil &amp; Gas sector confirms a norm where object-based, rather than file-based, transactions have shaped data exchange models, formats, use case methodologies, and collaboration mechanisms, thus contributing towards semantic connectivity across its diverse systems. Key research questions address the nature of these sectors, the promise that object-based data exchange offers, and examine recommendations that would improve standards and systems. The paper affirms that measures taken to improve interoperability in the Oil &amp; Gas sector have relevance for the AECO sector, and that better understanding, and recognition of the structure of AECOs interoperability ecosystem is central to effecting lasting and significant change. Thus, we make recommendations that acknowledge the hybrid nature of AECO data exchanges and propose an interoperability ecosystem that connects both distributed and centralised federated models. Improved standards to define application programming interfaces (APIs) and adaptors, based on a modular approach, would be central to this proposal. We also make recommendations to improve use case definitions, and to ensure that semantic connectivity at the object level is scalable to web-based transactions. Finally, we assert that, to realise these changes, the developers and vendors of its systems should recognise and address the AECO sector’s pressing needs and concerns.
[DC] Annotation in Asynchronous Collaborative Immersive Analytic Environments using Augmented Reality

Immersive Analytics (IA) and Augmented Reality (AR) head-mounted displays provide a different paradigm for people to analyze multidimensional data and externalize their thoughts by utilizing the stereoscopic nature of headsets. However, using annotation in IA-AR is challenging and not well-understood. In addition, IA collaborative environments add another complexity level for users operating on complex visualized datasets. Current AR systems focus mainly on synchronized collaboration, while asynchronous collaboration has remained unexplored. This project investigates annotation in IA for asynchronous collaborative environments. We present our research studies on virtual annotation types and introduce a new filtering annotation technique for IA.
Leveraging Digital Library to Enhance Research and Learning Experience of College Students: An In-Depth Study

A digital library is a platform that contains collections of books, services, and personnel to support the sharing of knowledge with creation, dissemination, and preservation. In this context, any university library should comprehensively embrace the developmental trend occurring in the library setting, which should be strictly followed by university libraries as a special mission. Digital libraries should also actively promote the updating of the embedded service model and further upgrade the various resources of the university library. Thus, the digital library provides a platform to assist students to develop an inclination towards learning and emotional shaping. Its functional system thus serves both comprehensive and harmonious development of students. Especially, the knowledge service module of the digital library incorporates users’ scientific research context. Most of the existing research studies focus on the individual researcher and neglects the context of the entire research team. Knowledge recommendation for the context of team-based scientific research activities can better serve more scientific research activities and team cooperation. In this work, we propose a knowledge recommendation algorithm for digital libraries based on team research-knowledge application in context matching. We leverage the context-aware learning model to construct the corresponding application context of the digital library knowledge and the context model of team scientific research. Subsequently, we select alternative knowledge and neighbor users as active users and further complete knowledge sorting and recommendation. According to the knowledge recommendation system in a digital library, it is confirmed that the proposed method can effectively deliver the knowledge of researchers in the context of the digital library.