Reviewerly/exHarmony huggingface dataset:
The Transformation of the Reading Model in a Digital Culture

The article explores the transformation of the traditional reading model in the conditions of the modern information society and digital culture. The author considers the problem in the anthropological aspect as a radical cognitive restructuring of reading perception and modernization of reading practices. This requires a revision of the essential concepts of the reading theory and of methodological approaches to their study. Firstly, this concerns texts that change their nature in the digital environment and acquire a multimodal polycode character. Changing the medium and the way in which it is recorded leads to a change from linear, sequential reading to a parallel, divergent way of reader’s thinking, which significantly affects the depth of attention and understanding of the text. In this regard, researchers note the gradual replacement of slow, thoughtful reading by widely used scanning techniques, superficial search reading that realizes specific pragmatic goals. The modern reading model replaces the traditional, consolidating function with a differentiating one, related to the stratification of the readership and the formation of different reading subcultures and online reading communities. Internet reading, which is especially popular among young people, is becoming “social”, aiming at active online communication and the creation of its own fanfiction content. This removes the institutional distance between the author and the reader, and significantly changes the status position of reading, which loses its role as a terminal value and becomes a secondary, collateral occupation. Reader differentiation is also evident in the mental digital generational divide; reading practices of adherents of the traditional reading model become increasingly elitist in today’s socio-cultural context. This gap is exacerbated by the current trend towards the visualisation of reading, which accelerates perception of information but negates essential reading competencies. The transformation of the reading model has deep historical roots and is legitimate, so the reader’s ability to be “multitextual”, i.e. to switch to different modes of perception, choosing different reading strategies depending on the text and the situations, is important today This can be facilitated by education and book publishing that adapt to the characteristics of the new reading model.
VAPKI: A Blockchain-Based Identification System with Validation and Authentication

The traditional public key infrastructure (PKI) issues certificates by trusted certification authority (CA). But due to the centralized structure of CA, it brings some problems, such as single-point failure, certificate opacity and so on. Another decentralized system web of trust (WOT) has a high access threshold. In addition, WOT cannot ensure the authenticity of users’ authentication because of lack of incentive, and it cannot authenticate the specific user identity. In this paper, we propose a new distributed identification system based on blockchain, called VAPKI. This system is implemented through smart contracts on Ethereum, which is transparent and immutable. Meanwhile, it can authenticate the fine-grained attributes of user identity and strengthen the authenticity of user identity through transparent authentication. In addition, VAPKI can identify the malicious user through the regular validation tasks (RVTs). In addition, it realizes the credit and deposit mechanism, ensuring users to be honest and trustworthy. This mechanism can also force users to authenticate strictly and punish malicious users found in RVTs.
Adaptive Controller based on Uncertainty Observer for a Class of Affine T-S Fuzzy Models

A class of affine T-S fuzzy models with uncertainty is considered in this paper. Compared with the T-S fuzzy model which is widely used in fuzzy control systems, affine T-S fuzzy model is regarded as a more effective model to describe the dynamics of the system concerned. However, the difficulty is how to handle the affine term in the model. In this paper, following a partition of state space that enables us to employ the S-procedure in an effort to deal with the affine term, a switching-type controller is proposed. The controller contains an uncertainty observer that is used to tackle the existence of the uncertainty and an adaptive law that is used to deal with others that are not covered by the uncertainty observer. In additions, examples are provided in order to demonstrate the effectiveness of the approach proposed in the paper.
Individuals redistribution based on differential evolution for covariance matrix adaptation evolution strategy

Among population-based metaheuristics, both Differential Evolution (DE) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) perform outstanding for real parameter single objective optimization. Compared with DE, CMA-ES stagnates much earlier in many occasions. In this paper, we propose CMA-ES with individuals redistribution based on DE, IR-CMA-ES, to address stagnation in CMA-ES. We execute experiments based on two benchmark test suites to compare our algorithm with nine peers. Experimental results show that our IR-CMA-ES is competitive in the field of real parameter single objective optimization.
Discovering Causal Structure with Reproducing-Kernel Hilbert Space $ε$-Machines

We merge computational mechanics' definition of causal states (predictively-equivalent histories) with reproducing-kernel Hilbert space (RKHS) representation inference. The result is a widely-applicable method that infers causal structure directly from observations of a system's behaviors whether they are over discrete or continuous events or time. A structural representation -- a finite- or infinite-state kernel $\epsilon$-machine -- is extracted by a reduced-dimension transform that gives an efficient representation of causal states and their topology. In this way, the system dynamics are represented by a stochastic (ordinary or partial) differential equation that acts on causal states. We introduce an algorithm to estimate the associated evolution operator. Paralleling the Fokker-Plank equation, it efficiently evolves causal-state distributions and makes predictions in the original data space via an RKHS functional mapping. We demonstrate these techniques, together with their predictive abilities, on discrete-time, discrete-value infinite Markov-order processes generated by finite-state hidden Markov models with (i) finite or (ii) uncountably-infinite causal states and (iii) continuous-time, continuous-value processes generated by thermally-driven chaotic flows. The method robustly estimates causal structure in the presence of varying external and measurement noise levels and for very high dimensional data.
Improved Yolov4-tiny Algorithm Based on Cascade Residual Dilated Fusion

Performance of object feature extraction and fusion is not high in the detection process of yolov4-tiny algorithm, which leads to the low accuracy. An improved algorithm, dcca-yolov4-tiny based on cascaded residual dilation fusion mechanism embedded in location information, is proposed. The dilated convolution is used to increase the receptive field, and based on the idea of residual, the cascade residual fusion mechanism is used for feature fusion to improve the effect. The spatial features containing position information are embedded into the dilation convolution, so that the network can adaptively learn the spatial feature information. Two fusion methods are used combined with the position information to further improve the fusion effect of different object receptive field features and improve the accuracy of object detection. Experimental results on the VOC(Visual Object Classes) test set show that the proposed cascade residual dilated fusion module can promote the algorithm to effectively improve the accuracy of object detection. The mAP can reach 80.86%, which is 4.73% higher than the original model.
Research on various image processing techniques

Digital image processing deals with the manipulation of digital images through a digital computer. It is a subfield of signals and systems but focuses particularly on images. DIP focuses on developing a computer system that can perform processing on an image. The input of that system is a digital image and the system process that image using efficient algorithms and gives an image as an output. The most common example is Adobe Photoshop. It is one of the widely used applications for processing digital images. The image processing techniques play a vital role in image Acquisition, image pre-processing, Clustering, Segmentation, and Classification techniques with different kinds of images such as Fruits, Medical, Vehicle, and Digital text images, etc. In this study, the various images remove unwanted noise and performance enhancement techniques such as contrast limited adaptive histogram equalization.
A Hierarchical Simple Probabilistic Population-Based Algorithm Applied to the Dynamic TSP

This work presents a Hierarchical Simple Probabilistic Population-Based algorithm (H-SPPBO) which is a hierarchical version of SPPBO. The SPPBO framework is a general scheme to design population-based algorithms for combinatorial (and other) optimization problems. The proposed H-SPPBO algorithm uses a hierarchical tree structure to make it particularly suitable for dynamic (combinatorial) optimization problems. The hierarchy is used to detect dynamic changes. H-SPPBO adapts to changes by discarding low-quality solutions whenever a change is detected. The H-SPPBO is applied to the dynamic Traveling Salesperson Problem (DTSP). The experimental results show that the hierarchical tree structure and the number of swap operations within that tree can be used as an indicator for the occurrence of dynamic changes. It is discussed how the performance of the algorithm is influenced by the threshold for detecting changes and by the number of solutions that are reset when a change is detected. The best results are obtained with medium to high thresholds and when only a certain subset of the solutions is reset.
Pedagogy of “Life and Practice” and a Reflection on Contemporary Education

Professor Lan Ye’s meta-research on pedagogy and education in Return to Breakthrough – An Outline of Life and Practice Pedagogy puts forward the problems of what pedagogy is and what education is, reflects on the problems of contemporary education, and proposes that education should return to its root, which lies in the traditional Chinese problems of education and adults as well as education and life. This paper attempts to clarify the problems raised by Professor Lan Ye and reflect about the problems of contemporary education under meta-education as well as the role and significance of the traditional Chinese educational spirit for contemporary education.
Industrial product design method using feature expression and multi‐view learning in <scp>ICN</scp>

In order to realize multi‐layer semantic integration and innovation of complex product industrial design, promote multi‐disciplinary knowledge sharing and coordination, and improve product quality, the hierarchical semantic concept of product industrial design is put forward based on the theories of product semiotics and cognitive semantics. In addition, the hierarchical semantic industrial design process context model is constructed based on complex product development process and information processing theory using the Information Centric Networking (ICN) technology, since ICN architecture can be used to optimize the product quality. In the ICN environment, the scientific semantic expression methods and the artistic semantic expression method of product semantics are also used to extract features and construct the feature expression in the specific design situation. The icon semantic features, indicative semantic features, symbolic semantic features and their relevance of complex products are obtained. Experimental results have been achieved and shown that the proposed method has benefits than the other methods.
A Survey of Online Course Recommendation Techniques

With the development of information technology, online learning has gradually become an indispensable way of knowledge acquisition.However, with the increasing amount of data information, it is increasingly difficult for people to find appropriate learning materials from a large number of educational resources.The recommender system has been widely used in various Internet applications due to its high efficiency in filtering information, helping users to quickly find personalized resources from thousands of information, thereby alleviating the problem of information overload.In addition, due to its great use value, many new researches have been proposed in the field of recommender systems in recent years, but there are not many works on online course recommendation at present.Therefore, this paper aims to sort out the existing cutting-edge recommendation algorithms and the work related to online course recommendation, so as to provide a comprehensive overview of the online course recommender system.Specifically, we will first introduce the main technologies and representative work used in the online course recommender system, explain the advantages and disadvantages of various technologies, and finally discuss the future research direction of the online course recommender system.
The Compact Support Neural Network

Neural networks are popular and useful in many fields, but they have the problem of giving high confidence responses for examples that are away from the training data. This makes the neural networks very confident in their prediction while making gross mistakes, thus limiting their reliability for safety-critical applications such as autonomous driving and space exploration, etc. This paper introduces a novel neuron generalization that has the standard dot-product-based neuron and the radial basis function (RBF) neuron as two extreme cases of a shape parameter. Using a rectified linear unit (ReLU) as the activation function results in a novel neuron that has compact support, which means its output is zero outside a bounded domain. To address the difficulties in training the proposed neural network, it introduces a novel training method that takes a pretrained standard neural network that is fine-tuned while gradually increasing the shape parameter to the desired value. The theoretical findings of the paper are bound on the gradient of the proposed neuron and proof that a neural network with such neurons has the universal approximation property. This means that the network can approximate any continuous and integrable function with an arbitrary degree of accuracy. The experimental findings on standard benchmark datasets show that the proposed approach has smaller test errors than the state-of-the-art competing methods and outperforms the competing methods in detecting out-of-distribution samples on two out of three datasets.
An Overview of the Competencies and Career Outcomes in Educational Technology

Competencies are defined as knowledge, skills, and abilities that professionals need to perform various roles and functions within their organizational settings. Graduate programs aiming to prepare educational technology professionals develop competencies for their students drawing from professional experiences, research, standards, and professional organizations. This chapter identifies both foundational and specialized educational technology competencies for graduates in this field based on an analysis of 12 educational technology graduate programs in the United States (U.S.). The alignment between the educational technology competencies of graduate programs and recent job announcements of educational technology professionals is analyzed and discussed.
The Linkages Between Democracy, Terrorism and Foreign Direct Investments in Developed and Developing Countries

In this study, the linkages between political dynamics and foreign direct investment inwards to 118 developed and developing countries over the 2002-2017 period were analysed by the System GMM Panel Model to avoid the potential endogeneity problem. Findings indicate that the most vital positive determinant of FDI inwards is trade liberalization or trade openness. Also, there is no statistically significant relationship between countries’ democratization and freedom level and FDI inwards. However, terrorism hinders FDI directly, especially in developed countries. Therefore, the decision of foreign investors might mainly depend on economic dynamics, which are related to them and host countries rather than politics.
Science maps for exploration, navigation, and reflection—A graphic approach to strategic thinking

The world of science is growing at an unprecedented speed with more and more scholarly papers produced each year. The scientific landscape is constantly changing as research specialties evolve, merge or become obsolete. It is difficult for researchers, research managers and the public alike to keep abreast with these changes and maintain a true and fair overview of the world of science. Such an overview is necessary to stimulate scientific progress, to maintain flexible and responsive research organizations, and to secure collaboration and knowledge exchange between different research specialties and the wider community. Although science mapping is applied to a wide range of scientific areas, examples of their practical use are sparse. This paper demonstrates how to use a topical, scientific reference maps to understand and navigate in dynamic research landscapes and how to utilize science maps to facilitate strategic thinking. In this study, the research domain of biology at Aarhus University serves as an example. All scientific papers authored by the current, permanent staff were extracted (6,830 in total). These papers were used to create a semantic cognitive map of the research field using a co-word analysis based on keywords and keyword phrases. A workflow was written in Python for easy and fast retrieval of information for topic maps (including tokens from keywords section and title) to generate intelligible research maps, and to visualize the distribution of topics (keywords), papers, journal categories, individual researchers and research groups on any scale. The resulting projections revealed new insights into the structure of the research community and made it possible to compare researchers or research groups to describe differences and similarities, to find scientific overlaps or gaps, and to understand how they relate and connect. Science mapping can be used for intended (top-down) as well as emergent (bottom-up) strategy development. The paper concludes that science maps provide alternative views of the intricate structures of science to supplement traditional bibliometric information. These insights may help strengthen strategic thinking and boost creativity and thus contribute to the progress of science.
Future Applications of Blockchain in Education Sector: A Semantic Review

Blockchain is a chain of blocks that are linked to each other via the Hash of the previous block. Digitalized Education sector has a lot of benefits for the student like providing one to one learning experience, flexibility in learning, etc. The Future Scope of the Education sector is vast, and new technology will be invented in the future for student’s benefit. But at the same time, Education Sector has been affected a lot due to digitalization. Security of data, the privacy of students, and working digitally are few challenges faced by the education sector. Education Sector has a pool of sensitive information for which hackers are looking. As sensitive information is stored in the traditional system they are considered to be less secure than Blockchain Technology. Forgery of a certificate, Grading assignments digitally becomes difficult, Copyright content is used for teaching students digitally, etc. are few common problems faced by the Education sector in the digital environment. Traditional systems are not capable of handling these problems as they are vulnerable to various security attacks. So, This research paper focuses on these issues and has contributed a theoretical Blockchain model to solve this problem. In this model, Verification of Accreditation and Certificate helps in solving forgery problems using smart contracts and digital signatures, which makes the work easy for recruiters to verify the certificate received from an accredited institution. Auto grading assignments make use of external AI-ML natural language processing model and smart contract to grade assignments automatically. The copyright checking model makes use of public blockchain for checking the copyrighted material and helps to monetize the copyrighted content used and then permits the rights to publish it in a private blockchain. These blockchain-based models will help in solving these issues.
Creating Adjustable Human-like AI Behavior in a 3D Tennis Game with Monte-Carlo Tree Search

Interaction with opponents is a core element in video sports games. Thus, user experience in single-player matches heavily depends on the quality of AI opponents, who are expected to vary in their skill level and play styles. One way to achieve this goal is to learn game-playing behavior from real human players and to improve it if necessary with an automated optimization method. Monte-Carlo tree search (MCTS) has been successfully used for this purpose in several card and board games, such as chess and poker. We explore the possibility to apply MCTS in an action sports game of 3D tennis, and show how a dataset of pre-recorded tennis games can be used to train an MCTS-based AI system, exhibiting believable and reasonably skillful behavior.
Nonlinear Causal Structure Learning for Mixed Data

Causal discovery from observational data is a fundamental problem. A large number of algorithms have been proposed over the years for that purpose, but they usually handle the data of a single type, either continuous or discrete variables only. Recently, a few causal structure discovery algorithms have been developed for mixed data types, and received many applications. In this paper, we propose a structural equation model for mixed data types, which allows the causal mechanisms to be nonlinear and can consequently model many read-world situations. We prove that the causal structure is identifiable from the data distribution generated by the model under certain conditions. Moreover, we propose a maximum likelihood estimator and develop an efficient order search algorithm benefiting from a novel method of order space cutting, which can handle several hundred variables. We adopt automatic relevance determination kernel-based variable selection after order learning to recover the causal structure. Experiments on synthetic datasets demonstrate the accuracy and scalability of our approach. Especially, we apply our method to publicly available causal-effect pairs and show its superiority in the causal direction identification of mixed causal pairs. In addition, we show that our method can sensibly recover causal relationships on a publicly available real dataset and a private real-world dataset.