Reviewerly/exHarmony huggingface dataset:
A Time Interval Aware Approach for Session-Based Social Recommendation

Users on social media like Facebook and Twitter are influenced by their friends. Social recommendation exploits users' social activities for modeling their interests to enhance the activeness and retention of users. Besides, their interests change from time to time. Session-based recommendation divides users' interaction history into sessions and predict users' behaviors with the context information in each session. It's essential but challenging to model the social activities and the dynamic property in an unified model. Besides, most of existing session-based recommendation approaches model users' interaction history as ordered sequence in regardless of real timestamps of those interactions. To solve the above issues together, we design a heterogeneous graph for modeling the complex interactions among users and items and propose a Time Interval aware graph neural network-based Recommendation approach(TiRec) to model both the social activities and the dynamic property of users' interaction with items. Furthermore, to capture users' dynamic preference, we propose a time interval aware and self-attention based aggregator to model users' preference in each session. Experimental results on several real-world datasets demonstrates the effectiveness of our proposed approach over some competitive baselines.
Implementation of a Mixed Reality Space Interior Layout System by Gesture Operation

In this study, we implemented an interior layout system in MR (Mixed Reality) space that enables gesture operation. This system constructs the MR space by scanning the real space through the head mounted display. The user can arrange interiors by gesture operation in MR space. The gesture operation supports the user’s intuitive operation.
Vehicle path tracking Based on Strong Tracking Cubature Kalman Filter While Encountering An Emergency Collision Avoidance

Abstract Considering the high speed emergency avoidance situations, the vehicle path tracking was studied in this paper. The three degrees of freedom nonlinear vehicle model and the cubature Kalman filter are applied to tracking vehicle emergency avoidance path. For the degradation of adaptive tracking performance in the vehicle emergency avoidance, an improved strong tracking cubature Kalman filter algorithm is proposed by introducing the fading factor into filtering process which is learnt from strong tracking filter. The algorithm has a simple implementation, high estimation accuracy and good robustness. Different target paths are selected and the cubature Kalman filter is applied to simulate path tracking in the vehicle emergency avoidance. The simulation results show that the proposed path tracking method can control the vehicle tracking the ideal collision avoidance path rapidly without collisions.
Node Embedding over Attributed Bipartite Graphs

This work investigates the modeling of attributes along with network structure for representation learning of the bipartite networks. Most of the attributed network representation learning (NRL) works consider the homogeneous type network only; However, these methods, when apply to bipartite type networks, may not be beneficial to learn an informative representation of nodes for predictive analysis. Hence, we propose a BIGAT2VEC framework that examines the internode relationships in the form of direct and indirect relations between two different as well as the same node type of bipartite network to preserve both structure and attribute context. In BIGAT2VEC, learning is enforced on two levels: (1) direct inter-node relationship between nodes of different type (either through the edge or attribute similarities perspective) by minimizing the probabilities through KL divergence; (2) indirect inter-node relationship within same node type (either through 2nd order neighborhood proximity and attributes similarities perspective) by employing shallow neural network model through maximizing the probabilities. These two levels are separately optimized, and we leverage its learned embeddings through late fusion to further execute the network mining tasks such as link prediction, node classification (multi-class and multilabel), and visualization. We perform extensive experiments on various datasets and evaluate our method with several baselines. The results show the BIGAT2VEC efficacy as compare to other (non)attributed representation learning methods.
Augmented Reality Application for the Mobile Measurement of Strain Distributions

In mechanical engineering studies hands-on laboratories are an integral part of the students’ education. In manufacturing related fields, material characterization labs are often used to enable students to foster their understanding of different materials. To enhance the laboratory experience and to educate about specific aspects of the uniaxial tensile test, an Augmented Reality (AR) application has been developed. With this applications, it is possible to visualize the inhomogeneous strain field that arises during the experiment on the surface of the specimen. The technical components and structure of the implementation are described in this paper. The usability of several algorithms, technical and software implementation is discussed and evaluated.
Twinned power: formations of cloud-edge control

From smart vehicles to smart city sensors, the millions of new devices connected over the next few years will generate huge amounts of highly personal data that needs to be processed in real time. Transmitting this data to the cloud, with its centralized data centers and high latency, is both economically and technically unviable. As a result, the industry is moving towards processing data closer to the source, a major shift from the cloud to the so-called edge. This new architecture is understood as a necessary augmentation to the cloud. Indeed, in many ways, the edge is the polar opposite of the cloud: ad hoc networks, composed of resource-poor devices, that function at the hyperlocal level of the home, office, or neighborhood. However, this article argues that such technical supplementation is also about control, filling a critical void in contemporary formations of power. Coupled together, this cloud-edge formation is both centralized and decentralized, resource intense yet geographically dispersed. Drawing on Foucauldian theory, such power augments heavy, situated force with a more flexible, economic architecture – conforming to a trajectory of ‘intensification’ while also complicating it. While each mode of power has certain strengths and weaknesses, combining their operations forms a twinned power with new capacities for subjectivation and governance.
Toward End-to-End Neural Cascading Strategies for Grammatical Error Correction

Neural sequence-to-sequence (seq2seq) grammatical error correction (GEC) models are usually computationally expensive both in training and in translation inference. Also, they tend to suffer from poor generalization and arrive at inept capabilities due to limited error-corrected data, and thus, incapable of effectively correcting grammar. In this work, we propose the use of neural cascading strategies in enhancing the effectiveness of neural sequence-to-sequence grammatical error correction models as inspired by post-editing processes of neural machine translations. The findings of our experiments show that adapting cascading techniques in low resource NMT models unleashes performances that is comparable to high setting NMT models. We extensively exploit and evaluate multiple cascading learning strategies and establish best practices toward improving neural seq2seq GECs.
Learning Connectivity of Neural Networks from a Topological Perspective

Seeking effective neural networks is a critical and practical field in deep learning. Besides designing the depth, type of convolution, normalization, and nonlinearities, the topological connectivity of neural networks is also important. Previous principles of rule-based modular design simplify the difficulty of building an effective architecture, but constrain the possible topologies in limited spaces. In this paper, we attempt to optimize the connectivity in neural networks. We propose a topological perspective to represent a network into a complete graph for analysis, where nodes carry out aggregation and transformation of features, and edges determine the flow of information. By assigning learnable parameters to the edges which reflect the magnitude of connections, the learning process can be performed in a differentiable manner. We further attach auxiliary sparsity constraint to the distribution of connectedness, which promotes the learned topology focus on critical connections. This learning process is compatible with existing networks and owns adaptability to larger search spaces and different tasks. Quantitative results of experiments reflect the learned connectivity is superior to traditional rule-based ones, such as random, residual and complete. In addition, it obtains significant improvements in image classification and object detection without introducing excessive computation burden.
Query Answering in Fuzzy DL-Lite with Graded Axioms

Fuzzy DL-Lite has been studied as a means to answer queries w.r.t. vague or imprecise knowledge and facts. Existing approaches consider only the Zadeh semantics or are limited to precise terminological knowledge, where only facts are graded. We study the problem of answering conjunctive queries over fuzzy DL-Lite $$_R$$ ontologies which allow also graded axioms, and whose semantics is based on mathematical fuzzy logic. We show that for the Gödel t-norm, the degree of an answer is computable through repeated calls to a classical query answering engine. For non-idempotent t-norms, we show the difficulty in dealing with degrees, and provide some partial solutions.
Sound and Keystroke Dynamics Analysis for User Authenticity Verification

The paper presents an approach to solving one of the main security problems of modern information systems. This problem is related to unauthorized access to its resources. To prevent unauthorized access, it is necessary to improve the user authentication procedure. Currently, there are several basic approaches to user identity verification, but the most challenging is undoubtedly the verification of the user's authenticity using its biometric features. One of such biometric features which can be used in computer systems is the dynamics of typing. The most interesting and popular ways how the authentication is performed in modern systems have been discussed in the first part of this work. The paper presents the outcome of research which used the following elements characterizing user style of typing: time intervals between keypress; duration of pressing the key; the speed of writing and sound. The novelty of the paper is related to combining the frequently used dynamic elements of typing with the sound spectrogram associated with the process of typing.
Analysis of Web Content Quality Information on the Koseeker Website Using the Web Content Audit Method and ParseHub Tools

Content Quality is one of the most critical dimensions of Website Quality. There are seven indicators to check the quality of the content. Content that has low quality can be determined with these indicators. To facilitate quality checking of the website content, the authors used a content audit method. However, there are currently not many standards that can be used for content auditing. So in this study, the authors proposed to use the website auditing framework and combine it with content quality indicators. With these methods, the authors can assess content by following the indicators of content quality. Complete information about the website was needed to conduct an audit. The authors used web scraping using Parsehub to get all these data. In this research, the case taken for quality checking was Koseeker Website. After conducting the content audit in Koseeker, the results showed that Koseeker has a quality that was not yet maximum on three of seven indicators, namely, Timely, Relevant, and Authority. After doing the test, it can be seen that the proposed web content audit can assess the quality of Koseeker website content.
Research on Aerial Image Stitching Technology

The rise of UAV has made UAV play a major role in various industries, especially in place of manpower to solve difficult problems such as complex terrain detection and on-site monitoring in disaster areas. The pictures taken by the UAV are spliced to realize the global display of the scene, which is convenient for analysis and research. The integrity and clarity of image stitching depends on the performance of the stitching algorithm. The key to image registration and stitching is the extraction and matching of feature points. The article compares and analyzes the traditional three feature point (key-point) extraction algorithms. The article summarizes their advantages and disadvantages and scope of application. In this paper, SIFT, SURF and ORB algorithms were used respectively to extract feature points from the two images. Then, the nearest neighbor matching method was used to select the optimal matching points to remove the pseudo-matching points and improve the matching accuracy. After image registration, the composite image is prone to splicing gaps and brightness differences due to error accumulation, color differences, and the like. Therefore, in order to make the final panoramic image better, it is more necessary to perform image fusion after image registration processing, correct the difference, and eliminate the stitching gap. In this paper, the improved ORB algorithm combined with the weighted average fusion algorithm is used to achieve smooth transition of the two images. The improved algorithm time is reduced and the efficiency is significantly improved. The experimental results also show that the weighted average algorithm has high effectiveness and practicability in image fusion.
Study of The Teacher’s Readiness Condition to Implement the Idea of Managing the Personal Trajectory of Professional Development Within the Framework of Digitalization of Education

The article deals with the topical issue of digitalization of education as a component of the digital transformation of contemporary society and the economy in general. The purpose of the research is to identify the readiness status of teaching staff for professional activity in the context of the digitalization of education. The main research methods were the questionnaire (implemented in the form of an online survey among students of the extramural form of study of the Moscow City Pedagogical University working at educational institutions of Moscow), and statistical analysis of the data obtained. The results of an online survey of teachers allowed concluding that today teachers have acquired a positive experience in the use of recommended tested digital educational resources, the ability to develop innovative digital educational and methodological support for the educational process, as well as recognized the need to build a personal trajectory of professional development in the framework of digital education.
Challenging the Concept of Digital Nativeness – Through the Assessment of Information Literacy and Digital Literacy

New opportunities for learning and teaching are introduced as the aftermath of the emergence of new digital technologies. Concurrently, change is taking place within the concept of literacy, as different dimensions of literacy have emerged. Information- and digital literacy are prominent examples of this, as their significance in the 21st century has received an array of scholarly attention. However, dimensions of literacy and technology acceptance within the educational context have been extensively bound to the discussion of digital natives and digital immigrants based on their age factor. A generalisation of this nature disregards diversity within the suggested groups, as differences can appear in other influencing factors. Hence, this study aims to look beyond age as a divider of digital nativeness, and instead takes a focus on other possible boundaries of characterisation. The results show that both information and digital literacy are important factors, and that literacy is a competence that depends on the access, frequency of use and the ability to use digital technologies. As such, the interactions of individuals with digital technologies should be used to classify individuals as digital native and digital immigrants.
Automatic Vector Graphic Organization and Asset Extraction

We present a novel method for organizing Bézier bounded geometry based on affine similarity and visual saliency. For this computationally expensive (many-to-many) problem, we propose a highly parallel algorithm, leveraging programmable GPU pipeline, computing pairwise affine transformations to classify Bézier geometry into clusters, where paths in each cluster are affine transforms of each other. Using these clusters, we propose a method to organize paths into meaningful groups, even from complex, unstructured geometry, common in real-world vector art. We propose a function to quantify relative importance of these groups, using attributes such as complexity and frequency of occurrence, resulting in meaningful, reusable assets. Our method is both robust and performant, capable of processing thousands of paths within milliseconds.
Search-Based Classification for Offline Tifinagh Alphabets Recognition

The main difficulty in developing a successful optical character recognition (OCR) system lies in the confusion between the characters. In the case of Amazigh writing (Tifinagh alphabets), some characters have similarities based on rotation or scale. Most of the researchers attempted to solve this problem by combining multiple descriptors and / or classifiers which increased the recognition rate, but at the expense of processing time that becomes more prohibitive. Thus, reducing the confusion of characters and their recognition times is the major challenge of OCR systems. In this chapter, the authors present an off-line OCR system for Tifinagh characters.
Time-Aware Attentive Neural Network for News Recommendation with Long- and Short-Term User Representation

News recommendation is very critical to help users quickly find news satisfying their preferences. Modeling user interests with accurate user representations is a challenging task in news recommendation. Existing methods usually utilize recurrent neural networks to capture the short-term user interests, and have achieved promising performance. However, existing methods ignore the user interest drifts caused by time interval in the short session. Thus they always assume the short-term user interests are stable, which might lead to suboptimal performance. To address this issue, we propose the novel model named Time-aware Attentive Neural Network with Long-term and Short-term User Representation (TANN). Specifically, to reduce the influence of interest drifts, we propose the Time-aware Self-Attention (T-SA) which considers the time interval information about user browsing history. We learn the short-term user representations from their recently browsing news through the T-SA. In addition, we learn more informative news representations from the historical readers and the contents of news articles. Moreover, we adopt the latent factor model to build the long-term user representations from the entire browsing history. We combine the short-term and long-term user representations to capture more accurate user interests. Extensive experiments on two public datasets show that our model outperforms several state-of-the-art methods.
Formation of student’s hypertext competence using hypertext technologies

The formation of hypertext competence emerges from a combination of methods, tools and technologies that provide communication between hypertext network devices with the aim of transmitting information. The information transmitted may be diverse depending on its type and content and is intended to be used collectively through network information and communication resources, where the network language and culture differ from traditional speech practices. The difference here lies at the macro level, i.e. at the level of texts, and at the micro level, the level of lexeme transformation, at the level of meaning and form, different from the norms existing in the language system of the community. Moreover, the level of fact acquisition and, as a result, memorization increase when the information is structured, taking into account common features, which are relevant for students. Objectivity, integrity and network identity pay an important role there. The formation of hypertext competence is carried out in the course of environment-oriented training, which means that a foreign language is acquired in natural situational conditions.
Software and Hardware Complex For Adaptive HF-and VHF-Communication

The article considers physical characteristics of meteors and meteor trails. The block diagram of the software and hardware complex for establishing meteor-burst communication in polar latitudes is offered. The examples of radiophysical measurements of meteor trails are given.
Performance and Fault Tolerance Trade-offs in Sharded Permissioned Blockchains

Blockchain has become a promising technology in distributed systems in recent years, but scalability remains a major problem. The traditional approach to scalability, namely sharding, does not solve the problem easily because the process of interleaving blocks stored in different shards to create a unified master ledger introduces overhead. This paper examines two techniques for interleaving the shards of permissioned blockchains, which we refer to as strong temporal coupling and weak temporal coupling. We implement these techniques in a prototype system with a Bitcoin-like transaction structure, using the EPaxos consensus protocol for transaction ordering. Our experimental results show that strong coupling can achieve lower latency as compared to weak coupling but same level of peak throughput. However, strong coupling requires all shards to grow at the same rate, and cannot tolerate any shard failure. In contrast, the higher latency of weak coupling is because of the consensus strategy it uses to order the blocks. However, if shard failure occurs, weak coupling can still make progress without stalling the whole system.
Study on Density Peaks Clustering Based on Hierarchical K-Nearest Neighbors

Density Peaks Clustering is a novel clustering algorithm, which can find clusters of arbitrary shapes with fast speed. However, it has a few disadvantages, for example, when the data are unevenly distributed, the clustering performance is not good. Therefore, a improved DPC based on hierarchical k-nearest neighbor (HKNN-DPC) algorithm is proposed, which divided k-nearest neighbors into three layers, each layer of data points has different weight, and redesigned the local density calculation approach. We adopt the proposed algorithm to compare with DPC, DBSCAN and K-means in synthesized and UCI data sets. The experimental results indicated HKNN-DPC had better performance.
The Devil Is in the Details

This chapter discusses in considerable detail many fundamental concepts and features of decision problems, and the complexities of real-world decision-making. Our context is Multiple Criteria Decision Making, where we abstract away from uncertainty. We make the distinction between Evaluation Problems and Design Problems.