Reviewerly/exHarmony huggingface dataset:
Valid measurement region segmentation method for the interferogram based on local fringe distribution features

Oblique laser interferometry is an effective method to measure the form deviation of gear tooth flank. In the measurement, segmenting the valid measurement region (VMR) of the tooth flank from the captured interference fringe patterns (IFPs) is a crucial step that affects the measurement accuracy. To address the problem that current segmentation algorithms are susceptible to multiple forms of noise, making it difficult to guarantee their segmentation accuracy, this paper proposes a segmentation method that introduces the simulation tooth image (STI) as a template of the active shape model (ASM) to improve the segmentation accuracy. Firstly, an STI with a stable and noiseless fringe grayscale distribution was used to build the dataset and train, in order to obtain the initial contour of the tooth flank VMR. Next, the local grayscale features of the STI fringe were used to build a grayscale model of the initial contour, and the transformation parameters were adaptively calculated based on their a priori shape information. The trained segmentation model is searched in the IFPs to segment the tooth flank VMR. Finally, the proposed method was verified by comparing with segmentation methods based on grayscale and phase-shifting information. Moreover, this paper proves that the proposed method can be applied to the VMR recognition of IFPs of complex precision surfaces.
Adversarial Learning Enhanced Social Interest Diffusion Model for Recommendation

With the rapid development of recommendation system, various side information has been utilized to remedy data sparsity and cold start problem. Social recommendation performs by modeling social information which brings high-order information beyond user-item interaction. However, existing works relay on GNN based social network embedding which may lead to over-smoothing problem. The process of graph diffusion encodes high-order feature also takes much noise into the model. We argue that the latent influence of social relations cannot be well captured which had not be well addressed in previous work. In this work, we propose a new recommendation framework named adversarial learning enhanced social influence graph neural network (SI-GAN) that can inherently fuses the adversarial learning enhanced social network feature and graph interaction feature. Specifically, we propose an interest-wise influence diffusion network which modeling the user-item interaction and learning the embedding of users and items through influence diffusion. We adopt the embedding of user by both interaction information and adversarial learning enhanced social network which are efficiently fused by feature fusion model. We utilize the structure of adversarial network to address the problem of over-smoothing and digging the latent feature representation. Comprehensive experiments on three real-world datasets demonstrate the superiority of our proposed model.
Cold-Start Based Multi-scenario Ranking Model for Click-Through Rate Prediction

Online travel platforms (OTPs), e.g., Ctrip.com or Fliggy.com, can effectively provide travel-related products or services to users. In this paper, we focus on the multi-scenario click-through rate (CTR) prediction, i.e., training a unified model to serve all scenarios. Existing multi-scenario based CTR methods struggle in the context of OTP setting due to the ignorance of the cold-start users who have very limited data. To fill this gap, we propose a novel method named Cold-Start based Multi-scenario Network (CSMN). Specifically, it consists of two basic components including: 1) User Interest Projection Network (UIPN), which firstly purifies users' behaviors by eliminating the scenario-irrelevant information in behaviors with respect to the visiting scenario, followed by obtaining users' scenario-specific interests by summarizing the purified behaviors with respect to the target item via an attention mechanism; and 2) User Representation Memory Network (URMN), which benefits cold-start users from users with rich behaviors through a memory read and write mechanism. CSMN seamlessly integrates both components in an end-to-end learning framework. Extensive experiments on real-world offline dataset and online A/B test demonstrate the superiority of CSMN over state-of-the-art methods.
MOEF: Modeling Occasion Evolution in Frequency Domain for Promotion-Aware Click-Through Rate Prediction

Promotions are becoming more important and prevalent in e-commerce to attract customers and boost sales, leading to frequent changes of occasions, which drives users to behave differently. In such situations, most existing Click-Through Rate (CTR) models can't generalize well to online serving due to distribution uncertainty of the upcoming occasion. In this paper, we propose a novel CTR model named MOEF for recommendations under frequent changes of occasions. Firstly, we design a time series that consists of occasion signals generated from the online business scenario. Since occasion signals are more discriminative in the frequency domain, we apply Fourier Transformation to sliding time windows upon the time series, obtaining a sequence of frequency spectrums which is then processed by Occasion Evolution Layer (OEL). In this way, a high-order occasion representation can be learned to handle the online distribution uncertainty. Moreover, we adopt multiple experts to learn feature representations from multiple aspects, which are guided by the occasion representation via an attention mechanism. Accordingly, a mixture of feature representations is obtained adaptively for different occasions to predict the final CTR. Experimental results on real-world datasets validate the superiority of MOEF and online A/B tests also show MOEF outperforms representative CTR models significantly.
A New Reconstruction Attack: User Latent Vector Leakage in Federated Recommendation

Federated Recommendation (FR) has received considerable attention in the past few years. For each user in FR, its latent vector and interaction data are kept on its local device and thus are private to others. However, keeping the training data locally can not ensure the user's privacy is compromised. In this paper, we show that the existing FR is vulnerable to a new reconstruction attack in which the attacker leverages the semi-trusted FR server to lunch the reconstruction attack. In this attack, the server rigidly follows the protocol of FR, but the attacker may compromise the system security by analyzing the gradient updates received by the server. Specifically, we design Generative Reconstruction Network (GRN), a model reconstructing attack against FR aiming to generate the target user's (i.e., the victim) latent vector including user's sensitive information. Moreover, a server-side generator is designed to take random vectors as inputs and outputs generated latent vectors. The generator is trained by the distance between the real victim's gradient updates and the generated gradient updates. We explain that the generator will successfully learn the target latent vector distribution to probe into the victim's privacy. The experimental results demonstrate the proposed attack's effectiveness and superiority over the baseline attacks.
KRec-C2: A Knowledge Graph Enhanced Recommendation with Context Awareness and Contrastive Learning

Knowledge graph (KG) has been widely utilized in recommendation system to its rich semantic information. There are two main challenges in real-world applications: high-quality knowledge graphs and modeling user-item relationships. However, existing methods try to solve the above challenges by adopting unified relational rules and simple node aggregation, which cannot cope with complex structured graph data. In this paper, we propose a Knowledge graph enhanced Recommendation with Context awareness and Contrastive learning (KRec-C2) to overcome the issue. Specifically, we design an category-level contrastive learning module to model underlying node relationships from noisy real-world graph data. Furthermore, we propose a sequential context-based information aggregation module to accurately learn item-level relation features from a knowledge graph. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our KRec-C2 model over existing state-of-the-art methods.
Temporal-Aware Multi-behavior Contrastive Recommendation

Modeling various types of users' interactions and jointly considering individual preferences from multiple perspectives, multi-behavior recommendation has attracted increasing attention recently. However, most existing multi-behavior recommendations only focus on the behavioral interaction itself, attempting to extract user preferences merely by modeling behaviors, while ignoring the properties of the interaction (e.g., the temporal information). Meanwhile, the introduction of multi-behavior information is bound to generate additional noise signals, which further affects the accuracy of recommendation. Thus, to tackle the above challenges, two aspects need to be explored: (1) How to fuse interaction attributes to construct unified cross-behavior dependencies while distinguishing subtle differences between individuals' behaviors. (2) How to mitigate the effects of noise to a certain extent while preserving multi-behavioral information. In this work, we propose a Temporal-Aware Multi-Behavior Contrastive Learning (TMCL) framework to explore the patterns of multiple behaviors of individuals through temporal information, jointly capture the correlation of users' preference evolution, and alleviate the impact of multi-behavior noise. Extensive experiments and ablation tests on the three datasets indicate our TMCL outperforms various state-of-the-art methods and verify the effectiveness of our model.
A Scalable Query Pricing Framework for Incomplete Graph Data

With the rapid growth of data, how to make full use of their value becomes a critical issue. In the past few years, it has been a popular method to buy and sell data through the data market. Meanwhile, a variety of data pricing mechanisms have been proposed. However, since most of them concentrate on relational data, little is known about graph data pricing, particularly incomplete graph data. In this paper, we mainly focus on the pricing problem for queries over incomplete graph data. We take data provenance as the key idea behind our pricing mechanism and assign a base price to each edge in the graph. Considering the arbitrage-free property of query price, and the lack of some potential answers due to data incompleteness, we propose two practical pricing functions for incomplete graph query respectively. Furthermore, we design feasible pricing algorithms based on subgraph matching to derive each type of query price. Extensive experiments on real graph datasets demonstrate the effectiveness and efficiency of our solutions.
PEG: A Partial Evaluation-based Distributed RDF Graph System

In this demo, we implement a Partial Evaluation-based distributed RDF Graph system (PEG for short), which can implement partial evaluation without modifying the single-machine RDF graph system at each site. When a query is newly input, PEG rewrites it into a set of local partial subqueries. Then, each local partial match defined in the framework of partial evaluation can be also found by evaluating one of the local partial subqueries. To further prune some unnecessary results of local partial subqueries, PEG also associates each vertex with an attribute to specify whether it belongs to the partition at the site. In this demo, we demonstrate the main features of PEG and show how PEG loads and queries large RDF graphs in a distributed environment.
What the Fork? The Impact of Social Norm Violation on User Behavior

Research indicates that exposure to swearing, an experience often perceived as a norm violation may affect individual and group behaviors. To further explore this, we examined whether personal and group norms for swearing influence the extent to which exposure to swearing spreads virally and affects group performance during an online group discussion. Participants engaged in an online group decision-making task that assesses group performance and opinion polarization. Results revealed that groups exposed to profanity and were permissive of swearing were more likely to demonstrate deviant behavior that spread beyond swearing (i.e., less task focus and less formal language) as compared to groups not exposed to profanity. Furthermore, exposure to swearing decreased the quality of group decisions and increased group polarization. These results have implications for social norms and user behavior and productivity in distributed teams.
Semantic-Driven Instance Generation for Table Question Answering

Recent studies exhibit that generating sufficient samples can improve the performance of Table QA, especially in complex cross-domain applications. However, most existing data augmentation approaches for Table QA adopt a top-down paradigm and rely on predefined rules designed by human experts. In this paper, we aim to generate training instances for Table QA while mitigating the dependence on the human experience. We propose an approach coined SIG-TQA in which an off-shelf parsing tool is utilized to extract semantic patterns of SQL queries from text-to-SQL corpus. Then, these semantic patterns are underpinned to generate question/SQL pair with a SQL generator and a natural language question generator, respectively. Both the semantic pattern extraction and question/SQL pair generation are performed based on the original text-to-SQL corpus with few manual efforts injected, our proposed SIG-TQA is making a different bottom-up paradigm. Extensive experiments on a widely-used benchmark and online experiments on a practical industry system demonstrate the superiority of SIG-TQA. Currently, our SIG-TQA has been applied to a real-world Table QA system, and its code is available on https://github.com/DHms2020/SIG-TQA .
Knowledge Hypergraph Reasoning Based on Representation Learning

The knowledge hypergraph, as a data carrier for describing real-world things and complex relationships, faces the challenge of incompleteness due to the proliferation of knowledge. It is an important research direction to use representation learning technology to reason knowledge hypergraphs and complete missing and unknown knowledge tuples. Most current methods extend directly from the binary relations of the knowledge graph to the n-ary relations without obtaining the position and role information of entities in each n-ary relation tuple, however, these semantic attribute information are crucial for knowledge hypergraph reasoning based on representation learning.
Fair and Privacy-Preserving Graph Neural Network

Graph neural networks (GNNs) have demonstrated superior performance in modeling graph-structured. They are vastly applied in various high-stakes scenarios such as financial analysis and social analysis. Among the fields, privacy issues and fairness issues have become the focus of attention. Currently, most studies focus on protecting data privacy or promoting the fairness of the model. However, how to both guarantee privacy and fairness is under-explored on GNN. To ensure GNNs behave in a socially responsible manner, it is necessary to protect the privacy and mitigate bias simultaneously. Therefore, our Ph.D. project aims at creating GNNs which can promote fairness and protect data privacy, preserving high utility performance.
Cleanits-MEDetect: Multiple Errors Detection for Time Series Data in Cleanits

Data quality problems are seriously prevalent in time series data, and the data suffer from types of errors including single-point errors, continuous errors, and contextual errors. Since it is challenging to achieve high accuracy and efficiency in error detection tasks for time series data, we develop error detection system MEDetect in Cleanits, a data cleaning tool for multi-dimensional industrial time series. We propose an integration detection model for multiple errors, which holds the hierarchical variational automatic encoder as the main structure, and we propose a dimensionality reduction method for k-shape based cluster- ing algorithm, which reduces the time costs of the detection process. MEDetect is designed to allow customized error detection, and users can choose detection and repairing algorithms on their demands.
VIRTUAL EDUCATION IN MUSIC THERAPY

The article deals with the possibilities of training highly qualified specialists in the field of modern scientific music therapy in the conditions of virtual education. The article substantiates the relevance of training modern music therapists to use digital and computerized technologies in health care practice. A brief historical overview of the development of this area, available educational programs, and their brief analysis are given.&#x0D; We analyze the existing modern scientific advanced methods and technologies of working with stress as one of the most pressing problems of society in more detail. The issues of high-level professional training in the specialty "music therapist" are discussed.
When the Light Goes Out: Ignorance and Multiplicity in Teaching and Learning

Purpose If education is the movement from dark to light, what does it mean for the light to go out? Following this question, this paper examines how conceptions of ignorance inform and are embedded in ideas of learning and pedagogy. Design/Approach/Methods Through historical and contemporary examples, I ask how an understanding of ignorance as absence frames some forms of teaching and learning, how ignorance might be understood differently, and how a different formulation of ignorance creates possibilities for imagining teaching and learning otherwise. Findings The framing of ignorance as absence articulates a distance between knowing and not knowing that defines what I call explicatory spacetime. This arrangement positions ignorance as a social problem and schooling as a solution aimed at closing the gap. I point to ways this framing persists today in research and practices that posit the acquisition of positive knowledge and the eradication of negative ignorance as a foremost educational responsibility. Originality/Value Engaging work across science and technology studies, psychoanalysis, and the philosophy of education, I argue that alternative conceptions of ignorance often maintain associations with absence. Building from this scholarship, I ask how reframing ignorance through multiplicity, that is, within a field of possibility out of which a thing called knowledge can cohere, offers a different framework and pedagogical arrangements. Finally, I explore how reframing ignorance in a spacetime of multiplicity opens possibilities for understanding and enacting pedagogical encounters.
Visual Task Performance and Spatial Abilities: An Investigation of Artists and Mathematicians

This study builds on past research to present a domain-specific empirical investigation of artists and math & computer scientists on their respective relationships to, perceptions of, and interactions with data visualization. We conducted a three-phase study utilizing mixed-methods to investigate performance on visual and text representations of data between domains. Our findings evidenced how math & computer scientists are proficient utilizing text representations of data while artists benefit more from visual chart representations. Finally, we present perspectives from artists to gain an understanding of their approach to visual and mathematical tasks. Our findings indicate that artists are especially adept at statistical visual tasks and that development of cognitive skills could be fostered by individuals to potentially benefit visualization task performance.
A differential privacy trajectory data storage and publishing scheme based on radix tree

Summary With the rapid development of information technology, location‐based services (LBSs) are becoming more and more popular, and at the same time, the users' privacy issues are becoming more and more serious. Therefore, this article proposes a differential private trajectory data storage and publishing scheme based on radix tree (TPRT). Firstly, the original trajectory is preprocessed by considering semantic information and simplified by dividing the stay area to reduce the risk of privacy leakage. Then, differential privacy technology is used to add Laplace noise to sensitive locations to achieve generalization. Finally, a radix tree structure satisfying differential privacy is defined, which alleviates the problem of large space consumption caused by tree height. To protect privacy, Laplace noise is added to the sensitive counts of the radix tree and limited by the Markov chain and privacy degree. Compared with several existing methods, our method has better performance and higher data availability.
Elements of a polyprogram methodology of teaching the theme "Creation and processing of text information" using the free office package LibreOffice at the level of basic general education

The article presents the author's polyprogram methodology of teaching the theme "Creation and processing of text information" for teaching informatics at the level of basic general education. The methodology is based on the use of four main components of the free cross-platform LibreOffice office suite: LibreOffice Writer text editor, LibreOffice Math mathematical formula editor, LibreOffice Calc spreadsheets (for creating diagrams), LibreOffice Draw vector graphics editor (for creating drawings and diagrams). The relevance of the work is determined, first of all, by the tightening of economic sanctions against Russian engineering universities by the governments of a number of Western countries in 2021, which consists, in particular, in the refusal of proprietary software developers to provide a license to use their software, as well as the need of domestic education in informatics teaching methods that are insensitive to the form of education (full-time, distance), suitable for use in the conditions of a sudden transfer of the educational process to a distance form due to a pandemic or other critical factors. The developed elements of the methodology mean a stack of practical tasks, during which students develop the set of digital skills necessary to create complex combined multi-page text documents. Moreover, the development of these skills takes place both in the classroom and when performing practical homework tasks. The article describes this set of practical tasks simultaneously with the tools used for these purposes and the functionality of LibreOffice Writer.
Quest on the history of informatics dedicated to the Programmer's Day in Russia

An important stimulus for the development of the cognitive interest of school students is the historical aspect of school knowledge. The use of elements of historicism in informatics lessons and as part of extracurricular activities expands the horizons of students and enhances their general culture, especially considering that the vast majority of modern schoolchildren have little idea of the development of informatics and often cannot even set the time frame for a particular invention. In modern school textbooks, the theme "History of the development of informatics" is not singled out in a separate paragraph, historical facts are given separately, which makes it difficult for students to form a holistic perception of this theme. However, this theme is important for the personal development of students, including the education of patriotism. The article presents an extracurricular activity — a quest dedicated to the Programmer's Day, which is celebrated annually in Russia on September 12 (in leap years) or September 13 (in non-leap years). The quest helps to introduce the children to the main scientists who contributed to the development of informatics, it develops reading literacy skills, the ability to search for the necessary information in the text, search for information on the Internet, including searching by image. The facts used in the quest can later be useful to schoolchildren at various competitions and Olympiads.