Reviewerly/exHarmony huggingface dataset:
Learning Item Attributes and User Interests for Knowledge Graph Enhanced Recommendation

Knowledge Graphs (KGs) manifest great potential in recommendation. This is ascribable to the rich attribute information contained in KG, such as the price attribute of goods, which is further integrated into item and user representations and improves recommendation performance as side information. However, existing knowledge-aware methods leverage attribute information at a coarse-grained level in two aspects: (1) item representations don't accurately learn the distributional characteristics of different attributes, and (2) user representations don't sufficiently recognize the pattern of user preferences towards attributes. In this paper, we propose a novel attentive knowledge graph attribute network(AKGAN) to learn item attributes and user interests via attribute information in KG. Technically, AKGAN adopts a novel graph neural network framework, which has a different design between the first layer and the latter layer. The first layer merges one-hop neighbors' attribute information by concatenation operation to avoid breaking down the independence of different attributes, and the latter layer recursively propagates attribute information without weight decrease of high-order significant neighbors. With one attribute placed in the corresponding range of element-wise positions, AKGAN employs a novel interest-aware attention unit, which releases the limitation that the sum of attention weight is 1, to model the complexity and personality of user interests. Experimental results on three benchmark datasets show that AKGAN achieves significant improvements over the state-of-the-art methods. Further analyses show that AKGAN offers interpretable explanations for user preferences towards attributes.
Correlation Guided Multi-teacher Knowledge Distillation

Knowledge distillation is a model compression technique that transfers knowledge from a redundant and strong network (teacher) to a lightweight network (student). Due to the limitations of a single teacher's perspective, researchers advocate for the inclusion of multiple teachers to facilitate a more diverse and accurate acquisition of knowledge. However, the current multi-teacher knowledge distillation methods only consider the integrity of integrated knowledge from the teachers' level in teacher weight assignments, which largely ignores the student's preference for knowledge. This will result in inefficient and redundant knowledge transfer, thereby limiting the learning effect of the student network. To more efficiently integrate teacher knowledge suitable for student learning, we propose Correlation Guided Multi-Teacher Knowledge Distillation (CG-MTKD), which utilizes the feedback of the student's learning effects to achieve the purpose of integrating the student's preferred knowledge. Through extensive experiments on two public datasets, CIFAR-10 and CIFAR-100, we demonstrate that our method, CG-MTKD, can effectively integrate the knowledge of student preferences during teacher weight assignments.
Modeling User’s Neutral Feedback in Conversational Recommendation

Conversational recommendation systems (CRS) enable the traditional recommender systems to obtain dynamic user preferences with interactive conversations. Although CRS has shown success in generating recommendation lists based on user's preferences, existing methods restrict users to make binary responses, i.e., accept and reject, after recommending, which limits users from expressing their needs. In fact, the user's rejection feedback may contain other valuable information. To address this limitation, we try to refine user's negative item-level feedback into attribute-level and extend CRS to a more realistic scenario that not only incorporates positive and negative feedback, but also neutral feedback. Neutral feedback denotes incomplete satisfaction with recommended items, which can help CRS infer user's preferences. To better cope with the new setting, we propose a CRS model called Neutral Feedback in Conversational Recommendation (NFCR). We adopt a joint learning task framework for feature extraction and use inverse reinforcement learning to train the decision network, helping CRS make appropriate decisions at each turn. Finally, we utilize the fine-grained neutral feedback from users to acquire their dynamic preferences in the update and deduction module. We conducted comprehensive evaluations on four benchmark datasets to demonstrate the effectiveness of our model.
Towards Undetectable Adversarial Examples: A Steganographic Perspective

Over the past decade, adversarial examples have demonstrated an enhancing ability to fool neural networks. However, most adversarial examples can be easily detected, especially under statistical analysis. Ensuring undetectability is crucial for the success of adversarial examples in practice. In this paper, we borrow the idea of the embedding suitability map from steganography and employ it to modulate the adversarial perturbation. In this way, the adversarial perturbations are concentrated in the hard-to-detect areas and are attenuated in predictable regions. Extensive experiments show that the proposed scheme is compatible with various existing attacks and can significantly boost the undetectability of adversarial examples against both human inspection and statistical analysis of the same attack ability. The code is available at github.com/zengh5/Undetectable-attack .
A Fast and Scalable Frame-Recurrent Video Super-Resolution Framework

The video super-resolution(VSR) methods based on deep learning have become the mainstream VSR methods and have been widely used in various fields. Although many deep learning-based VSR methods have been proposed, they cannot be applied to real-time VSR tasks due to the vast computation and memory occupation. The lightweight VSR networks have faster inference speeds, but their super-resolution performance could be better. In this paper, we analyze the explicit and implicit motion compensation methods commonly used in VSR networks and design a fast and scalable frame-recurrent VSR network(FFRVSR). FFRVSR incorporates the Frame-Recurrent Network and Recurrent-Residual Network. This network structure can extract information from low-resolution video frames more efficiently and alleviate error accumulation during inference. We also design a super-resolution flow estimation network(SRFnet) that can more accurately estimate optical flow between video frames while reducing error information ingress. Extensive experiments demonstrate that the proposed FFRVSR surpasses state-of-the-art methods in terms of inference speed. FFRVSR also has strong scalability and can be adapted for both real-time video super-resolution tasks and high-quality video super-resolution tasks.
Structural Properties of Associative Knowledge Graphs

This paper introduces a novel structural approach to constructing associative knowledge graphs. These graphs are composed of many overlapping scenes, with each scene representing a specific set of objects. In the knowledge graph, each scene is represented as a complete subgraph associating scene objects. Knowledge graph nodes represent various objects present within the scenes. The same object can appear in multiple scenes. The recreation of the stored scenes from the knowledge graph occurs through association with a given context, which includes some of the objects stored in the graph. The memory capacity of the system is determined by the size of the graph and the density of its synaptic connections. Theoretical dependencies are derived to describe both the critical graph density and the memory capacity of scenes stored in such graphs. The critical graph density represents the maximum density at which it is possible to reproduce all elements of the scene without errors.
Propheter: Prophetic Teacher Guided Long-Tailed Distribution Learning

The problem of deep long-tailed learning, a prevalent challenge in the realm of generic visual recognition, persists in a multitude of real-world applications. To tackle the heavily-skewed dataset issue in long-tailed classification, prior efforts have sought to augment existing deep models with the elaborate class-balancing strategies, such as class rebalancing, data augmentation, and module improvement. Despite the encouraging performance, the limited class knowledge of the tailed classes in the training dataset still bottlenecks the performance of the existing deep models. In this paper, we propose an innovative long-tailed learning paradigm that breaks the bottleneck by guiding the learning of deep networks with external prior knowledge. This is specifically achieved by devising an elaborated "prophetic" teacher, termed as "Propheter", that aims to learn the potential class distributions. The target long-tailed prediction model is then optimized under the instruction of the well-trained "Propheter", such that the distributions of different classes are as distinguishable as possible from each other. Experiments on eight long-tailed benchmarks across three architectures demonstrate that the proposed prophetic paradigm acts as a promising solution to the challenge of limited class knowledge in long-tailed datasets. The developed code is publicly available at https://github.com/tcmyxc/propheter .
Reconstructing Challenging Hand Posture from Multi-modal Input

3D Hand reconstruction is critical for immersive VR/AR, action understanding or human healthcare. Without considering actual skin or texture details, existing solutions have concentrated on recovering hand pose and shape using parametric models or learning techniques. In this study, we introduce a challenging hand dataset, CHANDS, which is composed of articulated precise 3D geometry corresponding to previously unheard-of challenging gestures performed by real hands. Specifically, we construct a multi-view camera setup to acquire multi-view images for initial 3D reconstructions and use a hand tracker to separately capture the skeleton. Then, we present a robust method for reconstructing an articulated geometry and matching the skeleton to the geometry using a template. In addition, we build a hand pose model from CHANDS that covers a wider range of poses and is particularly helpful for difficult poses.
A Domain Knowledge-Based Semi-supervised Pancreas Segmentation Approach

The five-year survival rate of pancreatic cancer is extremely low, and the survival time of patients can be extended by timely detection and treatment. Deep learning-based methods have been used to assist radiologists in diagnosis, with remarkable achievements. However, obtaining sufficient labeled data is time-consuming and labor-intensive. Semi-supervised learning is an effective way to alleviate dependence on annotated data by combining unlabeled data. Since the existing semi-supervised pancreas segmentation works are easier to ignore the domain knowledge, leading to location and shape bias. In this paper, we propose a semi-supervised pancreas segmentation method based on domain knowledge. Specifically, the prior constraints for different organ sub-regions are used to guide the pseudo-label generation for unlabeled data. Then the bidirectional information flow regularization is designed by further utilizing pseudo-labels, encouraging the model to align the labeled and unlabeled data distributions. Extensive experiments on NIH pancreas datasets show: the proposed method achieved Dice of 76.23% and 80.76% under 10% and 20% labeled data, respectively, which is superior to other semi-supervised pancreas segmentation methods.
Utilizing an Attention-Based LSTM Model for Detecting Sarcasm and Irony in Social Media

Sarcasm and irony represent intricate linguistic forms in social media communication, demanding nuanced comprehension of context and tone. In this study, we propose an advanced natural language processing methodology utilizing long short-term memory with an attention mechanism (LSTM-AM) to achieve an impressive accuracy of 99.86% in detecting and interpreting sarcasm and irony within social media text. Our approach involves innovating novel deep learning models adept at capturing subtle cues, contextual dependencies, and sentiment shifts inherent in sarcastic or ironic statements. Furthermore, we explore the potential of transfer learning from extensive language models and integrating multimodal information, such as emojis and images, to heighten the precision of sarcasm and irony detection. Rigorous evaluation against benchmark datasets and real-world social media content showcases the efficacy of our proposed models. The outcomes of this research hold paramount significance, offering a substantial advancement in comprehending intricate language nuances in digital communication. These findings carry profound implications for sentiment analysis, opinion mining, and an enhanced understanding of social media dynamics.
Mobile learning for science education: meta-analysis of K-12 research

The benefits of mobile learning for science education have been widely described; however, there is scarce evidence of its effect on student learning. This article presents a meta-analysis of 44 empirical studies to assess the effect of mobile learning on students’ learning at K-12 levels. Moreover, the study considered the moderating effect of science field, education level, pedagogical approach, learning environment, and control treatment. The results indicate that mobile learning has a large effect (g=0.75) on students’ learning. This effect is influenced by the science field, the pedagogical approach, the learning environment, and the control treatment, and not by the education level. The findings suggest that mobile learning produces the best results when the situated learning approach supports the educational intervention. Furthermore, semi-formal settings, such as museums and outdoor activities, performed better than formal settings inside classrooms or laboratories. The best results were obtained when comparing mobile learning with traditional lectures. Similarly, mobile learning showed the best results in environmental education and when implemented in primary education. Finally, mobile learning yields better results than traditional lectures, traditional pedagogical tools, and other multimedia resources. Therefore, it can be promoted as a pedagogical approach to foster quality education for all.
Educational and Methodological Support for the Theoretical Foundations of Initial Mathematics Course Discipline

The article examines the issues of the educational and methodological complex of the discipline of the theoretical foundations of the initial course of mathematics in teacher training colleges, which is of great importance in the training of future primary school teachers. It includes a combination of theoretical and methodological concepts. An analysis of work experience in this direction showed that professional and pedagogical orientation in teaching mathematics to future primary school teachers can be successfully implemented if certain conditions are met, such as motivation for various types of profession that involve the use of mathematics; orientation of methods and forms of teaching mathematics to prepare students for future practical work as a teacher; orientation of all classroom, extracurricular and research work with students towards practical needs and skills. In addition, the issues of motivation to study the course of theoretical foundations of the initial mathematics course were discussed. To achieve this goal, ideas have been put forward to link this college course with the elementary school mathematics course. It has been suggested that in this direction it is possible to successfully create the scientific foundations of an initial course in mathematics. The results of the study showed that with the targeted and systematic use of school textbooks, students’ assimilation of the basic concepts of the course becomes more meaningful and informative. Based on the educational program for training primary school teachers, ways to implement the educational and methodological complex in the Theoretical Foundations of the Primary Course in Mathematics discipline have been identified. A theoretical analysis of the approach to constructing an educational and methodological complex for the discipline was carried out. The model, structure, and content of the educational and methodological complex for the Theoretical Foundations of the Initial Course in Mathematics discipline at the Pedagogical College of Osh State University have been established. The results obtained will help improve the quality of teaching mathematics courses and improve the qualifications of teachers.
Formation of Mathematical Literacy of Students With the Help of Practice-oriented Tasks

The use of practice-oriented pedagogical tasks in the process of teaching mathematics, the task as a means of managing students’ learning activities, as a pedagogical form aimed at achieving results, and as a learning activity of students. The main features of practice-oriented tasks are defined. Considering the task as a material of educational discipline, practice-oriented tasks allow students to move from a passive object of the pedagogical process to an active subject of educational activity, as well as the use of tasks designed in accordance with local and national characteristics is the best example of establishing a link between mathematics and everyday life. Generalization of practical experience and active application of mathematical knowledge in everyday life contributes to the formation and development of students’ mathematical literacy.
The Connection Between the Innovation of Intellectual Activity of Federal Innovation Sites and the Uniqueness of the Results of Their Work, Expressed in the Form of Electronic Educational Resources

The article is devoted to the issue of correlation between the innovative activities of universities, which are federal innovation platforms, with the uniqueness of the results of their work, expressed in the form of electronic educational resources. The novelty (uniqueness) of an electronic educational resource is determined based on its demand in the educational services market to improve the quality of education and training in demand in the country. The commercialized demand for an electronic educational resource in the educational services market, which brings profit in the form of improving the quality of training of specialists, confirms the innovativeness of the university’s activities. The uniqueness (novelty) of an electronic educational resource is established during the process of its registration in the “Science and Education” United Fund of Electronic Resources. An electronic educational resource is assessed as meeting the requirement of novelty if, based on a combination of 17 indicators of consumer-oriented properties, it has no analogues in the foundation database.
Artificial Intelligence Using Python: Technologies and Applications

This article explores the significant role of Python in the development and application of artificial intelligence (AI). It covers fundamental AI concepts such as neural networks, machine learning, and deep learning, as well as key Python libraries used in this field. A brief overview of the history of AI is provided, starting from its early concepts and progressing to modern achievements. Python is regarded as the primary tool in AI development, and popular libraries and frameworks like TensorFlow, PyTorch, and scikit-learn are introduced. The article includes specific code examples and demonstrations of operations with Python libraries for machine and deep learning. It discusses AI application areas such as natural language processing, computer vision, recommendation systems, and robotics, along with examples of successful projects in these domains.
Methodology for Determining Professional Skills of Mathematics Teachers in Pedagogical Colleges

The article presents the results of experimental work carried out based on the Industrial Pedagogical College of Osh State University. To determine the opinions of students at a pedagogical college about the quality of teaching mathematical courses and the qualifications of teachers, students were presented with questionnaire questions to assess the professional skills and personal qualities of college teachers, including the author of this study, who teaches the subject “Theoretical Foundations of an Initial Mathematics Course” in these groups. In teaching mathematics courses in secondary specialized educational institutions with a pedagogical profile, formality and an abstract approach predominate. That is why there are many problems in modern technology, the discovery of sciences and practical activities. Therefore, the need to update the state of teaching the current mathematics course determined the relevance of scientific work. As a result of the survey, the level of the teacher’s professional technique, the teacher’s creative approach to the subject of mathematics, the level of communication skills and other personal qualities were determined. In addition, such teacher qualities were identified as a creative approach to work; the teacher knows well how to arouse and maintain the audience’s interest in the subject. And such qualities were identified as such qualities as the ability to relieve tension and fatigue of listeners, focuses on the application of the studied material in professional and social activities. Various pedagogical techniques were used. The conclusion was of a recommendatory nature, which was made based on the results of the analysis of this experiment, this: the creation and use of an educational and methodological complex in the learning process increases the professional level of the teacher, helps improve the relationship between the teacher and the student, and has a positive impact on the quality of education of students.
Demonstration of reversed non-Hermitian skin effect via quantum walks on a ladder

Abstract Quantum walks hold enormous potential applications in various areas such as quantum computing and quantum simulation. Discrete-time quantum walks on a ladder offer greater prospects compared to traditional quantum walks, especially in addressing physical problems in higher-dimension coupled systems. Here we give an experimental proposal of quantum walks on a two-leg ladder using linear optics, and further apply it to non-Hermitian systems by introducing loss-based non-unitary evolutions. Non-Hermitian systems under nonreciprocity-induced evolution present an exotic phenomenon, known as the non-Hermitian skin effect (NHSE). In a two-leg non-Hermitian system with the same preferred direction of NHSE, the direction has recently been found to reverse when interchain couplings are introduced. Based on quantum walks on a ladder, we also propose an experimentally feasible scheme to demonstrate the direction reversal of NHSE. Through the simulated results we show that particles on each chain accumulate to the preferred boundary driven by nonreciprocal hopping, while particles are transported in the opposite direction when interchain hopping is allowed, clearly demonstrating the existence of reversed NHSE. Our work further expands the application of the quantum walk platform and opens a door for the experimental investigation of reversed NHSE.