Reviewerly/exHarmony huggingface dataset:
Solving Quantum-Inspired Perfect Matching Problems via Tutte-Theorem-Based Hybrid Boolean Constraints

Determining the satisfiability of Boolean constraint-satisfaction problems with different types of constraints, that is hybrid constraints, is a well-studied problem with important applications. We study a new application of hybrid Boolean constraints, which arises in quantum computing. The problem relates to constrained perfect matching in edge-colored graphs. While general-purpose hybrid constraint solvers can be powerful, we show that direct encodings of the constrained-matching problem as hybrid constraints scale poorly and special techniques are still needed. We propose a novel encoding based on Tutte's Theorem in graph theory as well as optimization techniques. Empirical results demonstrate that our encoding, in suitable languages with advanced SAT solvers, scales significantly better than a number of competing approaches on constrained-matching benchmarks. Our study identifies the necessity of designing problem-specific encodings when applying powerful general-purpose constraint solvers.
LGI-GT: Graph Transformers with Local and Global Operators Interleaving

Since Transformers can alleviate some critical and fundamental problems of graph neural networks (GNNs), such as over-smoothing, over-squashing and limited expressiveness, they have been successfully applied to graph representation learning and achieved impressive results. However, although there are many works dedicated to make graph Transformers (GTs) aware of the structure and edge information by specifically tailored attention forms or graph-related positional and structural encodings, few works address the problem of how to construct high-performing GTs with modules of GNNs and Transformers. In this paper, we propose a novel graph Transformer with local and global operators interleaving (LGI-GT), in which we further design a new method propagating embeddings of the [CLS] token for global information representation. Additionally, we propose an effective message passing module called edge enhanced local attention (EELA), which makes LGI-GT a full-attention GT. Extensive experiments demonstrate that LGI-GT performs consistently better than previous state-of-the-art GNNs and GTs, while ablation studies show the effectiveness of the proposed LGI scheme and EELA. The source code of LGI-GT is available at https://github.com/shuoyinn/LGI-GT.
Hierarchical Prompt Learning for Compositional Zero-Shot Recognition

Compositional Zero-Shot Learning (CZSL) aims to imitate the powerful generalization ability of human beings to recognize novel compositions of known primitive concepts that correspond to a state and an object, e.g., purple apple. To fully capture the intra- and inter-class correlations between compositional concepts, in this paper, we propose to learn them in a hierarchical manner. Specifically, we set up three hierarchical embedding spaces that respectively model the states, the objects, and their compositions, which serve as three “experts” that can be combined in inference for more accurate predictions. We achieve this based on the recent success of large-scale pretrained vision-language models, e.g., CLIP, which provides a strong initial knowledge of image-text relationships. To better adapt this knowledge to CZSL, we propose to learn three hierarchical prompts by explicitly fixing the unrelated word tokens in the three embedding spaces. Despite its simplicity, our proposed method consistently yields superior performance over current state-of-the-art approaches on three widely-used CZSL benchmarks.
Self-supervised Graph Disentangled Networks for Review-based Recommendation

User review data is considered as auxiliary information to alleviate the data sparsity problem and improve the quality of learned user/item or interaction representations in review-based recommender systems. However, existing methods usually model user-item interactions in a holistic manner and neglect the entanglement of the latent intents behind them, e.g., price, quality, or appearance, resulting in suboptimal representations and reducing interpretability. In this paper, we propose a Self-supervised Graph Disentangled Networks for review-based recommendation (SGDN), to separately model the user-item interactions based on the latent factors through the textual review data. To this end, we first model the distributions of interactions over latent factors from both semantic information in review data and structural information in user-item graph data, forming several factor graphs. Then a factorized message passing mechanism is designed to learn disentangled user/item and interaction representations on the factor graphs. Finally, we set an intent-aware contrastive learning task to alleviate the sparsity issue and encourage disentanglement through dynamically identifying positive and negative samples based on the learned intent distributions. Empirical results over five benchmark datasets validate the superiority of SGDN over the state-of-the-art methods and the interpretability of learned intent factors.
Decoding the Underlying Meaning of Multimodal Hateful Memes

Recent studies have proposed models that yielded promising performance for the hateful meme classification task. Nevertheless, these proposed models do not generate interpretable explanations that uncover the underlying meaning and support the classification output. A major reason for the lack of explainable hateful meme methods is the absence of a hateful meme dataset that contains ground truth explanations for benchmarking or training. Intuitively, having such explanations can educate and assist content moderators in interpreting and removing flagged hateful memes. This paper address this research gap by introducing Hateful meme with Reasons Dataset (HatReD), which is a new multimodal hateful meme dataset annotated with the underlying hateful contextual reasons. We also define a new conditional generation task that aims to automatically generate underlying reasons to explain hateful memes and establish the baseline performance of state-of-the-art pre-trained language models on this task. We further demonstrate the usefulness of HatReD by analyzing the challenges of the new conditional generation task in explaining memes in seen and unseen domains. The dataset and benchmark models are made available here: https://github.com/Social-AI-Studio/HatRed
HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learning

Federated learning (FL) collaboratively models user data in a decentralized way. However, in the real world, non-identical and independent data distributions (non-IID) among clients hinder the performance of FL due to three issues, i.e., (1) the class statistics shifting, (2) the insufficient hierarchical information utilization, and (3) the inconsistency in aggregating clients. To address the above issues, we propose HyperFed which contains three main modules, i.e., hyperbolic prototype Tammes initialization (HPTI), hyperbolic prototype learning (HPL), and consistent aggregation (CA). Firstly, HPTI in the server constructs uniformly distributed and fixed class prototypes, and shares them with clients to match class statistics, further guiding consistent feature representation for local clients. Secondly, HPL in each client captures the hierarchical information in local data with the supervision of shared class prototypes in the hyperbolic model space. Additionally, CA in the server mitigates the impact of the inconsistent deviations from clients to server. Extensive studies of four datasets prove that HyperFed is effective in enhancing the performance of FL under the non-IID setting.
Contrastive Label Enhancement

Label distribution learning (LDL) is a new machine learning paradigm for solving label ambiguity. Since it is difficult to directly obtain label distributions, many studies are focusing on how to recover label distributions from logical labels, dubbed label enhancement (LE). Existing LE methods estimate label distributions by simply building a mapping relationship between features and label distributions under the supervision of logical labels. They typically overlook the fact that both features and logical labels are descriptions of the instance from different views. Therefore, we propose a novel method called Contrastive Label Enhancement (ConLE) which integrates features and logical labels into the unified projection space to generate high-level features by contrastive learning strategy. In this approach, features and logical labels belonging to the same sample are pulled closer, while those of different samples are projected farther away from each other in the projection space. Subsequently, we leverage the obtained high-level features to gain label distributions through a well-designed training strategy that considers the consistency of label attributes. Extensive experiments on LDL benchmark datasets demonstrate the effectiveness and superiority of our method.
Learning Survival Distribution with Implicit Survival Function

Survival analysis aims at modeling the relationship between covariates and event occurrence with some untracked (censored) samples. In implementation, existing methods model the survival distribution with strong assumptions or in a discrete time space for likelihood estimation with censorship, which leads to weak generalization. In this paper, we propose Implicit Survival Function (ISF) based on Implicit Neural Representation for survival distribution estimation without strong assumptions, and employ numerical integration to approximate the cumulative distribution function for prediction and optimization. Experimental results show that ISF outperforms the state-of-the-art methods in three public datasets and has robustness to the hyperparameter controlling estimation precision.
From Association to Generation: Text-only Captioning by Unsupervised Cross-modal Mapping

With the development of Vision-Language Pre-training Models (VLPMs) represented by CLIP and ALIGN, significant breakthroughs have been achieved for association-based visual tasks such as image classification and image-text retrieval by the zero-shot capability of CLIP without fine-tuning. However, CLIP is hard to apply to generation-based tasks. This is due to the lack of decoder architecture and pre-training tasks for generation. Although previous works have created generation capacity for CLIP through additional language models, a modality gap between the CLIP representations of different modalities and the inability of CLIP to model the offset of this gap, which results in the failure of the concept to transfer across modes. To solve the problem, we try to map images/videos to the language modality and generate captions from the language modality. In this paper, we propose the K-nearest-neighbor Cross-modality Mapping (Knight), a zero-shot method from association to generation. With vision-free unsupervised training, Knight achieves state-of-the-art performance in zero-shot methods for image captioning and video captioning.
Action Space Reduction for Planning Domains

Planning tasks succinctly represent labeled transition systems, with each ground action corresponding to a label. This granularity, however, is not necessary for solving planning tasks and can be harmful, especially for model-free methods. In order to apply such methods, the label sets are often manually reduced. In this work, we propose automating this manual process. We characterize a valid label reduction for classical planning tasks and propose an automated way of obtaining such valid reductions by leveraging lifted mutex groups. Our experiments show a significant reduction in the action label space size across a wide collection of planning domains. We demonstrate the benefit of our automated label reduction in two separate use cases: improved sample complexity of model-free reinforcement learning algorithms and speeding up successor generation in lifted planning. The code and supplementary material are available at https://github.com/IBM/Parameter-Seed-Set.
Unreliable Partial Label Learning with Recursive Separation

Partial label learning (PLL) is a typical weakly supervised learning problem in which each instance is associated with a candidate label set, and among which only one is true. However, the assumption that the ground-truth label is always among the candidate label set would be unrealistic, as the reliability of the candidate label sets in real-world applications cannot be guaranteed by annotators. Therefore, a generalized PLL named Unreliable Partial Label Learning (UPLL) is proposed, in which the true label may not be in the candidate label set. Due to the challenges posed by unreliable labeling, previous PLL methods will experience a marked decline in performance when applied to UPLL. To address the issue, we propose a two-stage framework named Unreliable Partial Label Learning with Recursive Separation (UPLLRS). In the first stage, the self-adaptive recursive separation strategy is proposed to separate the training set into a reliable subset and an unreliable subset. In the second stage, a disambiguation strategy is employed to progressively identify the ground-truth labels in the reliable subset. Simultaneously, semi-supervised learning methods are adopted to extract valuable information from the unreliable subset. Our method demonstrates state-of-the-art performance as evidenced by experimental results, particularly in situations of high unreliability. Code and supplementary materials are available at https://github.com/dhiyu/UPLLRS.
Incorporating Unlikely Negative Cues for Distinctive Image Captioning

While recent neural image captioning models have shown great promise in terms of automatic metrics, they still struggle with generating generic sentences, which limits their use to only a handful of simple scenarios. On the other hand, negative training has been suggested as an effective way to prevent models from producing frequent yet meaningless sentences. However, when applied to image captioning, this approach may overlook low-frequency but generic and vague sentences, which can be problematic when dealing with diverse and changeable visual scenes. In this paper, we introduce a approach to improve image captioning by integrating negative knowledge that focuses on preventing the model from producing undesirable generic descriptions while addressing previous limitations. We accomplish this by training a negative teacher model that generates image-wise generic sentences with retrieval entropy-filtered data. Subsequently, the student model is required to maximize the distance with multi-level negative knowledge transferring for optimal guiding. Empirical results evaluated on MS COCO benchmark confirm that our plug-and-play framework incorporating unlikely negative knowledge leads to significant improvements in both accuracy and diversity, surpassing previous state-of-the-art methods for distinctive image captioning.
Model Conversion via Differentially Private Data-Free Distillation

While massive valuable deep models trained on large-scale data have been released to facilitate the artificial intelligence community, they may encounter attacks in deployment which leads to privacy leakage of training data. In this work, we propose a learning approach termed differentially private data-free distillation (DPDFD) for model conversion that can convert a pretrained model (teacher) into its privacy-preserving counterpart (student) via an intermediate generator without access to training data. The learning collaborates three parties in a unified way. First, massive synthetic data are generated with the generator. Then, they are fed into the teacher and student to compute differentially private gradients by normalizing the gradients and adding noise before performing descent. Finally, the student is updated with these differentially private gradients and the generator is updated by taking the student as a fixed discriminator in an alternate manner. In addition to a privacy-preserving student, the generator can generate synthetic data in a differentially private way for other down-stream tasks. We theoretically prove that our approach can guarantee differential privacy and well convergence. Extensive experiments that significantly outperform other differentially private generative approaches demonstrate the effectiveness of our approach.
Black-box Prompt Tuning for Vision-Language Model as a Service

In the scenario of Model-as-a-Service (MaaS), pre-trained models are usually released as inference APIs. Users are allowed to query those models with manually crafted prompts. Without accessing the network structure and gradient information, it's tricky to perform continuous prompt tuning on MaaS, especially for vision-language models (VLMs) considering cross-modal interaction. In this paper, we propose a black-box prompt tuning framework for VLMs to learn task-relevant prompts without back-propagation. In particular, the vision and language prompts are jointly optimized in the intrinsic parameter subspace with various evolution strategies. Different prompt variants are also explored to enhance the cross-model interaction. Experimental results show that our proposed black-box prompt tuning framework outperforms both hand-crafted prompt engineering and gradient-based prompt learning methods, which serves as evidence of its capability to train task-relevant prompts in a derivative-free manner.
THE ROLE OF THE SCHOOL HEAD IN THE SELF-DEVELOPMENT OF STUDENTS THROUGH EXTRACULAR ACTIVITIES (CASE STUDY MIN 1 BENER MERIAH)

The purpose of this study was to describe why the role of the principal in self-development of students through extracurricular activities at MIN 1 Bener Meriah, and to describe the role of the principal in self-development of students through extracurricular activities at MIN 1 Bener Meriah. This study is a qualitative type using observational, interviews and documentation, data analysis using triangulation theory. Based on the results of the research: the role of the principal is very important in the self-development of students at MIN 1 Bener Meriah. Among these roles are (1) educator: teaching with teacher intermediaries, (2) manager: managing with POAC even though the field is narrow, (3) administrators: take care of correspondence and funding for extracurricular activities, (4) supervise extracurricular activities through joint meetings and direct involvement, (5) leaders: mobilize involved teachers and students in extracurricular activities, (6) innovator: Principals make reforms such as creative guel dance (7) motivator: the principal provides encouragement in the form of words and gifts for students who excel, including in extracurricular activities. So that the role of the principal is done well, it will also have a good impact on the development of students in the extracurricular program.
Transparency meets open citations

&lt;em&gt;This post was first published on &lt;/em&gt;&lt;em&gt;QUERTY: musings from the rabbit&lt;/em&gt; &lt;em&gt;hole&lt;/em&gt;, &lt;em&gt;a blog by Silvio Peroni&lt;/em&gt; In the scholarly ecosystem, a bibliographic citation is a &lt;em&gt;conceptual&lt;/em&gt; directional link from a citing entity to a cited entity, used to acknowledge or ascribe credit for the contribution made by the author(s) of the cited entity. Citations are one of the core elements of scholarly communication.
The Informationized Teaching Evaluation Indicator System for College English Teachers

The big data analysis has been applied to the public education in some countries and has become an important force in the educational reform.However, the current research on data intelligence is still relatively weak.How to improve the teachers' data intelligence and make them possess the ability to implement teaching activities in the era of big data have become urgent problems.Based on researches about systemic summary of teachers' informationized teaching and its structure, this paper aims at building a scientific and effective informationized teaching evaluation indicator system for college English teachers, so as to render a basis for the measurement and evaluation of college English teachers' informationized teaching and provide reference for the implementation of college English teacher training activities.
Deep learning based phishing website identification system using CNN-LSTM classifier

The term phishing refers to an attack that pretends to be the website of a large corporation, typically one dealing with money, such as a bank or other financial institution or an online retailer. Its primary objective is to acquire personally identifiable information from users, such as their social security numbers, credit card information, and passwords. Due to the rise of phishing attacks, various techniques have been developed in order to combat these threats. One of these is deep learning algorithms, which are capable of learning and analyzing massive datasets. Due to their capabilities, these algorithms are very useful in identifying and preventing phishing attacks. Due to the complexity of the phishing websites, many development systems have been created to detect them. Unfortunately, the output that was desired cannot be achieved by these systems, and they have a number of other flaws as well. The purpose of this paper is to propose a hybrid deep learning-based phishing detection system that is easy to put into practice. The quality of the input dataset is improved through the process of preprocessing the dataset. After that, the procedures of clustering and feature selection are carried out in order to improve the accuracy and decrease the amount of time required for the processing. The resulting features are then fed into the CNN_LSTM, which is a classification system that classifies websites that are phishing and legitimate. Proposed Hybrid deep learning models are proposed to combine the features of natural language processing (NLP) and character embedding. They can then reveal high-level connections between characters. In terms of the metric that is being used for the evaluation, the performance of the models that have been proposed is better than that of the other models.
Teaching Practice of Core Literacy of Chinese in Senior High School

With the release of the research results of Chinese students' core literacy development and the establishment of the framework of students' core literacy development and various disciplines, the core literacy idea has begun to deepen into various disciplines.The core literacy of Chinese discipline includes four aspects: language construction and application, thinking development and promotion, aesthetic appreciation and creation, cultural inheritance and understanding, among which language construction and application are related to the ontology of Chinese discipline.Teachers should pay attention to cultivating students' core abilities such as language application ability, innovative thinking ability and literary aesthetic ability in Chinese teaching, so as to effectively improve students' core literacy of Chinese subject and improve the quality of Chinese teaching in senior high schools.In the context of the new college entrance examination, new textbooks, and new curriculum standards, the transformation of high school Chinese classroom teaching is imperative.Three kinds of classroom teaching, namely, "reverse teaching design based on understanding", "thematic discussion under critical thinking" and "deep learning from the perspective of academic research", which point to the core quality of the discipline, help guide students to accumulate speech experience in real learning situations, develop thinking ability, cultivate aesthetic interest and accumulate cultural heritage.