bluuebunny/crossref_metadata_2025 huggingface dataset:
PANDEMİNİN SOSYAL ETKİLERİ VE DEĞİŞEN GÜNDELİK HAYAT TRABZON KENTİNİN KOVİD-19 DENEYİMİ
Authors: YADİGAROĞLU Hüseyin
Published: September 2023

Bu çalışmanın konusu KOVİD-19 salgınının Trabzon kentinde yaşayan insanlar tarafından nasıl deneyimlendiğini irdelemektir. KOVİD-19 salgını tüm dünyayı etkileyen bir pandemidir. Salgın hastalıkların büyük coğrafyalarda etkili olması anlamını taşıyan pandemi, dünyayı olduğu gibi Türkiye’yi de etkisi altına almış ve binlerce insana bulaşmıştır. Türkiye’nin Kuzey Doğu Bölgesi’nde yer alan ve büyükşehirlerden biri olan Trabzon, vaka sayılarının belli dönemlerde Türkiye ortalamasının üzerinde olması nedeniyle pandeminin etkilerini fazlasıyla deneyimlendiği bir kenttir. Sosyolojik düşüncede işlevselcilik ve sembolik etkileşimcilik teorileri hastalık deneyimine odaklanır ve hastalıkları yaşanmış bir deneyim olarak kavramsallaştırır. Buna göre çalışmanın temel kuramsal perspektifi işlevselci teoridir. Çalışmada KOVİD-19 salgını yaşanmış bir deneyim olarak ele alınmıştır. Hastalık deneyimi, hastalık belirtilerinin görülmeye başlanmasından iyileşme, rehabilitasyon veya ölüme kadarki süreçte yaşanılan ve yapılan tüm eylem ve duyguları kapsamaktadır. Bu çalışmada, işlevselci perspektif benimsenmiş ve pandeminin bir risk olarak toplumsal sistemde neden olduğu dönüşüm ele alınmış ve dönüşümün etkileri Trabzon’da yaşayan insanların deneyimleri üzerinden sorunsallaştırılmıştır. Bu bağlamda çalışmanın amacı, toplumsal sistemi tehdit eden pandemiye karşı sistemin uyum ve dengeyi yeniden sağlama adına gösterdiği tepkileri, bireylerin deneyimleri üzerinden irdelemektir. Çalışma bir tarama araştırması olarak tasarlanmış ve veriler anket tekniği ile elde edilmiştir. Anket; demografik bilgiler, ekonomik durum, sosyal hayat, geleceğe dair düşünce, pandeminin nedenleri ve korunma yolları, pandemi sürecinde devlet desteği ve uzaktan eğitim gibi temaları içeren sorulardan oluşmaktadır. Araştırmanın örneklemi Trabzon’da yaşayan (kır ve il merkezinde) ve yetişkin olan 391 (182 kadın ve 209 erkek) kişiden oluşmaktadır. Çalışmadan elde edilen veriler SPSS programı aracılığıyla analiz edilmiştir. Sonuç olarak pandemi, toplumsal sistem için bir risktir ve risk toplumsal sistemde işlevsizliklere neden olmuştur. Buna karşı kurumsal yapı (özellikle sağlık kurumu) riske karşı uyum ve dengeyi sağlama amacıyla yeni normal bağlamında “HES kodu” ve “temizlik, maske, mesafe” söylemi gibi uygulamalarla kendini korumuştur. Araştırma neticesinde katılımcıların % 30,9’u sosyal etkinliklere daha az katıldığını, %29,9’u evde geçirdiği zamanın arttığını ve %53,1 salgında sosyal etkileşimin sınırlandığını söylemiştir. Dolayısıyla yeni normal düzende temas ve etkileşim sınırlandırılarak bulaş riskine karşı önlem alınmış ve sağlık kurumu bireysel sorumlulukların (mesafeye dikkat etme, aşı vurulma ve maske kullanımı vb.) önemini vurgulamıştır. Bu bağlamda araştırma sonucunda katılımcıların %78,7’si aşı vurulacağını belirtmiştir.
The flagship of economic, mathematical and computer modeling: 60 years in line
Authors: Kleiner George
Published: September 2023

Main directions and evolution routes of the Central Economics and Mathematics Institute (CEMI) as a leader of domestic economic science in the development and application of economic-mathematical and information-computer models of socio-economic processes and systems were analyzed in the paper. The connection of modeling with such components of the economy as economic theory, economic policy, economic management and economic practice were clarified. It was shown that the economic and mathematical model plays an indispensable role in the integration and systematization of knowledge in the field of economic theory and economic practice. A generalized scheme and the sequence of modeling stages based on the concept of a model as a homomorphic image of a real object in the field of ontology (“the world of things”), ideology (“the world of ideas”), epistemology (“the world of knowledge”) and praxeology (“the world of creation”), were proposed. The evolution of the main components of modeling which had significantly changed over the past 60 years the ideas about the object, subject, goals, apparatus and empirical basis of modeling, were traced. The experience accumulated at CEMI RAS in this area allows us to propose the concept of evidence-based modeling, similar to the ideology of evidence-based medicine, evidence-based policy, evidence-based forecasting, aimed at qualitatively improving the validity and reliability of modeling results for applying in economic theory and practice. The main principles of evidence-based modeling that have to play the role of standards for evidence-based modeling were formulated. The modeling process was considered as an analogue of an innovative project, where every independent stage (likewise the entire project) should be carried out according to these standards. A list of the most important, in our opinion, pioneer works of CEMI RAS in the field of economic-mathematical and computer modeling, which determine significant directions for the further development of this field of science, were given.
ОЦІНКА ЯКОСТІ ЕФЕКТИВНОСТІ ЗАСТОСУВАННЯ БАГАТОКРИТЕРІАЛЬНОЇ МОДЕЛІ ІДЕНТИФІКАЦІЇ НЕШТАТНИХ СИТУАЦІЙ
Authors: СОРОКОПУД Тетяна , ПИСАРЧУК Олексій
Published: September 2023

В роботі наведено результати апробації багатокритеріальної моделі ідентифікації нештатних ситуацій за сукупністю ознак на об’єктах критичної інфраструктури.

Основним фактором якості побудованої моделі є оцінка результативності її показників, щодо виявлення та вирішення задачі ідентифікації нештатних ситуацій на об’єктах критичної інфраструктури.

Отримані результати демонструють, що запропонована модель за показниками ефективності може бути впроваджена в діючі об’єкти критичної інфраструктури.
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning
Authors: Castagna Alberto , Dusparic Ivana
Published: September 2023

Transfer learning in Reinforcement Learning (RL) has been widely studied to overcome training challenges in Deep-RL, i.e., exploration cost, data availability and convergence time, by bootstrapping external knowledge to enhance learning phase. While this overcomes the training issues on a novice agent, a good understanding of the task by the expert agent is required for such a transfer to be effective. As an alternative, in this paper we propose Expert-Free Online Transfer Learning (EF-OnTL), an algorithm that enables expert-free real-time dynamic transfer learning in multi-agent system. No dedicated expert agent exists, and transfer source agent and knowledge to be transferred are dynamically selected at each transfer step based on agents’ performance and level of uncertainty. To improve uncertainty estimation, we also propose State Action Reward Next-State Random Network Distillation (sars-RND), an extension of RND that estimates uncertainty from RL agent-environment interaction. We demonstrate EF-OnTL effectiveness against a no-transfer scenario and state-of-the-art advice-based baselines, with and without expert agents, in three benchmark tasks: Cart-Pole, a grid-based Multi-Team Predator-Prey (MT-PP) and Half Field Offense (HFO). Our results show that EF-OnTL achieves overall comparable performance to that of advice-based approaches, while not requiring expert agents, external input, nor threshold tuning. EF-OnTL outperforms no-transfer with an improvement related to the complexity of the task addressed.
Dual-Scale Interest Extraction Framework with Self-Supervision for Sequential Recommendation
Authors: Chen Liangliang , Lin Hongzhan , Ma Jinshan , Chen Guang
Published: September 2023

In the sequential recommendation task, the recommender generally learns multiple embeddings from a user’s historical behaviors, to catch the diverse interests of the user. Nevertheless, the existing approaches just extract each interest independently for the corresponding sub-sequence while ignoring the global correlation of the entire interaction sequence, which may fail to capture the user’s inherent preference for the potential interests generalization and unavoidably make the recommended items homogeneous with the historical behaviors. In this paper, we propose a novel Dual-Scale Interest Extraction framework (DSIE) to precisely estimate the user’s current interests. Specifically, DSIE explicitly models the user’s inherent preference with contrastive learning by attending over his/her entire interaction sequence at the global scale and catches the user’s diverse interests in a fine granularity at the local scale. Moreover, we develop a novel interest aggregation module to integrate the multi-interests according to the inherent preference to generate the user’s current interests for the next-item prediction. Experiments conducted on three real-world benchmark datasets demonstrate that DSIE outperforms the state-of-the-art models in terms of recommendation preciseness and novelty.
PARIS: Planning Algorithms for Reconfiguring Independent Sets
Authors: Christen Remo , Eriksson Salomé , Katz Michael , Muise Christian , Petrov Alice , Pommerening Florian , Seipp Jendrik , Sievers Silvan , Speck David
Published: September 2023

Combinatorial reconfiguration is the problem of transforming one solution of a combinatorial problem into another, where each transformation may only apply small changes to a solution and may not leave the solution space. An important example is the independent set reconfiguration (ISR) problem, where an independent set of a graph (a subset of its vertices without edges between them) has to be transformed into another by a sequence of transformations that can replace a vertex in the current subset such that the new subset is still an independent set. The 1st Combinatorial Reconfiguration Challenge (CoRe Challenge 2022) was a competition focused on the ISR problem. The PARIS team successfully participated with two solvers that model the ISR problem as a planning task and employ different planning techniques for solving it. In this work, we describe these models and solvers. For a fair comparison to competing ISR approaches, we re-run the entire competition under equal computational conditions. Besides showcasing the success of planning technology, we hope that this work will create a cross-fertilization of the two research fields.
Rank-Envy-Freeness in Roommate Matchings
Authors: Coutance Baptistin , Maddila Prasanna , Wilczynski Anaëlle
Published: September 2023

In the roommate problem, pairs of agents must be formed, based on ordinal preferences of the agents over each other. In this article, we examine fair roommate matchings by relaxing envy-freeness to account for justified envy based on the rank in the agents’ preferences. A rank-envy-free matching prevents that an agent prefers the partner of another agent whereas she has ranked it better. Although this requirement is pretty weak in house allocation [9], we show that it is more demanding in the roommate setting. We study parameterizations of rank-envy-freeness, as well as further natural relaxations of this concept. We also investigate the connections between the family of rank-based fairness criteria and known optimality or stability concepts.
Effective and Efficient Community Search with Graph Embeddings
Authors: Gou Xiaoxuan , Xu Xiaoliang , Wu Xiangying , Chen Runhuai , Wang Yuxiang , Wu Tianxing , Ke Xiangyu
Published: September 2023

Given a graph G and a query node q, community search (CS) seeks a cohesive subgraph from G that contains q. CS has gained much research interests recently. In the database research community, researchers aim to find the most cohesive subgraph satisfying a specific community model (e.g., k-core or k-truss) via graph traversal. These works obtain good precision, however suffering from the low efficiency issue. In the AI research community, a new thought of using the deep learning model to support CS without relying on graph traversal emerges. Supervised end-to-end models using GCN are presented, which perform efficiently, but leave a large room for precision improvement. None of them can achieve a good balance between the efficiency and effectiveness. This motivates our solution: First, we present an offline community-injected graph embedding method to preserve the community’s cohesiveness features into the learned node representations. Second, we resort to a proximity graph (PG) built from node representations, to quickly return the community online. Moreover, we develop a self-augmented method based on KL divergence to further optimize node representations. Extensive experiments on seven real-world graphs show our solution’s superiority on effectiveness (at least 39.3% improvement) and efficiency (one to two orders of magnitude faster).
Evolving Dictionary Representation for Few-Shot Class-Incremental Learning
Authors: Han Xuejun , Guo Yuhong
Published: September 2023

New objects are continuously emerging in the dynamically changing world and a real-world artificial intelligence system should be capable of continual and effectual adaptation to new emerging classes without forgetting old ones. In view of this, in this paper we tackle a challenging and practical continual learning scenario named few-shot class-incremental learning (FSCIL), in which labeled data are given for classes in a base session but very limited labeled instances are available for new incremental classes. To address this problem, we propose a novel and succinct approach by introducing deep dictionary learning which is a hybrid learning architecture that combines dictionary learning and visual representation learning to provide a better space for characterizing different classes. We simultaneously optimize the dictionary and the feature extraction backbone in the base session, but only finetune the dictionary in the incremental session for adaptation to novel classes, which can alleviate the forgetting problem on base classes compared to finetuning the entire model. To further facilitate future adaptation, we also incorporate multiple pseudo classes into the base session training so that certain space projected by dictionary can be reserved for future new concepts. Moreover, we adopt a novel optimization process by implicitly incorporating the dictionary reconstruction loss into the learning process without adding extra loss terms. The extensive experimental results on CIFAR100, miniImageNet and CUB200 validate the effectiveness of our approach compared to other SOTA methods.
Offline Decentralized Multi-Agent Reinforcement Learning
Authors: Jiang Jiechuan , Lu Zongqing
Published: September 2023

In many real-world multi-agent cooperative tasks, due to high cost and risk, agents cannot continuously interact with the environment and collect experiences during learning, but have to learn from offline datasets. However, the transition dynamics in the dataset of each agent can be much different from the ones induced by the learned policies of other agents in execution, creating large errors in value estimates. Consequently, agents learn uncoordinated low-performing policies. In this paper, we propose a framework for offline decentralized multi-agent reinforcement learning, which exploits value deviation and transition normalization to deliberately modify the transition probabilities. Value deviation optimistically increases the transition probabilities of high-value next states, and transition normalization normalizes the transition probabilities of next states. They together enable agents to learn high-performing and coordinated policies. Theoretically, we prove the convergence of Q-learning under the altered non-stationary transition dynamics. Empirically, we show that the framework can be easily built on many existing offline reinforcement learning algorithms and achieve substantial improvement in a variety of multi-agent tasks.
ALFR++: A Novel Algorithm for Learning Adversarial Fair Representations
Authors: Knobbout Max
Published: September 2023

As more decisions in our daily life become automated, the need to have machine learning algorithms that make fair decisions increases. In fair representation learning we are tasked with finding a suitable representation of the data in which a sensitive variable is censored. Recent work aims to learn fair representations through adversarial learning. With this paper, we build upon this work by introducing a novel algorithm, ALFR++, to learn adversarial fair representations independent of the downstream task that may be encountered (supervised or unsupervised). We introduce balanced error rate modulation, to stabilize the interaction between actor and adversary. We couple it with a recently introduced censoring technique called stacking, to learn strong censored representations within a restricted hypothesis space. Results show that our algorithm improves on previous work in both censoring and reconstruction, allowing us to create fairer data without knowing the task beforehand.
Extracting and Exploiting Bounds of Numeric Variables for Optimal Linear Numeric Planning
Authors: Kuroiwa Ryo , Shleyfman Alexander , Beck J. Christopher
Published: September 2023

In numeric AI planning, a state is represented by propositions and numeric variables, actions change the values of numeric variables in addition to adding and deleting propositions, and goals and preconditions of actions may include conditions over numeric variables. While domains of numeric variables are rational numbers in general, upper and lower bounds on variables affected only by constant increase and decrease can sometimes be determined and exploited by a heuristic function. In this paper, we generalize the existing method to variables that are changed by linear effects. We exploit the extracted bounds to improve the numeric LM-cut heuristic, a state-of-the-art admissible heuristic for linear numeric planning. Empirical evaluation shows that our method improves the performance of LM-cut in multiple domains. The proposed method can also detect unsolvability of some numeric tasks in polynomial time.
Self-Expressive Network-Based Subspace Clustering for Deep Embedding
Authors: Leng Tingting , Zhao Ling , Xiong Xiaolong , Cheng Peng , Zhou Jun
Published: September 2023

Existing deep subspace clustering algorithms are difficult to scale to large-scale data. There are two reasons: Firstly, the existing subspace clustering algorithms almost all need to find the self-expressive coefficient matrix whose size is proportional to the square of the data set size at once. Secondly, spectral clustering needs to solve the eigenvector of the affinity matrix. These two points make the computational complexity of clustering very high when the data scale is large. This paper proposes Self-Expressive Network-Based Deep Embedded Subspace Clustering (SE-DESC), a subspace clustering method that can be applied to large-scale single-view and multi-view data. Using the idea of siamese networks, we design a self-expressive network to calculate the self-expressive coefficient between two data points, reducing the parameter amount of the self-expressive model to a constant. It can effectively avoid computational complexity. Then, we use a deeply embedded network to learn an embedding for each data point to map the data into the spectral space, avoiding the high computational complexity of spectral clustering. Extensive experiments demonstrate that SE-DESC improves the clustering performance on large-scale data compared to state-of-the-art methods.
ESSL: Enhanced Spatio-Temporal Self-Selective Learning Framework for Unsupervised Video Anomaly Detection
Authors: Li Qun , Pan Xubei , Xiao Fu , Bhanu Bir
Published: September 2023

Unsupervised Video Anomaly Detection (UVAD) utilizes completely unlabeled videos for training without any human intervention. Due to the existence of unlabeled abnormal videos in the training data, the performance of UVAD has a large gap compared with semi-supervised VAD, which only uses normal videos for training. To address the problem of insufficient ability of the existing UVAD methods to learn normality and reduce the negative impact of abnormal events, this paper proposes a novel Enhanced Spatio-temporal Self-selective Learning (ESSL) framework for UVAD. This framework is designed for capturing both the appearance and motion features through effective network structures by solving the spatial and temporal jigsaw puzzles. Specially, we develop a Self-selective Learning Module (SLM) for UVAD, which prevents the model learning abnormal features and enhances the model by selecting normal features. Experimental results on three benchmark datasets show that the proposed method not only surpasses the state-of-the-art UVAD works, but also achieves the performance comparable to the classic semi-supervised methods for video anomaly detection that needs normal videos selected manually. Code is available at: https://github.com/xusuger/ESSL.
High Probability Analysis for Non-Convex Stochastic Optimization with Clipping
Authors: Li Shaojie , Liu Yong
Published: September 2023

Gradient clipping is a commonly used technique to stabilize the training process of neural networks. A growing body of studies has shown that gradient clipping is a promising technique for dealing with the heavy-tailed behavior that emerged in stochastic optimization as well. While gradient clipping is significant, its theoretical guarantees are scarce. Most theoretical guarantees only provide an in-expectation analysis and only focus on optimization performance. In this paper, we provide high probability analysis in the non-convex setting and derive the optimization bound and the generalization bound simultaneously for popular stochastic optimization algorithms with gradient clipping, including stochastic gradient descent and its variants of momentum and adaptive stepsizes. With the gradient clipping, we study a heavy-tailed assumption that the gradients only have bounded α-th moments for some α ∈ (1, 2], which is much weaker than the standard bounded second-moment assumption. Overall, our study provides a relatively complete picture for the theoretical guarantee of stochastic optimization algorithms with clipping.
Letting Go of Self-Domain Awareness: Multi-Source Domain-Adversarial Generalization via Dynamic Domain-Weighted Contrastive Transfer Learning
Authors: Ma Yuan , Chen Yiqiang , Yu Han , Gu Yang , Wen Shijie , Guo Shuai
Published: September 2023

Domain generalization (DG), which aims to learn a model that can generalize to an unseen target domain, has recently attracted increasing research interest. A major approach is to learn domain invariant representations to avoid greedily capturing all the correlations found in source domains caused by empirical risk minimization. Nevertheless, overly emphasizing learning of domain invariant representations might lead to learning overly-compressed domain invariant representations, causing confusion of different classes in a same domain. To address this limitation, we introduce a novel dynamic domain-weighted contrastive loss, which maximizes the subdomain differences between different classes especially those belonging to the same domain, while minimizing the average distance between the points of the convex hull of the aligned source domains. We propose Multi-source domain-adversarial generalization via dynamic domain-weighted Contrastive transfer learning (MsCtrl), a novel domain-adversarial generalization framework, which optimizes the distribution alignment of source and potential target subdomains in an adversarial manner under the “control” of the aforementioned contrastive loss. Extensive experiments based on real-world datasets demonstrate significant advantages of MsCtrl over existing state-of-the-art methods.
Story Ending Generation Using Commonsense Casual Reasoning and Graph Convolutional Networks
Authors: Park Eunkyung , Wong Raymond K. , Chu Victor W.
Published: September 2023

Story Ending Generation is a task of generating a coherent and sensible ending for a given story. The key challenges of this task are i) how to obtain a good understanding of context, ii) how to capture hidden information between lines, and iii) how to obtain causal progression. However, recent machine learning models can only partially address these challenges due to the lack of causal entailment and consistency. The key novelty in our proposed approach is to capture the hidden story by generating transitional commonsense sentences between each adjacent context sentence, which substantially enriches causal and consistent story flow. Specifically, we adopt a soft causal relation using people’s everyday commonsense knowledge to mimic the cognitive understanding process of readers. We then enrich the story with causal reasoning and utilize dependency parsing to capture long range text relations. Finally, we apply multi-level Graph Convolutional Networks to deliver enriched contextual information across different layers. Both automatic and human evaluation results show that our proposed model can significantly improve the quality of generated story endings.
Adversarial Discriminator to Mitigate Gender Bias in Abusive Language Detection
Authors: Park Jaeil , Cho Sung-Bae
Published: September 2023

Abusive language detection models tend to have a gender bias problem in which the model is biased towards sentences containing identity words of specific gender groups. Previous studies to reduce bias, such as projection methods, tend to lose information in word vectors and sentence context, resulting in low detection accuracy. This paper proposes a novel method that mitigates gender bias while preserving original information by regularizing sentence embedding vectors based on information theory. Latent vectors generated by an autoencoder are debiased through dual regularization using a gender discriminator, an abuse classifier, and a decoder. While the gender discriminator labels are randomized, the discriminator confuses the gender feature, and the classifier retains the abuse information. Latent vectors are regularized through information theoretic adversarial optimization that disentangles and mitigates gender features. We show that the proposed method successfully orthogonalizes the direction of the correlated information and reduces the gender feature through calculation of subspaces and embedding vector visualization. Moreover, the proposed method maintains the highest accuracy among the four state-of-the-art bias mitigation methods and shows superior performance in reducing gender bias in four different Twitter datasets for abusive language detection.
Causal Discovery and Knowledge Injection for Contestable Neural Networks
Authors: Russo Fabrizio , Toni Francesca
Published: September 2023

Neural networks have proven to be effective at solving machine learning tasks but it is unclear whether they learn any relevant causal relationships, while their black-box nature makes it difficult for modellers to understand and debug them. We propose a novel method overcoming these issues by allowing a two-way interaction whereby neural-network-empowered machines can expose the underpinning learnt causal graphs and humans can contest the machines by modifying the causal graphs before re-injecting them into the machines. The learnt models are guaranteed to conform to the graphs and adhere to expert knowledge, some of which can also be given up-front. By building a window into the model behaviour and enabling knowledge injection, our method allows practitioners to debug networks based on the causal structure discovered from the data and underpinning the predictions. Experiments with real and synthetic tabular data show that our method improves predictive performance up to 2.4x while producing parsimonious networks, up to 7x smaller in the input layer, compared to SOTA regularised networks.
Cognitive Effects in Large Language Models
Authors: Shaki Jonathan , Kraus Sarit , Wooldridge Michael
Published: September 2023

Large Language Models (LLMs) such as ChatGPT have received enormous attention over the past year and are now used by hundreds of millions of people every day. The rapid adoption of this technology naturally raises questions about the possible biases such models might exhibit. In this work, we tested one of these models (GPT-3) on a range of cognitive effects, which are systematic patterns that are usually found in human cognitive tasks. We found that LLMs are indeed prone to several human cognitive effects. Specifically, we show that the priming, distance, SNARC, and size congruity effects were presented with GPT-3, while the anchoring effect is absent. We describe our methodology, and specifically the way we converted real-world experiments to text-based experiments. Finally, we speculate on the possible reasons why GPT-3 exhibits these effects and discuss whether they are imitated or reinvented.
Uncertain Relational Hypergraph Attention Networks for Document-Level Event Factuality Identification
Authors: Sheng Jiawei , Cong Xin , Cao Jiangxia , Guo Shu , Li Chen , Wang Lihong , Liu Tingwen , Xu Hongbo
Published: September 2023

Document-level event factuality identification (DocEFI) is an important task in event knowledge acquisition, which aims to detect whether an event actually occurs or not from the perspective of the document. Unlike the sentence-level task, a document can have multiple sentences with different event factualities, leading to event factuality conflicts in DocEFI. Existing studies attempt to aggregate local event factuality by exploiting document structures, but they mostly consider textual components in the document separately, degrading complicated correlations therein. To address the above issues, this paper proposes a novel approach, namely UR-HAT, to improve DocEFI with uncertain relational hypergraph attention networks. Particularly, we reframe a document graph as a hypergraph, and establish beneficial n-ary correlations among textual nodes with relational hyperedges, which helps to globally consider local factuality features to resolve event factuality conflicts. To better discern the importance of event factuality features, we further represent textual nodes with uncertain Gaussian distributions, and propose novel uncertain relational hypergraph attention networks to refine textual nodes with the document hypergraph. In addition, we select factuality-related keywords as nodes to enrich event factuality features. Experimental results demonstrate the effectiveness of our proposed method, and outperforms previous methods on two widely used benchmark datasets.
Decoding Realistic Images from Brain Activity with Contrastive Self-Supervision and Latent Diffusion
Authors: Sun Jingyuan , Li Mingxiao , Moens Marie-Francine
Published: September 2023

Reconstructing visual stimuli from human brain activities provides a promising opportunity to advance our understanding of the brain’s visual system and its connection with computer vision models. Although deep generative models have been employed for this task, the challenge of generating high-quality images with accurate semantics persists due to the intricate underlying representations of brain signals and the limited availability of parallel data. In this paper, we propose a two-phase framework named Contrast and Diffuse (CnD) to decode realistic images from functional magnetic resonance imaging (fMRI) recordings. In the first phase, we acquire representations of fMRI data through self-supervised contrastive learning. In the second phase, the encoded fMRI representations condition the diffusion model to reconstruct visual stimulus through our proposed concept-aware conditioning method. Experimental results show that CnD reconstructs highly plausible images on challenging benchmarks. We also provide a quantitative interpretation of the connection between the latent diffusion model (LDM) components and the human brain’s visual system. In summary, we present an effective approach for reconstructing visual stimuli based on human brain activity and offer a novel framework to understand the relationship between the diffusion model and the human brain visual system. The code is released at https://github.com/Mingxiao-Li/BrainDecoding.
User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations
Authors: Tan Juntao , Ge Yingqiang , Zhu Yan , Xia Yinglong , Luo Jiebo , Ji Jianchao , Zhang Yongfeng
Published: September 2023

Modern recommender systems utilize users’ historical behaviors to generate personalized recommendations. However, these systems often lack user controllability, leading to diminished user satisfaction and trust in the systems. Acknowledging the recent advancements in explainable recommender systems that enhance users’ understanding of recommendation mechanisms, we propose leveraging these advancements to improve user controllability. In this paper, we present a user-controllable recommender system that seamlessly integrates explainability and controllability within a unified framework. By providing both retrospective and prospective explanations through counterfactual reasoning, users can customize their control over the system by interacting with these explanations. Furthermore, we introduce and assess two attributes of controllability in recommendation systems: the complexity of controllability and the accuracy of controllability. Experimental evaluations on MovieLens and Yelp datasets substantiate the effectiveness of our proposed framework. Additionally, our experiments demonstrate that offering users control options can potentially enhance recommendation accuracy in the future. Source code and data are available at https://github.com/chrisjtan/ucr.
Do Topic and Causal Consistency Affect Emotion Cognition? A Graph Interactive Network for Conversational Emotion Detection
Authors: Tu Geng , Liang Bin , Lyu Xiucheng , Gui Lin , Xu Ruifeng
Published: September 2023

Emotion recognition in conversations (ERC) typically requires modeling both intra- and inter-speaker context dependencies. However, when modeling inter-speaker dependencies, it may not capture differences among other participants in the conversation. Recent ERC research has attempted to improve utterance representations by utilizing speakers’ commonsense knowledge. Nonetheless, these studies ignore the causal consistency in knowledge between the two participants, which contradicts the above modeling of speaker-sensitive context dependencies. Additionally, it is observed that historical utterances from various topics are blindly leveraged in context modeling, which fails the inter- and intra-topic coherence. To address these issues, we propose the topic- and causal-aware interactive graph network (TCA-IGN). Specifically, we suggest a graph encoder to model topic-level context dependencies, achieving inter- and intra-topic coherence. The topics of utterances are derived from a context-sensitive neural topic model. Then, we present a causal-aware graph attention to keep the speaker’s causal consistency in commonsense knowledge, improving speaker-level context modeling. Finally, considering the defect of modeling inter-speaker or inter-topic context dependencies, we employ supervised contrastive learning to sweeten it. Experimental results show that TCA-IGN outperforms state-of-the-art methods on three public conversational datasets.
Finite Sample Guarantees of Differentially Private Expectation Maximization Algorithm
Authors: Wang Di , Ding Jiahao , Hu Lijie , Xie Zejun , Pan Miao , Xu Jinhui
Published: September 2023

(Gradient) Expectation Maximization (EM) is a widely used algorithm for estimating the maximum likelihood of mixture models or incomplete data problems. A major challenge facing this popular technique is how to effectively preserve the privacy of sensitive data. Previous research on this problem has already lead to the discovery of some Differentially Private (DP) algorithms for (Gradient) EM. However, unlike in the non-private case, existing techniques are not yet able to provide finite sample statistical guarantees. To address this issue, we propose in this paper the first DP version of Gradient EM algorithm with statistical guarantees. Specifically, we first propose a new mechanism for privately estimating the mean of a heavy-tailed distribution, which significantly improves a previous result in [25], and it could be extended to the local DP model, which has not been studied before. Next, we apply our general framework to three canonical models: Gaussian Mixture Model (GMM), Mixture of Regressions Model (MRM) and Linear Regression with Missing Covariates (RMC). Specifically, for GMM in the DP model, our estimation error is near optimal in some cases. For the other two models, we provide the first result on finite sample statistical guarantees. Our theory is supported by thorough numerical experiments on both real-world data and synthetic data.
A Conditional Denoising Diffusion Probabilistic Model for Radio Interferometric Image Reconstruction
Authors: Wang Ruoqi , Chen Zhuoyang , Luo Qiong , Wang Feng
Published: September 2023

In radio astronomy, signals from radio telescopes are transformed into images of observed celestial objects, or sources. However, these images, called dirty images, contain real sources as well as artifacts due to signal sparsity and other factors. Therefore, radio interferometric image reconstruction is performed on dirty images, aiming to produce clean images in which artifacts are reduced and real sources are recovered. So far, existing methods have limited success on recovering faint sources, preserving detailed structures, and eliminating artifacts. In this paper, we present VIC-DDPM, a Visibility and Image Conditioned Denoising Diffusion Probabilistic Model. Our main idea is to use both the original visibility data in the spectral domain and dirty images in the spatial domain to guide the image generation process with DDPM. This way, we can leverage DDPM to generate fine details and eliminate noise, while utilizing visibility data to separate signals from noise and retaining spatial information in dirty images. We have conducted experiments in comparison with both traditional methods and recent deep learning based approaches. Our results show that our method significantly improves the resulting images by reducing artifacts, preserving fine details, and recovering dim sources. This advancement further facilitates radio astronomical data analysis tasks on celestial phenomena. Our code is available at https://github.com/RapidsAtHKUST/VIC-DDPM.
On the Variation of Max Regret with Respect to the Scaling of the Objectives
Authors: Wilson Nic
Published: September 2023

In a multi-objective optimisation problem, when there is uncertainty regarding the correct user preference model, max regret is a natural measure for how far an alternative is from being necessarily optimal (i.e., optimal with respect to every candidate preference model). It can be used for recommending a relatively safe choice to the user, or used in the generation of an informative query, and in the decision to terminate the user interaction, because an alternative is sufficiently close to being necessarily optimal. We consider a common and simple form of user preference model: a weighted average over the objectives (with unknown weights). However, changing the scale of an objective by a linear factor leads to an essentially different set of preference models, and this changes the max regret values (and potentially their relative ordering), sometimes very considerably. Since the scaling of the objectives is often partly subjective and somewhat arbitrary, it is important to be aware of how sensitive the max regret values are to the choices of scaling of the objectives. We give mathematical results that characterise and enable computation of this variability, along with an asymptotic analysis.
Characterizations of Network Auctions and Generalizations of VCG
Authors: Xiao Mingyu , Lin Guixin , Khoussainov Bakh , Song Yuchao
Published: September 2023

With the growth of networks, promoting products through social networks has become an important problem. For auctions in social networks, items are needed to be sold to agents in a network, where each agent can bid and also diffuse the sale information to her neighbors. Thus, the agents’ social relations are intervened with their bids in the auctions. In network auctions, the classical VCG mechanism fails to retain key properties. In order to better understand network auctions, in this paper, we characterize network auctions for the single-unit setting with respect to weak budget balance, individual rationality, incentive compatibility, efficiency, and other properties. For example, we present sufficient conditions for mechanisms to be efficient and (weakly) incentive compatible. With the help of these properties and new concepts such as rewards, participation rewards, and so on, we show how to design efficient mechanisms to satisfy incentive compatibility as much as possible, and incentive compatibility mechanisms to maximize the revenue. Our results provide insights into understanding auctions in social networks.
Deep Interactions-Boosted Embeddings for Link Prediction on Knowledge Graph
Authors: Yin Hong , Zhong Jiang , Dai Qizhu
Published: September 2023

Link prediction for Knowledge Graphs (KGs) aims to predict missing links between entities. Previous works have utilized Graph Neural Networks (GNNs) to learn specific embeddings of entities and relations. However, these works only consider the linear aggregation of neighbors and do not consider interactions among neighbors, resulting in the neglect of partial indicating information. To address this issue, we propose Deep Interactions-boosted Embeddings (DInBE) which encodes interaction information to enrich the entity representations. To obtain interaction information, we disentangle the representation behind entities to learn diverse disentangled representations for each entity. Then, we learn intra-interactions among neighboring entities in the same component and inter-interactions among different components based on these disentangled representations. With the help of interaction information, our model generates more expressive representations. In addition, we propose a relation-aware scoring mechanism to select useful components based on the given query. Our experiments demonstrate that our proposed model outperforms existing state-of-the-art methods by a large margin in the link prediction task, and this verifies the effectiveness of exploring interactions and adaptive scoring.
Knowledge Injection for Multiagent Systems via Counterfactual Perception Shaping
Authors: Zerbel Nicholas , Tumer Kagan
Published: September 2023

Reward shaping can be used to train coordinated agent teams, but most learning approaches optimize for training conditions and by design, are limited by knowledge directly captured by the reward function. Advances in adaptive systems (e.g., transfer learning) may enable agents to quickly learn new policies in response to changing conditions, but retraining agents is both difficult and risks losing team coordination altogether. In this work we introduce Counterfactual Knowledge Injection (CKI), a novel approach to injecting high-level information into a multiagent system outside of the learning process. CKI encodes knowledge into counterfactual state representations to shape agent perceptions of the system so that their current policies better match the current system conditions. We demonstrate CKI in a multiagent exploration task where agents must collaborate to observe various Points of Interest (POI). We show that CKI successfully imparts high-level system knowledge to agents in response to imperceptible changes. We also show that CKI enables agents to adjust their level of agent-to-agent coordination ranging from tasks individuals can complete up to tasks that require the entire team.
Intelligent acquisition of single-valued neutrosophic numbers based on emotional tendency analysis and its application in emergency decision making
Authors: Tan Ruipu , Chen Chong , Zhang Wende , Yang Lehua , Ma Hangfei
Published: September 2023

With the rising incidences of emergencies, it is both challenging and meaningful to study how to make decisions quickly and take appropriate measures to control the spread and evolution of the situation. However, most current emergency decision-making focuses on mathematical model construction, whereas fuzzy decision-making is biased towards subjective assumptions, which are both insufficient for practicability. We studied the intelligent acquisition of single-valued neutrosophic numbers based on emotional tendency analysis and applied them to emergency decision-making. First, Python programming technology was used to crawl, preprocess, and statistically analyse the network comment data of emergencies, and a quantised single-value neutrosophic number was obtained. Second, the attribute values, represented as neutrosophic numbers, were uniformly converted into cloud droplets, and the weight of the attribute values was objectively determined according to the digital feature entropy in the cloud droplets. Subsequently, a case-based reasoning approach was used to calculate the combined weighted similarity between the alternatives and ideal solution (target case) to obtain a ranking of the alternatives and historical cases in the case base that best matches the target case. Finally, a typhoon disaster assessment was considered as an example to verify the feasibility and effectiveness of the proposed method, and the advantages of the proposed method were emphasised through multi-aspect and multi-angle comparative analyses. The relevant research can be used for public opinion monitoring during emergencies and emergency handling.
Research on innovation and entrepreneurship education environment evaluation in universities under the background of “Double Innovation” with interval-valued intuitionistic fuzzy information
Authors: Yang Lei , Gao Ge
Published: September 2023

As China’s economic and social development enters a new stage, the role of innovation and entrepreneurship is becoming increasingly prominent, and its importance is also being emphasized. As the main force for future employment and national economic construction, college students naturally become the new force for innovation and entrepreneurship. Therefore, it is imperative for universities to carry out in-depth innovation and entrepreneurship education (IEE) for college students. Then, with the continuous development of social development needs and the professional growth needs of college students, the “innovation and entrepreneurship” education for college students should also be adjusted in a timely manner in terms of educational concepts, models, and methods. The IEE environment evaluation in universities under the background of “ Double Innovation” is looked as multiple attribute decision-making (MADM). In this paper, the information entropy model is employed to calculate the objective weight of the evaluation attribute. Then, interval-valued intuitionistic fuzzy Combined Compromise Solution (IVIF-CoCoSo) is built based on the Hamming distance and Euclid distance to cope with MADM under interval-valued intuitionistic fuzzy sets (IVIFSs). The new MADM method is proposed for IEE environment evaluation in universities under the background of “ Double Innovation”. Finally, the IVIF-CoCoSo approach is compared with existing methods to verify the effectiveness of IVIF-CoCoSo algorithm. The main contributions of this constructed paper are: (1) the IVIF-CoCoSo method is built based on the Hamming distance and Euclid distance. (2) the information entropy model is employed to calculate the objective weight of the evaluation attribute. (3) The new MADM method is proposed for IEE environment evaluation in universities under the background of “ Double Innovation” based on IVIF-CoCoSo. (4) The IVIF-CoCoSo model is compared with existing methods to verify the effectiveness of the IVIF-CoCoSo algorithm.