bluuebunny/crossref_metadata_2025 huggingface dataset:
Digitization as a tool for revitalizing the livestock industry
Authors: Misiuk Mykola , Zakhodym Maryna
Published: September 2023

Digitalization of the country’s economy should currently be considered as an integral prerequisite for its development, the activation of which is necessary at all levels, from the state to the level of an individual citizen. In the agricultural sector, the introduction of digital tools and technologies is necessary in view of their significant potential in the context of increasing the productivity of resources and increasing the level of competitiveness of products, manufacturers, industries, and the state as a whole. The livestock industry in Ukraine is experiencing a severe lack of electronic digital services. The purpose of the article is to justify the importance of using digitization for the innovative development of the livestock industry. The following methods of scientific knowledge were used in the research process: theoretical generalization, analysis and synthesis, data analysis, economic-statistical, graphic, correlation-regression, cartographic and abstract -logical. The main tools of digitization of the process of resource provision of producers of livestock products were summarized. Systematized priority types of information technologies within the framework of digitalization of business processes of producers of livestock products depending on the field of application, the main of which are information systems for determining actual and potential resource needs, selection of potential suppliers and organization of purchase and sale operations (field of resource provision); technologies for monitoring the state of farm animals, optimizing the diet and veterinary measures, forecasting the state of the animal, disease outbreaks, livestock productivity (production); online platforms for selling livestock products, electronic document management services, etc. (sales of products). The directions of using geo-information systems and technologies in order to ensure the development of the animal husbandry industry, which involve taking into account the information needs of potential users (in particular, producers of livestock products, investors and/or creditors, executive authorities, consumers) and providing them with accurate, unbiased, operational data, are substantiated. necessary for making management decisions. Therefore, digitalization should be considered as one of the promising tools for intensifying the development of animal husbandry and increasing the efficiency of production and sale of animal products. The practical value of the work lies in the development of the systematization of priority types of information technologies within the framework of digitalization of business processes for the production of livestock products.
Agricultural land turnover in Ukraine: New challenges and new opportunities
Authors: Khodakivska Olga , Yurchenko Ihor
Published: September 2023

The experience of the market turnover of agricultural land in Ukraine has shown that untimely and insufficiently comprehensive analysis of the functioning of the land market can lead to incorrect conclusions and limit the understanding of its dynamics and impact on the country’s economic situation. The purpose of the study was to identify the main trends and indicators of the market turnover of agricultural land in Ukraine by analysing land ownership transactions. The methodological tools of the problem under study were general scientific and special methods, as well as the method of comparison and abstract and logical methods. The main results of the study are a cost and quantitative assessment of transactions of sale and purchase of ownership rights to agricultural land in Ukraine, which makes it possible to identify the main trends in the land market. Certain provisions can be used to formulate strategies for the development of land relations in Ukraine. The number and share of transactions involving the sale and purchase of ownership rights to agricultural land in the overall structure of transactions is determined. The area of land ownership rights to which were alienated through sale and purchase is determined. The author calculates the value of agricultural land depending on its remoteness from a settlement, regional location and designated purpose. Fluctuations in the value of arable land depending on their territorial location and in the overall dynamics by month are determined. The level of activity of agricultural land market participants in the country as a whole and by regions is determined. The results and trends in the market turnover of agricultural land in the territories where military operations were or are ongoing are investigated. The main key indicators of the market turnover of agricultural land are determined. Forecasts of the value of arable land in Ukraine by the end of 2023 are formed. The discrepancy between the Ukrainian model of agricultural land market turnover and the models operating in the European Union has been established. The results of the study can be used to develop ways to reorient the market turnover of agricultural land to the European principles of functioning aimed at the development of family farming as a fundamental goal of Ukraine’s agricultural policy
Credal Learning: Weakly Supervised Learning from Credal Sets
Authors: Campagner Andrea
Published: September 2023

In this article we study the problem of credal learning, a general form of weakly supervised learning in which instances are associated with credal sets (i.e., closed, convex sets of probabilities), which are assumed to represent the partial knowledge of an annotating agent about the true conditional label distribution. A variety of algorithms have been proposed in this setting, chiefly among them the generalized risk minimization method, a class of algorithms that extend empirical risk minimization. Despite its popularity and promising empirical results, however, the theoretical properties of this algorithm (as well as of credal learning more in general) have not been previously studied. In this article we address this gap by studying the problem of credal learning from the learning-theoretic and complexity-theoretic perspectives. We provide, in particular, three main contributions: 1) we show that, under weak assumptions about the accuracy of the annotating agent, credal learning is learnable in the convex learning setting, providing effective risk bounds; 2) we study the properties of generalized risk minimization and, in particular, identify the optimal instance of this approach, that we call trade-off risk minimization; 3) we study the computational complexity of generalized risk minimization, showing effective algorithms based on gradient descent and providing sufficient and necessary conditions for them being computationally efficient.
A Flexible Debiasing Framework for Fair Heterogeneous Information Network Embedding
Authors: Cao Meng , Chen Mingcai , Song Jianqing , Fang Chenxuan , Wang Chongjun
Published: September 2023

Heterogeneous Information Networks (HINs) are prevalent in real-world systems. Recent advances in network embedding provide an effective way of encoding HINs into low-dimensional vectors. However, there is a growing concern that existing HIN embedding algorithms may suffer from the problem of generating biased representations, resulting in discrimination against certain demographic groups. In this paper, we propose a flexible debiasing framework for fair HIN embedding to address this issue. Specifically, we first formalize measurements and the definition of fairness in HIN embedding. Then, we propose a debiasing framework named FairHGNN, including a novel meta-path sampling method that focuses on mitigating the bias in random walks, and a fairness constraint with Wasserstein distance to alleviate the algorithmic bias in Graph Neural Networks (GNNs). Experimental results on real-world datasets validate the efficacy of FairHGNN in promoting fairness and maintaining good utility.
PiercingEye: Identifying Both Faint and Distinct Clues for Explainable Fake News Detection with Progressive Dynamic Graph Mining
Authors: Ding Yasan , Guo Bin , Liu Yan , Wang Hao , Shen Haocheng , Yu Zhiwen
Published: September 2023

Explainability is crucial for the successful use of AI for fake news detection (FND). Researchers aim to improve the explainability of FND by highlighting important descriptions in crowd-contributed comments as clues. From the perspective of law and sociology, there are distinct clues that are easy to discover and understand, and faint clues that require careful observation and analysis. For example, in fake news related to COVID-Omicron showing increased pathogenicity and transmissibility, distinct clues might involve virologists’ opinions regarding the inverse correlation between pathogenicity and transmissibility. Meanwhile, faint clues might be reflected in an infected person’s claim that the symptoms are milder than a cold (indirectly indicating reduced pathogenicity). Occasionally, the statements of some ordinary eyewitnesses can decisively reveal the truth of the news, leading to the judgment of fake news. Existing methods generally use static networks to model the entire news life-cycle, which makes it fail to capture the subtle dynamic interactions between individual clues and news. Thereby faint clues, whose relations to the truth of news are challenging to be characterized and extracted directly, are more likely to be overshadowed by distinct clues. To address this issue, we propose an explainable FND method, dubbed as PiercingEye, which leverages dynamic interaction information to progressively mine valuable clues. PiercingEye models the news propagation topology as a dynamic graph, with interactive comments serving as nodes, and employs the time-semantic encoding mechanism to refine the modeling of temporal interaction information between comments and news to preserve faint clues. Subsequently, it utilizes the self-attention mechanism to aggregate distinct and faint clues for FND. Experimental results demonstrate that PiercingEye outperforms state-of-the-art methods and is capable of identifying both faint and distinct clues for humans to debunk fake news.
Data-Driven Self-Supervised Graph Representation Learning
Authors: Samy Ahmed E. , Kefato Zekarias T. , Girdzijauskas Šarūnas
Published: September 2023

Self-supervised graph representation learning (SSGRL) is a representation learning paradigm used to reduce or avoid manual labeling. An essential part of SSGRL is graph data augmentation. Existing methods usually rely on heuristics commonly identified through trial and error and are effective only within some application domains. Also, it is not clear why one heuristic is better than another. Moreover, recent studies have argued against some techniques (e.g., dropout: that can change the properties of molecular graphs or destroy relevant signals for graph-based document classification tasks). In this study, we propose a novel data-driven SSGRL approach that automatically learns a suitable graph augmentation from the signal encoded in the graph (i.e., the nodes’ predictive feature and topological information). We propose two complementary approaches that produce learnable feature and topological augmentations. The former learns multi-view augmentation of node features, and the latter learns a high-order view of the topology. Moreover, the augmentations are jointly learned with the representation. Our approach is general that it can be applied to homogeneous and heterogeneous graphs. We perform extensive experiments on node classification (using nine homogeneous and heterogeneous datasets) and graph property prediction (using another eight datasets). The results show that the proposed method matches or outperforms the SOTA SSGRL baselines and performs similarly to semi-supervised methods. The anonymised source code is available at https://github.com/AhmedESamy/dsgrl/
Towards a Rigorous Calibration Assessment Framework: Advancements in Metrics, Methods, and Use
Authors: Famiglini Lorenzo , Campagner Andrea , Cabitza Federico
Published: September 2023

Calibration is paramount in developing and validating Machine Learning models, particularly in sensitive domains such as medicine. Despite its significance, existing metrics to assess calibration have been found to have shortcomings in regard to their interpretation and theoretical properties. This article introduces a novel and comprehensive framework to assess the calibration of Machine and Deep Learning models that addresses the above limitations. The proposed framework is based on a modification of the Expected Calibration Error (ECE), called the Estimated Calibration Index (ECI), which grounds on and extends prior research. ECI was initially formulated for binary settings, and we adapted it to fit multiclass settings. ECI offers a more nuanced, both locally and globally, and informative measure of a model’s tendency towards over/underconfidence. The paper first outlines the issues related to the prevalent definitions of ECE, including potential biases that may arise in the evaluation of their measures. Then, we present the results of a series of experiments conducted to demonstrate the effectiveness of the proposed framework in supporting a more accurate understanding of a model’s calibration level. Additionally, we discuss how to address and potentially mitigate some biases in calibration assessment.
A Novel Differentiable Rank Learning Method Towards Stock Movement Quantile Forecasting
Authors: Fan Chenyou , Lu Hengyang , Huang Aimin
Published: September 2023

We focus on Stock Movement Forecasting (SMF) using AI techniques to develop modern automated trading systems. Previous studies with deep-learning-based methodology have only considered binary up-or-down trends, ignoring the importance of fine-grained categorization of the stock movements to facilitate decision-making. However, the challenges of SMF arise from the randomness of the global market impacting cross-sectional stocks and the volatility of internal dynamics in each time series. To address these challenges, we present a novel end-to-end learning-to-rank framework that incorporates both market-level and stock-level dynamics. Specifically, we aim to identify cross-sectional stocks that exhibit notable movements at every time step and learn to rank steps with the most significant movements in the temporal dimension. We conduct extensive evaluations of our multi-task learning framework utilizing real-world market data, which demonstrate superior performance when compared to state-of-the-art methods, with improvements in the Gain and Sharpe Ratio by 5–15%.
Generosity and the Emergence of Forgiveness in the Donation Game
Authors: Griffiths Nathan , Oren Nir
Published: September 2023

Research has shown that cooperative action struggles to emerge in the noisy variant of the donation game, a simple model of noisy multi-agent systems where indirect reciprocity is required to maximise utility. Such noise can arise when agents may have an incorrect view of the reputation of their interaction partners, or when the actions themselves may fail. Concepts such as generosity, as well as the use of higher-order norms, have been investigated as mechanisms to facilitate cooperation in such environments, but often are not effective or require additional assumptions or infrastructure in the system to operate. In this paper, we demonstrate both analytically and empirically that a simple form of generosity when combined with fine grained reputation can help cooperation emerge. We also show that the use of individual forgiveness strategies rather than the presence of global generosity can support cooperation in such environments.
Leximin Approximation: From Single-Objective to Multi-Objective
Authors: Hartman Eden , Hassidim Avinatan , Aumann Yonatan , Segal-Halevi Erel
Published: September 2023

Leximin is a common approach to multi-objective optimization, frequently employed in fair division applications. In leximin optimization, one first aims to maximize the smallest objective value; subject to this, one maximizes the second-smallest objective; and so on. Often, even the single-objective problem of maximizing the smallest value cannot be solved accurately. What can we hope to accomplish for leximin optimization in this situation? Recently, Henzinger et al. (2022) defined a notion of approximate leximin optimality. Their definition, however, considers only an additive approximation. In this work, we first define the notion of approximate leximin optimality, allowing both multiplicative and additive errors. We then show how to compute, in polynomial time, such an approximate leximin solution, using an oracle that finds an approximation to a single-objective problem. The approximation factors of the algorithms are closely related: an (α,ϵ)-approximation for the single-objective problem (where α ∈ (0,1] and ϵ ≥ 0 are the multiplicative and additive factors respectively) translates into an (α2/(1 − α + α2), ϵ/(1 − α + α2))-approximation for the multi-objective leximin problem, regardless of the number of objectives. Finally, we apply our algorithm to obtain an approximate leximin solution for the problem of stochastic allocations of indivisible goods.
LoSS: Local Structural Separation Hypergraph Convolutional Neural Network
Authors: Hu Bingde , Gao Yang , Feng Zunlei , Song Mingli , Wang Xinyu , Li Ying
Published: September 2023

Graph classification is a classic problem with practical applications in many real-life scenes. Existing graph neural networks, including GCN, GAT, and GIN, are proposed to extract useful features from complex graph structures. However, most existing methods’ feature extraction and aggregation inevitably mix the useful and redundant features, which will disturb the final classification performance. In this paper, to handle the above drawback, we put forward the Local Structural Separation Hypergraph Convolutional Neural Network (LoSS) based on two discoveries: most graph classification tasks only focus on a few groups of adjacent nodes, and different categories have their specific high response bits in graph embeddings. In LoSS, we first decouple the original graph into different hypergraphs and aggregate the features in each substructure, which aims to find useful features for the final classification. Next, the low-correlation feature suppression strategy is devised to suppress the irrelevant node-level and bit-level features in the forward inference process, effectively reducing the disturbance of redundant features. Experiments on five datasets show that the proposed LoSS can effectively locate and aggregate useful hypergraph features and achieve SOTA performance compared with existing methods.
Dynamic Graph Convolutional Network with Attention Fusion for Traffic Flow Prediction
Authors: Luo Xunlian , Zhu Chunjiang , Zhang Detian , Li Qing
Published: September 2023

Accurate and real-time traffic state prediction is of great practical importance for urban traffic control and web mapping services. With the support of massive data, deep learning methods have shown their powerful capability in capturing the complex spatial-temporal patterns of traffic networks. However, existing approaches use pre-defined graphs and a simple set of spatial-temporal components, making it difficult to model multi-scale spatial-temporal dependencies. In this paper, we propose a novel dynamic graph convolution network with attention fusion to tackle this gap. The method first enhances the interaction of temporal feature dimensions, and then it combines a dynamic graph learner with GRU to jointly model synchronous spatial-temporal correlations. We also incorporate spatial-temporal attention modules to effectively capture long-range, multifaceted domain spatial-temporal patterns. We conduct extensive experiments in four real-world traffic datasets to demonstrate that our method surpasses state-of-the-art performance compared to 18 baseline methods.
R-STAR: Robust Self-Taught Task-Wise Reweighting for Rehearsal-Based Class Incremental Learning
Authors: Luo Yutian , Gao Yizhao , Wu Haoran , Ma Ruitao , Lu Zhiwu
Published: September 2023

Class incremental learning (CIL) requires a model to learn the knowledge of new classes without overwriting that of old classes. The main challenge thus lies in catastrophic forgetting. Among all advances in addressing this challenge, rehearsal-based methods are the most widely-used due to their convenience and effectiveness. However, the (classification) scores bias between the old and new classes, known as the main cause of catastrophic forgetting for rehearsal-based methods, is still not fully addressed. Although some recent strategies are proposed to reduce the scores bias, they either take extra training time or sacrifice too much performance on the current task. In this paper, we propose a novel Robust Self-Taught Task-Wise Reweighting (R-STAR) method, which can act as a flexible and key component for improving existing rehearsal-based methods. Concretely, on top of the standard training process, it measures the forgetting degree of the model over the augmented buffer (for robust evaluation) on each task. Further, following the self-taught paradigm, it directly activates the task-wise forgetting degree into a reweighting ratio for scores bias reduction during the inference stage. Extensive experiments show that our R-STAR can improve most rehearsal-based methods with remarkable margins, but with (almost) no extra training cost or excessive performance sacrifice on the new task. Moreover, it also shows its advantages over existing scores bias correction strategies.
Piecewise-Stationary Combinatorial Semi-Bandit with Causally Related Rewards
Authors: Nourani-Koliji Behzad , Bilaj Steven , Balef Amir Rezaei , Maghsudi Setareh
Published: September 2023

We study the piecewise stationary combinatorial semi-bandit problem with causally related rewards. In our nonstationary environment, variations in the base arms’ distributions, causal relationships between rewards, or both, change the reward generation process. In such an environment, an optimal decision-maker must follow both sources of change and adapt accordingly. The problem becomes aggravated in the combinatorial semi-bandit setting, where the decision-maker only observes the outcome of the selected bundle of arms. The core of our proposed policy is the Upper Confidence Bound (UCB) algorithm. We assume the agent relies on an adaptive approach to overcome the challenge. More specifically, it employs a change-point detector based on the Generalized Likelihood Ratio test. Besides, we introduce the notion of group restart as a new alternative restarting strategy in the decision making process in structured environments. Finally, our algorithm integrates a mechanism to trace the variations of the underlying graph structure, which captures the causal relationships between the rewards in the bandit setting. Theoretically, we establish a regret upper bound that reflects the effects of the number of structural- and distribution changes on the performance. The outcome of our numerical experiments in real-world scenarios exhibits applicability and superior performance of our proposal compared to the state-of-the-art benchmarks.
Overlay Neural Networks for Heterophilous Graphs
Authors: Pirrò Giuseppe
Published: September 2023

Graph Neural Networks (GNNs) have become increasingly popular for their ability to capture complex relationships within graphs by aggregating node neighbor information. However, in graphs exhibiting high levels of heterophily relevant distant nodes are missed during neighbor aggregation, thus limiting the GNN performance in tasks like node classification. To tackle the problem of incorporating long-range relevant neighbors into the GNN node aggregation mechanism, this paper introduces the Overlay Graph Neural Networks (OGN) model. OGN is inspired by P2P overlay networks, where the idea is to find neighbor peers (nodes) that, although not directly connected to a given node (a peer), are semantically similar and could favorably improve both query routing and query results. In our context, the network is the graph, and the routing is the message passing a GNN performs to aggregate node features. OGN networks are built by stacking one or more overlay layers, each taking as input the graph and a node feature matrix either available or derivable (e.g., by analyzing the graph’s structure). Each overlay layer combines base embeddings, learned by considering node features and short-range node neighbors, with overlay embeddings computed by projecting nodes with similar features close in an overlay space and then aggregating (overlay) neighbor nodes via a sliding window attention mechanism. Base and overlay embeddings are combined to capture nodes’ immediate and global context in a graph. We evaluate OGN in a node classification task using state-of-the-art benchmarks and show that OGN is competitive with the advantage of being easily portable to any existing GNN model.
Associative Memories in the Feature Space
Authors: Salvatori Tommaso , Millidge Beren , Song Yuhang , Bogcaz Rafal , Lukasiewicz Thomas
Published: September 2023

An autoassociative memory model is a function that, given a set of data points, takes as input an arbitrary vector and outputs the most similar data point from the memorized set. However, popular memory models fail to retrieve images even when the corruption is mild and easy to detect for a human evaluator. This is because similarities are evaluated in the raw pixel space, which does not contain any semantic information about the images. This problem can be easily solved by computing similarities in an embedding space instead of the pixel space. We show that an effective way of computing such embeddings is via a network pretrained with a contrastive loss. As the dimension of embedding spaces is often significantly smaller than the pixel space, we also have a faster computation of similarity scores. We test this method on complex datasets such as CIFAR10 and STL10. An additional drawback of current models is the need of storing the whole dataset in the pixel space, which is often extremely large. We relax this condition and propose a class of memory models that only stores low-dimensional semantic embeddings, and uses them to retrieve similar, but not identical, memories. We demonstrate a proof of concept of this method on a simple task on the MNIST dataset.
Reinforcement Learning in RDPs by Combining Deep RL with Automata Learning
Authors: Shahar Tal , Brafman Ronen I.
Published: September 2023

Regular Decision Processes (RDPs) are a recently introduced model for decision-making in non-Markovian domains in which states are not postulated a-priori, and the next observation depends in a regular manner on past history. As such, they provide a more succinct and understandable model of the dynamics and reward function. Existing algorithms for learning RDPs attempt to learn an automaton that reflects the regularity of the underlying domain. However, their scalability is limited due to the practical difficulty of learning automata. In this paper we propose to leverage the power of Deep reinforcement learning in partially observable domain to learn RDPs: First, we learn an RNN-based policy. Then, we generate an automaton that reflects the policy’s structure and use our old data to transform it into an MDP, which we solve. This results in a finite, explainable policy structure, and, as our empirical evaluation on old and new RDP benchmarks shows, much better sample complexity.
Truthful and Equitable Lateral Transshipment in Multi-Retailer Systems
Authors: Shakya Garima , Danda Sai Koti Reddy , Nath Swaprava , Dayama Pankaj , Sajja Surya Shravan Kumar
Published: September 2023

We consider a multi-retailer system where the sellers are connected with each other via a transportation network and the transactions with the consumers happen on a platform. Each consumer is serviced by only one retailer. Since the demands to the sellers (i.e., the retailers on the platform) are stochastic in nature, supplies can be either in excess or in deficit. Transshipping these items laterally among the retailers benefits both, the platform and the retailers. For retailers, excess supply leads to wastage and deficit to a loss of revenue, while via transshipment, they get a better outcome. The platform can also earn some revenue in facilitating this process. However, only the sellers know their excess (which can be salvaged at a price or transshipped to another seller) or the deficit (which can be directly procured from a supplier or transshipped from another seller), both of which have multiple information that is private. We propose a model that allows lateral transshipment at a price and design mechanisms such that the sellers are incentivized to voluntarily participate and be truthful. Experimenting on different types of network topologies, we find that the sellers at more central locations in the network get an unfair advantage in the classical mechanism that aims for economic efficiency. We, therefore, propose a modified mechanism with tunable parameters which can ensure that the mechanism is more equitable for non-central retailers. Our synthetic data experiments show that such mechanisms do not compromise too much on efficiency, and also reduce budget imbalance.
SPAT: Semantic-Preserving Adversarial Transformation for Perceptually Similar Adversarial Examples
Authors: Swain Subrat Kumar , Kumar Vireshwar , Kim Dan Dongseong , Bai Guangdong
Published: September 2023

Although machine learning models achieve high classification accuracy against benign examples, they are vulnerable to adversarial machine learning (AML) attacks which generate adversarial examples by adding well-crafted perturbations to the benign examples. The perturbations can be increased to enhance the attack success rate, however, if the perturbations are added without considering the semantic or perceptual similarity between the benign and adversarial examples, the attack can be easily perceived/detected. As such, there exists a trade-off between the attack success rate and the perceptual similarity. In this paper, we propose a novel Semantic-Preserving Adversarial Transformation (SPAT) framework which facilitates an advantageous trade-off between the two metrics. SPAT modifies the optimisation objective of an AML attack to include the goal of increasing the attack success rate as well as the goal of maintaining the perceptual similarity between benign and adversarial examples. Our experiments on a variety of datasets including CIFAR-10, GTSRB, and MNIST demonstrate that SPAT-transformed AML attacks achieve better perceptual similarity while maintaining the attack success rates as the conventional AML attacks.
Enforcing Natural Properties of Choice Functions, with Application for Combination
Authors: Wilson Nic
Published: September 2023

One important and natural representation of preferences is a choice function, which returns the preferred options amongst any given subset of the alternatives. There are some very intuitive coherence conditions that might be assumed for an agent’s choice function, in particular path independence, and a consistency condition stating that there is always at least one preferred alternative among any non-empty set. However, an elicited choice function may not satisfy path independence, because of the elicitation being incomplete, or because of there being some incoherence in the agent’s reported choice function (despite the agent assenting to the general coherence conditions). Furthermore, if we wish to combine the choice functions of more than one agent, simple natural combination operations can lose path independence. This paper develops methods for enforcing path independence and restoring consistency, thus, making the user preferences coherent; this method also leads to approaches for combining two choice functions, in order to suggest the most promising alternatives for a pair of agents.
IPERS: Individual Prioritized Experience Replay with Subgoals for Sparse Reward Multi-Agent Reinforcement Learning
Authors: Xie Zaipeng , Zhang Yufeng , Qiao Chentai , Shen Sitong
Published: September 2023

Multi-agent reinforcement learning commonly uses a global team reward signal to represent overall collaborative performance. Value decomposition breaks this global reward into estimated individual value functions per agent, enabling efficient training. However, in sparse reward environments, agents struggle to assess if their actions achieve the team goal, slowing convergence. This impedes the algorithm’s convergence rate and overall efficacy. We present IPERS, an Individual Prioritized Experience Replay algorithm with Subgoals for Sparse Reward Multi-Agent Reinforcement Learning. IPERS integrates joint action decomposition and prioritized experience replay, maintaining invariance between global and individual loss gradients. Subgoals serve as intermediate goals that break down complex tasks into simpler steps with dense feedback and provide helpful intrinsic rewards that guide agents. This facilitates learning coordinated policies in challenging collaborative environments with sparse rewards. Experimental evaluations of IPERS in both the SMAC and GRF environments demonstrate rapid adaptation to diverse multi-agent tasks and significant improvements in win rate and convergence performance relative to state-of-the-art algorithms.
Specializing Small Language Models Towards Complex Style Transfer via Latent Attribute Pre-Training
Authors: Xu Ruiqi , Huang Yongfeng , Chen Xin , Zhang Lin
Published: September 2023

In this work, we introduce the concept of complex text style transfer tasks, and constructed complex text datasets based on two widely applicable scenarios. Our dataset is the first large-scale data set of its kind, with 700 rephrased sentences and 1,000 sentences from the game Genshin Impact. While large language models (LLM) have shown promise in complex text style transfer, they have drawbacks such as data privacy concerns, network instability, and high deployment costs. To address these issues, we explore the effectiveness of small models (less than T5-3B) with implicit style pre-training through contrastive learning. We also propose a method for automated evaluation of text generation quality based on alignment with human evaluations using ChatGPT. Finally, we compare our approach with existing methods and show that our model achieves state-of-art performances of few-shot text style transfer models.
DCNet: Weakly Supervised Saliency Guided Dual Coding Network for Visual Sentiment Recognition
Authors: Zhang Xinyue , Xiang Jing , Zhang Hanxiu , Wu Chunwei , Wang Hailing , Cao Guitao
Published: September 2023

Visual sentiment recognition is a challenging task with scientific significance in probing vision-processing mechanisms. Recent approaches mainly focused on using overall images or precise annotations to learn emotional representations, yet neglected to capture abstract semantics from regional information, or led to a heavy annotation burden. In this paper, we propose an end-to-end weakly supervised framework, called Dual Coding Network (DCNet), which models a dual coding process for both shallow features and high-level regional information. On the one hand, with the help of the fine-grained module (FG), visual features (e.g. texture features) are utilized to enhance the learning of distinguished representation. On the other hand, the DCNet innovatively leverages saliency information to imitate the neural decoding of perceived visual sentiment contents in human brain activity. Specifically, the saliency information guides the generation of sentiment-specific pseudo affective maps (SAMG), which serve as weak annotations. Then the DCNet couples fine-grained features with pseudo affective maps, and obtains semantic vectors for final sentiment prediction. Extensive experiments show that the proposed DCNet outperforms the state-of-the-art performance on five benchmark datasets.
Time-Series Data Imputation via Realistic Masking-Guided Tri-Attention Bi-GRU
Authors: Zhang Zhipeng , Zhang Yiqun , Zeng An , Pan Dan , Ji Yuzhu , Zhang Zhipeng , Lin Jing
Published: September 2023

Time series data with missing values are ubiquitous in real applications due to various unforeseen faults during data generation, storage, and transmission. Time-Series Data Imputation (TSDI) is thus crucial to many temporal data analysis tasks. However, existing works usually consider only one of the following two issues: (1) intra-feature temporal dependency, and (2) inter-feature correlation, leading to the overlook of complex coupling information in imputation. To achieve more accurate TDSI, we design a novel imputation model called TABiG, which delicately preserves the short-term, long-term, and inter-feature dependencies by attention mechanisms in a delay error-reduced bi-directional architecture. That is, it leverages GRU to model short-term temporal dependencies and adopts self-attention mechanisms hierarchically to capture long-term temporal dependencies and inter-feature correlations. The multiple self-attention mechanisms are nested in a bi-directional structure to alleviate the problem of delay errors in RNN-like structures. To facilitate model training with higher generalization, a masking strategy that mimics various extreme real missing situations beyond the simple random ones has been adopted for generating self-supervised learning tasks. Comprehensive experiments demonstrate that TABiG significantly outperforms most state-of-the-art imputation counterparts. Complementary results and source code can be accessed at https://github.com/Zhang2112105189/TABiG
Improving Adversarial Transferability with Ghost Samples
Authors: Zhao Yi , Mou Ningping , Ge Yunjie , Wang Qian
Published: September 2023

Adversarial transferability presents an intriguing phenomenon, where adversarial examples designed for one model can effectively deceive other models. By exploiting this property, various transfer-based methods are proposed to conduct adversarial attacks without knowledge of target models, posing significant threats to practical black-box applications. However, these methods either have limited transferability or require high resource consumption. To bridge the gap, we investigate adversarial transferability from the optimization perspective and propose the ghost sample attack (GSA). GSA improves adversarial transferability by alleviating the overfitting issue of adversarial examples on the surrogate model. Based on the insight that a slight shift of the adversarial example is similar to a minor change in the decision boundary, we aggregate gradients of perturbed adversarial copies (named ghost samples) to efficiently achieve a similar effect to calculating gradients of multiple ensemble surrogate models. Extensive experiments demonstrate that GSA achieves state-of-the-art adversarial transferability with restricted resources. On average, GSA improves the attack success rate by 4.8% on normally trained models compared to state-of-the-art attacks. Additionally, GSA reduces the computational cost by 62% compared with TAIG-R. When combined with other methods, GSA further improves transferability to 96.9% on normally trained models and 82.7% on robust models.
The Monad Platform – Temporal Aspects in Behavioral Modeling
Authors: Rychalska Barbara , Sieradzki Igor , Dąbrowski Jacek
Published: September 2023

Behavioral modeling is an emerging machine learning area which aims to predict user actions, especially in commercial settings. Companies actively gather data such as clicks, likes, page views, card transactions, add-to-basket, or purchase events. However, the large size of data combined with hardships in applying sophisticated graph-based machine learning often leads to data not being used for modeling at all. In response to this, we propose the Monad platform. The primary focus of Monad is to train a large, private behavioral foundation model for each client company. The foundation model is then fine-tuned to any user behavioral prediction task, such as recommendations or churn. The Monad foundation models are based on our algorithms – Cleora and EMDE – which are award winning solutions (KDD Cup and other anonymized AI contests). Cleora and EMDE allow to process real-life datasets composed of billions of events in record time. In this paper, we present and analyze the temporal side of Monad. Time is a crucial aspect of behavioral training, because many target tasks such as propensity to buy rely on seasonal aspects and predictions are usually required to include a time frame (e.g. “Will the user buy brand X within a week from now?”). To tackle this problem, we present the concept of time-based data splits in training, as well as approaches towards time-based feature encoding, which do not require normalization or any feature statistics.
Online Evaluation of Tail Project Boosting in Citizen Science
Authors: Sultan Amit , Segal Avi , Shani Guy , Cavalier Darlene , Gal Kobi
Published: September 2023

In citizen science, regular people provide invaluable information by contributing to scientific projects. Citizen science platforms, such as SciStarter, provide easy access to numerous such projects. Often, users contribute mainly to a relatively small set of popular projects, while it is difficult for many projects to draw the attention of users. Thus, increasing the contribution of users to such low-popularity projects may increase scientific and societal impact. In this paper, we explore the power of a recommender system to draw attention to less popular projects. Standard use of recommendation systems often leads to limited exposure of less popular (tail) projects. We thus propose a re-ranking approach based on “lift boosting,” which uses the statistical lift measure to enhance the exposure of tail projects. By combining lift and traditional relevance measures, our method re-ranks the recommendation list to emphasize projects that are both relevant to the user while also have a high lift value. We implement our approach on SciStarter, one of the biggest citizen science platforms on the web. We conduct an online experiment involving over 2000 real users. Our results show a positive shift towards less popular projects without compromising overall contribution rates. This work demonstrates the potential of our lift-boosting method for promoting the discovery of tail projects in citizen science platforms, thereby fostering a more diverse range of scientific contributions.
Racial/Ethnic Disparities in Misidentification of Dementia in Medicare Claims: Results from the Washington Heights-Inwood Columbia Aging Project
Authors: Zhu Carolyn W. , Gu Yian , Cosentino Stephanie , Kociolek Anton J. , Hernandez Michelle , Stern Yaakov
Published: September 2023

Background:

Misidentification of dementia in Medicare claims is quite common.

Objective:

We examined potential race/ethnic disparities in misidentification of dementia in Medicare claims in a diverse cohort of older adults who underwent careful clinical assessment.

Methods:

Participants were enrolled in the Washington Heights-Inwood Columbia Aging Project (WHICAP), a multiethnic, population-based, prospective study of cognitive aging in which dementia status was assessed using a rigorous clinical protocol. ICD-9-CM and ICD-10-CM diagnosis codes in all available Medicare claims (1999–2019) were compared to clinical dementia diagnosis and categorized into three mutually exclusive groups: 1) congruent-, 2) over-, and 3) under- identification during the study period. Multinomial logistic regression model was used to examine the relationship between race (White, African American/Black, other) and ethnicity (Hispanic/Latinx, non-Hispanic/Latinx) and congruency of dementia identification after controlling for clinical (cognition, function, comorbidities) and demographic characteristics (age, sex, education), and inpatient and outpatient utilization.

Results:

Across all person-years, 88.4% had congruent identification of dementia compared to clinical diagnosis, in 4.1% of the times participants were over-identified with dementia, and 7.5% of the times the participants were under-identified. Rates of misidentification was higher in minority participants than in White, non-Hispanic participants. Multivariable estimation results showed that the probability of over-identification with dementia was 2.2% higher for African American/Black than White ( p = 0.05) and 2.7% higher for Hispanic participants than non-Hispanics ( p = 0.03) participants. Differences in under-identification by race/ethnicity were not statistically significant.

Conclusions:

African American/Black and Hispanic participants were more likely over-identified with dementia in Medicare claims.
An early discovery of intrusion attack using novel optimized deep learning for internet of things
Authors: Anandha Kumar M. , Shanmuga Priya M. , Arunprakash R.
Published: September 2023

In the past couple of years, neural networks have gained widespread use in network security analysis. This type of analysis is usually performed in a nonlinear and highly correlated manner. Due to the immense amount of data traffic, the current models are prone to false alarms and poor detection. Deep-learning models can help security researchers identify and extract data features that are related to an attack. They can also minimize the data’s dimensionality and detect intrusions. Unfortunately, the complexity of the network structure and hidden neurons of a deep-learning model can be set by error-prone procedures. In order to improve the performance of deep learning models, a new algorithm is proposed. This method combines a gradient boost regression and particle swarm optimization. The proposes a method called the Spark-DBN-SVM-GBR algorithm. The simulations conducted proposed algorithm revealed that it has a better accuracy rate than other deep learning models and the experiments conducted on the PSO-GBR algorithm revealed that it performed better than the current optimization technique when detecting unauthorized attack activities.
Conflict evidence fusion method based on improved conflict coefficient and belief entropy
Authors: Li Junwei , Liu Huanyu , Jin Yong , Zhao Aoxiang
Published: September 2023

Research on conflict evidence fusion is an important topic of evidence theory. When fusing conflicting evidence, Dempster-Shafer evidence theory sometimes produces counter-intuitive results. Thus, this work proposes a conflict evidence fusion method based on improved conflict coefficient and belief entropy. Firstly, the proposed method uses an improved conflict coefficient to measure the degree of conflict, and the conflict matrix is constructed to get the support degree of evidence. Secondly, in order to measure the uncertainty of evidence, an improved belief entropy is proposed, and the information volume of evidence is obtained by the improve entropy. Next, connecting with the support degree and information volume, We get the weight coefficient, and use it to modify the evidence. Finally, using the combination rule of Dempster for fusion. Simulation experiments have demonstrated the effectiveness and superiority of the proposed method in this paper.
Adaptive active contour model based on local bias field estimation and saliency
Authors: Deng Ming , Zhou Zhiheng , Liu Guoqi , Zeng Delu , Zhang Mingyue
Published: September 2023

Some active contour models proposed based on intensity inhomogeneity are sensitive to initialization and cannot achieve ideal segmentation results for real images. An adaptive active contour model based on local bias field estimation and saliency is proposed in this paper. First of all, this model proposes an adaptive multi-local search algorithm, which avoids the initialization sensitivity by adaptively setting of the initial contour; Secondly, the local bias field is estimated by fusing the saliency map and fuzzy c-means clustering; Finally, the new bias field and the corrected energy fitting constant are used to define the new energy functional. The desired target object is obtained by minimizing the energy functional. The experimental results show that the segmentation accuracy of the model proposed in this paper is higher than that of the models participating in the comparison. The proposed model can not only avoid the interference of initialization and redundant information, but also segment images with intensity inhomogeneity effectively.