bluuebunny/crossref_metadata_2025 huggingface dataset:
Light Agents Searching for Hot Information
Authors: Kowalski Dariusz R. , Pajak Dominik
Published: July 2022

Agent-based crawlers are commonly used in network maintenance and information gathering. In order not to disturb the main functionality of the system, whether acting at nodes or being in transit, they need to operate online, perform a single operation fast and use small memory. They should also be preferably deterministic, as crawling agents have limited capabilities of generating a large number of truly random bits. We consider a system in which an agent receives an update, typically an insertion or deletion, of some information upon visiting a node. On request, the agent needs to output hot information, i.e., with the net occurrence above certain frequency threshold. A desired time and memory complexity of such agent should be poly-logarithmic in the number of visited nodes and inversely proportional to the frequency threshold. Ours is the first such agent with rigorous analysis and a complementary almost-matching lower bound.
Penalized Proximal Policy Optimization for Safe Reinforcement Learning
Authors: Zhang Linrui , Shen Li , Yang Long , Chen Shixiang , Wang Xueqian , Yuan Bo , Tao Dacheng
Published: July 2022

Safe reinforcement learning aims to learn the optimal policy while satisfying safety constraints, which is essential in real-world applications. However, current algorithms still struggle for efficient policy updates with hard constraint satisfaction. In this paper, we propose Penalized Proximal Policy Optimization (P3O), which solves the cumbersome constrained policy iteration via a single minimization of an equivalent unconstrained problem. Specifically, P3O utilizes a simple yet effective penalty approach to eliminate cost constraints and removes the trust-region constraint by the clipped surrogate objective. We theoretically prove the exactness of the penalized method with a finite penalty factor and provide a worst-case analysis for approximate error when evaluated on sample trajectories. Moreover, we extend P3O to more challenging multi-constraint and multi-agent scenarios which are less studied in previous work. Extensive experiments show that P3O outperforms state-of-the-art algorithms with respect to both reward improvement and constraint satisfaction on a set of constrained locomotive tasks.
A Universal PINNs Method for Solving Partial Differential Equations with a Point Source
Authors: Huang Xiang , Liu Hongsheng , Shi Beiji , Wang Zidong , Yang Kang , Li Yang , Wang Min , Chu Haotian , Zhou Jing , Yu Fan , Hua Bei , Dong Bin , Chen Lei
Published: July 2022

In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs)method emerges to be a promising method for solving both forward and inverse PDE problems. PDEs with a point source that is expressed as a Dirac delta function in the governing equations are mathematical models of many physical processes. However, they cannot be solved directly by conventional PINNs method due to the singularity brought by the Dirac delta function. In this paper, we propose a universal solution to tackle this problem by proposing three novel techniques. Firstly the Dirac delta function is modeled as a continuous probability density function to eliminate the singularity at the point source; secondly a lower bound constrained uncertainty weighting algorithm is proposed to balance the physics-informed loss terms of point source area and the remaining areas; and thirdly a multi-scale deep neural network with periodic activation function is used to improve the accuracy and convergence speed. We evaluate the proposed method with three representative PDEs, and the experimental results show that our method outperforms existing deep learning based methods with respect to the accuracy, the efficiency and the versatility.
Biased Majority Opinion Dynamics: Exploiting Graph k-domination
Authors: Lesfari Hicham , Giroire Frédéric , Pérennes Stéphane
Published: July 2022

We study opinion dynamics in multi-agent networks where agents hold binary opinions and are influenced by their neighbors while being biased towards one of the two opinions, called the superior opinion. The dynamics is modeled by the following process: at each round, a randomly selected agent chooses the superior opinion with some probability α, and with probability 1-α it conforms to the opinion manifested by the majority of its neighbors. In this work, we exhibit classes of network topologies for which we prove that the expected time for consensus on the superior opinion can be exponential. This answers an open conjecture in the literature. In contrast, we show that in all cubic graphs, convergence occurs after a polynomial number of rounds for every α.

We rely on new structural graph properties by characterizing the opinion formation in terms of multiple domination, stable and decreasing structures in graphs, providing an interplay between bias, consensus and network structure. Finally, we provide both theoretical and experimental evidence for the existence of decreasing structures and relate it to the rich behavior observed on the expected convergence time of the opinion diffusion model.
Learn Continuously, Act Discretely: Hybrid Action-Space Reinforcement Learning For Optimal Execution
Authors: Pan Feiyang , Zhang Tongzhe , Luo Ling , He Jia , Liu Shuoling
Published: July 2022

Optimal execution is a sequential decision-making problem for cost-saving in algorithmic trading. Studies have found that reinforcement learning (RL) can help decide the order-splitting sizes. However, a problem remains unsolved: how to place limit orders at appropriate limit prices?

The key challenge lies in the ``continuous-discrete duality'' of the action space. On the one hand, the continuous action space using percentage changes in prices is preferred for generalization. On the other hand, the trader eventually needs to choose limit prices discretely due to the existence of the tick size, which requires specialization for every single stock with different characteristics (e.g., the liquidity and the price range). So we need continuous control for generalization and discrete control for specialization. To this end, we propose a hybrid RL method to combine the advantages of both of them. We first use a continuous control agent to scope an action subset, then deploy a fine-grained agent to choose a specific limit price. Extensive experiments show that our method has higher sample efficiency and better training stability than existing RL algorithms and significantly outperforms previous learning-based methods for order execution.
Heterogeneous Interactive Snapshot Network for Review-Enhanced Stock Profiling and Recommendation
Authors: Wang Heyuan , Wang Tengjiao , Li Shun , Guan Shijie , Zheng Jiayi , Chen Wei
Published: July 2022

Stock recommendation plays a critical role in modern quantitative trading. The large volumes of social media information such as investment reviews that delegate emotion-driven factors, together with price technical indicators formulate a “snapshot” of the evolving stock market profile. However, previous studies usually model the temporal trajectories of price and media modalities separately while losing their interrelated influences. Moreover, they mainly extract review semantics via sequential or attentive models, whereas the rich text associated knowledge is largely neglected. In this paper, we propose a novel heterogeneous interactive snapshot network for stock profiling and recommendation. We model investment reviews in each snapshot as a heterogeneous document graph, and develop a flexible hierarchical attentive propagation framework to capture fine-grained proximity features. Further, to learn stock embedding for ranking, we introduce a novel twins-GRU method, which tightly couples the media and price parallel sequences in a cross-interactive fashion to catch dynamic dependencies between successive snapshots. Our approach excels state-of-the-arts over 7.6% in terms of cumulative and risk-adjusted returns in trading simulations on both English and Chinese benchmarks.
Aspect-based Sentiment Analysis with Opinion Tree Generation
Authors: Bao Xiaoyi , Zhongqing Wang , Jiang Xiaotong , Xiao Rong , Li Shoushan
Published: July 2022

Existing studies usually extract these sentiment elements by decomposing the complex structure prediction task into multiple subtasks. Despite their effectiveness, these methods ignore the semantic structure in ABSA problems and require extensive task-specific designs. In this study, we introduce an Opinion Tree Generation task, which aims to jointly detect all sentiment elements in a tree. We believe that the opinion tree can reveal a more comprehensive and complete aspect-level sentiment structure. Furthermore, we propose a pre-trained model to integrate both syntax and semantic features for opinion tree generation. On one hand, a pre-trained model with large-scale unlabeled data is important for the tree generation model. On the other hand, the syntax and semantic features are very effective for forming the opinion tree structure. Extensive experiments show the superiority of our proposed method. The results also validate the tree structure is effective to generate sentimental elements.
FastRE: Towards Fast Relation Extraction with Convolutional Encoder and Improved Cascade Binary Tagging Framework
Authors: Li Guozheng , Chen Xu , Wang Peng , Xie Jiafeng , Luo Qiqing
Published: July 2022

Recent work for extracting relations from texts has achieved excellent performance. However, most existing methods pay less attention to the efficiency, making it still challenging to quickly extract relations from massive or streaming text data in realistic scenarios. The main efficiency bottleneck is that these methods use a Transformer-based pre-trained language model for encoding, which heavily affects the training speed and inference speed. To address this issue, we propose a fast relation extraction model (FastRE) based on convolutional encoder and improved cascade binary tagging framework. Compared to previous work, FastRE employs several innovations to improve efficiency while also keeping promising performance. Concretely, FastRE adopts a novel convolutional encoder architecture combined with dilated convolution, gated unit and residual connection, which significantly reduces the computation cost of training and inference, while maintaining the satisfactory performance. Moreover, to improve the cascade binary tagging framework, FastRE first introduces a type-relation mapping mechanism to accelerate tagging efficiency and alleviate relation redundancy, and then utilizes a position-dependent adaptive thresholding strategy to obtain higher tagging accuracy and better model generalization. Experimental results demonstrate that FastRE is well balanced between efficiency and performance, and achieves 3-10$\times$ training speed, 7-15$\times$ inference speed faster, and 1/100 parameters compared to the state-of-the-art models, while the performance is still competitive. Our code is available at \url{https://github.com/seukgcode/FastRE}.
CUP: Curriculum Learning based Prompt Tuning for Implicit Event Argument Extraction
Authors: Lin Jiaju , Chen Qin , Zhou Jie , Jin Jian , He Liang
Published: July 2022

Implicit event argument extraction (EAE) aims to identify arguments that could scatter over the document. Most previous work focuses on learning the direct relations between arguments and the given trigger, while the implicit relations with long-range dependency are not well studied. Moreover, recent neural network based approaches rely on a large amount of labeled data for training, which is unavailable due to the high labelling cost. In this paper, we propose a Curriculum learning based Prompt tuning (CUP) approach, which resolves implicit EAE by four learning stages. The stages are defined according to the relations with the trigger node in a semantic graph, which well captures the long-range dependency between arguments and the trigger. In addition, we integrate a prompt-based encoder-decoder model to elicit related knowledge from pre-trained language models (PLMs) in each stage, where the prompt templates are adapted with the learning progress to enhance the reasoning for arguments. Experimental results on two well-known benchmark datasets show the great advantages of our proposed approach. In particular, we outperform the state-of-the-art models in both fully-supervised and low-data scenarios.
Learning by Interpreting
Authors: Tang Xuting , Khan Abdul Rafae , Wang Shusen , Xu Jia
Published: July 2022

This paper introduces a novel way of enhancing NLP prediction accuracy by incorporating model interpretation insights. Conventional efforts often focus on balancing the trade-offs between accuracy and interpretability, for instance, sacrificing model performance to increase the explainability. Here, we take a unique approach and show that model interpretation can ultimately help improve NLP quality. Specifically, we employ our learned interpretability results using attention mechanisms, LIME, and SHAP to train our model. We demonstrate a significant increase in accuracy of up to +3.4 BLEU points on NMT and up to +4.8 points on GLUE tasks, verifying our hypothesis that it is possible to achieve better model learning by incorporating model interpretation knowledge.
Clickbait Detection via Contrastive Variational Modelling of Text and Label
Authors: Yi Xiaoyuan , Zhang Jiarui , Li Wenhao , Wang Xiting , Xie Xing
Published: July 2022

Clickbait refers to deliberately created sensational or deceptive text for tricking readers into clicking, which severely hurts the web ecosystem. With a growing number of clickbaits on social media, developing automatic detection methods becomes essential. Nonetheless, the performance of existing neural classifiers is limited due to the underutilization of small labelled datasets. Inspired by related pedagogy theories that learning to write can promote comprehension ability, we propose a novel Contrastive Variational Modelling (CVM) framework to exploit the labelled data better. CVM models the conditional distributions of text and clickbait labels by predicting labels from text and generating text from labels simultaneously with Variational AutoEncoder and further differentiates the learned spaces under each label by a mixed contrastive learning loss. In this way, CVM can capture more underlying textual properties and hence utilize label information to its full potential, boosting detection performance. We theoretically demonstrate CVM as learning a joint distribution of text, clickbait label, and latent variable. Experiments on three clickbait detection datasets show our method's robustness to inadequate and biased labels, outperforming several recent strong baselines.
Targeted Multimodal Sentiment Classification based on Coarse-to-Fine Grained Image-Target Matching
Authors: Yu Jianfei , Wang Jieming , Xia Rui , Li Junjie
Published: July 2022

Targeted Multimodal Sentiment Classification (TMSC) aims to identify the sentiment polarities over each target mentioned in a pair of sentence and image. Existing methods to TMSC failed to explicitly capture both coarse-grained and fine-grained image-target matching, including 1) the relevance between the image and the target and 2) the alignment between visual objects and the target. To tackle this issue, we propose a new multi-task learning architecture named coarse-to-fine grained Image-Target Matching network (ITM), which jointly performs image-target relevance classification, object-target alignment, and targeted sentiment classification. We further construct an Image-Target Matching dataset by manually annotating the image-target relevance and the visual object aligned with the input target. Experiments on two benchmark TMSC datasets show that our model consistently outperforms the baselines, achieves state-of-the-art results, and presents interpretable visualizations.
Reasoning over Hybrid Chain for Table-and-Text Open Domain Question Answering
Authors: Zhong Wanjun , Huang Junjie , Liu Qian , Zhou Ming , Wang Jiahai , Yin Jian , Duan Nan
Published: July 2022

Tabular and textual question answering requires systems to perform reasoning over heterogeneous information, considering table structure, and the connections among table and text. In this paper, we propose a ChAin-centric Reasoning and Pre-training framework (CARP). CARP utilizes hybrid chain to model the explicit intermediate reasoning process across table and text for question answering. We also propose a novel chain-centric pre-training method, to enhance the pre-trained model in identifying the cross-modality reasoning process and alleviating the data sparsity problem. This method constructs the large-scale reasoning corpus by synthesizing pseudo heterogeneous reasoning paths from Wikipedia and generating corresponding questions. We evaluate our system on OTT-QA, a large-scale table-and-text open-domain question answering benchmark, and our system achieves the state-of-the-art performance. Further analyses illustrate that the explicit hybrid chain offers substantial performance improvement and interpretablity of the intermediate reasoning process, and the chain-centric pre-training boosts the performance on the chain extraction.
Grape: Grammar-Preserving Rule Embedding
Authors: Zhu Qihao , Sun Zeyu , Zhang Wenjie , Xiong Yingfei , Zhang Lu
Published: July 2022

Word embedding has been widely used in various areas to boost the performance of the neural models. However, when processing context-free languages, embedding grammar rules with word embedding loses two types of information. One is the structural relationship between the grammar rules, and the other one is the content information of the rule definition.

In this paper, we make the first attempt to learn a grammar-preserving rule embedding. We first introduce a novel graph structure to represent the context-free grammar. Then, we apply a Graph Neural Network (GNN) to extract the structural information and use a gating layer to integrate content information.

We conducted experiments on six widely-used benchmarks containing four context-free languages. The results show that our approach improves the accuracy of the base model by 0.8 to 6.4 percentage points. Furthermore, Grape also achieves 1.6 F1 score improvement on the method naming task which shows the generality of our approach.
Explaining Soft-Goal Conflicts through Constraint Relaxations
Authors: Eifler Rebecca , Frank Jeremy , Hoffmann Jörg
Published: July 2022

Recent work suggests to explain trade-offs between soft-goals in terms of their conflicts, i.e., minimal unsolvable soft-goal subsets. But this does not explain the conflicts themselves: Why can a given set of soft-goals not be jointly achieved? Here we approach that question in terms of the underlying constraints on plans in the task at hand, namely resource availability and time windows. In this context, a natural form of explanation for a soft-goal conflict is a minimal constraint relaxation under which the conflict disappears (``if the deadline was 1 hour later, it would work''). We explore algorithms for computing such explanations. A baseline is to simply loop over all relaxed tasks and compute the conflicts for each separately. We improve over this by two algorithms that leverage information -- conflicts, reachable states -- across relaxed tasks. We show that these algorithms can exponentially outperform the baseline in theory, and we run experiments confirming that advantage in practice.
Neural Network Pruning by Cooperative Coevolution
Authors: Shang Haopu , Wu Jia-Liang , Hong Wenjing , Qian Chao
Published: July 2022

Neural network pruning is a popular model compression method which can significantly reduce the computing cost with negligible loss of accuracy. Recently, filters are often pruned directly by designing proper criteria or using auxiliary modules to measure their importance, which, however, requires expertise and trial-and-error. Due to the advantage of automation, pruning by evolutionary algorithms (EAs) has attracted much attention, but the performance is limited for deep neural networks as the search space can be quite large. In this paper, we propose a new filter pruning algorithm CCEP by cooperative coevolution, which prunes the filters in each layer by EAs separately. That is, CCEP reduces the pruning space by a divide-and-conquer strategy. The experiments show that CCEP can achieve a competitive performance with the state-of-the-art pruning methods, e.g., prune ResNet56 for 63.42% FLOPs on CIFAR10 with -0.24% accuracy drop, and ResNet50 for 44.56% FLOPs on ImageNet with 0.07% accuracy drop.
Sound2Synth: Interpreting Sound via FM Synthesizer Parameters Estimation
Authors: Chen Zui , Jing Yansen , Yuan Shengcheng , Xu Yifei , Wu Jian , Zhao Hang
Published: July 2022

Synthesizer is a type of electronic musical instrument that is now widely used in modern music production and sound design. Each parameters configuration of a synthesizer produces a unique timbre and can be viewed as a unique instrument. The problem of estimating a set of parameters configuration that best restore a sound timbre is an important yet complicated problem, i.e.: the synthesizer parameters estimation problem. We proposed a multi-modal deep-learning-based pipeline Sound2Synth, together with a network structure Prime-Dilated Convolution (PDC) specially designed to solve this problem. Our method achieved not only SOTA but also the first real-world applicable results on Dexed synthesizer, a popular FM synthesizer.
Towards the Quantitative Interpretability Analysis of Citizens Happiness Prediction
Authors: Li Lin , Wu Xiaohua , Kong Miao , Zhou Dong , Tao Xiaohui
Published: July 2022

Evaluating the high-effect factors of citizens' happiness is beneficial to a wide range of policy-making for economics and politics in most countries. Benefiting from the high-efficiency of regression models, previous efforts by sociology scholars have analyzed the effect of happiness factors with high interpretability. However, restricted to their research concerns, they are specifically interested in some subset of factors modeled as linear functions. Recently, deep learning shows promising prediction accuracy while addressing challenges in interpretability. To this end, we introduce Shapley value that is inherent in solid theory for factor contribution interpretability to work with deep learning models by taking into account interactions between multiple factors. The proposed solution computes the Shapley value of a factor, i.e., its average contribution to the prediction in different coalitions based on coalitional game theory. Aiming to evaluate the interpretability quality of our solution, experiments are conducted on a Chinese General Social Survey (CGSS) questionnaire dataset. Through systematic reviews, the experimental results of Shapley value are highly consistent with academic studies in social science, which implies our solution for citizens' happiness prediction has 2-fold implications, theoretically and practically.
Gigs with Guarantees: Achieving Fair Wage for Food Delivery Workers
Authors: Nair Ashish , Yadav Rahul , Gupta Anjali , Chakraborty Abhijnan , Ranu Sayan , Bagchi Amitabha
Published: July 2022

With the increasing popularity of food delivery platforms, it has become pertinent to look into the working conditions of the `gig' workers in these platforms, especially providing them fair wages, reasonable working hours, and transparency on work availability. However, any solution to these problems must not degrade customer experience and be cost-effective to ensure that platforms are willing to adopt them. We propose Work4Food, which provides income guarantees to delivery agents, while minimizing platform costs and ensuring customer satisfaction. Work4Food ensures that the income guarantees are met in such a way that it does not lead to increased working hours or degrade environmental impact. To incorporate these objectives, Work4Food balances supply and demand by controlling the number of agents in the system and providing dynamic payment guarantees to agents based on factors such as agent location, ratings, etc. We evaluate Work4Food on a real-world dataset from a leading food delivery platform and establish its advantages over the state of the art in terms of the multi-dimensional objectives at hand.
Psychiatric Scale Guided Risky Post Screening for Early Detection of Depression
Authors: Zhang Zhiling , Chen Siyuan , Wu Mengyue , Zhu Kenny Q.
Published: July 2022

Depression is a prominent health challenge to the world, and early risk detection (ERD) of depression from online posts can be a promising technique for combating the threat. Early depression detection faces the challenge of efficiently tackling streaming data, balancing the tradeoff between timeliness, accuracy and explainability. To tackle these challenges, we propose a psychiatric scale guided risky post screening method that can capture risky posts related to the dimensions defined in clinical depression scales, and providing interpretable diagnostic basis. A Hierarchical Attentional Network equipped with BERT (HAN-BERT) is proposed to further advance explainable predictions. For ERD, we propose an online algorithm based on an evolving queue of risky posts that can significantly reduce the number of model inferences to boost efficiency. Experiments show that our method outperforms the competitive feature-based and neural models under conventional depression detection settings, and achieves simultaneous improvement in both efficacy and efficiency for ERD.
Allocating Opportunities in a Dynamic Model of Intergenerational Mobility (Extended Abstract)
Authors: Heidari Hoda , Kleinberg Jon
Published: July 2022

Opportunities such as higher education can promote intergenerational mobility, leading individuals to achieve levels of socioeconomic status above that of their parents. In this work, which is an extended abstract of a longer paper in the proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, we develop a dynamic model for allocating such opportunities in a society that exhibits bottlenecks in mobility; the problem of optimal allocation reflects a trade-off between the benefits conferred by the opportunities in the current generation and the potential to elevate the socioeconomic status of recipients, shaping the composition of future generations in ways that can benefit further from the opportunities. We show how optimal allocations in our model arise as solutions to continuous optimization problems over multiple generations, and we find in general that these optimal solutions can favor recipients of low socioeconomic status over slightly higher-performing individuals of high socioeconomic status --- a form of socioeconomic affirmative action that the society in our model discovers in the pursuit of purely payoff-maximizing goals. We characterize how the structure of the model can lead to either temporary or persistent affirmative action, and we consider extensions of the model with more complex processes modulating the movement between different levels of socioeconomic status.
Evidential Reasoning and Learning: a Survey
Authors: Cerutti Federico , Kaplan Lance M. , Şensoy Murat
Published: July 2022

When collaborating with an artificial intelligence (AI) system, we need to assess when to trust its recommendations. Suppose we mistakenly trust it in regions where it is likely to err. In that case, catastrophic failures may occur, hence the need for Bayesian approaches for reasoning and learning to determine the confidence (or epistemic uncertainty) in the probabilities of the queried outcome. Pure Bayesian methods, however, suffer from high computational costs. To overcome them, we revert to efficient and effective approximations. In this paper, we focus on techniques that take the name of evidential reasoning and learning from the process of Bayesian update of given hypotheses based on additional evidence. This paper provides the reader with a gentle introduction to the area of investigation, the up-to-date research outcomes, and the open questions still left unanswered.
A Survey of Risk-Aware Multi-Armed Bandits
Authors: Tan Vincent Y. F. , L.A. Prashanth , Jagannathan Krishna
Published: July 2022

In several applications such as clinical trials and financial portfolio optimization, the expected value (or the average reward) does not satisfactorily capture the merits of a drug or a portfolio. In such applications, risk plays a crucial role, and a risk-aware performance measure is preferable, so as to capture losses in the case of adverse events. This survey aims to consolidate and summarise the existing research on risk measures, specifically in the context of multi-armed bandits. We review various risk measures of interest, and comment on their properties. Next, we review existing concentration inequalities for various risk measures. Then, we proceed to defining risk-aware bandit problems, We consider algorithms for the regret minimization setting, where the exploration-exploitation tradeoff manifests, as well as the best arm identification setting, which is a pure exploration problem—both in the context of risk-sensitive measures. We conclude by commenting on persisting challenges and fertile areas for future research.
Recent Advances in Concept Drift Adaptation Methods for Deep Learning
Authors: Yuan Liheng , Li Heng , Xia Beihao , Gao Cuiying , Liu Mingyue , Yuan Wei , You Xinge
Published: July 2022

In the ``Big Data'' age, the amount and distribution of data have increased wildly and changed over time in various time-series-based tasks, e.g weather prediction, network intrusion detection. However, deep learning models may become outdated facing variable input data distribution, which is called concept drift. To address this problem, large number of samples are usually required to update deep learning models, which is impractical in many realistic applications. This challenge drives researchers to explore the effective ways to adapt deep learning models to concept drift. In this paper, we first mathematically describe the categories of concept drift including abrupt drift, gradual drift, recurrent drift, incremental drift. We then divide existing studies into two categories (i.e., model parameter updating and model structure updating), and analyze the pros and cons of representative methods in each category. Finally, we evaluate the performance of these methods, and point out the future directions of concept drift adaptation for deep learning.
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps (Extended Abstract)
Authors: Huber Tobias , Weitz Katharina , André Elisabeth , Amir Ofra
Published: July 2022

With advances in reinforcement learning (RL), agents are now being developed in high-stakes application domains such as healthcare and transportation. Explaining the behavior of these agents is challenging, as they act in large state spaces, and their decision-making can be affected by delayed rewards. In this paper, we explore a combination of explanations that attempt to convey the global behavior of the agent and local explanations which provide information regarding the agent's decision-making in a particular state. Specifically, we augment strategy summaries that demonstrate the agent's actions in a range of states with saliency maps highlighting the information it attends to. Our user study shows that intelligently choosing what states to include in the summary (global information) results in an improved analysis of the agents. We find mixed results with respect to augmenting summaries with saliency maps (local information).
Learning Realistic Patterns from Visually Unrealistic Stimuli: Generalization and Data Anonymization (Extended Abstract)
Authors: Nikolaidis Konstantinos , Kristiansen Stein , Plagemann Thomas , Goebel Vera , Liestøl Knut , Kankanhalli Mohan , Traaen Gunn-Marit , Øverland Britt , Akre Harriet , Aakeroy Lars , Steinshamn Sigurd
Published: July 2022

Good training data is a prerequisite to develop useful Machine Learning applications. However, in many domains existing data sets cannot be shared due to privacy regulations (e.g., from medical studies). This work investigates a simple yet unconventional approach for anonymized data synthesis to enable third parties to benefit from such anonymized data. We explore the feasibility of learning implicitly from visually unrealistic, task-relevant stimuli, which are synthesized by exciting the neurons of a trained deep neural network. As such, neuronal excitation can be used to generate synthetic stimuli. The stimuli data is used to train new classification models. Furthermore, we extend this framework to inhibit representations that are associated with specific individuals. Extensive comparative empirical investigation shows that different algorithms trained on the stimuli are able to generalize successfully on the same task as the original model.
A Theoretical Perspective on Hyperdimensional Computing (Extended Abstract)
Authors: Thomas Anthony , Dasgupta Sanjoy , Rosing Tajana
Published: July 2022

Hyperdimensional (HD) computing is a set of neurally inspired methods for computing on high-dimensional, low-precision, distributed representations of data. These representations can be combined with simple, neurally plausible algorithms to effect a variety of information processing tasks. HD computing has recently garnered significant interest from the computer hardware community as an energy-efficient, low-latency, and noise-robust tool for solving learning problems. We present a novel mathematical framework that unifies analysis of HD computing architectures, and provides general, non-asymptotic, sufficient conditions under which HD information processing techniques will succeed.
Mechanism Design Powered by Social Interactions: A Call to Arms
Authors: Zhao Dengji
Published: July 2022

Mechanism design has traditionally assumed that the participants are fixed and independent. However, in reality, the participants are well-connected (e.g., via their social networks) and we can utilize their connections to power the design. One interesting trend is to incentivize the existing participants to use their connections to invite new participants. This helps to form larger games in auctions, coalitional games, matching etc., which is not achievable with the traditional solutions. The challenge is that the participants are competitors and they would not invite each other by default. Solving this is well-coupled with the existing challenges. For example, in auctions, solving it may require revenue monotonicity and false-name-proofness, which were proved impossible to achieve under certain sensible conditions. In matching, this cannot get along with standard optimality and stability. Hence, we believe there is an important theoretical value to discover and the study will stimulate many interesting applications, especially under decentralized systems with blockchain.
Equilibria in Strategic Nominee Selection
Authors: Lisowski Grzegorz
Published: July 2022

In my PhD project I explore the game-theoretic problems related to the strategic selection of party nominees. I aim at establishing the complexity of computational problems in this setting. I further study aspects of opinion diffusion protocols related to this problem.
Application of Neurosymbolic AI to Sequential Decision Making
Authors: Núñez-Molina Carlos
Published: July 2022

In the history of AI, two main paradigms have been proposed to solve Sequential Decision Making (SDM) problems: Automated Planning (AP) and Reinforcement Learning (RL). Among the many proposals to unify both fields, the one known as neurosymbolic AI has recently attracted great attention. It combines the Deep Neural Networks used in modern RL with the symbolic representations typical of AP. The main goal of this PhD is to progress the state of the art in neurosymbolic AI for SDM, developing methods for both solving these problems and learning aspects of their structure.
Interactive Reinforcement Learning for Symbolic Regression from Multi-Format Human-Preference Feedbacks
Authors: Crochepierre Laure , Boudjeloud-Assala Lydia , Barbesant Vincent
Published: July 2022

In this work, we propose an interactive platform to perform grammar-guided symbolic regression using a reinforcement learning approach from human-preference feedback. To do so, a reinforcement learning algorithm iteratively generates symbolic expressions, modeled as trajectories constrained by grammatical rules, from which a user shall elicit preferences. The interface gives the user three distinct ways of stating its preferences between multiple sampled symbolic expressions: categorizing samples, comparing pairs, and suggesting improvements to a sampled symbolic expression. Learning from preferences enables users to guide the exploration in the symbolic space toward regions that are more relevant to them. We provide a web-based interface testable on symbolic regression benchmark functions and power system data.
CARBEN: Composite Adversarial Robustness Benchmark
Authors: Hsiung Lei , Tsai Yun-Yun , Chen Pin-Yu , Ho Tsung-Yi
Published: July 2022

Prior literature on adversarial attack methods has mainly focused on attacking with and defending against a single threat model, e.g., perturbations bounded in Lp ball. However, multiple threat models can be combined into composite perturbations. One such approach, composite adversarial attack (CAA), not only expands the perturbable space of the image, but also may be overlooked by current modes of robustness evaluation. This paper demonstrates how CAA's attack order affects the resulting image, and provides real-time inferences of different models, which will facilitate users' configuration of the parameters of the attack level and their rapid evaluation of model prediction. A leaderboard to benchmark adversarial robustness against CAA is also introduced.
Correlation-Based Algorithm for Team-Maxmin Equilibrium in Multiplayer Extensive-Form Games
Authors: Zhang Youzhi , An Bo , Subrahmanian V. S.
Published: July 2022

Efficient algorithms computing a Nash equilibrium have been successfully applied to large zero- sum two-player extensive-form games (e.g., poker). However, in multiplayer games, computing a Nash equilibrium is generally hard, and the equilibria are not exchangeable, which makes players face the problem of selecting one of many different Nash equilibria. In this paper, we focus on an alternative solution concept in zero-sum multiplayer extensive-form games called Team-Maxmin Equilibrium (TME). It is a Nash equilibrium that maximizes each team member’s utility. As TME is unique in general, it avoids the equilibrium selection problem. However, it is still difficult (FNP- hard) to find a TME. Computing it can be formulated as a non-convex program, but existing algorithms are capable of solving this program for only very small games. In this paper, we first refine the complexity result for computing a TME by using a correlation plan to show that a TME can be found in polynomial time in a specific class of games according to our boundary for complexity. Second, we propose an efficient correlation-based algorithm to solve the non-convex program for TME in games not belonging to this class. The algorithm combines two special correlation plans based on McCormick envelopes for convex relaxation and von Stengel-Forges polytope for correlated equilibria. We show that restricting the feasible solution space to von Stengel-Forges polytope will strictly reduce the feasible solution space after convex re- laxation of nonlinear terms. Finally, experiments show that our algorithm is about four orders of magnitude faster than the prior state of the art and can solve many previously unsolvable games.
Evolutionary Approach to Security Games with Signaling
Authors: Żychowski Adam , Mańdziuk Jacek , Bondi Elizabeth , Venugopal Aravind , Tambe Milind , Ravindran Balaraman
Published: July 2022

Green Security Games have become a popular way to model scenarios involving the protection of natural resources, such as wildlife. Sensors (e.g. drones equipped with cameras) have also begun to play a role in these scenarios by providing real-time information.

Incorporating both human and sensor defender resources strategically is the subject of recent work on Security Games with Signaling (SGS). However, current methods to solve SGS do not scale well in terms of time or memory.

We therefore propose a novel approach to SGS, which, for the first time in this domain, employs an Evolutionary Computation paradigm: EASGS. EASGS effectively searches the huge SGS solution space via suitable solution encoding in a chromosome and a specially-designed set of operators. The operators include three types of mutations, each focusing on a particular aspect of the SGS solution, optimized crossover and a local coverage improvement scheme (a memetic aspect of EASGS). We also introduce a new set of benchmark games, based on dense or locally-dense graphs that reflect real-world SGS settings.

In the majority of 342 test game instances, EASGS outperforms state-of-the-art methods, including a reinforcement learning method, in terms of time scalability, nearly constant memory utilization, and quality of the returned defender's strategies (expected payoffs).
Clinical Evaluation of the Efficacy of Fitostimoline Hydrogel used for the Autolytic Debridement of Venous Ulcers
Authors:   University Of Messina, Motolese Alfonso
Published: July 2022

Venous ulcers are the most common type of chronic ulcers, affecting frequently the lower limbs. Chronic venous ulcers significantly impact quality of life because of pain, risk of infections, need of medications and possibility of malignant transformation [1]. Accurate wound assessment is essential to guarantee appropriate goal setting.Debridement and bacterial load control are two initial steps of wound care [2]. There are many procedures for wounds cleaning and to reduce bacterial bioburden. It is important the appropriate treatment choice for any single case. Firstly, venous ulcers often present fibrin, one of the major factor that prevents healing process, and for this reason, a debridement of fibrinous tissue is necessary [3]. There are different type of debridement (enzymatic, autolytic, surgical etc.) and the common choice of not aggressive debridement depends on the impossibility to obtain an adequate anesthesia at home or in first level/private offices. Autolytic debridement is a selective and not aggressive method which causes removal of necrotic tissue due to the physiological moist wound environment, which exerts its own debriding capacity [4]. It is a painless and safe technique with effective action and with low costs because dressing are usually changed every two days.
Sistem Peramalan Waktu Masak Fisiologis Benih Padi Menggunakan Double Exponential Smoothing
Authors: Shofi Najmi Nurus , Arifianto Aji Seto , Bintoro Mochamat
Published: July 2022

The success of the rice harvest is influenced by various factors, one of which is the selection of quality seeds. Good rice seeds are those obtained during physiological maturity where the moisture content in the seeds is not too low/high. The physiological cooking time of rice seeds can be done by calculating heat accumulation and testing germination. Heat accumulation is the total heat energy from solar radiation received by rice plants, this value can be calculated by recording the maximum, minimum, and humidity temperature data continuously for 90-120 days. Meanwhile, post-harvest laboratory tests were carried out for germination. Temperature and humidity data that were stored and processed quickly of course could be predicted the physiological ripening time of seeds better. Therefore in this article, a web-based forecasting system with the double exponential smoothing method was developed and supported by a graphical display of the data development. This research was conducted on 3 rice varieties namely IR64, Sinta Nur, and Ciherang. The yield that could be conveyed for the IR64 variety reached 85% germination with heat accumulation of 1147 and 105 DAS. The Sinta Nur variety had 92% germination and 86% Ciherang variety with heat accumulation of 1266 at 115 DAS. In this forecasting process, the MAPE value is 0.205 with alpha 0.9 and beta 0.1.
Perancangan User Interface Sistem Informasi Alumni Menggunakan Metode Webuse Dan User Centered Design
Authors: Pramudhita Agung Nugroho , Arhandi Putra Prima , Sukmadewi Ferina Bayu
Published: July 2022

Alumni Information System is a system that facilitates data collection process and obtains information about alumni. Based on results of interviews with several alumni who just graduated in 2020 at State Polytechnic of Malang, results were obtained, they had problems accessing website, such as updating data, searching for alumni data, and registration for graduation ceremony. Based on results of online survey to several alumni of State Polytechnic of Malang, problems were founded in content organizing, ease of reading content, navigating links, UI/UX, and website effectiveness. To make website with interface is user friendly, design improvements are needed to provide recommendations for problems that arise. User Centered Design method was chosen to design user interface according to user experience and webuse was chosen because it focuses on evaluating usability of website and helping web designers and developers based on responses given by visitors. Results Both of evaluation from UI design in content organizing & readability category and navigation & links category are 0.85 with an “Excellent” usability level. In user interface design category result is 0.86 with an “Excellent” usability level, and performance & effectiveness category result is 0.81 with an “Excellent” usability level. So that entire webuse category has increased.
The value method in literary studies (on the interpretation of N.A. Nekrasov's work in the axiological aspect)
Authors: Wei Xinyi
Published: July 2022

Nekrasov's work presents a certain problem for studying from the point of view of the value system expressed in it. Despite the fact that quite a large number of works were devoted to this problem in Soviet times, it does not seem to be solved. The most productive approach to solving the question of the ethical foundations of Nekrasov's poetry seems to be the reconstruction of the understanding of the connection between aesthetics and ethics, characteristic of the socio-philosophical thought of his time. Throughout the 1830s and 1850s, the Russian liberal intelligentsia turned to German classical philosophy in search of answers to the question of the place of art in the world. From the philosophy of Kant, who sharply divided ethics and aesthetics and denied the possibility of art's direct impact on the world and society, she moved on to Schelling's "system of transcendental idealism". Not satisfied with the potential "immoralism" of this system, Russian liberal thought accepted Hegel's philosophy, on the one hand, recognizing the need for the philosopher to "accept" reality, on the other – reserving the artist the right to criticize this reality as not corresponding to his "concept". In the 1850s, the thinkers of the circle of Chernyshevsky and Dobrolyubov made additions based on Feuerbach's philosophy, but in fact it remains unchanged. These concepts allow us to truly understand Nekrasov's axiology, to truly approach his specific "poetry of reality", combining the pursuit of aesthetic perfection, philosophical acceptance of the world in its entirety and sharp criticism of modern society.