bluuebunny/crossref_metadata_2025 huggingface dataset:
Effectiveness of Size, Liquidity, Leverage, and Growth on CSR Disclosure Moderating Role by Profitability in Indonesian Stock Exchange (IDX)
Authors: Sari Dwi Fatmah , Hariyanto Eko , Wahyuni Sri , Dirgantari Novi
Published: September 2023

The conduct of this study emphasizes the purpose of determining the presence or absence of the influence of profitability which acts as moderating in the influence of company size, liquidity, leverage, and company growth on CSR disclosure. This research focuses on sectors that have been classified as consumer non-cyclicals listed on the Indonesia Stock Exchange (IDX) during 2018-2021. The theory carried in this study refers to the theory of legitimacy and stakeholders. With regard to the theory of legitimacy, it is clearly affirmed that organizations always seek to be perceived as operating within the limits and norms of the prevailing society and put more emphasis on public rights. Meanwhile, in stakeholder theory, it is said that a company must be an organization whose actions can contribute to its stakeholders. Therefore, the support provided by stakeholders affects the existence of the company itself and this support plays an important role in maintaining the company's life.  The procedure in the sampling process is by purposive sampling and obtained by 28 companies. The data analysis model applied in this study includes descriptive statistics, multiple linear regression analysis, and residual test MRA and by utilizing SPSS 26 software. The results of this study prove that CSR disclosure cannot be influenced by company size or company growth. Meanwhile, CSR disclosure can be affected by liquidity and leverage. The results of moderated regression analysis through residual testing have proven that the relationship between company size, liquidity, leverage, and company growth with CSR disclosure cannot be strengthened by profitability.
Review of: Explanatory dictionary of the Tatar language. Kazan, 2015–2021
Authors: Ivanova Alena Mikhajlovna
Published: September 2023

Review, abstract, keywords.
Table-algorithmic method for anti-aliazing image of line segments
Authors: Bashkov E. O. , Kurinnyi M. S.
Published: September 2023

Increasing the informativeness of computer graphics is achieved due to the formation of images that accurately reproduce the structural and visual features of the object. When forming such images, it is necessary to display graphic scenes with great detail, therefore, at this stage of the development of computer graphics, special attention is paid not only to the speed of forming graphic images, but also to their realism. In most modern computer graphics systems, the raster principle of image formation is used. When creating raster images, distortions occur due to the insufficient resolution of the raster. Artifacts appear on the images, one of the manifestations of which are pronounced steps or teeth on the edges of objects. The aliasing effect significantly affects the realism of the formed image, which necessitates the development of special methods and means of its elimination The tabular method of vector anti-aliasing was further developed. It is proposed to calculate the values of the evaluation function to store its values with a larger quantization step, and to calculate the intermediate ones according to the derived formula. The analysis showed that the use of the proposed approach allows reducing by 8¸17 times the amount of memory needed to calculate the pixel coverage area. The method involves hardware and software implementation and can be used in high-performance computer graphics systems.
Correlation-phase intellectual analysis of the complex degree of coherence of laser autofluorescence images of blood plasma and diagnosis of oncological changes in human organs
Authors: Ushenko Yu. O. , Dubolazov O. V. , Soltys I. V. , Angelsky A.-P. I. , Ushenko O. H. , Gordey I. Yu. , Ilin V. A. , Aksynti Voronka A. F. G.
Published: September 2023

The theoretical foundations of correlation-phase analysis of laser images of human blood plasma are presented. The values and ranges of change of statistical (moments of the 1st - 4th orders), correlation (Gram-Charlier decomposition coefficients of autocorrelation functions) and fractal (slopes and variance of extremes of logarithmic dependences of power spectra) parameters of the coordinate distributions of CSP are investigated. The objective criteria for diagnosing oncological changes in the blood plasma of a cancer patient are determined.
Explore the feasibility study of magnetic stereotaxic system
Authors: Hunkun Jiao , Avrunin O. G.
Published: September 2023

This paper introduces the magnetic stereotaxis system in detail, and establishes the basic model of the magnetic stereotaxic system in three-dimensional space through the computer simulation software COMSOL, and conducts theoretical analysis and computer simulation experiments on the non-contact control of implants. After that, the slide rail system controlled by Arduino was constructed in actual experiments, and the results of computer simulation experiments were verified. It has also been demonstrated that changing the external magnetic field strength in a magnetic stereotaxic system enables non-contact control of implant movement.
Infographics experiences of international baccalaureate teacher candidates in educational technologies
Authors: ÜZÜMCÜ Özlem
Published: September 2023

This study aims to investigate the process of designing infographics through participants' knowledge construction in the assessment and evaluation process of IB Educator Certificate (IBEC) educational technologies education. The study, in which the case study method was used, was conducted with 12 participants who attended the educational technologies and material design course in IBEC education. 7 infographic assignments were given in 14 weeks and 42 hours of online training and analyzed according to the relevant analytical rubric. To measure the development in the training process, pre-test and post-test were measured with the Web 2.0 tools usage competencies scale. At the end of the training, the participants' views on the effect of infographics on their learning processes and its use in education were analyzed by descriptive analysis. Accordingly, it was observed that the participants produced designs with effective content in their infographic assignments during the process. At the end of the training, there was a significant increase in their use of web 2.0 tools, which is the scope of the course, in support of this. According to the participants’ views, results such as facilitating learning, permanent learning and having fun while learning were also obtained.
The principles of issuing a crucial decision (selected problems)
Authors: Dubiński Łukasz
Published: September 2023

The erection and commissioning of a nuclear power plant requires a number of administrative pro­cedures. At the same time, in the majority of cases, it concerns proceedings that are mainly regulated outside the provisions on nuclear energy, and thus have already been discussed many times in the doctrine (e.g. cases regarding the building permit).
Against the background of the above, the procedure for issuing a crucial decision stands out. This is because this decision is not present outside the regulations concerning nuclear energy. However, it should be noted that the decision in question is of key importance as far as the possibility of starting the construction and operation of nuclear power is concerned. Thus it is surprising that the manner in which this ruling is regulated does not allow for determining such basic issues as the character of the crucial decision or the premises for issuing it. The goal of this study is to solve these dilemmas. The adopted research method is the dogmatic and legal method.
On the basis of the conducted analysis, it was established that in the current legal status, the crucial decision is of a binding nature, and the premises for its issuance are included in Article 22 Section 3 of the Act of 29 June 2011 on preparing and implementing investments in the field of nuclear power facilities and accompanying investments. It should be added that the presented results of the interpretative analysis are inconsistent with the position of the doctrine. In the literature, it is assumed that the crucial decision has a discretionary (‘political’) character. Moreover, the wording of the aforementioned act does not specify the conditions for issuing a crucial decision. Instead, it is suggested to look for them within the so-called legislative materials.
Konflikt między wolnością słowa a wolnością religijną. Sprawa Rabczewska v. Polska, skarga nr 8257/13. Glosa aprobująca
Authors: Zawiślak Michał
Published: September 2023

Ocena dokonana przez ETPC, w myśl której sądy krajowe nie oceniły, czy wypowiedzi skarżącej mogły wzbudzić uzasadnione oburzenie lub czy miały one charakter podżegania do nienawiści albo w inny sposób zakłócania pokoju i tolerancji religijnej w Polsce, ma kluczowe znaczenie w kontekście glosowanego wyroku. ETPC słusznie orzekł, że grupa religijna musi tolerować zaprzeczanie przez innych jej przekonaniom religijnym, a nawet propagowanie przez innych doktryn wrogich ich wierze, o ile kwestionowane wypowiedzi nie nawołują do nienawiści lub nietolerancji religijnej. Wobec braku definicji prawnej trudno ocenić społecz­ną szkodliwość czynu. Trybunał stwierdził, że ochrona uczuć religijnych jest nieskuteczna i nieadekwatna do stopnia zniewagi czynu zabronionego, tym samym uznał istniejący od lat w polskim prawie karnym problem ze stopniowaniem społecznej szkodliwości czynu. Polska naruszyła art. 10 Konwencji, gdyż sądy krajowe nie dokonały rzetelnej oceny społecznej szkodliwości czynu.
ВПЛИВ ФУНКЦІОНУВАННЯ ПРІОРИТЕТНИХ ГАЛУЗЕЙ ЕКОНОМІКИ НА СТАН ФІНАНСОВО-ЕКОНОМІЧНОЇ БЕЗПЕКИ УКРАЇНИ
Authors: СВИДРУК Ірена , ІЛЬЧИШИН Степан
Published: September 2023

Статтю присвячено проблемі забезпечення фінансово-економічної безпеки пріоритетних галузей економіки України, яка гостро постає у період захисту суверенітету держави через військову агресію рф. Накладання фінансово-економічних шоків довоєнного періоду посилює виклики і загрози фінансово-економічного характеру на шляху  післявоєнного відновлення економіки. Мета статті полягає у формуванні методологічної основи для проведення дослідження взаємовпливу результатів діяльності пріоритетних галузей економіки та рівня фінансово-економічної безпеки України.  Представлене дослідження започатковує науковий напрям, який дозволить чітко планувати стратегічні орієнтири забезпечення фінансово-економічної безпеки пріоритетних галузей економіки України. Структурно-динамічне дослідження соціально-економічних показників діяльності пріоритетних галузей дозволило сформувати матрицю регресорів для визначення факторного впливу на динаміку фінансово-економічної безпеки. Досліджено гіпотези і статистично підтверджено існування залежностей, що підтверджує доцільність використання запропонованої методологічної основи дослідження. З’ясовано, що найбільший позитивний вплив на динаміку показників фінансово-економічної безпеки мали темпи зростання обсягів реалізованої продукції в галузі промисловості та темпи зростання кількості зайнятих працівників в торговельній галузі, негативний – темпи зростання обсягів реалізованої продукції в загальному по економіці України та темпи зростання кількості зайнятих працівників в галузі сільського господарства. Подальші дослідження в обраному напрямку запропоновано сконцентрувати на розробленні деталізованої метричної основи з врахуванням впливу факторів макроекономічного рівня.
Which sub-compartments of fat mass and fat-free mass are related to blood viscosity factors?
Authors: Brun Jean-Frédéric , Varlet-Marie Emmanuelle , Myzia Justine , Vachoud Laurent , Marion Bénédicte , Roques Céline , Raynaud de Mauverger Eric , Mercier Jacques
Published: September 2023

The size of body compartments is a determinant of several factors of blood viscosity. Red cell aggregation is proportional to fat mass while hematocrit is proportional to both fat-free mass and abdominal adiposity, but which parts of these body components are involved in this relationship is not known. Segmental bioelectrical impedance analysis (sBIA) provides a possibility to delineate the relationships more precisely between various subdivisions of the body and blood viscosity factors, going farther than preceding studies using non segmental BIA. In this study we investigated in 38 subjects undergoing a standardized breakfast test with mathematical modelling of glucose homeostasis and a segmental bioelectrical impedance analysis (sBIA) the relationships between the various compartments of the body and viscosity factors. Blood and plasma viscosity were measured with the Anton Paar rheometer and analyzed with Quemada’s model. The parameters better correlated to hematocrit are fat free mass (r = 0.562) and its two components muscle mass (r = 0.516) and non-muscular fat-free mass (r = 0.452), and also trunk fat mass (r = 0.383) and waist-to hip ratio (r = 0.394). Red cell aggregation measurements were correlated with both truncal and appendicular fat mass (r ranging between 0.603 and 0.728). Weaker correlations of M and M1 are found with waist circumference and hip circumference. This study shows that the correlation between lean mass and hematocrit involves both muscle and non-muscle moieties of lean mass, and that both central and appendicular fat are determinants of red cell aggregation.
TrojBits: A Hardware Aware Inference-Time Attack on Transformer-Based Language Models
Authors: Al Ghanim Mansour , Santriaji Muhammad , Lou Qian , Solihin Yan
Published: September 2023

Transformer-based language models demonstrate exceptional performance in Natural Language Processing (NLP) tasks but remain susceptible to backdoor attacks involving hidden input triggers. Trojan injection via hardware bitflips presents a significant challenge for contemporary language models. However, previous research overlooks practical hardware considerations, such as DRAM and cache memory structures, resulting in unrealistic attacks that demand the manipulation of an excessive number of parameters and bits. In this paper, we present TrojBits, a novel approach requiring minimal bit-flips to effectively insert Trojans into real-world Transformer language model systems. This is achieved through a three-module framework designed to efficiently target Transformer-based language models, consisting of Vulnerable Parameters Ranking (VPR), Hardware-aware Attack Optimization (HAO), and Vulnerable Bits Pruning (VBP). Within the VPR module, we are the first to employ Gradient-guided Fisher information to identify the most susceptible Transformer parameters, specifically in the word embedding layer. The HAO module then redistributes these parameters across multiple triggers, conforming to hardware constraints by incorporating a regularization term in the trojan optimization methodology. Finally, the VBP module aims to reduce the number of bit-flips by discarding less significant bits. We evaluate TrojBits on two representative NLP models, BERT and XLNE, on three classification tasks (SST2, OffensEval, and AG’s News). Our results demonstrate that our TrojBits successfully achieves the inference-time attack with only 64 parameters out of 116 million and 90-bit flips while maintaining the model performance.
A Bilevel Formalism for the Peer-Reviewing Problem
Authors: Auricchio Gennaro , Zhang Ruixiao , Zhang Jie , Cai Xiaohao
Published: September 2023

Due to the large number of submissions that more and more conferences experience, finding an automatized way to well distribute the submitted papers among reviewers has become necessary. We model the peer-reviewing matching problem as a bilevel programming (BP) formulation. Our model consists of a lower-level problem describing the reviewers’ perspective and an upper-level problem describing the editors’. Every reviewer is interested in minimizing their overall effort, while the editors are interested in finding an allocation that maximizes the quality of the reviews and follows the reviewers’ preferences the most. To the best of our knowledge, the proposed model is the first one that formulates the peer-reviewing matching problem by considering two objective functions, one to describe the reviewers’ viewpoint and the other to describe the editors’ viewpoint. We demonstrate that both the upper-level and lower-level problems are feasible and that our BP model admits a solution under mild assumptions. After studying the properties of the solutions, we propose a heuristic to solve our model and compare its performance with the relevant state-of-the-art methods. Extensive numerical results show that our approach can find fairer solutions with competitive quality and less effort from the reviewers.(Our code website: https://github.com/Galaxy-ZRX/Bilevel-Review.)
Diverse, Top-k, and Top-Quality Planning Over Simulators
Authors: Benke Lyndon , Miller Tim , Papasimeon Michael , Lipovetzky Nir
Published: September 2023

Diverse, top-k, and top-quality planning are concerned with the generation of sets of solutions to sequential decision problems. Previously this area has been the domain of classical planners that require a symbolic model of the problem instance. This paper proposes a novel alternative approach that uses Monte Carlo Tree Search (MCTS), enabling application to problems for which only a black-box simulation model is available. We present a procedure for extracting bounded sets of plans from pre-generated search trees in best-first order, and a metric for evaluating the relative quality of paths through a search tree. We demonstrate this approach on a path-planning problem with hidden information, and suggest adaptations to the MCTS algorithm to increase the diversity of generated plans. Our results show that our method can generate diverse and high-quality plan sets in domains where classical planners are not applicable.
Expediting Self-Play Learning in AlphaZero-Style Game-Playing Agents
Authors: Björnsson Yngvi , Jónsson Róbert Leó Þormar , Jónsson Sigurjón Ingi
Published: September 2023

One of the main appeals of AlphaZero-style game-playing agents, which combine deep learning and Monte Carlo Tree Search, is that they can be trained autonomously without external expert-level domain knowledge. However, training such agents is generally computationally expensive, with the most computationally time-consuming step being generating training data via self-play. Here we propose an improved strategy for generating self-play training data, resulting in higher-quality samples, especially in earlier training phases. The new strategy initially emphasizes the latter game phases and gradually extends those phases to entire games as the training progresses. In our test domains, the games Connect4 and Breakthrough, we show that game-playing agents using the improved training approach learn significantly faster than counterpart agents using a standard approach. Furthermore, we empirically show that the proposed strategy is (in our test domains) superior to several recently proposed strategies for expediting self-play learning in game playing.
Evaluating Explanation Methods for Vision-and-Language Navigation
Authors: Chen Guanqi , Yang Lei , Chen Guanhua , Pan Jia
Published: September 2023

The ability to navigate robots with natural language instructions in an unknown environment is a crucial step for achieving embodied artificial intelligence (AI). With the improving performance of deep neural models proposed in the field of vision-and-language navigation (VLN), it is equally interesting to know what information the models utilize for their decision-making in the navigation tasks. To understand the inner workings of deep neural models, various explanation methods have been developed for promoting explainable AI (XAI). But they are mostly applied to deep neural models for image or text classification tasks and little work has been done in explaining deep neural models for VLN tasks. In this paper, we address these problems by building quantitative benchmarks to evaluate explanation methods for VLN models in terms of faithfulness. We propose a new erasure-based evaluation pipeline to measure the step-wise textual explanation in the sequential decision-making setting. We evaluate several explanation methods for two representative VLN models on two popular VLN datasets and reveal valuable findings through our experiments.
Enhancing Document-Level Relation Extraction with Relation-Specific Entity Representation and Evidence Sentence Augmentation
Authors: Dai Qizhu , Zhong Jiang , Zhu Wei , Wang Chen , Yin Hong , Lei Qin , Li Xue , Li Rongzhen
Published: September 2023

Document-level relation extraction (DocRE) is an important task in natural language processing, with applications in knowledge graph construction, question answering, and biomedical text analysis. However, existing approaches to DocRE have limitations in predicting relations between entities using fixed entity representations, which can lead to inaccurate results. In this paper, we propose a novel DocRE model that addresses these limitations by using a relation-specific entity representation method and evidence sentence augmentation. Our model uses evidence sentence augmentation to identify top-k evidence sentences for each relation and a relation-specific entity representation method that aggregates the importance of entity mentions using an attention mechanism. These two components work together to capture the context of each entity mention in relation to the specific relation being predicted and select evidence sentences that support accurate relation identification. Finally, we re-predicts entity relations based on the evidence sentences, called relationship reordering module. This module re-predicts entity relationships based on the predicted set of evidence sentences to form k sets of relationship predictions, and then averages these k+1 sets of results to obtain the final relationship predictions. Experimental results on the DocRED dataset demonstrate that our proposed model achieves an F1 score of 62.84% and an lgn F1 score of 60.79%, outperforming state-of-the-art methods.
LTLf Best-Effort Synthesis in Nondeterministic Planning Domains
Authors: De Giacomo Giuseppe , Parretti Gianmarco , Zhu Shufang
Published: September 2023

We study best-effort strategies (aka plans) in fully observable nondeterministic domains (FOND) for goals expressed in Linear Temporal Logic on Finite Traces (LTLf). The notion of best-effort strategy has been introduced to also deal with the scenario when no agent strategy exists that fulfills the goal against every possible nondeterministic environment reaction. Such strategies fulfill the goal if possible, and do their best to do so otherwise. We present a game-theoretic technique for synthesizing best-effort strategies that exploit the specificity of nondeterministic planning domains. We formally show its correctness and demonstrate its effectiveness experimentally, exhibiting a much greater scalability with respect to a direct best-effort synthesis approach based on re-expressing the planning domain as generic environment specifications.
Omega-Regular Reward Machines
Authors: Hahn Ernst Moritz , Perez Mateo , Schewe Sven , Somenzi Fabio , Trivedi Ashutosh , Wojtczak Dominik
Published: September 2023

Reinforcement learning (RL) is a powerful approach for training agents to perform tasks, but designing an appropriate reward mechanism is critical to its success. However, in many cases, the complexity of the learning objectives goes beyond the capabilities of the Markovian assumption, necessitating a more sophisticated reward mechanism. Reward machines and ω-regular languages are two formalisms used to express non-Markovian rewards for quantitative and qualitative objectives, respectively. This paper introduces ω-regular reward machines, which integrate reward machines with ω-regular languages to enable an expressive and effective reward mechanism for RL. We present a model-free RL algorithm to compute ε-optimal strategies against ω-regular reward machines and evaluate the effectiveness of the proposed algorithm through experiments.
Dimensions of Similarity: Towards Interpretable Dimension-Based Text Similarity
Authors: Hatzel Hans Ole , Petersen-Frey Fynn , Fischer Tim , Biemann Chris
Published: September 2023

This paper paves the way for interpretable and configurable semantic similarity search, by training state-of-the-art models for identifying textual similarity guided by a set of aspects or dimensions. The similarity models are analyzed as to which interpretable dimensions of similarity they place the most emphasis on. We conceptually introduce configurable similarity search for finding documents similar in specific aspects but dissimilar in others. To evaluate the interpretability of these dimensions, we experiment with downstream retrieval tasks using weighted combinations of these dimensions. Configurable similarity search is an invaluable tool for exploring datasets and will certainly be helpful in many applied natural language processing research applications.
Reinforcement Learning with Reward Machines in Stochastic Games
Authors: Hu Jueming , Gaglione Jean-Raphaël , Wang Yanze , Xu Zhe , Topcu Ufuk , Liu Yongming
Published: September 2023

We investigate multi-agent reinforcement learning for stochastic games with complex tasks, where the reward functions are non-Markovian. We utilize reward machines to incorporate high-level knowledge of complex tasks. We develop an algorithm called Q-learning with reward machines for stochastic games (QRM-SG), to learn the best-response strategy at Nash equilibrium for each agent. In QRM-SG, we define the Q-function at a Nash equilibrium in augmented state space. The augmented state space integrates the state of the stochastic game and the state of reward machines. Each agent learns the Q-functions of all agents in the system. We prove that Q-functions learned in QRM-SG converge to the Q-functions at a Nash equilibrium if the stage game at each time step during learning has a global optimum point or a saddle point, and the agents update Q-functions based on the best-response strategy at this point. We use the Lemke-Howson method to derive the best-response strategy given current Q-functions. The three case studies show that QRM-SG can learn the best-response strategies effectively. QRM-SG learns the best-response strategies after around 7500 episodes in Case Study I, 1000 episodes in Case Study II, and 1500 episodes in Case Study III, while baseline methods such as Nash Q-learning and MADDPG fail to converge to the Nash equilibrium in all three case studies.
Oracle-Based Local Search for Pseudo-Boolean Optimization
Authors: Iser Markus , Berg Jeremias , Järvisalo Matti
Published: September 2023

Significant advances have been recently made in the development of increasingly effective in-exact (or incomplete) search algorithms—particularly geared towards finding good though not provably optimal solutions fast—for the constraint optimization paradigm of maximum satisfiability (MaxSAT). One of the most successful recent approaches is a new type of stochastic local search in which a Boolean satisfiability (SAT) solver is used as a decision oracle for moving from a solution to another. In this work, we strive for extending the success of the approach to the more general realm of pseudo-Boolean optimization (PBO), where constraints are expressed as linear inequalities over binary variables. As a basis for the approach, we make use of recent advances in practical approaches to satisfiability checking pseudo-Boolean constraints. We outline various heuristics within the oracle-based approach to anytime PBO solving, and show that the approach compares in practice favorably both to a recently-proposed local search approach for PBO that is in comparison a more traditional instantiation of the stochastic local search paradigm as well as a recent exact PBO approach when used as an anytime solver.
FedPerturb: Covert Poisoning Attack on Federated Learning via Partial Perturbation
Authors: Jin Tongsai , Fu Zhihui , Meng Dan , Wang Jun , Qi Yue , Cao Guitao
Published: September 2023

Federated learning breaks through the barrier of data owners by allowing them to collaboratively train a federated machine learning model without compromising the privacy of their own data. However, Federation Learning also faces the threat of poisoning attacks, especially from the client model updates, which may impair the accuracy of the global model. To defend against the poisoning attacks, previous work aims to identify the malicious updates in high dimensional spaces. However, we find that the distances in high dimensional spaces cannot identify the changes in a small subset of dimensions, and the small changes may affect the global models severely. Based on this finding, we propose an untargeted poisoning attack under the federated learning setting via the partial perturbations on a small subset of the carefully selected model parameters, and present two attack object selection strategies. We experimentally demonstrate that the proposed attack scheme achieves high attack success rate on five state-of-the-art defense schemes. Furthermore, the proposed attack scheme remains effective at low malicious client ratios and still circumvents three defense schemes with a malicious client ratio as low as 2%.
Robust Assignment of Labels for Active Learning with Sparse and Noisy Annotations
Authors: Kałuża Daniel , Janusz Andrzej , Ślęzak Dominik
Published: September 2023

The performance of classification models depends on the quality of training data labels. Unfortunately, acquiring good-quality annotations for many tasks is infeasible or too expensive. To address this challenge, active learning algorithms are commonly employed to select only the most relevant data for labeling. However, this is possible only when the quality and quantity of labels acquired from experts are sufficient. In many applications, a trade-off between annotating individual samples by multiple annotators to increase label quality vs. annotating new samples to increase the total number of labeled instances is necessary. In this paper, we address the issue of faulty data annotations in the context of active learning. In particular, we propose two novel annotation unification algorithms that utilize unlabeled parts of the sample space. The proposed methods require little to no intersection between samples annotated by different experts. Our experiments on four public datasets indicate the robustness and superiority of the proposed methods in both, the estimation of the annotator’s reliability, and the assignment of actual labels, against the state-of-the-art algorithms and the simple majority voting.
Mechanism Design for Ad Auctions with Display Prices
Authors: Li Bin , Lei Yahui
Published: September 2023

In various applications, ads are displayed together with prices, so as to provide a direct comparison among similar products or services. The price-displaying feature not only influences the consumers’ decision, but also affects the bidding behavior of advertisers. In this paper, we study ad auctions with display prices from the perspective of mechanism design, in which advertisers are asked to submit both the product costs and the display prices of their commodities. We first provide a characterization for all individually rational and incentive-compatible mechanisms in the presence of display prices, then use it to design ad auctions in two scenarios. In the former scenario, the display prices are assumed to be exogenously determined. For this scenario, we derive the welfare-maximizing and revenue-maximizing auctions for any given display price profile. In the latter, advertisers are allowed to strategize their display prices freely. We investigate two families of allocation policies within the scenario and identify the equilibrium display prices accordingly. Our findings demonstrate the impact of display prices on the design of ad auctions, and highlight how platforms can utilize display price information to optimize the performance of ad delivery.
Integrated Private Data Trading Systems for Data Marketplaces
Authors: Li Weidong , Zhang Mengxiao , Zhang Libo , Liu Jiamou
Published: September 2023

In the digital age, data is a valuable commodity, and data marketplaces offer lucrative opportunities for data owners to monetize their private data. However, data privacy is a significant concern, and differential privacy has become a popular solution to address this issue. Private data trading systems (PDQS) facilitate the trade of private data by determining which data owners to purchase data from, the amount of privacy purchased, and providing specific aggregation statistics while protecting the privacy of data owners. However, existing PDQS with separated procurement and query processes are prone to over-perturbation of private data and lack trustworthiness. To address this issue, this paper proposes a framework for PDQS with an integrated procurement and query process to avoid excessive perturbation of private data. We also present two instances of this framework, one based on a greedy approach and another based on a neural network. Our experimental results show that both of our mechanisms outperformed the separately conducted procurement and query mechanism under the same budget regarding accuracy.
Verifying Belief-Based Programs via Symbolic Dynamic Programming
Authors: Liu Daxin , Huang Qinfei , Belle Vaishak , Lakemeyer Gerhard
Published: September 2023

Belief-based programming is a probabilistic extension of the Golog programming language family, where every action and sensing could be noisy and every test refers to the subjective beliefs of the agent. Such characteristics make it rather suitable for robot control in a partial-observable uncertain environment. Recently, efforts have been made in providing formal semantics for belief programs and investigating the hardness of verifying belief programs. Nevertheless, a general algorithm that actually conducts the verification is missing. In this paper, we propose an algorithm based on symbolic dynamic programming to verify belief programs, an approach that generalizes the dynamic programming technique for solving (partially observable) Markov decision processes, i.e. (PO)MDP, by exploiting the symbolic structure in the solution of first-order (PO)MDPs induced by belief program execution.
On Observability Analysis in Multiagent Systems
Authors: Mu Chunyan , Pang Jun
Published: September 2023

In multiagent systems (MASs), agents’ observation upon system behaviours may improve the overall team performance, but may also leak sensitive information to an observer. A quantified observability analysis can thus be useful to assist decision-making in MASs by operators seeking to optimise the relationship between performance effectiveness and information exposure through observations in practice. This paper presents a novel approach to quantitatively analysing the observability properties in MASs. The concept of opacity is applied to formally express the characterisation of observability in MASs modelled as partially observable multiagent systems. We propose a temporal logic oPATL to reason about agents’ observability with quantitative goals, which capture the probability of information transparency of system behaviours to an observer, and develop verification techniques for quantitatively analysing such properties. We implement the approach as an extension of the PRISM model checker, and illustrate its applicability via several examples.
Transparency in Sum-Product Network Decompilation
Authors: Papantonis Ioannis , Belle Vaishak
Published: September 2023

Sum-product networks guarantee that conditionals and marginals can be computed efficiently, for a wide range of models, bypassing the hardness of inference. However, this advantage comes at the expense of transparency, since it is unclear how variables interact in sum-product networks. Due to this, a series of decompilation algorithms transform sum-product networks back to Bayesian networks. In this work, we first study the transparency and causal utility of the resulting Bayesian networks. We then propose a novel decompilation algorithm to address the identified limitations.
Stock Movement Prediction via Attention-Aware Multi-Order Relation Graph Neural Network
Authors: Peng Hao , Yang Jie
Published: September 2023

Stock Movement Prediction (SMP) is a challenging task that aims at predicting the future stock price trend of companies in the stock. Recent advances mainly apply the Graph Convolutional Network (GCN) to learn connections among companies for SMP. However, these methods usually ignore the semantics of the specific relations (e.g., investment and share) between two entities (i.e., companies and persons) on the market knowledge graph. Meanwhile, considering the long-chain cross-shareholding structures among entities, it is difficult for GCN to obtain high-order neighbor information over long distances. To address these two problems, we present an Attention-aware Multi-order Relation GCN for SMP (AMRGCN-SMP). Specifically, an attention-aware multi-channel aggregation manner achieves the weighted fusion of nodes across multiple semantic channels. Moreover, the dynamic update of the adjacent tensor can fuse the multi-order relation representations and bring more abundant long-chain connections. The experiments on the CSI100E and CSI300E datasets demonstrate that the proposed method achieves state-of-the-art performances compared with the recent advances.
Ada-QPacknet – Multi-Task Forget-Free Continual Learning with Quantization Driven Adaptive Pruning
Authors: Pietron Marcin , Zurek Dominik , Faber Kamil , Corizzo Roberto
Published: September 2023

Continual learning (CL) is a challenging machine learning setting that is attracting the interest of an increasing number of researchers. Among recent CL works, architectural strategies appear particularly promising due to their potential to expand and adapt the model architecture as new tasks are presented. However, existing solutions do not efficiently exploit model sparsity due to the adoption of constant pruning ratios. Moreover, current approaches exhibit a tendency to quickly saturate model capacity since the number of weights is limited and each weight is restricted to a single value. In this paper, we propose Ada-QPacknet, a novel architectural CL method that resorts to adaptive pruning and quantization. These two features allow our model to overcome the two crucial issues of effective exploitation of model sparsity and efficient use of model capacity. Specifically, adaptive pruning restores model capacity by reducing the number of weights assigned to each task to a smaller subset of weights that preserves the performance of the full set, allowing other weights to be used for future tasks. Adaptive quantization separates each weight into multiple components with adaptively reduced bit-width, allowing a single weight to solve more than one task without significant performance drops, leading to improved exploitation of model capacity. Experimental results on benchmark CL scenarios show that our proposed method achieves better results in terms of accuracy than existing rehearsal, regularization, and architectural CL strategies. Moreover, our method significantly outperforms forget-free competitors in terms of efficient exploitation of model capacity.
Generating Replanning Goals Through Multi-Objective Optimization in Response to Execution Observation
Authors: Pozanco Alberto , Borrajo Daniel , Veloso Manuela
Published: September 2023

In some applications, planning-monitoring systems generate plans and monitor their execution by other agents. During execution, agents might deviate from these plans for various reasons. The deviation from the expected behavior will be observed by the planning-monitoring system, which will replan in order to provide the agent a new suggested plan. Most existing replanning approaches maintain the goals and compute a plan that achieves them under the new circumstances. This is often not realistic, as achieving the original goal might be very costly or impossible under the current conditions. Furthermore, replanning approaches usually overlook agent’s behavior up to the observed deviation from the original plan. In this paper we introduce GREPLAN, a novel approach that proposes new replanning goals (and plans) by solving a multi-objective optimization problem that considers all goals within a perimeter of the original goal. Empirical results in several planning benchmarks show that GREPLAN successfully reacts to deviations from the original plan by generating new appropriate replanning goals.
Disentangling Interaction Using Maximum Entropy Reinforcement Learning in Multi-Agent Systems
Authors: Rother David , Weisswange Thomas H. , Peters Jan
Published: September 2023

Research on multi-agent interaction involving both multiple artificial agents and humans is still in its infancy. Most recent approaches have focused on environments with collaboration-focused human behavior, or providing only a small, defined set of situations. When deploying robots in human-inhabited environments in the future, it will be unlikely that all interactions fit a predefined model of collaboration, where collaborative behavior is still expected from the robot. Existing approaches are unlikely to effectively create such behaviors in such “coexistence” environments. To tackle this issue, we introduce a novel framework that decomposes interaction and task-solving into separate learning problems and blends the resulting policies at inference time. Policies are learned with maximum entropy reinforcement learning, allowing us to create interaction-impact-aware agents and scale the cost of training agents linearly with the number of agents and available tasks. We propose a weighting function covering the alignment of interaction distributions with the original task. We demonstrate that our framework addresses the scaling problem while solving a given task and considering collaboration opportunities in a co-existence particle environment and a new cooking environment. Our work introduces a new learning paradigm that opens the path to more complex multi-robot, multi-human interactions.
GridFormer: Spatial-Temporal Transformer Network for Citywide Crowd Flow Prediction
Authors: Su Chaoqun , Wu Chenwang , Lian Defu
Published: September 2023

Crowd flow prediction plays a vital role in various fields such as traffic management, public safety, and urban planning. The main challenge in crowd flow prediction lies in effectively modeling the periodic temporal dependency and long-range spatial dependency. In the temporal domain, crowd flow shows a strong periodicity which is exploited by existing works to build multi-time-scale spatial-temporal features. However, these works hardly consider the disturbance of periods, that is, the crowd flow is not strictly periodic. In the spatial domain, existing works mainly utilize CNN to capture spatial dependency, but the small receptive field of the convolution operator limits the ability to capture the long-range dependency between crowd flows in different regions. In this paper, we propose GridFormer, a Transformer network, in which a periodically shifted sampling method and attention mechanism are employed to handle the temporal shifting in the daily and weekly periodicity, and a pyramid 3D Swin Transformers network is designed to capture long-range spatial dependency in a hierarchical manner. Meanwhile, the pyramid 3D Swin Transformers network jointly models spatial-temporal features to enable better interaction between the spatial and temporal domains. Experimental results on three crowd flow datasets demonstrate that our GridFormer outperforms the state-of-the-art crowd flow prediction methods.
Tuning in to Neural Encoding: Linking Human Brain and Artificial Supervised Representations of Language
Authors: Sun Jingyuan , Zhang Xiaohan , Moens Marie-Francine
Published: September 2023

To understand the algorithm that supports the human brain’s language representation, previous research has attempted to predict neural responses to linguistic stimuli using embeddings generated by artificial neural networks (ANNs), a process known as neural encoding. However, most of these studies have focused on probing neural representations of Germanic languages, such as English, with unsupervised ANNs. In this paper, we propose to bridge the gap between human brain and supervised ANN representations of the Chinese language. Specifically, we investigate how task tuning influences a pretained Transformer for neural encoding and which tasks lead to the best encoding performances. We generate supervised representations on eight Natural Language Understanding (NLU) tasks using prompt-tuning, a technique that is seldom explored in neural encoding for language. We demonstrate that prompt-tuning yields representations that better predict neural responses to Chinese stimuli than traditional fine-tuning on four tasks. Furthermore, we discover that tasks that require a fine-grained processing of concepts and entities lead to representations that are most predictive of brain activation patterns. Additionally, we reveal that the proportion of tuned parameters highly influences the neural encoding performance of fine-tuned models. Overall, our experimental findings could help us better understand the relationship between supervised artificial and brain language representations.
Intractability of Optimal Multi-Agent Pathfinding on Directed Graphs
Authors: Tan Xing , Bercher Pascal
Published: September 2023

In Multi-Agent Pathfinding (MAPF) problems, multiple agents move simultaneously to reach their individual destinations without colliding with each other. The computational complexity of the problem has been extensively studied for undirected graphs over the past decades. However, plan existence for Directed MAPF (diMAPF) was only recently studied and was shown to be in PSPACE as well as NP-hard. In this paper, we study the optimization versions (on makespan and on travel distance of agents) of diMAPF problems and show that they remain NP-hard even when various important non-trivial restrictions are imposed (e.g., when considering the problem on directed, acyclic, and planar graphs where the vertex-degrees are bounded). We have also provide membership results, thus presenting the first set of NP-completeness results for various optimal diMAPF variants.
Uniform Training and Marginal Decoding for Multi-Reference Question-Answer Generation
Authors: Vakulenko Svitlana , Byrne Bill , de Gispert Adrià
Published: September 2023

Question generation is an important task that helps to improve question answering performance and augment search interfaces with possible suggested questions. While multiple approaches have been proposed for this task, none addresses the goal of generating a diverse set of questions given the same input context. The main reason for this is the lack of multi-reference datasets for training such models. We propose to bridge this gap by seeding a baseline question generation model with named entities as candidate answers. This allows us to automatically synthesize an unlimited number of question-answer pairs. We then propose an approach designed to leverage such multi-reference annotations, and demonstrate its advantages over the standard training and decoding strategies used in question generation. An experimental evaluation on synthetic, as well as manually annotated data shows that our approach can be used in creating a single generative model that produces a diverse set of question-answer pairs per input sentence.