bluuebunny/crossref_metadata_2025 huggingface dataset:
Front-to-End Bidirectional Heuristic Search with Consistent Heuristics: Enumerating and Evaluating Algorithms and Bounds
Authors: Siag Lior , Shperberg Shahaf , Felner Ariel , Sturtevant Nathan
Published: August 2023

Recent research on bidirectional heuristic search (BiHS) is based on the must-expand pairs theory (MEP theory), which describes which pairs of nodes must be expanded during the search to guarantee the optimality of solutions. A separate line of research in BiHS has proposed algorithms that use lower bounds that are derived from consistent heuristics during search. This paper links these two directions, providing a comprehensive unifying view and showing that both existing and novel algorithms can be derived from the MEP theory. An extended set of bounds is formulated, encompassing both previously discovered bounds and new ones. Finally, the bounds are empirically evaluated by their contribution to the efficiency of the search
A Novel Learnable Interpolation Approach for Scale-Arbitrary Image Super-Resolution
Authors: Chao Jiahao , Zhou Zhou , Gao Hongfan , Gong Jiali , Zeng Zhenbing , Yang Zhengfeng
Published: August 2023

Deep convolutional neural networks (CNNs) have achieved unprecedented success in single image super-resolution over the past few years. Meanwhile, there is an increasing demand for single image super-resolution with arbitrary scale factors in real-world scenarios. Many approaches adopt scale-specific multi-path learning to cope with multi-scale super-resolution with a single network. However, these methods require a large number of parameters. To achieve a better balance between the reconstruction quality and parameter amounts, we proposes a learnable interpolation method that leverages the advantages of neural networks and interpolation methods to tackle the scale-arbitrary super-resolution task. The scale factor is treated as a function parameter for generating the kernel weights for the learnable interpolation. We demonstrate that the learnable interpolation builds a bridge between neural networks and traditional interpolation methods. Experiments show that the proposed learnable interpolation requires much fewer parameters and outperforms state-of-the-art super-resolution methods.
On the Complexity of Counterfactual Reasoning
Authors: Han Yunqiu , Chen Yizuo , Darwiche Adnan
Published: August 2023

We study the computational complexity of counterfactual reasoning in relation to the complexity of associational and interventional reasoning on structural causal models (SCMs). We show that counterfactual reasoning is no harder than associational or interventional reasoning on fully specified SCMs in the context of two computational frameworks. The first framework is based on the notion of treewidth and includes the classical variable elimination and jointree algorithms. The second framework is based on the more recent and refined notion of causal treewidth which is directed towards models with functional dependencies such as SCMs. Our results are constructive and based on bounding the (causal) treewidth of twin networks---used in standard counterfactual reasoning that contemplates two worlds, real and imaginary---to the (causal) treewidth of the underlying SCM structure. In particular, we show that the latter (causal) treewidth is no more than twice the former plus one. Hence, if associational or interventional reasoning is tractable on a fully specified SCM then counterfactual reasoning is tractable too. We extend our results to general counterfactual reasoning that requires contemplating more than two worlds and discuss applications of our results to counterfactual reasoning with partially specified SCMs that are coupled with data. We finally present empirical results that measure the gap between the complexities of counterfactual reasoning and associational/interventional reasoning on random SCMs.
Approximate Inference in Logical Credal Networks
Authors: Marinescu Radu , Qian Haifeng , Gray Alexander , Bhattacharjya Debarun , Barahona Francisco , Gao Tian , Riegel Ryan
Published: August 2023

The Logical Credal Network or LCN is a recent probabilistic logic designed for effective aggregation and reasoning over multiple sources of imprecise knowledge. An LCN specifies a set of probability distributions over all interpretations of a set of logical formulas for which marginal and conditional probability bounds on their truth values are known. Inference in LCNs involves the exact solution of a non-convex non-linear program defined over an exponentially large number of non-negative real valued variables and, therefore, is limited to relatively small problems. In this paper, we present ARIEL -- a novel iterative message-passing scheme for approximate inference in LCNs. Inspired by classical belief propagation for graphical models, our method propagates messages that involve solving considerably smaller local non-linear programs. Experiments on several classes of LCNs demonstrate clearly that ARIEL yields high quality solutions compared with exact inference and scales to much larger problems than previously considered.
Quantifying Consistency and Information Loss for Causal Abstraction Learning
Authors: Zennaro Fabio Massimo , Turrini Paolo , Damoulas Theodoros
Published: August 2023

Structural causal models provide a formalism to express causal relations between variables of interest. Models and variables can represent a system at different levels of abstraction, whereby relations may be coarsened and refined according to the need of a modeller.

However, switching between different levels of abstraction requires evaluating a trade-off between the consistency and the information loss among different models.

In this paper we introduce a family of interventional measures that an agent may use to evaluate such a trade-off. We consider four measures suited for different tasks, analyze their properties, and propose algorithms to evaluate and learn causal abstractions. Finally, we illustrate the flexibility of our setup by empirically showing how different measures and algorithmic choices may lead to different abstractions.
Evaluating Human-AI Interaction via Usability, User Experience and Acceptance Measures for MMM-C: A Creative AI System for Music Composition
Authors: Bougueng Tchemeube Renaud , Ens Jeffrey , Plut Cale , Pasquier Philippe , Safi Maryam , Grabit Yvan , Rolland Jean-Baptiste
Published: August 2023

With the rise of artificial intelligence (AI), there has been increasing interest in human-AI co-creation in a variety of artistic domains including music as AI-driven systems are frequently able to generate human-competitive artifacts. Now, the implications of such systems for the musical practice are being investigated. This paper reports on a thorough evaluation of the user adoption of the Multi-Track Music Machine (MMM) as a minimal co-creative AI tool for music composers. To do this, we integrate MMM into Cubase, a popular Digital Audio Workstation (DAW), by producing a "1-parameter" plugin interface named MMM-Cubase, which enables human-AI co-composition. We conduct a 3-part mixed method study measuring usability, user experience and technology acceptance of the system across two groups of expert-level composers: hobbyists and professionals. Results show positive usability and acceptance scores. Users report experiences of novelty, surprise and ease of use from using the system, and limitations on controllability and predictability of the interface when generating music. Findings indicate no significant difference between the two user groups.
Q&A: Query-Based Representation Learning for Multi-Track Symbolic Music re-Arrangement
Authors: Zhao Jingwei , Xia Gus , Wang Ye
Published: August 2023

Music rearrangement is a common music practice of reconstructing and reconceptualizing a piece using new composition or instrumentation styles, which is also an important task of automatic music generation. Existing studies typically model the mapping from a source piece to a target piece via supervised learning. In this paper, we tackle rearrangement problems via self-supervised learning, in which the mapping styles can be regarded as conditions and controlled in a flexible way. Specifically, we are inspired by the representation disentanglement idea and propose Q&A, a query-based algorithm for multi-track music rearrangement under an encoder-decoder framework. Q&A learns both a content representation from the mixture and function (style) representations from each individual track, while the latter queries the former in order to rearrange a new piece. Our current model focuses on popular music and provides a controllable pathway to four scenarios: 1) re-instrumentation, 2) piano cover generation, 3) orchestration, and 4) voice separation. Experiments show that our query system achieves high-quality rearrangement results with delicate multi-track structures, significantly outperforming the baselines.
For Women, Life, Freedom: A Participatory AI-Based Social Web Analysis of a Watershed Moment in Iran's Gender Struggles
Authors: Khorramrouz Adel , Dutta Sujan , KhudaBukhsh Ashiqur R.
Published: August 2023

In this paper, we present a computational analysis of the Persian language Twitter discourse with the aim to estimate the shift in stance toward gender equality following the death of Mahsa Amini in police custody. We present an ensemble active learning pipeline to train a stance classifier. Our novelty lies in the involvement of Iranian women in an active role as annotators in building this AI system. Our annotators not only provide labels, but they also suggest valuable keywords for more meaningful corpus creation as well as provide short example documents for a guided sampling step. Our analyses indicate that Mahsa Amini's death triggered polarized Persian language discourse where both fractions of negative and positive tweets toward gender equality increased. The increase in positive tweets was slightly greater than the increase in negative tweets. We also observe that with respect to account creation time, between the state-aligned Twitter accounts and pro-protest Twitter accounts, pro-protest accounts are more similar to baseline Persian Twitter activity.
GreenFlow: A Computation Allocation Framework for Building Environmentally Sound Recommendation System
Authors: Lu Xingyu , Liu Zhining , Guan Yanchu , Zhang Hongxuan , Zhuang Chenyi , Ma Wenqi , Tan Yize , Gu Jinjie , Zhang Guannan
Published: August 2023

Given the enormous number of users and items, industrial cascade recommendation systems (RS) are continuously expanded in size and complexity to deliver relevant items, such as news, services, and commodities, to the appropriate users. In a real-world scenario with hundreds of thousands requests per second, significant computation is required to infer personalized results for each request, resulting in a massive energy consumption and carbon emission that raises concern.

This paper proposes GreenFlow, a practical computation allocation framework for RS, that considers both accuracy and carbon emission during inference. For each stage (e.g., recall, pre-ranking, ranking, etc.) of a cascade RS, when a user triggers a request, we define two actions that determine the computation: (1) the trained instances of models with different computational complexity; and (2) the number of items to be inferred in the stage. We refer to the combinations of actions in all stages as action chains. A reward score is estimated for each action chain, followed by dynamic primal-dual optimization considering both the reward and computation budget. Extensive experiments verify the effectiveness of the framework, reducing computation consumption by 41% in an industrial mobile application while maintaining commercial revenue. Moreover, the proposed framework saves approximately 5000kWh of electricity and reduces 3 tons of carbon emissions per day.
AudioQR: Deep Neural Audio Watermarks For QR Code
Authors: Qu Xinghua , Yin Xiang , Wei Pengfei , Lu Lu , Ma Zejun
Published: August 2023

Image-based quick response (QR) code is frequently used, but creates barriers for the visual impaired people. With the goal of ``AI for good", this paper proposes the AudioQR, a barrier-free QR coding mechanism for the visually impaired population via deep neural audio watermarks. Previous audio watermarking approaches are mainly based on handcrafted pipelines, which is less secure and difficult to apply in large-scale scenarios. In contrast, AudioQR is the first comprehensive end-to-end pipeline that hides watermarks in audio imperceptibly and robustly. To achieve this, we jointly train an encoder and decoder, where the encoder is structured as a concatenation of transposed convolutions and multi-receptive field fusion modules. Moreover, we customize the decoder training with a stochastic data augmentation chain to make the watermarked audio robust towards different audio distortions, such as environment background, room impulse response when playing through the air, music surrounding, and Gaussian noise. Experiment results indicate that AudioQR can efficiently hide arbitrary information into audio without introducing significant perceptible difference. Our code is available at https://github.com/xinghua-qu/AudioQR.
A Quantitative Game-theoretical Study on Externalities of Long-lasting Humanitarian Relief Operations in Conflict Areas
Authors: Xiao Kaiming , Chen Haiwen , Huang Hongbin , Liu Lihua , Wu Jibing
Published: August 2023

Humanitarian relief operations are often accompanied by regional conflicts around the globe, at risk of deliberate, persistent and unpredictable attacks. However, the long-term channeling of aid resources into conflict areas may influence subsequent patterns of violence and expose local communities to new risks. In this paper, we quantitatively analyze the potential externalities associated with long-lasting humanitarian relief operations based on game-theoretical modeling and online planning approaches. Specifically, we first model the problem of long-lasting humanitarian relief operations in conflict areas as an online multi-stage rescuer-and-attacker interdiction game in which aid demands are revealed in an online fashion. Both models of single-source and multiple-source relief supply policy are established respectively, and two corresponding near-optimal online algorithms are proposed. In conjunction with a real case of anti-Ebola practice in conflict areas of DR Congo, we find that 1) long-lasting humanitarian relief operations aiming alleviation of crises in conflict areas can lead to indirect funding of local rebel groups; 2) the operations can activate the rebel groups to some extent, as evidenced by the scope expansion of their activities. Furthermore, the impacts of humanitarian aid intensity, frequency and supply policies on the above externalities are quantitatively analyzed, which will provide enlightening decision-making support for the implementation of related operations in the future.
Optimization-driven Demand Prediction Framework for Suburban Dynamic Demand-Responsive Transport Systems
Authors: Zigrand Louis , Wolfler Calvo Roberto , Traversi Emiliano , Alizadeh Pegah
Published: August 2023

Demand-Responsive Transport (DRT) has grown over the last decade as an ecological solution to both metropolitan and suburban areas. It provides a more efficient public transport service in metropolitan areas and satisfies the mobility needs in sparse and heterogeneous suburban areas. Traditionally, DRT operators build the plannings of their drivers by relying on myopic insertion heuristics that do not take into account the dynamic nature of such a service. We thus investigate in this work the potential of a Demand Prediction Framework used specifically to build more flexible routes within a Dynamic Dial-a-Ride Problem (DaRP) solver. We show how to obtain a Machine Learning forecasting model that is explicitly designed for optimization purposes. The prediction task is further complicated by the fact that the historical dataset is significantly sparse. We finally show how the predicted travel requests can be integrated within an optimization scheme in order to compute better plannings at the start of the day. Numerical results support the fact that, despite the data sparsity challenge as well as the optimization-driven constraints that result from the DaRP model, such a look-ahead approach can improve up to 3.5% the average insertion rate of an actual DRT service.
Certified CNF Translations for Pseudo-Boolean Solving (Extended Abstract).
Authors: Gocht Stephan , Martins Ruben , Nordström Jakob , Oertel Andy
Published: August 2023

The dramatic improvements in Boolean satisfiability (SAT) solving since the turn of the millennium have made it possible to leverage conflict-driven clause learning (CDCL) solvers for many combinatorial problems in academia and industry, and the use of proof logging has played a crucial role in increasing the confidence that the results these solvers produce are correct. However, the fact that SAT proof logging is performed in conjunctive normal form (CNF) clausal format means that it has not been possible to extend guarantees of correctness to the use of SAT solvers for more expressive combinatorial paradigms, where the first step is an unverified translation of the input to CNF.

In this work, we show how cutting-planes-based reasoning can provide proof logging for solvers that translate pseudo-Boolean (a.k.a. 0-1 integer linear) decision problems to CNF and then run CDCL. We are hopeful that this is just a first step towards providing a unified proof logging approach that will extend to maximum satisfiability (MaxSAT) solving and pseudo-Boolean optimization in general.
Finite-Trace Analysis of Stochastic Systems with Silent Transitions (Extended Abstract)
Authors: Leemans Sander J.J. , Maggi Fabrizio M. , Montali Marco
Published: August 2023

In this paper, we summarise the main technical results obtained for specification probability. That is, we compute the probability that if a bounded stochastic Petri net produces a trace, that trace satisfies a given specification.
Unsupervised Deep Subgraph Anomaly Detection (Extended Abstract)
Authors: Zhang Zheng , Zhao Liang
Published: August 2023

Effectively mining anomalous subgraphs in networks is crucial for various applications, including disease outbreak detection, financial fraud detection, and activity monitoring in social networks. However, identifying anomalous subgraphs poses significant challenges due to their complex topological structures, high-dimensional attributes, multiple notions of anomalies, and the vast subgraph space within a given graph. Classical shallow models rely on handcrafted anomaly measure functions, limiting their applicability when prior knowledge is unavailable. Deep learning-based methods have shown promise in detecting node-level, edge-level, and graph-level anomalies, but subgraph-level anomaly detection remains under-explored due to difficulties in subgraph representation learning, supervision, and end-to-end anomaly quantification. To address these challenges, this paper introduces a novel deep framework named Anomalous Subgraph Autoencoder (AS-GAE). AS-GAE leverages an unsupervised and weakly supervised approach to extract anomalous subgraphs. It incorporates a location-aware graph autoencoder to uncover anomalous areas based on reconstruction mismatches and introduces a supermodular graph scoring function module to assign meaningful anomaly scores to subgraphs within the identified anomalous areas. Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness of our proposed method.
DFVSR: Directional Frequency Video Super-Resolution via Asymmetric and Enhancement Alignment Network
Authors: Dong Shuting , Lu Feng , Wu Zhe , Yuan Chun
Published: August 2023

Recently, techniques utilizing frequency-based methods have gained significant attention, as they exhibit exceptional restoration capabilities for detail and structure in video super-resolution tasks. However, most of these frequency-based methods mainly have three major limitations: 1) insufficient exploration of object motion information, 2) inadequate enhancement for high-fidelity regions, and 3) loss of spatial information during convolution. In this paper, we propose a novel network, Directional Frequency Video Super-Resolution (DFVSR), to address these limitations. Specifically, we reconsider object motion from a new perspective and propose Directional Frequency Representation (DFR), which not only borrows the property of frequency representation of detail and structure information but also contains the direction information of the object motion that is extremely significant in videos. Based on this representation, we propose a Directional Frequency-Enhanced Alignment (DFEA) to use double enhancements of task-related information for ensuring the retention of high-fidelity frequency regions to generate the high-quality alignment feature. Furthermore, we design a novel Asymmetrical U-shaped network architecture to progressively fuse these alignment features and output the final output. This architecture enables the intercommunication of the same level of resolution in the encoder and decoder to achieve the supplement of spatial information. Powered by the above designs, our method achieves superior performance over state-of-the-art models on both quantitative and qualitative evaluations.
Recent Advances in Direct Speech-to-text Translation
Authors: Xu Chen , Ye Rong , Dong Qianqian , Zhao Chengqi , Ko Tom , Wang Mingxuan , Xiao Tong , Zhu Jingbo
Published: August 2023

Recently, speech-to-text translation has attracted more and more attention and many studies have emerged rapidly. In this paper, we present a comprehensive survey on direct speech translation aiming to summarize the current state-of-the-art techniques. First, we categorize the existing research work into three directions based on the main challenges --- modeling burden, data scarcity, and application issues. To tackle the problem of modeling burden, two main structures have been proposed, encoder-decoder framework (Transformer and the variants) and multitask frameworks. For the challenge of data scarcity, recent work resorts to many sophisticated techniques, such as data augmentation, pre-training, knowledge distillation, and multilingual modeling. We analyze and summarize the application issues, which include real-time, segmentation, named entity, gender bias, and code-switching. Finally, we discuss some promising directions for future work.
Survey and Evaluation of Causal Discovery Methods for Time Series (Extended Abstract)
Authors: Assaad Charles K. , Devijver Emilie , Gaussier Eric
Published: August 2023

We introduce in this survey the major concepts, models, and algorithms proposed so far to infer causal relations from observational time series, a task usually referred to as causal discovery in time series. To do so, after a description of the underlying concepts and modelling assumptions, we present different methods according to the family of approaches they belong to: Granger causality, constraint-based approaches, noise-based approaches, score-based approaches, logic-based approaches, topology-based approaches, and difference-based approaches. We then evaluate several representative methods to illustrate the behaviour of different families of approaches. This illustration is conducted on both artificial and real datasets, with different characteristics. The main conclusions one can draw from this survey is that causal discovery in times series is an active research field in which new methods (in every family of approaches) are regularly proposed, and that no family or method stands out in all situations. Indeed, they all rely on assumptions that may or may not be appropriate for a particular dataset.
SAMBA: A Generic Framework for Secure Federated Multi-Armed Bandits (Extended Abstract)
Authors: Ciucanu Radu , Lafourcade Pascal , Marcadet Gael , Soare Marta
Published: August 2023

We tackle the problem of secure cumulative reward maximization in multi-armed bandits in a cross-silo federated learning setting. Under the orchestration of a central server, each data owner participating at the cumulative reward computation has the guarantee that its raw data is not seen by some other participant. We rely on cryptographic schemes and propose SAMBA, a generic framework for Secure federAted Multi-armed BAndits. We show that SAMBA returns the same cumulative reward as the non-secure versions of bandit algorithms, while satisfying formally proven security properties. We also show that the overhead due to cryptographic primitives is linear in the size of the input, which is confirmed by our implementation.
LION: Label Disambiguation for Semi-supervised Facial Expression Recognition with Progressive Negative Learning
Authors: Du Zhongjing , Jiang Xu , Wang Peng , Zhou Qizheng , Wu Xi , Zhou Jiliu , Wang Yan
Published: August 2023

Semi-supervised deep facial expression recognition (SS-DFER) has recently attracted rising research interest due to its more practical setting of abundant unlabeled data. However, there are two main problems unconsidered in current SS-DFER methods: 1) label ambiguity, i.e., given labels mismatch with facial expressions; 2) inefficient utilization of unlabeled data with low-confidence. In this paper, we propose a novel SS-DFER method, including a Label DIsambiguation module and a PrOgressive Negative Learning module, namely LION, to simultaneously address both problems. Specifically, the label disambiguation module operates on labeled data, including data with accurate labels (clear data) and ambiguous labels (ambiguous data). It first uses clear data to calculate prototypes for all the expression classes, and then re-assign a candidate label set to all the ambiguous data. Based on the prototypes and the candidate label set, the ambiguous data can be relabeled more accurately. As for unlabeled data with low-confidence, the progressive negative learning module is developed to iteratively mine more complete complementary labels, which can guide the model to reduce the association between data and corresponding complementary labels. Experiments on three challenging datasets show that our method significantly outperforms the current state-of-the-art approaches in SS-DFER and surpasses fully-supervised baselines. Code will be available at https://github.com/NUM-7/LION.
Mean-Semivariance Policy Optimization via Risk-Averse Reinforcement Learning (Extended Abstract)
Authors: Ma Xiaoteng , Ma Shuai , Xia Li , Zhao Qianchuan
Published: August 2023

Keeping risk under control is often more crucial than maximizing expected rewards in real-world decision-making situations, such as finance, robotics, autonomous driving, etc. The most natural choice of risk measures is variance, while it penalizes the upside volatility as much as the downside part. Instead, the (downside) semivariance, which captures negative deviation of a random variable under its mean, is more suitable for risk-averse proposes. This paper aims at optimizing the mean-semivariance (MSV) criterion in reinforcement learning w.r.t. steady reward distribution. Since semivariance is time-inconsistent and does not satisfy the standard Bellman equation, the traditional dynamic programming methods are inapplicable to MSV problems directly. To tackle this challenge, we resort to Perturbation Analysis (PA) theory and establish the performance difference formula for MSV. We reveal that the MSV problem can be solved by iteratively solving a sequence of RL problems with a policy-dependent reward function. Further, we propose two on-policy algorithms based on the policy gradient theory and the trust region method. Finally, we conduct diverse experiments from simple bandit problems to continuous control tasks in MuJoCo, which demonstrate the effectiveness of our proposed methods.
Memory-Limited Model-Based Diagnosis (Extended Abstract)
Authors: Rodler Patrick
Published: August 2023

Model-based diagnosis is a principled and broadly applicable AI-based approach to tackle debugging problems in a wide range of areas including software, knowledge bases, circuits, cars, and robots. Whenever the sound and complete computation of fault explanations in a given preference order (e.g., cardinality or probability) is required, all existing diagnosis algorithms suffer from an exponential space complexity. This can prevent their application on memory-restricted devices and for memory-intensive problem cases. As a remedy, we propose RBF-HS, a diagnostic search based on Korf’s seminal RBFS algorithm which can enumerate an arbitrary ﬁxed number of fault explanations in best-ﬁrst order within linear space bounds, without sacriﬁcing other desirable properties. Evaluations on real-world diagnosis cases show that RBF-HS, when used to compute minimum-cardinality fault explanations, in most cases saves substantial space while requiring only reasonably more or even less time than Reiter’s HS-Tree, one of the most inﬂuential diagnostic algorithms with the same properties.
Incremental Event Calculus for Run-Time Reasoning (Extended Abstract)
Authors: Tsilionis Efthimis , Artikis Alexander , Paliouras Georgios
Published: August 2023

We present a system for online, incremental composite event recognition. In streaming environments, the usual case is for data to arrive with a (variable) delay from, and to be revised by, the underlying sources. We propose RTEC_inc, an incremental version of RTEC, a composite event recognition engine with formal, declarative semantics, that has been shown to scale to several real-world data streams. RTEC deals with delayed arrival and revision of events by computing all queries from scratch. This is often inefficient since it results in redundant computations. Instead, RTEC_inc deals with delays and revisions in a more efficient way, by updating only the affected queries. We compare RTEC_inc and RTEC experimentally using real-world and synthetic datasets. The results are compatible with our complexity analysis and show that RTEC_inc outperforms RTEC in many practical cases.
Ethical By Designer - How to Grow Ethical Designers of Artificial Intelligence (Extended Abstract)
Authors: Vanhée Loïs , Borit Melania
Published: August 2023

Ethical concerns regarding Artificial Intelligence technology have fueled discussions around the ethics training received by its designers. Training designers for ethical behaviour, understood as habitual application of ethical principles in any situation, can make a significant difference in the practice of research, development, and application of AI systems. Building on interdisciplinary knowledge and practical experience from computer science, moral psychology, and pedagogy, we propose a functional way to provide this training.
Efficient and Equitable Deployment of Mobile Vaccine Distribution Centers
Authors: Chen Da Qi , Li Ann , Li George Z. , Marathe Madhav , Srinivasan Aravind , Tsepenekas Leonidas , Vullikanti Anil
Published: August 2023

Vaccines have proven to be extremely effective in preventing the spread of COVID-19 and potentially ending the pandemic. Lack of access caused many people not getting vaccinated early, so states such as Virginia deployed mobile vaccination sites in order to distribute vaccines across the state. Here we study the problem of deciding where these facilities should be placed and moved over time in order to minimize the distance each person needs to travel in order to be vaccinated. Traditional facility location models for this problem fail to incorporate the fact that our facilities are mobile (i.e., they can move over time). To this end, we instead model vaccine distribution as the Dynamic k-Supplier problem and give the first approximation algorithms for this problem. We then run extensive simulations on real world datasets to show the efficacy of our methods. In particular, we find that natural baselines for Dynamic k-Supplier cannot take advantage of the mobility of the facilities, and perform worse than non-mobile k-Supplier algorithms.
Towards Formal Verification of Neuro-symbolic Multi-agent Systems
Authors: Kouvaros Panagiotis
Published: August 2023

This paper outlines some of the key methods we developed towards the formal verification of multi- agent systems, covering both symbolic and connectionist systems. It discusses logic-based methods for the verification of unbounded multi-agent systems (i.e., systems composed of an arbitrary number of homogeneous agents, e.g., robot swarms), optimisation approaches for establishing the robustness of neural network models, and methods for analysing properties of neuro-symbolic multi-agent systems.
A Pathway Towards Responsible AI Generated Content
Authors: Lyu Lingjuan
Published: August 2023

AI Generated Content (AIGC) has received tremendous attention within the past few years, with content ranging from image, text, to audio, video, etc. Meanwhile, AIGC has become a double-edged sword and recently received much criticism regarding its responsible usage. In this article, we focus on three main concerns that may hinder the healthy development and deployment of AIGC in practice, including risks from privacy; bias, toxicity, misinformation; and intellectual property (IP). By documenting known and potential risks, as well as any possible misuse scenarios of AIGC, the aim is to sound the alarm of potential risks and misuse, help society to eliminate obstacles, and promote the more ethical and secure deployment of AIGC.
AI Planning for Hybrid Systems
Authors: Scala Enrico
Published: August 2023

When planning the tasks of some physical entities that need to perform actions in the world (e.g., a Robot) it is necessary to take into account quite complex models for ensuring that the plan is actually executable. Indeed the state of these systems evolves according to potentially non-linear dynamics where interdependent discrete and continuous changes happen over the entire course of the task. Systems of this kind are typically compactly represented in planning using languages mixing propositional logic and mathematics. However, these languages are still poorly understood and exploited. What are the difficulties for planning in these settings? How can we build systems that can scale up over realistically sized problems? What are the domains which can benefit from these languages? This short paper shows the main two ingredients that are needed to build a heuristic search planner, outline the main impact that such techniques have on application, and provide some open challenges. These models and relative planners hold the promise to deliver explainable AI solutions that do not rely on large amounts of data.
Sources and Information Reliability Measures
Authors: Elsaesser Quentin
Published: August 2023

More and more information is shared on the web or on social media platforms, and the information provided can be conflicting. In this case, we need to decide which information is reliable and should be taken into account. We want to define measures of reliability for each source that provides information, but also to find the truth among the conflicting information, and propose properties of these measures.
Human-AI Collaboration in Recruitment and Selection
Authors: Natarajan Neil
Published: August 2023

My research focuses on using algorithmic systems alongside human collaborators to outperform either humans or machines individually. I specialize in human-machine collaboration in recruiting and selecting talented groups of people.
A Framework for Participatory Budgeting with Resource Pooling
Authors: Vollen Jeremy
Published: August 2023

Participatory budgeting (PB) is an implementation of direct democracy that allows members of a community to make collective budgeting decisions. However, existing PB processes rely upon a pre-defined central budget. We introduce a framework for pooling resources in addition to selecting projects, which we call PB with Resource Pooling. We motivate the key characteristics of this model and the basic properties we would like a mechanism to satisfy. We summarize results and discuss interesting questions related to our framework.
VideoMaster: A Multimodal Micro Game Video Recreator
Authors: Yu Yipeng , Chen Xiao , Zhan Hui
Published: August 2023

To free human from laborious video production, this paper proposes the building of VideoMaster, a multimodal system equipped with four capabilities: highlight extraction, video describing, video dubbing and video editing. It extracts interesting episodes from long game videos, generates subtitles for each episode, reads the subtitles through synthesized speech, and finally re-creates a better short video through video editing. Notably, VideoMaster takes a combination of deep learning and traditional computer vision techniques to extract highlights with fine-to-coarse labels, utilizes a novel framework named PCSG-v (probabilistic context sensitive grammar for video) for video description generation, and imitates a target speaker's voice to read the description. To the best of our knowledge, VideoMaster is the first multimedia system that can automatically produce product-level micro-videos without heavy human annotation.
Ciência Cidadã e a Educação Básica
Authors: Pacheco Jailson , Reis Rodrigo Arantes , Joucoski Emerson , Low Russanne
Published: August 2023

A ciência cidadã consiste no envolvimento de cidadãos como voluntários na coleta e análise de dados, definição de objetivos e divulgação nas pesquisas científicas das mais variadas áreas do conhecimento. Dessa forma, ela pode se tornar uma valiosa ferramenta a ser utilizada por professores no ensino de ciências pensando no currículo da Educação Básica. Além disso, ela pode atuar na promoção da alfabetização científica e do conhecimento sobre a natureza da ciência. Este trabalho objetiva discutir teoricamente ciência cidadã, suas diferentes tipologias e modelos existentes, além de apresentar alguns projetos nacionais e internacionais com atuação na rede básica de ensino. Nossas considerações finais apontam o potencial da ciência cidadã na Educação Básica, sobretudo no ensino de ciências, relacionando-a às competências e habilidades presentes na BNCC.
Potential of Impatiens walleriana Hook.f. as medicinal herb and otherwise - An update
Authors: Kulkarni Gauri
Published: August 2023

The healing potential and usefulness of plants is known since historic times. Impatiens walleriana Hook.f. (Family - Balsaminaceae) is the most cultivated plant from the genus “Impatiens”. It is explored by floriculture and horticulture industries. It has culinary uses with the potential to be researched by the food industry. Many modern medicines are plant-derived and I. walleriana has the potential to be used as medicine as backed by ethnopharmacological data. I. walleriana finds a number of traditional/folk medicinal claims. All parts of the plant and in varied formulations are used traditionally to treat various ailments. The data reviewed shows its safety in uses and its nontoxic nature. Among the Impatiens species I. walleriana is the less researched one. Few studies pertaining to its antioxidant, antimicrobial and antitumor activities are reported. Phytochemical constituents have been identified and reported by some researchers. Following a detailed study of published information and reported activities current review on the one hand summarizes the research work that has been carried out on the other hand gives food for thought for researchers to undertake more work in the direction of finding its usefulness using scientific methodologies and establishing the ethnic claims.
Dominio afectivo en referentes curriculares de matemáticas en Colombia durante el periodo 1998-2018
Authors: Miranda Vargas Wildebrando
Published: August 2023

Se presenta una revisión sobre la presencia del dominio afectivo en las políticas curriculares de matemáticas en Colombia. Se analizaron siete documentos curriculares que el Ministerio de Educación Nacional (MEN) y el Instituto Colombiano para la Evaluación de la Educación (ICFES) publicaron entre 1998 y 2018. El análisis se centró en responder la pregunta ¿Se contempla en los documentos curriculares de matemáticas de Colombia el dominio afectivo? Los resultados más llamativos señalan, por un lado, la poca presencia del dominio afectivo en algunos de los documentos curriculares revisados, y por el otro, un tratamiento débil, cuando lo hay, que muestran una tendencia del currículo nacional hacia aspectos cognitivos.
VALIDAÇÃO DE SIMULADOR DE EMISSÕES OTOACÚSTICAS PARA O ENSINO EM AUDIOLOGIA
Authors: Massih Cristiane Guerreiro Pereira Abdul , Jorge Marília Santa Brígida Silva , Marinho Christiane de Carvalho , Silva Renata Cunha , De Lima Anderson Bentes , Kietzer Kátia Simone
Published: August 2023

O teste Emissões Otoacústicas (EOA) é parte da avaliação Audiológica para detecção precoce da surdez na infância. Desse modo, o aprendizado das EOA na graduação do curso de Fonoaudiologia é fundamental conforme a Diretriz Curricular Nacional (DCN). As diretrizes educacionais sugerem a inclusão de tecnologias de informação no ensino da graduação para melhor desempenho dos futuros profissionais da saúde. Objetivo: Validar o protótipo simulador OTOBABY para o Teste de Emissões Otoacústicas como ferramenta de ensino em Fonoaudiologia. Metodologia: Trata-se de uma pesquisa metodológica, experimental, com a participação de dez fonoaudiólogos professores e/ou preceptores, denominados juízes e 20 alunos de graduação de Fonoaudiologia em Belém do Pará/Brasil. Os juízes responderam a um questionário de validação de Escala Likert sobre a usabilidade e efetividade do simulador OTOBABY como ferramenta de ensino. Os alunos participaram de um curso de capacitação com o uso do protótipo e foram avaliados com checklists antes e depois do curso para a verificação da eficácia do simulador como instrumento de ensino. Os dados foram analisados  estatisticamente com o teste de Wilcoxon. Resultados: O simulador alcançou 98% de aprovação como ferramenta de ensino para as EOA pelos juízes. Quanto a habilidade dos alunos, observou-se que era de 60,3%, antes do curso, obtendo-se um desempenho de 97,7% após curso com o OTOBABY. Conclusão: Conclui-se que o protótipo simulador OTOBABY foi aprovado e validado como um instrumento facilitador do aprendizado das Emissões Otoacústicas no ensino da prática Audiológica.
AVALIAÇÃO DO ALFABETISMO EM SAÚDE BUCAL DOS RESPONSÁVEIS POR CRIANÇAS E ADOLESCENTES ATENDIDOS NA CLÍNICA ESCOLA DE ODONTOLOGIA UNINASSAU RECIFE
Authors: De Macêdo Thuanny Silva , Silva Alícia Marcelly Souza de Mendonça , Da Silva Ana Cláudia , De Melo Beatriz  Lira Tavares , Silva Bruna Santana , Santos Giovana Pereira de Holanda , Santos Maria  Eduarda Ferreira da Silva , Melo Mikael Maik dos Santos , Barros Raquel Carvalho de Paula Ferreira , Barros Raquel Carvalho de Paula Ferreira , dos Santos Rute Oliveira
Published: August 2023

Objetivo: Analisar a influência do nível de alfabetismo dos pais e/ou responsáveis na saúde bucal de crianças e adolescentes atendidos na Clínica Escola de Odontologia do Centro Universitário Maurício de Nassau, em Recife. Métodos: Estudo de campo de desenvolvimento transversal e natureza observacional realizado na Clínica Escola de Odontologia do Centro Universitário Maurício de Nassau, no período de janeiro a abril de 2023. A amostra foi composta por 32 pais e/ou responsáveis. Os instrumentos de coleta utilizados foram: ficha específica, Índice de Placa Visível (IPV) modificado e Índice de dentes cariados, perdidos e obturados (CPO-D)/ Índice de dentes cariados, extraídos e obturados (ceo-d), Critério de Classificação Econômica (CCEB) e Rapid Estimate of Adult Literacy in Dentistry – Brazil (REALD-B). Resultados: Observou-se uma predominância do acompanhamento materno nas consultas odontológicas de suas crianças/adolescentes. Quanto a procura por atendimento odontológico para seus filhos, a maioria afirmou buscar em caso de prevenção, diferente do resultado para o atendimento próprio, que prevaleceu os casos de tratamento. Ademais, ao associar o resultado obtido pelo CCEB e teste REALD-B, obteve-se que dos 19 participantes classificados como “ruim”, ao acertarem entre 0 a 18 palavras, 18 são da Classe “C-D,E”, com nível econômico médio-baixo. A inspeção de saúde bucal das crianças/adolescentes foi considerada razoável, com a média de 2 a 3 dentes acometidos, no entanto a faixa etária predominante da amostra variou entre 3 e 10 anos, ratificando o ínicio precoce dos problemas de saúde bucal. Conclusão: Foi observado uma associação entre o nível de alfabetismo em saúde bucal dos pais e/ou responsáveis e as condições clínicas de saúde bucal de suas crianças/adolescentes.
AVALIAÇÃO DA INFLUÊNCIA DA AGITAÇÃO ULTRASSÔNICA NA RESISTÊNCIA DE UNIÃO DE DOIS MATERIAIS REPARADORES UTILIZADOS PARA SELAR PERFURAÇÕES NA FURCA
Authors: Carneiro Liziane Vasconcelos , Aguiar Bernardo Almeida , Cruz Suyane Maria Luna , Pinheiro Júnior Elilton Cavalcante , Vivacqua-Gomes Nilton , De Vasconcelos Bruno Carvalho
Published: August 2023

Avaliou-se a influência da utilização da agitação ultrassônica na resistência de união à dentina, por meio de push-out, proporcionada pelos cimentos MTA-Angelus branco (MTA) e Biodentine (BD), empregados no selamento de perfurações de furca. Foram utilizados 48 primeiros ou segundos molares inferiores, com raízes não fusionadas, distantes cerca de 2 mm entre si na porção cervical das mesmas. Os dentes foram acessados e, em seguida, as raízes foram seccionadas transversalmente abaixo da junção cemento-esmalte. Os dentes foram montados em blocos de silicona de adição e perfurações de furca realizadas em seus assoalhos paralelamente ao longo eixo dos dentes e perpendiculares aos assoalhos dentais. A amostra foi dividida em quatro grupos (n = 12) em função do material reparador e da utilização ou não da agitação ultrassônica (AU). Quando empregada, a AU foi aplicada em 2 ciclos de 20 segundos por meio de inserto cônico liso acionado por ultrassom piezoelétrico. Concluídos os selamentos eles foram protegidos com cimento de ionômero de vidro e os dentes imersos em PBS. Decorridos 7 dias discos com 1 mm de altura foram retirados dos assoalhos dentais com auxílio de cortadora metalográfica e levados à máquina de ensaio universal. Os resultados expressos em MPa foram submetidos aos testes ANOVA dois fatores e de Bonferroni. Os maiores valores de resistência obtidos foram no grupo Biodentine com ultrassom (29,41 MPa), seguidos em ordem decrescente pelos grupos Biodentine sem ultrassom; MTA com ultrassom e MTA sem ultrassom (3,72 MPa). Nas condições do estudo concluiu-se que o BD apresentou maior resistência de união à dentina radicular; ainda, que a agitação ultrassônica influenciou positivamente na resistência de união do material à dentina.
ANALISIS POTENSI ENERGI LISTRIK DI GEDUNG UMPAR MENGGUNAKAN PANEL SURYA 100WP
Authors: Y Alauddin , Amir Ashadi , Asrul Asrul , Sahidin Sudirman
Published: August 2023

Electricity plays a very important role in everyday life. Most human activities are related to the use of electrical energy, including in the Campus environment. Technological advances have resulted in an increase in the consumption of electrical energy and this has resulted in reduced reserves of fossil energy sources as a result of being used up and causing emissions that can affect the global climate. The energy transition from the use of fossil energy to new and renewable energy is one solution to overcome this. Solar Power Plant (PLTS) is an alternative energy solution that is environmentally friendly that can be developed on campus. This is also supported by Indonesia's geographical location which has a very high intensity of sunlight. In this study, an analysis of the potential of electrical energy generated at Universitas Muhammadiyah Parepare Building was carried out. The research was conducted by measuring the surface voltage and temperature of solar panels for 20 days. The test was carried out for 7 hours from 10.00 WITA to 16.00 WITA. Voltage and temperature measurements were carried out in 1 hour intervals. The test results show that the average voltage value generated by solar panels is 21.31 volts. The highest average voltage value was generated at 13.00 WITA, which was 21.82 volts with an average surface temperature of 37.79 oC