bluuebunny/crossref_metadata_2025 huggingface dataset:
Methods and software for capturing three-dimensional digital images using spatial demodulation
Authors: Borovytsky V. M. , Gudz О. E.
Published: September 2023

The paper proposes the generalized technique of demodulation, which can be applied for capturing digital three-dimensional images in optical microscopes. This technique allows the calculation of the contrast of harmonic spatial modulation with any values of phase angles. The input data is a stack of digital images with harmonic spatial modulation. Three digital images with different phases of modulation captured in the same focus position are processed to get one data set about the contrast distribution. This data makes it possible to compose the three-dimensional images by the selection of the maximal values of the contrast in three-dimensional space. The paper contains the analytical solution of the system with three linear equations that helps to identify the principal parameters of harmonic spatial modulation, including its average value and contrast. As a result, the expression for contrast calculation is present in an analytical form. This expression is valid for any modulation phase angles that do not coincide. The paper contains MathCAD modeling, which illustrates the procedure of composing the three-dimensional images.

The modeling confirms the correctness of described calculations and demonstrates the built of the three-dimensional surface image as a composition of the contrast distribution map and the corresponded height map. The advantage of the proposed technique is the possibility to apply the economical units for the formation of a harmonious spatial modulation. These units can be installed on most optical microscopes with digital cameras and motorized focusing drives without purchasing expensive commercial software. It significantly reduces the price of transforming these optical microscopes into instruments for capturing digital three-dimensional images.
Comparison of science teachers' thoughts on face-to-face and online STEM-based activities
Authors: ÖNER ARMAĞAN Fulya , METİN Mustafa , ATALAY Reyhan
Published: September 2023

This study aims to compare the views of science teachers on face-to-face and online STEM-based activities. The study was carried out with phenomenology; one of the qualitative research methods. Five science teachers (three female, two male) participated in the study. Data were collected with a structured interview form. Since they were in the distance education process, audio and video recordings were taken during the interviews. Descriptive and content analysis methods were used to analyze the data. In the interviews, STEM-based activities of science teachers; It is seen that they mostly associate with "Physical Events" and "Science, Engineering and Entrepreneurship" from the learning fields. Teachers stated that it is easier to provide classroom management, group work, and collaboration in face-to-face STEM activities. However, in some large group studies, classroom management becomes difficult. They mentioned the physical exhaustion and the difficulty of completing the program. However, in STEM-based activities in the distance education process, the teachers stated that being in cooperation with the family is an advantage, and the problems in terms of materials, time, technological infrastructure, and mobile devices are disadvantages. According to the results of the research, it can be ensured that STEM activities can be better structured in face-to-face lessons than in the distance education process. Because in face-to-face education the characteristics of the students can be determined in advance and the course contents and teaching strategies can be prepared accordingly, communication problems (lack of technical infrastructure, internet speed problems, etc.) can be resolved.
Technology supported teaching implementations designed to improve critical thinking skills
Authors: GÜRSAN Serkan , TAPAN BROUTIN Menekşe Seden , İPEK Jale
Published: September 2023

This research evaluated the influence of technology-based teaching models on the development of critical thinking abilities in elementary school mathematics teacher candidates, as measured using Facione's (1990) criteria. The research was conducted with fourth-grade students studying in the Faculty of Education, Elementary Mathematics Education at a state university in the Marmara Region of Türkiye during the 2019-2020 Fall Semester. A case study research design was used to obtain a qualitative understanding of the participants' experiences. Two pre-service teachers were video-recorded throughout the research implementation process (5 weeks in total, from the 3rd week when the teaching model practices started to the 7th week when it ended). The dialogues in the video recording were analyzed using the descriptive analysis technique. Keywords and sentences describing the critical thinking sub-skills identified by Facione (1990) were observed to be included in the student dialogues, especially in the implementations of the designed teaching model that required the use of GeoGebra. These dialogues concluded that the proposed instructional model programs are consistent with the critical thinking competencies, include activities to use critical thinking skills, and can provide opportunities for developing critical thinking skills.
The views of associate degree students from child development on distance education after the earthquake
Authors: KOÇ Nuray
Published: September 2023

This study aims to investigate the satisfaction and experiences of associate degree students from the child development department on distance education after the earthquake in Türkiye. The research was carried out in a mixed method embedded design with the participation of 133 associate degree students who studied in the child development department in a vocational school affiliated with Bursa Uludağ University in the spring term of the 2022-2023 academic year. The quantitate data were collected through "The Distance Education Satisfaction Scale of University Students.” The qualitative data were obtained through a semi-structured interview with 20 students from the same study group. Descriptive statistics, Independent samples t-test, Mann-Whitney U test and Kruskal-Wallis H Test, and content analysis were used to analyze the data obtained. According to the findings, online education was less effective than face-to-face education due to the earthquake. The student’s satisfaction with distance education differs significantly depending on having technological tools. The students stated that they had difficulties due to the adverse psychological effects of the earthquake and the infrastructure problems. It was determined that distance education's most significant advantage is having a flexible environment and listening to the course recordings. However, they were disadvantaged in not making enough applications during the lessons in online education.
Paper-Based vs. Digitalized Glossaries in Laboratory Scripts
Authors: Koenen Jenna , Mariot Lars , Tiemann Rüdiger
Published: September 2023

Abstract: In the future, learning will be essentially characterized by the ability to regulate the learning process and monitor success independently from a teacher. The technical possibilities offer better access to learning contents, precise and more individualized feedback, and learning phases adapted precisely to the needs of the learner in terms of scope and pace. In this study, we investigate an important aspect of the digitization of teaching/learning processes using the example of laboratory scripts for chemistry students at university. The focus is on looking up terms and concepts in preparation for the lab internships, firstly in a paper-based glossary and secondly in a digital glossary. During a two-day study, a total of 16 students prepared for experiments on two topics with completely identical materials. We then studied the influence of content knowledge, motivation, and cognitive load. While all students show significant learning achievements, there are no significant differences between the groups. Furthermore, results show that pure digitization of information has no effect, despite the theoretically assumed advantages. 
Inhibición de la IL 23 en la psoriasis refractaria
Authors: Romo Cevallos Howard , Martínez Guillen María Mercedes
Published: September 2023

Comprender la inmunopatogénesis de la psoriasis ha llevado a nuevos avances en agentes terapéuticos. La IL-23, originada principalmente por las células dendríticas, conserva la diferenciación de las células T vírgenes a las células Th17, las células efectoras clave en la psoriasis. Los agentes terapéuticos dirigidos a esta citocina han demostrado eficacia clínica en la psoriasis en placas de moderada a grave. Guselkumab, es el primer anticuerpo humano contra la subunidad p40 del receptor IL-23 aprobado por la Administración de Alimentos y Medicamentos (FDA) de EE. UU. y la Agencia Europea de Medicamentos (EMA) para esta indicación en pacientes adultos (2017). De igual manera ha sido aprobado para el tratamiento de artritis psoriásica en Japón (abril de 2018). Presentamos un caso de un hombre de 41 años de edad con diagnóstico de psoriasis con gran afectación de extensión de placas eritematosas descamativas, con afectación de superficie corporal del 68 %, con evolución de 7 años, que recibió múltiples tratamientos que incluyeron tópicos, sistémicos, fototerapia con respuesta poca satisfactoria. Por lo que se decide empezar tratamiento con Guselkumab, obteniéndose una respuesta clínica favorable, demostrándose así la eficacia y seguridad de este producto biológico.
The Effect of Quality of Work Life, Job Satisfaction, and Job Commitment on Employee Performance Through Work Motivation in BPJS Employment Employees in the DKI Jakarta Region in 2023
Authors: Rahman Irsyadi Abdur
Published: September 2023

Literature Review Article the Effect of Quality of Work Life, Job Satisfaction, Organisational Commitment, and Work Motivation on Employee Performance is a scientific article that aims to build a research hypothesis of the influence between variables that will be used in further research, within the scope of Human Resource Management science. The method of writing this Literature Review article is by library research method, which is sourced from online media such as Google Scholar, Mendeley and other academic online media. The results of this literature review article are: 1) Quality of Work Life affects Employee Performance; 2) Job Satisfaction affects Employee Performance; and 3) Organisational Commitment affects Employee Performance; 4) Work Motivation affects Employee Performance; 5) Quality of Life, Job Satisfaction, Organisational Commitment and Work Motivation affect Employee Performance.
FAKTOR-FAKTOR YANG MEMPENGARUHI KEAMANAN SISTEM INFORMASI: KEAMANAN INFORMASI, TEKNOLOGI INFORMASI DAN NETWORK (LITERATURE REVIEW SIM)
Authors: Nurul Shinta , Shynta Anggrainy  , Siska Aprelyani
Published: September 2023

Keamanan informasi menggambarkan usaha untuk   melindungi   komputer   dan   non   peralatan   komputer,  fasilitas,   data,   dan   informasi   dari penyalahgunaan   oleh   orang  yang  tidak   bertanggung jawab. Keamanan   informasi   dimaksudkan untuk mencapai kerahasiaan, ketersediaan, dan integritas di dalam sumber daya informasi dalam suatu perusahaan. Keamanan Sistem informasi terdiri atas perlindungan harian, yang disebut keamanan informasi  (information security) dan persiapan-persiapan operasional. Tujuan penulisan artikel ini guna membangun hipotesis pengaruh antar variabel untuk digunakan pada riset selanjutnya.   Hasil artikel literature review ini adalah:  1) Keamanan Informasi  berpengaruh terhadap Keamanan Sistem Informasi; 2) Teknologi Informasi berpengaruh terhadap Keamanan Sistem Informasi; dan 3) Network berpengaruh terhadap Keamanan Sistem Informasi.
Transferring experiences in k-nearest neighbors based multiagent reinforcement learning: an application to traffic signal control
Authors: Bazzan Ana Lucia C. , de Almeida Vicente N. , Abdoos Monireh
Published: September 2023

The increasing demand for mobility in our society poses various challenges to traffic engineering, computer science in general, and artificial intelligence in particular. Increasing the capacity of road networks is not always possible, thus a more efficient use of the available transportation infrastructure is required. Another issue is that many problems in traffic management and control are inherently decentralized and/or require adaptation to the traffic situation. Hence, there is a close relationship to multiagent reinforcement learning. However, using reinforcement learning poses the challenge that the state space is normally large and continuous, thus it is necessary to find appropriate schemes to deal with discretization of the state space. To address these issues, a multiagent system with agents learning independently via a learning algorithm was proposed, which is based on estimating Q-values from k-nearest neighbors. In the present paper, we extend this approach and include transfer of experiences among the agents, especially when an agent does not have a good set of k experiences. We deal with traffic signal control, running experiments on a traffic network in which we vary the traffic situation along time, and compare our approach to two baselines (one involving reinforcement learning and one based on fixed times). Our results show that the extended method pays off when an agent returns to an already experienced traffic situation.
FASTC: A Fast Attentional Framework for Semantic Traversability Classification Using Point Cloud
Authors: Chen Yirui , Wei Pengjin , Liu Zhenhuan , Wang Bingchao , Yang Jie , Liu Wei
Published: September 2023

Producing traversability maps and understanding the surroundings are crucial prerequisites for autonomous navigation. In this paper, we address the problem of traversability assessment using point clouds. We propose a novel pillar feature extraction module that utilizes PointNet to capture features from point clouds organized in vertical volume and a 2D encoder-decoder structure to conduct traversability classification instead of the widely used 3D convolutions. This results in less computational cost while even better performance is achieved at the same time. We then propose a new spatio-temporal attention module to fuse multi-frame information, which can properly handle the varying density problem of LIDAR point clouds, and this makes our module able to assess distant areas more accurately. Comprehensive experimental results on augmented Semantic KITTI and RELLIS-3D datasets show that our method is able to achieve superior performance over existing approaches both quantitatively and quantitatively. Our code is publicly available at https://github.com/chenyirui/FASTC.
Conditionally Acyclic CO-Networks for Efficient Preferential Optimization
Authors: Gimenez Pierre-François , Mengin Jérôme
Published: September 2023

This paper focuses on graphical models for modelling preferences in combinatorial space and their use for item optimisation. The preferential optimisation task seeks to find the preferred item containing some defined values, which is useful for many recommendation settings in e-commerce. We show that efficient (i.e., with polynomial time complexity) preferential optimisation is achieved with a subset of cyclic CP-nets called conditional acyclic CP-net. We also introduce a new graphical preference model, called Conditional-Optimality networks (CO-networks), that are more concise than conditional acyclic CP-nets and LP-trees but have the same expressiveness with respect to optimisation. Finally, we empirically show that preferential optimisation can be used for encoding alternatives into partial instantiations and vice versa, paving the way towards CO-nets and CP-nets unsupervised learning with the minimal description length (MDL) principle.
Declarative Encoding of Fairness in Logic Tensor Networks
Authors: Greco Greta , Alberici Federico , Palmonari Matteo , Cosentini Andrea
Published: September 2023

Algorithms are vulnerable to biases that might render their decisions unfair toward particular groups of individuals. Fairness comes with a range of facets that strongly depend on the application domain and that need to be enforced accordingly. However, most mitigation models embed fairness constraints as fundamental component of the loss function thus requiring code-level adjustments to adapt to specific contexts and domains. Rather than relying on a procedural approach, our model leverages declarative structured knowledge to encode fairness requirements in the form of logic rules capturing unambiguous and precise natural language statements. We propose a neuro-symbolic integration approach based on Logic Tensor Networks that combines data-driven network-based learning with high-level logical knowledge, allowing to perform classification tasks while reducing discrimination. Experimental evidence shows that performance is as good as state-of-the-art (SOTA) thus providing a flexible framework to account for non-discrimination often at a modest cost in terms of accuracy.
Mitigating Long-Tail Language Representation Collapsing via Cross-Lingual Bootstrapped Unsupervised Fine-Tuning
Authors: Guo Ping , Hu Yue , Ren Yubing , Li Yunpeng , Zhang Jiarui , Zhang Xingsheng
Published: September 2023

Large Language Models have shown great capability to comprehend natural language and provide reasonable responses. However, previous researches have shown weak performance of these models on low-resource (long-tail) languages. It remains to be a problem to mitigate the performance gap between long-tail languages and rich-resource ones, which is referred to as long-tail language representation collapsing. Though some previous works can generate pseudo-parallel corpora with the auto-regressive generation, this generation progress is time-consuming and remains low quality, particularly for long-tail languages. In this paper, we propose a (X) Cross-lingual Bootstrapped Unsupervised Fine-tuning Framework (X-BUFF) to mitigate long-tail language representation collapsing. X-BUFF iteratively updates cross-lingual PLMs in a curriculum way. In each iteration of X-BUFF, we (1) select sentences with complementary semantics from monolingual corpora in long-tail languages. (2) match these selected sentences with semantic equivalent sentences in many other languages to create parallel sentence pairs, which we then merge with previous sentence pairs to build a larger and more difficult bootstrapped parallel queue. (3) fine-tune the PLMs with the bootstrapped parallel queue. Extensive experiments show that X-BUFF can mitigate the long-tail language representation collapsing problem in cross-lingual PLMs and achieve significant improvements over the previous baselines on several cross-lingual evaluation benchmarks.
Sensitivity Analysis for Saturated Post-Hoc Optimization in Classical Planning
Authors: Höft Paul , Speck David , Seipp Jendrik
Published: September 2023

Cost partitioning is the foundation of today’s strongest heuristics for optimal classical planning. However, computing a cost partitioning for each evaluated state is prohibitively expensive in practice. Thus, existing approaches make an approximation and compute a cost partitioning only for a set of sampled states, and then reuse the resulting heuristics for all other states evaluated during the search. In this paper, we present exact methods for cost partitioning heuristics based on linear programming that fully preserve heuristic accuracy while minimizing computational cost. Specifically, we focus on saturated post-hoc optimization and establish several sufficient conditions for when reusing a cost partitioning computed for one state preserves the estimates for other states, mainly based on a sensitivity analysis of the underlying linear program. Our experiments demonstrate that our theoretical results transfer into practice, and that our exact cost partitioning algorithms are competitive with the strongest approximations currently available, while usually requiring fewer linear program evaluations.
Multi-Modal Fusion with Semantic Supervision for Radiology Report Generation
Authors: Jia Xing , Xiong Yun , Zhang Yao , Luo Li
Published: September 2023

Radiology report generation, one way of analyzing radiology images, is to generate a textual report automatically for the given image, and it is of great significance to assist diagnosis and alleviate the workload of radiologists. Some report generation methods have been therefore proposed. However, these methods suffer from the problem of low-quality generation, because of the visual and textual bias and training with text similarity oriented objective. To solve this problem, we propose a novel radiology report generation model with multi-modal fusion and semantic supervision, namely MS-Gen. MS-Gen consists of two main components, i.e., the semantic-visual fusion module and the semantic weighted contrastive loss. Specifically, the main idea of the semantic-visual fusion module is to make use of the domain-specific prior knowledge contained in a large pre-trained visual-language model and also the complementary nature between the image and text modalities. Moreover, a novel optimization term, i.e., the semantic weighted contrastive loss, is proposed to guide the optimization process with semantic similarity objective, and further enforce the generated reports with higher clinical accuracy. Extensive experiments conducted on two real datasets of IU X-Ray and MIMIC-CXR demonstrate the effectiveness of MS-Gen.
Answer Set Automata: A Learnable Pattern Specification Framework for Complex Event Recognition
Authors: Katzouris Nikos , Paliouras Georgios
Published: September 2023

Complex Event Recognition (CER) systems detect event occurrences in streaming input using predefined event patterns. Techniques that learn event patterns from data are highly desirable in CER. Since such patterns are typically represented by symbolic automata, we propose a family of such automata where the transition-enabling conditions are defined by Answer Set Programming (ASP) rules, and which, thanks to the strong connections of ASP to symbolic learning, are learnable from data. We present such a learning approach in ASP, capable of jointly learning the structure of an automaton, while synthesizing the transition guards from building-block predicates, and a scalable, incremental version thereof that progressively revises models learnt from mini-batches using Monte Carlo Tree Search. We evaluate our approach on three CER datasets and empirically demonstrate its efficacy.
Reducing Oversmoothing in Graph Neural Networks by Changing the Activation Function
Authors: Kelesis Dimitrios , Vogiatzis Dimitrios , Katsimpras Georgios , Fotakis Dimitris , Paliouras Georgios
Published: September 2023

The performance of Graph Neural Networks (GNNs) deteriorates as the depth of the network increases. That performance drop is mainly attributed to oversmoothing, which leads to similar node representations through repeated graph convolutions. We show that in deep GNNs the activation function plays a crucial role in oversmoothing. We explain theoretically why this is the case and propose a simple modification to the slope of ReLU to reduce oversmoothing. The proposed approach enables deep networks without the need to change the network architecture or to add residual connections. We verify the theoretical results experimentally and further show that deep networks, which do not suffer from oversmoothing, are beneficial in the presence of the “cold start” problem, i.e. when there is no feature information about unlabeled nodes.
On Explaining Agent Behaviour via Root Cause Analysis: A Formal Account Grounded in Theory of Mind
Authors: Khan Shakil M. , Rostamigiv Maryam
Published: September 2023

Inspired by a novel action-theoretic formalization of actual cause, Khan and Lespérance (2021) recently proposed a first account of causal knowledge that supports epistemic effects, models causal knowledge dynamics, and allows sensing actions to be causes of observed effects. To date, no other study has looked specifically at these issues. But their formalization is not sufficiently expressive to model explanations via causal analysis of mental states as it ignores a crucial aspect of theory of mind, namely motivations. In this paper, we build on their work to support causal reasoning about conative effects. In our framework, one can reason about causes of motivational states, and we allow motivation-altering actions to be causes of observed effects. We illustrate that this formalization along with a model of goal recognition can be utilized to explain agent behaviour in communicative multiagent contexts.
Neural Architecture Search for Explainable Networks
Authors: Li Yaoman , King Irwin
Published: September 2023

One of the main challenges in machine learning is providing understandable explanations for complex models. Despite outperforming humans in many tasks, machine learning models are often treated as black boxes that are difficult to interpret. Post-hoc explanation methods have been developed to create interpretable surrogate models that explain the behavior of black-box models. However, these methods have been shown to perpetuate bad practices and lack stability. Recently, inherent explainable approaches have been proposed to provide built-in explainability to models. However, most of these methods sacrifice performance. This paper proposes the Neural Architecture Search for Explainable Networks (NASXNet) approach to address the trade-off between performance and interpretability. Our method applies architecture search to generate high-performance and explainable neural networks for image classification tasks. We conduct experiments on four datasets: CUB-200-2011, Stanford Cars, CIFAR 10, and CIFAR 100. The results demonstrate that our models provide a high-level interpretation of prediction results, achieving state-of-the-art performance that is on par with non-explainable models. This paper contributes by solving the trade-off problem between performance and interpretability. It is the first to apply neural architecture search to develop explainable deep learning models, generating state-of-the-art explainable models that outperform existing approaches. Additionally, a new training process is proposed that enables faster convergence during model training.
A Dynamic Selective Parameter Sharing Mechanism Embedded with Multi-Level Reasoning Abstractions
Authors: Liu Yan , He Ying , Ming Zhong , Yu F. Richard
Published: September 2023

Cooperative multi-agent reinforcement learning (Co-MARL) commonly employs different parameter sharing mechanisms, such as full and partial sharing. However, imprudent application of these mechanisms can potentially constrain policy diversity and limit cooperation flexibility. Recent methods that group agents into distinct sharing categories often exhibit poor performance due to challenges in precisely differentiating agents and neglecting the issue of promoting cooperation among these categories. To address these issues, we introduce a dynamic selective parameter sharing mechanism embedded with multi-level reasoning abstractions (DSPS-MA). Our approach uses self-comparison sequences to infer agents’ abstract concepts, defining the differences between agents and allowing them to dynamically select partners to share parameters based on these abstract concepts. We also design an intrinsic reward to offer comprehensive collaboration guidance for agents, and introduce a policy cosine similarity regularization term to ensure sufficient policy diversity. Empirical evaluations demonstrate that our approach yields higher returns and faster convergence than state-of-the-art methods.
H2T-FAST: Head-to-Tail Feature Augmentation by Style Transfer for Long-Tailed Recognition
Authors: Meng Ziyao , Gu Xue , Shen Qiang , Tavares Adriano , Pinto Sandro , Xu Hao
Published: September 2023

Deep learning algorithms perform poorly on long-tailed datasets because there is insufficient data in the tail classes to recover its original distribution, resulting in an under-representation of the tail classes in the model. In this work, we propose H2T-FAST, a Head-to-Tail Feature Augmentation method by Style Transfer to improve the performance of the tail. H2T-FAST has the following advantages: (1) It is a fast and universal method that acts on the feature space and so, it can be applied to different backbone networks as well as easily integrated into various imbalanced algorithms with stable performance gains; and (2) it is used only in the training phase and therefore, imposes no additional burden on the deep neural network in the testing phase. In particular, we firstly and randomly select the same number of head samples as the tail ones in each training mini batch. Secondly, the style of the head is transferred to the tail to generate new tail data containing the head style, as a way to increase the number of the tail and get better feature representations. We test our methods on several benchmark vision tasks with state-of-the-art performances.
SUDS: Sanitizing Universal and Dependent Steganography
Authors: Robinette Preston K. , Wang Hanchen D. , Shehadeh Nishan , Moyer Daniel , Johnson Taylor T.
Published: September 2023

Steganography, or hiding messages in plain sight, is a form of information hiding that is most commonly used for covert communication. As modern steganographic mediums include images, text, audio, and video, this communication method is being increasingly used by bad actors to propagate malware, exfiltrate data, and discreetly communicate. Current protection mechanisms rely upon steganalysis, or the detection of steganography, but these approaches are dependent upon prior knowledge, such as steganographic signatures from publicly available tools and statistical knowledge about known hiding methods. These dependencies render steganalysis useless against new or unique hiding methods, which are becoming increasingly common with the application of deep learning models. To mitigate the shortcomings of steganalysis, this work focuses on a deep learning sanitization technique called SUDS that is not reliant upon knowledge of steganographic hiding techniques and is able to sanitize universal and dependent steganography. SUDS is tested using least significant bit method (LSB), dependent deep hiding (DDH), and universal deep hiding (UDH). We demonstrate the capabilities and limitations of SUDS by answering five research questions, including baseline comparisons and an ablation study. Additionally, we apply SUDS to a real-world scenario, where it is able to increase the resistance of a poisoned classifier against attacks by 1375%.
XFLT: Exploring Techniques for Generating Cross Lingual Factually Grounded Long Text
Authors: Singh Bhavyajeet , Hari Aditya , Mehta Rahul , Abhishek Tushar , Gupta Manish , Varma Vasudeva
Published: September 2023

Multiple business scenarios require an automated generation of descriptive human-readable long text from structured input data, where the source is typically a high-resource language and the target is a low or medium resource language. We define the Cross-Lingual Fact to Long Text Generation (XFLT) as a novel natural language generation (NLG) task that involves generating descriptive and human-readable long text in a target language from structured input data (such as fact triples) in a source language. XFLT is challenging because of (a) hallucinatory nature of the state-of-the-art NLG models, (b) lack of good quality training data, and (c) lack of a suitable cross-lingual NLG metric. Unfortunately previous work focuses on different related problem settings (cross-lingual facts to short text or monolingual graph to text) and has made no efforts to handle hallucinations. In this paper, we contribute a novel dataset, XLALIGN with over 64,000 paragraphs across 12 different languages, and English facts. We propose a novel solution to the XFLT task which addresses these challenges by training multilingual Transformer-based encoder-decoder models with coverage prompts and grounded decoding. Further, it improves on the XFLT quality by defining task-specific reward functions and training on them using reinforcement learning. On XLALIGN, we compare this novel solution with several strong baselines using a new metric, cross-lingual PARENT. We also make our code and data publicly available https://drive.google.com/file/d/1sHgcwXKribjrm2grbs-LzXUUqXQitD2N/.
Lifted Successor Generation by Maximum Clique Enumeration
Authors: Ståhlberg Simon
Published: September 2023

Classical planning instances are often represented using first-order logic; however, the initial step for most classical planners is to transform the given instance into a propositional representation. For example, action schemas are converted into ground actions, aiming to generate as few ground actions as possible without eliminating any viable solutions to the problem. This step can become a bottleneck in some domains due to the exponential blowup caused by the grounding process. A recent approach to alleviate this issue involves using the lifted (first-order) representation of the instance and generating all applicable ground actions on-the-fly during the search for each expanded state. In this paper, we propose a method that addresses this problem by enumerating all maximum cliques of a graph encoding the state and the action schema’s preconditions. We compare our method with state-of-the-art across 47 domains, showcasing improved performance in 23 domains. In some cases, simply changing the maximum clique enumeration algorithm results in a significant speedup compared to the state-of-the-art.
Motivated Agent with Semantic Memory
Authors: Starzyk Janusz , Kowalik Marcin , Horzyk Adrian
Published: September 2023

This paper introduces a motivated agent scheme that enables an agent to create its own goals using prior knowledge about its environment. A motivated agent operates in a dynamically changing environment and is capable of setting and achieving its own goals, as well as those set by the designer. The agent has access to additional knowledge about the environment, which is represented in associative semantic memory. This memory is constructed based on ANAKG associative knowledge graphs, which have been shown to have several advantages over other semantic memories for processing symbolic sequential inputs. They are easy to organize and train. In this paper, we demonstrate that a motivated agent with semantic memory learns to achieve its goals more easily and utilizes environmental resources more effectively. To simplify comparisons with reinforcement learning, we represent the environment as an environmental graph that shows the principles governing it. By exploring the environment, the agent learns these principles and can use them to accomplish its tasks. Our experiments and tests confirm our claims about the higher efficiency of agents with memory. Moreover, an extensive comparison with reinforcement learning agents highlights the advantages of motivated learning over reinforcement learning.
ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging
Authors: Struski Łukasz , Rymarczyk Dawid , Lewicki Arkadiusz , Sabiniewicz Robert , Tabor Jacek , Zieliński Bartosz
Published: September 2023

Multiple Instance Learning (MIL) is a weakly-supervised problem in which one label is assigned to the whole bag of instances. An important class of MIL models is instance-based, where we first classify instances and then aggregate those predictions to obtain a bag label. The most common MIL model is when we consider a bag as positive if at least one of its instances has a positive label. However, this reasoning does not hold in many real-life scenarios, where the positive bag label is often a consequence of a certain percentage of positive instances. To address this issue, we introduce a dedicated instance-based method called ProMIL, based on deep neural networks and Bernstein polynomial estimation. An important advantage of ProMIL is that it can automatically detect the optimal percentage level for decision-making. We show that ProMIL outperforms standard instance-based MIL in real-world medical applications. We make the code available.
Tab-Attention: Self-Attention-Based Stacked Generalization for Imbalanced Credit Default Prediction
Authors: Tan Yandan , Zhu Hongbin , Wu Jie , Chai Hongfeng
Published: September 2023

Accurately credit default prediction faces challenges due to imbalanced data and low correlation between features and labels. Existing default prediction studies on the basis of gradient boosting decision trees (GBDT), deep learning techniques, and feature selection strategies can have varying degrees of success depending on the specific task. Motivated by this, we propose Tab-Attention, a novel self-attention-based stacked generalization method for credit default prediction. This approach ensembles the potential proprietary knowledge contributions from multi-view feature spaces, to cope with low feature correlation and imbalance. We organize multi-view feature spaces according to the latent linear or nonlinear strengths between features and labels. Meanwhile, the f1 score assists the model in imbalance training to find the optimal state for identifying minority default samples. Our Tab-Attention achieves superior Recall1 and f11 of default intention recognition than existing GBDT-based models and advanced deep learning by about 32.92% and 16.05% on average, respectively, while maintaining outstanding overall performance and prediction performance for non-default samples. The proposed method could ensemble essential knowledge through the self-attention mechanism, which is of great significance for a more robust future prediction system.
Neuro-Symbolic Procedural Semantics for Reasoning-Intensive Visual Dialogue Tasks
Authors: Verheyen Lara , Ekila Jérôme Botoko , Nevens Jens , Van Eecke Paul , Beuls Katrien
Published: September 2023

This paper introduces a novel approach to visual dialogue that is based on neuro-symbolic procedural semantics. The approach builds further on earlier work on procedural semantics for visual question answering and expands it on the one hand with neuro-symbolic reasoning operations, and on the other hand with mechanisms that handle the challenges that are inherent to dialogue, in particular the incremental nature of the information that is conveyed. Concretely, we introduce (i) the use of a conversation memory as a data structure that explicitly and incrementally represents the information that is expressed during the subsequent turns of a dialogue, and (ii) the design of a neuro-symbolic procedural semantic representation that is grounded in both visual input and the conversation memory. We validate the methodology using the reasoning-intensive MNIST Dialog and CLEVR-Dialog benchmark challenges and achieve a question-level accuracy of 99.8% and 99.2% respectively. The methodology presented in this paper responds to the growing interest in the field of artificial intelligence in solving tasks that involve both low-level perception and high-level reasoning using a combination of neural and symbolic techniques.
Fair Few-Shot Learning with Auxiliary Sets
Authors: Wang Song , Ma Jing , Cheng Lu , Li Jundong
Published: September 2023

Recently, there has been a growing interest in developing machine learning (ML) models that can promote fairness, i.e., eliminating biased predictions towards certain populations (e.g., individuals from a specific demographic group). Most existing works learn such models based on well-designed fairness constraints in optimization. Nevertheless, in many practical ML tasks, only very few labeled data samples can be collected, which can lead to inferior fairness performance. This is because existing fairness constraints are designed to restrict the prediction disparity among different sensitive groups, but with few samples, it becomes difficult to accurately measure the disparity, thus rendering ineffective fairness optimization. In this paper, we define the fairness-aware learning task with limited training samples as the fair few-shot learning problem. To deal with this problem, we devise a novel framework that accumulates fairness-aware knowledge across different meta-training tasks and then generalizes the learned knowledge to meta-test tasks. To compensate for insufficient training samples, we propose an essential strategy to select and leverage an auxiliary set for each meta-test task. These auxiliary sets contain several labeled training samples that can enhance the model performance regarding fairness in meta-test tasks, thereby allowing for the transfer of learned useful fairness-oriented knowledge to meta-test tasks. Furthermore, we conduct extensive experiments on three real-world datasets to validate the superiority of our framework against the state-of-the-art baselines.
Using Self-Supervised Dual Constraint Contrastive Learning for Cross-Modal Retrieval
Authors: Wang Xintong , Li Xiaoyu , Ding Liang , Zhao Sanyuan , Biemann Chris
Published: September 2023

In this work, we present a self-supervised dual constraint contrastive method for efficiently fine-tuning the vision-language pre-trained (VLP) models that have achieved great success on various cross-modal tasks, since full fine-tune these pre-trained models is computationally expensive and tend to result in catastrophic forgetting restricted by the size and quality of labeled datasets. Our approach freezes the pre-trained VLP models as the fundamental, generalized, and transferable multimodal representation and incorporates lightweight parameters to learn domain and task-specific features without labeled data. We demonstrated that our self-supervised dual contrastive model performs better than previous fine-tuning methods on MS COCO and Flickr 30K datasets on the cross-modal retrieval task, with an even more pronounced improvement in zero-shot performance. Furthermore, experiments on the MOTIF dataset prove that our self-supervised approach remains effective when trained on a small, out-of-domain dataset without overfitting. As a plug-and-play method, our proposed method is agnostic to the underlying models and can be easily integrated with different VLP models, allowing for the potential incorporation of future advancements in VLP models.
A Convolutional Neural Network Approach to General Game Playing
Authors: Wang Yu , Zhang Heng , Jiang Guifei
Published: September 2023

General Game Playing (GGP), a research field aimed at developing agents that master different games in a unified way, is regarded as a necessary step towards creating artificial general intelligence. With the success of deep reinforcement learning (DRL) in games like Go, chess, and shogi, it has been recently introduced to GGP and is regarded as a promising technique to achieve the goal of GGP. However, the current work uses fully connected neural networks and is thus unable to efficiently exploit the topological structure of game states. In this paper, we propose an approach to applying general-purposed convolutional neural networks to GGP and implement a DRL-based GGP player. Experiments indicate that the built player not only outperforms the previous algorithm and UCT benchmark in a variety of games but also requires less training time.
A Generalization of the Shortest Path Problem to Graphs with Multiple Edge-Cost Estimates
Authors: Weiss Eyal , Felner Ariel , Kaminka Gal A.
Published: September 2023

The shortest path problem in graphs is a cornerstone of AI theory and applications. Existing algorithms generally ignore edge weight computation time. We present a generalized framework for weighted directed graphs, where edge weight can be computed (estimated) multiple times, at increasing accuracy and run-time expense. This raises several generalized variants of the shortest path problem. We introduce the problem of finding a path with the tightest lower-bound on the optimal cost. We then present two complete algorithms for the generalized problem, and empirically demonstrate their efficacy.
Enhancing Text Generation with Cooperative Training
Authors: Wu Tong , Wang Hao , Zeng Zhongshen , Wang Wei , Zheng Hai-Tao , Zhang Jiaxing
Published: September 2023

Recently, there has been a surge in the use of generated data to enhance the performance of downstream models, largely due to the advancements in pre-trained language models. However, most prevailing methods trained generative and discriminative models in isolation, which left them unable to adapt to changes in each other. These approaches lead to generative models that are prone to deviating from the true data distribution and providing limited benefits to discriminative models. While some works have proposed jointly training generative and discriminative language models, their methods remain challenging due to the non-differentiable nature of discrete data. To overcome these issues, we introduce a self-consistent learning framework in the text field that involves training a discriminator and generator cooperatively in a closed-loop manner until a scoring consensus is reached. By learning directly from selected samples, our framework are able to mitigate training instabilities such as mode collapse and non-convergence. Extensive experiments on four downstream benchmarks, including AFQMC, CHIP-STS, QQP, and MRPC, demonstrate the efficacy of the proposed framework.
A Declarative Approach to Compact Controllers for FOND Planning via Answer Set Programming
Authors: Yadav Nitin , Sardina Sebastian
Published: September 2023

We present an approach to non-deterministic planning under full observability via Answer Set Programming. The technique can synthesise compact policies, handle both fair and unfair actions simultaneously, and readily accommodate control knowledge and procedural domain constraints. We show that whereas compact controllers may yield sub-optimal behavior under a naive executor, optimality can be recovered under a smarter executor. The developed planner is succinct, elegant, and directly implementable, thus providing higher confidence of its correctness and ease of elaboration. Experimental results show that its performance is competitive.
Enhancing Link Prediction with Self-Discriminating Augmentation for Structure-Aware Contrastive Learning
Authors: Yang Hao-Wei , Chang Ming-Yi , Shen Chih-Ya
Published: September 2023

Link prediction is a crucial research area for both data mining and machine learning. Despite the success of contrastive learning in node classification tasks, applying it directly to link prediction tasks has revealed two major weaknesses, i.e., single positive sample contrasting and random augmentation, resulting in inferior performance. To overcome these issues, we propose a new contrastive learning approach for link prediction, called Structure-aware Contrastive Representation Learning with Self-discriminating Augmentation (SECRET). Our approach includes a novel data augmentation scheme based on the prediction model itself and takes into account both the contrastive objective and the reconstruction loss, which jointly improve the performance of link prediction. Our experiments on 11 benchmark datasets demonstrate that SECRET significantly outperforms the other state-of-the-art baselines.