bluuebunny/crossref_metadata_2025 huggingface dataset:
Building Bridges: Michel Chiha and the Construction of Modern Lebanon
Authors: González Fernández Borja W.
Published: June 2023

For almost three decades, the banker, journalist—and occasional poet—Michel Chiha (1891-1951) constructed, through the pages of his newspaper, Le Jour, a possibilist approach to Lebanese nationhood that was directly influential in the configuration of the National Pact and, ultimately, Lebanese independence. However, and despite the prominent role his thought played in the construction of the ‘Lebanese formula’, his work has been sorely overseen and misunderstood by scholarship. Analyzing Chiha’s editorial production, this paper will underline how this author’s cosmopolitan philosophy came to define the Lebanese self-perceptions of identity, defining constitutional practices until the present.
(Des)Igualdad de Género en Educación Física Escolar Chilena desde la Perspectiva Estudiantil: Estudio de Casos
Authors: Mujica-Johnson Felipe , Díaz Vásquez Pedro , González Robles Camila , Ramírez Rojas Joaquín
Published: June 2023

Esta investigación tiene como principal objetivo comprender las interpretaciones del alumnado sobre la desigualdad e igualdad de género en las clases de Educación Física escolar, siendo abordado desde la perspectiva estudiantil del género femenino y masculino de dos establecimientos educativos chilenos. Se trabajó bajo un enfoque cualitativo y de carácter fenomenológico-interpretativo. La población correspondió a ocho estudiantes que cursan tercer año medio. La recogida de datos se hizo con una entrevista semiestructurada, aplicándose un análisis de contenido deductivo-inductivo. Los resultados obtenidos evidencian situaciones explicitas de igualdad y desigualdad de género en las clases de Educación Física. La desigualdad se relacionó a la exigencia física, a las oportunidades y a la segregación. Por otro lado, la igualdad de género se vinculó al trato del profesorado y al ambiente escolar. Se concluye que la igualdad y la desigualdad de género son fenómenos culturales presentes en las clases de Educación Física del contexto escolar chileno investigado. No obstante, no son fenómenos rígidos y estables, sino que se encuentran condicionados e influenciados por características propias del contexto y la subjetividad de cada estudiante.
Efectos del Aprendizaje-Servicio Universitario sobre la Competencia Docente del Alumnado el Ámbito de la Actividad Física y el Deporte
Authors: García-Rico Luis , Santos-Pastor Mª Luisa , Ruiz-Montero Pedro Jesús , Martínez-Muñoz Luis Fernando
Published: June 2023

El Aprendizaje-Servicio Universitario ha tenido un importante impulso en los últimos años por su contribución a la formación profesional del alumnado. Este estudio se centra conocer y comprender los efectos que tiene sobre la competencia docente del alumnado universitario. La investigación ha seguido un diseño mixto. Han participado 145 estudiantes que han llevado a cabo experiencias de Aprendizaje-Servicio en diferentes asignaturas de las titulaciones del Grado en Ciencias de la Actividad Física y el Deporte y del Grado en Maestro/a en Educación Primaria, con mención en Educación Física, de la Universidad Autónoma de Madrid. Para la recogida de la información se utilizó el cuestionario E-ASAF, grupos focales, entrevistas grupales, el portafolio del alumnado y observaciones no participantes. El análisis estadístico se realizó mediante el programa SPSS v.25 y el análisis de contenido para la información cualitativa. Los principales hallazgos de la investigación nos permiten afirmar que el Aprendizaje-Servicio Universitario incide positivamente en los diferentes ámbitos de la formación docente del alumnado, destacando su contribución eficaz para el desarrollo de la competencia profesional en el ámbito de la Actividad Física y el Deporte. Las limitaciones del estudio proponen mejorar la diversidad de contextos y de participantes.
Animación a la Lectura en la Universidad de Mayores: Experiencia Artística Multidisciplinar
Authors: Gisbert Vicenta , Vela Marta
Published: June 2023

Asistimos a un considerable incremento en el envejecimiento y longevidad de la población, por lo que se ha de prestar especial atención para garantizar un aprendizaje de calidad para toda la vida que permita un envejecimiento activo. Se presenta en este trabajo una propuesta multidisciplinar que pretende incentivar la lectura, un recurso de aprendizaje con el que incorporar nuevos contenidos culturales que faciliten la interacción social, la participación en el entorno y el acercamiento a estímulos artísticos. Se trata de una experiencia desarrollada en la Universidad de Mayores (Zaragoza, España) donde participan un total de 121 sujetos con una edad media de 65 años. Además de la contextualización teórica, confluyen en este artículo un diseño de investigación longitudinal y cuantitativo que ha precisado del diseño de un cuestionario ad hoc en dos fases, previa y posterior a la implementación de la propuesta. Se involucran aspectos relacionados con la música y el cine que han incrementado la motivación por la lectura de obras literarias de gran envergadura, demostrando que la contextualización artística y multidisciplinar obtiene buenos resultados de aprendizaje y un incremento motivacional.
Conocimientos y Creencias de las Personas Mayores en torno a la Educación para la Muerte
Authors: Martínez-Heredia Nazaret , Bedmar Matías , Santaella Esther
Published: June 2023

Este estudio tiene como objetivo principal analizar los conocimientos y creencias de las personas mayores sobre la educación y preparación para la muerte. Para ello, se desarrolla una investigación cualitativa, a través de 28 entrevistas, agrupando sus interpretaciones en dos categorías iniciales: educación para la muerte y preparación para la muerte. Los resultados muestran que la mayoría asume la importancia de esta educación, pero que únicamente se la recibieron durante la etapa de la infancia y en relación al ámbito religioso. La muerte se encuentra presente en nuestra vida diaria pero, en el ámbito educativo, sigue siendo un tema tabú, aunque cobra especial relevancia en la vida de las personas mayores como un medio para afrontar la propia muerte frente a situaciones de ansiedad y miedo o como acompañamiento ante dicho proceso. También, exponen la necesidad de una educación para la muerte en el ámbito familiar, como ayuda educativa para superar el duelo. En conclusión, las personas mayores demandan una preparación para la muerte, a través de su inclusión como materia transversal en espacios educativos.
Biomimetinių sistemų tyrimas suminio dažnio generacijos spektroskopijos metodu
Authors:   , Navakauskas Edvinas
Published: June 2023

The application of sum-frequency generation spectroscopy in studies of biomimetic systems
Фундаментальна теплофізична інженерія будівель у контексті відновлення України
Authors: Басок Борис Іванович
Published: June 2023

У доповіді проаналізовано досвід Інституту технічної теплофізики НАН України з проведення робіт за напрямом фундаментальної теплофізичної інженерії будівель і споруд з метою доведення їх до стану пасивних будівель, будівель «нуль енергії», «розумних» будівель за умови підвищення їхньої енергетичної стійкості та мінімізації викидів парникових газів. Результати цих досліджень спрямовано на вирішення актуальних і стратегічно важливих науково-технічних проблем, пов’язаних із суттєвим підвищенням енергетичної ефективності наявних, відбудованих і новозбудованих будівель та споруд України згідно з вимогами енергетичного і кліматичного законодавства ЄС.
Філософське мистецтво Григорія Сковороди і дискусії щодо розвитку гуманітарної сфери України
Authors: Хамітов Назіп Віленович
Published: June 2023

У статті обстоюється ідея, що розвиток гуманітарної сфери країни світоглядно спрямовується філософією цієї країни у поліфонії її проявів у культурі — і теоретичною філософією, і філософським мистецтвом. Автор доводить продуктивність підходів філософського мистецтва Григорія Сковороди для розв’язання суперечності в дискусіях представників державницько-соціальної і ліберальної стратегій розвитку гуманітарної сфери України. Констатовано необхідність і можливість гармонійного розв’язання цієї суперечності в діалозі, шляхом виходу на метапозицію і знаходження балансу між крайнощами. Показано, що це глибинно відповідає архетипам української культури, виразником і творцем яких був Григорій Сковорода та послідовники його традиції.
Order parameters in Dicke model
Authors: Lyagushyn S. F. , Sokolovsky A. I.
Published: June 2023

Dicke model provides describing the superradiance effect when the spontaneous emission of a great number of excited two-level atoms is running in a correlated way due to their interaction via field. The process is identical to a giant dipole emission. Since the ordering takes place, it is interesting to study the possibility for using the terms of phase transition theory in such physical situation. Equilibrium properties of Dicke type models were studied for a long time because of their relevance for explaining some phenomena in matter-field interaction. The existence of an ordered phase in the emitter subsystem together with macroscopic photon mode filling is established, wherein the correct solution requires the Bogolyubov concept of quasiaverages to exclude the phase uncertainty of quasispin component structures  and  (for the concentrated model). In this paper, the behavior of similar order parameters for the non-equilibrium process description is analyzed and using the order parameter  associated with the squared electric dipole moment of the emitter complex is substantiated. Such fact justifies  application in the literature devoted to the superradiance theory. The situation with quasiaverages is compared with the problem of uniformity breaking in the numerical modeling.
Polarization features of Bi12SiO20:Cr and Bi12SiO20:Mn crystals
Authors: Panchenko T. V. , Karpova L. M. , Potapovich Yu. N. , Trubitsyn M. P.
Published: June 2023

The effect of impurity ions and polarization conditions on the dielectric characteristics and polarization features of Bi12SiO20 crystals doped with Cr and Mn ions is studied by dielectric spectroscopy in the sound frequency range and by thermoactivation spectroscopy in the temperature range 280 ÷ 800 K. The mechanisms of the observed phenomena are discussed at the atomic level.
Structure and properties of ion-plasma deposited films of CoCrFeMiMn high-entropy alloy
Authors: Kushnerov O. I. , Ryabtsev S. I. , Bashev V. F.
Published: June 2023

High-entropy Co19Cr18Fe22Mn21Ni20 thin films were obtained by the modernized method of three-electrode ion-plasma sputtering of mosaic targets consisting of pure metals. The structure, electrical resistance, and magnetic properties of films were investigated. Single diffuse halo was seen on the XRD patterns of the as-deposited films, which confirms their amorphous structure. Some of the thin films, which were annealed at 900 K in a vacuum, were identified to be oxidized by a small amount of oxygen in the work chamber. After the heat treatment, the Co19Cr18Fe22Mn21Ni20 films were transformed from an amorphous state into a crystallized FCC solid solution with the lattice parameter a=0.3613 nm. Also, the cubic B2 phase of FeCo with a lattice parameter of 0.2857 nm was formed in the annealed films. As a result of oxidation processes, a dispersed phase of manganese oxide also arose after annealing. The temperature dependencies of electrical resistivity of films were measured by the four-point technique upon continuously heating in the high vacuum. Both as-deposited and annealed films clearly revealed a typical ferromagnetic behavior. The as-deposited high-entropy film exhibited the soft magnetic properties while the annealed films could be attributed to hard magnetic materials.
Sensitive element of an intellectual sensor (for measuring physical and chemical characteristics)
Authors: Kovalchuk V. , Mamuka K. , Smorgh M.
Published: June 2023

The work is devoted to the development of a sensing element of an intellectual sensor for the creation of an analyser of meteorological characteristics. The use of such a sensor gives new opportunities for creating devices for portable gadgets controlled by a microcontroller. The practical application of such devices opens the possibility of correct analysis of air pressure, temperature, and humidity data in real time.
Visualizing hyperparathyroidism: A pictorial essay of Tc-99m MIBI parathyroid imaging across different etiologies
Authors: Buakhao Chanittha , Vachatimanont Sira
Published: June 2023

Parathyroid scintigraphy is an imaging technique that uses gamma-emitting radionuclide to locate hyperfunctioning parathyroid glands in patients with hyperparathyroidism. It is valuable for preoperative assessment before parathyroidectomy, which is a curative surgery in most cases of primary hyperparathyroidism and some cases of secondary hyperparathyroidism. There are several different techniques for parathyroid scintigraphy. In general, the scintigraphy is performed with Tc-99m MIBI, a mitochondria-targeting radiotracer. Some techniques also supply the Tc-99m MIBI scintigraphy with thyroid scintigraphy to differentiate between thyroid and parathyroid tissue. Parathyroid scintigraphy can detect primary hyperparathyroidism with a sensitivity of 80% and a specificity of 84%. It can also detect secondary hyperparathyroidism with a sensitivity of 58% and a specificity of 93%. The unique advantage of parathyroid scintigraphy is the ability to identify supernumerary and ectopic parathyroid abnormalities, which can significantly affect surgical planning and outcomes.
Piezoelectric based V-shape cantilever beam design of energy harvester for biomedical applications
Authors: Dewanjee Joy , Islam Md Shabiul
Published: June 2023

This research paper exhibits the design of a V-shaped cantilever beam as a micro Energy Harvester (EH) having Piezoelectric (PZT) as its energy source for biomedical applications. PZT source based materials have the ability to convert the mechanical energy into electrical energy. Low-power biomedical devices mostly operate using electrical energy (i.e. batteries). But batteries are treated as a bio-hazard due to the massive use of biomedical applications. To overcome this toxic bio-hazard, the proposed PZT based V-shaped cantilever beam of micro EH can solve the limitations. To perform the experimental work, the cantilever beam design parameters - length, width and thickness have been considered and simulated using COMSOL Multiphysics to get the resonant frequency of 156.19 Hz which is lower than previous research work. It was observed that the obtained lower resonant frequency can be converted into AC voltage (mV) using PZT material. To convert the output AC voltage (mV) into DC voltage, a circuit of an Ultra-Low-Power (ULP) EH will be designed in LTSPICE software. Finally, the integration of the both V-shape cantilever beam and the ULP EH circuit will be implemented in PCB hardware to generate the output power (10 µW), will be stored in super-capacitor for biomedical devices-pacemaker.
Faith, Hope, and Resilience: Unveiling the Spiritual Dimensions of Hope in Christian Education
Authors: Odia Cyril
Published: June 2023

Many people can endure economic hardships and do suffer considerably due to the social and political structures within which they live. One way to understand why such people do not give up in life or stop working hard despite low economic rewards is connected to the element of the Christian faith. It is a spiritual gift that enables people to ‘endure’ challenges in life and grow strong amid adversity. This belief is mentioned in Romans 5:1-5 but does it really work that way? This research explored the possibility of people relying primarily on the word of God and participating in religious activities to cope with life's difficulties. It was significant to have focus group interviews with people living under such conditions of faith and economic hardship to understand how this gift works in real life. From this research carried out in 2022, it was discovered that people who are active in practising their Christian faith could cope with hardships that come their way in life because of a strong sense of hope.
La mujer en la Iglesia Católica: situación canónica actual y perspectivas abiertas por la sinodalidad
Authors: Peña Carmen
Published: June 2023

El artículo parte de la descripción de las múltiples vías de participación femenina responsable en la actuación eclesial, destacando aquellos campos donde más avances ha habido en las cuatro décadas de vigencia del Código de 1983, o aquellos que abren posibilidades más significativas para avanzar hacia la implementación efectiva del principio de igualdad de género, como son la docencia en facultades eclesiásticas y seminarios, su nombramiento como jueces eclesiásticos o la intervención femenina en investigaciones y procedimientos penales abiertos contra sacerdotes. Y hace también una reflexión valorativa sobre las perspectivas de futuro que, en este tema, abre el actual proceso sinodal sobre la sinodalidad, señalando posibles concreciones de este principio, prestando atención a la reforma de la Curia Romana por la c.a. Praedicate Evangelium. Se concluye apuntando algunos aspectos susceptibles de revisión normativa, como la condición clerical de los jueces de los Tribunales Apostólicos, el cardenalato o la posible reforma de órganos consultivos diocesanos.
Progressive quality estimation of oyster mushrooms using neural network–based image analysis
Authors: Sarkar Tanmay , Mukherjee Alok , Chatterjee Kingshuk , Smaoui Slim , Pati Siddhartha , Shariati Mohammad Ali
Published: June 2023

We have developed an artificial intelligence–based quality prediction model for oyster mushroom samples in this work. The proposed model tends to predict the progressively deteriorating quality of the samples in terms of predicted Hedonic number, which is adjudged as one of the most reliable scales of raw fruit quality assessment parameters. The present scheme attempts to continuously assess the quality of mushrooms by judging the extent of deterioration of the sample images; instead of discrete classification asserting only the edibility or non-edibility of the samples. Thus, the extent of the freshness of any test sample could also be approximated using the predicted Hedonic number from the model. The proposed scheme uses an artificial neural network to develop the estimator. The simplicity of analysis of the scheme and high accuracy of prediction of freshness allow for basic screening of the samples without requiring a panel of experts to judge the same, which is a difficult task, especially under this pandemic circumstance. Besides, implementing the proposed algorithm in designing possible mobile-based application software would widen its applicability in a practical scenario.
Consumo Online das Mulheres na Pandemia da Covid-19 no Brasil: Características e Influências
Authors: Assis Evange Elias , Rodrigues Maria Clotilde Perez
Published: June 2023

O e-commerce que já mostrava uma curva ascendente nos últimos anos, com maior participação das mulheres, apresentou uma rápida aceleração nas vendas após o isolamento social causado pela Covid-19. O objetivo deste estudo é verificar os fatores que influenciam o consumo on-line das mulheres durante a pandemia da Covid-19. A pesquisa exploratória com abordagem qualitativa ocorreu em duas etapas. A primeira, consistiu na revisão teórica que subsidiou a segunda etapa, a pesquisa empírica, realizada com entrevistas em profundidade. Os resultados do estudo reforçam que a Covid-19 impactou de forma decisiva a compra no e-commerce e sugerem que os fatores de maior influência sobre o consumo online das  mulheres durante a pandemia são: confiança, conveniência e preço. Indicação de pessoas conhecidas e comentários na internet sobre os produtos e marcas também foram considerados importantes para a tomada de decisão de compra online.
Ray Tracing Algorithm to Simulate Laser-Cut Panel Light-Redirecting Elements
Authors:   Universidade Federal de Alagoas, Maceió, Brasil, Ribeiro Pedro Vitor Sousa , Cabús Ricardo Carvalho ,   Universidade Federal de Alagoas, Maceió, Brasil
Published: June 2023

Daylighting simulation software is an important tool to improve the quality of building design and to improve the quality of the built environment. For its application to correspond to reality, its algorithm needs to reflect real behaviour in the best possible way. This paper aims to propose an algorithm to simulate the behaviour of fenestration complex systems, such as the laser-cut panel, based on ray tracing techniques. The algorithm was developed in MatLab as an add-on for the TropLux software, using the physical formulas associated with the concepts of daylight coefficients, ray tracing and Monte Carlo method. After validation it is possible to realize that the developed algorithm can simulate the light behaviour through complex fenestration systems using plane-based modelling with precision and accuracy. The tests using laser cut panel, ordinary glass and prismatic glass obtained behaviour close to that observed in similar measurements and algorithms.
The influence of project management on the performance of projects to implement procedural improvements: A multiple case study.
Authors: Santos Manoela Pastor dos , Carvalho Marly Monteiro , Do Amaral Caio Senna , Berssaneti Fernando Tobal
Published: June 2023

Purpose – Project management is well established in the literature as a useful methodology for planning, organizing, monitoring projects in order to ensure its completion. Improvement projects are internal initiatives within an organization that can originate from different sources, especially from the search for continued improvement. Theoretical framework – Within this context, the research will seek to understand what are the benefits and influences of Project Management when applied to these types of projects, using as a research method a multiple case study in two companies, in units in Brazil and the United States, in which interviews were conducted with professionals directly involved improvement projects within the organization. Design/methodology/approach – The data from the recorded interviews were transcribed, organized, grouped, compared and analyzed and the discussions and results are presented in the present study. Findings – Relevant findings are that Project Management, at first, provides companies with guidance on how to plan their improvement projects to better execute them, but none of the evaluated companies showed homogeneous adoption.Research, Practical & Social implications - Some of the limitations found in the present study are that only two companies of specific branches were evaluated.Originality/value – This article will seek to investigate what is the influence of the application of the project management methodology on the performance of procedural improvement projects, from its definition, to its planning, execution and observation of the results arising from the practice.Keywords - Project Management, Performance, Results, Processes and Improvement Projects.
ScatterFormer: Locally-Invariant Scattering Transformer for Patient-Independent Multispectral Detection of Epileptiform Discharges
Authors: Zheng Ruizhe , Li Jun , Wang Yi , Luo Tian , Yu Yuguo
Published: June 2023

Patient-independent detection of epileptic activities based on visual spectral representation of continuous EEG (cEEG) has been widely used for diagnosing epilepsy. However, precise detection remains a considerable challenge due to subtle variabilities across subjects, channels and time points. Thus, capturing fine-grained, discriminative features of EEG patterns, which is associated with high-frequency textural information, is yet to be resolved. In this work, we propose Scattering Transformer (ScatterFormer), an invariant scattering transform-based hierarchical Transformer that specifically pays attention to subtle features. In particular, the disentangled frequency-aware attention (FAA) enables the Transformer to capture clinically informative high-frequency components, offering a novel clinical explainability based on visual encoding of multichannel EEG signals. Evaluations on two distinct tasks of epileptiform detection demonstrate
the effectiveness our method. Our proposed model achieves median AUCROC and accuracy of 98.14%, 96.39% in patients with Rolandic epilepsy. On a neonatal seizure detection benchmark, it outperforms the state-of-the-art by 9% in terms of average AUCROC.
Progress and Limitations of Deep Networks to Recognize Objects in Unusual Poses
Authors: Abbas Amro , Deny Stéphane
Published: June 2023

Deep networks should be robust to rare events if they are to be successfully deployed in high-stakes real-world applications. Here we study the capability of deep networks to recognize objects in unusual poses. We create a synthetic dataset of images of objects in unusual orientations, and evaluate the robustness of a collection of 38 recent and competitive deep networks for image classification. We show that classifying these images is still a challenge for all networks tested, with an average accuracy drop of 29.5% compared to when the objects are presented
upright. This brittleness is largely unaffected by various design choices, such as training losses, architectures, dataset modalities, and data-augmentation schemes. However, networks trained on very large datasets substantially outperform others, with the best network tested—Noisy Student trained on JFT-300M—showing a relatively small accuracy drop of only 14.5% on unusual poses. Nevertheless, a visual inspection of the failures of Noisy Student reveals a remaining gap in robustness with humans. Furthermore, combining multiple object transformations—3D-rotations and scaling—further degrades the performance of all networks. Our results provide another measurement of the robustness of deep networks to consider when using them in the real world. Code and datasets are available at https://github.com/amro-kamal/ObjectPose.
Rethinking Interpretation: Input-Agnostic Saliency Mapping of Deep Visual Classifiers
Authors: Akhtar Naveed , Jalwana Mohammad Amir Asim Khan
Published: June 2023

Saliency methods provide post-hoc model interpretation by attributing input features to the model outputs. Current methods mainly achieve this using a single input sample, thereby failing to answer input-independent inquiries about the model. We also show that input-specific saliency mapping is intrinsically susceptible to misleading feature attribution. Current attempts to use `general' input features for model interpretation assume access to a dataset containing those features, which biases the interpretation. Addressing the gap, we introduce a new perspective of input-agnostic saliency mapping that computationally estimates the high-level features attributed by the model to its outputs. These features are geometrically correlated, and are computed by accumulating model's gradient information with respect to an unrestricted data distribution. To compute these features, we nudge independent data points over the model loss surface towards the local minima associated by a human-understandable concept, e.g., class label for classifiers. With a systematic projection, scaling and refinement process, this information is transformed into an interpretable visualization without compromising its model-fidelity. The visualization serves as a stand-alone qualitative interpretation. With an extensive evaluation, we not only demonstrate successful visualizations for a variety of concepts for large-scale models, but also showcase an interesting utility of this new form of saliency mapping by identifying backdoor signatures in compromised classifiers.
Layout Representation Learning with Spatial and Structural Hierarchies
Authors: Bai Yue , Manandhar Dipu , Wang Zhaowen , Collomosse John , Fu Yun
Published: June 2023

We present a novel hierarchical modeling method for layout representation learning, the core of design documents (e.g., user interface, poster, template).
Existing works on layout representation often ignore element hierarchies, which is an important facet of layouts, and mainly rely on the spatial bounding boxes for feature extraction.
This paper proposes a Spatial-Structural Hierarchical Auto-Encoder (SSH-AE) that learns hierarchical representation by treating a hierarchically annotated layout as a tree format.
On the one side, we model SSH-AE from both spatial (semantic views) and structural (organization and relationships) perspectives, which are two complementary aspects to represent a layout.
On the other side, the semantic/geometric properties are associated at multiple resolutions/granularities, naturally handling complex layouts.
Our learned representations are used for effective layout search from both spatial and structural similarity perspectives.
We also newly involve the tree-edit distance (TED) as an evaluation metric to construct a comprehensive evaluation protocol for layout similarity assessment, which benefits a systematic and customized layout search.
We further present a new dataset of POSTER layouts which we believe will be useful for future layout research.
We show that our proposed SSH-AE outperforms the existing methods achieving state-of-the-art performance on two benchmark datasets.
Code is available at github.com/yueb17/SSH-AE.
Multi-Level Compositional Reasoning for Interactive Instruction Following
Authors: Bhambri Suvaansh , Kim Byeonghwi , Choi Jonghyun
Published: June 2023

Robotic agents performing domestic chores by natural language directives are required to master the complex job of navigating environment and interacting with objects in the environments. The tasks given to the agents are often composite thus are challenging as completing them require to reason about multiple subtasks, e.g., bring a cup of coffee. To address the challenge, we propose to divide and conquer it by breaking the task into multiple subgoals and attend to them individually for better navigation and interaction. We call it Multi-level Compositional Reasoning Agent (MCR-Agent). Specifically, we learn a three-level action policy. At the highest level, we infer a sequence of human-interpretable subgoals to be executed based on language instructions by a high-level policy composition controller. At the middle level, we discriminatively control the agent’s navigation by a master policy by alternating between a navigation policy and various independent interaction policies. Finally, at the lowest level, we infer manipulation actions with the corresponding object masks using the appropriate interaction policy. Our approach not only generates human interpretable subgoals but also achieves 2.03% absolute gain to comparable state of the arts in the efficiency metric (PLWSR in unseen set) without using rule-based planning or a semantic spatial memory. The
code is available at https://github.com/yonseivnl/mcr-agent.
Self-Supervised Joint Dynamic Scene Reconstruction and Optical Flow Estimation for Spiking Camera
Authors: Chen Shiyan , Yu Zhaofei , Huang Tiejun
Published: June 2023

Spiking camera, a novel retina-inspired vision sensor, has shown its great potential for capturing high-speed dynamic scenes with a sampling rate of 40,000 Hz. The spiking camera abandons the concept of exposure window, with each of its photosensitive units continuously capturing photons and firing spikes asynchronously. However, the special sampling mechanism prevents the frame-based algorithm from being used to spiking camera. It remains to be a challenge to reconstruct dynamic scenes and perform common computer vision tasks for spiking camera. In this paper, we propose a self-supervised joint learning framework for optical flow estimation and reconstruction of spiking camera. The framework reconstructs clean frame-based spiking representations in a self-supervised manner, and then uses them to train the optical flow networks. We also propose an optical flow based inverse rendering process to achieve self-supervision by minimizing the difference with respect to the original spiking temporal aggregation image. The experimental results demonstrate that our method bridges the gap between synthetic and real-world scenes and achieves desired results in real-world scenarios. To the best of our knowledge, this is the first attempt to jointly reconstruct dynamic scenes and estimate optical flow for spiking camera from a self-supervised learning perspective.
Cross-Modality Person Re-identification with Memory-Based Contrastive Embedding
Authors: Cheng De , Wang Xiaolong , Wang Nannan , Wang Zhen , Wang Xiaoyu , Gao Xinbo
Published: June 2023

Visible-infrared person re-identification (VI-ReID) aims to retrieve the person images of the same identity from the RGB to infrared image space, which is very important for real-world surveillance system. In practice, VI-ReID is more challenging due to the heterogeneous modality discrepancy, which further aggravates the challenges of traditional single-modality person ReID problem, i.e., inter-class confusion and intra-class variations. In this paper, we propose an aggregated memory-based cross-modality deep metric learning framework, which benefits from the increasing number of learned modality-aware and modality-agnostic centroid proxies for cluster contrast and mutual information learning. Furthermore, to suppress the modality discrepancy, the proposed cross-modality alignment objective simultaneously utilizes both historical and up-to-date learned cluster proxies for enhanced cross-modality association. Such training mechanism helps to obtain hard positive references through increased diversity of learned cluster proxies, and finally achieves stronger ``pulling close'' effect between cross-modality image features. Extensive experiment results demonstrate the effectiveness of the proposed method, surpassing state-of-the-art works significantly by a large margin on the commonly used VI-ReID datasets.
Domain-General Crowd Counting in Unseen Scenarios
Authors: Du Zhipeng , Deng Jiankang , Shi Miaojing
Published: June 2023

Domain shift across crowd data severely hinders crowd counting models to generalize to unseen scenarios. Although domain adaptive crowd counting approaches close this gap to a certain extent, they are still dependent on the target domain data to adapt (e.g. finetune) their models to the specific domain. In this paper, we instead target to train a model based on a single source domain which can generalize well on any unseen domain. This falls into the realm of domain generalization that remains unexplored in crowd counting. We first introduce a dynamic sub-domain division scheme which divides the source domain into multiple sub-domains such that we can initiate a meta-learning framework for domain generalization. The sub-domain division is dynamically refined during the meta-learning. Next, in order to disentangle domain-invariant information from domain-specific information in image features, we design the domain-invariant and -specific crowd memory modules to re-encode image features. Two types of losses, i.e. feature reconstruction and orthogonal losses, are devised to enable this disentanglement. Extensive experiments on several standard crowd counting benchmarks i.e. SHA, SHB, QNRF, and NWPU, show the strong generalizability of our method. Our code is available at: https://github.com/ZPDu/Domain-general-Crowd-Counting-in-Unseen-Scenarios
InstanceFormer: An Online Video Instance Segmentation Framework
Authors: Koner Rajat , Hannan Tanveer , Shit Suprosanna , Sharifzadeh Sahand , Schubert Matthias , Seidl Thomas , Tresp Volker
Published: June 2023

Recent transformer-based offline video instance segmentation (VIS) approaches achieve encouraging results and significantly outperform online approaches. However, their reliance on the whole video and the immense computational complexity caused by full Spatio-temporal attention limit them in real-life applications such as processing lengthy videos. In this paper, we propose a single-stage transformer-based efficient online VIS framework named InstanceFormer, which is especially suitable for long and challenging videos. We propose three novel components to model short-term and long-term dependency and temporal coherence. First, we propagate the representation, location, and semantic information of prior instances to model short-term changes. Second, we propose a novel memory cross-attention in the decoder, which allows the network to look into earlier instances within a certain temporal window. Finally, we employ a temporal contrastive loss to impose coherence in the representation of an instance across all frames. Memory attention and temporal coherence are particularly beneficial to long-range dependency modeling, including challenging scenarios like occlusion. The proposed InstanceFormer outperforms previous online benchmark methods by a large margin across multiple datasets. Most importantly, InstanceFormer surpasses offline approaches for challenging and long datasets such as YouTube-VIS-2021 and OVIS. Code is available at https://github.com/rajatkoner08/InstanceFormer.
Curriculum Multi-Negative Augmentation for Debiased Video Grounding
Authors: Lan Xiaohan , Yuan Yitian , Chen Hong , Wang Xin , Jie Zequn , Ma Lin , Wang Zhi , Zhu Wenwu
Published: June 2023

Video Grounding (VG) aims to locate the desired segment from a video given a sentence query. Recent studies have found that current VG models are prone to over-rely the groundtruth moment annotation distribution biases in the training set. To discourage the standard VG model's behavior of exploiting such temporal annotation biases and improve the model generalization ability, we propose multiple negative augmentations in a hierarchical way, including cross-video augmentations from clip-/video-level, and self-shuffled augmentations with masks. These augmentations can effectively diversify the data distribution so that the model can make more reasonable predictions instead of merely fitting the temporal biases. However, directly adopting such data augmentation strategy may inevitably carry some noise shown in our cases, since not all of the handcrafted augmentations are semantically irrelevant to the groundtruth video. To further denoise and improve the grounding accuracy, we design a multi-stage curriculum strategy to adaptively train the standard VG model from easy to hard negative augmentations. Experiments on newly collected Charades-CD and ActivityNet-CD datasets demonstrate our proposed strategy can improve the performance of the base model on both i.i.d and o.o.d scenarios.
Linking People across Text and Images Based on Social Relation Reasoning
Authors: Lei Yang , Zhao Peizhi , Li Pijian , Cai Yi , Huang Qingbao
Published: June 2023

As a sub-task of visual grounding, linking people across text and images aims to localize target people in images with corresponding sentences. Existing approaches tend to capture superficial features of people (e.g., dress and location) that suffer from the incompleteness information across text and images. We observe that humans are adept at exploring social relations to assist identifying people. Therefore, we propose a Social Relation Reasoning (SRR) model to address the aforementioned issues. Firstly, we design a Social Relation Extraction (SRE) module to extract social relations between people in the input sentence. Specially, the SRE module based on zero-shot learning is able to extract social relations even though they are not defined in the existing datasets. A Reasoning based Cross-modal Matching (RCM) module is further used to generate matching matrices by reasoning on the social relations and visual features. Experimental results show that the accuracy of our proposed SRR model outperforms the state-of-the-art models on the challenging datasets Who's Waldo and FL: MSRE, by more than 5\% and 7\%, respectively. Our source code is available at https://github.com/VILAN-Lab/SRR.
Learning Semantic Alignment with Global Modality Reconstruction for Video-Language Pre-training towards Retrieval
Authors: Li Mingchao , Shi Xiaoming , Leng Haitao , Zhou Wei , Zheng Hai-Tao , Zhang Kuncai
Published: June 2023

Video-language pre-training for text-based video retrieval tasks is vitally important. Previous pre-training methods suffer from the semantic misalignments. The reason is that these methods ignore sequence alignments but focusing on critical token alignment. To alleviate the problem, we propose a video-language pre-training framework, termed videolanguage pre-training For lEarning sEmantic aLignments (FEEL), to learn semantic alignments at the sequence level. Specifically, the global modality reconstruction and the cross- modal self-contrasting method is utilized to learn the alignments at the sequence level better. Extensive experimental results demonstrate the effectiveness of FEEL on text-based video retrieval and text-based video corpus moment retrieval.
Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy Gradient
Authors: Chen Wubing , Li Wenbin , Liu Xiao , Yang Shangdong , Gao Yang
Published: June 2023

Cooperative multi-agent policy gradient (MAPG) algorithms have recently attracted wide attention and are regarded as a general scheme for the multi-agent system. Credit assignment plays an important role in MAPG and can induce cooperation among multiple agents. However, most MAPG algorithms cannot achieve good credit assignment because of the game-theoretic pathology known as centralized-decentralized mismatch. To address this issue, this paper presents a novel method, Multi-Agent Polarization Policy Gradient (MAPPG). MAPPG takes a simple but efficient polarization function to transform the optimal consistency of joint and individual actions into easily realized constraints, thus enabling efficient credit assignment in MAPPG. Theoretically, we prove that individual policies of MAPPG can converge to the global optimum. Empirically, we evaluate MAPPG on the well-known matrix game and differential game, and verify that MAPPG can converge to the global optimum for both discrete and continuous action spaces. We also evaluate MAPPG on a set of StarCraft II micromanagement tasks and demonstrate that MAPPG outperforms the state-of-the-art MAPG algorithms.
Zero-Shot Assistance in Sequential Decision Problems
Authors: De Peuter Sebastiaan , Kaski Samuel
Published: June 2023

We consider the problem of creating assistants that can help agents solve new sequential decision problems, assuming the agent is not able to specify the reward function explicitly to the assistant. Instead of acting in place of the agent as in current automation-based approaches, we give the assistant an advisory role and keep the agent in the loop as the main decision maker. The difficulty is that we must account for potential biases of the agent which may cause it to seemingly irrationally reject advice. To do this we introduce a novel formalization of assistance that models these biases, allowing the assistant to infer and adapt to them. We then introduce a new method for planning the assistant's actions which can scale to large decision making problems. We show experimentally that our approach adapts to these agent biases, and results in higher cumulative reward for the agent than automation-based alternatives. Lastly, we show that an approach combining advice and automation outperforms advice alone at the cost of losing some safety guarantees.
Reward-Based Negotiating Agent Strategies
Authors: Higa Ryota , Fujita Katsuhide , Takahashi Toki , Shimizu Takumu , Nakadai Shinji
Published: June 2023

This study proposed a novel reward-based negotiating agent strategy using an issue-based represented deep policy network. We compared the negotiation strategies with reinforcement learning (RL) by the tournaments toward heuristics-based champion agents in multi-issue negotiation. A bilateral multi-issue negotiation in which the two agents exchange offers in turn was considered. Existing RL architectures for a negotiation strategy incorporate rich utility function that provides concrete information even though the rewards of RL are considered as generalized signals in practice. Additionally, in existing reinforcement learning architectures for negotiation strategies, both the issue-based representations of the negotiation problems and the policy network to improve the scalability of negotiation domains are yet to be considered. This study proposed a novel reward-based negotiation strategy through deep RL by considering an issue-based represented deep policy network for multi-issue negotiation. Comparative studies analyzed the significant properties of negotiation strategies with RL. The results revealed that the policy-based learning agents with issue-based representations achieved comparable or higher utility than the state-of-the-art baselines with RL and heuristics, especially in the large-sized domains. Additionally, negotiation strategies with RL based on the policy network can achieve agreements by effectively using each step.
DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learning
Authors: Yuan Tingting , Chung Hwei-Ming , Yuan Jie , Fu Xiaoming
Published: June 2023

Communication is supposed to improve multi-agent collaboration and overall performance in cooperative Multi-agent reinforcement learning (MARL). However, such improvements are prevalently limited in practice since most existing communication schemes ignore communication overheads (e.g., communication delays). In this paper, we demonstrate that ignoring communication delays has detrimental effects on collaborations, especially in delay-sensitive tasks such as autonomous driving. To mitigate this impact, we design a delay-aware multi-agent communication model (DACOM) to adapt communication to delays. Specifically, DACOM introduces a component, TimeNet, that is responsible for adjusting the waiting time of an agent to receive messages from other agents such that the uncertainty associated with delay can be addressed. Our experiments reveal that DACOM has a non-negligible performance improvement over other mechanisms by making a better trade-off between the benefits of communication and the costs of waiting for messages.
Improvement-Focused Causal Recourse (ICR)
Authors: König Gunnar , Freiesleben Timo , Grosse-Wentrup Moritz
Published: June 2023

Algorithmic recourse recommendations inform stakeholders of how to act to revert unfavorable decisions. However, existing methods may recommend actions that lead to acceptance (i.e., revert the model's decision) but do not lead to improvement (i.e., may not revert the underlying real-world state). To recommend such actions is to recommend fooling the predictor. We introduce a novel method, Improvement-Focused Causal Recourse (ICR), which involves a conceptual shift: Firstly, we require ICR recommendations to guide toward improvement. Secondly, we do not tailor the recommendations to be accepted by a specific predictor. Instead, we leverage causal knowledge to design decision systems that predict accurately pre- and post-recourse, such that improvement guarantees translate into acceptance guarantees. Curiously, optimal pre-recourse classifiers are robust to ICR actions and thus suitable post-recourse. In semi-synthetic experiments, we demonstrate that given correct causal knowledge ICR, in contrast to existing approaches, guides toward both acceptance and improvement.