bluuebunny/crossref_metadata_2025 huggingface dataset:
Design A Precision Motion Control of an Upper Limb Robotic Arm
Authors: Kwan Ze Zhen , Mohd Khairuddin Ismail , Mohd Razman Mohd Azraai , P. P. Abdul Majeed Anwar , Mohd Isa Wan Hasbullah
Published: June 2023

Trajectory tracking is utilized in medical rehabilitation programs at the early stage of rehabilitation in order to track the performance of the patient in performing the prescribed task. The robotic arm has been utilized to accomplish this due to it precision and provide repetitive motion. The goal of this study is to design and simulate a two-degree-of-freedom robotic arm that can effectively track a trajectory. As a result, this study discusses the modelling, simulation, and control of a Two Degree of Freedom (2-DOF) Robot Arm to attain that goal. First, the robot specifications are provided, as well as the forward and inverse kinematics of a 2-DOF robot arm. The dynamics of the 2-DOF robot arm were then defined using the Euler- Lagrange Equation to obtain motion equations. A PID controller was used to construct a control design for the robot's controller. MATLAB is used to record all the data, including the margin of error, overshoot, and peak settling time. The data is identified using the PI and PID controllers, in which the error is smaller than 7 and 1.5, respectively. The controller was then used to create a prototype model by using MATLAB Sim Mechanics. The data obtained indicates that a PID controller is the best fit for this rehabilitation robot.
Support-Free Fused Deposition Modelling Printing for Overhangs Structure
Authors: Alhamed Abdulrahman , Mohd Romlay Fadhlur Rahman , Muhamad Khairul Fikri , Mohd Zawawi Mohd Zairulnizam , Ishak Ismayuzri
Published: June 2023

Fused deposition modelling printing utilizes layer by layer printing process. The accumulation of those layers created the 3D object during the printing process. An overhang structure is defined by shapes that extend outward without direct support from a printing base. When printing an overhang structure with a fused deposition modelling 3D printer, the process generally requires support material which may need post processing to remove the support structures. The three-dimensional printing of overhang models without using supporting structures is useful in the current fused deposition modelling manufacturing because it allows to significantly reduce printing time and material usage. In this project, a procedure and algorithm for making a specific toolpath that is capable of printing overhang structures and bridges without using supports will be proposed. The algorithm was tested on three different shapes of models, and it successfully achieved the objective to 3D printed an overhangs structure without support material
Intelligent Classification Of Stingless Bee Honey Using Enose
Authors: Mohd Nasir Akrimi Mathwa , Najib Muhammad Sharfi , Mohd Daud Suhaimi , Mohamad Mujahid , Che Mohd Che Mohd Aizal
Published: June 2023

Honey is an essential product produced by both honey bees and stingless bees. It is one of the most powerful natural products used for wound healing also known as natural sweetener that is widely available across the entire world. One of the problems required to sustain the bee honey by measuring and quantifying the quality. One of the methods to detect bee honey via odor signature. However, the difficulties in identifying the odour profile feature are common by using human or animal nose. The second challenge is to find efficient and accurate artificial intelligence methods to identify the odours. The objective of this research is to identify the stingless bee honey (SBH) using odour-profile feature. SBH is one of a bee species, yet different in size, it produces a honey that is clearer in colour as compared to natural honey bee. However, study on SBH grade is not yet extensively explored. Other than that, this research is done to measure the odour-profile by using E-nose which comprises of sensor array has been used to measure the samples of dataset from a few different types of SBH. Hence, this research aim is to classify stingless bee honey based on smell pattern recognition. The final step is the measured data were normalised and analysed using case-based reasoning (CBR) method. Interestingly, CBR classification had shown significant findings whereby it could achieved 100% rate of accuracy, specificity and sensitivity. In conclusion, classification of SBH odor-profile using CBR is feasible.
Development of Controllino Program to Integrate the Sensor Inputs for Motors Control
Authors: Zulkifli Muhammad Shafinaim Akmal , Abdul Ghani Ahmad Shahrizan , Abu Mohd Yazid , Fusaomi Nagata
Published: June 2023

The system fishcake machine available in the market cannot be controlled by its motor speed based on the desired speed. In addition, this machine uses PLC which causes the production cost of the machine to increase, cannot use components that have a small voltage such as 5V and indirectly makes the PLC a difficult component to integrate all types of components in one PLC only. So, in this paper, we will develop a program using MAXI Automation Controllino for motor speed control that can be changed according to the desired speed with PWM method. In addition, to analyse and validate the integration sensors and motors to control the entire system. Finally, in this study we use Controllino type Maxi Automation to replace the existing PLC in the market and this Controllino will relate to a sensor that is an ultrasonic sensor, infrared range sensor and a DC motor. To control this system, we use a software method that is Arduino IDE to write all the coding. This coding will be written based on all the programs or processes involved in the fishcake machine performed.
Parametric Investigations of Drying Roselle Calyces Flower
Authors: Kamarulbahrin Muhammad Nur Qayyum , Zamri Zubaidah , Abdul Sani Amiril Sahab
Published: June 2023

In this research, a new dryer which use halogen lamp must be designed and the drying performance of Roselle is to be investigate. The power of lamp is varied (50, 75, 100, 125, 150, 175, 200, 225) W. The result shows that higher power of lamp produce high temperature and low humidity which shorten the drying time of Roselle. The drying rate of each parameter shows slightly different pattern as temperature does not constant since the power supply does not produce constant voltage or current. The heat of vaporization increase as the power of lamp is increase and quantity of heat energy to raise temperature of Roselle at certain mass also increase. The most suitable parameter is 200W since the colour of Roselle does not degrade very much with short drying time 240mins, heat, transfer coefficient 91.51 W/2K . Maximum   has a sour taste and is ordinarily utilized in the readiness of cold and hot drinks and as   energy needed to heat Roselle at 62⁰C is 17.02 kJ and heat of vaporization are determined to be 2612.3 kJ/kg.
Nilai-nilai Pendidikan dalam Novel Mitra Sejati Karya Tulus Setiyadi
Authors: Afriza Hesti , Aryanto Aris , Widoyono Yuli
Published: June 2023

This study aims to describe the intrinsic elements in the novel Mitra Sejati by Tulus Setiyadi and the educational values ​​in the novel Mitra Sejati by Tulus Setiyadi. This type of descriptive qualitative research. The data sources for the novel Mitra Sejati by Tulus Setiyadi and the data for this study are quotes that contain intrinsic elements and educational values. Data collection techniques used library techniques and note-taking techniques. The instrument used by the researcher himself was the main instrument. The validity of the data used triangulation of sources, researchers, and theories. The analysis of this research uses an interactive analysis model. The technique of presenting the results of the analysis used the informal presentation method. The results found (1) intrinsic elements include: (a) the theme of the novel Mitra Sejati is friendship and the struggle of two children in continuing their education (b) The characters and characterizations consist of the main characters, namely Arga and Bayu, the additional characters are Pak Karyo, Ratih, Mbok Suminah, Mas Guntur, Mbah Kromo, Budhe Tumi, Indri, Nuning, Raras (c) The plot used is a forward plot (d) The setting consists of place setting, time setting, and socio-cultural setting (e) The point of view used is the point of view the third person's point of view is omniscient. (2) educational values ​​consist of: (a) the value of religious education includes 2 (two) aspects (b) the value of moral education includes 2 (two) aspects and (c) the value of cultural education includes 2 (two) aspects. In this study, it was concluded that the researcher only examined the intrinsic elements and educational values ​​of the Mitra Sejati novel by Tulus Setiyadi.
Pincaka (Pintar Hanacaraka): Pengembangan Media Berbasis Android Untuk Meningkatkan Kompetensi Menulis Huruf Jawa
Authors: Zein Ahmad Baharuddin , Kurniati Endang
Published: June 2023

The students of grade 11th MAN 2 Semarang have difficulty in writing Javanese script-based text. Hence, Android-based Pincaka (pintar hanacaraka) media is necessary to help them practice writing Javanese scripts. This research aims to develop Android-based Pincaka learning media. This research belongs to Research and Development which was done in five steps, including potentials and problems analysis, gathering information, product design, design validation, and design revision. The data was collected through observation, interview, and questionnaire. Then, the data were analyzed by using the descriptive qualitative analytical technique. The result of the study shows that students and teachers need supported media to learn to write Javanese scripts text. The content of the media consists of materials, exercises for writing various Javanese scripts text, and evaluation. The results of the media validation test obtained an average score of 3,875, while the material validation showed an average score of 3 from a range of 1 to 4. Therefore, the android-based pincaka (pintar hanacaraka) media can be used and developed to support the material lesson of writing Javanese text, both in class XI MAN 2 Kota Semarang and class XI SMA/SMK/MA in general.
Le Mouvement du 20 février au Rif. Mémoire de violence et réconciliation politique
Authors: Nahhass Badiha , Rhani Zakaria
Published: June 2023

Le mouvement du 20 février (M20F), survenu en 2011, est la version marocaine de ce qu’a été nommé par les observateurs « Printemps arabe ». Le M20F a connu des articulations différentes d’une région à l’autre. Des spécificités qui sont surtout liées aux particularités politico-historiques et socioéconomiques. Cet article propose d’analyser les dynamiques protestataires dans la région du Rif, Al Hoceima en particulier. Nous arguons que ces dynamiques, tout en s’inscrivant en continuité avec le mouvement M20F global, présentent des spécificités liées à l’histoire de cette région, notamment des rapports singuliers à l’histoire des mouvements de protestation postindépendance, marqués par la violence, et leurs mémoires incorporées.
Justicia Social y Desarrollo Sostenible en la Legislación Educativa Español
Authors: Rosado-Castellano Fátima , Flores-Rodríguez Cruz
Published: June 2023

Los Objetivos de Desarrollo Sostenible (ODS) suponen una nueva forma de afrontar los retos contemporáneos de la humanidad. Con su declaración, se establece una variable irrenunciable que debe acompañar al crecimiento: la justicia social. En el presente artículo se exploran las implicaciones que esto supone para el desarrollo económico y humano, así como su impacto sobre la legislación educativa española. El objetivo es identificar la inclusión de descriptores afines al desarrollo sostenible y la justicia social, nacidos de las metas de los ODS, en las tres últimas leyes educativas españolas. Mediante un proceso descriptivo, se realiza un estudio comparativo de carácter longitudinal. Los resultados muestran cómo la legislación educativa española ha venido incorporando y actualizando contenidos y sensibilidades acerca del desarrollo sostenible. Asimismo, y teniendo en cuenta el carácter pluridimensional de la justicia social, la educación española ha sabido abordar desde diversos flancos sus retos. Quedan pendientes asuntos que aún no han recibido la atención necesaria, así como un abordaje de lo que la justicia social, desde sus cinco dimensiones, supone. La legislación educativa española tiene margen de mejora en cuanto a su capacidad de atender los retos presentes.
Extraction of polymers from enzyme-treated softwood
Authors: Azhar Shoaib , Lawoko Martin , Henriksson Gunnar , Lindström Mikael E.
Published: June 2023

In a biorefinery context it is an advantage to fractionate and extract different wood components in a relatively pure form. However, one major obstacle for efficient extraction of wood polymers (lignin, polysaccharides etc.) is the covalent lignin-polysaccharide networks present in lignified cell walls. Enzymatic catalysis might be a useful tool for a controlled degradation of these networks, thereby enhancing the extraction of high molecular weight polymers. In this work, a methanol-alkali mixture was used to extract two different wood samples treated with endoxylanase and gammanase, respectively. Wood chips were pretreated with alkali prior to enzymatic treatment to enhance the cell-wall accessibility to enzymes. Extractions were also carried out on non-enzyme-treated samples to evaluate the enzymatic effects. Results showed that the enzymatic treatment increased the extraction yield, with gammanase as the more efficient of the two enzymes. Furthermore, polymers extracted from xylanase-treated wood had a higher degree of polymerization than the reference.
The use of twin-screw extrusion in processing of wood: The effect of processing parameter and pretreatment
Authors: Hietala Maiju , Niinimäki Jouko , Oksman Kristiina
Published: June 2023

In this study the effect of processing parameters on different types of wood raw material in extrusion was examined. The study consisted of two parts: the first part was to break and separate individual fibers from wood chips during the extrusion process; in the second part the effect of chemical pre-treatment and screw elements on wood raw material was evaluated. Statistical analysis was performed to evaluate the most important factors affecting wood particle size in extrusion. The statistical analysis showed that the screw speed is the main factor affecting wood fiber length in twin-screw extrusion of wood chips. This study showed that a twin-screw extruder can be used to separate individual fibers from wood chips, and the separated fibers have higher aspect ratios than the wood flour particles typically used in wood-polymer composites. When more fibrous and chemically softened wood raw material was used, fibers with even higher aspect ratios were obtained.
Preparation and rheological properties of cellulose/chitosan homogeneous solution in ionic liquid
Authors: Duan Xianquan , Xu Jigang , He Beihai , Li Junrong , Sun Yushan
Published: June 2023

Cellulose/chitosan homogeneous solutions were prepared in 1-ethyl-3-methylimidazolium acetate ([EMIM]Ac) ionic liquid. Steady and oscillatory dynamic rheological properties were studied with a Haake Mars-ΙΙΙ rotational rheometer. Rheological results show that cellulose/chitosan [EMIM]Ac solutions are a pseudoplastic liquid, and the viscosity of the solution decreases with increasing of chitosan content. The zero shear rate viscous activation energy of solution is about 52.10 to 62.50KJ/mol, calculated from the Cross and Arrhenius equation. The structural viscosity index of the solution is similar to that of cellulose/N-methylmorpholine-N-oxide solution, which indicates a potential spinnability. The dynamic rheological responses reveal that the Cox-Merz rule holds well with the solutions, and the crossover point of loss modulus and storage modulus shifts to a higher frequency range with increasing temperature.
Solution behaviours and adsorption characteristics of sodium lignosulfonate under different pH conditions
Authors: Qian Yong , Deng Yonghong , Yi Conghua , Yu Haifeng , Qiu Xueqing
Published: June 2023

Solution behaviors and adsorption characteristics of sodium lignosulfonate (NaLS) were studied under different pH conditions. The changes of Mw and particle size of NaSL with solution pH were detected by laser light scattering (LLS). Film thickness was determined with a spectroscopic ellipsometer, and surface roughness was measured by atomic force microscopy (AFMaSL was changed from compact to loose. When the NaSL with loose structure and low degree of aggregation was adsorbed on a solid substrate, the adsorption thickness and the surface roughness decreased correspondingly.
Методологія досліджень та прогнозування довговічності зварних конструкцій критичної інфраструктури
Authors: Ниркова Л.І.
Published: June 2023

У доповіді наведено найважливіші науково-практичні результати в галузі хімічного опору матеріалів та захисту від корозії, отримані в Інституті електрозварювання ім. Є.О. Патона НАН України. Зокрема, розглянуто розроблену методологію досліджень та прогнозування довговічності зварних конструкцій критичної інфраструктури. Актуальність цих робіт зумовлена необхідністю забезпечення надійної та безпечної експлуатації об’єктів критичної інфраструктури України, що є стратегічно важливим для безпеки держави, функціонування економіки, розвитку суспільства та зростання добробуту населення.
Рукописні фонди Інституту літератури ім. Т.Г. Шевченка НАН України — основа академічних, науково-критичних та енциклопедичних видань
Authors: Жулинський Микола Григорович
Published: June 2023

У доповіді наголошено на необхідності вирішення нагальних проблем, пов’язаних зі збереженням та використанням унікального рукописного фонду Інституту літератури ім. Т.Г. Шевченка НАН України, який є архівом національного значення. Запропоновано актуалізувати концепцію створення науково-дослідного і культурно-інформаційного центру «Шевченківський дім», у складі якого передбачити розміщення та функціонування центру збереження рукописних фондів класиків української літератури й видань літературної спадщини з відповідними архівосховищами.
Transferência de Conhecimento e Aposentadoria: uma Revisão Integrativa de Literatura
Authors: Fossatti Emanuele Canali , Mozzato Anelise Rebelato
Published: June 2023

Esta revisão integrativa de literatura tem como objetivo identificar e descrever a relação existente entre a temática da transferência de conhecimento e a aposentadoria na área das Ciências Sociais Aplicadas no período de 2008 a 2020. As temáticas transferência de conhecimento e aposentadoria vêm sendo abordadas por diferentes áreas de estudo, visto que preveem uma contribuição multisciplinar, porém, quando relacionadas, apresentam pouca ênfase na área das Ciências Sociais Aplicadas. O cenário demográfico brasileiro aponta para o envelhecimento dos trabalhadores, ampliando os desafios existentes no âmbito organizacional. Neste contexto, as organizações nem sempre estão preparadas para planejar a transferência de conhecimento dos trabalhadores mais experientes para as gerações subsequentes e, assim, a perda de conhecimento resultante da aposentadoria pode representar uma ameaça as organizações. Os resultados fornecem uma perspectiva ampla e integrada sobre o campo de debate na temática proposta quando abordada pelas Ciências Sociais Aplicadas, contribuindo para embasar futuros estudos. Por fim, apresenta-se os resultados mais relevantes e uma agenda para futuras pesquisas, destacando-se algumas lacunas que podem ser aprofundadas, em especial pela área das Ciências Sociais Aplicadas.
Um ajuste nada justo para a educação: reflexões sobre o documento do Banco Mundial
Authors: Santos Rodrigo Oliveira , Azevedo Adriana Barroso de
Published: June 2023

Este trabalho tem como finalidade apresentar uma análise sobre a proposta de reformas apresentado pelo Banco Mundial, a pedido do governo brasileiro em 2017, com foco nas ações sugeridas para a área da educação. O papel desempenhado por tal documento preconiza a reordenação social e econômica em um momento de grave recessão e, também, crise institucional, como forma de retomada política e econômica através de um “novo” governo, mas, o não investimento ou a imposição de mecanismos que reduzem a eficácia de políticas não se apresentam como coerentes dentro das demandas sociais encontradas no Brasil. As reflexões sobre a ação financista sobre a Educação apontam para um erro conceitual da proposta e com consequências visíveis.
LA HABILIDAD PROFESIONAL PEDAGÓGICA: DIRIGIR LA ACTIVIDAD PRÁCTICO-EXPERIMENTAL EN ESTUDIANTES DE LICENCIATURA EN EDUCACIÓN QUÍMICA
Authors: Pérez Hernández Iraida , Quintana Andrés Israel Yera , Jimenez Gerardo Martínez
Published: June 2023

El artículo presenta un estudio desarrollado en la Facultad de Ciencias Pedagógicas de la Universidad de Ciego de Ávila “Máximo Gómez Báez”, en Cuba, sobre las insuficiencias en la habilidad profesional pedagógica: dirigir la actividad práctico-experimental que poseen los estudiantes de la Carrera Licenciatura en Educación Química. El objetivo fue diagnosticar el estado actual del desarrollo de esta habilidad profesional pedagógica en estudiantes de la carrera mencionada. Se adoptó un enfoque metodológico mixto para el desarrollo de un estudio de campo con diseño de investigación no experimental transversal. La muestra quedó conformada por 3 directivos, 7 estudiantes de segundo año, 14 profesores y 7 tutores que representan el 100.0 %, 53.84 %, 51.85 % y 53.84 % de los integrantes de la población, respectivamente. Los principales resultados evidenciaron insuficiencias teóricas, metodológicas y prácticas relacionadas con el proceso de formación de pregrado del Licenciado en Educación Química desde la integración de los componentes académico, laboral e investigativo.
Hierarchical ConViT with Attention-Based Relational Reasoner for Visual Analogical Reasoning
Authors: He Wentao , Zhang Jialu , Ren Jianfeng , Bai Ruibin , Jiang Xudong
Published: June 2023

Raven’s Progressive Matrices (RPMs) have been widely used to evaluate the visual reasoning ability of humans. To tackle the challenges of visual perception and logic reasoning on RPMs, we propose a Hierarchical ConViT with Attention-based Relational Reasoner (HCV-ARR). Traditional solution methods often apply relatively shallow convolution networks to visually perceive shape patterns in RPM images, which may not fully model the long-range dependencies of complex pattern combinations in RPMs. The proposed ConViT consists of a convolutional block to capture the low-level attributes of visual patterns, and a transformer block to capture the high-level image semantics such as pattern formations. Furthermore, the proposed hierarchical ConViT captures visual features from multiple receptive fields, where the shallow layers focus on the image fine details while the deeper layers focus on the image semantics. To better model the underlying reasoning rules embedded in RPM images, an Attention-based Relational Reasoner (ARR) is proposed to establish the underlying relations among images. The proposed ARR well exploits the hidden relations among question images through the developed element-wise attentive reasoner. Experimental results on three RPM datasets demonstrate that the proposed HCV-ARR achieves a significant performance gain compared with the state-of-the-art models. The source code is available at: https://github.com/wentaoheunnc/HCV-ARR.
A Semi-parametric Model for Decision Making in High-Dimensional Sensory Discrimination Tasks
Authors: Keeley Stephen , Letham Benjamin , Sanders Craig , Tymms Chase , Shvartsman Michael
Published: June 2023

Psychometric functions typically characterize binary sensory decisions along a single stimulus dimension. However, real-life sensory tasks vary along a greater variety of dimensions (e.g. color, contrast and luminance for visual stimuli). Approaches to characterizing high-dimensional sensory spaces either require strong parametric assumptions about these additional contextual dimensions, or fail to leverage known properties of classical psychometric curves. We overcome both limitations by introducing a semi-parametric model of sensory discrimination that applies traditional psychophysical models along a stimulus intensity dimension, but puts Gaussian process (GP) priors on the parameters of these models with respect to the remaining dimensions. By combining the flexibility of the GP with the deep literature on parametric psychophysics, our semi-parametric models achieve good performance with much less data than baselines on both synthetic and real-world, high-dimensional psychophysics datasets. We additionally show strong performance in a Bayesian active learning setting, and present a novel active learning paradigm for the semi-parametric model.
Disentangling Reafferent Effects by Doing Nothing
Authors: Wilkins Benedict , Stathis Kostas
Published: June 2023

An agent's ability to distinguish between sensory effects that are self-caused, and those that are not, is instrumental in the achievement of its goals. This ability is thought to be central to a variety of functions in biological organisms, from perceptual stabilisation and accurate motor control, to higher level cognitive functions such as planning, mirroring and the sense of agency. Although many of these functions are well studied in AI, this important distinction is rarely made explicit and the focus tends to be on the associational relationship between action and sensory effect or success. Toward the development of more general agents, we develop a framework that enables agents to disentangle self-caused and externally-caused sensory effects. Informed by relevant models and experiments in robotics, and in the biological and cognitive sciences, we demonstrate the general applicability of this framework through an extensive experimental evaluation over three different environments.
VASR: Visual Analogies of Situation Recognition
Authors: Bitton Yonatan , Yosef Ron , Strugo Eliyahu , Shahaf Dafna , Schwartz Roy , Stanovsky Gabriel
Published: June 2023

A core process in human cognition is analogical mapping: the ability to identify a similar relational structure between different situations.
We introduce a novel task, Visual Analogies of Situation Recognition, adapting the classical word-analogy task into the visual domain. Given a triplet of images, the task is to select an image candidate B' that completes the analogy (A to A' is like B to what?). Unlike previous work on visual analogy that focused on simple image transformations, we tackle complex analogies requiring understanding of scenes.

We leverage situation recognition annotations and the CLIP model to generate a large set of 500k candidate analogies. Crowdsourced annotations for a sample of the data indicate that humans agree with the dataset label ~80% of the time (chance level 25%). Furthermore, we use human annotations to create a gold-standard dataset of 3,820 validated analogies.
Our experiments demonstrate that state-of-the-art models do well when distractors are chosen randomly (~86%), but struggle with carefully chosen distractors (~53%, compared to 90% human accuracy). We hope our dataset will encourage the development of new analogy-making models. Website: https://vasr-dataset.github.io/
Scalable Spatial Memory for Scene Rendering and Navigation
Authors: Chen Wen-Cheng , Chen Chu-Song , Chiu Wei-Chen , Hu Min-Chun
Published: June 2023

Neural scene representation and rendering methods have shown promise in learning the implicit form of scene structure without supervision. However, the implicit representation learned in most existing methods is non-expandable and cannot be inferred online for novel scenes, which makes the learned representation difficult to be applied across different reinforcement learning (RL) tasks. In this work, we introduce Scene Memory Network (SMN) to achieve online spatial memory construction and expansion for view rendering in novel scenes. SMN models the camera projection and back-projection as spatially aware memory control processes, where the memory values store the information of the partial 3D area, and the memory keys indicate the position of that area. The memory controller can learn the geometry property from observations without the camera's intrinsic parameters and depth supervision. We further apply the memory constructed by SMN to exploration and navigation tasks. The experimental results reveal the generalization ability of our proposed SMN in large-scale scene synthesis and its potential to improve the performance of spatial RL tasks.
MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection
Authors: Chen Yingxian , Liu Zhengzhe , Zhang Baoheng , Fok Wilton , Qi Xiaojuan , Wu Yik-Chung
Published: June 2023

Weakly supervised detection of anomalies in surveillance videos is a challenging task. Going beyond existing works that have deficient capabilities to localize anomalies in long videos, we propose a novel glance and focus network to effectively integrate spatial-temporal information for accurate anomaly detection. In addition, we empirically found that existing approaches that use feature magnitudes to represent the degree of anomalies typically ignore the effects of scene variations, and hence result in sub-optimal performance due to the inconsistency of feature magnitudes across scenes. To address this issue, we propose the Feature Amplification Mechanism and a Magnitude Contrastive Loss to enhance the discriminativeness of feature magnitudes for detecting anomalies. Experimental results on two large-scale benchmarks UCF-Crime and XD-Violence manifest that our method outperforms state-of-the-art approaches.
Imperceptible Adversarial Attack via Invertible Neural Networks
Authors: Chen Zihan , Wang Ziyue , Huang Jun-Jie , Zhao Wentao , Liu Xiao , Guan Dejian
Published: June 2023

Adding perturbations via utilizing auxiliary gradient information or discarding existing details of the benign images are two common approaches for generating adversarial examples. Though visual imperceptibility is the desired property of adversarial examples, conventional adversarial attacks still generate traceable adversarial perturbations. In this paper, we introduce a novel Adversarial Attack via Invertible Neural Networks (AdvINN) method to produce robust and imperceptible adversarial examples. Specifically, AdvINN fully takes advantage of the information preservation property of Invertible Neural Networks and thereby generates adversarial examples by simultaneously adding class-specific semantic information of the target class and dropping discriminant information of the original class. Extensive experiments on CIFAR-10, CIFAR-100, and ImageNet-1K demonstrate that the proposed AdvINN method can produce less imperceptible adversarial images than the state-of-the-art methods and AdvINN yields more robust adversarial examples with high confidence compared to other adversarial attacks. Code is available at https://github.com/jjhuangcs/AdvINN.
Adversarial Alignment for Source Free Object Detection
Authors: Chu Qiaosong , Li Shuyan , Chen Guangyi , Li Kai , Li Xiu
Published: June 2023

Source-free object detection (SFOD) aims to transfer a detector pre-trained on a label-rich source domain to an unlabeled target domain without seeing source data. While most existing SFOD methods generate pseudo labels via a source-pretrained model to guide training, these pseudo labels usually contain high noises due to heavy domain discrepancy. In order to obtain better pseudo supervisions, we divide the target domain into source-similar and source-dissimilar parts and align them in the feature space by adversarial learning.Specifically, we design a detection variance-based criterion to divide the target domain. This criterion is motivated by a finding that larger detection variances denote higher recall and larger similarity to the source domain. Then we incorporate an adversarial module into a mean teacher framework to drive the feature spaces of these two subsets indistinguishable. Extensive experiments on multiple cross-domain object detection datasets demonstrate that our proposed method consistently outperforms the compared SFOD methods. Our implementation is available at https://github.com/ChuQiaosong.
Dual-Domain Attention for Image Deblurring
Authors: Cui Yuning , Tao Yi , Ren Wenqi , Knoll Alois
Published: June 2023

As a long-standing and challenging task, image deblurring aims to reconstruct the latent sharp image from its degraded counterpart. In this study, to bridge the gaps between degraded/sharp image pairs in the spatial and frequency domains simultaneously, we develop the dual-domain attention mechanism for image deblurring. Self-attention is widely used in vision tasks, however, due to the quadratic complexity, it is not applicable to image deblurring with high-resolution images. To alleviate this issue, we propose a novel spatial attention module by implementing self-attention in the style of dynamic group convolution for integrating information from the local region, enhancing the representation learning capability and reducing computational burden. Regarding frequency domain learning, many frequency-based deblurring approaches either treat the spectrum as a whole or decompose frequency components in a complicated manner. In this work, we devise a frequency attention module to compactly decouple the spectrum into distinct frequency parts and accentuate the informative part with extremely lightweight learnable parameters. Finally, we incorporate attention modules into a U-shaped network. Extensive comparisons with prior arts on the common benchmarks show that our model, named Dual-domain Attention Network (DDANet), obtains comparable results with a significantly improved inference speed.
Weakly-Supervised Semantic Segmentation for Histopathology Images Based on Dataset Synthesis and Feature Consistency Constraint
Authors: Fang Zijie , Chen Yang , Wang Yifeng , Wang Zhi , Ji Xiangyang , Zhang Yongbing
Published: June 2023

Tissue segmentation is a critical task in computational pathology due to its desirable ability to indicate the prognosis of cancer patients. Currently, numerous studies attempt to use image-level labels to achieve pixel-level segmentation to reduce the need for fine annotations. However, most of these methods are based on class activation map, which suffers from inaccurate segmentation boundaries. To address this problem, we propose a novel weakly-supervised tissue segmentation framework named PistoSeg, which is implemented under a fully-supervised manner by transferring tissue category labels to pixel-level masks. Firstly, a dataset synthesis method is proposed based on Mosaic transformation to generate synthesized images with pixel-level masks. Next, considering the difference between synthesized and real images, this paper devises an attention-based feature consistency, which directs the training process of a proposed pseudo-mask refining module. Finally, the refined pseudo-masks are used to train a precise segmentation model for testing. Experiments based on WSSS4LUAD and BCSS-WSSS validate that PistoSeg outperforms the state-of-the-art methods. The code is released at https://github.com/Vison307/PistoSeg.
Point-Teaching: Weakly Semi-supervised Object Detection with Point Annotations
Authors: Ge Yongtao , Zhou Qiang , Wang Xinlong , Shen Chunhua , Wang Zhibin , Li Hao
Published: June 2023

Point annotations are considerably more time-efficient than bounding box annotations. However, how to use cheap point annotations to boost the performance of semi-supervised object detection is still an open question. In this work, we present Point-Teaching, a weakly- and semi-supervised object detection framework to fully utilize the point annotations. Specifically, we propose a Hungarian-based point-matching method to generate pseudo labels for point-annotated images. We further propose multiple instance learning (MIL) approaches at the level of images and points to supervise the object detector with point annotations. Finally, we propose a simple data augmentation, named Point-Guided Copy-Paste, to reduce the impact of those unmatched points. Experiments demonstrate the effectiveness of our method on a few datasets and various data regimes. In particular, Point-Teaching outperforms the previous best method Group R-CNN by 3.1 AP with 5% fully labeled data and 2.3 AP with 30% fully labeled data on the MS COCO dataset. We believe that our proposed framework can largely lower the bar of learning accurate object detectors and pave the way for its broader applications. The code is available at https://github.com/YongtaoGe/Point-Teaching.
Flexible 3D Lane Detection by Hierarchical Shape Matching
Authors: Guan Zhihao , Liu Ruixin , Yuan Zejian , Liu Ao , Tang Kun , Zhou Tong , Li Erlong , Zheng Chao , Mei Shuqi
Published: June 2023

As one of the basic while vital technologies for HD map construction, 3D lane detection is still an open problem due to varying visual conditions, complex typologies, and strict demands for precision. In this paper, an end-to-end flexible and hierarchical lane detector is proposed to precisely predict 3D lane lines from point clouds. Specifically, we design a hierarchical network predicting flexible representations of lane shapes at different levels, simultaneously collecting global instance semantics and avoiding local errors. In the global scope, we propose to regress parametric curves w.r.t adaptive axes that help to make more robust predictions towards complex scenes, while in the local vision the structure of lane segment is detected in each of the dynamic anchor cells sampled along the global predicted curves. Moreover, corresponding global and local shape matching losses and anchor cell generation strategies are designed. Experiments on two datasets show that we overwhelm current top methods under high precision standards, and full ablation studies also verify each part of our method. Our codes will be released at https://github.com/Doo-do/FHLD.
RAFaRe: Learning Robust and Accurate Non-parametric 3D Face Reconstruction from Pseudo 2D&3D Pairs
Authors: Guo Longwei , Zhu Hao , Lu Yuanxun , Wu Menghua , Cao Xun
Published: June 2023

We propose a robust and accurate non-parametric method for single-view 3D face reconstruction (SVFR). While tremendous efforts have been devoted to parametric SVFR, a visible gap still lies between the result 3D shape and the ground truth. We believe there are two major obstacles: 1) the representation of the parametric model is limited to a certain face database; 2) 2D images and 3D shapes in the fitted datasets are distinctly misaligned. To resolve these issues, a large-scale pseudo 2D&3D dataset is created by first rendering the detailed 3D faces, then swapping the face in the wild images with the rendered face. These pseudo 2D&3D pairs are created from publicly available datasets which eliminate the gaps between 2D and 3D data while covering diverse appearances, poses, scenes, and illumination. We further propose a non-parametric scheme to learn a well-generalized SVFR model from the created dataset, and the proposed hierarchical signed distance function turns out to be effective in predicting middle-scale and small-scale 3D facial geometry. Our model outperforms previous methods on FaceScape-wild/lab and MICC benchmarks and is well generalized to various appearances, poses, expressions, and in-the-wild environments. The code is released at https://github.com/zhuhao-nju/rafare.
RankDNN: Learning to Rank for Few-Shot Learning
Authors: Guo Qianyu , Haotong Gong , Wei Xujun , Fu Yanwei , Yu Yizhou , Zhang Wenqiang , Ge Weifeng
Published: June 2023

This paper introduces a new few-shot learning pipeline that
casts relevance ranking for image retrieval as binary ranking
relation classification. In comparison to image classification,
ranking relation classification is sample efficient and
domain agnostic. Besides, it provides a new perspective on
few-shot learning and is complementary to state-of-the-art
methods. The core component of our deep neural network is
a simple MLP, which takes as input an image triplet encoded
as the difference between two vector-Kronecker products,
and outputs a binary relevance ranking order. The proposed
RankMLP can be built on top of any state-of-the-art feature
extractors, and our entire deep neural network is called
the ranking deep neural network, or RankDNN. Meanwhile,
RankDNN can be flexibly fused with other post-processing
methods. During the meta test, RankDNN ranks support images
according to their similarity with the query samples,
and each query sample is assigned the class label of its
nearest neighbor. Experiments demonstrate that RankDNN
can effectively improve the performance of its baselines
based on a variety of backbones and it outperforms previous
state-of-the-art algorithms on multiple few-shot learning
benchmarks, including miniImageNet, tieredImageNet,
Caltech-UCSD Birds, and CIFAR-FS. Furthermore, experiments
on the cross-domain challenge demonstrate the superior
transferability of RankDNN.The code is available at:
https://github.com/guoqianyu-alberta/RankDNN.
Social Relation Reasoning Based on Triangular Constraints
Authors: Guo Yunfei , Yin Fei , Feng Wei , Yan Xudong , Xue Tao , Mei Shuqi , Liu Cheng-Lin
Published: June 2023

Social networks are essentially in a graph structure where persons act as nodes and the edges connecting nodes denote social relations. The prediction of social relations, therefore, relies on the context in graphs to model the higher-order constraints among relations, which has not been exploited sufficiently by previous works, however. In this paper, we formulate the paradigm of the higher-order constraints in social relations into triangular relational closed-loop structures, i.e., triangular constraints, and further introduce the triangular reasoning graph attention network (TRGAT). Our TRGAT employs the attention mechanism to aggregate features with triangular constraints in the graph, thereby exploiting the higher-order context to reason social relations iteratively. Besides, to acquire better feature representations of persons, we introduce node contrastive learning into relation reasoning. Experimental results show that our method outperforms existing approaches significantly, with higher accuracy and better consistency in generating social relation graphs.
Self-Emphasizing Network for Continuous Sign Language Recognition
Authors: Hu Lianyu , Gao Liqing , Liu Zekang , Feng Wei
Published: June 2023

Hand and face play an important role in expressing sign language. Their features are usually especially leveraged to improve system performance. However, to effectively extract visual representations and capture trajectories for hands and face, previous methods always come at high computations with increased training complexity. They usually employ extra heavy pose-estimation networks to locate human body keypoints or rely on additional pre-extracted heatmaps for supervision. To relieve this problem, we propose a self-emphasizing network (SEN) to emphasize informative spatial regions in a self-motivated way, with few extra computations and without additional expensive supervision. Specifically, SEN first employs a lightweight subnetwork to incorporate local spatial-temporal features to identify informative regions, and then dynamically augment original features via attention maps. It's also observed that not all frames contribute equally to recognition. We present a temporal self-emphasizing module to adaptively emphasize those discriminative frames and suppress redundant ones. A comprehensive comparison with previous methods equipped with hand and face features demonstrates the superiority of our method, even though they always require huge computations and rely on expensive extra supervision. Remarkably, with few extra computations, SEN achieves new state-of-the-art accuracy on four large-scale datasets, PHOENIX14, PHOENIX14-T, CSL-Daily, and CSL. Visualizations verify the effects of SEN on emphasizing informative spatial and temporal features. Code is available at https://github.com/hulianyuyy/SEN_CSLR
PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models against Adversarial Examples
Authors: Hu Shengshan , Zhang Junwei , Liu Wei , Hou Junhui , Li Minghui , Zhang Leo Yu , Jin Hai , Sun Lichao
Published: June 2023

Point cloud completion, as the upstream procedure of 3D recognition and segmentation, has become an essential part of many tasks such as navigation and scene understanding. While various point cloud completion models have demonstrated their powerful capabilities, their robustness against adversarial attacks, which have been proven to be fatally malicious towards deep neural networks, remains unknown. In addition, existing attack approaches towards point cloud classifiers cannot be applied to the completion models due to different output forms and attack purposes. In order to evaluate the robustness of the completion models, we propose PointCA, the first adversarial attack against 3D point cloud completion models. PointCA can generate adversarial point clouds that maintain high similarity with the original ones, while being completed as another object with totally different semantic information. Specifically, we minimize the representation discrepancy between the adversarial example and the target point set to jointly explore the adversarial point clouds in the geometry space and the feature space. Furthermore, to launch a stealthier attack, we innovatively employ the neighbourhood density information to tailor the perturbation constraint, leading to geometry-aware and distribution-adaptive modifications for each point.
Extensive experiments against different premier point cloud completion networks show that PointCA can cause the performance degradation from 77.9% to 16.7%, with the structure chamfer distance kept below 0.01. We conclude that existing completion models are severely vulnerable to adversarial examples, and state-of-the-art defenses for point cloud classification will be partially invalid when applied to incomplete and uneven point cloud data.
PolarFormer: Multi-Camera 3D Object Detection with Polar Transformer
Authors: Jiang Yanqin , Zhang Li , Miao Zhenwei , Zhu Xiatian , Gao Jin , Hu Weiming , Jiang Yu-Gang
Published: June 2023

3D object detection in autonomous driving aims to reason “what” and “where” the objects of interest present in a 3D world. Following the conventional wisdom of previous 2D object detection, existing methods often adopt the canonical Cartesian coordinate system with perpendicular axis. However, we conjugate that this does not fit the nature of the ego car’s perspective, as each onboard camera perceives the world in shape of wedge intrinsic to the imaging geometry with radical (non perpendicular) axis. Hence, in this paper we advocate the exploitation of the Polar coordinate system and propose a new Polar Transformer (PolarFormer) for more accurate 3D object detection in the bird’s-eye-view (BEV) taking as input only multi-camera 2D images. Specifically, we design a cross-attention based Polar detection head without restriction to the shape of input structure to deal with irregular Polar grids. For tackling the unconstrained object scale variations along Polar’s distance dimension, we further introduce a multi-scale Polar representation learning strategy. As a result, our model can make best use of the Polar representation rasterized via attending to the corresponding image observation in a sequence-to-sequence fashion subject to the geometric constraints. Thorough experiments on the nuScenes dataset demonstrate that our PolarFormer outperforms significantly state-of-the-art 3D object detection alternatives.