Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Bot Detection Based on Window Strategy

With social bots evolving continually, the new bots post highly anthropomorphic posts to evade detection. For post content information, existing methods choose to extract features from single posts or use the rough characterization of the pretraining language model for bot detection. However, the evolution of bots has made them better at camouflage in previous posts processing techniques. According to our observation, the purpose of bot posting is to publicize different contents in different periods, and its posting often shows abnormal interest changes, while human posting is to express hobbies and daily life, and its interest changes are relatively stable. Therefore, we extract the interest changes between multiple posts published by users for bot detection. In this paper, we propose a social Bot detection model based on the Window Strategy(BotWS). Specifically, we first employ the window strategy to obtain the user posting windows, each containing multiple posts. Then, we extract the interest changes between the posting windows by multi-head attention mechanism. Finally, we embed the interest changes information into the user representation and construct a heterogeneous graph classification module to classify. We conduct experiments on three challenging datasets. Results indicate that our approach achieves state-of-the-art.
Identifying Human Grasp Properties During Robot-to-Human Handovers

One of the most important challenges in Human-Robot Interaction (HRI) is the perception of the human state. When a robot physically engages with a human, such as during physical interaction and assistance, it is vital that the robot perceives as much information as possible about the human to properly guide its behavior. We examined the specific case of a robot handing a rigid object to a human and used only the robot’s force and motion sensors to determine when the human’s grasp was secure enough for the robot to safely release the object. From biomechanical reasoning, we assumed that safer grasps are stiffer grasps. We commanded our robot to impose small motions on the object being passed and measured the resulting force changes and used system identification techniques to measure and visualize changes in the human’s grasp stiffness. When subjects were instructed to grasp more tightly an object being passed, our robot could reliably detect increases in several measures of the multi-dimensional stiffness of the human’s grasp. Our technique also enabled us to measure the increases in damping with tighter grasps. This preliminary work demonstrates promising value for active haptic perception during physical HRI.
Bioremediation impact on intertidal communities affected by the Prestige oil spill at Sorrizo beach (Galicia)

In November 2002 the sinking of the oil tanker Prestige caused one of the worst oil spills off the European coastline. The Marine Biology Station of A Grana carried out a study aimed at assessing the effect of certain bioremediating products used to remove oil from the coast as well as other cleaning techniques used (e.g. hydrocleaning) both on the flora and the fauna. This study was carried out during a year (2003-2004) at Sorrizo inlet (A Coruna, Galicia, NW Iberian Peninsula). This paper sets out the results obtained in the study of both the impact on the fauna and flora caused by bioremediating products and cleaning techniques used at the inlet and the impact caused by the Prestige fuel itself. The use of bioremediation hardly influenced the flora and fauna at Sorrizo beach. A temporary increase of fauna was observed in all sampling areas as shown in the number of species and total abundance. Besides, certain similarities between samples of September 2003 and 2004 were observed both in abundance and specific richness
Abnormal Behavior Analysis for Surveillance in Poultry Farms using Deep Learning

The poultry farming sector plays a vital role in supplying sustenance to an expanding population. Nevertheless, the birds’ well-being remains a crucial issue, as inadequate living conditions result in abnormal behaviors that impact the productivity and health of the entire crowd. In order to improve and sustain the health of the birds, an automated surveillance system that monitors and analyzes chickens behaviors needs to take in place. The study of abnormal behavior analysis for surveillance in poultry farms is of essential importance in ensuring the health, welfare, and productivity of the birds. This research aims to develop a comprehensive understanding of the various factors contributing to abnormal behavior patterns and the methods for effectively monitoring and detecting these behaviors. By identifying and addressing issues related to illness, stress, or discomfort at an early stage using the proposed system, farmers can implement targeted interventions to improve the overall well-being of the birds, leading to enhanced production efficiency and profitability. Furthermore, this research contributes to the development of sustainable poultry farming practices, protecting public health, and safeguarding food safety, highlighting the significance of abnormal behavior analysis in the poultry industry. This paper proposes a computer vision based system that monitors and analyzes the chickens’ behaviors in poultry farms. The system takes video input and segments them into 10-second segments. The proposed system achieves an accuracy of 96.43% using a convolutional neural network for heatmaps classification.
Latent Feature Regularization based Adversarial Network for Brain Tumor Anomaly Detection

Brain tumor anomaly detection plays a critical role in the field of computer-aided diagnosis, which has attracted ever-increasing focus from the medical community However, brain tumor data are scarce and tough to classify. Unsupervised methods enable the reduction of huge labeling costs to be applied to brain tumor anomaly detection during the training only given normal brain images. However, the existing unsupervised methods distinguish whether the input image is abnormal in the image space, which cannot effectively learn the discriminative features. In this paper, we propose a novel brain tumor anomaly detection method via Latent Feature Regularization based Adversarial Network (LFRA-Net), which leverages a latent feature regularizer into adversarial learning to obtain the discriminative features. Comprehensive experiments on BraTS, HCP, MNIST, and CIFAR-10 datasets evaluate the effectiveness of our LFRANet, which outperforms state-of-the-art unsupervised learning methods.
Deep Homography Estimation With Feature Correlation Transformer

Homography estimation is an important image alignment method that has been widely used in computer vision applications. Traditional methods heavily rely on the distribution of features and usually fail in low-texture and large-baseline scenes. Most learning-based methods use convolutional neural networks(CNNs) to extract features. However, the dense features extracted in this way have a limited receptive field, leading to poor accuracy of results. In this paper, we propose a novel method for homography estimation. We first estimate the projective transformation between the reference image and the target image at a coarse level and then refine the estimated homography at the fine level. Unlike approaches that use simple CNNs or global correlations to search correspondences, we add self- and cross-attention layers in the transformer to enhance the feature correlations. The experiments show that our method significantly outperforms the existing solutions in challenging large-baseline scenes.
Exploration of FPGA PLB Architecture Base on LUT and Microgates

Because of the logic redundancy of look-up table (LUT) based field programmable gate array (FPGA), we explore a series of programmable logic block (PLB) architectures to increase logic density by adding different kinds of microgates including AND gate and exclusive-OR gate. Because individual LUT inputs have different delays to the output, adding microgates to the shortest input-output path does not influence the LUT critical path delay. To evaluate the proposed PLB architectures, the Boolean matching based on disjoint-support decomposition (DSD) library in ABC is used to realize technology mapping and followed by VPR 8.0 to pack, place and route. Evaluation results based on VTR and Koios benchmarks show that the proposed PLB architecture by adding two AND gates to LUT6 can achieve the best performance, which reduces the logic depth, the utilization of PLBs, and the critical path delay by 19.8%, 4.36%, and 6.25% respectively on average compared to Intel Stratix-10 PLB architecture in VTR.
Suserup Skov : structures and processes in a temperate, deciduous forest reserve

The history and present conditions of Suserup Skov - a nemoral, deciduous forest reserve in a cultural landscape. J. Heilmann-Clausen,R. H. W. Bradshaw, J. Emborg and G. Hannon. The structure of Suserup Skov, 2002. The first re-measurement of a long-term permanent plot study of forest dynamics started in 1992. J. Emborg and J. Heilmann-Clausen. The forest cycle of Suserup Skov - revisited and revisted. M. Christensen, J. Emborg and A. B. Nielsen. What is beneath the canopy? Structural complexity and understorey light intensity in Suserup Skov, eastern. Denmark. A. B. Nielsen and K. Hahn. Suppression and release during canopy recruitment in Fagus sylvatica and Fraxinus excelsior, a dendroecological. study of natural growth patterns and competition. J. Emborg. Structural impact of gale damage on Suserup Skov, a near-natural temperate deciduous forest in Denmark. J. Bigler and A. Wolf. Above and below ground gaps - the effects of a small canopy opening on throughfall, soil moisture and tree. transpiration in Suserup Skov, Denmark. L. Dalsgaard. Nitrate in soil solution and nitrogen availability in litter and soil after gap formation in the semi-natural Suserup. Skov and two managed beech Fagus sylvativa forests in Denmark. E. Ritter. The carbon pools in a Danish semi-natural forest. L. Vesterdal and M. Christensen. Nematode assemblges and their responses to soil disturbance differ between microsites in Suserup. Skov, a semi-natural forest. L. Bjornlund and J. D. Lekfeldt. Gap regeneration in four natural gaps in Suserup Skov - a mixed deciduous forest reserve in Denmark. K. Hahn, P. Madsen and S. Lindholt. Growth and photosynthesis of ash Fraxinus excelsior and beech Fagus sylvatica seedlings in response to a light. gradient following natural gap formation. K. S. Einhorn. Ground flora in Suserup Skov: characterized by forest continuity and natural gap dynamics or edge-effect. and introduced species? K. Hahn and R. P. Thomsen. Natural forest stand dynamics in time and space - synthesis of research in Suserup Skov, Denmark and perspectives for forest management. K. Hahn, J. Emborg, L. Vesterdal, S. Christensen, R. H. W. Bradshaw, K. Raulund-Rasmussen and. J. B. Larsen
Stability of delay differential equations in the sense of Ulam on unbounded intervals

In this paper, we consider the stability problem of delay differential equations in the sense of Hyers-Ulam-Rassias. Recently this problem has been solved for bounded intervals, our result extends and improve the literature by obtaining stability in unbounded intervals. An illustrative example is also given to compare these results and visualize the improvement.
Influence of Electrical Stimulation Intensity on the Perception of Piquancy

The interface designed to enhance the perceived piquancy is expected to overcome the trade-off problem between the positive and negative effects of consuming piquant substances on human health. Previous research has shown that anodal electrical stimulation of the tongue can enhance the perceived piquancy. However, a method for dynamically controlling the intensity of perceived piquancy is yet to be established. In this study, we focused on the relationship between the intensity of perceived piquancy and the amount of current generated via electrical stimulation. Psychophysical experiments were conducted to evaluate the relationship. The results demonstrate an enhancement in the intensity of the piquancy perception induced by chili peppers and wasabi through changes in the amount of current via electrical tongue stimulation.
Android Malware Detection Using Machine Learning Techniques

The exponentially growing use and popularity of the Android Operating System made it more prone to malware attacks. Easy to use android smartphones attracted intruders which resulted in the need for a novel malware detection method. Several works are proposed which makes use of machine learning techniques to detect malign and benign applications. To provide the solution of addressed problem of detection of malware in android, in this paper, we have provided a decent approach based on static analysis of the android apk for feature extraction like API Calls, Intents, Permissions and Command signature. We have used the Drebin[2] [26] and Malgenome[35] datasets, both the datasets have a good amount of goodwares and malwares to train the machine learning classifier. After feature extraction from android APK (Android Package Kit), we performed various experiments taking into consideration all the combined extracted features as well as generated the twosome combination of permission. By taking all four extracted features our model achieved a high classification accuracy of 98.19% with Drebin Dataset and with the twosome permission combination it achieved an accuracy of 96.27% with the RF model. Additionally, in the case of Malgenome Dataset, we have an accuracy of 98.84% considering all four features and 97.63% in the case of permission twosome combination SVM with PCA (Principal Component Analysis). Both experiments have given a decent result compared to some of the existing work considering the same scenario in the android malware domain.
Visual Garment Collar Style Identification using Deep Neural Networks

Modeling fashion style is key for fashion industry. While traditional manually analysis is slow and inefficient, we aim to automate it using deep neural networks. Particularly, in this paper, we propose a VGG16-based collar recognition network that is more suitable for commercial use to solve the problem of difficult collar recognition in the apparel field. Furthermore, to enable our research and further research using deep learning, we create a dataset called COLLAR 2000. To achieve the commercial network for recognition accuracy, network stability, network complexity and other requirements, we explored traditional machine learning methods as well as mature neural networks, respectively, by adjusting the parameters of PCA, SVM, LeNet, AlexNet, VGG16 and combining migration learning methods to make them more suitable for garment collar recognition. The experimental results show that the VGG16-based collar recognition network is the best for collar recognition (81.7% recognition accuracy, 83.0%precision, 81.7% recall, 0.82 F1-Score), the network stability and complexity are moderate, which is more suitable for commercial use.
EANet: Towards Lightweight Human Pose Estimation With Effective Aggregation Network

Existing solutions to lightweight human pose estimation typically adopt a depthwise separable strategy, i.e., a normal 2D convolution is factorized into channel aggregation and spatial aggregation. However, this strategy cannot well capture multi-scale Effective Receptive Field (ERF), which is essential to dense prediction tasks like human pose estimation. To address this issue, we propose a novel lightweight network for human pose estimation, namely effective aggregation net (EANet). In EANet, we introduce two lightweight computational units: effective channel aggregating (ECA) and effective spatial aggregating (ESA), which are respectively responsible for channel-wise feature aggregation and pixel-wise feature aggregation. Unlike typical channel-wise aggregation using pointwise (1 × 1) convolution, the ECA aggregates few feature points that are estimated as effective ones. Moreover, the ESA is designed with re-parameterizing techniques, and it aggregates effective spatial feature points with multi-scale shared convolutions. Comprehensive experiments are conducted on three challenging datasets, i.e., COCO, Crowd-Pose, Wholebody-COCO. Our EANet demonstrates superior results on human pose estimation over previous lightweight methods, reaching a new state-of-the-art performance with a good trade-off. Our code and models are publicly available1.
Indoor environment : airborne particles and settled dust

Foreword.Preface. List of ContributorsList of Symbols and Abbreviations.PART 1. FUNDAMENTALS.Fundamentals of Indoor Particles and Settled Dust (L. Morawska & T. Salthammer). PART 2. SAMPLING AND MEASUREMENT.Introduction to Sampling and Measurement Techniques (L. Morawska & T. Salthammer). Measurement of Airborne Particles (A. Schmidt-Ott & Z. Ristovski). Sampling of Surface Dust in Buildings (T. Schneider). Analysis of Chemical and Biological Properties (K. BeruBem, et al.). PART 3. APPLICATIONS AND CASE STUDIES.Organic Compounds Adsorbed on Particles and Settled House Dust (G. Ayoko & E. Uhde). Indoor Chemistry as a Source of Particles (C. Weschler). Particle Concentration Levels and Size Distribution Characteristics in Residential and Non-Industrial Workplace Environments (L. Morawska & C. He). Asbestos and Mineral Fibers (D. Bake). Environmental Tobacco Smoke Particles (W. Nazaroff & N. Klepeis). The Effect of Filtration in Heating, Ventilation, and Air-Conditioning Systems (D. Ensor, et al.). Motor vehicle Emissions as a Source of Indoor Particles (L. Morawska). Modeling of Indoor Particle Concentration (M. Jamriska). The Phenomenon of "Black Magic Dust" in Housing Units (M. Wensing, et al.). PART 4. EXPOSURE AND RISK ASSESSMENT.Assessment of Exposure to Airborne Particles (M. Singh & C. Sioutas). Health Effects of Airborne Dust and Particulate Matter Indoors: A Review of Three Climate Chambers Studies (L. Molhave). Reference Values of Environmental Pollutants in House Dust (W. Butte). Subject Index.
THEORIES OF COUNSELING AND PSYCHOTHERAPY : A Multicultural Perspective , 6 / e © 2007

The pages of this Sample Chapter may have slight variations in final published form. c h a p t e r g o a l s This chapter is designed to: 1. Foster an increased understanding of the Adlerian and Jungian theories of counseling and psychotherapy. 2. Assist you in exercising some of the skills that are associated with Alfred Adler's and Carl Jung's helping theories. 3. Deepen your thinking about the strengths and limitations of Adlerian and Jungian counseling from a multicultural-feminist-social justice perspective. 4. Expand your understanding of some of the ways in which Adlerian and Jungian concepts can be used in other aspects of your work.
Information Transfer of Full-Body Vibrotactile Stimuli: An Initial Study with One to Three Sequential Vibrations

Full-body vibrotactile stimuli are a promising means by which haptic interaction designers can substantially increase information transfer (IT). This paper reports the IT values of full-body sequential vibrotactile stimuli applied to ten body sites. We designed 10, 100, and 1,000 stimuli by increasing the number of vibrations in a sequence from one to three and estimated IT for each condition. We also obtained the lower and upper bounds of the IT estimates using several empirical methods to compensate for their biases. The IT estimate for each condition was 3.32, 6.55, and 8.05 bits, the highest IT ever reported in the haptics literature. This study is the first attempt to quantity the IT of full-body vibrotactile sequences, providing guidelines for the design of effective full-body vibrotactile stimuli.
Experimental study on the partial discharge and AC breakdown properties of C 4 F 7 N/CO 2 mixture

: As an environmental friendly insulating medium, C 4 F 7 N has received extensive attention in the past two years. In this study, the partial discharge (PD) and breakdown characteristics of C 4 F 7 N/CO 2 gas mixture were investigated using the gas insulation performance test platform. The influence of gas pressure and mixing ratio on the PD initial voltage (PDIV) and breakdown voltage of C 4 F 7 N/CO 2 gas mixture in different electric fields was tested. It was found that the PDIV and breakdown voltage of gas mixture with 2–8% C 4 F 7 N in the highly non-uniform field showed a linear saturation increasing trend with the change of gas pressure and mixing ratio. The breakdown voltage of gas mixture increases linearly with gas pressure in the quasi-uniform electric field. The relative breakdown voltage of gas mixture in the highly non-uniform field under high-pressure conditions is inferior to those of low-pressure conditions. The C 4 F 7 N/CO 2 gas mixture containing 2–8% C 4 F 7 N is sensitive to the non-uniformity of electric field. According to the PD and breakdown tests results, C 4 F 7 N/CO 2 gas mixture with 2–8% C 4 F 7 N has the potential to replace SF 6 using in gas-insulated equipment.
Cascade Trajectory Optimization with Phase Duration Adaption and Control for Wheel-Legged Robots Overcoming High Obstacles

In this paper, we propose a framework that contains a cascade trajectory optimization (CTO) method and a hybrid control architecture for wheel-legged quadruped robots. Our framework allows the robot to go over high obstacles efficiently without pause in body movement and maintain stability simultaneously. In detail, the CTO is first presented for planning the support motion of the entire process over obstacles. The CTO is used to optimize the body states, the locations and contact forces of the end-effector (EE) for each leg in the support state, and the duration of the phase, with multiple constraints taken into count. Then, the output of the CTO is reprocessed to connect the support motion with the swing part for generalizing the final trajectory. A hybrid control architecture consisting of body motion control, EE control, and swing leg control is designed to track the reprocessed trajectory. Validation in simulation demonstrates that our wheel-legged robot can overcome high obstacles up to 0.90m, showing the feasibility of this framework.
Learning Group Residual Representation for Group Activity Prediction*

The goal of group activity prediction is to infer the group activity involved multiple individuals before it is completely executed. Previous methods focused on capturing pair-wise relationships between individuals, but lacked the exploration of group-wise interactions which can provide global guidance from a macroscopic perspective. To further explore the group-wise interaction, we propose a Group Residual Module (GRM) which constructs a virtual leader node to summarize the group representation and designs a bidirectional message passing mechanism to build the bridge between group and individuals. To capture the spatial-temporal correlation jointly, we propose a Spatial-Temporal Group Residual Network composed of spatial GRMs and temporal GRMs. Different from existing methods that obtain additional information from the complete activity execution, temporal masks in the temporal GRMs are designed to enforce our network to excavate as much discriminative information as possible from the observed activity sequence. Moreover, experimental results show that our network achieves state-of-the-art performance on Volleyball Dataset and Collective Activity Dataset.
refractive and visual outcomes after Keraring intrastromal corneal ring segment implantation for keratoconus assisted by femtosecond laser at 6 months follow-up

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