Miracle-dz/UniFAR-FTUs huggingface dataset:
DccGraph: Detecting Criminal Communities with Augmented Criminal Network Construction and Graph Neural Network

A criminal community is an interior group where individuals commit criminal activities with high intention. Therefore, the detection is of great importance to prevent potential crimes early in the stage. Prior studies focused on methods in modularity or network analysis based on topology. These approaches, however, do not work well for detecting minority communities, which is also a key issue in criminal detection. The main reasons are: 1) modularity-based approach cannot identify the inside community structure due to the resolution limit, 2) topology-based network analysis cannot fully leverage personal feature information, such as the amount and frequency of criminal transactions. To address these problems, this paper proposes a novel framework named DccGraph (Detect criminal communities using a Graph neural network) to enhance the overall performance of detecting criminal communities, especially minority ones. First, we extract the feature information of criminals and balance the distribution of criminal community members to construct an Augmented Criminal Network(ACN), which alleviates representation collapse and distinguish the feature of criminals in minority communities. In that case, it is capable to go beyond the resolution limit and locate minority communities effectively. Then, we design a criminal-oriented siamese graph encoder to capture both structural and feature information of criminals in the ACN. Specifically, feature interference and connection disturbance of criminals are employed to enrich the feature representation. To the best of our knowledge, DccGraph is the first framework to apply a graph neural network on criminal community detection. Experiments on several real-life dataset and benchmark datasets show that: DccGraph successfully outperforms eight baselines by 29.25%, 48.04%, 35.37%, and 40.98% on ACC, NMI, ARI, and F1, respectively. The dataset and the code for this framework are publicly available.
Traffic Policeman Gesture Recognition With Spatial Temporal Graph Convolution Network

With the rise of the popularity of self-driving cars, there would be more scenarios where the autonomous cars would be used in environments which are not properly designed for them. Building an automatic gesture recognition system is essential for self-driving cars, especially in the developing countries where the traffic signal infrastructure could be not present or working. In this project, we focus on the video understanding of the automatic gesture recognition system to classify the gesture of traffic policemen who stands at traffic intersections and give commands to the oncoming traffic. Computer vision and Deep learning has been finding more and more applications in the autonomous vehicles these days [1] – [4] We use skeleton data for pose recognition because:
OOD-Robust Boosting Tree for Intrusion Detection Systems

Out-of-distribution (OOD) detection is indispensable to security applications because they are deployed in the real world, and therefore, often face zero-day attacks. Training machine learning-based applications in an i.i. d. setting, which assumes that training and test distributions are identical, brings vulnerability to OOD samples. This paper discusses OOD sample detection in a multi-class classification on tabular data. This aspect has not been fully discussed in the literature, but tabular data are commonly analyzed in security applications. A challenging issue for OOD detection in a multi-class classification is that classifiers often make overly confident prediction on OOD samples and thus fail to detecting them. To address this issue, we designed (i) a tree-based OOD data generation approach and (ii) a training strategy for gradient boosting tree-based classifiers to increase the robustness against OOD samples. In our experiments, our OOD-robust boosting tree and ten prior arts are evaluated on eight tabular datasets, including three intrusion detection system (IDS) datasets. Our proposed method improves OOD detection performance, when measured by the true positive rates, from 58.21% to 71.48%, while maintaining classification performance. We also demonstrate case studies on the vulnerability of security applications to unseen attacks through the experiments on the IDS datasets.
Fine-Tuning Languages: Epistemological Foundations for Ethical AI in Journalism

The development of AI-based solutions in newsrooms aims to automate specific tasks or help journalists understand complex data to improve reporting and news dissemination. The professionals involved in these editorial processes are neither trained in journalism nor consider themselves journalists. They also have different views on what technology and journalism are or should be. At the same time, there is a recognized need for blending AI systems with journalistic values. From an epistemological perspective, the challenge is to apprehend the shifts in meanings and approaches to the critical concepts of accuracy, objectivity, and transparency.
Sphygmopalpation of Pulse Waves with Variable Applied Fingertip Pressure using a MEMS Flexible Tactile Sensor Array

With the advancement of Digital Chinese Medicine (DCM) using micro/nano sensors and AI technologies, researchers have invested significant efforts in acquiring pulse wave information and relating them to disease symptoms. Many flexible sensors have been developed in the past decade attempting to improve and digitize Traditional Chinese Medicine Pulse Sphygmopalpation (TCMPS) data. Typically, TCM doctors use their fingers to obtain physiological information from the wrist radial artery and determine a patient’s physical condition by sensing temporal and spatial information of the pulse waves. However, an important element in the TCMPS method is that the fingertips must apply three different pressure levels onto a patient’s wrist in order to properly obtain the wave patterns described by TCM doctors. Unfortunately, past work failed at addressing this fundamental problem for TCMPS – existing flexible sensors have not reported the capability of measuring the truly applied pressure onto a patient’s skin while measuring temporal variations of the pulse waves. We present here a method that allows TCM doctors to record a patient’s pulse in real-time using a tactile sensor array while the fingertip pressure applied to the skin can also be recorded. We believe our work will significantly advance TCM by providing a repeatable and stable pulse wave collection methodology incorporating the vital concept of sphygmopalpation with variable applied fingertip pressures.
PAD-Phys: Exploiting Physiology for Presentation Attack Detection in Face Biometrics

Presentation Attack Detection (PAD) is a crucial stage in facial recognition systems to avoid leakage of personal information or spoofing of identity to entities. Recently, pulse detection based on remote photoplethysmography (rPPG) has been shown to be effective in face presentation attack detection. This work presents three different approaches to the presentation attack detection based on rPPG: (i) The physiological domain, a domain using rPPG-based models, (ii) the Deepfakes domain, a domain where models were retrained from the physiological domain to specific Deepfakes detection tasks; and (iii) a new Presentation Attack domain was trained by applying transfer learning from the two previous domains to improve the capability to differentiate between bona-fides and attacks. The results show the efficiency of the rPPG-based models for presentation attack detection, evidencing a 21.70% decrease in average classification error rate (ACER) (from 41.03% to 19.32%) when the presentation attack domain is compared to the physiological and Deepfakes domains. Our experiments highlight the efficiency of transfer learning in rPPG-based models and perform well in presentation attack detection in instruments that do not allow copying of this physiological feature.
Forecasting Congestive Heart Failure using Deep Learning

Cardiovascular failure is one of the leading causes of death worldwide. Although significant advances have been made in the area of cardiovascular healthcare over the years, it is still challenging to accurately predict and prevent cardiovascular disease in individuals. Deep learning has been increasingly employed to develop powerful forecasting models that may be used to detect and predict cardiovascular failure. This research will provide a comparative stratification of forecasting cardiovascular failure using deep learning techniques. the strengths and weaknesses of popular deep learning methods are compared and contrasted to identify how best to use them for forecasting cardiovascular failure. The goal is to provide researchers with a comprehensive overview of deep learning for forecasting cardiovascular failure and help them make informed decisions and in the end models implemented by us are demonstrated with their accuracies to portray the advancements in the field.
Delineating minor landslide displacements using GPS and terrestrial laser scanning-derived terrain surfaces and trees: a case study of the Slumgullion landslide, Lake City, Colorado

Multi-temporal high-density terrestrial laser scanning (TLS) datasets are processed to delineating possible movements from terrain surfaces and trees. Terrain surface movements are estimated with the help of segmentation and random sample consensus (RANSAC) algorithm. Tree movements are interpreted by iterative closest point (ICP) solved translations and rotations of tree point clouds. The capabilities of the proposed methodology were tested using a case study of the Slumgullion landslide, where the trees without clear trunks cover the terrain surfaces. The displacements from the terrain surfaces and trees are similar with the results observed using our global positioning system (GPS) and historic results.
Approximating the Atomic Composition of Drug-like Molecules from Quantum Derived Properties: Inverse Design

This paper further explores an inverse design approach to molecular design consisting of using machine learning methods to approximate the atomic composition of molecules. In this generative approach the input is given by a set of desired properties of the molecule and the output is an approximation of the atomic composition in terms of its constituent chemical elements. This could serve as the starting region for further search in the huge space determined by the set of possible chemical compounds. The quantum mechanic's dataset QM9 is used in the study, composed of 133885 small organic molecules and 19 electronic properties. Different multi-target regression approaches were considered for predicting the atomic composition from the properties, including feature engineering techniques in an auto-machine learning framework. It was found that the data consist of an outer region predominantly composed of scattered outliers, and an inner, core region that concentrates clustered inliner objects. The spatial structure exhibits a relationship with molecular weight. High-quality models were found that predict the atomic composition of the molecules from their electronic properties, as well as from a subset of only 52.6 % size. Feature selection worked better than feature generation. The results validate the generative approach to inverse molecular design.
Dynamic Manipulation Like Normal-type Pen Spinning by a High-speed Robot Hand and a High-speed Vision System

This study presents a novel approach for dynamically manipulating an object, akin to standard pen spinning, by utilizing a high-speed robotic hand and visual feedback from a high-speed vision system. First, the dynamics of both standard pen spinning and the robotic hand are analyzed. Next, a pen spinning motion is developed for the robotic hand based on these dynamics. Experimental results of dynamic manipulation resembling conventional pen spinning demonstrate that the proposed simple motions of the robotic hand can achieve the desired dynamic (unstable) manipulation without the need for complex feedback controls.
Gravity-Shift-VIO: Adaptive Acceleration Shift and Multi-Modal Fusion with Transformer in Visual-Inertial Odometry

Visual-inertial odometry (VIO) estimates the 6-degree-of-freedom (6-DoF) ego-motion of an agent based on sequential data from cameras and inertial measurement units (IMUs). The acceleration measured through IMUs is affected by gravity, which is typically addressed by initialization methods in traditional VIO approaches. However, this problem has not received much attention in recent end-to-end deep learning methods. For raw accelerations, gravity causes overlapping be-tween different motion patterns, degenerating the representation embedding, which limits the performance of pose estimation. In this paper, we propose Gravity-Shift-VIO, an attention-based approach that addresses this issue by adaptively shifting the acceleration vector before the representation embedding. Further, a cross-frame multimodal transformer is introduced to fuse multimodal information. Experimentation on the KITTI dataset shows that Gravity-Shift-VIO exhibits strong performance and shows promising results in terms of ego-motion estimation. Further ablation study indicates that the Gravity-Shift- Viois highly effective in reducing the overlap of acceleration representation caused by gravity. And the cross-frame transformer effectively improves the multi-sensor fusion and time-series feature extraction.
TLMIX: Twin Leader Mixing Network for Cooperative Multi-Agent Reinforcement Learning

Recent methods of cooperative multiagent rein-forcement learning built upon the individual global max value decomposition principle show promising results using variants of deep mixing networks, and credit assignment plays a crucial role in it. However, each agent in a multiagent system requires not only credit assignment but also credit feedback which tells each agent how many rewards it should obtain to maximize expected cumulative global rewards. In this work, we propose TLMIX, a novel Twin Leader Mixing Network for multiagent cooperation reinforcement learning while maintaining the centralized training and decentralized execution paradigm. TLMIX introduces a leader network to address the credit feedback issue by utilizing global information to provide reasonable objectives for agent networks. TLMIX also introduces a twin mixing network to find a more accurate target function from the Q-value functions, which avoids the rapid increase in parameter scale caused by introducing individual agents' twin networks and effectively mitigates the accumulation of high overestimation errors caused by temporal difference updates. Extensive results on SMAC experimental scenarios and the Predator-Prey environment demonstrate that TLMIX significantly outperforms comparable benchmark algorithms on convergence speed and performance.
A Robust Wavelet-integrated Residual Network for Fault Diagnosis of Machines with Adversarial Training

In engineering applications, the performance of intelligent fault diagnosis models can be significantly impacted by noise interference in the signals. This paper aims to address this issue by analyzing the influence of noise interference on diagnosis models, focusing on the network structure and training methods. Based on the analysis findings, a wavelet-integrated residual network (WResNet) is proposed to improve the noise-robustness. WResNet integrates discrete wavelet transformation (DWT) into the residual network architecture to mitigate potential problems related to frequency aliasing caused by traditional down-sampling operations. By incorporating DWT, WResNet could reduce the impact of noise interference. In addition, a gradient-based adversarial training method is adopted for optimizing the loss function of WResNet. By minimizing the maximal risk for label-preserving fluctuations of input signals, adversarial training is able to enhance the stability of WResNet. The effectiveness of WResNet is validated by using the monitoring data from a motor with different signal-noise-ratio. The results show that compared with ResNet and the method that using wavelet transformation as a pre-processing step, WResNet is able to achieve higher diagnosis accuracy while owning better noise-robustness.
Partial Outsourcing of Malware Dynamic Analysis Without Disclosing File Contents

Dynamic analysis is one of the methods to analyze malware. However, if the file to be analyzed contains confidential information, disclosing it to the analyst outside the organization is undesirable. Previous works proposed classifying malware while preserving privacy or outsourcing dynamic analysis, but it is challenging to outsource dynamic analysis without disclosing file contents. The proposed method builds the Local Environment for users and the Remote Environment for analysts outside the organization. We proposed partial outsourcing, which opens a file in the Local Environment, reproduces its behavior in the Remote Environment, and conducts dynamic analysis based on this information. The Local Environment hooks an API call and retrieves information on the function name and arguments. Then, the Local Environment sends the information to the Remote Environment to reproduce file behavior. Our method could reproduce most operations on files and registries but could not reproduce some operations on files.
Gradient Descent Optimizes Normalization-Free ResNets

Recent empirical studies observe that even without normalization, a deep residual network can be trained reliably. We call such a structure as normalization-free Residual Networks (N-F ResNets), which add a learnable parameter $\alpha$ to control the scale of the residual block instead of normalization. However, the theoretical understanding on N-F ResNets is still limited despite their empirical success. In this paper, we provide the first theoretical understanding of N-F ResNets from two perspectives. Firstly, we prove that the gradient descent (GD) algorithm can find the global minimum of the training loss at a linear rate for over-parameterized N-F ResNets. Secondly, we prove that N-F ResNets can avoid the gradient exploding or vanishing problem, by initializing the key parameter $\alpha$ to be a small constant. Notably, we demonstrate that the gradients of N-F ResNets are more stable than those of ResNets with Kaiming initialization. Moreover, empirical experiments on benchmark datasets verify our theoretical results.
Modified Low Power High Speed Approximate Adders For Energy Efficient Arithmetic Applications

The suggested circuits are crucial for increasing the effectiveness of circuit design in approximate computing since they are related to the design of approximate full adders for energy-efficient arithmetic applications. Adders, which are essential to computing machines, are composed of the building blocks of arithmetic circuitry. Digital signal processing applications commonly use arithmetic circuits, based on MOSFET technology. Using the Cadence Virtuoso tool and 45 nm CMOS technology, the suggested new designs are simulated. The proposed innovative circuits are examined and evaluated using Cadence Software for latency, power consumption, and mistakes.
Low-cost, accurate robotic harvesting system for existing mushroom farms

This paper presents a robotic mushroom harvesting solution, consisting of an actuated scanning vision system integrated into a gantry robot. The system is capable of performing segmentation and pose estimation of the mushrooms on Dutch shelves commonly used in growing farms worldwide. The vision system employs an active stereo RGB-D camera able to capture a 360° scene of the mushroom bed, providing a high quality reconstruction of the mushroom caps. The YOLOv5 algorithm is used for the detection and size classification of the mushrooms, while a two-step model-fitting method is developed for the pose estimation task. The actuated carriage is compact, designed for operation in real mushroom-growing farms and intended to be used together with a soft gripper. The robot has five actuated degrees of freedom (DoFs), three for the linear motion on the shelves, and two DoFs for achieving the desired orientation for the gripper. In a real harvesting scenario, the robot sequentially scans the selected areas and accurately places the gripper in the appropriate angle of attack utilising our pose estimation method together with our visual servoing module for minor adjustments. The results were promising on all trials using 3D printed white button mushrooms on real soil.
Learning to Fight Against Cell Stimuli: A Game Theoretic Perspective

Current genomics interventions have limitations in accounting for cell stimuli and the dynamic response to intervention. Although genomic sequencing and analysis have led to significant advances in personalized medicine, the complexity of cellular interactions and the dynamic nature of the cellular response to stimuli pose significant challenges. These limitations can lead to chronic disease recurrence and inefficient genomic interventions. Therefore, it is necessary to capture the full range of cellular responses to develop effective interventions. This paper presents a game-theoretic model of the fight between the cell and intervention, demonstrating analytically and numerically why current interventions become ineffective over time. The performance is analyzed using melanoma regulatory networks, and the role of artificial intelligence in deriving effective solutions is described.
Exact invariant solutions for grooved Couette and channel flows

The dynamical systems approach to turbulence has gained a lot of traction since the turn of the century. A large set of exact invariant solutions for canonical wall-bounded flows such as Couette, channel, and pipe flows has been found by researchers. These solutions, and their connections, are thought to form a skeleton for trajectories of turbulent flow. However, this vision of turbulence has not been extended to rough-walled flows despite the practical signficance of such flows in engineering applications. This thesis describes continuation, by numerical homotopy, of known equilibria from smooth-walled plane shear flows to grooved plane shear flows using a domain transformation method, with the hope that this exploratory work would inform later efforts to extend such solutions to rough-walled flows. As a precursor to computing non-laminar equilibria, laminar solutions are computed for grooved channel flows for transverse, longitudinal, and oblique grooves. In addition to the numerical solutions, analytical solutions are also derived for asymptotically long groove-wavelengths, employing the Stokes-flow approximation for transverse and oblique grooves. Exact invariant solutions can indeed be continued from plane Couette flow (PCoF) with smooth walls to grooved PCoF with longitudinal grooves using a simple domain transformation method. However, smooth PCoF equilibria exist as continuous families of solutions that are identical up to a translational shift; the loss of spanwise homogeneity due to the grooves restricts such continuous families to discrete families due to symmetry-breaking. This phase-based restriction can also be expected to be reflected in turbulent statistics. Continuation of equilibria in grooves of different wavelengths shows a drag increasing tendency for grooves of the same wavelength as the vortex-streak structure, and a drag reducing tendency for grooves of significantly smaller wavelengths. This can relate the optimal spacing of riblets for maximal drag reduction to the spanwise spacing of the vortex-streak structures observed in the self-sustaining near-wall cycle.