Miracle-dz/UniFAR-FTUs huggingface dataset:
VALID: Voltage-Based Lightweight Intrusion Detection for the Controller Area Network

The Controller Area Network (CAN), a broadcasting bus for intra-vehicle communication, does not provide any security mechanisms, although it is implemented in almost every vehicle. Attackers can exploit this issue, transmit malicious messages unnoticeably and cause severe harm. As the utilization of Message Authentication Codes (MACs) is only possible to a limited extent in resource-constrained systems, the focus is put on the development of Intrusion Detection Systems (IDSs). Due to their simple idea of operation, current developments are increasingly utilizing physical signal properties like voltages to realize these systems. Although the feasibility for CAN-based networks could be demonstrated, the least approaches consider the constrained resource-availability of vehicular hardware. To close this gap, we present Voltage-Based Lightweight Intrusion Detection (VALID), which provides physics-based intrusion detection with low resource requirements. By utilizing solely the individual voltage levels on the network during communication, the system detects unauthorized message transmissions without any sophisticated sampling approaches and feature calculations. Having performed evaluations on data from two real vehicles, we show that VALID is not only able to detect intrusions with an accuracy of 99.54 %, but additionally is capable of identifying the attack source reliably. These properties make VALID one of the most lightweight intrusion detection approaches that is ready-to-use, as it can be easily implemented on hardware already installed in vehicles and does not require any further components. Additionally, this allows existing platforms to be retrofitted and vehicular security systems to be improved and extended.
A Miniaturised Neuromorphic Tactile Sensor integrated with an Anthropomorphic Robot Hand

Restoring tactile sensation is essential to enable in-hand manipulation and the smooth, natural control of upper-limb prosthetic devices. Here we present a platform to contribute to that long-term vision, combining an anthropomorphic robot hand (QB SoftHand) with a neuromorphic optical tactile sensor (neuroTac). Neuromorphic sensors aim to produce efficient, spike-based representations of information for bio-inspired processing. The development of this 5-fingered, sensorized hardware platform is validated with a customized mount allowing manual control of the hand. The platform is demonstrated to succesfully identify 4 objects from the YCB object set, and accurately discriminate between 4 directions of shear during stable grasps. This platform could lead to wide-ranging developments in the areas of haptics, prosthetics and telerobotics.
Reinforcement Learning-based Hierarchical Control for Path Following of a Salamander-like Robot

Path following is a challenging task for legged robots. In this paper, we present a hierarchical control architecture for path following of a quadruped salamander-like robot, in which, the tracking problem is decomposed into two sub-tasks: high-level policy learning based on the framework of reinforcement learning (RL) and low-level traditional controller design. More specifically, the high-level policy is learned in a physics simulator with a low-level controller designed in advance. To improve the tracking accuracy and to eliminate static errors, a soft Actor-Critic algorithm with state integral compensation is proposed. Additionally, to enhance the generalization and transferability, a compact state representation, which only contains the information of the target path and the abstract action similar to front-back and left-right, is proposed. The proposed algorithm is trained offline in the simulation environment and tested on the self-developed real quadruped salamander-like robot for different path following tasks. Simulation and experiments results validate the satisfactory performance of the proposed method.
Encoder-Decoder Structure With the Feature Pyramid for Depth Estimation From a Single Image

We address the problem of depth estimation from a single monocular image in the paper. Depth estimation from a single image is an ill-posed and inherently ambiguous problem. In the paper, we propose an encoder-decoder structure with the feature pyramid to predict the depth map from a single RGB image. More specifically, the feature pyramid is used to detect objects of different scales in the image. The encoder structure aims to extract the most representative information from the original image through a series of convolution operations and to reduce the resolution of the input image. We adopt Res2-50 as the encoder to extract important features. The decoder section uses a novel upsampling structure to improve the output resolution. Then, we also propose a novel loss function that adds gradient loss and surface normal loss to the depth loss, which can predict not only the global depth but also the depth of fuzzy edges and small objects. Additionally, we use Adam as our optimization function to optimize our network and speed up convergence. Our extensive experimental evaluation proves the efficiency and effectiveness of the method, which is competitive with previous methods on the Make3D dataset and outperforms state-of-the-art methods on the NYU Depth v2 dataset.
QoS Aware and Fair Resource Distribution for Uplink NOMA Cellular Networks

Non orthogonal multiple access (NOMA) technique achieved a lot of interest due to its extraordinary spectral efficiency and ready to become the best possible solution for fifth generation (5G). In this paper, system throughput maximization is analyzed under the uplink NOMA scenario. The scarcity of resources motivates the researchers for finding some optimal way to allocate or share the resources among users. This paper focuses on the objective of system throughput maximization under the optimal channel and power allocation constraint. Our problem is mixed integer nonlinear programming (MINLP) problem and NP-hard to solve. Consequently, it is solved in two sequential steps. The first is channel assignment through different schemes and second is power allocation that has been done (while satisfying constraints such as power budget and quality of service (QoS)) to the optimal channel paired users. The simulation results are discussed, analyzed and the considered performance metric is system throughput.
Autonomous Robot Navigation Based on Multi-Camera Perception

In this paper, we propose an autonomous method for robot navigation based on a multi-camera setup that takes advantage of a wide field of view. A new multi-task network is designed for handling the visual information supplied by the left, central and right cameras to find the passable area, detect the intersection and infer the steering. Based on the outputs of the network, three navigation indicators are generated and then combined with the high-level control commands extracted by the proposed MapNet, which are finally fed into the driving controller. The indicators are also used through the controller for adjusting the driving velocity, which assists the robot to adjust the speed for smoothly bypassing obstacles. Experiments in real-world environments demonstrate that our method performs well in both local obstacle avoidance and global goal-directed navigation tasks.
Estimating Pure Component Vapor Pressures of Complex Organic Molecules

Modifications of the entropy of boiling term and heat capacity change upon boiling term are made to the Yalkowsky−Mishra vapor pressure equation. These modifications eliminate a systematic error and enable the pure component vapor pressure estimation of complex organic compounds, including those that are hydrogen bonding. The new vapor pressure equation, which requires only the knowledge of transition temperatures and molecular structure, is shown to be more accurate than the Yalkowky−Mishra equation for a wide variety of compounds over a wide range of temperatures (vapor pressures from 1 atm and below).
RDS-SLAM: Real-Time Dynamic SLAM Using Semantic Segmentation Methods

The scene rigidity is a strong assumption in typical visual Simultaneous Localization and Mapping (vSLAM) algorithms. Such strong assumption limits the usage of most vSLAM in dynamic real-world environments, which are the target of several relevant applications such as augmented reality, semantic mapping, unmanned autonomous vehicles, and service robotics. Many solutions are proposed that use different kinds of semantic segmentation methods (e.g., Mask R-CNN, SegNet) to detect dynamic objects and remove outliers. However, as far as we know, such kind of methods wait for the semantic results in the tracking thread in their architecture, and the processing time depends on the segmentation methods used. In this paper, we present RDS-SLAM, a real-time visual dynamic SLAM algorithm that is built on ORB-SLAM3 and adds a semantic thread and a semantic-based optimization thread for robust tracking and mapping in dynamic environments in real-time. These novel threads run in parallel with the others, and therefore the tracking thread does not need to wait for the semantic information anymore. Besides, we propose an algorithm to obtain as the latest semantic information as possible, thereby making it possible to use segmentation methods with different speeds in a uniform way. We update and propagate semantic information using the moving probability, which is saved in the map and used to remove outliers from tracking using a data association algorithm. Finally, we evaluate the tracking accuracy and real-time performance using the public TUM RGB-D datasets and Kinect camera in dynamic indoor scenarios.Source code and demo: https://github.com/yubaoliu/RDS-SLAM.git
Program Slice based Vulnerable Code Clone Detection

Vulnerabilities in software will not only lead to security problems of the software itself, but also cause the spread of vulnerabilities through code clones. It is important to detect and locate vulnerabilities among the source code to facilitate the fix. Although many methods are proposed to detect code clones in source code, most of them fail to detect code clones that involve statement addition and deletion effectively or are not suitable for vulnerability detection. In this paper, we propose a method that can detect vulnerabilities caused by code clones. Program slices are used to filter statements that are not related to vulnerabilities and extract important vulnerable statements in function. Hash function and bitvector are applied to improve efficiency during the detection. The results are displayed in html, among which the vulnerable statements are highlighted to help subsequent patching work. Our method is evaluated on open source software (Openssl, Linux Kernel, FFmpeg and QEMU). The results of experiments show that our method detects 12.72% more vulnerable clones in acceptable time compared with Vuddy, proving the effectiveness of our method.
Dynamic Relay Selection using a Greedy Cluster Strategy

One of the primary challenges of relay assisted communications in ad hoc network and device-to-device (D2D) communications in cellular network is sustenance of QoS requirements of mobile users. This paper proposes a relay selection technique based on a greedy clustering scheme that takes relay load and willingness into consideration during relay selection. With a dynamic selection imperative in a mobile environment this technique aims at optimizing the number of selected relays of a node by monitoring the current status of the relays. We compare our proposed technique with the relay selection technique of optimized Link State Routing (RFC 7181) over two scenarios: the first in an ad hoc scenario with a collection of mobile nodes and the second in D2D communication over cellular scenario. Simulation results show an overall improvement in performance in terms of average network throughput, packet loss rate and end-to-end delay for our proposed technique.
VD-GAN: A Unified Framework for Joint Prototype and Representation Learning From Contaminated Single Sample per Person

Single sample per person (SSPP) face recognition with a <inline-formula> <tex-math notation="LaTeX">${c}$ </tex-math></inline-formula>ontaminated biometric <inline-formula> <tex-math notation="LaTeX">${e}$ </tex-math></inline-formula>nrolment database (SSPP-ce FR) is an emerging practical FR problem, where the SSPP in the enrolment database is no longer standard but contaminated by nuisance facial variations such as expression, lighting, pose, and disguise. In this case, the conventional SSPP FR methods, including the patch-based and generic learning methods, will suffer from serious performance degradation. Few recent methods were proposed to tackle SSPP-ce FR by <italic>either</italic> performing prototype learning on the contaminated enrolment database <italic>or</italic> learning discriminative representations that are robust against variation. Despite that, most of these approaches can only handle a specified <italic>single</italic> variation, e.g., pose, but cannot be extended to <italic>multiple</italic> variations. To address these two limitations, we propose a novel Variation Disentangling Generative Adversarial Network (VD-GAN) to <italic>jointly</italic> perform prototype learning and representation learning in a unified framework. The proposed VD-GAN consists of an encoder-decoder structural generator and a multi-task discriminator to handle universal variations including single, multiple, and even mixed variations in practice. The generator and discriminator play an adversarial game such that the generator learns a discriminative identity representation and generates an identity-preserved prototype for each face image, while the discriminator aims to predict face identity label, distinguish real vs. fake prototype, and disentangle target variations from the learned representations. Qualitative and quantitative evaluations on various real-world face datasets containing single/multiple and mixed variations demonstrate the effectiveness of VD-GAN.
Differential Privacy Preservation in Interpretable Feedforward-Designed Convolutional Neural Networks

Feedforward-designed convolutional neural network (FF-CNN) is an interpretable network. The parameter training of the model does not require backpropagation (BP) and optimization algorithms (SGD). The entire network is based on the statistical data output by the previous layer, and the parameters of the current layer are obtained through one-pass manner. Since the network complexity under the FF design is lower than the BP algorithm, FF-CNN has better utility than the BP training method in the directions of semi-supervised learning, ensemble learning, and continuous subspace learning. However, the FF-CNN training process or model release will cause the privacy of training data to be leaked. In this paper, we analyze and verify that the attacker can obtain the private information of the original training data after mastering the training parameters of FF-CNN and the partial output responses. Therefore, the privacy protection of training data is imperative. However, due to the particularity of the FF-CNN training method, the existing deep learning privacy protection technology is not applicable. So we proposed an algorithm called differential privacy subspace approximation with adjusted bias (DPSaab) to protect the training data in FF-CNN. According to the different contribution of the model filters to the output response, we design the privacy budget allocation according to the ratio of the eigenvalues and allocate a larger privacy budget to the filter with a large contribution, and vice versa. Extensive experiments on MNIST, Fashion-MNIST, and CIFAR-10 datasets show that DPSaab algorithm has better utility than existing privacy protection technologies,
A Novel Frame model for Cluster Head Selection and Codeword Detection in the 5G Cellular Networks

4G communications were ruling the entire world with its high-speed network; however, if the users increased, then its speed gets decreased. The 5G model developed, and its rate of data is higher than the 4G frame model. Also, the dense weight node in the cellular network consumed more energy that tends to signal failure. So to make the 5G mobile communications efficient, the present article aimed to develop a novel Grey Wolf (GW) clustering model to choose the cluster head. Moreover, the codeword selection refined by a novel Generalized Intelligent Fuzzy (GIF) mode. Finally, the predictive model as a novel African Buffalo-based Recurrent Model (ABRM) deep learning model developed as the predictive model for continuous multiuser (MU) prediction and monitoring. Subsequently, the data transferred effectively, and its success rate is evaluated with existing models our proposed model gained an excellent outcome by attaining 98.8% of accuracy and reduced complexity rate as 17%.
Data Authentication and Anonymization in IoT Scenarios and Future 5G Networks Using Chaotic Digital Watermarking

Future Internet-of-Things (IoT) scenarios and applications are envisioned to be supported by emerging 5G networks. In this context, complex routing schemes for pervasive infrastructures are highly simplified, as every hardware element may stablish its own communication link with a 5G base station. However, this situation also introduces new risks, especially in the security field where innovative cyber-physical attacks and distributed denial of service attacks are becoming more popular and dangerous each day. Thus, data authentication, protection and anonymization in those new applications and schemes is a key challenge to be addressed. Besides, most devices in future IoT systems will be resource constrained, so traditional solutions based on private keys stored in devices’ memory and computationally heavy cryptographic algorithms will turn unsecure, inefficient or, directly, impossible to run. Therefore, in this paper we propose a new mechanism to protect, authenticate and anonymize data in IoT systems supported by future 5G networks. The proposed solution employs both digital watermarking techniques and lightweight cryptographic technologies. To generate keys in a secure and simple manner, physical unclonable functions are employed. Besides, to reduce as much as possible the computational cost of algorithms, chaotic dynamics will be considered. In order to evaluate the performance of the proposed solution an experimental validation based on simulation techniques is also carried out.
2DSlicesNet: A 2D Slice-Based Convolutional Neural Network for 3D Object Retrieval and Classification

3D data can be instrumental to the computer vision field as it provides insightful information about the full 3D models’ geometry. Recently, with easy access to both computational power and huge 3D databases, it is feasible to apply convolutional neural networks to automatically extract the 3D models’ features. This paper presents a novel approach, called 2DSlicesNet, which deals with the issue of 3D model retrieval and classification using a 2D slice-based representation with a 3D convolutional neural network. The assumption in this context is that similar 3D models will be composed of almost identical 2D slices. Therefore, we first transform each normalized 3D model into a set of 2D slices corresponding to its first main axis, and then use them as input data to our 3D convolutional neural network. Experimental results and comparison with state-of-the-art approaches, using ModelNet10 and ModelNet40 datasets, prove that our proposed 2DSlicesNet approach can reach notable rates of accuracy in classification and retrieval.
Unsupervised Anomaly Detection on Multisensory Data from Honey Bee Colonies

Beekeepers face the situation that the health state of honey bee colonies is inherently difficult to observe without stressing the bees by opening the hive. We address this problem by proposing an approach that relies on a sensor setup to gather multisensory data inside the bee colony and focus on the detection of outliers in the data stream as indicators of critical situations during the colony's development. Based on data recorded during the citizen science project Bee Observer BOB we demonstrate that algorithms exploiting an ARIMA Model and Receiver Operating Characteristics in combination with an underlying multi agent system are well able to detect and classify anomalies in honey bee colonies. In future applications this concept can be used for both - as a stealth monitoring tool in honey bee research as well as a precise technical assistance for minimally invasive, bee friendly practices in hobbyist and professional beekeeping.
Computer Modelling the Effects of Chloroquine on KCNJ2 D172N and E299V Mutations-Linked Short QT Syndrome

Short QT syndrome (SQTS) is an inherited cardiac disease characterized by ventricular tachyarrhythmias leading to sudden cardiac death (SCD). SQT3 has been linked to the D172N or E299V gain-in-function mutation to Kir2.1, which preferentially increases outward current through channels responsible for inward rectifier K+ current (IK1). Until now, the only effective therapy is implantation of a defibrillator whereas clinical drug therapy is little known about it. Previous studies reported that anti-malarial drug chloroquine was an effective inhibitor of the mutant Kir2.1 channels. The effectiveness of chloroquine has demonstrated in SQT3 associated with D172N mutation, there is a reason to believe that it may be useful in the other mutations in SQT3, such as E299V mutation. Therefore, we aim to predict and compare the potential effects of drug chloroquine on SQT3 D172N and E299V mutations. In this study, the ten Tusscher model of ventricular cell was coupled with the SQT3 mutant IK1 patch clamp data. Our simulation data showed that chloroquine prolonged the ventricular cell action potential duration (APD) and QT interval under SQT3 condition, and demonstrated that chloroquine produced a therapeutic effect on different mutant SQT3. This study provides new evidence that chloroquine may be a potential generic drug for the treatment of SQTS.
Bandits Under the Influence

Recommender systems should adapt to user interests as the latter evolve. A prevalent cause for the evolution of user interests is the influence of their social circle. In general, when the interests are not known, online algorithms that explore the recommendation space while also exploiting observed preferences are preferable. We present online recommendation algorithms rooted in the linear multi-armed bandit literature. Our bandit algorithms are tailored precisely to recommendation scenarios where user interests evolve under social influence. In particular, we show that our adaptations of the classic LinREL and ThompsonSampling algorithms maintain the same asymptotic regret bounds as in the non-social case. We validate our approach experimentally using both synthetic and real datasets.