Miracle-dz/UniFAR-FTUs huggingface dataset:
An Approach to Reduce Uncertainty Problem in Network Intrusion Detection Systems

In the current scenario pertaining to cyberattacks, Denial of Service attacks are the most common type. Denial of Service (DoS) has now become an attack category that has different types of attacks such as Back, Neptune, Smurf, Teardrop, etc. As common as these attacks are, they are one of the most troublesome to deal with and have become an annoyance in the industry. Along with those, attacks like User-to-Root (U2R), Remote-to-Local (R2L) and Probe are used to gain access to the system and hence form the cycle of an attack. A network intrusion detection system is proposed which is tailored to detect these attacks. The main objective is to classify the aforementioned types of attacks with minimum uncertainty and reduce the number of false positives for more reliable detection. With data mining coupled with machine learning and deep learning algorithms, a feature selection and a classification model is built by primarily training it on the KDDCup99 dataset and the ISTS Dataset, then tweaking the models by testing it on real-time data gathered from tcpdump. Real-time data collected using the ISTS dataset is firstly labelled using unsupervised machine learning methods and also by matching the data with the KDDCup99 dataset records. A model with the most optimum algorithms used for feature selection and classification procedure is developed. Also, different algorithms used on various parameters are compared.
TT-TSDF: Memory-Efficient TSDF with Low-Rank Tensor Train Decomposition

In this paper we apply the low-rank Tensor Train decomposition for compression and operations on 3D objects and scenes represented by volumetric distance functions. Our study shows that not only it allows for a very efficient compression of the high-resolution TSDF maps (up to three orders of magnitude of the original memory footprint at resolution of 5123), but also allows to perform TSDF-Fusion directly in the low-rank form. This can potentially enable much more efficient 3D mapping on low-power mobile and consumer robot platforms.
Retrieving Input from Touch Interfaces via Acoustic Emanations

Security for mobile devices have largely focused on the development of trusted hardware and securing software, however these secure platforms are still vulnerable to physical side channel attacks. Side channel attacks bypass secure hardware access controls, exploiting the physical characteristics of devices and onboard sensors to compromise and leak sensitive information. In this paper, we investigate the use of onboard sensors to recover user input on touchscreen interfaces. We evaluate the use of motion and acoustic sensors to categories user interactions with the device and apply machine learning techniques to find a strong correlation between acoustic emanations and user input. The acoustic output of a touch-screen mobile device is used to build a model that predicts user input with up to 86 % accuracy in a rpa listie scpnario_
Hybrid Precoding Aided Secure Generalized Spatial Modulation in Millimeter Wave MIMO Systems

This letter studies the secrecy capacity of the millimeter Wave (mmWave) Generalized Spatial Modulation (GSM) system with hybrid precoding. First of all, the capacity of the mmWave GSM system with Gram-Schmidt precoding is analyzed. Secondly, the influence of the shared scatterers on the secrecy capacity is explored under the wire-tap channel. Finally, artificial noise, which has no influence on legitimate user but can interfere with the eavesdropping user, is adopted to prevent eavesdropping. Simulation results show that in the most SNR region, the secrecy capacity of the proposed scheme is higher than that of the system directly adopting orthogonal matching pursuit, phase extraction alternating minimization, and semidefinite relaxation alternating minimization precoding algorithms.
Robust real-time monitoring of human task advancement for collaborative robotics applications

A crucial problem in human-robot collaboration is to achieve seamless coordination among the agents. Robots have to adapt to human behaviour, which is highly uncertain. In fact, humans can perform each task in many ways and with different speeds, occasional errors and short pauses. This paper offers a robust method to monitor the advancement of the current human activity in real-time in order to predict its duration. The algorithm learns online templates of new variants of the task and uses them as references for a Dynamic Time Warping-based algorithm. The proposed strategy has been tested within a realistic assembly task. Results show its ability to give accurate predictions also in case of peculiar variants, such as those associated with errors.
Personalized Online Learning with Pseudo-Ground Truth

Personalized online machine learning allows a very accurate modelling of individual behavior and demands. In particular, a system that dynamically adapts during runtime can initiate a continuous collaboration with its user where both alternately adjust to each other to maximize the system’s utility. However, in application scenarios based on supervised learning it is often unclear how to obtain the required ground truth for such dynamic systems. In this paper, we focus on applications where a real-time classification of sequential data is crucial. Concretely, we propose to adapt an online personalized model solely based on pseudo-ground-truth information which is provided by another machine learning model. This model has the advantage to classify sequences in retrospective with a small delay and thus is able to achieve a higher performance than real-time systems. In particular, it is a pre-trained offline model, which means that no ground-truth information is necessary during runtime. We apply the proposal on the task of online action classification, for which the benefits of personalization have been recently emphasized.
SaD-SLAM: A Visual SLAM Based on Semantic and Depth Information

Simultaneous Localization and Mapping (SLAM) is considered significant for intelligent mobile robot autonomous pathfinding. Over the past years, many successful SLAM systems have been developed and works satisfactorily in static environments. However, in some dynamic scenes with moving objects, the camera pose estimation error would be unacceptable, or the systems even lose their locations. In this paper, we present SaD-SLAM, a visual SLAM system that, building on ORB-SLAM2, achieves excellent performance in dynamic environments. With the help of semantic and depth information, we find out feature points that belong to movable objects. And we detect whether those feature points are keeping still at the moment. To make the system perform accurately and robustly in dynamic scenes, we use both feature points extracted from static objects and static feature points derived from movable objects to finetune the camera pose estimation. We evaluate our algorithm in TUM RGB-D datasets. The results demonstrate the absolute trajectory accuracy of SaD-SLAM can be improved significantly compared with the original ORB-SLAM2. We also compare our algorithm with DynaSLAM and DS-SLAM, which are designed to fit dynamic scenes.
SGM-MDE: Semi-global optimization for classification-based monocular depth estimation

Depth estimation plays a crucial role in robotic applications that require environment perception. With the introduction of convolutional neural networks, monocular depth estimation (MDE) methods have become viable alternatives to LiDAR and stereo reconstruction-based solutions. Such methods require less equipment, fewer resources and do not need additional sensor alignment requirements. However, due to the ill-posed formulation of MDE, such algorithms can only rely on learning mechanisms, which makes them less reliable and less robust.In this work we propose a novel method to cope with the lack of geometric constraints inherent to monocular depth computation. Towards this goal, we initially mathematically transform the feature vectors from the last layer inside a MDE CNN such that a 3D stereo-like cost volume is generated. We then adapt the semi-global stereo optimization to the aforementioned volume, global consistency of the map being ensured. Furthermore, we enhance the results by adding a sub-pixel stereo post-processing be means of interpolation functions, a larger range of depth values being obtained. Our method can be applied to any classification-based MDE, experiments showing an increase in accuracy with an additional time cost of only 8 ms on a regular GPU, making the technique usable for real-time applications.
Open-Domain question classification and completion in conversational information search

Searching for new information requires talking to the system. In this research, an Open-domain Conversational information search system has been developed. This system has been implemented using the TREC CAsT 2019 track, which is one of the first attempts to build a framework in this area. According to the user's previous questions, the system firstly completes the question (using the first and the previous question in each turn) and then classifies it (based on the question words). This system extracts the related answers according to the rules of each question. In this research, a simple yet effective method with high performance has been used, which on average, extracts 20% more relevant results than the baseline.
Fined-grained Aspect Extraction from Online Reviews for Decision Support

With the flourish of the Web 2.0, online reviews offer valuable information for customers and businesses. Deep investigation on the online reviews can help the businesses understand customers and their needs, which can assist decision making in product design and marketing. However, the massive records with irregular structure and ambiguous words pose great challenges for online review analysis. In this paper, we focus on the movie reviews and propose a framework to mine the aspect-based opinions, and utilize the results for decision making support. Based on the different sentence characteristics of movie reviews collected from Douban, the most popular movie community in China, we divide the reviews into two categories, short reviews and long reviews. Firstly, we develop different methods to extract the fine-grained aspects including the global and local aspects from the short reviews and long reviews respectively. Secondly, a lexical updating algorithm is proposed to identify the opinion words towards different aspects. In contrast to most studies that focus on determining the overall sentiment orientation (positive versus negative), the proposed method performs fine-grained analysis to mine both the various aspects and their corresponding opinions of a movie. Finally, based on the positive and negative opinions towards different aspects, the producers can improve the marketing strategy and future products. Experimental results based on the data collected from Douban verify the efficiency and accuracy of the developed methods.
Sample-Efficient Learning for Industrial Assembly using Qgraph-bounded DDPG

Recent progress in deep reinforcement learning has enabled agents to autonomously learn complex control strategies from scratch. Model-free approaches like Deep Deterministic Policy Gradients (DDPG) seem promising for applications with intricate dynamics, such as contact-rich manipulation tasks. However, these methods typically require large amounts of training data or meticulous hyperparameter tuning, limiting their usefulness for real-world robotics applications. In this paper, we evaluate and benchmark our recently proposed approach for improving model-free reinforcement learning with DDPG through Qgraph-based bounds in temporal difference learning. We directly apply the algorithm to a challenging real-world industrial insertion task and assess its performance (see https://youtu.be/Z_GcNbCWE-E). Empirical results show that the insertion task can be learned despite significant frictional forces and uncertainty, even in sparse-reward settings. We present an in-depth comparison based on a large number of experiments and demonstrate the advantages and performance of Qgraph-bounded DDPG: the learning process can be significantly sped up, robustified against bad choices of hyperparameters and runs with less memory requirements. Lastly, the presented results extend the current theoretical understanding of the link between data graph structure and soft divergence in DDPG.
Combining LiDAR Scan Matching with Stereo Visual Odometry Using Curvefusion

In this paper, we present a novel algorithm, namely Curvefusion for integrating LiDAR scan matching with stereo visual odometry. First, 6-DOF pose trajectories are estimated by utilizing SOFT odometry, which is the state of the art stereo visual odometry based on feature selection and tracking, and the well-known ICP scan matching algorithm, respectively. Second, a deformation-based multi-sensor fusion method, namely curvefusion is applied. The proposed fusion method does not rely on a sensor model. As long as the trajectories of the sensors to be fused are given, we can obtain an optimized fusion trajectory, which greatly improves the computational efficiency. Experiments based on publicly available KITTI data set show that the proposed method outperforms or achieves similar performance compared with the state-of-the-art odometry methods.
Perfect: A Hyperbolic Embedding for Joint User and Community Alignment

Social network alignment shows fundamental importance in a wide spectrum of applications. To the best of our knowledge, existing studies mainly focus on network alignment at the individual user level, requiring abundant common information between shared individual users. For the networks that cannot meet such requirements, social community structures actually provide complementary and critical information at a slightly coarse-grained level, alignment of which will provide additional information for user alignment. In turn, user alignment also reveals more clues for community alignment. Hence, in this paper, we introduce the problem of joint social network alignment, which aims to align users and communities across social networks simultaneously. Key challenges lie in that (1) how to learn the representations of both users and communities, and (2) how to make user alignment and community alignment benefit from each other. To address these challenges, we first elaborate on the characteristics of real-world networks with the notion of $\delta$-hyperbolicity, and show the superiority of hyperbolic space for representing social networks. Then, we present a novel hyperbolic embedding approach for the joint social network alignment, referred to as Perfect, in a unified optimization. Extensive experiments on real-world datasets show the superiority of Perfect in both user alignment and community alignment.
An Efficient Design of Anderson PUF by Utilization of the Xilinx Primitives in the SLICEM

Physical unclonable functions (PUFs) are known as one of the most recent promising technologies for cryptographic key generation. A PUF circuit is designed in such a way to produce random digits based on true-random and uncontrollable variations during the integrated circuits (IC) manufacturing process. The response of PUF can be used as a unique identity for the device where the PUF is embedded in it. Field-programmable gate arrays (FPGAs) are usually considered as one of the first choices for implementing PUFs. This paper proposes a novel FPGA-derived Anderson PUF by optimizing all elements located in one configurable logic blocks (CLBs). The experimental results on Spartan-6 family Xilinx XC6SLX9 FPGAs show that the proposed architecture improves the PUF’s uniformity, uniqueness, and reliability to 49.41%, 50.89%, and 91.25%, respectively. Furthermore, the proposed structure increases the complexity and unpredictability of the PUF while decreases the hardware area overhead.
GraphTTE: Travel Time Estimation Based on Attention-Spatiotemporal Graphs

This letter proposes a new travel time estimation model based on graph neural network (GraphTTE) to improve the accuracy of travel time estimation. We design a Multi-layer Spatiotemporal Graph frame (MSG), which consists of static network and dynamic networks, to fully consider the influence of traffic temporal characteristics and road network topological characteristics on travel time. Moreover, we design an Attention Graph Nodes Impact Index algorithm (AGNII) to score the impact of each node on travel time. In particular, the dynamic networks utilize the graph convolution network and gate recurrent unit to obtain the traffic characteristics, the static network utilizes graph convolution network to obtain the road basic attributes. We combine the real paths sequence with the impact score of nodes to extract the subgraph with a great impact on the trajectory. After the graph representation learning and deep residual network, the estimated time is obtained. A simulator was designed to train and test our model in Chengdu and Xi’an datasets, the results show that the mean absolute percent error (MAPE) is 12.58% and 14.01%, which is 1.54% and 1.78% lower than the baselines.
Unusual host properties. X-Ray structures of three salt-like crystalline aggregates of 1,1′-binaphthyl-8,8′-dicarboxylic acid

In contrast with 1,1′-binaphthyl-2,2′-dicarboxylic acid (1), isomeric 1,1′-binaphthyl-8,8′-dicarboxylic acid (2) does not form inclusion compounds with aprotic or proton-donor organic molecules. Instead, salt-like associations of (2) with basic components such as pyridine, derivatives of pyridine, or related heterocycles are readily obtained, and recrystallization of (2) from pyridine in the presence of other non-basic solvents, including cyclic ethers, dipolar aprotic compounds, and acids, gives, crystalline ternary complexes. Crystal structures of the two-component aggregates (2)·pyridine (1:1) and (2)·3-(hydroxymethyl)pyridine, (1:1) and of the ternary crystal aggregate of (2)·pyridine-acetic acid (1:1:1) have been studied by X-ray diffraction. In the crystals of (2)·pyridine (1:1) and (2)·pyridine·acetic acid (1:1:1), the structures are built up from finite blocks of H-bonded (2)·pyridine and (2)·pyridine·acetic acid associations. In the case of (2)·3-(hydroxymethyl)pyridine (1:1) the side-chain hydroxy group of the pyridine moiety acts as a proton donor to the carboxyl group of a neighbouring molecule of (2), thus forming infinite undulating chains in the crystallographic c-direction. A characteristic feature of these structures is the intramolecular H bond between the carboxylic groups of (2), which works against the net bulkiness of the molecule and thus seems to result in the modest clathrate-forming ability of this host. Crystal data for (2)·pyridine (1:1): monoclinic (P21), a= 8.080(1), b= 17.254(2), c= 7.715(1)A, β= 106.28(3)°, R= 0.049 for 1 860 reflections; for (2)·3-(hydroxymethyl)pyridine (1:1): orthorhombic: (Pbca), a= 15.426 7(4), b= 21.189 5(6), c= 13.372 7(4)A, R= 0.049 for 2 085 reflections; and for (2)·pyridine·acetic acid (1:1:1): triclinic (P), a= 14.525(6), b= 10.481(4), c= 8.862(4)A, α= 105.69(4), β= 111.10(6), γ= 86.37(6)°, R= 0.053 for 2 686 observations.
Learning Object Manipulation with Dexterous Hand-Arm Systems from Human Demonstration

We present a novel learning and control framework that combines artificial neural networks with online trajectory optimization to learn dexterous manipulation skills from human demonstration and to transfer the learned behaviors to real robots. Humans can perform the demonstrations with their own hands and with real objects. An instrumented glove is used to record motions and tactile data. Our system learns neural control policies that generalize to modified object poses directly from limited amounts of demonstration data. Outputs from the neural policy network are combined at runtime with kinematic and dynamic safety and feasibility constraints as well as a learned regularizer to obtain commands for a real robot through online trajectory optimization. We test our approach on multiple tasks and robots.
Inception C-Net(IC-Net): Altered Inception Module for Detection of Covid-19 and Pneumonia using Chest X-rays

Data Science, being the most imperative part of Medical Science, is contributing its vital role in finding solutions to the greatest existing hurdles. It is being used to develop drugs, diagnose diseases, automate ventilators, and what not in a very short span of time which can otherwise become a very lengthy process. In early 2020 we have seen the arrival of another deleterious disease named as Covid-19. Artificial Intelligence is playing a major role in controlling the outbreak of this disease which has been declared a pandemic by WHO. Elaborating the power of computer vision, in this project we are trying to detect the Covid-19 pandemic with the help of Chest X-rays of the patients suffering from this disease.
Effect of elevated nitrogen levels on endogenous gibberellin and jasmonic acid contents of three rice (Oryza sativa L.) cultivars

Three rice (Oryza sativa L.) cultivars ( cv. Daesanbyeo, cv. Dongjinbyeo, cv. Junambyeo) were analyzed for endogenous gibberellin (GA) and jasmonic acid (JA) contents and their changes in response to elevated nitrogen (N) levels. The N fertilizer was applied in the form of urea [(NH 2 ) 2 CO] at three rates (0, 36.8, 73.6kg N ha -1 ). Plant growth (height and dry weight) was enhanced by the first N rate but not further enhanced by the highest rate. The endogenous GA contents were analyzed through high-performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry-selected ion monitoring (GC-MS-SIM) while that of JA with GC-MS-SIM. They were analyzed one week after N application and were significantly increased with elevated N levels in all rice cultivars. The bioactive GA 1 markedly increased, but its concentration differed in different rice cultivars. Similar fluctuations were observed for endogenous GA 8 , GA 12 , GA 19 , GA 20 , and GA 53 in response to elevated N levels, showing that the rates of biosynthesis of GAs were differently affected by elevated N levels within different rice cultivars. The level of GA 20 , a precursor of GA 1 biosynthesis, was not significantly increased, though GA 19 , a precursor of GA 20 , was found to be the most abundant GA type in all rice cultivars. Jasmonic acid content in the plants increased with the basic urea application (36.8kg N ha -1 ), but significantly decreased with the double urea level (73.6kg N ha -1 ). The results demonstrate that GA and JA are differentially affected in response to elevated N application in rice.
Detection and Research on Unsafe Driving of Taxi Drivers

In response to the unsafe driving of taxi drivers during the epidemic, the design and implementation of the driver's face registration and detection of whether the driver wears a mask, detection of the driver's fatigue physiological signals and multiple integration are designed. Target detection algorithm based on MobileNetV2 to realize mask detection. Combine MTCNN and Face-Net organically to realize driver's face login. The cerebellar neural network model is used to fuse the 12 fatigue monitoring signals EMG, EEG, etc. extracted by the simulated driving experiment platform to obtain a multi-integrated fatigue monitoring control model. After the fatigue driving multiple physiological indicators of the simulated driving platform, the technical model is studied and verified. It is concluded that the multiple fusion fatigue monitoring control model has higher accuracy than the traditional single signal monitoring.