Miracle-dz/UniFAR-FTUs huggingface dataset:
Android Malware Classification Using Machine Learning and Bio-Inspired Optimisation Algorithms

In recent years the number and sophistication of Android malware have increased dramatically. A prototype framework which uses static analysis methods for classification is proposed which employs two feature sets to classify Android malware, permissions declared in the Androidmanifest.xml and Android classes used from the Classes.dex file. The extracted features were then used to train a variety of machine learning algorithms including Random Forest, SGD, SVM and Neural networks. Each machine learning algorithm was subsequently optimised using optimisation algorithms, including the use of bio-inspired optimisation algorithms such as Particle Swarm Optimisation, Artificial Bee Colony optimisation (ABC), Firefly optimisation and Genetic algorithm. The prototype framework was tested and evaluated using three datasets. It achieved a good accuracy of 95.7 percent by using SVM and ABC optimisation for the CICAndMal2019 dataset, 94.9 percent accuracy (with fl-score of 96.7 percent) using Neural network for the KuafuDet dataset and 99.6 percent accuracy using an SGD classifier for the Andro-Dump dataset. The accuracy could be further improved through better feature selection.
Augmented Reality User Interfaces for Heterogeneous Multirobot Control

Recent advances in the design of head-mounted augmented reality (AR) interfaces for assistive human-robot interaction (HRI) have allowed untrained users to rapidly and fluently control single-robot platforms. In this paper, we investigate how such interfaces transfer onto multirobot architectures, as several assistive robotics applications need to be distributed among robots that are different both physically and in terms of software. As part of this investigation, we introduce a novel head-mounted AR interface for heterogeneous multirobot control. This interface generates and displays dynamic joint-affordance signifiers, i.e. signifiers that combine and show multiple actions from different robots that can be applied simultaneously to an object. We present a user study with 15 participants analysing the effects of our approach on their perceived fluency. Participants were given the task of filling-out a cup with water making use of a multirobot platform. Our results show a clear improvement in standard HRI fluency metrics when users applied dynamic joint-affordance signifiers, as opposed to a sequence of independent actions.
A Novel Multi-Mode Bayesian Method for the Process Monitoring and Fault Diagnosis of Coal Mills

Process monitoring and fault diagnosis (PM-FD) of coal mills are essential to the security and reliability of the coal-fired power plant. However, traditional methods have difficulties in addressing the strong nonlinearity and multi-modality of coal mills. In this paper, a novel multi-mode Bayesian PM-FD method is proposed. Gaussian mixture model (GMM) is first applied to identify the operating modes of the coal mill. Subsequently, combined with multi-output relevance vector regression (MRVR), Bayesian inference is introduced to reconstruct and monitor the newly observed samples from different running modes. Additionally, the squared prediction error and the contribution plot method are employed for fault detection and isolation. The performance of the proposed PM-FD method is verified through its application in a self-defined nonlinear system and two actual fault cases of a medium-speed coal mill. Compared with the traditional methods, the experimental results demonstrate the effectiveness of the proposed method.
GR-SLAM: Vision-Based Sensor Fusion SLAM for Ground Robots on Complex Terrain

In recent years, many excellent SLAM methods based on cameras, especially the camera-IMU fusion (VIO), have emerged, which has greatly improved the accuracy and robustness of SLAM. However, we find through experiments that most of the existing VIO methods perform well on drones or drone datasets, but for ground robots on complex terrain, they cannot continuously provide accurate and robust localization results. Some researchers have proposed methods for ground robots, but most of them have limited applications due to the assumption of plane motion. Therefore, this paper proposes GR-SLAM for the localization of ground robots on complex terrain, which can fuse camera, IMU, and encoder data in a tightly coupled scheme to provide accurate and robust state estimation for robots. First, an odometer increment model is proposed, which can fuse the encoder and IMU data to calculate the robot pose increment on manifold, and calculate the frame constraints through the pre-integrated increment. Then we propose an evaluation algorithm for multi-sensor measurements, which can detect abnormal data and adjust its optimization weight. Finally, we implement a complete factor graph optimization framework based on sliding window, which can tightly couple camera, IMU, and encoder data to perform state estimation. Extensive experiments are conducted based on a real ground robot and the results show that GR-SLAM can provide accurate and robust state estimation for ground robots.
Explore Bravely: Wheeled-Legged Robots Traverse in Unknown Rough Environment

This paper addressed a challenging problem of wheeled-legged robots with high degrees of freedom exploring in unknown rough environments. The proposed method works as a pipeline to achieve prioritized exploration comprising three primary modules: traversability analysis, frontier-based exploration and hybrid locomotion planning. Traversability analysis provides robots an evaluation about surrounding terrain according to various criteria ( roughness, slope etc.) and other semantic information (small step, stair, bridge etc.), while novel gravity point frontier-based exploration algorithm can effectively decide which direction to go even in unknown environments based on robots’ current pose and desired one. Given all these information, hybrid locomotion planner will generate a path with motion mode (driving or walking) encoded by optimizing among different objectives and constraints. Lastly, our approach was well verified in both simulation and experiment on a wheeled quadrupedal robot Pholus.
Uncoordinated Channel Tracking for High-Rate Internet of Things in Multiuser Massive MIMO

The integration of high-rate internet of things (IoT) service into a massive MIMO network is considered. Existing approaches are limited in several ways: i) they incur large overhead for high data rates, ii) they do not consider channel aging explicitly, iii) channel tracking is performed for one device only treating others as noise, iv) pilot access patterns are assumed known at base station (BS) severely limiting network flexibility. Here, several algorithms are proposed that circumvent all of the aforementioned shortcomings with modest complexity. Specifically, ideas from target tracking literature is leveraged to model each unknown pilot contamination pattern as a different target-measurement association. Subsequently, several algorithms based on probabilistic data association filter (PDAF), multiple hypotheses tracking (MHT), global nearest neighbor (GNN), and soft and hard least-squares (LS) are presented. By numerical analysis, it is determined that their performance comes surprisingly close to that of the optimum tracker which knows access patterns when number of BS antennas are large.
A Target Tracking and Positioning Framework for Video Satellites Based on SLAM

With the booming development in aerospace technology, the video satellite which observes the live phenomena on the ground by video shooting has gradually emerged as a new Earth observation method. And remote sensing comes into a "dynamic" era with the demand for new processing techniques, especially the near-real-time tracking and geo-positioning algorithm for ground moving targets. However, many researchers merely extract pixel-level trajectories in post-processed video products, resulting in fairly limited applications. We regard the video satellite as a robot flying in space and adopt the SLAM framework for the positioning of ground moving targets. The designed framework is based on the representative ORB-SLAM and we make improvements mainly in feature extraction, satellite pose estimation, moving target tracking and positioning. We coordinate a moving fishing boat with GPS-RTK (Real-time Kinematic) devices and a video satellite observing it simultaneously for verification and evaluation of our method. Experiments demonstrate that our framework provides reasonable geolocation of the moving target in satellite videos. Finally, some open problems and potential research directions are discussed.
Meat Quality and Volatile Flavor Traits of Duroc, Berkshire and Yorksire Breeds

The present study evaluated the difference in objective and subjective meat quality properties among the pure-breed boars of Duroc, Berkshire and Yorkshire. Ten longissimus lumborum (LD) muscles were collected from each breed after 24 h slaughtering. The breed type showed a significant effect on intramuscular fat content, moisture (p<0.01), pH, sarcoplasmic protein solubility and color (p<0.05), whereas cooking loss and Warner Blazer shear force (WBsf) did not differ among the breeds. The Yorkshire breed showed significant (p<0.05) lower sarcoplasmic protein solubility, pH and CIE a* value when compared with other breeds. The sensory panels identified Duroc as having greater overall acceptability and higher rating values than other breeds. The oleic acid content was significantly lower in the Berkshire (29.85 %) than in the Duroc (40.19 %) and Yorkshire breeds (38.18 %, p<0.05). The Yorkshire breed showed the most desirable ratio of polyunsaturated and saturated fatty acids (0.31) than the Berkshire (0.16) and Duroc breeds (0.15, p<0.05). 40 volatile compounds have been identified and quantified, while aldehydes were the most abundant among flavor substances. Aldehydes were negatively correlated with oleic acid content (p<0.05). Current data indicated that each breed had their own merits and deficiencies in terms of meat qualityThe Yorkshire breed showed a greater number of weak points. Furthermore, this study indicated that individual fat-driven flavor components were greatly influenced by fatty acid composition. The polyunsaturated fatty acids did not show any negative effects on meat flavor if cooked meats were consumed soon after cooking.
Ciphertext-Policy Attribute-Based Encryption with Multi-Keyword Search over Medical Cloud Data

Over the years, public health has faced a large number of challenges like COVID-19. Medical cloud computing is a promising method since it can make healthcare costs lower. The computation of health data is outsourced to the cloud server. If the encrypted medical data is not decrypted, it is difficult to search for those data. Many researchers have worked on searchable encryption schemes that allow executing searches on encrypted data. However, many existing works support single-keyword search. In this article, we propose a patient-centered fine-grained attribute-based encryption scheme with multi-keyword search (CP-ABEMKS) for medical cloud computing. First, we leverage the ciphertext-policy attribute-based technique to construct trapdoors. Then, we give a security analysis. Besides, we provide a performance evaluation, and the experiments demonstrate the efficiency and practicality of the proposed CP-ABEMKS.
Deep Semantic Network Representation

Network representation aims to learn low-dimensional vector representations of network nodes while preserving the inherent properties of the network. For all its popularity, majority of the existing methods focus on exploitation of diverse information, including network topology and semantic information on nodes of network, and ignore their implicit semantics. For example, we all know the saying that birds of a feather flock together. More concretely, semantic information of one node can be influenced by its neighbors' semantic information. Furthermore, even two nodes are not directly connected, they may have similar implicit semantic information (i.e., high-order semantic proximity). Thus, they should be close in the represented vector space. To this end, we propose a Deep Semantic Network Representation approach (DSNR) in the self-translation framework from sequence to sequence. To excavate the implicit semantic information of nodes and capture the high-order semantic proximity, three key components make our approach effective, i.e., aggregation of nodes neighbors' semantic information and enhancement to the semantic feature representations of nodes by a deep autoencoder, integration of nodes semantic information in node identity sequence to generate node semantic sequence, and translation from node semantic sequence to node identity sequence to capture the high-order semantic proximity in an attention-enhanced seq2seq framework. Extensive experiments based on three real-world datasets have verified the effectiveness of our proposed approach11Code is available at https://github.com/DASE4/DSNR.
Energy management strategy based on velocity prediction for parallel plug-in hybrid electric bus

For plug-in hybrid electric vehicle, an optimal energy management strategy can maximize its potential to achieve high efficiency. However, energy management strategy without condition information cannot achieve optimal fuel economy in real-time. In order to obtain higher efficiency and adapt to unexpected situation, we develop an energy management strategy based on velocity prediction using digital map information. The detailed model of the hybrid powertrain system such as engine, battery pack and vehicle model are established. The typical driving cycles are constructed to minimize the fuel consumption with equivalent consumption minimization strategy. To adapt to sudden congestions, a realtime strategy based on velocity prediction is proposed. Results indicates that equivalent consumption minimization strategy with velocity prediction is more efficient than the traditional equivalent consumption minimization strategy.
Cryfind: Using Static Analysis to Identify Cryptographic Algorithms in Binary Executables

In this paper, we present an automatic static tool CryFind to identify cryptographic algorithms in a binary executable. Our main strategy is using string match to search for cryptographic constants and API names. To expand our search range and improve our hit rate, our tool matches strings under different encodings and XOR'ed with different keys, as well as incorporates techniques to extract strings on stack. As a result, we have a more effective and efficient detection tool compared with a wide range of state-of-the-art static analysis tools.
Towards Vision-Based Impedance Control for the Contact Inspection of Unknown Generically-Shaped Surfaces with a Fully-Actuated UAV

The integration of computer vision techniques for the accomplishment of autonomous interaction tasks represents a challenging research direction in the context of aerial robotics. In this paper, we consider the problem of contact-based inspection of a textured target of unknown geometry and pose. Exploiting state of the art techniques in computer graphics, tuned and improved for the task at hand, we designed a framework for the projection of a desired trajectory for the robot end-effector on a generically-shaped surface to be inspected. Combining these results with previous work on energy-based interaction control, we are laying the basis of what we call vision-based impedance control paradigm. To demonstrate the feasibility and the effectiveness of our methodology, we present the results of both realistic ROS/Gazebo simulations and preliminary experiments with a fully-actuated hexarotor interacting with heterogeneous curved surfaces whose geometric description is not available a priori, provided that enough visual features on the target are naturally or artificially available to allow the integration of localization and mapping algorithms.
Evaluation of the Effect of Surfactants on the Movement of Pesticides in Soils Using a Soil Thin-Layer Chromatography Technique

In this study, the effect of concentration (1/2 CMC, at CMC and 2 x CMC) of surfactants, cetyl trimethyl ammonium bromide (cationic), sodium dodecyl sulfate (anionic), and tween ‘20’ (non-ionic) on the movement of carbofuran, chlorpyrifos and en-dosulfan in soils was evaluated by using a soil thin-layer chromatographic technique. The movement of pesticides was detected by spray reagents and expressed in terms of Rf values. The penetrability K was found to increase by decreasing the plate angle and followed the order as: sandy loam > loam > silt loam soils. The penetrability K also decreases in surfactant-free and surfactant-amended soils when developed in distilled water and aqueous surfactant solutions of different CMCs, respectively. The higher movement of pesticides was observed in sandy loam soil followed by loam and silt loam soils. On the basis of Rf values, the movement of pesticides follows the order as: carbofuran > chlorpyrifos > endosulfan, both in surfactant-amended and surfactant-free soils when developed in distilled water and aqueous surfactant solutions of different CMCs. The movement is directly proportional to the aqueous solubilities, polarities, and carbon numbers and inversely related to the molecular weights of pesticides. A significant increase or decrease of pesticides movement in soils was discussed on the basis of adsorption of pesticides on soils, chemical nature of the surfactants, and its concentrations in terms of critical micelle concentrations (CMCs) in soils and eluents. Results obtained may provide insights pertaining to the use of surfactants for solving soil pollution problems posed by pesticides.
Measurement Unit Placement Against Injection Attacks for the Secured Operation of an IIoT-based Smart Grid

Carefully constructed cyber-attacks directly influence the data integrity and the operational functionality of the smart energy grid. In this paper, we have explored the data integrity attack behaviour in a wide-area sensor-enabled IIoT-SCADA system. We have demonstrated that an intelligent cyber-attacker can inject false information through the sensor devices that may remain stealthy in the traditional detection module and corrupt estimated system states at the utility control centres. Next, to protect the operation, we defined a set of critical measurements that need to be protected for the resilient operation of the grid. Finally, we placed the measurement units using an optimal allocation strategy by ensuring that a limited number of nodes are protected against the attack while the system observability is satisfied. Under such scenarios, a wide range of experiments has been conducted to evaluate the performance considering IEEE 14-bus, 24 bus-reliability test system, 85-bus, 141-bus and 145-bus test systems. Results show that by ensuring the protection of around 25% of the total nodes, the IIoT-SCADA enabled energy grid can be protected against injection attacks while observability of the network is well-maintained.
Impact of Multiple Shadows on Visible Light Communication Channel

This letter investigates the impact of multiple shadows for indoor visible light communication (VLC) systems composed of a VLC access point and multiple terminals. The blockage is a crucial problem in the VLC system, which would result in great deterioration of users’ communication quality due to the shadow area caused by the blockage. Considering the room boundaries and overlap between shadows, we derive the mathematical expressions of the shadow area and position. In the simulation part, the effect of multiple shadows on throughput and outage performance is appraised. Furthermore, we also take the effect of the room size into account.
Trajectory Tracking of a One-Link Flexible Arm via Iterative Learning Control

Trajectory tracking of flexible link robots is a classical control problem. Historically, the link elasticity was considered as something to be removed. Hence, the control performance was guaranteed by adopting high-gain feedback loops and, possibly, a dynamic compensation with the result to stiffen up the dynamic behavior of the robot. Nowadays, robots are pushed more and more towards a safe physical interaction with a less and less structured environment. Hence, the design and control of the robots moved to an on-purpose introduction of highly compliant elements in the robot bodies, the so-called soft robotics, and towards control approaches that aim to provide the tracking performance without a substantial change in the robot dynamic behavior. Following this approach, we present an iterative learning control that relies mainly on a feedforward component, hence preserves the robot dynamics, for trajectory tracking of a one-link flexible arm. We provide a condition, based on the system dynamics and similar to the Strong Inertially Coupled property, that ensures the applicability of the proposed control method. Finally, we report simulation and experimental tests to validate the theoretical results.
A Measurement Study of IoT-Based Attacks Using IoT Kill Chain

Manufacturing limitations, configuration and maintenance flaws associated with the Internet of Things (IoT) devices have resulted in an ever-expanding attack surface. Attackers exploit IoT devices to steal private information, take part in botnets, perform Denial of Service (DoS) attacks and use their resources for the mining of cryptocurrency. In this paper, we experimentally evaluate a hypothesis that attacks on IoT devices follow the generalised Cyber Kill Chain (CKC) model. We used a medium-interaction honeypot to capture and analyse more than 30,000 attacks targeting IoT devices. We classified the steps taken by the attackers using the CKC model and extended CKC to an IoT Kill Chain (IoTKC) model. The IoTKC provides details about IoT-specific attack characteristics and attackers' activities in the exploitation of IoT devices.
Fruit quality control by surface analysis using a bio-inspired soft tactile sensor

The growing consumer demand for large volumes of high quality fruit has generated an increasing need for auto-mated fruit quality control during production. Optical methods have been proved successful in a few cases, but with limitations related to the variability of fruit colors and lighting conditions during tests. Tactile sensing provides a valuable alternative, although it comes with the need of a physical interaction that could damage the fruit. To overcome these limitations, we propose the usage of a recently developed soft tactile sensor for non-invasive fruit quality control. The ability of the sensor to detect very small forces and to finely analyze surfaces allows the collection of relevant information about the fruit by performing a very delicate physical interaction, that does not cause any damage. We report experiments in which such information is used to determine whether apples and strawberries are ripe or senescent. We test different configurations of the sensor and different classification algorithms, achieving very good accuracy for both apples (96%) and strawberries (83%).
Vision-Based Proprioceptive Sensing: Tip Position Estimation for a Soft Inflatable Bellow Actuator

This paper presents a vision-based sensing approach for a soft linear actuator, which is equipped with an internal camera. The proposed vision-based sensing pipeline predicts the three-dimensional tip position of the actuator. To train and evaluate the algorithm, predictions are compared to ground truth data from an external motion capture system. An off-the-shelf distance sensor is integrated in a second actuator of the same type, providing only the vertical component of the tip position and used as a baseline for comparison. The camera-based sensing pipeline runs at 40 Hz in real-time on a standard laptop and is additionally used for closed loop elongation control of the actuator. It is shown that the approach can achieve comparable accuracy to the distance sensor for measuring the linear expansion of the actuator, but additionally provide the full three-dimensional tip position.