Miracle-dz/UniFAR-FTUs huggingface dataset:
Credibility in Online Social Networks: A Survey

The importance of information credibility in society cannot be underestimated given that it is at the heart of all decision-making. Generally, more information is better; however, knowing the value of this information is essential for the decision-making processes. Information credibility defines a measure of the fitness of the information for consumption. It can also be defined in terms of reliability, which denotes the probability that a data source will appear credible to the users. A challenge in this topic is that there is a great deal of literature that has developed different credibility dimensions. In addition, information science dealing with online social networks has grown in complexity, attracting interest from researchers in information science, psychology, human–computer interaction, communication studies, and management studies, all of whom have studied the topic from different perspectives. This work will attempt to provide an overall review of the credibility assessment literature over the period 2006–2017 as applied to the context of the microblogging platform, Twitter. The known interpretations of credibility will be examined, particularly as they relate to the Twitter environment. In addition, we investigate levels of credibility assessment features. We then discuss recent works, addressing a new taxonomy of credibility analysis and assessment techniques. At last, a cross-referencing of literature is performed while suggesting new topics for future studies of credibility assessment in a social media context.
Optimal Time Allocation for Quadrotor Trajectory Generation

In this paper, we present a framework to do optimal time allocation for quadrotor trajectory generation. Using this method, we can generate minimum-time piecewise polynomial trajectories for quadrotor flights. We decouple the quadrotor trajectory generation problem into two folds. Firstly we generate a smooth and safe curve which is parameterized by a virtual variable. This curve named spatial trajectory is independent of time and has fixed spatial properties. Then a mapping function which decides how the quadrotor moves along the spatial trajectory respecting kinodynamic limits is found by minimizing total trajectory time. The mapping function maps the virtual variable to time is named temporal trajectory. We formulate the minimum-time temporal trajectory generation problem as a convex program which can be efficiently solved. We show that the proposed method can corporate with various types of previous trajectory generation method to obtain the optimal time allocation. The proposed method is integrated into a customized light-weight quadrotor platform and is validated by presenting autonomous flights in indoor and outdoor environments. We release our code for time optimization as an open-source ros-package.
Evaluating the Effects of Hardware Configurations on Bro under DDoS Attacks

The exponential growth of network traffic and the growing sophistication of network attacks call for faster, efficient and scalable intrusion detection systems (IDS) that will be able to quickly look into the traffic and timeously produce alerts when malicious traffic has been detected. Snort has been the de-facto standard of IDS for so many years but has been ineffective under heavy loads. In both multi-core and single core hardware configuration snort shows no improvement in detection capability of Transmission Control Protocol (TCP) flooding Distributed Denial of Service (DDoS) attack. This has led to the development of alternative IDS which try to address the limitations of Snort. Bro is a flexible script-driven intrusion detection system, which provides a ‘worker’ based architecture to utilize multiple processors. The aim of this paper is to evaluate Bro in terms of performance and packet handling against TCP flooding DDoS attacks under different hardware configurations. To achieve this aim, Bro was installed on different hardware configurations. Tests were conducted to assess its performance under each configuration. Packet loss, Throughput and resource utilization metrics were measured. The results show that utilizing better hardware increases resources availability hence gives the system better performance.
Balun Bandpass Low-Noise Amplifier in GaAs

In this work, the design of an on-chip balun bandpass low-noise amplifier (LNA) is proposed so as to achieve the functional integration of balun, bandpass filter, and LNA in a compact circuit structure. Specifically, the input matching network of the LNA is designed as a bandpass filter while its output matching network is realized with a balun. The resulted multi-functional circuit effectively reduces the circuit size and cost of RF front-end circuitry. The proposed balun bandpass LNA is implemented using a commercial GaAs pHEMT process. The measured in-band small signal gain is within 11.2±0.35 dB from 5.4 to 6 GHz, and the in-band input and output return losses are all better than 10.8 dB. The measured in-band noise figure is better than 6.2 dB with a minimum noise figure of 4.9 dB at 6 GHz. The measured in-band amplitude imbalance between the balanced output ports is with 1.2 dB while the inband phase imbalance is within 6.4 degree. Notably, the proposed balun bandpass LNA achieves 30-dBc stopband rejection from DC to 4.3 GHz and from 8.3 to 20 GHz.
Machine Learning Aided Simulation of Public Transport Utilization

Despite having one of the most efficient transportation systems in the world, Singapore is still faced with congestion issues regularly, especially during peak hour periods, due to a number of reasons. We investigate some of the factors contributing to this issue and propose a simulator supplied with predictive travel times through congestion prediction, in order to evaluate and improve bus utilization through effective scheduling. We introduced a conceptual framework to integrate neural network models into simulation so as to improve real-time supply based on several possibilities of demands. This paper will delineate the steps taken to produce the simulator and discuss the evaluation of these models.
Impedance Control of a High Performance Twisted-Coiled Polymer Actuator

This paper presents a 1-link robotic arm that is antagonistically driven by one pair of a high performance super-coiled polymer actuators with an embedded controller. The actuator which is made from Spandex and nylon fibers is low-cost, easy to fabricate and light-weight. Moreover, it can generate large displacement and provide Joule heating capability. The main contribution of the paper is the model-based impedance controller, which enables position control of the antagonistic joint with variable stiffness and damping. The impedance control is a torque-based law, which in turn depends on a proposed backstepping control law to control the force of each actuator. The control system is proved to be stable using dissipativity stability theory and verified through experiments. Experimental results show that our system can track the angular position reference with the worst position error of 0.43deg and root-mean squared error of 0.16deg at steady state for sinusoidal waveform tracking (with the frequency of 0.1Hz), and the worst position error of 0.2deg for set-point regulation.
Learning Deep C3D Features For Soccer Video Event Detection

Soccer video event identification has been an interesting task in research community during past few decades. Numerous machine learning techniques and C2D (Convolution 2-dimensional) have been used for this problem, but C3D has not been implemented for this task. By taking advantage from C3D (Convolution 3-dimensional), to completely exploit spatio-temporal relation, deep convolution network is developed to highlight distinct video events in proposed research work. Initially, we detect soccer video event marks by pixel differencing and edge change ratio. After this semantic features of segmented frames are extracted followed by CNN to map soccer event categories: Corner, Shoot, Goal Attempt, Penalty Kick. Because no effective and suitable dataset is available, we categorized soccer videos into four classes and developed soccer videos dataset for training CNN network. Evaluation results on soccer match clips generated results with high efficiency.
Artificial neural network–based method to classify sedimentary rocks

In a geological study, an important step is to determine the type of sedimentary rock or its grain size. Such a determination requires accurate analysis in the field or in a laboratory. As the size of the study area grows, this activity can be time consuming and error prone because the number of specialists working under rigid criteria also increases. This paper proposes a novel methodology to classify grain size using unique wavelength reflectance data and artificial neural networks. The results indicate that the proposed method can be reliably used in the field.
An Aggregation Modeling Method of Large-scale Wind Farms in Power System Transient Stability Analysis

The operation states of wind turbines of large-scale wind farms are not identical due to the influence of voltage distribution and wake effect, etc. So the accuracy of traditional single-machine dynamic aggregation model of wind farms is hard to meet the demand of security and stability analysis of power system with large-scale wind power integration. In order to improve the credibility of transient stability analysis of power systems. A simple fixed-scale clustering method of wind turbines was proposed, which takes “the sensitivity of the wind turbines affected by power systems fault” as the grouping index. Based on the clustering method, the aggregation modeling method of wind turbines and power collection system was proposed. In addition, the detailed model, multi-machine aggregation model and dual-machine aggregation model were established by taking an actual grid-connected large-scale wind farm as an example. And the validity and reliability of the proposed aggregation modeling method of wind farms were verified by simulation analysis.
Underwater Robot Navigation for Maintenance and Inspection of Flooded Mine Shafts

The maintenance and inspection of the flooded shafts, specially coal ones, is an important environmental problem. There are thousands of shafts of this type in Europe with the danger of pollution, flood and collapse. This paper presents some of the main ongoing works of the EU project STAMS that develop an autonomous underwater robotic system for periodic monitoring of flooded shafts in hazardous and complex conditions. The accurate navigation is very cluttered at 1.000 m depth conditions, where minimum visibility and unexpected obstacles are some of the difficulties to overcome. We are going beyond classical navigation approaches using only few sensor information. Another innovation is the installation of Reference Points (RPs) in the shaft's walls by the robot using a special fixation mechanism. The specially designed cases of the RPs allow to house specific sensors and help in the navigation, and will be used in periodic monitoring and assessment of the mine shafts. The positioning and attachment of these RPs is another contribution of this paper.
Smoothing Net Load Demand Variations Using Residential Demand Management

One of the major obstacles facing the large-scale integration of renewable energy sources to existing power networks is the large magnitude of fluctuations and uncertainties introduced to aggregated demand profiles. Such variations make the supply–demand matching a more challenging task, and increase the operational cost of the power system. In this paper, we provide an effective, yet simple, methodology for smoothing power variations using the demand response of a large number of residential appliances. We first present a demand aggregation model based on queueing theory that can accommodate both deferrable and thermostatically controlled loads. Second, controllable demands are scheduled using an online algorithm to smooth the net noncontrollable demand profile. The performance of our methodology is evaluated using realistic data.
Identification of the User's Habits based on Activity Information

This work proposes a system able to recognize the user habits based on his daily activities in a smart house. The habit estimation system uses the information provided by an activity recognition module, which provides the sequence and the duration of the activities performed by the user. Based on those parameters, the activities are represented as a signal by using Fourier series representation. Several output signals from different users are clustered into groups using the k-means method, where each cluster corresponds to a specific habit from a group of people. The proposed system was tested with dataset from the experiment that took place in an environment similar to a smart house. The users were asked to perform a set of 6 activities in any desired orders. In total, twenty-four subjects took part in the experiments. All activities were successfully recognized by the system and three different habits were found. The proposed system along with its habit representation can be potentially used to trace the relationships between the habits observed and some aspects of the user personality traits.
Learning-Based Modular Task-Oriented Grasp Stability Assessment

Assessing grasp stability is essential to prevent the failure of robotic manipulation tasks due to sensory data and object uncertainties. Learning-based approaches are widely deployed to infer the success of a grasp. Typically, the underlying model used to estimate the grasp stability is trained for a specific task, such as lifting, hand-over, or pouring. Since every task has individual stability demands, it is important to adapt the trained model to new manipulation actions. If the same trained model is directly applied to a new task, unnecessary grasp adaptations might be triggered, or in the worst case, the manipulation might fail. To address this issue, we divide the manipulation task used for training into seven sub-tasks, defined as modular tasks. We deploy a learning-based approach and assess the stability for each modular task separately. We further propose analytical features to reduce the dimensionality and the redundancy of the tactile sensor readings. A main task can thereby be represented as a sequence of relevant modular tasks. The stability prediction of the main task is computed based on the inferred success labels of the modular tasks. Our experimental evaluation shows that the proposed feature set lowers the prediction error up to 5.69% compared to other sets used in state-of-the-art methods. Robotic experiments demonstrate that our modular task-oriented stability assessment avoids unnecessary grasp force adaptations and regrasps for various manipulation tasks.
A Study on the Acceleration Optimization Control Method for the Integrated Helicopter/Engine System Based on Torsional Vibration Suppression

In order to solve the problem of insufficient power supply for helicopter and to increase the response speed of engine output power during the climbing process, the research on acceleration optimization control for the integrated helicopter/engine system is conducted. Meanwhile, the optimization control method with remarkable robustness for turboshaft engine based on torsional vibration suppression and min-batch gradient descent-neural network is proposed. The modified notch filter is available to suppress the torsional vibration from the frequency domain. Selecting the maximum output power as the optimization objective, the response speed of the engine output power can increase significantly through relaxing the restriction boundary of power turbine speed and taking into account the constraints of rotational speed, static strength, and temperature. The results show that the optimization control method can effectively reduce the low-order torsional amplitude by more than 70% and can decrease the response time of output power by more than 3s compared with the conventional cascade PID control, which allows the turboshaft engine to reach the potential on the premise of hardly exceeding the limit boundary.
Quadtree-Accelerated Real-Time Monocular Dense Mapping

In this paper, we propose a novel mapping method for robotic navigation. High-quality dense depth maps are estimated and fused into 3D reconstructions in real-time using a single localized moving camera. The quadtree structure of the intensity image is used to reduce the computation burden by estimating the depth map in multiple resolutions. Both the quadtree-based pixel selection and the dynamic belief propagation are proposed to speed up the mapping process: pixels are selected and optimized with the computation resource according to their levels in the quadtree. Solved depth estimations are further interpolated and fused temporally into full resolution depth maps and fused into dense 3D maps using truncated signed distance function (TSDF). We compare our method with other state-of-the-art methods using the public datasets. Onboard UAV autonomous flight is also used to further prove the usability and efficiency of our method on portable devices. For the benefit of the community, the implementation is also released as open source at https://github.com/HKUST-Aerial-Robotics/open_quadtree_mapping.
Importance of the Antenna Model to Assess GNSS Multipath in Airport Environments

For GNSS systems, the gain, phase and group-delay patterns of the receiver antenna have an impact on the pseudo-ranges, i.e. the apparent distances to the satellites estimated by the receiver. In the presence of multipath, this impact is difficult to assess because multipath and antenna effects are mixed. In this article, these sources of pseudo-range error are analyzed by means of simulations for an aircraft moving at the surface of an airport. For the simulations, we rely on a hybrid statistical-deterministic GNSS multipath simulator combined with a receiver simulator. Various antenna models are tested, e.g. an ideal isotropic antenna and a realistic antenna mounted on an aircraft, with and without the phase and group-delay patterns.
Software Implementation of the Accurate Regulators a Three-Dimensional Object: Heating Chamber Evaporator Heater Steam Boiler

The article proposes the software implementation of precise controllers in steam boiler control system. The practical significance of the study consists in the development of a step-time algorithm for the fuzzification of continuous physical quantities of the boiler process and the control flow-chart of a steam boiler with variable coefficients. The flow-chart for regulating the combustion and vaporization process is based on the system of production rules of two-valued logic. The indicators of quality and energy efficiency of a steam boiler automatic control with precise controllers using precise term sets are presented. A 3-D model for controlling the combustion and vaporization process in a steam boiler was developed in the fuzzyTech environment. The surface analysis of the constructed control model in the graphical shell of fuzzyTech on the basis of production rules is presented. The article is addressed to the teachers of technical disciplines in a university, as well as to postgraduate students.
An Iterative Parameters Estimator for Damped Exponential Signal in Noise

The paper proposed a hybrid-iteration algorithm to estimate the parameters (frequency and damping factor) of damped exponential signal. With regard to core idea of this method, the new algorithm utilizes the phase difference based on asymmetric window as the first implementation and interpolation Discrete Fourier Transform (IpDFT) as the second one. The estimator introduces the asymmetric phase difference method to the decaying case and avoids the error generated from wrong location of spectral line. The root mean squares error (RMSE) was investigated in the case of white Gauss noise to evaluate the capability of robustness against adaptive noise. Extensive numerical simulations show that the RMSEs of the proposed algorithm closely attach to Cramer-Rao lower bound (CRLB). In addition, simulation results also indicate that the new method has excellent performance against random noise compared with other interpolation DFT algorithms.
Phenol and Methanol Detector Based on Pristine Graphene Nano-sheet: A First Principles Study

Over the last decade graphene and its derivatives have stimulated the interest of researchers due to their extraordinary electrical, optical and chemical properties. Graphene has emerged as a potential candidate for electronic sensors and optical sensors applications. The purpose of the work presented in this article is to contribute in this novel field by simulating graphene nano-sheet based sensor to detect phenol and methanol molecules. QuantumWise Atomistix Toolkit (ATK) software has been used to carry out the sensor simulations. A significant change in the density of states and conductivity of the graphene have been noticed in the proximity of the target molecules. This change in the conductivity of graphene nano-sheet has been used as a detection signal. These simulations lay the groundwork for our physical experiments, which are targeted to produce a cheap sensor.
A study on development of a hybrid aerial / terrestrial robot system for avoiding ground obstacles by flight

To date, many studies related to robots have been performed around the world. Many of these studies have assumed operation at locations where entry is difficult, such as disaster sites, and have focused on various terrestrial robots, such as snake-like, humanoid, spider-type, and wheeled units. Another area of active research in recent years has been aerial robots with small helicopters for operation indoors and outdoors. However, less research has been performed on robots that operate both on the ground and in the air. Accordingly, in this paper, we propose a hybrid aerial / terrestrial robot system. The proposed robot system was developed by equipping a quadcopter with a mechanism for ground movement. It does not use power dedicated to ground movement, and instead uses the flight mechanism of the quadcopter to achieve ground movement as well. Furthermore, we addressed the issue of obstacle avoidance as part of studies on autonomous control. Thus, we found that autonomous control of ground movement and flight was possible for the hybrid aerial / terrestrial robot system, as was autonomous obstacle avoidance by flight when an obstacle appeared during ground movement.
Improving Association Rule Mining Using Clustering-based Discretization of Numerical Data

Association rule mining is an important data mining technique that help discover interesting attribute relationships that are useful for decision making. Most association rule mining methods use item-set manipulation approach, whereby data type must be categorical in nature. When a dataset contains numerical attributes, they will need to be discretized before rule mining. At the moment, most unsupervised data discretization methods do not account for data distributions, and users have to try different methods and discretization settings in order to improve rule mining results. In this paper, we propose using TwoStep clustering for data discretization. Unlike simple discretization methods, TwoStep automatically determines the discretization intervals by taking into account the unique data distribution property of each attribute. In our experiments, we evaluated the performance of Apriori algorithm based on four datasets, whereby each dataset was pre-processed using TwoStep and three other commonly used discretization methods. Our results show that TwoStep produced the greatest number of high-quality rules, as compared to common discretization methods.