Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Attack Strategy Against Wireless Networked Control Systems With Proactive Channel Hopping

This article investigates the security issue based on the proactive channel hopping scheme in wireless networked control systems (WNCS), where the sensor sends the measurement data to the controller through multiple channels attacked by a periodic denial-of-service jammer. For a jammer with the limited energy, the number of attacks, which denotes the amount of time expended in launching the attack, increases with the decline in the channel number attacked at each time, which has an effect on the success of receiving the data. On the basis of this, the problem of making an optimal tradeoff between the channel number attacked at each time and the attack number to degrade the WNCS performance maximally is investigated. We formulate this problem as an integer programming problem based on the linear quadratic Gaussian control cost function. Then, a necessary condition of the optimal solution without integrality constraints for this optimization problem is given for the scenario where the sensor utilizes one channel at each time to perform the data transmission. We further investigate this problem for the case where the sensor can select several channels at each time to transmit the measurement data. Moreover, for both cases the corresponding algorithms are presented to approximate optimal schedules. Finally, the theoretical results and the validity of the algorithm proposed are verified via the numerical results.
Examining the Implementation of PhET Simulations into General Chemistry Laboratory

Traditional general chemistry laboratories are highly structured and used to verify concepts that are covered in the textbook or lecture. This practice is very different from a scientific investigation and is often referred to as “cookbook”. This project aims to transform the traditional emphasis in general chemistry laboratory experiments from individual or paired “cookbook” exercises to an innovative approach. We also aim to further enhance student achievement by fostering problemsolving skills while incorporating the use of cybermedia in the form of PhET interactive labs and Excel program. Incorporating the use of internet-based technologies into chemistry laboratory improves students’ problem-solving skills as well as adding relevance and interest to students’ mastery of the content in the chemistry curriculum. Our method of data collection is a Likert-type and open-ended questionnaire, that was distributed at the end of each of the newly integrated labs into the General Chemistry I curriculum in an anonymous fashion. The collected data enabled us to examine the impact of implementing Excel, PhET interactive labs, and problem-solving session in General Chemistry I laboratory at The City College of New York. Overall, these experiments had a positive impact on the students’ attitudes towards chemistry, contributed to the learning experience, increased their involvement in the topics, and complemented the traditional tabs.
A grid-connected single-phase photovoltaic micro inverter

In this paper, the topology of a single-phase grid-connected photovoltaic (PV) micro-inverter is proposed. The PV micro-inverter consists of DC-DC stage with high voltage gain boost and DC-AC conversion stage. In the first stage, we apply the active clamp circuit and two voltage multipliers to achieve soft switching technology and high voltage gain. In addition, the flower pollination algorithm (FPA) is employed for the maximum power point tracking (MPPT) in the PV module in this stage. The second stage cascades a H-bridge inverter and LCL filter. To feed high quality sinusoidal power into the grid, the software phase lock, outer voltage loop and inner current loop control method are adopted as the control strategy. The performance of the proposed topology is tested by Matlab/Simulink. A PV module with maximum power 300W and maximum power point voltage 40V is applied as the input source. The simulation results indicate that the proposed topology and the control strategy are feasible.
A new data analytics framework emphasising preprocessing of data to generate insights into complex manufacturing systems

Recent emphasis has been placed on improving the processes in manufacturing by employing early detection or fault prediction within production lines. Whilst companies are increasingly including sensors to record observations and measurements, this brings challenges in interpretation as standard approaches do not highlight the presence of unknown relationships. To address this, we have proposed a new data analytics framework for predicting faults in a large-scale manufacturing system and validated it using both a publicly available Bosch manufacturing dataset with a focus on preprocessing of the data and the open-source SECOM industrial dataset. This paper is an extension to the work presented at International Conference on Intelligent Manufacturing and Internet of Things. The additional material includes a detailed focus on feature selection and the various approaches for identifying important features in the data, an updated framework methodology and description, an extension of XGBoost to allow this model to be used for prediction/classification and a comparison for classification with a Random Forest, tree-based model. The framework was used to explore two public manufacturing datasets and successfully identified the most influential features related to product failure in each production line data.
Connected-Dense-Connected Subgraphs in Triple Networks

Finding meaningful communities - subnetworks of interest within a large scale network - is a problem with a variety of applications. Most existing work towards community detection focuses on a single network. However, many real-life applications naturally yield what we refer to as Triple Networks. Triple Networks are comprised of two networks, and the network of bipartite connections between their nodes. In this paper, we formulate and investigate the problem of finding Connected-Dense-Connected subgraph (CDC), a subnetwork which has the largest density in the bipartite network and whose sets of end points within each network induce connected subnetworks. These patterns represent communities based on the bipartite association between the networks. To our knowledge, such patterns cannot be detected by existing algorithms for a single network or heterogeneous networks. We show that finding CDC subgraphs is NP-hard and develop novel heuristics to obtain feasible solutions, the fastest of which is O(nlogn+m) with n nodes and m edges. We also study different variations of the CDC subgraphs. We perform experiments on a variety of real and synthetic Triple Networks to evaluate the effectiveness and efficiency of the developed methods. Employing these heuristics, we demonstrate how to identify communities of similar opinions and research interests, and factors influencing communities.
High-Throughput Hardware Design for 3D-HEVC Disparity Estimation

Editor’s note: Disparity refers to the fact, that, for example, human beings see an object from a different angle with the left eye and the right eye. The disparity es-timation in images produces a computationally intensive effort. The HEVC disparity estimation can be analyzed according to its efficient implementa-tion and high throughput. Disparity estimation is one crucial part of HEVC, and the optimization in terms of throughput affects, therefore, the complete realization on hardware. --Michael Hübner, Brandenburg University of Technology
REACT-R and unity integration

This paper presents REACT-R which is a modification of an existing cognitive architecture, ACT-R, to incorporate robot embodiment. Robot embodiment is facilitated by situating the cognitive architecture within the robot and allowing it to interact with the environment through its actuators and sensors. The REACT-R module offers flexibility by using a UDP connection to integrate with the Robot Operating System (ROS) allowing REACT-R to be used in simulations or on physical models. We have successfully integrated REACT-R with an interactive 3D Game Engine to autonomously control a simulated quadrotor flying vehicle. The cognitive model also contains varying levels of autonomy, providing a human pilot with the option of controlling different aspects or levels of robot operation.
Big Data Analytics for Intrusion Detection

Intrusion detection has become a problem of big data, with a semantic gap between vast security data sources and real knowledge about threats. The use of machine learning (ML) algorithms on big data has already been successfully applied in other domains. Hence, this approach is promising for dealing with cyber security’s big data problem. Rather than relying on human analysts to create signatures or classify huge volumes of data, ML can be used. ML allows the implementation of advanced algorithms to extract information from data using behavioral analysis or to find hidden correlations. However, the adversarial setting and the dynamism of the cyber threat landscape stand as difficult challenges when applying ML. The next generation security information and event management (SIEM) systems should provide security monitoring with the means for automation, orchestration and real-time contextual threat awareness. However, recent research shows that further work is needed to fulfill these requirements. This chapter presents a survey on recent work on big data analytics for intrusion detection.
BSBL‐based multiband fusion ISAR imaging

Multiband fusion imaging can effectively improve the range resolution of inverse synthetic aperture radar (ISAR) imaging. In this study, the block sparse Bayesian learning (BSBL) method is applied to multiband fusion imaging to achieve high-resolution ISAR imaging of a block-structured target. The BSBL method is suitable for the ISAR imaging of numerous and continuous scatterers because it considers the block structure characteristics of the signal. The validity of the proposed method is verified by the simulation and real-data experimental results.
Counteracting Attacks From Malicious End Hosts in Software Defined Networks

This paper proposes security techniques for counteracting attacks from malicious end hosts in a software defined networking (SDN) environment. This paper describes the design of a security architecture, which comprises a security management application running in the SDN controller for specifying and evaluating security policies, and security components in the switches for enforcing these security policies on network flows. Our proposed security solution helps to detect the attacking end hosts even before the flow requests from the malicious end hosts are forwarded to the SDN controller. Furthermore, if the end hosts become malicious after the interactions with the SDN controller and generate attacks in the data plane, then our architecture has mechanisms to address these attacks that occur after the establishment of routes by the SDN controller. The domain wide network visibility of the SDN controller enables our security architecture to achieve dynamic management of the security policies. The enforcement of security policies in the data plane is tailored to the functionality available in the network switches, making the proposed security solution practical. We describe the implementation of the proposed security architecture and analyze its security and performance characteristics. We also discuss the advantages of the proposed security architecture over existing solutions.
Efficient Elliptic Localization in the Presence of Antenna Position Uncertainties and Clock Parameter Imperfections

In this paper, an algebraic solution for elliptic localization in multiple-input multiple-output (MIMO) radar systems is proposed by taking the uncertainties in antenna positions and their clock parameters into account. Through nonlinear transformation and multi-stage processing, the target position is estimated in closed-form by successively utilizing the weighted least squares estimator. Theoretical analysis demonstrates that the proposed method is able to attain the Cramer-Rao lower bound (CRLB) performance under mild Gaussian error. The conditions for achieving the CRLB are derived as well. The numerical simulations confirm the analytical results and demonstrate significant performance improvement of the proposed estimator in comparison with the state-of-the-art methods.
SSPA: an effective semi-supervised peer assessment method for large scale MOOCs

ABSTRACT Peer assessment has become a primary solution to the challenge of evaluating a large number of students in Massive Open Online Courses (MOOCs). In peer assessment, all students need to evaluate a subset of other students’ assignments, and then these peer grades are aggregated to predict a final score for each student. Unfortunately, due to the lack of grading experience or the heterogeneous grading abilities, students may introduce unintentional deviations in the evaluation. This paper proposes and implements a semi-supervised peer assessment method (SSPA) that incorporates a small number of teacher’s gradings as ground truth, and uses them to externally calibrate the procedure of aggregating peer grades. Specifically, each student’s grading ability is directly (if students have common peer assessments with teacher) or indirectly (if students have no common peer assessments with teacher) measured with the grading similarity between the student and teacher. Then, SSPA utilizes the weighted aggregation of peer grades to infer the final score of each student. Based on both real dataset and synthetic datasets, the experimental results illustrate that SSPA performs better than the existing methods.
The final chapter: implementing effective learning designs

This ALTC-sponsored project demonstrated that generic learning designs can serve as pedagogical frameworks to support academic staff in creating new learning experiences. It explored the issues to emerge from the implementation of learning designs and identified barriers to their widespread adoption and ways of overcoming them. These findings underpin the implementation of learning design templates which address these adoption challenges in their design and streamlines the planning process. The templates can be used by academic staff to tailor exemplary examples to meet the individual lecturer’s, and/or course co-ordinator’s, particular requirements, whilst providing them with the underlying pedagogical principals involved in the learning design.
On the Assimilation of Instructions: Stimulus-response Associations are Implemented but not Stimulus-task Associations

The assimilation of instructions consists of two stages. First, a task model is formed on the basis of instructions. Second, this model is implemented, resulting in highly accessible representations, which enable reflexive behavior that guides the application of instructions. Research frequently demonstrated that instructions can lead to automatic response activation, which indicates that stimulus-response associations can be implemented on the basis of a task model. However, instructions not only indicate how to respond (stimulus-response mappings) but also when (i.e., the conditions under which mappings apply). Accordingly, we tested whether instruction implementation leads both to the activation of stimulus-response associations and of associations between stimuli and the context or task in which the instructed stimulus-response mappings are relevant (i.e., stimulus-task associations). In four experiments, we measured if implementing newly instructed stimulus-response mappings also leads to bivalence costs (i.e., shorter latencies when a stimulus can only occur in one task compared to when it can occur in two tasks), which indicate the presence of stimulus-task associations. We consistently observed automatic response activation on the basis of instructions, but no bivalence costs. A discrepancy thus exists between information conveyed in an instructed task model and the elements of that task model that are implemented. We propose that future research on automatic effects of instructions should broaden its scope and focus both on the formation of an instructed task model and its subsequent implementation.
Eksperimen Estimasi Biaya Proyek Perangkat Lunak Menggunakan Metode Function Point Analysis (FPA)

In building software, it should start with project planning. This activity is initialized with estimation activity. When estimation is done, it is necessary to make predictions in the future and handle the uncertainty that will be passed in running a software project. There are several options in project estimation. However, in this study, we used the most common and familiar technique to be tested on project data that had been running in the field. From the results of experiments conducted in the study on 20 (twenty) data software development projects in one of the software houses in Jambi city using the Function Point Analysis (FPA) method, the results of the calculation of the RMSE (root mean squared error) of Rp 1,073,001, - and an error value of 0.25. This means that it can be concluded that the difference in RMSE from the real price comparison transacted in the field with the price estimated using the Function Point Analysis (FPA) method is equal to the value mentioned above, which value includes a value with a fairly small gap. As well, the error value produced is also quite small at 0.25 .
Latency-Based Dynamic Controller Assignment in Hybrid SDNs: Considering the Impact of Legacy Routers

Software-defined networking (SDN) is a modern network architecture, which separates the network control plane from the data plane. Considering the gradual migration from traditional networks to SDNs, the hybrid SDN, which consists of SDN-enabled devices and legacy devices, is an intermediate state. For wide-area hybrid SDNs, to guarantee the control performance, such as low latency, multi SDN controllers are usually needed to be deployed at different places. How to assign them to switches and partition the network into several control domains is a critical problem. For this problem, the control latency and the packet loss rate of control messages are important metrics, which have been considered in a lot of previous works. However, hybrid SDNs have their unique characters, which can affect the assignment scheme and have been ignored by previous studies. For example, control messages pass through Legacy Forwarding Devices (LFDs) in hybrid SDNs and cause more latency and packet loss rate for queuing compared with SDN-enabled Forwarding Devices (SFDs). In this paper, we propose a dynamic controller assignment scheme in hybrid SDNs, which is called the Legacy Based Assignment (LBA). This scheme can dynamically delegate each controller with a subset of SFDs in the hybrid SDNs, whose objective is to minimize average SFD-to-control latency. We performed some experiments compared with other schemes, which show that our scheme has a better performance in terms of the latency and the packet loss rate.
Theoretical analysis and experimental verification of wideband multiple‐input multiple‐output imaging radar

Multiple-input multiple-output (MIMO) radar is a novel radar system which can achieve more individual observation echoes than the number of the actual antennas by transmitting orthogonal waveforms. At present, pattern theory is usually utilised to evaluate the azimuth imaging indicator of the MIMO imaging radar system. Although it cannot describe the two-dimensional performance, it is quite effective to analyse the azimuth resolution of far-field targets. However, the pattern based on the far-field condition becomes invalid for targets in the near-field area, thus it cannot explain the appearance of near-field azimuth grating lobes. In this article, spatial spectrums and generalised ambiguity functions (GAFs) of near-field targets are calculated. By describing the near-field GAF (NGAF) approximately, the relationship between the distortions of NGAF and the spatial spectrum is studied qualitatively, which can explain the appearance of azimuth near-field grating lobes. In addition, the calibration method of amplitude and phase errors by an isolated corner reflector is presented in this article. Finally, a simulation and an experiment are carried out to verify the conclusions.
Asymmetrical neural processing of amplitude modulated sounds : a psychophysical, fMRI and TMS investigation

Human speech is the most behaviourally important and complex signal that the human brain is required to process yet it does so with remarkable ease. Speech is composed of highly complex amplitude modulations over time and these modulations are known to be crucial for intelligibility. There is evidence for hemispheric asymmetries in processing auditory modulations over different timescales and multiple models have been proposed to account for these. The procedure by which the auditory system extracts and processes these modulations is not fully understood. Psychophysical, neuroimaging and non-invasive neurostimulation techniques can be combined in complementary ways to potentially provide unique insights into this problem. Functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS) are relatively novel methods that have not been previously applied in combination to investigate amplitude modulation processing. Three psychophysical and fMRI-guided TMS studies were conducted in order to address the following research questions. Firstly, is fMRI-guided TMS an effective method for modulating AM processing? Secondly, are different TMS protocols more or less effective at modulating AM processing? Finally, is fMRI-guided TMS an effective method for further understanding the functional asymmetry of speech processing? Online dual pulse TMS to right auditory cortex was shown to be effective at modulating 4 Hz AM detection accuracy. State-dependent TMS to left auditory cortex was shown to be effective at modulating 40 Hz AM detection accuracy, but the effects were complex. Continuous theta burst stimulation was not shown to be effective at modulating AM depth discrimination ability. It was thus found that fMRI-guided TMS can be an effective tool for modulating AM processing, however, efficacy differs depending on the specific TMS protocol used. Further, fMRI-guided TMS can be used to investigate functional asymmetry of speech processing, however some important caveats apply.
On the trigger of Greek triggered inversion

This article discusses the possible triggers of subject-verb inversion in Greek constituent questions ( obligatory inversion ). We lay out the main proposals put forth in the literature so far and we spot a couple of inconsistencies in Kotzoglou’s (2005, 2006) proposal. We, then, note how Chomsky’s (2013, 2015) model of projection might help explain both aspects of the phenomenon, i.e. the lack of T-to-C movement and the adjacency between the fronted wh -phrase and T.
Harvesting and Threat Aware Security Configuration Strategy for IEEE 802.15.4 Based IoT Networks

IEEE 802.15. 4 protocol defines multiple security specifications to provide different levels of safety protection in the link layer. How to choose the security level for harvesting enabled Internet of Things (IoT) nodes is an important problem as it decides the security protection and also impacts on network performance. Considering the dynamics of network threats and harvesting performance, in this letter, a harvesting and threat aware security configuration strategy is proposed. Simulation results illustrate that the proposal can choose the suitable security configuration according to the network security threats, service requirements, and the variable harvested energy. Comparison with conventional methods, the proposed strategy can extend the working time, resulting in improved network performance. Moreover, the proposed security configuration method can also meet the diversified service requirements.