Miracle-dz/UniFAR-FTUs huggingface dataset:
Reducing the effect of offshore wind farms on the REWS CFAR detection threshold

Radar Early Warning Systems (REWS) are an important part of the protection and management infrastructure for offshore Oil and Gas (O&G) platforms and subsea assets. REWS are typically installed onboard O&G platforms to monitor and track nearby surface traffic and alert operators of potential collision threats. However, it is not uncommon for offshore wind farms to be located within few nautical miles from offshore O&G assets. Wind farms can interfere with the REWS due to their large and varying returns, which may cause loss of detection of targets of interest. This paper will consider the complex radar scattering from turbines within the nearfield region and will apply the radar returns modeling to a realistic wind farm scenario. The paper will present some modeling results to illustrate the potential effects of wind farm returns on the detection threshold. The paper will then propose a theoretical approach to minimize the adverse effects of wind farms on the Constant False Alarm Rate (CFAR) threshold.
Design of project based positioning and project planning system for mobile employee

It is important that the employee of mobile-based companies (software consultancy, etc.) on the computer are able to make position takings and integrate these positions with project planning systems. This study targets an abstractly designed software product that can integrate with other enterprise applications where location identification and follow-up of personnel using IP based system (tablet, phone, pc, etc.) can be done. After this software product collects the physical location definitions of the employee (GPS, WPS and IP) and performs the appropriate validation steps, it provides outputs that can be done on a project / customer basis based on location / employee location by converting logical locations like project, customer etc. It is anticipated that this study will provide significant gains and administrative facilities for mobile employee companies by designing to meet the need to know which employee is in which location.
H∞ control of constrained-input nonlinear systems with unknown model based on adaptive dynamic programming

An Adaptive dynamic programming(ADP) algorithm that contain online measurement and off-policy learning two phase is proposed to solve the H∞ control problem of continuous-time nonlinear system with constrained-input and unknown model only based on online data. The model-free Hamiton-Jacobi-Isaacs(HJI) equation is derived by the policy iteration(PI) and the model-free iteration reinforcement learning(IRL) method. Three neural networks(NN) are structured, after collecting online data of system is finished, then off-policy learning method is used to approximate solve the model-free HJI equation. And the value function, control strategy and disturbance strategy are obtained by the NN. The weights of the neural network are solved by the least square method. The simulation results verify the feasibility of the algorithm.
My Personal Images as My Graphical Password

In 1996, Blonder introduced the first authentication system based on a graphical password. Since then, researchers have proposed several systems in the literature enhancing security properties to prevent brute-force, guessing, and shoulder-surfing attacks. However, many systems were developed using impersonal images, hindering their identification and retention. As a solution, Takada-Toike, and Herzberg-Margulies introduced systems using personal images in 2002 and 2012, respectively. Nonetheless, users require passing many stages during the authentication phase, making the systems unsecured. As a solution, we propose a system where each user creates a graphical personal password and needs to pass a stage. Security analysis demonstrates that the proposal can resist very well-known attacks, making it secure and useful for web services.
HomeNetRescue: An SDN service for troubleshooting home networks

The number of smart devices in home networks is rapidly increasing, making it more complex to manage their faults. In addition, the lack of customer knowledge and tools to automatically diagnose and fix faults aggravate the problem. In this paper, we propose HomeNetRescue, a Software-Defined Network (SDN) service for autonomous management of wireless and wired home networks focused on fault and configuration management. We evaluate HomeNetRescue in a real world prototype, considering throughput, delay, and jitter. Our results show that HomeNetRescue can increase the throughput of the network by up to 131%, reducing wireless transmission delay and jitter by 46% and 24%, respectively.
Constructing a Word Similarity Graph from Vector based Word Representation for Named Entity Recognition

In this paper, we discuss a method for identifying a seed word that would best represent a class of named entities in a graphical representation of words and their similarities. Word networks, or word graphs, are representations of vectorized text where nodes are the words encountered in a corpus, and the weighted edges incident on the nodes represent how similar the words are to each other. We intend to build a bilingual word graph and identify seed words through community analysis that would be best used to segment a graph according to its named entities, therefore providing an unsupervised way of tagging named entities for a bilingual language base.
Analysis on container fleet competitiveness after COSCO and China shipping reorganization based on multi-attribute decision making

In order to meet the development trend of world economy and improve the growth of shipping market freight, China Ocean Shipping (Group) Company and China Shipping (Group) Company merged into China COSCO Shipping Corporation Limited in the area of container transport, financial business and other business segments as the goal of enhancing the level of international management and avoiding homogeneous competition. After reorganization, COSCO Shipping Lines will be the world's fourth largest container shipping business. As the core of liner shipping enterprise, container fleet plays an important role in promoting the healthy development of liner enterprises, so it is necessary to compare the competitiveness after their reorganization. In this paper, using the relevant knowledge of decision-making theory, the problem to be studied can be considered as a multi-attribute model problem. Then this paper analyzes the factors which have influence on the competitiveness of the fleet and builds the appropriate evaluation index system and uses the TOPSIS method to carry on empirical analysis of the world's outstanding liner shipping companies with COSCO Shipping Lines. According to the result, container fleet competitiveness has significantly improved.
Majority-Based Spin-CMOS Primitives for Approximate Computing

Promising for digital signal processing applications, approximate computing has been extensively considered to tradeoff limited accuracy for improvements in other circuit metrics such as area, power, and performance. In this paper, approximate arithmetic circuits are proposed by using emerging nanoscale spintronic devices. Leveraging the intrinsic current-mode thresholding operation of spintronic devices, we initially present a hybrid spin-CMOS majority gate design based on a composite spintronic device structure consisting of a magnetic domain wall motion stripe and a magnetic tunnel junction. We further propose a compact and energy-efficient accuracy-configurable adder design based on the majority gate. Unlike most previous approximate circuit designs that hardwire a constant degree of approximation, this design is adaptive to the inherent resilience in various applications to different degrees of accuracy. Subsequently, we propose two new approximate compressors for utilization in fast multiplier designs. The device-circuit SPICE simulation shows 34.58% and 66% improvement in power consumption, respectively, for the accurate and approximate modes of the accuracy-configurable adder, compared to the recently reported domain wall motion-based full adder design. In addition, the proposed accuracy-configurable adder and approximate compressors can be efficiently utilized in the discrete cosine transform (DCT) as a widely-used digital image processing algorithm. The results indicate that the DCT and inverse DCT (IDCT) using the approximate multiplier achieve $\sim$2x energy saving and 3x speed-up compared to an exactly-designed circuit, while achieving comparable quality in its output result.
An implementation of a lightweight end-to-end secured communication system for patient monitoring system

Securing wireless body area network (WBAN), which is often used for collecting precious patient's data, is an inevitable challenge in a modern patient monitoring system (PMS). In general, a PMS consists of several network segments, starting from a WBAN to the user's access network via a server where the patients' data is stored. Apart from the communication links between the WBAN gateway and the server, and between the server and the user's device, the wireless link between the body sensors and the gateway remains susceptible to threat by eavesdropper or malicious users. Moreover, the sensor nodes are often battery powered and thus cannot be overloaded with highly computational algorithms and protocols. In this context, we develop and implement an end-to- end secure PMS by emphasizing on securing sensor to gateway wireless link with a lightweight encryption protocol that will consume minimal energy. In addition, we use MQ telemetry transport (MQTT) as a transport protocol rather than hypertext transport protocol (HTTP) for its simplicity and lightweight features. The encrypted sensor data thus transported and stored in the server from where an authentic user access the data and decrypt it.
Tracking control for helicopters with input stochastic perturbation

In this paper, the flight control problem for helicopters is investigated under input stochastic perturbation. The unmeasurable states of the helicopter are estimated by a state observer. Then, combining the estimated states with the other measurable states of the helicopter, a state feedback controller is designed. In the controller design, the input stochastic perturbation is considered. By applying the designed controller, the helicopter is maintaining a hovering state at the predefined positions and yaw angle. In order to guarantee the stochastic stability of the closed-loop system, a sufficient condition is presented by using the theory of stochastic differential equation, Lyapunov stability method and linear matrix inequalities (LMIs) technique. Finally, the simulation is given to show the effectiveness of the proposed results.
Deep Learning for Launching and Mitigating Wireless Jamming Attacks

An adversarial machine learning approach is introduced to launch jamming attacks on wireless communications and a defense strategy is presented. A cognitive transmitter uses a pre-trained classifier to predict the current channel status based on recent sensing results and decides whether to transmit or not, whereas a jammer collects channel status and ACKs to build a deep learning classifier that reliably predicts the next successful transmissions and effectively jams them. This jamming approach is shown to reduce the transmitter’s performance much more severely compared with random or sensing-based jamming. The deep learning classification scores are used by the jammer for power control subject to an average power constraint. Next, a generative adversarial network is developed for the jammer to reduce the time to collect the training dataset by augmenting it with synthetic samples. As a defense scheme, the transmitter deliberately takes a small number of wrong actions in spectrum access (in form of a causative attack against the jammer) and therefore prevents the jammer from building a reliable classifier. The transmitter systematically selects when to take wrong actions and adapts the level of defense to mislead the jammer into making prediction errors and consequently increase its throughput.
Grey wolf RNA-GA modeling method for FCCU main fractionator

This paper proposes a grey wolf RNA-GA modeling method for FCCU main fractionator. In this method, the grey wolf search strategy is used to design the new crossover operator to improve the local optimization ability, and the grey wolf RNA-GA algorithm is proposed. The validity of the grey wolf RNA-GA is illustrated with some typical test functions. The grey wolf RNA-GA is adopted for parameter estimation of the FCCU main fractionator model. The simulation results show that the proposed modeling method can accurately reflect the dynamic characteristics of the main fractionator.
Course recommendation of MOOC with big data support: A contextual online learning approach

With the advent of the big data era of MOOC, enrolled students and offered courses become numerous and diverse, resulting in a large amount of data and complex curriculum relationships. Thus how to recommend appropriate course to improve students' learning outcomes has become a daunting task. The state-of-the-art works ignore some significant features in course recommendation of MOOC: heterogeneity of large-scale user groups, sequence problem in courses and foreseeable quantitative explosion of courses and users. This paper proposes a systematic methodology for recommending personalized courses with considering the sequence of learning curriculum. The system works by recommending the course with the highest reward to a user. New feedback of the user is then recorded and will be used to improving the performance of recommendation for future students. The core component is a novel online learning algorithm based on hierarchical bandits with known smoothness. We analyze the performance of our proposed online learning algorithm in terms of regret, and prove the asymptotic optimality of the proposed algorithm. Experimental results are provided to verify our theory.
Traffic time series prediction based on CS and SVR

Numerous data of traffic parameters consume a large amount of storage space and take long time to be predicted. It doesn't achieve real-time performance. And compressed sensing is the algorithm that the sparse signal can be reconstructed to recover the original signal. Due to the problems, a novel CSSVR algorithm is proposed. Firstly, the theoretical analysis proves the influence to recovered error by the correlation between measure matrix and sparse basis. And then, a general reconstruction framework is provided to reconstruct a single measurement or a multiple measurement of arbitrary sparse structure. At last, the reconstruction of the predicted sparse signal by support vector machine to get the predicted result will be explained. The simulation results show that it can realize the prediction of traffic parameter based on the sparse reconstruction efficiently and the accuracy is of high quality. So the algorithm is robust and practical.
Effects of assets on attitudes and behaviors: Advance test of a social policy proposal

Raising asset limits and creating individual development accounts have been proposed in welfare reform. In part the rationale for these proposals is that assets have positive effects on attitudes and behaviors, including long-term planning, greater work effort, and improved social connectedness. The Panel Study of Income Dynamics (PSID), between 1968 and 1972, included a wide range of attitude and behavioral measures. In this study, data from the PSID are analyzed to test for the following: a) the effect of assets on attitudes and behaviors; b) the effect of attitudes and behaviors on assets; c) the effect of income on attitudes and behaviors; and d) the effect of attitudes and behaviors on income. A path analytic model is estimated using LISREL. Results indicate modest effects of some assets that are (a) consistently positive as predicted, and (b) overall as strong as the effects of income. Results support the proposition that assets have a positive effect on expectations and confidence about the future; influence people to make specific plans with regard to work and family; induce more prudent and protective personal behaviors; and lead to more social connectedness with relatives, neighbors, and organizations. These results suggest that policy demonstrations are
Combat effectiveness evaluation of real combat exercise based on data-driven

War is a typical complex giant system. In the field of real combat exercise, the experience-driven effectiveness evaluation method has the problems of subjectivity and inefficiency in evaluation, and has been unable to meet the requirements of informationized warfare. In this paper, combat effectiveness is divided into three components: win-lose effectiveness, military casualty effectiveness and weapon damage effectiveness. Data-driven algorithm-least square support vector machine (LSSVM) is introduced to build combat effectiveness evaluation model of real combat exercise. The historical data train the model and establish the nonlinear mapping complex relationship between efficiency component and operational effectiveness. Experiment shows that the LS-SVM-based tactical effectiveness assessment method has higher efficiency and accuracy.
A big data management system for providing real time services using fog infrastructure

In Modern ages, the classical Cloud computing (CC) standard is not adequate for big data(BD) storage that is generated by internet of things (IoT). For solving this issue, an additional platform is required and the name of this platform is Fog Computing. Providing real time services from cloud is too many time consuming. Here, Fog Computing plays a vital role. The maximum functions of processing data are implemented outside of cloud in the case of Fog Computing and one thing considered in Fog Computing is that here memory of fog devices is very little. So, a well-organized communication system is needed for data processing. Here we have suggested Smart Grid (SG) and Smart Local Grid (SLG) for getting an affordable, robust and secure power supply. The SG and SLG support the customer's real time services using fog infrastructure. In this paper we have proposed a hierarchical architecture for security purposes. We have also proposed a well-organized communication system for data processing in the case of SG and SLG using fog infrastructure or nodes.
A 22 year assessment of deforestation and restoration in riparian forests in the eastern Brazilian Amazon

SUMMARY Brazilian environmental law imposes more restrictions on land-use change by private landowners in riparian forests than in non-riparian forest areas, reflecting recognition of their importance for the conservation of biodiversity and key ecosystem services. A 22-year time series of classified Landsat images was used to evaluate deforestation and forest regeneration in riparian permanent preservation areas over the past two decades, focusing on the municipality of Paragominas in the state of Pará in eastern Amazonia. There was no evidence that riparian forests had been more effectively protected than non-riparian forests. Instead, deforestation was found to be comparatively higher inside riparian permanent preservation areas as recently as 2010, indicating a widespread failure of private property owners to comply with environmental legislation. There was no evidence for higher levels of regeneration in riparian zones, although property owners are obliged by law to restore such areas. A number of factors limit improvements in the protection and restoration of riparian forests. These include limited awareness of environmental compliance requirements, the need for improved technical capacity in mapping the distribution and extent of riparian forests and the boundaries of private properties, and improved access to the financial resources and technical capacity needed to support restoration projects.
A Two-test Protocol for the Precise Determination of the Maximal Lactate Steady State

The purpose of this study was to determine the efficacy of a two-test method for precisely identifying the Maximal Lactate Steady State (MLSS). Eight male competitive cyclists performed two bouts on a cycle ergometer. Following a maximal oxygen consumption (V̇O2max) test (66.91 ± 5.29 mL·kg−1·min−1) we identified the lactate deflection point using the visual deflection (TVis), Log-Log (TLog), Dmax (TDmax), RER = 1.00 (TRER), ventilatory threshold (TVent), and the 1.0 mmol·L−1 increase above baseline (T+1) methods. The second incremental test (SIT) consisted of 6–7 stages (5 min each) starting 20–30 W below to 20–30 W above the predetermined deflection point, in 10 W increments. Comparison of the two tests yielded different threshold estimates (range 11–46W) for all methods (P = 0.001–0.019) except the TLog (P = 0.194) and TRER (P = 0.100). The SIT resulted in significantly (P = 0.007) more narrow range of thresholds (27.5 ± 11.01W) compared to the V̇O2max test (70 ± 42.51W). The TVis from the SIT was identified as the MLSS and was verified using three 45-minute steady-state exercise bouts at 95%, 100%, and 105% of MLSS intensity (average increment 12.8 W). Blood lactate and V̇O2 were recorded every 5 minutes and differed between the three intensities at every time point (P < 0.001). V̇O2 increased from the 5th to the 45th minute by 7.02 mL·kg−1·min−1 (100% MLSS), 3.63 mL·kg−1·min−1 (95% MLSS) and 7.5 mL·kg−1·min−1 (105% MLSS, to the 30th minute). These results indicate that the MLSS was identified correctly by the SIT, the single incremental test overestimated the MLSS intensity, and the TVis provides a very accurate determination of the lactate breakpoint. The use of a second submaximal test is required for a precise identification of MLSS.
Formally Reasoning about the Cost and Efficacy of Securing the Email Infrastructure

Security in the Internet has historically been added post-hoc, leaving services like email, which, after all, is used by 3.7 billion users, vulnerable to large-scale surveillance. For email alone, there is a multitude of proposals to mitigate known vulnerabilities, ranging from the introduction of completely new protocols to modifications of the communication paths used by big providers. Deciding which measures to deploy requires a deep understanding of the induced benefits, the cost and the resulting effects. This paper proposes the first automated methodology for making formal deployment assessments. Our planning algorithm analyses the impact and cost-efficiency of different known mitigation strategies against an attacker in a formal threat model. This novel formalisation of an infrastructure attacker includes routing, name resolution and application level weaknesses. We apply the methodology to a large-scale scan of the Internet, and assess how protocols like IPsec, DNSSEC, DANE, SMTP STS, SMTP over TLS and other mitigation techniques like server relocation can be combined to improve the confidentiality of email users in 45 combinations of attacker and defender countries and nine cost scenarios. This is the first deployment analysis for mitigation techniques at this scale.
QRS detection on IOS platform based on single channel practical ECG measurement for remote patient monitoring

In this study, a real time QRS detection of ECG signal is implemented on IOS smart phone in order to determine arrhythmia type heart diseases of ambulatory patients over remote patient monitoring system. Data is measured by single channel, pratical ECG device that is attached to the smart phone and transferred to, monitored, and processed on this phone. In the mobile application developed using Objective-C programming language, QRS detection of received ECG signal is implemented, which is processed according various stages in Pan-Tomkins method. In addition, measured ECG signal is transferred in real time to Monitoring and Server System of the remote patient monitoring system that we have designed and developed before.