Miracle-dz/UniFAR-FTUs huggingface dataset:
The goatfish Pseudupeneus maculatus and its follower fishes at an oceanic island in the tropical west Atlantic

The influence of a substratum-disturbing forager, the spotted goatfish Pseudupeneus maculatus on the assemblage of its escorting, opportunistic-feeding fishes was examined at Fernando de Noronha Archipelago (tropical west Atlantic). Followers attracted to spotted goatfish foraging singly differed from followers of spotted goatfish foraging in groups in several characteristics. The larger the nuclear fish group, the greater the species richness and number of individuals of followers. Moreover, groups of foraging spotted goatfish attracted herbivores, not recorded for spotted goatfish foraging singly. The size of follower individuals increased with the size and the number of foraging spotted goatfish. The zoobenthivorous habits of the spotted goatfish and its ability to disturb a variety of soft substrata render it an important nuclear fish for several follower species of the reef fish assemblage at Fernando de Noronha. # 2006 The Authors Journal compilation # 2006 The Fisheries Society of the British Isles
A replica management strategy based on MOEA/D

As distributed storage systems have been used more and more widely nowadays, data replication management has become a hot research topic. Although replica technology can increase data availability and improve load balancing, an unreasonable replica management strategy will waste too much storage and increase the energy consuming sharply. In this paper, we developed a replica management strategy based on MOEA/D, in which we take five factors affecting replication decisions into consideration such as system data unavailability, network traffic, disk performance, load balancing and energy consuming. By using the improved MOEA/D algorithm, we get a Pareto solution sets, then an elite selection strategy based on individual density was proposed to pick out the suitable solution from the set. At last, we adjust the replica dynamically depending on the file access record log. Those steps are gathered to form the final strategy: A Replica Management Strategy Based On MOEA/D(MDRMS). Series of experiments are designed to demonstrate the validity and universality of the strategy. The experimental results show that MDRMS performs better than HDFS(Hadoop Distributed File System) and DRPMO (Dynamic Replica Placement based on Multi-objective Optimization), especially in load balancing and energy consuming.
Formulation of the measurement noise covariance matrix in linear state estimation

The paper aims at assessing the accuracy performance of power system state estimators that rely on synchrophasor measurements and are thus formulated in rectangular coordinates. The algorithm that is used to conduct the performance assessment of the proposed state estimator is Linear Weighted Least Squares (LWLS). In this direction, the contribution of the paper lies in the analytical mathematical proof related to the transformation of the measurement errors and the projection of the associated standard deviations from polar to rectangular coordinates. The latter are used as input to calculate the measurement noise covariance matrix whose proper formulation plays a significant role in the performance of Linear State Estimation (LSE). The validation of the proposed state estimator is carried out by using as a case study a suitably modified version of the IEEE 123-bus distribution test feeder.
Voltage control simulations in distribution systems with high penetration of PVs using the OpenDSS

With the increasing number of photovoltaic generators (PVs) in the distribution system, new challenges arise to maintain the delivered energy quality to consumers. Injection of current by the PV system can create an overvoltage condition around the point of common coupling to the network. To overcome this problem, recent studies show some solutions using the inverter capacity to absorb or inject reactive power to voltage control. In this context, this paper presents a case study to investigate the impact of increased levels of PV penetration in network voltage and the use of the Volt / Var and Volt/ Watt control in different topologies, also including storage systems, as some possible solutions. The program used for the study was the OpenDSS, the steady-state simulation performed took into account typical variations of residential and commercial loads in a modified IEEE 13 Node test feeder. The result shows the importance of the impact analysis caused by the new types of energy generation and how the use of different solutions can mitigate the overvoltage in the most critical busbar.
Identification of Sparse Reciprocal Graphical Models

In this letter we propose an identification procedure of a sparse graphical model associated to a Gaussian stationary stochastic process. The identification paradigm exploits the approximation of autoregressive (AR) processes through reciprocal processes in order to improve the robustness of the identification algorithm, especially when the order of the AR process becomes large. We show that the proposed paradigm leads to a regularized, circulant matrix completion problem whose solution only requires computations of the eigenvalues of matrices of dimension equal to the dimension of the process.
Efficient manifold ranking for cross-media retrieval

With the advent of the era of big data, all kinds of data are fused together which form a new modality-multimedia data, how to find information in multimedia data become a key problem, so cross-media retrieval has drawn great attention in recent year. Precisely and effectively measuring the similarity between different modalities of data is a key step of cross-media retrieval. The traditional methods usually use Cosine distance or Euclidean distance, which is difficult to reflect the real similarity between different modalities of data. For address this problem, in this paper, we use a novel approach to replace the above two methods, called efficient manifold ranking (EMR), which is able to better reflect the similarity between different modalities of data. Experiments on Wikipedia dataset and the challenging XMedia dataset which includes 5 media types show the effectiveness of the EMR algorithm, as compared with the 4 state-of the-art methods.
Four-channel behavior recognition algorithm based on DRN

Concerning the fact that previous behavior recognition algorithms tend to be affected by background and illumination noise, we propose a four-channel behavior recognition algorithm based on fine-tuning deep network Dilated Residual (DRN). Our algorithm can extract more human behavior information through the high frequency grayscale channel. Specifically, we decompose video datasets into multiframe color images, and convert them to high frequency grayscale images with high-pass filtering and normalization. Then, the four-channel dataset consists of RGB and above high frequency grayscale images. The effectiveness of the algorithm is evaluated by testing the classification accuracy on Olympic Sports dataset with DRN. The result shows that the algorithm achieves 85.5% accuracy on behavior recognition, which is 3.4% higher than the TOI+SVM method.
PAPR reduction of FBMC-OQAM using A-law and Mu-law companding

Filter bank multicarrier scheme is an efficient multicarrier scheme and candidate waveform for 5G. Similar to OFDM and other multicarrier schemes, FBMC also has high Peak-to-Average Power Ratio (PAPR) which leads us to the use of high power amplifiers with high dynamic range. Companding techniques are used to reduce PAPR at the expense of bit error rate (BER) performance degradation. In this paper, a novel usage of A-law and Mu-law companding techniques for PAPR reduction of FBMC-OQAM scheme are proposed. The paper also investigates the tradeoff between PAPR reduction and Bit error rate performance of FBMC-OQAM using A-law and Mu-law companding techniques. Simulation results have shown a significant decrease in PAPR but BER of the system has increased. Both the companding techniques have shown similar results but Mu-law companding has given a slightly better performance than A-law companding in PAPR reduction but BER of A-law companding is better than Mu-law companding.
EMI-Based Current and Voltage Sensing for the Control of Power Electronic Converters

In this brief, a new method for current and voltage sensing in power electronic converters is demonstrated. This concept, which uses the converter’s own electromagnetic interference, associates an electromagnetic field characterization method and the inverse Fourier transform. These two combined with a circuit analysis allow the prediction of the needed parameters to achieve the current programed control chosen here. Results obtained on a Buck converter show the feasibility of this concept.
Understanding Botnet-driven Blog Spam: Motivations and Methods

Spam, or unsolicited commercial communication, has evolved from telemarketing schemes to a highly sophisticated and profitable black-market business. Although many users are aware that email spam is prominent, they are less aware of blog spam (Thomason, 2007). Blog spam, also known as forum spam, is spam that is posted to a public or outward facing website. Blog spam can be to accomplish many tasks that email spam is used for, such as posting links to a malicious executable. Blog spam can also serve some unique purposes. First, blog spam can influence purchasing decisions by featuring illegitimate advertisements or reviews. Second, blog spam can include content with target keywords designed to change the way a search engine identifies pages (Geerthik, 2013). Lastly, blog spam can contain link spam, which spams a URL on a victim page to increase the inserted URLs search engine ranking. Overall, blog spam weakens search engines’ model of the Internet popularity distribution. Much academic and industrial effort has been spent to detect, filter, and deter spam (Dinh, 2013), (Spirin and Han, 2012). Less effort has been placed in understanding the underlying distribution mechanisms of spambots and botnets. One foundational study in characterizing blog spam (Niu et al., 2007) provided a quantitative analysis of blog spam in 2007. This study showed that blogs in 2007 included incredible amounts of spam but does not try to identify linked behavior that would imply botnet behavior. A later study on blog spam (Stringhini, 2015) explores using IPs and usernames to detect botnets but does not characterize the behavior of these botnets. In 2011, a research team (StoneGross et al., 2011) infiltrated a botnet, which allowed for observations of the logistics around botnet spam campaigns. Overall, our understanding of blog spam generated by botnets is still limited.
Robonaut 2 and Watson: Cognitive dexterity for future exploration

Future exploration missions will dictate a level of autonomy never before experienced in human spaceflight. Mission plans involving the uncrewed phases of complex human spacecraft in deep space will require a coordinated autonomous capability to be able to maintain the spacecraft when ground control is not available. One promising direction involves embedding intelligence into the system design both through the employment of state-of-the-art system engineering principles as well as through the creation of a cognitive network between a smart spacecraft or habitat and embodiments of cognitive agents. The work described here details efforts to integrate IBM's Watson and other cognitive computing services into NASA Johnson Space Center (JSC)'s Robonaut 2 (R2) anthropomorphic robot. This paper also discusses future directions this work will take. A cognitive spacecraft management system that is able to seamlessly collect data from subsystems, determine corrective actions, and provide commands to enable those actions is the end goal. These commands could be to embedded spacecraft systems or to a set of robotic assets that are tied into the cognitive system. An exciting collaboration with Woodside provides a promising Earth-bound testing analog, as controlling and maintaining not normally manned off-shore platforms have similar constraints to the space missions described.
PaintCopter: An Autonomous UAV for Spray Painting on Three-Dimensional Surfaces

This letter describes a system for autonomous spray painting using an unmanned aerial vehicle (UAV), suitable for industrial applications. The work is motivated by the potential for such a system to achieve accurate and fast painting results. The PaintCopter is a quadrotor that has been custom fitted with an arm plus a spray gun on a pan-tilt mechanism. To enable long deployment times for industrial painting tasks, power and paint are delivered by lines from an external unit. The ability to paint planar surfaces, such as walls in single color, is a basic requirement for a spray painting system. But this work addresses more sophisticated operation that subsumes the basic task, including painting on three-dimensional (3D) structure, and painting of a desired texture appearance. System operation consists of (a) an offline component to capture a 3D model of the target surface, (b) an offline component to design the painted surface appearance and generate the associated robotic painting commands, and (c) a live system that carries out the spray painting. Experimental results demonstrate autonomous spray painting by the UAV, doing area fill and versatile line painting on a 3D surface.
Online Incentive Mechanism for Mobile Crowdsourcing Based on Two-Tiered Social Crowdsourcing Architecture

Mobile crowdsourcing has become an efficient paradigm for performing large scale tasks. The incentive mechanism is important for the mobile crowdsourcing system to stimulate participants, and to achieve good service quality. In this paper, we focus on solving the insufficient participation problem in the budget constrained online crowdsourcing system. We present a two-tiered social crowdsourcing architecture, which can enable the selected registered users to recruit their social neighbors by diffusing the tasks to their social circles. In the two-tiered social crowdsourcing system, the tasks are associated with different end times, and both the registered users and their social neighbors have different arrival/departure times. An online incentive mechanism, MTSC, which consists of two steps: Agent Selection and Online Reverse Auction, is proposed for this novel mobile crowdsourcing system. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed incentive mechanism achieves computational efficiency, individual rationality, budget feasibility, cost truthfulness, and time truthfulness.
Proportional appraisal of access control schemes

Wireless sensor network (WSN) is sparse or dense cluster of sensor nodes that are spatially arranged. In many WSN applications such as critical military, home security, health care application where user's access security is necessary which is provided by Access Control Scheme(ACS). For the security purposes user's access restriction is implemented with different access rights. Using cryptography techniques many ACS are implemented. In this paper Hop Based Queuing (HBQ) and Energy-Efficient Access control scheme Based on ECC (ENABLE) are studied, implemented and compared. It shows that an energy-efficient access control scheme based on a symmetric cryptography technique i.e. ECC can be used to overcome limitations of security and more importantly to provide high energy-efficiency. Appraisal and simulation concludes and proves that ENABLE scheme overcomes the security problems and has better energy-efficiency over HBQ. Also the ENABLE ACS is tested with A-star routing algorithm which gives better energy and delay efficiency.
Multi-objective optimization of charging infrastructure to improve suitability of commercial drivers for electric vehicles using real travel data

Electric mobility has gained much interest in the automotive industry and with commercial customers. A well-developed charging infrastructure is necessary to meet the rising customer demand for electricity. The aim of this paper is to evaluate how the suitability of commercial customers for the conversion to electric vehicles (EVs) is improved by the expansion of new charging stations. Here, the impact of an expanded charging infrastructure is measured by a multi-objective genetic algorithm. The location and type of charging stations is optimized with respect to the number of failed trips, due to empty batteries, and the total cost of infrastructure. Travel data from commercial vehicle fleets is approximated to EVs and discloses a pareto front to support decision makers in placing optimal public charging stations.
3D GMR sensor detection ability in nondestructive evaluation of austenitic biomaterials

This paper deals with nondestructive evaluation of concrete conductive biomaterials that are nowadays widely used on the field of biomedicine. The study focuses on detection ability of a commercial magnetic field sensor for detection of residual magnetic field of inspected biomaterials. The gained results are presented and discussed in this paper.
CSV-ANNOTATE: Generate annotated tables from CSV file

The Semantic Web is a part of the current World Wide Web (WWW), which can facilitate a common mechanism to publish, share, and reuse data beyond the boundaries of web applications. It is widely believed that the majority of the datasets stored on the current web are in tabular data format (CSV, spreadsheets, SQL dumps, HTML tables etc), commonly in the comma-separated values (CSV) format. In order to prepare the CSV data semantically structured, interoperable, accessible and reusable for various web applications, they need to be extracted from the CSV files and converted into annotated table. Therefore, we propose an effective approach to generate annotated tables from CSV file. However, annotated table for CSV provides possibilities for data publishers to refer data validating, converting, displaying and inputting by following the Semantic Web standard. This research presents the conversion strategies of CSV file into annotated tables. Here, we design a parsing algorithm and development techniques to demonstrate the annotated tabular data model (column, row, and cell). An experiment is carried out to observe and compare the time efficiency of the annotation process. This method and findings provide a valuable reference for potential implementers to further operate the Semantic data.
A supporting survey to step into a novel approach for providing automated emotion recognition service in mobile phones

Human face plays an important role to exhibit one's emotions. Beyond verbal communication, facial expression contributes much in human-to-human communication. In human-machine interaction system, emotion recognition plays a vital role. A wide range of techniques has been proposed to automate the emotion detection process in computerized systems. Basically six types of expressions are recognized, i.e., happiness, surprise, fear, sadness, anger, and disgust. The parts of face which reveal the emotion are eyes, eyebrows, lips, nostrils, muscles bulging area and wrinkles. The properties of images under consideration are grayscale or colour image, strong or light background and the lightings. The source image can be a photo captured from the camera or it can be the image from video stream. There are three stages of processes involved in this emotion detection system which are face detection from the given image, extracting its features, and classification. The techniques involved in these three major processes and their sub processes are analyzed in this survey paper. Some of the soft computing techniques are also discussed to train the system to fine tune the system. The datasets utilized in various techniques includes JAFFE dataset, eNTERFACE database, Cohn Kanade AU-Coded database, MMI facial expression database, Berlin emotion database, CK+ database, and Chen generated dataset. This paper aims at identifying the techniques for recognizing facial emotions which are suitable for mobile phones.
Determining the Necessary Frame Rate of Video Data for Object Tracking under Accuracy Constraints

Network cameras, a type of surveillance cameras, generate real-time, versatile, and high quality video content that can be used for applications such as public safety and surveillance. Analyzing high frame rate video streams im- poses heavy computing needs and significant loads to the network. High frame rates may not be essential for meeting the accuracy requirements of the analyses. For example, high frame rates may not be required to track cars inside a garage compared with cars on a highway. In this paper, we study object tracking and propose a method to automatically determine the necessary frame rate for videos in network cameras for object tracking and adapt to run- time conditions. We demonstrate that the frame rates can be reduced up to 80% based on accuracy constraints.
First comparison of InGaAsP and InGaAlAs electroabsorption modulator in COBRA generic platform

Introduction Electro-absorption modulators (EAMs) are an attractive solution in applications where high speed modulation is required with high extinction ratio, low drive voltage and integration with lasers. They are particularly promising for highbandwidth optical communication systems. The possibility of using InGaAlAs compounds, instead of InGaAsP, has shown improved performance at high operating temperatures due to the favourably high band offset for electrons [1].
Computational vision applied to mobile robotics with position control and trajectory planning: Study and application

This paper has the primary goal of developing position control for a track driven robot on a flat surface through computer vision. Therefore, the robot is observed by a camera fixed upon the framework. Additionally, this work uses a path planning technique which is capable of driving the robot on the surface and avoiding possible obstacles, also detected by the camera. To conduct the practical tests together with the theoretical development, the robots kinematic and dynamic models are derived. The camera is a standard smartphone with the Android operating system. The robot driving system consists of two continuous rotation servomotors controlled by an Arduino microcontroller, which is also responsible for promoting the communication between the robot and computer. The driving signals are obtained through a specific control method, and the chosen path planning technique is based on A∗ (A Star) algorithm. All the computational steps were implemented in MATLAB software, in exception of the microcontroller source code. Finally, the results show that the simple model and controller derived are able to achieve good performance in the problem of position control.
A survey on miscellaneous attacks in Hadoop framework

The world is progressing towards innovative advancement step by step bringing about an exponential rise in data. This enormous volume of data has presented the possibility of Big data, which has caught the consideration of business and IT researchers as a gift and a source of various opportunities for extensive associations. Securing this enormous measure of information has turned into an extraordinary concern in the field of data and communication technology. Hadoop, as an open-source distributed computing and Big data framework, is progressively utilized as a part of the business world, however the imperfection of security mechanism now ends up noticeably one of the fundamental issues obstructing its improvement. This paper depicts the different attack in detail which is possible on the Hadoop framework. The objective of this paper is to draw out an overview on the security risk and their identification technique in the Hadoop environment.