Miracle-dz/UniFAR-FTUs huggingface dataset:
Task Allocation in Spatial Crowdsourcing: Current State and Future Directions

Spatial crowdsourcing (SC) is an emerging paradigm of crowdsourcing, which commits workers to move to some particular locations to perform spatio-temporal-relevant tasks (e.g., sensing and activity organization). Task allocation or worker selection is a significant problem that may impact the quality of completion of SC tasks. Based on a conceptual model and generic framework of SC task allocation, this paper first gives a review of the current state of research in this field, including single task allocation, multiple task allocation, low-cost task allocation, and quality-enhanced task allocation. We further investigate the future trends and open issues of SC task allocation, including skill-based task allocation, group recommendation and collaboration, task composition and decomposition, and privacy-preserving task allocation. Finally, we discuss the practical issues on real-world deployment as well as the challenges for large-scale user study in SC task allocation.
Learning to Communicate in UAV-Aided Wireless Networks: Map-Based Approaches

We consider a scenario where an unmanned aerial vehicle (UAV)-mounted flying base station is providing data communication services to a number of radio nodes spread over the ground. We focus on the problem of resource-constrained UAV trajectory design with: 1) optimal channel parameters learning and 2) optimal data throughput as key objectives, respectively. While the problem of throughput optimized trajectories has been addressed in prior works, the formulation of an optimized trajectory to efficiently discover the propagation parameters has not yet been addressed. When it comes to the communication phase, the advantage of this paper comes from the exploitation of a 3-D city map. Unfortunately, the communication trajectory design based on the raw map data leads to an intractable optimization problem. To solve this issue, we introduce a map compression method that allows us to tackle the problem with standard optimization tools. The trajectory optimization is then combined with a node scheduling algorithm. The advantages of the learning-optimized trajectory and of the map compression method are illustrated in the context of intelligent Internet of Things data harvesting.
Speaker verification using mel-frequency cepstrum coefficient and linear prediction coding

Identity verification is one of the most important concern in today's era of information technology. Conventionally, it is done using keys or password which could be stolen and are not suitable for critical purposes. To overcome these problem various biometric verification system have emerged, such as iris recognition, figure print recognition and speaker recognition system. In this paper the author has provided brief study of feature extraction method, linear prediction coding (LPC) and Mel-Frequency Cepstrum Coefficient (MFCC) in speaker recognition system. It also presents implementation of various blocks of MFCC using the following design suit : 1. Design is done using VHDL and simulated using ISE 2. Device technology used 90nm. 3. Device family SPARTAN3E; Device XC3S500E and Device package FG320.
Low voltage ride-through of a synchronous reluctance generator based variable speed wind energy conversion system

Grid codes want the wind energy conversion systems (WECS) to stay connected even when low voltage occurs at the point of common coupling (PCC) for a short-while. This paper is an attempt to propose a control strategy to achieve low voltage ride through (LVRT) for a variable speed grid-connected WECS based on Synchronous reluctance machine (SyRM) as a generator. The SyRM is controlled by field oriented control technique and grid-side-converter is controlled by grid voltage oriented control. Hill climbing algorithm is used to implement maximum power point tracking in the WECS. The LVRT capability of the system under three-phase grid fault condition is achieved using three different control techniques namely, modulation index control, de-loading and crowbar protection. These methods have been simulated in Simulink/MATLAB environment and the results obtained show that these techniques work very effectively for realizing LVRT in a SyRM based WECS.
GCPiN: Group caching for privacy in named data networking

Router architecture in Named Data Networks (NDN) is intricate, and entails in-network caching of Data packets. Protecting the privacy of this cached content, while maintaining the performance benefits obtained due to caching, is a major concern. Although this problem has been addressed, most existing solutions dramatically compromise the performance gain that NDN provides in order to ensure data privacy. In this paper, we formulate a new approach to enhance the privacy of cached content at each NDN router, while ensuring minimal performance loss. This is achieved by segregating NDN routers into groups, and maintaining a Distributed Content Store across each group. The proposed approach has been named Group Caching for Privacy in NDN (GCPiN). We provide a preliminary mathematical analysis which shows that GCPiN is a promising approach with scope for further evaluation.
Low blue carbon storage in eelgrass (Zostera marina) meadows on the Pacific Coast of Canada

Seagrass habitats provide important ecosystem services, including their ability to take up and store substantial amounts of organic carbon, known as ‘blue carbon. ’ However, the paucity of geospatial and carbon storage information along the Pacific Coast of Canada hinders the inclusion of blue carbon storage data in conservation planning and policy development in coastal habitats. We assessed the carbon storage and accumulation rates in three eelgrass (Zostera marina) meadows in southern Clayoquot Sound on the Pacific Coast of British Columbia. The intertidal and subtidal portions of each meadow were mapped and sampled to estimate eelgrass density, biomass, and carbon, and sediment cores were analyzed to estimate sediment carbon storage and accumulation rates. Aboveground biomass measurements were consistent with estimates for Z. marina in other regions, with average aboveground carbon biomass estimates of 16.78 g C m-2 and 16.25 g C m-2 in the intertidal and subtidal areas, respectively. However, the estimated aboveground to belowground biomass ratio was an order of magnitude higher than for seagrass species in temperate/tropical areas, largely because belowground biomass was up to 10 times lower than for other Z. marina meadows, averaging 6.17 g C m-2 and 5.03 g C m-2 in the intertidal and subtidal zones, respectively. Sediment carbon concentrations did not exceed 1.30%Corg, and carbon accumulation rates ranged from 2.90–39.61 g Corg m-2 yr-1, decreasing with depth and averaging 10.8 ± 5.2 g Corg m-2 yr-1. While sediment carbon stocks were generally higher in the eelgrass meadows relative to non-vegetated reference sites, carbons stocks averaged 1343 ± 482 g Corg m-2, substantially less than global averages. These carbon results confirm that eelgrass does contribute to carbon storage in Clayoquot Sound but at lower rates than identified for more tropical seagrasses. By improving the quantification of site-specific carbon dynamics, eelgrass’ role in climate change mitigation and conservation planning can be assessed.
Robust and Secure Indoor Optical Wireless Communications Supporting Multiple Users

We experimentally demonstrate a robust and secure multiple-access mechanism for indoor optical wireless communications using time-slot-coding and chaotic phase. Results show that it is secure against eavesdropper's exhaustive searching accuracy as high as 1e-10 and is robust against 67% (4-QAM) and 23% (16-QAM) code misalignment.
Supervised classification of structural brain networks reveals gender differences

The human brain anatomical organization can be modeled as a complex network, called a connectome. This representation enables the use of powerful mathematical tools to detect individual differences in brain structure. In this paper, we propose three methods based on Support Vector Machines, to classify connectomes according to a given label. We demonstrate the efficiency of our methods compared to existing approaches through experiments on a dataset built from publicly available MRI data, and for the classification between male and female brains. We further interpret the classification outcome in regards to one particular wiring feature: the inter-hemispheric ratio.
Anxiety-like behavior and structural changes of the bed nucleus of the stria terminalis (BNST) in gestational protein-restricted male offspring

Abstract Animal evidence has suggested that maternal emotional and nutritional stress during pregnancy is associated with behavioral outcomes in offspring. The nature of the stresses applied may differ, but it is often assumed that the mother’s hippocampus–hypothalamic–pituitary–adrenal (HHPA) axis response releases higher levels of glucocorticoid hormones. The bed nucleus of the stria terminalis (BNST) is in a pivotal position to regulate the HHPA axis and the stress response, and it has been implicated in anxiety behavior. In the current study, to search whether BNST structural changes and neurochemical alterations are associated with anxiety-related behavior in adult gestational protein-restricted offspring relative to an age-matched normal protein diet (NP) rats, we conduct behavioral tests and, BNST dendritic tree analysis by Sholl analysis, associated to immunoblotting–protein quantification [11β-HSD2, GR, MR, AT1R, 5HT1A and 5HT2A, corticotrophin-releasing factor (CRH) and CRH1]. Dams were maintained either on isocaloric standard rodent chow [with NP content, 17% casein or low protein content (LP), 6% casein] chow throughout their entire pregnancy. Here, in rats subjected to gestational protein restriction, we found: (a) a significant reduction in dendritic length and impoverished dendritic arborization in BNST neurons; (b) an elevated plasmatic corticosterone levels; and (c) associated with enhanced anxiety-like behavior when compared with age-matched NP offspring. Moreover, altered protein (11β-HSD2, GR, MR and type 1 CRH receptors) expressions may underlie the increase in anxiety-like behavior in LP offspring. This work represents the first demonstration that BNST developmental plasticity by maternal protein restriction, resulting in fine structural changes and neurochemical alterations that are associated with modified behavioral states.
Replication Fork Breakage and Restart in Escherichia coli

SUMMARY In all organisms, replication impairments are an important source of genome rearrangements, mainly because of the formation of double-stranded DNA (dsDNA) ends at inactivated replication forks. Three reactions for the formation of dsDNA ends at replication forks were originally described for Escherichia coli and became seminal models for all organisms: the encounter of replication forks with preexisting single-stranded DNA (ssDNA) interruptions, replication fork reversal, and head-to-tail collisions of successive replication rounds. Here, we first review the experimental evidence that now allows us to know when, where, and how these three different reactions occur in E. coli. Next, we recall our recent studies showing that in wild-type E. coli, spontaneous replication fork breakage occurs in 18% of cells at each generation. We propose that it results from the replication of preexisting nicks or gaps, since it does not involve replication fork reversal or head-to-tail fork collisions. In the recB mutant, deficient for double-strand break (DSB) repair, fork breakage triggers DSBs in the chromosome terminus during cell division, a reaction that is heritable for several generations. Finally, we recapitulate several observations suggesting that restart from intact inactivated replication forks and restart from recombination intermediates require different sets of enzymatic activities. The finding that 18% of cells suffer replication fork breakage suggests that DNA remains intact at most inactivated forks. Similarly, only 18% of cells need the helicase loader for replication restart, which leads us to speculate that the replicative helicase remains on DNA at intact inactivated replication forks and is reactivated by the replication restart proteins.
Gesture Recognition With the Linear Optical Sensor and Recurrent Neural Networks

In this paper, the optical linear sensor, a representative of low-resolution sensors, was investigated in the multiclass recognition of near-field hand gestures. The recurrent neural network (RNN) with a gated recurrent unit (GRU) memory cell was utilized as a gestures classifier. A set of 27 gestures was collected from a group of volunteers. The 27 000 sequences obtained were divided into training, validation, and test subsets. The primary research goal was to define the most appropriate model architecture in terms of the accurate recognition of each gesture. An additional aim of the research was to investigate the kind of input data, i.e., raw data or preprocessed (feature) data, which generally produces better results. Therefore, three datasets were generated: raw data, simple features data, and high-level features data. (This includes information about hand poses which are already recognized.) The random search method was applied to achieve hyperparameter optimization to find the best possible topology for the neural network. The analysis performed shows that selected models were characterized by a test score at a level of 96.89% for the raw data, 95.75% for simple features, and 93.38% for high-level features. Results indicate that the direct use of raw data obtained from the optical linear sensor evaluated on the RNN with GRU memory cells allows for the reliable recognition of even complex gestures. Therefore, such solutions may have the potential to serve as a support to or as an alternative to video-based sensors especially for mobile devices.
Analysis of the effective production Kanban size with triggering system for achieving just-in-time (JIT) production

In this paper, the improvement activity in manufacturing plant has been conducted by focusing on the effective Kanban size to achieve just-in-time (JIT) production system. The results of the implementation in case study was identified, analyzed and verified by utilizing the effective feeders of Kanban size through queuing system analysis. The ultimate objective was to ensure adaptation of the developed Kanban approach using effective feeders for in-house production system was implemented, in which work-in-progress (wip) “triggering system” was introduced to ensure sustainability of the Kanban system and to achieve the JIT production system. Based on the implementation from the case study industry, the obtained result for effective feeders of Kanban size was dual feeders and with the waste of waiting eliminated by 36.64%. Through effective production Kanban size, the waiting time of the production process in production losses time due to the pre-preparation part had been identified and successfully minimized.
Power-Efficient Communication in UAV-Aided Wireless Sensor Networks

This letter investigates a power-efficient unmanned aerial vehicle (UAV)-based wireless sensor network, where the UAV is used as a flying base station to communicate with the sensor nodes (SNs) with flexible movement path. We aim at minimizing the total power consumption of the UAV with a guarantee of the required transmission rate of SNs by jointly optimizing the SNs-UAV scheduling scheme, power allocation strategy, and flight trajectory of the UAV. Due to the non-convex and mixed-integer nature of the original problem, block coordinate decent and successive convex optimization techniques are employed to decompose it into two sub-problems, which leads to an efficient iterative algorithm. Numerical results show the performance gain of our proposed scheme.
Stochastic geometry and Markov chain model based throughput analysis in dense WLANs

This paper analyzes the dense IEEE 802.11 wireless local area networks (WLANs) with random topologies using stochastic geometry and Markov chain model. Since Markov chain model for WLANs only focuses on 802.11 MAC layer performance and stochastic geometry model mainly focuses on physical layer effects, our mathematical models take both of these two layers into consideration. The locations of access points (APs) and stations (STAs) are distributed according to two independent homogeneous Poisson point process (PPP) models. Then, an algorithm is proposed for systematically constructing the continuous time Markov chain (CTMC) corresponding to dense WLANs with random topologies. We use a Markov chain model to analyze the throughput performance of dense WLANs based on the CTMC model. The accuracy of our mathematical models is validated by simulations.
Loss-Tolerant Event Communications Within Industrial Internet of Things by Leveraging on Game Theoretic Intelligence

Internet of Things (IoT) is one of the key technologies paving the way for the next industrial revolution named as Industry 4.0, since it promises to realize smarter factories by optimizing costs and productivity. Traditionally, the adopted communication protocols among the sensors are required to manage the large scale of the infrastructure in terms on the high number of interconnected nodes and the massive volume of exchanged data. However, due to the key role of those Industrial IoT in exchanging business critical data, such protocols need to also provide high resiliency guarantees to the message exchange, with as few delivery misses as possible. The publish/subscribe interaction pattern and the protocol implementing it are a technically sound approach for achieving scalability and elasticity, thanks to their intrinsic decoupling among the interacting nodes. However, they are often unsuitable in their current form, because they provide only best-effort delivery guarantees, or they adopt naive solutions to achieve resilient communication, especially when wireless networks are used. This paper presents a clustered lightweight gossiping algorithm for resilient event-based communications among the sensors, without requiring a predeployed brokering infrastructure supporting the adopted publish/subscribe protocol. A simulation-based assessment has been performed in order to empirically show the improvements in terms of successfully delivered notification without the excessive costs of the state-of-the-art solutions available in the literature.
Cuffless blood pressure monitoring using PTT and PWV methods

Cuff based methods for measuring Blood Pressure (BP) are obtrusive and uncomfortable and demands continuous, non-invasive measurement methods. The aim of this work is to develop a platform that measures BP using indirect techniques such as Pulse Transit Time (PTT) and Pulse Wave Velocity (PWV) methods. The Systolic BP (SBP) and Diastolic BP (DBP) measurement based on PTT and PWV method was found out from the experiments. It is concluded that BP measurements based on PWV methods have less error compared to PTT methods. The absolute error in SBP and DBP measurement based on PWV is +11 mmHg to −12 mmHg and +10 mmHg to −14 mmHg respectively compared to standard cuff based method. Further to this, the ECG and the PPG data were transferred to a nearby data aggregation device over bluetooth and subsequently to a server to enable remote health monitoring.
Contribution of peroxisomal docking machinery to mycotoxin biosynthesis, pathogenicity and pexophagy in the plant pathogenic fungus Fusarium graminearum

Peroxisomal proliferation is highly stimulated during the biosynthesis of mycotoxins and plant infection by Fusarium graminearum. Currently, the functions of the peroxisome in these cellular processes are poorly understood. In this study, we applied genetic, cell biological and biochemical analyses to investigate the functions of the peroxisomes. We constructed targeted deletion of docking machinery components, including FgPex13, FgPex14 and the filamentous fungal specific peroxin FgPex33. Our results indicated that peroxisome dysfunction resulted in a shortage of acetyl-CoA, the precursor of trichothecene biosynthesis, and subsequently decreased deoxynivalenol (DON) production. Deletion mutants of ΔFgPex13, ΔFgPex14 or ΔFgPex33 showed an increased accumulation of endogenous reactive oxygen species (ROS) and reduced phosphorylation of MAP (Mitogen-Activated Protein) kinase FgMgv1. In addition, mutants of the docking peroxin exhibited increased sensitivity toward host oxidative bursts and cell wall integrity stress agents and reduced virulence on host plants. More importantly, we found for the first time that FgPex14 is required for pexophagy in F. graminearum. Overall, our study suggests that peroxisomes play critical roles in DON biosynthesis and virulence in F. graminearum.
Ego-Motion Estimation of a Quadrotor Based on Nonlinear Observer

In this paper, an ego-motion estimation problem of a quadrotor unmanned aerial vehicle is studied. A nonlinear observer is proposed to simultaneously estimate the depth of the point features and the translational velocity of the quadrotor using visual feedback from an on-board monocular camera. Image moments defined on a rotated image plane are chosen as image features, and a decoupled image feature dynamics is achieved to facilitate the observer design. Based on the image feature dynamics and the dynamic model of the quadrotor, an observer is proposed to estimate the ego-motion of the quadrotor. Only several point features need to be tracked in consecutive acquired images, which makes the observer computationally efficient and robust. The stability of the observer is proved by means of Lyapunov analysis. Finally, computer simulations and experimental tests, including closed loop image-based visual servoing control and comparison with extended-Kalman-filter based observer, are conducted to verify the validity of the observer.
Monarch butterfly optimization algorithm for localization in wireless sensor networks

In this paper we adjusted recent monarch butterfly optimization algorithm for solving NP-hard wireless sensor networks optimization problem, as one of the most important challenges in this area. The main goal of node localization problem is to find geographical coordinates of each sensor node with unknown position that are randomly deployed in the monitoring area. Monarch butterfly optimization is novel swarm intelligence approach and it was not tested on this or similar problems before. We adjusted basic implementation of the monarch butterfly optimization method and tested it on several problem instances that are found in the literature. We also performed a comparative analysis with other swarm intelligence metaheuristics that were tested on the same problem instances. By examining results of empirical experiments and by comparative analysis of the results from other approaches, we established that butterfly optimization algorithm can be successfully applied to wireless sensor networks optimization problem.
Correction of range cell migration with FIR filter for passive radar

In the paper a method is shown for correcting range cell migration in passive radar. The method is based on filtering the crossambiguity function (CAF) with a two-dimensional finite impulse response (FIR) filter. It is shown that despite heuristic origin of the method, it provides satisfactory results in comparison with rigorous approach in terms of signal to noise ratio loss. The method is tested with simulated signals, as well as signals recorded with DVB-T-based passive radar.