Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Client Involvement towards Agile Project Success in Sri Lankan Software Industry

During the recent past, the software industry in Sri Lanka has developed rapidly. Every company that implements software products has the goal to deliver a quality product to the client. To achieve that goal, software companies use different processes, technologies and methods. Agile software development methodology has gained wide acceptance due to its flexibility and client involvement; Agile software development plan and process are inline according to the client’s desires and expectations. Hence one of the main factors for the success of an Agile project is the active client participation. Sri Lankan software industry heavily relies on outsourced projects where the client is often not physically present with the dev team. In this research, the client involvement level was examined in Sri Lankan software companies to evaluate its contribution to the success of Agile projects. The research further discusses the organizational culture to be maintained and how it contributes to having a better client relationship. The finding of the research will be beneficial for Sri Lankan software development companies to deliver successful software products.
Tasbi: Multisensory Squeeze and Vibrotactile Wrist Haptics for Augmented and Virtual Reality

Augmented and virtual reality are poised to deliver the next generation of computing interfaces. To fully immerse users, it will become increasingly important to couple visual information with tactile feedback for interactions with the virtual world. Small wearable devices which approximate or substitute for sensations in the hands offer an attractive path forward. In this work, we present Tasbi, a multisensory haptic wristband capable of delivering squeeze and vibrotactile feedback. The device features a novel mechanism for generating evenly distributed and purely normal squeeze forces around the wrist. Our approach ensures that Tasbi’s six radially spaced vibrotactors maintain position and exhibit consistent skin coupling. In addition to experimental device characterization, we present early explorations into Tasbi’s utility as a sensory substitution device for hand interactions, employing squeeze, vibration, and pseudo-haptic effects to render a highly believable virtual button.
Load Balanced Clustering protocol for lifetime enhancement of WSN

WSN have large numbers of spatially distributed and dedicated sensing devices, which are used for sensing the physical condition of the environment in which they are deployed. Todays time, WSN have lots of areas for research and it plays important role like tracking of area, health-scanning, earthquake-sensing and so on. WSN sets supplementary life as well as nodes time-period in sensor nodes. Network life cycle is dependent on many factors like their topology type, residual energy after some rounds, sensor-node distance to sink node etc. There are already lots of work done on this concept. Clustering is one of the most important approach to make efficient communication. In this paper, we used clustering approach that uses the minimum energy consumption by choosing the Clusters-Heads (CHs) using five parameters: delay, standard deviation of Eres, average distance of CHs, standard deviation of average life time of cluster head and max distance to Base Station (BS). Results verify that our proposed algorithm will prolong the lifetime of network over LEACH and base algorithm.
Seasonal variability and its impact on the sub-tidal macrobenthic fauna of Diu Island, north-eastern Arabian Sea: A multivariate approach

COPYRIGHT © 2022 Pandey, Jha, Ratnam, Venkatnarayanan, Sathish Kumar, Verma and Dharani. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. TYPE Data Report PUBLISHED 15 November 2022 DOI 10.3389/fmars.2022. 1006689
Dynamic Virtual Machine Placement Considering CPU and Memory Resource Requirements

In cloud data centers, cloud providers can offer computing infrastructure as a service in the form of virtual machines (VMs). With the help of virtualization technology, cloud data centers can consolidate VMs on physical machines to minimize costs. VM placement is the process of assigning VMs to the appropriate physical machines. An efficient VM placement solution will result in better VM consolidation ratios which ensures better resource utilization and hence more energy savings. The VM placement process consists of both the initial as well as the dynamic placement of VMs. In this paper, we are experimenting with a dynamic VM placement solution that considers different resource types (namely, CPU and memory). The proposed solution makes use of a genetic algorithm for the dynamic reallocation of the VMs based on the actual demand of the individual VMs aiming to minimize under-utilization and over-utilization scenarios in the cloud data center. Empirical evaluation using CloudSim highlights the importance of considering multiple resource types. In addition, it demonstrates that the genetic algorithm outperforms the well-known best-fit decreasing algorithm for dynamic VM placement.
Mobility Prediction with Missing Locations Based on Modified Markov Model for Wireless Users

Mobility prediction is an interesting topic attracting many researchers and both prediction theory and models are explored in the existing literature. The entropy metric to evaluate the mobility predictability of individuals gives a theoretical upper bound and lower bound of prediction probability, although the achieved accuracies of users with the same predictability vary. In this work, we investigate the missing locations phenomenon which means the users visit new locations in the testing set. The major difference of theoretical bound between with and without missing locations are found, which shows that users without missing locations are easier to predict. After discussing the impact of missing locations on the prediction accuracy, a modified Markov chain prediction model is proposed to deal with the presence of missing positions. Finally, the correlation between accuracy and predictability can be modeled as the Gaussian distribution and the standard deviation modeled with missing locations can be modeled as double Gaussian function, while that without missing locations can be modeled as the third-order polynomial function.
Guidewire Tracking based on Visual Algorithm for Endovascular Interventional Robotic System

During the Minimally invasive vascular intervention surgery, deformable guidewire tracking is still a challenging task due to background clutter of the image and the complex motion of the target. However, existing researches about guidewire tracking for robot-assisted endovascular catheterization system are still limited. In this paper, scale-adaptive mean-shift method is adopted in endovascular interventional robotic system to detect the position of the guidewire tip. To evaluate the performance of this algorithm in guidewire tracking, two interventional experiment using the rigid model of cerebral vascular were designed. The experimental results show that the ratio of frames with the center location error less than 5 pixels is 97.6% in these two tasks, and the average processing speed for each frame is 1.24ms. The result shows that this algorithm with high precision and real-time has the potential to apply in endovascular interventional robotic system.
Modeling of Offshore Wind Turbine Under Lightning Stroke and Analysis of Impact Factors on Transient Overvoltage

In order to prevent and reduce the offshore wind farm's damage caused by lightning, it's necessary to establish an electromagnetic transient model of the actual discharge flow path, calculate and evaluate the transient overvoltage at the wind turbine's engine room caused by lightning stroke. The accuracy of grounding resistance is an important factor affecting the reliability of the lightning channel model. Considering the wide distribution of China's offshore wind farms and the great difference in sea conditions, the intertidal grounding environment is divided into three layers: seawater layer, silt layer and sandstone layer. The influence of physical depth and resistivity of each layer on grounding resistance is analyzed. Taking the 2MW wind turbine of Shanghai Electric in the intertidal wind farm in Yangkou, Rudong, Jiangsu as an example, the actual discharge flow path is divided into five parts: excitation source, blade, bearing of engine room, tower and grounding resistance. And mathematical models are established respectively. The tower is equivalent to a vertical hollow section cylinder; the bearing between the engine room and the tower is equivalent to the capacitance; the equivalent resistance of each layer is calculated by using the Green function of the soil point current, and the total impact grounding resistance is obtained by superposition. Finally, the whole model is calculated in ATP-EMTP. The relationship between impact factors and the lightning transient effect is analyzed by taking the transient overvoltage at the engine room as an example.
z-READ: Towards Efficient and Transparent Zero-Copy Read

In cloud computing, I/O-intensive workloads can be co-located with other applications or virtual machines on a single physical machine. In this case, copy-based I/O (buffered I/O) can lead to severe performance interference to other memoryintensive workloads. It is because that the buffered I/O consumes memory bandwidth during memory copy even though it benefits from caching. To address this problem, many zero-copy I/O schemes have been proposed but none of them provides both 1) transparent copy avoidance through read/write system calls and 2) benefits of kernel-level caching at the same time. To this end, this paper presents z-READ, an efficient and transparent zero-copy read I/O scheme based on page remapping and copy-on-write techniques. In our scheme, we introduce several optimizations that minimize the overheads of page remapping by reducing the number of remote TLB shootdown.We implement z- READ prototype in memory management of Linux kernel 4.12. 9. Our experimental results show that the performance of the colocated memory-intensive workloads can be negatively affected by I/O-intensive workloads in the case of copy-based I/O (up to 1.96x slowdown in-memory configurations) while z-READ incurs only up to 1.07x slowdown for the respective configuration.
Design of a Dual-core Processor Based Controller with RTOS-GPOS Dual Operating System

The controller is one of the key components of the robot system. The general-purpose controller usually uses multiprocessor scheme with master-slave structure or distributed structure. This kind of scheme is complex and high-cost. It is difficult to adapt to the design requirements of embedded miniaturized robots. In view of this situation, this paper proposes a controller design based on dual-core dual-operating system, solving various real-time and non-real-time tasks in robot control through a single chip. According to the design requirements, a dual-core chip ZYNQ works as the core of the controller hardware platform. Linux and FreeRTOS are transplanted as master-slave system on different core, and the asymmetric multiprocessing (AMP) architecture is introduced as the working framework of the dual-core processor. After that, the key technologies of ZYNQ running under the AMP architecture are analyzed, including the allocation of shared resources, the dual-core booting mode and the dual-core communication mode. Finally, using the open source architecture OpenAMP to transplant the Linux+FreeRTOS dual system and achieve parallel running. The communication capability and real-time performance of the dual system are tested, and the results verify the effectiveness of the controller.
Displacement Control of Piezoelectric Actuator Based on Fuzzy Fractional Order PID

The hysteresis and creep of piezoelectric actuator greatly reduce the positioning accuracy. In order to acquire precise control and reduction of hysteresis effect, this paper proposes a control scheme based on fuzzy self-tuning fractional order PID to meet the requirements and establishes an experimental platform, including a fixed support, a driving and monitoring unit, to verify the experimental effect. The result shows that under the square wave voltage at different frequencies, the effective displacement, the system stability and the ability of the actuator to follow signals can be improved by the fuzzy fractional PID controller. This verifies the feasibility of the control scheme and indicates that the fuzzy fractional PID controller can reduce the hysteresis of the piezoelectric actuator and maintain high precision of displacement for the signal.
Multi-Agent Reinforcement Learning for Autonomous On Demand Vehicles

In this study, we elaborate the procedure of designing a supervisory controller for the Autonomous Transit on Demand Vehicle (ATODV) system. Reinforcement learning is implemented to reduce the mean waiting time of the passengers, and a cost function is introduced to penalize the energy consumption of the electric vehicles. A stochastic simulation environment for an ATODV pilot project is coded in the Python environment to train the autonomous cart decision process as agents with artificial intelligence. Passenger group behavior, get-on and get-off times, destinations are modeled as random variables. A single Deep Q-Learning Network is trained subject to multi-agent settings. The ATODV system's independent decision making for the carts to reduce the passenger's waiting time while constraining the energy consumption and empty vehicle motion is evaluated.
Tunable Frequency-Selective Rasorber Based on Varactor-Embedded Square-Loop Array

This paper presents a varactor-based tunable frequency-selective rasorber (FSR) with embedded bias grid. The proposed structure consists of a lossy layer based on square-loop arrays and a lossless layer. Each layer has an embedded wire grid that provides the bias voltage for the varactors through metallic vias. This arrangement can avoid unnecessary effects associated with the additional biasing network. An equivalent circuit model is developed to explain how the structure can provide a tunable transmission window within the absorption band. Simple design guidelines for the tunable FSR are then provided. The performance of the proposed structure is measured in a parallel-plate waveguide setup. Both simulated and measured results show that by changing the bias voltage from 16 to 4 V, the center transmission frequency of the FSR can be tuned from 5.2 to 3.8 GHz continuously. The insertion loss of the FSR within the transmission window is only 0.59 dB at 5.2 GHz and a fractional bandwidth of 93.1% (2.4–6.58 GHz) for −10 dB reflectivity is achieved under the normal incidence.
Transferable Driver Behavior Learning via Distribution Adaption in the Lane Change Scenario*

Because of the high accuracy and low cost, learning-based methods have been widely used to model driver behaviors in various scenarios. However, the performance of learning-based methods depend heavily on the quantity and coverage of the driving data. When the new driver with insufficient data is considered, the accuracy of these methods cannot be guaranteed any more. To solve this problem, the balanced distribution adaptation (BDA) is used to build the new driver's decision making model in the lane change (LC) scenario. Meanwhile, a transfer learning (TL) based regression model, modified BDA (MBDA) is proposed to predict the driver's steering behavior during the LC maneuver. Cross validation (CV) based model selection (MS) method is developed to obtain the optimal parameters in model training process. A series of experiments are carried out based on the simulated and naturalistic driving data to verify the TL based classification and regression models. The experimental results indicate that the BDA and MBDA have an outstanding ability in knowledge transfer. Compared with support vector machine (SVM) and Gaussian mixture regression (GMR), the proposed methods show a better performance in the decision making of lane keep/change and the prediction of the driver's steering operation.
Mapping Design for $2^{M}$ -Ary Bit-Interleaved Polar Coded Modulation

This paper proposes a mapping design for bit-interleaved polar coded modulation (BIPCM) systems with belief propagation (BP) decoding. We first introduce a two-layer bipartite graph to represent BIPCM, where a new mapping graph linking polar graph to modulator is added to the conventional factor graph. Then, a mapping design is proposed and the design paradigm is to separate sub-channels with lower reliability to different stopping trees of polar codes, aiming to make sure that each stopping tree receives reliable extrinsic information from demodulator. The proposed mapping algorithm is employed for BIPCM with traditional polar codes over 16-quadrature amplitude modulation (QAM) and 256-QAM. Numerical results show that our scheme can improve the error-correcting performance compared to the conventional scheme with a random mapping. Furthermore, to meet code-length requirement of different modulation orders, we propose an efficient method to construct flexible-length polar code (FLPC) by coupling several short length polar codes with a repeat-accumulate (RA) code. Also, the proposed FLPC is employed in the BIPCM system, with the designed mapping algorithm, simulation result also reveals that the block error rate performance of proposed BIPCM scheme with BP decoding outperforms the one with successive cancellation decoding by providing a gain up to 1 dB.
Machine Learning-Based Channel Estimation in Massive MIMO with Channel Aging

To support the ever increasing number of devices in massive multiple-input multiple-output systems, an excessive amount of overhead is required for conventional orthogonal pilot-based channel estimation (CE) schemes. To relax this stringent constraint, we design a machine learning (ML)-based time-division duplex scheme in which channel state information (CSI) can be obtained by leveraging the temporal channel correlation. The proposed ML-based predictors involve a pattern extraction and CSI predictor, which can be implemented via either a convolutional neural network (CNN) and autoregressive (AR) predictor or an autoregressive network with exogenous inputs recurrent neural network (NARX-RNN), respectively. Numerical results demonstrate that ML-based predictors can remarkably improve the prediction quality, and the optimal CE overhead is provided for practical reference.
Simplified Model of DFIG in Wind Integrated Power System

Due to increase in demand of electricity and due to limitations of conventional energy sources, renewable energy sources are coming up to take their share in the energy mix. The progress of technology has made wind as a promising supplementary energy source and wind power plants are being installed all over the world to support the conventional system. Consequently, contribution of wind power in power system is going up at higher rate, and it has become important to know that how wind power alters the parameters of the power system. There are variety of generators that are employed for wind power generation like SCIG, DFIG and SG. Owing to the advantages such as maximum power tracking, variable speed operation, independent control of active and reactive power DFIG is the most commonly used technology. The paper describes working of DFIG and its simplified model that can be used in the study of wind integrated power system. The paper describes effective use of a double-fed induction generator (DFIG) for variable speed wind power generation.
Self-Management of Containers Deployment in Decentralized Environments

The increasing presence of IoT and fog computing resources are the changing computing environments, leading the applications towards an elastic and decentralized execution. To simplify the deployment and management of applications, containers are widely adopted nowadays. However, although several solutions for orchestrating containers exist, most of them are not suitable to operate in the emerging computing environment, which exposes new challenges due to heterogeneity of computing resources and dynamism of working conditions. The doctorate work, presented in this paper, investigates the run-time adaptation of containerized applications with Quality of Service requirements, deployed over heterogeneous computing and networking resources. We propose decentralized policies based on reinforcement learning (RL) to adapt the application deployment, in terms of elasticity and container migrations. As first research step, we design and evaluate RL solutions that exploit different degrees of knowledge about the system dynamics (i.e., Q-learning, Dyna-Q, and Model-based). As future research, we want to design more efficient adaptation heuristics, exploiting the possibilities of IoT and fog environments, and considering mobility of users and resources within the adaptation loop. Furthermore, we plan to investigate multi-level elasticity, that jointly optimizes applications and infrastructure elasticity.
Perception of a Wearable Haptic Feedback Device to Render the Sensation of Flight

The FlyJacket is a wearable jacket designed to allow a user to intuitively control a fixed-wing drone by moving their torso. We embedded haptic feedback into the jacket using closed inflated air pouches compressed by motors to provide haptic feedback on the torso. This haptic feedback represents the drone centripetal forces while maintaining the essential lightweight and portable qualities of the jacket. We evaluate this haptic feedback in a user study to test whether it can convey useful haptic information to the user and whether the feedback enhances the user experience of flying. Users were able to understand the direction of the cues without prompting and could distinguish directional cues from the device quickly and with high accuracy. They also described the sensation provided by the device as pleasant. The device was used in a simulated flight task and compared to flying with no haptic feedback. Users indicated that the device increased the realism of the flight, but were split on whether it improved their sense of immersion.
Graph-based Analysis of Functional Brain Networks for Processing Semantic Auditory and Visual Stimuli

The human brain processes plenty of information from different senses, auditory and visual information for example, and then makes decision to guide our behaviors depending on their meaning. The brain is a rather intricate system in which different brain functional regions are linked by structural or functional connectivity. Although many studies have investigated the activated areas in cognitive task or in resting-state, the properties of functional network processing semantic auditory and visual stimuli remain unclear. Here, we acquired fMRI data while subjects performed a semantic discrimination task, and then explored the organization of functional brain network using graph theoretical analysis. We identified that increased demand for processing semantic auditory and visual inputs made the local efficiency lower to support more communication across global network. Hub regions with high degree distributed in the temporo-parieto-occipital network were found, which may reflect the mutual assistance of visual and auditory related regions in processing the correlated semantic information. These results provide evidence that processing semantic auditory and visual stimuli is associated with the communication across the global network, specially the regions in temporo-parieto-occipital network with high degree.