Miracle-dz/UniFAR-FTUs huggingface dataset:
On using PointNet Architecture for Human Body Segmentation

In the case of structured data, such as 2D images, many variants of traditional convolution neural network architectures have been successfully proposed. Learning from unstructured sets of data, such as sets of 3D point clouds, is a challenging task due to numerous reasons among which two most important ones are: 3D point cloud is generally (i) unordered and (ii) sparse data set. Therefore, the architectures have been proposed which are invariant to both ordering and number of points in the point cloud. PointNet is one such architecture, originally introduced and demonstrated on the task of classification and segmentation of the ModelNet40 data set. In this work we study the performance of PointNet on an even more demanding task, segmentation of human body parts. Finding enough training data of enough quality is generally a problem in deep learning, and especially for human body segmentation. To that end we take advantage of SMPL model which provides human body models in many shapes and sizes in an essentially automatic fashion, therefore avoiding a cumbersome procedure of manual collection and preparation of training data. Our results show that the proposed PointNet variant trained using SMPL model provides competitive segmentation results on the task of human body segmentation.
An analysis of energy storage system interaction in a multi objective model predictive control based energy management in DC microgrid

Non-deterministic generation from renewable sources have resulted in the incorporation energy storage systems in modern grids. Management of energy between different storage elements need to done optimally to ensure efficient operation of the grid. The intraday energy management problem is addressed in this work through an online model predictive control using multi objective optimisation. This work analyses the energy interaction among different storages when penalty weights in a multi objective optimisation problem is varied, in order to find an optimal scenario in terms of weight distribution. Different scenarios are identified and performance indices are proposed to achieve the same. The work also addresses implicitly the objective of minimising rate of degradation batteries. Simulation results are presented to aid in the analysis.
Linked data through the lens of machine learning: an Enterprise view

Enterprises have adopted semantic Web technologies for data reuse and sharing purposes and machine learning techniques for data analysis. Semantic Web is a generative technology which aims to build a giant global dataspace on the Web by interlinking diverse data sources available on the Web. Machine learning is a field of computational science that includes several generative algorithms that adaptively learn and make data-driven predictions. However, their confluence presents significant challenges from an enterprise context – that has not been effectively addressed. To solve the challenges, linked data can be chosen as the best practice for data representation while machine learning as a paradigm for data analysis. The paper proposes a conceptual model that confluences machine learning and linked data approaches, specifically to cater to enterprise needs. The conceptual model is applicable across varied enterprises viz., education, banking, government and medical, to name a few.
Common Flexible Channel Structure for Integration of Three-Axis Force Sensor and Soft Micro Actuator

This paper presents integrated sensor and actuator in the same soft structure. Three-axis force sensor composed of three standing strain sensors is enabled by making the best use of its deformable flexible structure. Both force sensor and pneumatic actuator are fabricated by using common channel structures. The force sensor uses liquid metal introduced into the channel and the pneumatic actuator uses pressure supplied through the channel. We focused on the characteristic of the liquid metal strain sensor and three-axis force can be sensed by standing the strain sensors. Sensitivity of the sensor according to Fx, Fy and Fz was calculated 0.016 N−1, 0.019 N−1 and 0.035 N−1, respectively. The maximum generating force of integrated soft actuator was 13.3mN.
Efficient Image Transmission Using LoRa Technology In Agricultural Monitoring IoT Systems

Reliable image transmission using LoRa in IoT monitoring systems is considered to be challenging due to insufficient LoRa data rate and payload size. Existing approaches transmit an image in a sequence of packets each of which is individually acknowledged. This approach results in a long image transmission time due to the time spent waiting for the many individual acknowledgements. The acknowledgement traffic also inflates network load. To facilitate LoRa-based image transmission in agricultural monitoring IoT systems, this paper proposes a new reliable delivery protocol, Multi-Packet LoRa (MPLR), for transmission of large messages, such as images, in LoRa networks. The proposed protocol is implemented and evaluated using a LoRa testbed network. In point-to-point experiments with a single sender/receiver pair, MPLR reduced image transmission time by an average of 24% in scenarios with no packet loss, and by averages of 30%, 42%, and 49% in scenarios with 2%, 5%, and 10% loss rate, respectively. When multiple LoRa nodes send images to a single gateway, high channel utilization and an unacceptable collision probability can be experienced with the standard LoRa MAC ALOHA protocol. In experiments with between 5 and 20 nodes, MPLR in conjunction with a channel reservation protocol can successfully send more images and reduce the maximum successful image transmission time between 2 and 7 times, compared to stop-and-wait packet transmission with ALOHA.
Free-space coupling of terahertz whispering-gallery modes

We report on free-space coupling of high quality (Q) spherical THz whispering-gallery mode resonators (WGMRs) as a simple alternative to more complex waveguide and prism coupling. Excitation efficiencies up to 50% to WGMs with Q-factors of $1.5 {\times} 10 ^{4}$ at 0.7THz are achieved.
Semi-Global Leader-Following Cluster Consensus of Heterogeneous Systems With Input Saturation

This paper investigates the semi-global leader-following cluster consensus problem for linear heterogeneous systems with input saturation. A distributed control scheme is constructed by using the low gain feedback technique under the assumptions that, each node is asymptotically null controllable and the underlying topology of each extended cluster is assumed to be directed graphs having spanning tree. Sufficient conditions for guaranteeing leader-following cluster consensus with input saturation are presented. Finally, a numerical example is presented to illustrate this result.
Trajectory Tracking and Disturbance Rejection for Quadrotor Helicopter by a Novel Method

Various control problems for quadrotor helicopter have been studied in the literature. In this paper, we will study the trajectory tracking and disturbance rejection problem for a generic model of quadrotor helicopter with the attitude being represented by unit quatemion. In our problem formulation, the translational dynamics of the system are subject to external disturbances generated by an exosystem, the position reference trajectory is also generated by the exosystem, and the attitude reference trajectory is some smooth time function. We deal with the attitude control and the position control by feedback linearization and output regulation theory, respectively. We show that, by appropriately specifying the attitude reference trajectory, our problem is solvable by combining the attitude control law and the position control law. A numerical example is used to illustrate our design.
Permutation Encoding for Automatic Reconstruction of Connections in Closed-Loop Control System using Evolutionary Algorithm

Search-based software engineering aims to apply different search-based techniques to software engineering problems. Automation of software development is one such problem. In this paper we evaluate the permutation-based individual encoding for automatic reconstruction of measurement connections in a closed-loop control system using evolutionary algorithm and model checking. Using the permutation-based encoding greatly increases the difficulty of the considered problem, but makes it much closer to the real world scenarios. The results show that even the simple (1+1) evolutionary algorithm can successfully solve the realistic optimization problem with large search space size, although it struggles to find the optimal solution within reasonable time on the hardest problem instance.
Decentralized Finite Time Consensus for Discrete-time High-order Linear Multi-agent System

The consensus problem for the multi-agent system with high-order linear dynamics is considered, where the state information is exchanged under a time-invariant topology. The formulation of a final consensus value is given. Then a fully decentralized consensus algorithm is proposed, which allows each agent to reach the consensus value using only finite number of steps of its past state information. The numerical simulation verifies the effectiveness of the algorithm.
A New Proposed Sensor Cloud Architecture Based on Fog Computing for Internet of Things

Sensor cloud is a cloud platform for Internet of Things (IoT) that addresses the challenges of virtualization, multi-tenant, and dynamic provisioning encountered by the IoT industry today. Though it is an effective solution for sharing physical sensor resources and on-demand provisioning of Sensing-as-a-Service, it encounters the challenges of providing real-time application and supporting for the high dispersion of things, and meanwhile it has some problems need to be deal with such as the sensor data fusion, network load balance, effective control and management of physical sensors. In this work, we propose a new sensor cloud architecture for IoT based on fog computing, which preprocesses raw sensor data on fog node and provides temporary storage of the preprocessing results if it is needed, controls and manages diverse type of sensors in IoT devices through the virtualization of physical sensors, and ultimately provides dynamic, on-demand, elastic and standardized Sensing-as-a-Service to end users. We also design the architecture and the components of fog-based sensor cloud system, give a detail function view of the components, and put forward the preliminary solutions for physical sensor virtualization, dynamic provisioning of virtual sensor group and service instance.
Using Oliver API for emotion-aware movie content characterization

This paper demonstrates the utilization of Oliver 11https://behavioralsignals.com/oliver/, the speech emotion recognition (SER) API created by Behavioral Signals, in the context of a movie content visualization application. Oliver API provides an emotion recognition as-a-service solution that can be accessed via a Web API. In this work, we demonstrate how one can send sound recordings from famous movies, retrieve respective emotional descriptors and use simple aggregations on these descriptors to visualize movie content. We have compiled a dataset of 60 movies, categorized over 8 directors. The classification examples included in this paper indicate the ability of simple emotion aggregations to discriminate between movie directors. In order for others to also experiment with the output of both the API's Emotional and Automatic Speech Recognition, the responses are provided as JSON files in this link: https://tinyurl.com/yxeqvvy2.
Job satisfaction and stress of teaching professionals in DEIS and non DEIS Schools and between primary and post-primary

Recent research in Ireland has been developed in regards to identifying the cause of stress and job satisfaction in the teaching profession. Darmondy & Smyth (2010) a study on primary school teachers and Kerr et al.,(2011) a study on secondary school teachers both showed high levels of stress in teachers as a result of pupil behaviour, collegial relations and student/teacher relationship. Although it has been a noted limitation in international research (Klassen & Chiu, 2010) relevant research in Ireland has yet to be established determining teachers stress and job satisfaction relative to the socioeconomic designation of schools. Also as current research is specific to primary and post primary is was decided to analyse the differences in stress and job satisfaction in an attempt to investigate and compare the different teaching levels. A quantitative questionnaire was used to collect data from one hundred and thirty nine participants (N=139). SPSS software to formulate a database enabling the investigation of differences of stress and job satisfaction in teachers of DEIS and Non DEIS schools and between primary and post primary. Results revealed that there was differences in stress in DEIS and Non DEIS schools with highest stress levels being associated with teachers in Non DEIS schools. Findings showed that Non DEIS teacher believed their class sizes too big and workloads are too high. Teachers of DEIS schools had lower levels of stress but showed high levels of discipline and classroom management problems. There were significant findings in the differences of job satisfaction and stress between teaching levels. Post Primary had higher stress and lower levels of job satisfaction. This was again accounted to high workload and the difficulty of dealing with pupil’s rejection of authority. This research was an introduction to analysis of how socioeconomic factors are affecting Irish teachers. As evident from the high levels of stress and an insufficient satisfaction at work in DEIS and Non DEIS schools and across levels of primary and post primary, it is clear that further research could only benefit the teaching profession.
Application of Impedance Control in Robotic Manipulators for Spacecraft On-orbit Servicing

On-orbit satellite servicing is a technology that is expected to transform the space sector in the coming years. Space robotics is a promising approach to refuel, repair, update, and transport satellites on orbit. However, safe and reliable docking with the client satellite, needed as part of most servicing operations, is still considered a challenge. This paper presents an autonomous robot-based approach for this purpose. An impedance control strategy is added to the controller of a conventional robotic manipulator to allow compliant and safe manipulation of a spacecraft docking mechanism. This setup is expected to facilitate autonomous docking and manipulation operations with cooperative and non-cooperative on–orbit serviced satellites. Platform-art©, a dynamic test bench for hardware-in-the-loop validation of space GNC technologies is used to test the proposed approach.
Forecasting in Indian Day-Ahead Electricity Market using ANN optimized with Biogeography Based Optimization Algorithm

Forecasting plays a crucial role in building market strategy for any players in restructured electricity market. Good forecasting technique can significantly increase surplus obtained by any Generating company or Distribution company. Therefore, in this paper we presents an efficient forecasting algorithm using Artificial Neural Network (ANN) optimized with Biogeography Based Optimization (BBO) for forecasting application in Indian Day-Ahead Electricity Market. Four quarter hourly quantity for 9th October 2018 were forecasted viz. Total Purchase Volume, Total Sell Volume, Market Clearing Volume and Market Clearing Price. Forecasted quarter hourly quantity by the proposed method were obtained and plotted along with actual value. The performance of proposed algorithm is compared with that obtained by ANN optimized with Genetic Algorithm(GA) and Particle Swarm Optimization (PSO) in terms of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) in forecasted results. Results clearly demonstrates the superiority of the proposed algorithm in precisely forecasting various quarter hourly quantity in Indian Day-Ahead Electricity Market.
Slant Polarised Crossed Bow-tie Antenna Array with Reduced Number of Phase Shifters and an Embedded Feed Network

this paper presents a two-element dual slant polarized crossed bow-tie phased array antenna with an integrated feed network to decrease the overall number of phase shifters and ports. The dual polarised antenna is composed of two perpendicular bow-tie antennas. In the proposed groundless feeding structure each half of the crossed bow-tie antenna shares its feeding network with the half of the neighbouring crossed bowtie element to reduce the number of phase shifters and excitation ports by 50%. The structure is designed to operate at 4.5 GHz centre frequency with 400 MHz bandwidth and 9.21 dBi realized gain.
Coordinated Path Following Control of Multiple Nonholonomic Mobile Robots With Prescribed Performance

In this paper, the problem of coordinated path following control is investigated for multiple nonholonomic mobile robots subject to the prescribed tracking error performance. By combining tan-type barrier Lyapunov functions, the graph theory and the backstepping technique, this paper proposes a novel coordinated path following control scheme. The proposed scheme not only achieves the path following control of each robot without the violation of the prescribed transient performance, but also makes multiple robots maintain the given formation pattern along one parameterized path. To make each robot satisfy the prescribed performance, the novel tan-type barrier Lyapunov functions are employed to describe the performance restrictions on tracking errors. To realize the coordinated control, the path variable containment method is used to make the mobile robots follow one parameterized path only based on the path variable information from its neighbours. Finally, the performance of the proposed controller is illustrated by simulation results.
Time-frequency and recurrence quantification analysis detect limb movement execution from EEG data

In present paper we consider application of recurrence quantification analysis (RQA) in detection of motor-related electroencephalograms (EEG). Particularly, we apply RQA to reveal transitions of mu-rhythm dynamics extracted from multichannel EEG recorded in motor cortex. The results show that the considering RQA measures of EEG in time-frequency domain one can effectively reveal dynamical features of motor-related brain activity.
Commit Time Materialized View Maintenance for Bulk Load Operations in Teradata

Materialized view is called Join Index (JI) in Teradata. JIs can store tables pre-joined, solely change a table to a different schema or aggregation results. JIs are stored permanently on the disks and cannot be peeped through. Also, their usage or evasion isn’t subject to user choice. Teradata optimizer decides when to use a JI, or the underlying table(s) directly from a cost point of view. While the advantage of having JIs is to increase read performance, its downside could be their maintenance. In case of row insertion or modification of an indexed column in base table, JI must be updated. This can create a big performance impact if multiple DMLs within same transaction result in same JI(s) maintenance, bulk load operations for example.On tables with Multiversion Concurrency Control (MVCC), readers and writers work concurrently (Load Isolation in Teradata for example). Readers continue to access the last committed rows while concurrent modifications are happening on the base table(s). JIs defined on multiversioned tables also follow the MVCC principle and continue allowing readers to access the last committed rows. Currently, JIs are maintained immediately with every DML request on base table(s). The proposed approach is to defer the JI maintenance till commit time for MVCC tables, resulting in performance improvement for write operations, as all the JI related operations are performed at the end.
Hybrid derivative functions for identification of unknown loads and physical parameters with application on slider-crank mechanism

Mechatronic systems are plagued by nonlinearities and contain uncertainties amongst others due to interactions with their environment. Having models of these systems that have accurate multistep predictive capabilities can be of value for control and decision making purposes. This paper proposes hybrid derivative functions that closely combine expert knowledge of the system, captured by ordinary differential equations, with data-driven feedforward neural networks. Euler’s method is used to apprehend the system’s multistep dynamics. The proposed formalism is dedicated to identifying unknown loads that are dependent on the state of the system together with the identification of physical parameter values. We apply the methodology on a slider-crank mechanism where synthetic data is used to investigate the feasibility and accuracy of the proposed approaches. Multiple numerical tests show that the network structure of the hybrid derivative function enables convergence and we show the ability to simultaneously uncover physical parameter values and explain unknown phenomena related to the unknown state dependent load. The latter suggest the possibility of discovering unknown loads without additional measurements. Further research should investigate the robustness of the presented approach.
Configuration Design of Bilinear Networked Systems with Minimum Cost Constraints

This paper addresses the control configuration design problem of bilinear networked systems. The cost values of controlling or measuring the variables of different states are nonnegative and nonuniform among the agents. These state variables are directly actuated or measured by the virtual nodes which are introduced for configuration design. Firstly, we model the design as an optimization problem: the minimum cost minimum control configuration (MCMCC). Next, the necessary condition for the solvability of MCMCC is derived. At the same time, an algorithm is given to provide the optimal solution to MCMCC via issue transformation. This algorithm is based on the Dulmage-Mendelson (DM) decomposition of the system bipartite graph and the directed acyclic graph decomposition of the system graph. Furthermore, we provide a novel dynamic graph to verify the coprime paths. This new dynamic graph can avoid circulation when exist the cycles in the system graph. We prove that this algorithm has polynomial computational complexity. Finally, one example is also provided to illustrate the validity of the algorithm.
Study of Aspect level Sentiment Analysis and Word Embeddings

Reviews play critical part in decision making of consumers. It’s becoming tedious for consumers to go through an ever-increasing number of reviews. Hence, it is becoming more important to analyze all these reviews and present in easily understandable format. User is unable to comprehend the essence of all these reviews from average ratings and also, they are unsuitable for providing information related to various features of that entity. Aspect or feature level sentiment analysis captures sentiments of features related to entities providing quick overview of each aspect of entities. This paper studies various methods and approaches for aspect detection and corresponding sentiment analysis. Also, various challenges faced while both sentiment analysis and aspect detection, and their approaches are discussed. Word embeddings for efficient sentiment analysis are also investigated. A system is proposed which uses machine learning as well as word embedding for aspect detection and corresponding sentiment analysis.