Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling of a heavy-lift airship carrying a payload by a cable-driven parallel manipulator

In this article, we present a preliminary analysis of a heavy-lift airship carrying a payload through a cable-driven parallel robot. With unlimited access to isolated locations around the globe, heavy-lift airship enables affordable and safe delivery of heavy cargo thanks to its vertical takeoff and landing capabilities. By considering the airship and the cable-driven parallel robot as a combined system, the kinematic and dynamic models are developed. The choice of the proposed decentralized control structure is justified by the weak coupling of the two subsystems (i.e. airship and cable-driven parallel robot) which makes it possible to control the above two subsystems independently. A robust sliding mode control, capable of auto-piloting and controlling the airship, is developed. Furthermore, an inverse dynamic controller is applied to the cable-driven parallel robot in order to ensure loading and unloading phase. The feature of the proposed control system is that the coupled dynamics between the airship and the cable-driven parallel robot are explicitly incorporated into control system design, without any simplifying assumption. Numerical simulation results are presented and a stability analysis is provided to confirm the accuracy of our derivations.
Autonomous Driving: A Multi-Objective Deep Reinforcement Learning Approach

Autonomous driving is a challenging domain that entails multiple aspects: a vehicle should be able to drive to its destination as fast as possible while avoiding collision, obeying traffic rules and ensuring the comfort of passengers. It’s representative of complex reinforcement learning tasks humans encounter in real life. The aim of this thesis is to explore the effectiveness of multi-objective reinforcement learning for such tasks characterized by autonomous driving. In particular, it shows that: • Multi-objective reinforcement learning is effective at overcoming some of the difficulties faced by scalar-reward reinforcement learning, and a multi-objective DQN agent based on a variant of thresholded lexicographic Q-learning is successfully trained to drive on multi-lane roads and intersections, yielding and changing lanes according to traffic rules. • Data efficiency of (multi-objective) reinforcement learning can be significantly improved by exploiting the factored structure of a task. Specifically, factored Q functions learned on the factored state space can be used as features to the original Q function to speed up learning. • Inclusion of history-dependent policies enables an intuitive exact algorithm for multiobjective reinforcement learning with thresholded lexicographic order.
Flower Pollination Algorithm Based PDFF Controller for a Two- Area Interconnected Thermal Power System with Gas / Diesel Units

This paper presents a new approach for designing a Pseudo Derivative Feed Forward (FDFF) controller for the loadfrequency control of the interconnected power system comprising Thermal power system and Gas / Diesel power plants. The proposed PDFF controller is designed to improve the dynamic performance of the frequency and tie line power under a sudden load disturbance in an area with the computation of Ancillary Service Requirement Assessment Indices (ASRAI). The PDFF controller is optimized using Flower Pollination Algorithm (FPA) which is based on the quality of pollination process of flowers. The optimized PDFF controller is implemented to bring back the frequency to stable state and the net interchanges to their desired values for each control area in the shortest possible time based on the settling time and peak over shoot concept of control input deviations of each area. Simulation result reveals that the interconnected thermal power system with Gas power plant ensures a better dynamic and steady state performance than that of the system incorporated with Diesel power plant.
Sprinting Out of Stuckness: Overcoming Moments of Stuckness to Support the Creativity Flow in Agile Team Settings

Multidisciplinary agile teams working in fast paced, delivery-oriented sprint cycles of two weeks can experience moments of stuckness. Typically, these moments can be characterised by the inability to continue, which can be quite detrimental in agile settings, where time is pressured. This paper aims to explore these moments of stuckness, to understand when and why they occur and to understand the different strategies Scrum teams use to overcome them, both on a personal as well as team level. A combination of interviews and observations were conducted with six Scrum team members and two experts to understand their process and experiences while working in an agile set up. We have identified five strategies, which strongly rely on agile values of collaboration, communication, and creativity. These are: looking for expert guidance, open communication, creating spike stories, visual communication and incubation. The findings from this study provide both practice and academia with a deeper understanding into how can creativity be supported in agile settings.
GammaLearn - first steps to apply Deep Learning to the Cherenkov Telescope Array data

The Cherenkov Telescope Array (CTA) is the next generation of ground-based gamma-ray telescopes for gamma-ray astronomy. Two arrays will be deployed composed of 19 telescopes in the Northern hemisphere and 99 telescopes in the Southern hemisphere. Due to its very high sensitivity, CTA will record a colossal amount of data that represent a computing challenge to the reconstruction software. Moreover, the vast majority of triggered events come from protons that represent a background for gamma-ray astronomy. Deep learning developments in the last few years have shown tremendous improvements in the analysis of data in many domains. Thanks to the huge amount of simulated data and later of real data, produced by CTA, these algorithms look well-suited and very promising. Moreover, the trained neural networks show very good computing performances during execution. Here we present a first study of deep learning architectures applied to CTA simulated data to perform the reconstruction of the particles energy and incoming direction and the development of a specific framework, GammaLearn, to accomplish this task.
Barriers to acceptance of information and communication technology in agricultural extension in Indonesia

The use of information and communication technology (ICT) has become progressively widespread in various sectors including agriculture. This study investigated the barriers to the diffusion of ICT in agricultural extension. Further, the study examined the effect of barriers toward ICT acceptance in agricultural extension. A paper and pencil survey by mail was conducted to collect data from 355 respondents in the Greater Surakarta Region, Indonesia. The study discovered that individual barriers, cultural barriers, government policy barriers, support and technological barriers significantly influenced the acceptance of ICT. In line with the Technology Acceptance Model (TAM), these variables influenced perceived ease of use, perceived usefulness and perceived intention to use of ICT. The findings suggested the methods that could be adopted by governments and non-government bodies to overcome the barriers in ICT implementation.
Integrated Communication and Localization System With OFDM-Chirp Waveform

This paper proposes an integrated communication and localization system scheme using orthogonal frequency division multiplexing (OFDM) chirp waveform. A new and efficient generation method is proposed to produce the OFDM-chirp waveform. The OFDM-chirp waveform is quantitatively compared with conventional OFDM waveform with respect to complementary cumulative distribution function of the peak-to-average power ratio. The OFDM-chirp waveform is analyzed by the ambiguity function together with its matched filtering performance. Furthermore, we implemented the OFDM-chirp waveform for integrated communication and localization with the universal software radio peripheral platform. Experimental results show that it outperforms conventional OFDM waveform for integrated communication and localization applications.
Gamifying user interaction to increase collaboration: the g.a.m.e. conceptual framework

Collaborative software, also known as groupware, supports groups in collaborative activities. Groupware are computer-based systems that support groups of people engaged in a common task (or goal) and that provide an interface to a shared environment. A means for collaboration is crowdsourcing the act of taking tasks traditionally done by an individual and outsourcing it to a large group of people. Online communities use crowdsourcing for several purposes. Crowdsourcing systems call a crowd of users to collaborate and solve problems. Critical mass is mandatory to the success of crowdsourcing. In addition to having a mass of users, crowdsourcing also depends on people’s motivation and behavior in participating. It is necessary to understand the user’s motivation to participate, and designers should incorporate motivation elements to provide incentive mechanisms. One main crowdsourcing challenge is how to design user interfaces that can attract and sustain many people to joining in crowdsourcing. Gamification, the use of game elements in non-game contexts, has been used to engage people in online communities that require collaboration from the participants. It takes advantage of how games engage players to solve fictional problems in fictional worlds to engage the same players to solve real world problems. Although gamification in collaborative software studies have been growing, there is no systematic approach to implement gamification in collaborative software. This work aims to investigate how gamification can be used to design user interaction aiming to increase collaboration in crowdsourcing systems. In this dissertation, we present our findings and discuss our decision making for gamifying our case study. We propose a systematic approach (named G.A.M.E.) to support designing user interaction of collaboration in crowdsourcing systems. Moreover, we present a qualitative study on the usage of G.A.M.E. by di↵erent developers. The qualitative study allowed us to gather impressions from point-of-view of professionals from di↵erent areas. From the results achieved with this evaluation we obtained evidence of the usefulness of the G.A.M.E. approach, and critical aspects of gamification design for collaborative software.
Development of Problem-Based Learning Devices to Improve Elementary School Students Ability on problem solving in Fraction Topics

- The low level of problem solving abilities of students and the lack of learning activities that hone problem solving skills are one of education problems that must be overcome. This study aims to develop and examine the feasibility, practicality, and effectiveness of problem-based learning devices to improve problem solving skills of fifth grade students of elementary school. This study used the Thiagarajan research and development (R & D) model known as 4D (define, design, develop, & disseminate). The feasibility of learning devices based on the results of expert validation. The practicality of learning devices based on the results of observations of the implementation of learning and student activities. While the effectiveness of learning device based on the results of the student questionnaire response and the problem solving ability test used One Group Pretest-Posttest Design. The results of the study showed that problem-based learning devices are feasible, practical, and effective to improve students' problem solving abilities. So that the problem-based learning device is recommended to be used as a learning tool for improving problem solving skills of fifth grade students of elementary school.
Performance Analysis of Device-to-Device Communication Underlaying Dense Networks (DenseNets)

Given the exponential proliferation of mobile devices and broadband services, there is a pressing need to amalgamate multiple technologies accommodating infrastructural changes for meeting the ever-increasing demands of user capacity. Therefore, in this paper, we propose the integration of proximity-based direct device-to-device (D2D) communication with the network densification concept. In this paper, we consider user-centric non-overlapping clusters associated with the closest access point for serving user equipment (UE) in the downlink under limited availability of orthogonal resource blocks (RBs), while the D2D links also reuse these downlink RBs. We analytically derive the coverage probability as well as the average transmission rate of D2D links under the proposed model, which results in a complex interference scenario. The performance results verify that our analytical results closely match with simulations for different parametric settings. The impact of the presence of D2D links on the coverage probability of the UEs is also quantified, revealing that the UEs’ performance is only modestly affected under the proposed system model.
Identify and Delimitate Urban Hotspot Areas Using a Network-Based Spatiotemporal Field Clustering Method

Pick-up and drop-off events of taxi trajectory data contain rich information about residents’ travel activities and road traffic. Such data have been widely applied in urban hotspot detection in recent years. However, few studies have attempted to delimitate the urban hotspot scope using taxi trajectory data. On this basis, the current study firstly introduces a network-based spatiotemporal field (NSF) clustering approach to discover and identify hotspots. Our proposed method expands the notion from spatial to space–time dimension and from Euclidean to network space by comparing with traditional spatial clustering analyses. In addition, a concentration index of hotspot areas is presented to refine the surface of centredness to delimitate the hotspot scope further. This index supports the quantitative depiction of hotspot areas by generating two standard deviation isolines. In the case study, we analyze the spatiotemporal dynamic patterns of hotspots at different days and times of day using the NSF method. Meanwhile, we also validate the effectiveness of the proposed method in identifying hotspots to evaluate the delimitating results. Experimental results reveal that the proposed approach can not only help detect detailed microscale characteristics of urban hotspots but also identify high-concentration patterns of pick-up incidents in specific places.
How do fashion retail customers search on the Internet?: Exploring the use of data mining tools to enhance CRM

This paper seeks to determine the usefulness of data mining tools to SMEs in developing customer relationship management (CRM) in the fashion retail sector. Kalakota & Robinson’s (1999, p.114) model of ‘The Three Phases of CRM’ acts as a basis to explore the use of data mining software. This paper reviews the nature and type of data that is available for collection and its relevance to CRM; providing an advisory framework for practitioners for them to examine the scope and limitations of using data analysis to improve CRM. The data mining tool examined was Google Analytics (GA); an online freeware tool that enables businesses to understand how people find their site, how they navigate through it, and, ultimately, how they do or don’t become customers of it (Google Analytics, 2009). Establishing these relationships should lead to retailer development of enhanced web site aesthetics and functionality to coincide with consumer expectations. The paper finds that the competitive nature and homogeneity of the fashion retail sector requires retailers to improve the ‘reach, richness and affiliation’ (Hackney et al) of their sites by using technology to explore CRM.
Insight on the utilization of the noise estimation techniques in phase aware modified multiband spectral subtraction

ABSTRACT The standard technique of all Spectral Subtraction algorithms that are used for Speech Enhancement (SE) is to find out the clean speech by subtracting the noise estimate from the noisy speech spectrum. So the essential criterion is the accuracy of noise estimation (NE) and it affects the potency of the SE. The traditional NE algorithms use voice activity detection (VAD) for noise estimation, which is troublesome at the cases of low SNR condition and when the noise characteristics are not stationary. The effect of noise is also different in different frequency bands of the noisy speech spectrum. In this paper, several NE techniques are implemented along with a phase aware and frequency dependent modified multi-band spectral subtraction. To give additional intelligibility to SE, the phase of the enhanced signal is modified using the phase spectrum compensation function. Finally, the mentioned methods are simulated and compared using several objective evaluation measures with the help of standard speech database.
CANvas: Fast and Inexpensive Automotive Network Mapping

Modern vehicles contain tens of Electronic Control Units (ECUs), several of which communicate over the Controller Area Network (CAN) protocol. As such, in-vehicle networks have become a prime target for automotive network attacks. To understand the security of these networks, we argue that we need tools analogous to network mappers for traditional networks that provide an in-depth understanding of a network’s structure. To this end, our goal is to develop an automotive network mapping tool that assists in identifying a vehicle’s ECUs and their communication with each other. A significant challenge in designing this tool is the broadcast nature of the CAN protocol, as network messages contain no information about their sender or recipients. To address this challenge, we design and implement CANvas, an automotive network mapper that identifies transmitting ECUs with a pairwise clock offset tracking algorithm and identifies receiving ECUs with a forced ECU isolation technique. CANvas generates network maps in under an hour that identify a previously unknown ECU in a 2009 Toyota Prius and identify lenient message filters in a 2017 Ford Focus.
Modified Artificial immune system as Feature Selection

Feature selection algorithms play a big role in machine learning applications. There are several feature selection strategies based on metaheuristic algorithms. In this paper a feature selection strategy based on Modified Artificial Immune System (MAIS) has been proposed. The proposed algorithm exploits the advantages of Artificial Immune System AIS to increase the performance and randomization of features. The experimental results based on NSL-KDD dataset, have showed increasing in performance of accuracy compared with other feature selection algorithms (best first search, correlation and information gain).
Reinforcement Learning-designed LSTM for Trajectory and Traffic Flow Prediction

Trajectory and traffic flow prediction will play an essential role in Intelligent Transportation Systems (ITS) to enable a whole new set of applications ranging from traffic management to infotainment applications. In this scenario, deep learning approaches such as Recurrent Neural Networks (RNN) and its variant Long Short Term Memory (LSTM) are excellent alternatives due to their ability to learn spatiotemporal dependencies. However, these neural networks tend to be over-complex and hard to design due to the broad set of hyper-parameters. We propose an automated framework to predict future trajectories and traffic flows in urban areas without human interventions. We employ Reinforcement Learning (RL) and Transfer Learning (TL) to generate high-performance LSTM predictors, which is referred as RL-LSTM. In addition, we introduce HERITOR (High ordE r tR affI c convoluTiOn R 1-lstm), a novel deep learning algorithm for traffic flow prediction. Specifically, HERITOR attempts to capture pure spatiotemporal features of urban traffic. The extracted features are fed into the RL-LSTM to realize a high performance LSTM for traffic flow prediction. We examine the proposed trajectory and traffic flow predictors on two real-world, large-scale datasets and observe consistent improvements of 15% - 25% over the state-of-the-art.
Non-Orthogonal Multiple Access (NOMA) in Uplink Poisson Cellular Networks With Power Control

This paper develops an analytical framework for multi-cell uplink NOMA systems based on stochastic geometry. We propose two scenarios for the clustering of NOMA UEs and derive the Laplace transform of the inter-cell interference taking into account uplink power control. We utilize two different ordering techniques, namely mean signal power- (MSP-) and instantaneous signal-to-intercell-interference-and-noise-ratio- (ISĨNR-) based, for the successive interference cancellation process at the BSs. For each technique, we present a signal-to-interference-and-noise-ratio (SINR) analysis and derive the transmission success probabilities for the NOMA UEs. We show that uplink power control, which generally reduces the signal power disparity between UEs, does not necessarily degrade the NOMA performance. We discuss how UE clustering and the power control exponent impact this finding. ISĨNR-based ordering, which jointly considers path loss, fading, inter-cell interference, and noise, is generally superior to MSP-based ordering. Moreover, we show that the advantage of NOMA vanishes when the target SINR exceeds a certain threshold. A comparison of the two UE clustering scenarios indicates that excluding the UEs which are relatively far from the serving BS may improve the NOMA performance.
Ai Motion Control – A Generic Approach to Develop Control Policies for Robotic Manipulation Tasks

Current robotic solutions are able to manage specialized tasks, but they cannot perform intelligent actions which are based on experience. Autonomous robots that are able to succeed in complex environments like production plants need the ability to customize their capabilities. With the usage of artificial intelligence (AI) it is possible to train robot control policies without explicitly programming how to achieve desired goals. We introduce AI Motion Control (AIMC) a generic approach to develop control policies for diverse robots, environments and manipulation tasks. For safety reasons, but also to save investments and development time, motion control policies can first be trained in simulation and then transferred to real applications. This work uses the descriptive study I according to Blessing and Chakrabarti and is about the identification of this research gap. We combine latest motion control and reinforcement learning results and show the potential of AIMC for robotic technologies with industrial use cases.