Miracle-dz/UniFAR-FTUs huggingface dataset:
A Robust Channel Estimation Scheme for 5G Massive MIMO Systems

Channel state information (CSI) feedback in massive MIMO systems is too large due to large pilot overhead. It is due to the large channel matrix dimension which depends on the number of base station (BS) antennas and consumes the majority of scarce radio resources. To solve this problem, we proposed a scheme for efficient CSI acquisition and reduced pilot overhead. It is based on the separation mechanism for the channel matrix. The spatial correlation among multiuser channel matrices in the virtual angular domain is utilized to split the channel matrix. Then, the two parts of the matrix are estimated by deploying the compressed sensing (CS) techniques. This scheme is novel in the sense that the user equipment (UE) directly transmits the received symbols from the BS to the BS, so a joint CSI recovery is performed at the BS. Simulation results show that the proposed channel estimation scheme effectively estimates the channel with reduced pilot overhead and improved performance as compared with the state-of-the-art schemes.
Kecenderungan Pembelian Impulsif Online Ditinjau dari Penjelajahan Website Yang Bersifat Hedonis dan Jenis Kelamin pada Generasi Y

Generasi Y merupakan generasi yang rentan terpapar oleh kemajuan teknologi informasi. Dalam proses transaksi jual beli, generasi Y begitu mudah mengakses informasi. Kondisi ini tentu dapat memberi dampak terhadap pola pembelian yang dilakukan remaja salah satunya adalah pembelian impulsive yang dilakukan secara online. Penelitian ini menguji dua hal yakni apakah pembelian impulsive pada remaja pria dan wanita berbeda. Tujuan kedua adalah menguji korelasi antara penjelajahan website yang bersifat hedonis dengan pembelian impulsive pada generasi Y. Pengambilan data dilakukan dengan menggunakan skala pembelian impulsif online dan skala penjelajahan website secara hedonis. Subjek yang terlibat berjumlah 84 orang. Analisis menggunakan analisis korelasional untuk menguji hubungan antara penjelajahan website hedonis dengan pembelian impulsive. Uji beda digunakan untuk menguji perbedaan pembelian impulsif antara pria dan wanita. Dari hasil analisis menunjukkan jika kedua hipotesis diterima. Hasil penelitian ini dapat bermanfaat dalam pengembangan psikologi konsumen serta dapat dimanfaatkan untuk menanggulangi pembelian impulsive online melalui penjelajahan website hedonis remaja. Kata kunci : hedonis, generasi y, online, pembelian impulsif
A Community Detection Approach for Smart-Phone Addiction Recognition

In this paper, we present a novel approach for Smart-Phone Addiction recognition that leverages community detection algorithms from the Social Network Analysis (SNA) theory. Our basic idea is to model data concerning users’ behavior while they are using mobile devices as a particular social graph, discovering by means of SNA facilities patterns that better identify users with a high predisposition to smart phone addiction. Eventually, several experiments on a sample of users monitored for several weeks have been carried out to verify effectiveness of the proposed approach in correctly recognizing the related addiction degree.
Prosumption: bibliometric analysis using HistCite and VOSviewer

Purpose Over the past decade, the term prosumption (denoting simultaneous consumption and production) has exhibited a dramatic increase in frequency of use in publications in the social sciences and business studies. This paper aims to explore the current state of research into prosumption, particularly related to marketing. Design/methodology/approach This study systematically reviews papers on prosumption extracted from the Web of Science, using two bibliometric analyses on 20 years of data: citation counts from HistCite and bibliographic coupling and cartography analysis from the visualization of similarities software VOSviewer. A total of 75 papers on prosumption were found from the period 1997-2017, and the most influential authors, articles, journals, institutions and countries among these were determined. Furthermore, bibliographic coupling and most co-occurrent keywords in the title, keywords and abstracts were found. Findings This study found that the USA and the UK were the most influential among prosumption publications. Ritzer was the most prominent author and Journal of Consumer Culture was the top-ranking journal. Three clusters were found using bibliographic coupling and cartography analysis: prosumer and co-creation, prosumer and user-generated content and prosumer and informational capital. Research limitations/implications This analysis provided a basis for conceptualizing publications on prosumption related to business and sociology in the discipline of marketing. Content analysis found that prosumption research in marketing is in early stages: little quantitative study has been conducted yet. Researchers have not yet constructed a quantitative measure for prosumption. Practical implications Business firms can engage prosumers to gain market share and competitive advantage, especially relative to value co-creation, with near-zero marginal cost. Originality/value This may be the first bibliometric analysis and systematic review of prosumption research in marketing studies. The achievements of this paper open new avenues for other prosumption researchers.
Nonlinear Noncausal Optimal Control of Wave Energy Converters Via Approximate Dynamic Programming

This article proposes a novel nonlinear receding horizon optimal control algorithm for wave energy converter (WECs) with nonlinear dynamics. It is well accepted that the WEC control problem is essentially a noncausal constrained optimal control problem, where the energy output can be improved by incorporating the short-term wave prediction into the control synthesis. Inspired by this fact, we suggest a new nonlinear noncausal optimal control (NNOC) for WECs based on the principle of approximate dynamic programming, which can, first, explicitly use the wave prediction to improve the energy conversion efficiency; second, handle the state and control input constraints; third, reduce the computational burden. Different to the existing linear noncausal optimal control, the derived Hamilton–Jacobi–Bellman equation for NNOC does not have an analytic solution. To tackle this problem, a critic neural network (NN) is adopted to approximate its solution in a receding horizon manor. The weights of NN are determined via a policy iteration algorithm. The resulting NNOC consists of a causal state feedback part and a noncausal feedforward part to explicit incorporate wave prediction information. Numerical simulations are provided to verify the efficacy of the proposed NNOC method.
Feudal Networks for Hierarchical Reinforcement Learning Revisited

Hierarchical Reinforcement Learning (RL) has gained popularity in recent years in designing RL algorithms that converge in complex environments. Convergence of RL algorithms remains an active area of research, and no single approach has been found to work for all RL applications. Feudal networks (FuNs) are a hierarchical RL technique attempting to address portability and other problems by defining an internal structure for an RL agent using a Manager-Worker hierarchy. A Manager is that portion of the system utilizing a low temporal resolution component for setting goals to maximize rewards from the environment, while the Worker utilizes a high temporal resolution component for selecting among action primitives to maximize rewards from the Manager. This thesis provides an overview of reinforcement learning and the FuN architecture, then compares the relative convergence rates of untrained FuNs to FuNs constructed by Workers with different physical embodiments under a trained Manager.
Topics in bootstrap methods for survey sampling and spatially balanced design

This dissertation consists of three parts. In the first part, we propose new bootstrap methods for three commonly used sampling designs, including the Poisson sampling, simple random sampling, and probability-proportional-to-size sampling. We show that the proposed bootstrap methods are second-order accurate and easy to be implemented in practice. Two simulation studies are conducted to compare the proposed bootstrap methods with the Wald method, and the proposed bootstrap methods outperform the Wald method in terms of coverage rate. It is well-known that a spatially balanced sample, which spread over the study domain well, can improve the estimation efficiency under dependent settings. In the second part, we propose to use a block bootstrap method to estimate the variance and make inference based on a sample generated by a one-per-stratum sampling design. We show the validity of the block bootstrap method and compare it with another commonly used sampling design theoretically. Simulation study shows that the block bootstrap method can provide valid variance estimator and inference for the one-per-stratum sampling design. Although there are many researches about spatially balanced sampling design, there are few discussing the spatio-temporal balanced sampling design. In the third part, we propose a spatiotemporal balanced sampling design to generate annual samples, such that the sample for each year is spatially balanced, and the one combining from consecutive years is also spatially balanced. We also propose design-based variance estimator for the estimates of annual status and annual change. The proposed sampling design is used in the National Resources Inventory rangeland on-site survey, and it shows that the proposed design performs better than the current design and estimators.
Handling Missing Data in Growth Mixture Models

A Monte Carlo simulation was performed to compare methods for handling missing data in growth mixture models. The methods considered in the current study were (a) a fully Bayesian approach using a Gibbs sampler, (b) full information maximum likelihood using the expectation–maximization algorithm, (c) multiple imputation, (d) a two-stage multiple imputation method, and (e) listwise deletion. Of the five methods, it was found that the Bayesian approach and two-stage multiple imputation methods generally produce less biased parameter estimates compared to maximum likelihood or single imputation methods, although key differences were observed. Similarities and disparities among methods are highlighted and general recommendations articulated.
TECHNOLOGY OF E-GOVERNMENT ACCEPTANCE ANALYSIS ON ELECTRONIC RESIDENT IDENTIFICATION CARD (E-KTP) ADOPTION

The aim of this study is to analyze the effects of Technology Readiness Index (TRI) toward Technology Perceptions that will determine the Actual Usage of e-KTP adoption by taking of 50 people which is the all the population as the respondents. This study investigates the relationship between the personality dimensions of TRI (Technology Readiness Index) and the system specific dimensions of TAM (Technology Acceptance Model). Multiple regressions was used to test the relationship between the dimensions of TRI and TAM. The results show that Optimism influence perceived usefulness and perceived ease of use. Innovativeness and Discomfort influence perceived ease of use. Further, perceived usefulness and perceived ease of use has a significant positive influence on actual usage.
Macro Forecasting Using Alternative Data

Abstract Traditional macroeconomic data used by economic agents to make decisions are noisy, lack richness, and are produced with considerable lag. This chapter explores how alternative, web-scale data sources (“big data”) can help. We present a case study using a common alternative data source, web search, to predict one of the most important data releases—nonfarm payrolls. We discuss the efficacy of various machine learning techniques, the live performance of alternative data prediction models, and the typical problems faced in practice.
Fundamentals Limits Of Communication In Interference Limited Environments

In multiuser wireless communications, interference not only limits the performance of the system, but also allows users to eavesdrop on other users’ messages. Hence, interference management in multiuser wireless communication has received significant attention in the last decade, both in the academia and industry. The interference channel (IC) is one of the simplest information theoretic models to analyze the effect of interference on the throughput and secrecy of individual messages in a multiuser setup. In this thesis, the IC is studied under different settings with and without the secrecy constraint. The main contributions of the thesis are as follows: • The generalized degrees of freedom (GDOF) has emerged as a useful approximate measure of the potential throughput of a multiuser wireless system. Also, multiple antennas at the transmitter and receiver can provide additional dimension for signaling, which can in turn improve the GDOF performance of the IC. In the initial part of the thesis, aK-user MIMO Gaussian IC (GIC) is studied from an achievable GDOF perspective. An inner bound on GDOF is derived using a combination of techniques such as treating interference as noise, zero-forcing receiving, interference alignment (IA), and extending the Han-Kobayashi (HK) scheme toK users. Also, outer bounds on the sum rate of the K-user MIMO GIC are derived, under different assumptions of cooperation and providing side information to the receivers. The derived outer bounds are simplified to obtain outer bounds on the GDOF. The relative performance of these bounds yields insight into the performance limits of the multiuser MIMOGIC and the relative merits of different schemes for interference management. • Then, the problem of designing the precoding and receive filtering matrices for IA is explored for K-user MIMO (M × N) GIC. Two algorithms for designing iii
Harvest-Or-Access: Slotted ALOHA for Wireless Powered Communication Networks

This paper proposes a distributed energy-harvesting random access protocol called harvest-or-access by applying slotted ALOHA to wireless powered communication networks (WPCNs). From the fact that there are a considerable number of idle slots during which any nodes do not attempt transmission in slotted ALOHA, the idle slots are made available for wireless energy transfer (WET). If any random access (RA) slot is recognized as an idle slot, the hybrid access point (HAP) opportunistically performs the WET in this slot. The wireless devices (WDs) can harvest energy for every idle slot and attempt random access by using this harvested energy. An asymptotic throughput analysis in a high signal-to-noise ratio (SNR) environment with a sufficient number of accessing WDs shows that there is an optimal number of RA slots allocated to maximize throughput and this optimal number of RA slots depends only on the average of the minimum SNRs of the WDs without knowledge of full channel state information. Monte Carlo simulation results further show that the proposed harvest-or-access protocol outperforms the conventional harvest-then-transmit-based random access protocols especially when the number of WDs is large.
Assimilating Green Skills in Building Construction Programme: Crucial to Realizing Environmental Sustainability

Greening of any curriculum through the infusion of green skills is needed in the construction sector. This study identified important green skill areas in relation to building energy efficiency that are suitable for inclusion in the Building Construction Programme (BCP) at universities for the attainment of environmental sustainability. A questionnaire was used with 308 respondents. Data analyses were carried out using Descriptive Statistics, Exploratory Factor Analysis (EFA), and Confirmatory Factor Analysis (CFA). The supremacy of the identified energy efficiency skills over the non-energy efficiency skills is confirmed as its integration into BCP helps in realising environmental sustainability. Findings include the important areas to be included into the building construction programme curriculum. Findings can be used by the government for the adoption and practical implementation of these findings.
Deep Learning Based Image Segmentation of RGB-D Data in Warehouse Automation

The ability to locate specific objects within images is an essential step in various computer vision based engineering applications. Image segmentation is the task of dividing an image into "segments" that are uniform as well as homogeneous with respect to some characteristics, for example grey tone or texture as in Haralick et al. This thesis seeks to perform image segmentation using a Deep Learning (DL) approach in the area of warehouse automation, specifically focusing on an order picking use case of Vanderlande Industries (VI). Generally in literature, DL algorithms for image segmentation are split into two main classes: algorithms for RGB images and algorithms for RGB-D images. RGB stands for the Red, Green, and Blue values of a pixel. RGB-D stands for the Red, Green, Blue, and Depth values of a pixel. The depth value in this case differs from the RGB values in that it does not give a value for a colour intensity, but rather it gives a value for physical distance between the camera and the object it is capturing. The challenge addressed by this thesis focuses on whether the introduction of depth data results in a substantially better performance than using RGB-only images, based on a data-set provided by VI. Also, this thesis looks into the maximum allowed deviation along the X-axis in the registration of the depth data to the RGB images. Two networks from literature were investigated and implemented in MATLAB for this purpose: the SegNet architecture proposed by Badrinarayanan et al. and the FuseNet architecture proposed by Hazirbas et al. Through experiments we have found that, for this use case, the introduction of complementary depth data leads to an improvement over the use of RGB-only images. We also find that, for this use case, the maximum allowed deviation along the X-axis in the registration of the depth data to the RGB images is approximately equal to 1.67 millimetres. The results in this thesis seem to indicate that investing in acquiring an additional depth band does have a positive effect on the accuracy of image segmentation for order picking in warehouse automation.
EXTENSIVE READING: WHAT, WHY, AND HOW?

The best way to improve our knowledge of a foreign language is to go and live among its speakers. The next best way is to read extensively in it (Nuttal, 1982). Current literature points to the importance and benefits of extensive reading. Extensive reading provides contextualized clues for better reading comprehension (Krashen, 1982), and substantial linguistic input (Bell, 1998) needed for language development. Several studies have found a correlation between extensive reading and specific linguistic skills including breadth of reading choices, improved writing skills, ease in oral communication, and improvement in other aspects of language. This paper briefly tries to describe the nature of extensive reading, the reasons why extensive reading is important, and how is the best practice of extensive reading in EFL classroom .
A Robust Adaptive Chattering-Free Sliding Mode Control Strategy for Automotive Electronic Throttle System via Genetic Algorithm

A robust adaptive chattering-free sliding mode (ACFSM) control method for electronic throttle (ET) system is proposed in this paper. It is well known that nonlinearities in the throttle system including friction, return-spring, limp-home (LH) and gear backlash affect the control accuracy of the throttle valve. Compared with the traditional sliding mode control methods, the ACFSM control not only overcomes the influence of nonlinearities and parameter uncertainties in the ET system, but also eliminates chattering in nature such that excellent throttle tracking performance and robustness are maintained. The proposed ACFSM control method is superior to the traditional sliding mode control in the following two aspects: 1) The chattering-free sliding mode control method is able to attenuate the control chattering without weakening the output tracking performance and robustness. 2) The upper bounds of the uncertainty and disturbance are not required any more in control design. They are online estimated by the adaptive law in the sense of Lyapunov. Moreover, due to the difficulty in selecting proper control parameters, the genetic algorithm (GA) is introduced to optimate the control parameters for the ACFSM controller prior to practical implementation. The comparative experimental results are given to demonstrate the excellent control performance of the proposed ACFSM control.
QoE evaluation of WebRTC-based Mobile Multiparty Video Calls in Light of Different Video Codec Settings

Technological advances in mobile networks and end user devices are paving the way for widespread use of interactive multiparty audiovisual communication services. The emergence of the WebRTC (Web Real-Time Communication) open framework has provided an opportunity to redefine the video conferencing communication landscape. However, in the mobile context, challenges remain in optimizing video encoding parameters due to limited device processing power and dynamic wireless network conditions. In this paper, we investigate how different video encoding parameters in terms of encoding bit rate, resolution, and frame rate, influence Quality of Experience (QoE). While video parameters are manually manipulated, we observe also dynamic video adaptation invoked as a result of the Google Congestion Control (GCC) algorithm. We conduct an experimental study involving twenty-seven users participating in three-party audiovisual WebRTC calls on symmetric mobile devices with a given conversation task to simulate natural conversation. Participants reported subjective ratings for test scenarios with different combinations of video encoding parameters, for which we collect real-time session data using the Chrome WebRTC-internals tool.
Agent-based simulation of consumer purchase behaviour based on quality, price and promotion

ABSTRACT This paper studies the influence factors in consumer purchase behaviour. First, through reading and analysing the related paper, the authors summarise a lot of influence factors. Second, combined with the theme of the study, experts and scholars discuss these influence factors and choose quality, price and promotion of commodity as the object of this paper. Third, this paper presents an agent-based model and simulation method on the consumer purchase behaviours based on quality, price and promotion of commodity. The utility function is used to describe the model, which is implemented under NetLogo simulation environment. The results show that the proposed model can analyse and predict the effects of different quality, price and promotion strategies by the emergence of consumer purchase behaviour.