Miracle-dz/UniFAR-FTUs huggingface dataset:
Active learning in teaching English language support courses to first-year students in some Ethiopian universities

iii TABLE OF CONTENTS . v ABBREVIATIONS . xiii CHAPTER ONE: INTRODUCTION . 1 1.1 Background of the Study . 1 1.2 Statement of the Problem . 4 1.3 Research Questions . 5 1.4 Research Aim and Objectives . 6 1.5 Significance of the Study. 6 1.6 Definition of Key Terms . 7 1.7 Literature Review . 8 1.7. 1 What is Active Learning? 8 1.8. 2 Problems Related to the Use of Active Learning . 10 1. 8.3 Theories of Active Learning . 13 1.8.3.2 Constructivist Theories of Learning . 14 1.9 Research Design and Methodology . 16 1.9. 1 Population and Sampling . 16 1.9. 2 Instruments and Data-Collection Techniques . 17 1.9. 4 Data Analysis and Interpretation . 18 1.10 Limitations of the Study . 19 1.11 Chapter Overview . 20 CHAPTER 2: REVIEW OF RELATED LITERATURE . 21 2.1 Theoretical Framework . 21 2.1.1 The Nature of the Constructivist School of Thought . 21 2.1.2 What is Cognitive Constructivism? 25
The dual roles of web personalization on consumer decision quality in online shopping

Many online merchants today have adopted web personalization in the form of personalized product recommendations (PPRs) to improve consumer’s decision quality. The purpose of this paper is to reveal the roles of PPRs on consumer decision quality in online shopping from the theoretical perspective of information load. ,To explore the dual roles of PPRs on consumer decision quality, this paper develops a research model for it. A 2 (information load: high vs low) × 2 (web personalization: PPRs vs non-PPRs) between-subjects design is conducted to empirically test the model. ,The results indicate that: first, information load can increase perceived information overload and decrease perceived information underload; second, PPRs can weaken (enhance) the positive (negative) effect of information load on perceived information overload (perceived information underload); third, both perceived information overload and perceived information underload are negatively associated with consumer’s decision quality. ,This paper originally develops a research model that explains the roles of PPRs on consumer decision quality from the theoretical perspective of information load in the online shopping context, which could add new insights to the field of web personalization, especially the impact of web personalization on consumer decision making.
Emergent Jaw Predominance in Vocal Development Through Stochastic Optimization

Infant vocal babbling strongly relies on jaw oscillations, especially at the stage of canonical babbling, which underlies the syllabic structure of world languages. In this paper, we propose, model and analyze an hypothesis to explain this predominance of the jaw in early babbling. This hypothesis states that general stochastic optimization principles, when applied to learning sensorimotor control, automatically generate ordered babbling stages with a predominant exploration of jaw movements in early stages. The reason is that those movements impact the auditory effects more than other articulators. In previous computational models, such general principles were shown to selectively freeze and free degrees of freedom in a model reproducing the proximo-distal development observed in infant arm reaching. The contribution of this paper is to show how, using the same methods, we are able to explain such patterns in vocal development. We present three experiments. The two first ones show that the recruitment order of articulators emerging from stochastic optimization depends on the target sound to be achieved but that on average the jaw is largely chosen as the first recruited articulator. The third experiment analyses in more detail how the emerging recruitment order is shaped by the dynamics of the optimization process.
Reinforcement Learning in Structured and Partially Observable Environments

Author(s): Azizzadenesheli, Kamyar | Advisor(s): Singh, Sameer | Abstract: Sequentially making-decision abounds in real-world problems ranging from robots needing to interact with humans to companies aiming to provide reasonable services to their customers. It is as diverse as self-driving cars, health-care, agriculture, robotics, manufacturing, drug discovery, and aerospace. Reinforcement Learning (RL), as the study of sequential decision-making under uncertainty, represents a core aspect challenges in real-world applications. While most of the practical application of interests in RL are high dimensions, we study RL problems from theory to practice in high dimensional, structured, and partially observable settings. We show how statistically develop efficient RL algorithm for a variety of RL problems, from recommendation systems to robotics and games. We theoretically study these problems from their first principles to provide RL agents which efficiently interact with their surrounding environment and learn the desired behavior while minimizing their regrets. We study linear bandit problems where we propose Projected Stochastic Linear Bandit (PSLB), upper confidence bound based algorithm in linear bandit which exploit the intrinsic structure of the decision-making problem to significantly enhance the performance of RL agents. We study the problem of RL in Markov Decision Process (MDP) where we propose the first sample efficient model-free algorithm for the general continuous state and action space MDPs. We further investigate safe RL setting and introduce a safe RL algorithm to avoid catastrophic mistakes that can be made by an RL agent. We extensively study tree-based methods, a well-popularized method in RL which is also the core to Alpha-Go, a technique to beat the masters of board games such as Go game.We extend our study to partially observable environments, such as partially observable Markov decision processes (POMDP) where we propose the first regret analysis for the class of memoryless policies. We continue this study to a class of problems known as rich observable Markov decision processes (ROMPD) and propose the first regret bound with no dependency in the ambient dimension in the dominating terms.We empirically study the significance of all these theoretically guaranteed methods and show their value in practice.
INTERGRATING VIDEOCONFERENCE TECHNOLOGY INTO PEDAGOGICAL INQUIRY: USER PERCEPTIONS

Effective integration of Videoconference technology depends on users’ perception. There seem to be reluctance and ignorance amongst tutors to integrate Videoconference technology effectively, for students to comprehend the subject content when delivering the lesson. Following a qualitative research approach, this paper explores tutors’ perceptions in integrating Videoconference technology into teaching and learning. Data were collected through the individual semi-structured interviews with Videoconference tutors and tutorial lesson observations. The findings of the study highlight some factors contributory to tutors’ perceptions in using Videoconference technology with various suitable teaching methodologies. The findings of the study recommend modification in tutor training on how to integrate this technology with various suitable teaching methodologies and motivating these tutors to exploit the Videoconference medium in teaching and learning. We conclude in this study that Videoconference, if effectively used in Distance Learning, will bridge the learning distance that students experience.
Enabling Situational Awareness in Millimeter Wave Massive MIMO Systems

Situational awareness in wireless networks refers to the availability of position information on transmitters and receivers as well as information on their propagation environments to aid wireless communications. In millimeter wave massive multiple-input multiple-output communication systems, situational awareness can significantly improve the quality and robustness of communications. In this paper, we establish a model that describes the statistical dependencies between channel state information and the position, orientation, and clock offset of a user equipment along with the locations of features in the propagation environment. Based on this model, we introduce COMPAS (COncurrent Mapping, Positioning, And Synchronization); an inference engine that can provide accurate and reliable situational awareness in millimeter wave massive multiple-input multiple-output communication systems. Numerical results show that COMPAS is able to infer the positions of an unknown and time-varying number of features in the propagation environment and, at the same time, estimate the position, orientation, and clock offset of a user equipment.
Feminism! What Is It Good For? The Role of Feminism and Political Self-Efficacy in Women’s Online Political Participation

Despite initial hopes for more egalitarian access to democracy, research has shown that political participation on the Internet remains as stratified as its offline counterpart. Gender is among the characteristics affecting an individual’s degree of political engagement on the Internet—even when controlling for socioeconomic status. To explain this gender divide, it is necessary to go beyond purely resource-based perspectives. Social cognitive theory allows for an analysis of how environmental factors shape cognitions, such as political efficacy, which, in turn, foster political participation. Political efficacy has been shown to be lower among women compared to men. This study explores determinants of gendered online political participation (OPP) by analyzing how self-efficacy mediates the effect of newly developed measures of three different waves of feminist attitudes on OPP. Based on a survey of 1,078 Internet users in Germany, 70% of them women, we analyze the effects of feminism on political efficacy and participation. Feminism is associated with higher internal political efficacy. Also, some feminist paradigms are shown to empower women to participate politically online. This effect, however, is not mediated through efficacy. This finding sheds light on opportunities to foster women’s political participation.
New Results in Perceptually Lossless Compression of Hyperspectral Images

Hyperspectral images (HSI) have hundreds of bands, which impose heavy burden on data storage and transmission bandwidth. Quite a few compression techniques have been explored for HSI in the past decades. One high performing technique is the combination of principal component analysis (PCA) and JPEG-2000 (J2K). However, since there are several new compression codecs developed after J2K in the past 15 years, it is worthwhile to revisit this research area and investigate if there are better techniques for HSI compression. In this paper, we present some new results in HSI compression. We aim at perceptually lossless compression of HSI. Perceptually lossless means that the decompressed HSI data cube has a performance metric near 40 dBs in terms of peak-signal-to-noise ratio (PSNR) or human visual system (HVS) based metrics. The key idea is to compare several combinations of PCA and video/ image codecs. Three representative HSI data cubes were used in our studies. Four video/image codecs, including J2K, X264, X265, and Daala, have been investigated and four performance metrics were used in our comparative studies. Moreover, some alternative techniques such as video, split band, and PCA only approaches were also compared. It was observed that the combination of PCA and X264 yielded the best performance in terms of compression performance and computational complexity. In some cases, the PCA + X264 combination achieved more than 3 dBs than the PCA + J2K combination.
Post-selection-based continuous variable quantum information processing

Over the past three decades, there have been great advances in the burgeoning field of quantum information science, from the proposal of quantum key distribution that holds the promise of unconditionally secure communication, to quantum teleportation that harnesses entanglement to achieve communication that was previously confined to the realm of science fiction. Most of these possibilities were not foreseen before the union of quantum mechanics and the conventional information theory. The remarkable achievements emerged alongside the recognition that ultimate limits, such as the nogo theorem on noiseless linear amplification and the no-cloning theorem, exist on the performance of quantum devices. In practice, limited experimental resources also pose critical constraints on the benefits one can obtain from quantum mechanical systems over their classical counterparts. In this thesis, we introduce various post-selection-based continuous variable (CV) quantum information protocols, showing that the aforementioned limitations can be efficiently surmounted by forgoing determinism. We demonstrate quantum information tasks with a finite success probability that would otherwise be impossible deterministically. Moreover, we showcase that the inclusion of such post-selection obviates the requirement for highly non-classical resources in a variety of quantum information processings–when benchmarked on the same criterion. First, we heuristically study the properties of the post-selection filter that was originally proposed to emulate a noiseless linear amplifier (NLA). In particular, two equivalent implementations of NLA, i.e. the measurement-based NLA and the physical NLA, are investigated and compared in terms to their abilities to preserve the Gaussianity of an arbitrary input state and their success probability. The link between these two amplifiers is further clarified by integrating them into a measure-and-prepare setup. We conclude that the equivalence between the measurement-based and the physical approaches holds valid only when the effective parameters–the amplification gain, the cut-off, and the amplitude of the input state–are taken into account. We provide one concrete application of the measurement-based NLA as a 1-to-infinity cloner with fidelity surpassing its quantum limit. The first application of the post-selection filter is concerned with quantum amplification. The noise properties of deterministic phase-insensitive amplifiers are discussed, whereby the signal to noise ratio inevitably degrades in compliance with the Heisenberg uncertainty principle. We demonstrate a noise-reduced quantum amplifier that circumvents this noise penalty at the expense of finite success probability. Here a measurementbased NLA is profitably combined with a deterministic linear amplifier, making it possible for the signal-to-noise ratio of an incoming optical signal to be enhanced phase
A Spintronics Memory PUF for Resilience Against Cloning Counterfeit

With the widespread use of electronic devices in embedding or processing sensitive information, new hardware security primitives have emerged to improve the shortcomings of traditional secure data storage. One of these solutions, the physically unclonable function (PUF), which is widely used for authentication and cryptography applications, extracts the unique and unclonable value from the circuit physical properties. However, a cloning attack on memory-based PUF has been demonstrated from the circuit back-side tampering, questioning its unclonable property and opening counterfeiting vulnerability in an untrusted supply chain. Spin transfer torque-magnetic random-access memory (STT-MRAM) is a promising technology due to its nonvolatility, scalability, and CMOS compatibility, therefore envisioned to be used in many embedded secure devices. In this paper, we reveal the vulnerability of existing STT-MRAM PUF solutions to back-side attacks by modeling different levels of tampering and their impact at electrical and logical levels. We propose a tamper resilient methodology for the STT-MRAM PUF design based on its switching properties. The resilience of the proposed solution to different levels of tampering and a 100% detection are confirmed with simulation results.
Printed Monopole Antennas with multiple bends for rfid wlan and UWB Applications

Printed monopole antenna (PMA) with the microstrip feed line are an important class of broadband antennas. Because of the stripped ground plane on the back side of the substrate, these classes of antennas provide large bandwidth. Dual band operation can be achievable for these classes of antennas by creation of two independent resonating paths in the form of protruding stub in the ground plane and the radiating element printed on the top of the substrate. The major challenge in designing these antennas is the tuning of the protruding stub in the ground plane with the radiating element on the substrate to provide the dual-band operation for RFID, WLAN, etc. These antennas are light weight, low profile, less fragile and easily integrable with the hand held devices. UWB antennas are wideband antennas (3.1-10.6 GHz), widely used for application in modern wireless hand held devices. Miniaturization is a major challenge for the design of these UWB antennas. Apart from the compact size, consistent omni-directional radiation pattern, appropriate gain characteristics and suitable time domain characteristics are needed for designing a proper UWB antenna. In the UWB frequency region of 3.1-10.6 GHz, many narrowband wireless systems exist such as WiMAX (3.3-3.6 GHz) and WLAN (5-6 GHz), which interferes with the UWB operation. Hence, these narrowband wireless systems’ spectrum should be notched up for coexistence of UWB with the other existing narrowband devices. In our work, we have designed, simulated, fabricated and tested PMAs with multiple bends for applications in RFID, WLAN and UWB systems. Antennas are categorized depending upon the number of bends either in the radiating element or in the protruding strip in the ground plane. In this work, single, double, triple and quadruple bend PMAs for the dual-band WLAN and RFID applications are proposed. It has been observed that our proposed novel PMAs are considerably better than the existing planar antennas in terms of size compactness, bandwidth improvement and gain enhancement for dualTH-1109_07610204 band RFID/WLAN operations. Besides, rectangular PMA with multiple bends in the feed region with appropriate time and frequency domain characteristics, are proposed for singleand double-notched UWB communications for coexistence of UWB devices with other narrowband devices viz., WIMAX and WLAN.
Consumer Adoption of Self-Service Technologies in the Context of the Jordanian Banking Industry: Examining the Moderating Role of Channel Types

ABSTRACT This study aimed to test if the impacts of the main predictors of customer intention and adoption of self-service technologies (SSTs) could be moderated by channel type. The required data were collected from a convenience sample of Jordanian banking customers using a survey questionnaire. The results of the X2 differences test also indicate that there are significant differences in the influence of the main predictors due to the moderating effect of channel type.
Using geodesign to support collaborative planning workshops

Geodesign tools can be used to support collaborative processes (STEINITZ 2012). Typical tools combine different methods, such as simulation models, spatial multicriteria analysis, visualization, and optimization. User-friendly interfaces allow multiple users to provide input and generate real-time output to support negotiated spatial decisions (SIEBER 2006; GEERTMAN & STILLWELL 2009; PELZER et al. 2013).
THE EFFECT OF DIGITAL VIDEO GAMES ON EFL STUDENTS’ LANGUAGE LEARNING MOTIVATION

The study examined the effect of a commercial digit al video game on high school students’ language learning motivation. Participants were 241 male students randomly assigned to one of the following three treatments: Readers, who int ensively read the game’s story; Players, who played the digital video game; and Watchers, wh o atched two classmates play the digital video game. A language learning motivation scale was given to the participants as a preand post-test. Also, field notes were taken. R esults indicated a significant language learning motivation increase over time. Only the Wa tchers, however, showed significantly higher motivation than the Readers in the end. Thus , the use of commercial digital video games can help enhance high school students’ langua ge le rning motivation.
A General Framework of Optimal Investment

In this paper, we propose a general framework of optimal investment and a collection of trading ideas, which combine probability and statistical theory with, potentially, machine learning techniques, e.g., machine learning regression, classification and reinforcement learning. The trading ideas are easy to implement and their validity is justified by full mathematical rigor. The framework is model-free and can, in principle, incorporate all categories of trading ideas into it. Simulation and backtesting studies show good performance of selected trading strategies under the proposed framework. Sharpe ratios are above 8.00 in simulation study and Sortino ratios are above 4.00 in backtesting, with very limited drawdowns, using 20 years of monthly data of US equities (NASDAQ, NYSE and AMEX from 1999.1 to 2018.12) and 17 years of monthly data of China A-Share equities (Shanghai and Shenzhen Stock Exchange from 2002.1 to 2018.8).
On the Parameterized Complexity of Approximating Dominating Set

We study the parameterized complexity of approximating the k-Dominating Set (DomSet) problem where an integer k and a graph G on n vertices are given as input, and the goal is to find a dominating set of size at most F(k) ⋅ k whenever the graph G has a dominating set of size k. When such an algorithm runs in time T(k) ⋅ poly (n) (i.e., FPT-time) for some computable function T, it is said to be an F(k)-FPT-approximation algorithm for k-DomSet. Whether such an algorithm exists is listed in the seminal book of Downey and Fellows (2013) as one of the “most infamous” open problems in parameterized complexity. This work gives an almost complete answer to this question by showing the non-existence of such an algorithm under W[1] ≠ FPT and further providing tighter running time lower bounds under stronger hypotheses. Specifically, we prove the following for every computable functions T, F and every constant ε > 0: • Assuming W[1] ≠ FPT, there is no F(k)-FPT-approximation algorithm for k-DomSet. • Assuming the Exponential Time Hypothesis (ETH), there is no F(k)-approximation algorithm for k-DomSet that runs in T(k) ⋅ no(k) time. • Assuming the Strong Exponential Time Hypothesis (SETH), for every integer k ≥ 2, there is no F(k)-approximation algorithm for k-DomSet that runs in T(k) ⋅ nk − ε time. • Assuming the k-SUM Hypothesis, for every integer k ≥ 3, there is no F(k)-approximation algorithm for k-DomSet that runs in T(k) ⋅ n⌈ k/2 ⌉ − ε time. Previously, only constant ratio FPT-approximation algorithms were ruled out under sf W[1] ≠ FPT and (log1/4 &minus ε k)-FPT-approximation algorithms were ruled out under ETH [Chen and Lin, FOCS 2016]. Recently, the non-existence of an F(k)-FPT-approximation algorithm for any function F was shown under Gap-ETH [Chalermsook et al., FOCS 2017]. Note that, to the best of our knowledge, no running time lower bound of the form n&delta k for any absolute constant δ > 0 was known before even for any constant factor inapproximation ratio. Our results are obtained by establishing a connection between communication complexity and hardness of approximation, generalizing the ideas from a recent breakthrough work of Abboud et al. [FOCS 2017]. Specifically, we show that to prove hardness of approximation of a certain parameterized variant of the label cover problem, it suffices to devise a specific protocol for a communication problem that depends on which hypothesis we rely on. Each of these communication problems turns out to be either a well-studied problem or a variant of one; this allows us to easily apply known techniques to solve them.