Miracle-dz/UniFAR-FTUs huggingface dataset:
Model-based reinforcement learning and navigation in animals and machines

For decades, neuroscientists and psychologists have observed that animal performance on spatial navigation tasks suggests an internal learned map of the environment. More recently, map–based (or model–based) reinforcement learning has become a highly active research area in machine learning. With a learned model of their environment, both animals and artificial agents can generalize between tasks and learn rapidly. In this thesis, I present approaches for developing efficient model–based behaviour in machines and explaining model–based behaviour in animals. From a neuroscience perspective, I focus on the hippocampus, believed to be a major substrate of model–based behaviour in the brain. I consider how hippocampal connectivity enable path–finding between different locations in an environment. The model describes how environments with boundaries and barriers can be represented in recurrent neural networks (i.e. attractor networks), and how the transient activity in these networks, after being stimulated with a goal location, could be used for determining a path to the goal. I also propose how the connectivity of these map–like networks can be learned from the spatial firing patterns observed in the input pathway to the hippocampus (i.e. grid cells and border cells). From a machine learning perspective, I describe a reinforcement learning model that integrates model–based methods and “episodic control”, an approach to reinforcement learning based on episodic memory. According to episodic control, the agent learns how to act in the environment by storing snapshot–like memories of its observations, then comparing its current observations to similar snapshot memories where it took an action that resulted in high reward. In our approach, the agent augments these real–world memories with episodes simulated offline using a learned model of the environment. These “simulated memories” allow the agent to adapt faster when the reward locations change. Next, I describe Variational State Tabulation (VaST), a model–based method for learning quickly with continuous and high–dimensional observations (like those found in 3D navigation tasks). The VaST agent learns to map its observations to a limited number of discrete abstract states, and build a transition model over those abstract states. The long–term values of different actions in each state are updated continuously and efficiently in the background as the agent explores the environment. I show how the VaST agent can learn faster than other
Compression and visual quality assessment for light field contents

Since its invention in the 19th century, photography has allowed to create durable images of the world around us by capturing the intensity of light that flows through a scene, first analogically by using light-sensitive material, and then, with the advent of electronic image sensors, digitally. However, one main limitation of both analog and digital photography lays in its inability to capture any information about the direction of light rays. Through traditional photography, each Three-dimensional scene is projected onto a 2D plane; consequently, no information about the position of the 3D objects in space is retained. Light field photography aims at overcoming these limitations by recording the direction of light along with its intensity. In the past, several acquisition technologies have been presented to properly capture light field information, and portable devices have been commercialized to the general public. However, a considerably larger volume of data is generated when compared to traditional photography. Thus, new solutions must be designed to face the challenges light field photography poses in terms of storage, representation and visualization of the acquired data. In particular, new and efficient compression algorithms are needed to sensibly reduce the amount of data that needs to be stored and transmitted, while maintaining an adequate level of perceptual quality. In designing new solutions to address the unique challenges posed by light field photography, one cannot forgo the importance of having reliable, reproducible means of evaluating their performance, especially in relation to the scenario in which they will be consumed. To that end, subjective assessment of visual quality is of paramount importance to evaluate the impact of compression, representation, and rendering models on user experience. Yet, the standardized methodologies that are commonly used to evaluate the visual quality of traditional media content, such as images and videos, are not equipped to tackle the challenges posed by light field photography. New subjective methodologies must be tailored for the new possibilities this new type of imaging offers in terms of rendering and visual experience. In this work, we address the aforementioned problems by both designing new methodologies for visual quality evaluation of light field contents, and outlining a new compression solution to efficiently reduce the amount of data that needs to be transmitted and stored. We first analyse how traditional methodologies for subjective evaluation of multimedia contents can be adapted to suit light field data, and we propose new methodologies to reliably assess the visual quality while maintaining user engagement. Furthermore, we study how user behavior is affected by the visual quality of the data. We employ subjective quality assessment to compare several state-of-the-art solutions in light field coding, in order to find the most promising
TDCTFIC: A Novel Recommendation Framework Fusing Temporal Dynamics, CNN-Based Text Features and Item Correlation

The traditional recommendation system (RS) can learn the potential personal preferences of users and potential attribute characteristics of items through the rating records between users and items to make recommendations.However, for the new items with no historical rating records,the traditional RS usually suffers from the typical cold start problem. Additional auxiliary information has usually been used in the item cold start recommendation,we further bring temporal dynamics,text and relevance in our models to release item cold start.Two new cold start recommendation models TmTx(Time,Text) and TmTI(Time,Text,Item correlation) proposed to solve the item cold start problem for different cold start scenarios.While well-known methods like TimeSVD++ and CoFactor partially take temporal dynamics,comments,and item correlations into consideration to solve the cold start problem but none of them combines these information together.Two models proposed in this paper fused features such as time,text,and relevance can effectively improve the performance under item cold start.We select the convolutional neural network (CNN) to extract features from item description text which provides the model the ability to deal with cold start items.Both proposed models can effectively improve the performance with item cold start.Experimental results on three real-world data set show that our proposed models lead to significant improvement compared with the baseline methods. key words: recommendation system, matrix factorization, temporal dynamics, convolutional neural networks, item correlation
A Distributed Mode Selection Scheme for Full-Duplex Device-to-Device Communication

Networks with device-to-device (D2D) technology allow for two possible communication modes: traditional communication via the base station, and direct communication between the users. Recent studies show that in-band full-duplex (IBFD) operations can be advantageously combined with D2D communication to improve the spectral efficiency. However, no algorithms for selecting the communication mode of mobile users in IBFD networks have yet appeared in the literature. In this paper, we design a distributed mode selection scheme for users in D2D-enabled IBFD networks. The proposed scheme maximizes the users’ prob-ability of successful communication by leveraging only existing signaling mechanisms.
TARNN: Task‐aware autonomic resource management using neural networks in cloud environment

Resource management is one of the major issue in cloud computing for IaaS. Among several resource management problems allocation, provisioning, and requirement mapping directly affects the performance of cloud. Resource allocation signifies assignment of available resources to different workloads in an economically optimal manner. Precise and accurate allocation is required to maximize the usage of resources. Current method of task allocation do not take previously acquired knowledge, type of the tasks, and the QoS parameters altogether into account in the allocation phase, and it has not been trained for different set of tasks. Furthermore, the self‐optimization of the autonomous system fails to address the task type and identify the relationship between tasks and the resource demands along with its requirements. Important aspects like task management and resource utilization are the primary factors to consider for such a characteristic. This paper will present a novel autonomic resource management framework named task‐aware autonomic resource allocation strategy using neural networks (TARNN), which aims to use knowledge about the behavior of the task over an extended period of time and use this knowledge to allocate resources when a similar task is submitted in future by the user. To effectively do the allocation, a neural network–based approach is adopted to classify the tasks appropriately based on the task parameters, task type, and QoS parameters and allocate the resource optimally for a new task autonomously, without the intervention of the cloud provider. Moreover, to identify and improve the relationship of the tasks with the resources in the context of scheduling, we have proposed a novel modified Particle Swarm Optimization (m‐PSO) algorithm to schedule the tasks to resources based on resource demands. In TARNN, we have separated the collected synthetic dataset into 60‐40 ratio for training and testing purposes. We found that the neural network–based approach provides almost 80% accurate classification w.r. t. task type and QoS parameters. We have also compared our results with support vector machine (SVM) and got 69% accuracy. Since the tasks are classified appropriately, the occurrence of resource reconfiguration and VM migration is drastically reduced. Hence, our system provides better allocation of resources and schedules the tasks appropriately to the resources, thereby improving the performance of the cloud.
Higher Order Basis Based Integral Equation Solver - HOBBIES

The latest in parallel EM solutions with both in-core and out-of-core solversThe solution of complex electromagnetic (EM) problems requires one to address the issues related with numerical accuracy and efficient distribution of the solution procedure over multiple computational nodes. With the advent of multicore processors and high performance computing (HPC) technology, the EM software designers need to know how to add new functionality to computational EM codes so that they can run efficiently on these new processors. Higher Order Basis Based Integral Equation Solver [HOBBIES] presents a road map for the analysis of complex material structures using the high-performance parallel simulation software known as HOBBIES. Focusing on the Method of Moments (MoM), the book features new parallel programming techniques and user-friendly code with superior capabilities for solving challenging EM radiation and scattering problems. It provides readers with complete guidance on how to extend the capability of MoM and achieve faster and more accurate EM analysis and utilize multicore CPUs on desktop computers. Complete with an academic version of the HOBBIES software, this book:Explains the unique features of the higher order basis functions in the solution of integral equations in a MoM contextShows how to generate a properly load balanced parallel computational procedure for MoM matrix filling and matrix equation solving in both in-core and out-of-core implementationPresents a professional graphical users interface (GUI) for generating the geometrical structure based on NURBS modelingIllustrates various automatic meshing procedures based on an a-priori defined error between the actual geometry and the meshed structureOutlines all the key features of the HOBBIES software, including multiple optimization procedures for EM synthesisThe bottleneck of traditional MoM arises from the lack of memory in computers for solution of large problems. This is mitigated by using higher order basis functions and out-of-core solver in HOBBIES. HOBBIES has the capability to perform numerically accurate EM simulations using thousands of CPU cores in an HPC environment using a properly load balanced out-of-core solver. In this way, it provides a cost-effective choice for addressing modern engineering and scientific challenges that arise from the extremely complicated real-life applications.
The Network of Firms Implied by the News

We show that the news is a rich source of data on distressed firm links that drive firm-level and aggregate risks. The news tends to report about links in which a less popular firm is distressed and may contaminate a more popular firm. This constitutes a contagion channel that yields predictable returns and downgrades. Shocks to the degree of news-implied firm connectivity predict increases in aggregate volatilities, credit spreads, and default rates, and declines in output. To obtain our results, we propose a machine learning methodology that takes text data as input and outputs a data-implied firm network.
Human-in-the-loop Topic Modelling Assessing topic labelling and genre-topic relations with a movie plot summary corpus

A much-used but not yet mainstream text analysis approach, topic modelling allows the identification of lexical themes for a document collection. Against principles for interpretable AI and sociotechnical design, there are definite strengths from its speed and ability to discover structure, but remain challenges in how results can be interpreted whether this be by analysts, domain experts, or potential end users. Automated coherence and labelling measures go some of the way toward bridging the understanding and trust gap, and user empowerment through visualisation and design intervention is starting to show how the remaining ground might be made up. This study uses topic modelling on a corpus of Wikipedia movie summaries to illustrate challenges and potential. Topic labelling for naive users was found to only be easy in a quarter of cases, and difficulty increased markedly with 100 topics compared to 50. While automated measures suggested 88 topics, the number manageable by users was closer to 50. The unsupervised topic model was compared to the movie genre labels and indicated that the two might work together well to complement genres, match content across genre and highlight within-genre variability. It is suggested that unsupervised models might work better for creativity and discovery than semi-supervised versions.
Learning Embeddings for Text and Images from Structure of the Data

xii List of Abbreviations . xiii Acknowledgements . xvii Chapter
Investigating Students’ Acceptance of Online Courses at Al-Ahliyya Amman University

Online courses allow students to access the course materials anytime and anywhere. Those courses are meant to enhance and improve the learning processes. Unfortunately, by analyzing data of an online course in Al-Ahliyya Amman University, it was found that only 51% of enrolled students accessed the animated course material. This study proposed a model to understand the factors which affect students’ intention to use an online course by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) and Technology Acceptance Model (TAM). The proposed research model investigated the effects of experience, perceive usefulness, awareness, effort expectancy, cost, subjective (social) norms, and behavioral intentions to use online courses on students’ adoption of online courses. Besides, the model investigated the effects of moderators, such as: college, college level, personal computer ownership, an internet access, and an online course enrollment on the relations. A questionnaire was distributed and then a structural equation modeling (SEM) approach was used to analyze the responses using SmartPLS.
Consistent Modelling of the Impact Model of Modular Product Structures with Linking Boundary Conditions in SysML

The challenges related to product structures, which go hand in hand with megatrends such as individualization, can be met with the modularity of product structures. With the help of various modularization methods, modular product structures are created with regard to different goals. There are many references to the effects of modular product structures on life phases and economic targets in the literature. These effects were collected in previous research in a generic impact model. Since there is a lot of information about the effects, such models become very comprehensive and thereby difficult to handle. For this reason, the impact model is consistently generated using SysML. The adaptation to company scenarios is possible through the use of simulations with which, for example, company-related and product-related boundary conditions can be controlled by means of a User Interface.
How to Design and Implement a Flipped Classroom Lesson: A Bottom up Procedure for More Effective Lessons

The purpose of this study is to examine organizational decision-making processes of problemsolving groups. This experimental design study utilized a case study method bounded by a fictional problem-solving scenario to illicit problem solving in groups. Fourteen undergraduate pre-service teachers were placed into six groups and given 45 minutes to create a collaborative solution to the problem-solving scenario. Data sources consisted of group observations and focus group interviews. Data were analyzed using an inductive thematic technique. Results indicated seven themes that illuminate the nature of the group’s organization of cognitive processes and possible solutions. The results of this study may help understand how groups organize information, solutions, and cognitions in a complex group decision-making environment.
Enhancing Controllability Robustness of q-Snapback Networks through Redirecting Edges

The well-known small-world network model was established by randomly rewiring edges, aiming to enhance the synchronizability of an undirected nearest-neighbor regular network. This paper demonstrates via extensive numerical simulations that randomly redirecting edges could enhance the robustness of the network controllability for directed snapback networks against both random and intentional node-removal and edge-removal attacks.
Prediction of miRNA-circRNA associations based on k-NN multi-label with random walk restart on a heterogeneous network

Circular RNAs (circRNAs) play important roles in various biological processes, as essential non -coding RNAs that have effects on transcriptional and posttranscriptional gene expression regulation. Recently, many studies have shown that circRNAs can be regarded as micro RNA (miRNA) sponges, which are known to be associated with certain diseases. Therefore efficient computation methods are needed to explore miRNA-circRNA interactions, but only very few computational methods for predicting the associations between miRNAs and circRNAs exist. In this study, we adopt an improved random walk computational method, named KRWRMC, to express complicated associations between miRNAs and circRNAs. Our major contributions can be summed up in two points. First, in the conventional Random Walk Restart Heterogeneous (RWRH) algorithm, the computational method simply converts the circRNA/miRNA similarity network into the transition probability matrix; in contrast, we take the influence of the neighbor of the node in the network into account, which can suggest or stress some potential associations. Second, our proposed KRWRMC is the first computational model to calculate large numbers of miRNA-circRNA associations, which can be regarded as biomarkers to diagnose certain diseases and can thus help us to better understand complicated diseases. The reliability of KRWRMC has been verified by Leave One Out Cross Validation (LOOCV) and 10-fold cross validation, the results of which indicate that this method achieves excellent performance in predicting potential miRNA-circRNA associations.
A new cellular automata framework of urban growth modeling by incorporating statistical and heuristic methods

ABSTRACT We develop a new geographical cellular automata (CA) modeling framework, named UrbanCA, through reconstructing the essential CA structure and incorporating nonspatial, spatial, and heuristic approaches. The new UrbanCA is featured by 1) the improvement of the CA modeling framework by reformulating relationships among CA components, 2) the development of two scaling parameters to adjust the effects of transition probability and neighborhood, 3) the incorporation of a variety of statistical and heuristic methods to construct transition rules, and 4) the inclusion of urban planning regulations and spatial heterogeneities to project future urban scenarios. To illustrate the effectiveness of UrbanCA, we calibrate a CA model using artificial bee colony (ABC) to simulate the past urban patterns and predict future scenarios in Shanghai of China. The results show that UrbanCA under different scaling parameters is comparable to CA-Markov (as a reference model) concerning the accuracy of the end-state and change simulations, and is better than CA-Markov regarding the driving factor’s ability to explain the modeling outcomes. UrbanCA provides more choices compared to existing CA software packages, and the models are readily calibrated elsewhere to simulate the dynamic urban growth and assess the resulting natural and socioeconomic impacts.
The Efficacy of MALL Instruction in Business English Learning

The existing technological expansion has transformed pervasive revolutions in modern civilization, with continuing innovation of services and products. Nowadays people are having different channels of communication. Currently, mobile learning (M-learning) is being used extensively in teaching and learning foreign languages which further widens the mobility of learners. The usage of mobile instruction can lead learners towards a real interruption to learn effectively when used inappositely. The current study is an attempt to explore the potential usage of M-Learning in English for specific purpose (ESP) classes. For this intention smartphone were used in the ESP context for teaching Business English at the College of Business Administration at a public sector university in Saudi Arabia. A questionnaire was distributed to 21 participants of the present study after a 5-week exposure to M-learning activities in the formal classroom. The findings revealed that ESP learners acknowledge M-learning as a prompt source of feedback in ESP. The results of this study also indicate that incorporation of M-learning in ESP learning with conventional classroom can support learners to develop their learning proficiency. Moreover, learners showed significantly positive attitude in mobile instruction in a formal classroom.