Miracle-dz/UniFAR-FTUs huggingface dataset:
An exploratory design science study on theory testing using crowdsourcing

Theory in Information Systems (IS) is very important to the development of the field. Theory building, and theory testing seeks to accumulate knowledge about the relationships between people and technology. Testing theory can be difficult to accomplish, especially when it involves humans, a diversity of methods and sources, multiple experiments, large data sets, and careful tuning of conditions and instruments. Crowdsourcing is a strategy supporting the distribution of activities to crowd workers, which suggests that it may be used to support theory testing. This exploratory study seeks to analyse the adoption of crowdsourcing in theory testing, and to develop guidance for researchers to instantiate the strategy in their research projects. The study adopts the design science research paradigm to explore incorporating the crowdsourcing strategy in theory testing, and to evaluate its viability and utility. According to the principles of design science research, the study is structured around the construction of several interconnected IS artefacts: 1) a conceptual framework articulating the main principles of theory testing; 2) a pattern model of theory testing, which codifies existing research approaches to theory testing; and 3) a decision tool, which codifies guidelines for researchers making decisions on which research activities to crowdsource. In order to build the conceptual framework and pattern model, the study conducts a systematic review of theory testing in the IS domain. Both the conceptual framework and pattern model are then operationalized in the decision tool. The utility of the various artefacts is then assessed with the participation of research practitioners. This study is relevant because it synthesizes knowledge about theory testing, builds innovative artefacts supporting the adoption of crowdsourcing in theory testing, helps academic researchers understanding the theory testing process, and enables them to adopt crowdsourcing for theory testing.
Bidirectional GRU-Based Attention Model for Kid-Specific URL Classification

The Gen Z kids highly rely on internet for various purposes like entertainment, sports, and school projects. There is a demand for parental control systems to monitor the children during their surfing time. Current web page classification approaches are not effective as handcrafted features are extracted from the web content and machine learning techniques are used that need domain knowledge. Hence, a deep learning approach is proposed to perform URL-based web page classification. As the URL is a short text, the model should learn to understand where the important information is present in the URL. The proposed system integrates the strength of attention mechanism with recurrent convolutional neural network for effective learning of context-aware URL features. This enhanced architecture improves the design of kids-relevant URL classification. By conducting various experiments on the benchmark collection Open Directory Project, it is shown that an accuracy of 0.8251 was achieved. Bidirectional GRU-Based Attention Model for KidSpecific URL Classification
Disentanglement in conceptual space during sensorimotor interaction

The disentanglement of different objective properties from the external world is the foundation of language development for agents. The basic target of this process is to summarise the common natural properties and then to name it to describe those properties in the future. To realise this purpose, a new learning model is introduced for the disentanglement of several sensorimotor concepts (e.g. sizes, colours and shapes of objects) while the causal relationship is being learnt during interaction without much a priori experience and external instructions. This learning model links predictive deep neural models and the variational auto-encoder (VAE) and provides the possibility that the independent concepts can be extracted and disentangled from both perception and action. Moreover, such extraction is further learnt by VAE to memorise their common statistical features. The authors examine this model in the affordance learning setting, where the robot is trying to learn to disentangle about shapes of the tools and objects. The results show that such a process can be found in the neural activities of the β -VAE unit, which indicate that using similar VAE models is a promising way to learn the concepts, and thereby to learn the causal relationship of the sensorimotor interaction.
Deep Learning Approaches Assessment for Underwater Scene Understanding and Egomotion Estimation

This paper address the use of deep learning approaches for visual based navigation in confined underwater environments. State-of-the-art algorithms have shown the tremendous potential deep learning architectures can have for visual navigation implementations, though they are still mostly outperformed by classical feature-based techniques. In this work, we apply current state-of-the-art deep learning methods for visual-based robot navigation to the more challenging underwater environment, providing both an underwater visual dataset acquired in real operational mission scenarios and an assessment of state-of-the-art algorithms on the underwater context. We extend current work by proposing a novel pose optimization architecture for the purpose of correcting visual odometry estimate drift using a Visual-Inertial fusion network, consisted of a neural network architecture anchored on an Inertial supervision learning scheme. Our Visual-Inertial Fusion Network was shown to improve results an average of 50% for trajectory estimates, also producing more visually consistent trajectory estimates for both our underwater application scenarios.
A Comparison Evaluation of Demographic and Contextual Information of Movies using Tensor Factorization Model

Recommendation systems have procured massive attention due to the fast and eruptive expansion of information on the internet. Traditionally, the recommendation systems recommend products based only on the rating criteria but nowadays user expects suggestions in accordance with his requirements and might have varying preferences in different circumstances. Thus, this work presents an innovative framework to consider additional information beyond ratings that is demographic details and under what situations user interact with the system known as contextual information. This additional information is modelled as varying dimensions of the tensor factorization model. The main motive of this study is to determine the more influential dimensions among demographic and contextual dimensions and it is observed that contextual dimensions are more influential than demographic dimensions. The results validate that usage of contextual dimensions mitigates the sparsity and cold-start problems by 16% and 22% respectively in comparison to demographic information.
PickMe : task allocation in mobile crowdsensing

With rapid advancements in mobile and sensing technology, smartphones—equipped with communication, processing, and sensor modules—are now capable of sensing various phenomena of interest. This has led to Mobile CrowdSensing (MCS) paradigm which relies on the collective sensing power of a large number of mobile devices to execute large-scale sensing tasks. Several domains, including transportation and disaster management, benefit from applications based on MCS that process and analyze sensor data collected from a large set of mobile devices. Sensor data collected by multiple devices at similar locations and times show high tempo-spatial correlations. Hence, it is often sufficient to collect data from a representative subset of devices. Task allocation algorithms are used to identify this representative subset of devices for sensor data collection to reduce data redundancy and conserve resources on other devices as mobile devices have limited resources. Efficient task allocation as per varying application requirements, such as optimized resource consumption and semantically rich sensors, is a pressing issue that must be addressed while developing MCS applications. Moreover, inefficient battery consumption during task execution continues to be a major concern among users of these devices that often discourages user participation with MCS applications. In this dissertation, we study the challenges associated with limited resources (more specifically battery) and task allocation algorithms in MCS applications. At an algorithmic-level, we propose load-balanced algorithms that consider the battery depletion rate of mobile devices to distribute sensing workload across devices based on the amount of resources available with them. We show that a balanced allocation of tasks eventually helps achieve longer device participation. We propose a model of “richness” to measure the capabilities of mobile devices to execute sensing tasks. Based on this richness model, we propose two heuristic-based dynamic load-balanced task allocation algorithms—NVGreedy and NVWeighted. We conduct extensive simulations and real-world experiments to show that our algorithms ser-
A Self-Regulatory Viewpoint on Human Behavior

This chapter describes a set of ideas bearing on the structure of action and emotion and how they are regulated. Some of the ideas are associated with the terms feedback control and cybernetics. Those ideas have roots in many sources, including the concept of homeostasis and the creation of mechanical devices to serve as governors for engines. With respect to motivation, the ideas yield a viewpoint in which action is goal directed and reflects a hierarchy of control processes. The creation and reduction of affect are seen as reflecting another set of feedback processes. The portion of the model devoted to affect is of particular interest because it generates two predictions that differ substantially from those deriving from other theories. The first prediction is that both approach and avoidance can give rise to both positive and negative feelings; the second is that positive affect leads to coasting, a reduction in effort regarding the pursued goal. The latter suggests a way in which positive affect is involved in priority management when many goals are in play concurrently. Recent interest in dual process models, which distinguish between top-down guidance of goal pursuit and reflexive responses to cues of the moment, has caused a reexamination of some previous assumptions and consideration of the possibility that behavior emerges in two distinct ways. This line of thought has also recently been applied to conceptualizing diverse types of psychopathology. The chapter closes with brief consideration of how the ideas considered here might be compatible with other viewpoints on motivation.
Reinforcement learning for grid control in an electric distribution system

The increasing amount of variable renewable energy (VRE) sources such as solar and wind power in the power mix brings new challenges to existing power system infrastructure. A fundamental property of an electric power system is that the production of power at all times must be consumed somewhere in the grid. Therefore, excess power from VRE must be exported and consumed elsewhere, which can break the power capacity in distribution lines and damage the voltage quality in an electric grid. A workaround this problem is that consumers shift their power consumption pattern such that more solar power is consumed locally during daytime. Methods for achieving a change in consumption patterns are called demand response programs. The purpose of this thesis is to make a Python implementation of an automatic and simplified demand response program by using reinforcement learning (RL), a subcategory of machine learning. The RL algorithm is allowed to increase or decrease the power consumption every hour in an electric grid that has a high amount of local solar production and high peak demand. Decreasing the power consumption can for instance correspond to a collection of electric vehicles postponing the charging to later in the day. Once the RL algorithm has modified the power consumption, the resulting line currents and voltages in grid are calculated. The goal for the algorithm is to learn a behaviour that reduces the number of current and voltage violations in the grid. The trained RL algorithm is found to reduce the number of safety violations in the grid by 14 % in a test simulation. However, investigating the results reveals that the RL algorithm only avoids safety violations in hours of peak demand, and that it actually produces more violations during hours of peak solar production. The algorithm is better overall because more violations occur during the afternoon. Further investigation is needed to fine-tune the algorithm such that it behaves well in an entire day.
An emergent deep developmental model for auditory learning

ABSTRACT Speech recognition performance of the machine has been greatly improved using artificial intelligence. However, compared with the superior recognition ability of human auditory system, the machine still has some problems to deal with. Based on the existing physiological principle of human auditory system, this paper proposes a novel emergent auditory model. This model simulates each key part of the human auditory pathway with a deep developmental network (DDN). Furthermore, this model simulates the function of the superior colliculus in the thalamus, i.e., context integration, as an additional layer in the DDN. Mel-frequency cepstral coefficients (MFCC) are used to extract the speech signal features to be inputs of the DDN. This work is different from other previous models as we emphasise the mechanism that makes a system to develop its emergent representations from its operational experience, i.e., the internal unsupervised neurons of the DDN are utilised to depict the short contexts, and competitions among them afford an interpretation of how such internal neurons denote the different speech contexts when they are not supervised by the external world. Experimental results show the advantage of the proposed DNN compared to the state-of-the-art methods for the recognition accuracies of English words and phrases.
Evaluación del Enlazado de Entidades para Sistemas Pregunta-Respuesta sobre Grafos de Conocimiento

espanolEl Enlazado de Entidades (EE) consiste en asociar partes de un texto con nodos de una Base de Conocimiento (BC). A pesar de que se ha prestado bastante atencion a la tarea de EE en documentos, apenas hay estudios relativos a su impacto en el campo de la Busqueda de Respuestas (BR). En este trabajo estudiamos la composicion de varias colecciones de BR y realizamos varias observaciones relativas a su adecuacion para evaluar sistemas BR, especialmente en lo relativo a realizar EE. Tambien proponemos un metodo semiautomatico para crear colecciones de EE en el contexto de BR reaprovechando colecciones existentes de BR. Posteriormente, aplicamos nuestro metodo a varias colecciones actuales de BR, analizamos los resultados obtenidos y ponemos a disposicion de la comunidad cientifica la coleccion de EE generada, incluyendo un subconjunto que contiene los ejemplos donde es mas dificil realizar EE. Consideramos que la disponibilidad de esta nueva coleccion permitira una mejor evaluacion de la tarea de EE en el contexto de la BR. EnglishEntity Linking (EL) is the process of anchoring a part of a question to a node (entity) already known in a Knowledge Base (KB). Although EL has been widely studied with large documents such as webpages, there have not been studies about its impact on Question Answering (QA). In this paper, we study benchmarks for QA and how they are composed, providing insights about its suitability for a real evaluation about the state of the art in QA, specillay if we want to take into account the subtask of EL. We propose a semi-automatic method to generate an EL dataset linked to the QA task taking advantage of pre-existing QA datasets. We apply this method to benchmarking QA collections, analyze the results and release the created dataset to the research community, including a subset focused on complex EL in QA. We believe that EL e ectiveness in the context of QA can be better assessed through the use of the proposed dataset. |
Feasibility study of microgrid application for Murdoch University

In this modern era, technology continues to develop and improve, experiencing a signification transformation from the traditional concept and centralized power generation to an incorporated system with distributed energy resources (DER) which is then located closer to the local loads. A sustainable solution with this future concept would be the Microgrid application, and it is a growing system in recent days. This system consists of an integration of the generation from different renewable energy sources that contain energy storage systems. Microgrid also acts as a backup source to failure of the main power supply allowing the network to operate independently. This technology has provided a high reliance on the economy and the growing demand of the technologies such as the energy generated from wind and solar. The growth in several Microgrid utilization has also decreased the reliance on the usage of fossil fuels. The idea of the Microgrid application and implementing it into the network of Murdoch University would provide such significant benefits to the commercial ground. The objective of this thesis is to analyse the opportunity of self-generation of electricity with the possibility of implementing Photovoltaic systems, and a battery energy storage system. The demand for the electricity of the campus would also be analysed with the university’s existing renewable energy source, which is the diesel generator. MU has also experienced several events of power failure. Therefore, another aim of the project is also to have these Microgrid components to act as a back-up source. Risks from the failure of the main power supply will be identified in the introductory part of this thesis before flowing into the concept of Microgrid. The design and the specifications of the components to be integrated into the targeted network will be explained throughout the body of this chapter. The output of the research will provide a summary of potential generation from the chosen components. To have a self-generation of electricity would not only lead MU to be investing in the early stages but would also lead MU to an amount of cost-saving upon the project lifetime.
Predictive and prescriptive methods in operations research and machine learning : an optimization approach

The availability and prevalence of data have provided a substantial opportunity for decision makers to improve decisions and outcomes by effectively using this data. In this thesis, we propose approaches that start from data leading to high-quality decisions and predictions in various application areas. In the first chapter, we consider problems with observational data, and propose variants of machine learning (ML) algorithms that are trained by taking into account decision quality. The traditional approach to such a task has often focused on two-steps, separating the estimation task from the subsequent optimization task which uses these estimated models. Consequently, this approach can miss out on potential improvements in decision quality by considering these tasks jointly. Crucially, this leads to stronger prescriptive performance, particularly for smaller training set sizes, and improves the decision quality by 3 − 5% over other state-of-the-art methods. We introduce the idea of uncertainty penalization to control the optimism of these methods which improves their performance, and propose finite-sample regret bounds. Through experiments on real and synthetic data sets, we demonstrate the value of this approach. In the second chapter, we consider observational data with decision-dependent uncertainty; in particular, we focus on problems with a finite number of possible decisions (treatments). We present our method of prescriptive trees, that prescribes the best treatment option by learning from observational data while simultaneously predicting counterfactuals. We demonstrate the effectiveness of such an approach using real data for the problem of personalized diabetes management. In the third chapter, we consider stochastic optimization problems when the sample average approximation approach is computationally expensive. We introduce a novel measure, called the Prescriptive divergence which takes into account the decision quality of the scenarios, and consider scenario reduction in this context. We demonstrate the power of this optimization-based approach on various examples. 3 In the fourth chapter, we present our work on a problem in predictive analytics where we focus on ML problems from a modern optimization perspective. For sparse shape-constrained regression problems, we propose modern optimization based algorithms that are scalable, and recover the true support with high accuracy and low false positive rates. Thesis Supervisor: Dimitris J. Bertsimas Title: Boeing Leaders for Global Operations Professor Sloan School of Management Co-Director, Operations Research Center
Validation of a vehicle motion planner in an open‐source simulator

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Attribute-Aware Graph Recurrent Networks for Scholarly Friend Recommendation Based on Internet of Scholars in Scholarly Big Data

The academic society is stepping into the age of scholarly big data, where finding suitable scholars for collaboration has become ever difficult. Scholarly recommendation approaches are designed to overcome the information overload problems. However, previous methods mainly consider network topology without considering scholars’ academic information and the manually designed similarity measurements may not have a good performance when applying to large-scale sparse networks. To this end, this article proposes to design a scholarly friend recommendation system by taking advantages of network embedding and scholar attributes. It is worth mentioning that different from traditional scientific collaborator recommendations, our goal is to recommend potential friends for scholars using academic social networks. We first construct an attributed social network by extracting scholars’ academic attributes from digital libraries. Then, we perform an attributed random walk which can jointly model network structure and scholar attributes. Finally, a novel graph recurrent neural framework is adopted to embed attributed scholar interactions within the model for recommendations. Experimental results on two real-world scholarly datasets demonstrate the effectiveness of our proposed method.