Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Resource Sharing Access Through System Integration

ABSTRACT The application and IT ecosystem of academic libraries typically includes multiple systems, with crucial functions requiring using or sharing information between them. However, library systems are often not well integrated, making workflows and system interactions less than optimal for both staff and patrons. The method to integrate systems that the IDS Project took was to create a middleware platform, IDS Logic, that can connect multiple library systems and open or vendor web services to create the best resource sharing experience for staff and patrons. One specific application that is hosted within the IDS Logic middleware platform is Article Gateway, which uses resource-sharing technology and workflows to deliver fast or instant access to research material to users with little or no staff time and removes as many barriers to user access as possible. Where resource sharing has typically sought to deliver articles in one-to-two days, libraries using Article Gateway typically deliver a significantly higher percentage of articles to patrons within a few hours.
An Optimized Genetic Approach for Scheduling Task Duplication in Parallel Systems

Task Scheduling deals with the set of tasks assigned to parallel multiprocessor system and the execution order of the schedule so that the total execution time is minimized. The role of a good scheduling algorithm is to efficiently assign each task to a processor depending on the resources needed, the communication overhead between related tasks is reduced and the precedence relations among tasks are satisfied. It can be efficiently used for tasks that have a large calculation, and have time constraints to complete the schedule. The efficient execution of the task scheduling on parallel system takes the structure of the task and the performance characteristics of the proposed genetic algorithm. It falls in the category of NP-complete problem. This study proposes a parallel genetic algorithm-based approach to schedule tasks on parallel system with task duplication heuristics. Task duplication can minimize inter-processor communication and hence results in shorter finish times. Its performance is measured in comparison with the Round Robin (RR), First Come First Serve (FCFS), and Multi-level queue scheduling (MQS), Shortest Job First (SJF), Largest Job First (LJF) and Priority scheduling methods.
Schedulability using native non-preemptive groups on an AUTOSAR/OSEK platform with caches

Fixed-priority preemption threshold scheduling (FPTS) is a limited preemptive scheduling scheme that generalizes both fixed-priority preemptive scheduling (FPPS) and fixed-priority non-preemptive scheduling (FPNS). By increasing the priority of tasks as they start executing it reduces the set of tasks that can preempt any given task. A subset of FPTS task configurations can be implemented natively on any AUTOSAR/OSEK compatible platform by utilizing the platform's native implementation of non-preemptive task groups via so called internal resources. The limiting factor for this implementation is the number of internal resources that can be associated with any individual task. OSEK and consequently AUTOSAR limit this number to one internal resource per task. In this work, we investigate the impact of this limitation on the schedulability of task sets when cache related preemption delays are taken into account. We also consider the impact of this restriction on the stack size when the tasks are executed on a shared-stack system.
Leveraging Wikipedia-based features for entity relatedness and recommendation

Entities such as people, locations, organizations play a key role in natural language understanding. Most of the approaches that deal with natural language processing tasks, require a method to measure the relatedness between such entities. Is “Tom Cruise” more related to “Brad Pitt” than “Steve Jobs”? A human may easily provide their judgement by using common sense and their knowledge about these entities. However, a computer would require an immense amount of world knowledge to reason about the semantic relatedness between such entities. Moreover, a human has the ability of using their knowledge to understand an entity. On the other hand, a computer requires an algorithmic approach to process the background knowledge to do the same. Wikipedia is a great source to obtain background knowledge about millions of such entities. In this thesis, we introduce Wikipedia-based Distributional Semantics for Entity Relatedness (DiSER), which analyzes the semantics of an entity by its distribution in a high dimensional concept space derived from Wikipedia. DiSER measures the semantic relatedness between two entities by quantifying the distance between the corresponding high-dimensional vectors. The DiSER model is built by considering only the manually linked entities provided in a corpus such as Wikipedia. Thus, it provides an unambiguous and more accurate distributional vector for an entity comparing to existing approaches which do not distinguish between an entity and its textual surface form. We evaluate the approach on a benchmark dataset that contains relative entity relatedness scores for 420 entity pairs. DiSER improves the accuracy by more than 10% on state of the art methods for computing entity relatedness. In order to provide a resource that can be used to obtain the related entities for a given entity, we construct a graph called Entity Relatedness Graph (EnRG), where nodes represent Wikipedia entities and the relatedness scores are represented by the edges. Wikipedia contains more than 4 million entities, which requires efficient computation of the relatedness scores between the corresponding 16 trillions of entity-pairs in a fully connected graph. We present the processing behind EnRG to efficiently compute the relatedness scores between Wikipedia entities. EnRG can be seen as an entity recom-
Governance infrastructure and FDI flows in developing countries

Abstract With the existence and management of certain national and global-level institutions being called into question by their electorates and members, the aim of this paper based on a sample of 122 countries over the period 2002–2017 was to investigate the continuing applicability and relevance of current and existing macro governance infrastructure on patterns of foreign direct investment (FDI) in developing countries. Employing the World Bank’s ‘good governance index’ our findings demonstrate that certain aspects of country – level governance infrastructure continue to be a significant predictor of host country FDI, with particular emphasis placed on the ability of governments to effectively formulate and implement policies alongside an effective regulatory environment promoting private sector development. Thus, countries with weak institutional capacity to design and implement an effective investment regime, and secondly absorb FDI will struggle to attract and benefit from the positive externalities associated with FDI.
How growth opportunities are related to corporate leverage decisions

Growth opportunity has been considered as a significant determinant of capital structure. The literature generally favors the negative relationship between the growth opportunities and leverage of firms. However, another school of thought finding such a relationship to be positive also exists. The purpose of this study is to find out how growth opportunities in Pakistan are related to leverage decisions for the listed manufacturing corporate concerns. We use financial data from a sample of 110 manufacturing companies listed on Karachi Stock Exchange for 15 years (1982-1997) from 9 different sectors along with estimation of fixed-effects regression analysis to assess the subject relationship. We find a positive relationship between the growth opportunities and debt levels of the corporate firms. This positive relationship is highly significant for the segments of firms with ‘low’ and ‘medium’ growth opportunities. The reason for this finding may be that the owners of these firms view the available growth opportunities as unsustainable and more risky and intend to pass on that higher risk to the creditors. The socio-economic and political networks of such owners may help provide them easy access to credit market resulting in high debt level. Consequently, they might delay issuance of new common stocks to be issued at the future higher prices if the risky investment succeeds. We also observe a general tendency of the credit market, having limited options for profitable credit, to finance companies with little better future prospects. Moreover, unsustainable growth opportunities in economy, less developed capital markets, a high number of firms with low growth in Pakistan (in real terms) and their limited goodwill among the investors and general public (restricting them to issue shares of common stock) may also be the underlying reasons behind the corporate behavior causing such an overall positive relationship. Another important finding of this study is that industry type is also a relevant variable which affects the relationship between growth opportunities and leverage.
Real-time vehicle detection and tracking in video based on faster R-CNN

Vehicle detection and tracking is a significant part in auxiliary vehicle driving system. Using the traditional detection method based on image information has encountered enormous difficulties, especially in complex background. To solve this problem, a detection method based on deep learning, Faster R-CNN, which has very high detection accuracy and flexibility, is introduced. An algorithm of target tracking with the combination of Camshift and Kalman filter is proposed for vehicle tracking. The computation time of Faster R-CNN cannot achieve realtime detection. We use multi-thread technique to detect and track vehicle by parallel computation for real-time application.
Energy-Aware Routing in Mobile Wireless Sensor Networks

 Abstract — Wireless sensor networks are resource constrained networks, where energy is the major resource in such networks. Therefore, energy conservation is major aspect in the deployment of Wireless Sensor Network. This work makes use of an extended Greedy Perimeter Stateless Routing (eGPSR) protocol that mainly focuses on energy efficient data transmission. This data transmission is based on the fact that the message that is sent to a distant node consumes more energy than the message that is sent to a short range transmission. Every cluster contains a head set that consists of many virtual cluster heads. Routing is decided by head set members. The energy level of the received signal is the major constraint to choose head set from its members. The experimental result shows that the use of eGPSR in routing has improved throughput with comparatively less delay.
Evaluating the disparity between active areas of biomedical research and the global burden of disease employing Linked Data and data-driven discovery

Although biomedical research has brought substantial benefit to people all over the world, by dramatically improving their life expectancy and the quality of life, the distribution of this benefit is not equitable. An important contributor to this is the current absence of accurate, interlinked data and information that enables a precise description of the degree of inequality between current efforts in biomedical research and global health care needs. In this position paper we present an approach for evaluating this disparity, which involves converting and inter-linking relevant datasets into Linked Data, and analyzing them to represent the disparity as a visual map. We identify different data sets, relevant for answering the research question. Since bio-medical statistical data is of paramount importance in this data integration project, we describe a tool and methodology of representing such bio-medical statistical data as RDF. We perform an preliminary integration of the datasets and outline how prospective queries can be formulated. We conclude by discussing the limitations of the approach taking the current bio-medical data curation landscape into account.
Optimizing Robinson Operator with Ant Colony Optimization As a Digital Image Edge Detection Method

Edge detection serves to identify the boundaries of an object against a background of mutual overlap. One of the classic method for edge detection is operator Robinson. Operator Robinson produces a thin, not assertive and grey line edge. To overcome these deficiencies, the proposed improvements to edge detection method with the approach graph with Ant Colony Optimization algorithm. The repairs may be performed are thicken the edge and connect the edges cut off. Edge detection research aims to do optimization of operator Robinson with Ant Colony Optimization then compare the output and generated the inferred extent of Ant Colony Optimization can improve result of edge detection that has not been optimized and improve the accuracy of the results of Robinson edge detection. The parameters used in performance measurement of edge detection are morphology of the resulting edge line, MSE and PSNR. The result showed that Robinson and Ant Colony Optimization method produces images with a more assertive and thick edge. Ant Colony Optimization method is able to be used as a method for optimizing operator Robinson by improving the image result of Robinson detection average 16.77 % than classic Robinson result.
Instruction-Level NBTI Stress Estimation and Its Application in Runtime Aging Prediction for Embedded Processors

Lifetime reliability management of miniaturized CMOS devices continuously gets more importance with the shrinking of technology size. Neither of existing design-time solutions (like guard-banding) and runtime methods (like reactive monitoring) does efficiently address this issue; rather, proactive approaches, which use runtime aging prediction, are getting more promising to provide resiliency. Among various reliability threatening mechanisms in recent technologies, negative bias temperature instability is the dominant factor; it depends on multiple time-varying operational parameters, including temperature, supply voltage, and stress. This paper proposes an efficient instruction-level stress estimation model; accordingly, it introduces a runtime aging prediction approach for embedded processors, taking simultaneous impacts of the temperature, supply voltage, and stress variations. We propose instruction degradation factor and architecture degradation factor metrics, respectively, for fine-grained stress estimation and recurring runtime aging prediction. We also provide a simulation environment for model validation. Simulation results of several benchmarks show that the proposed stress estimation model has an accuracy of about 92%, indicating that the method is accurate enough, yet simple for runtime usage.
POCO-MOEA: Using Evolutionary Algorithms to Solve the Controller Placement Problem

One of the central tenets of a Software De ned Network (SDN) is the use of controllers, which are responsible for managing how tra c ows through switches, routers, and other data-passing devices on a computer network. Most modern SDNs use multiple controllers to divide responsibility for network switches while keeping communication latency low. A problem that has emerged since approximately 2011 is the decision of where to place these controllers to create the most 'optimum' network. This is known as the Controller Placement Problem (CPP). Such a decision is subject to multiple and sometimes con icting goals, making the CPP a type of Multi-Objective Problem (MOP). The Controller Placement Problem is NP-Hard. This means nding the 'optimum' solution can become a time-intensive process as network size increases. Multiple algorithms exist to solve MOPs using shortcut (or 'heuristic') methods which can produce a 'near-optimal' solution in times much shorter than those necessary to guarantee an 'optimal' solution. One popular class of algorithms is known as Evolutionary Algorithms (EAs); EAs designed to solve Multi-Objective problems are called Multi-Objective Evolutionary Algorithms (MOEAs). While many MOEAs exist, their application to the Controller Placement Problem is not well explored. The theory of this thesis is that an MOEA can produce solutions to the Controller Placement Problem which are 'nearly optimal' while keeping execution time low compared to an exhaustive 'optimal' search. This research extends a network modeling tool called the Pareto Optimal Controller Placement (POCO) Framework with custom designed MOEA, called POCO-MOEA. A series of full-factorial experiments is designed and executed to gather data on POCO-MOEA performance to a series of
A Regression based Sensor Data Prediction Technique to Analyze Data Trustworthiness in Cyber-Physical System

A Cyber-Physical System strongly depends on the sensor data to understand the current condition of the environment and act on that. Due to network faults, insufficient power supply, and rough environment, sensor data become noisy and the system may perform unwanted operations causing severe damage. In this paper, a technique has been proposed to analyze the trustworthiness of a sensor reading before performing operation based on the record. The technique employs regression analysis to select nearby sensors and develops a linear model for a target sensor. Using the linear model, target sensor reading is predicted in a particular time stamp with respect to each nearby sensor’s reading. If the difference between the predicted and actual value is within a given limit, the reading is considered as trustworthy for the corresponding nearby sensor. At last, majority consensus is taken to consider the reading as trustworthy. To evaluate the proposed technique, a data set containing temperature reading of 8 sensors for 24 hours was used where first 90% data was used for nearby sensor selection and linear model construction, and rest 10% for testing. The result analysis shows that the proposed technique detects 19, 69, and 73 trustworthy data from 73 records with respect to 3%, 4% and 5% deviation from actual reading.
Enhancing maritime defence and security through persistently autonomous operations and situation awareness systems

This thesis is concerned with autonomous operations with Autonomous Underwater Vehicles (AUVs) and maritime situation awareness in the context of enhancing maritime defence and security. The problem of autonomous operations with AUVs is one of persistence. That is, AUVs get stuck due to a lack of cognitive ability to deal with a situation and require intervention from a human operator. This thesis focuses on addressing vehicle subsystem failures and changes in high level mission priorities in a manner that preserves autonomy during Mine Countermeasures (MCM) operations in unknown environments. This is not a trivial task. The approach followed utilizes ontologies for representing knowledge about the operational environment, the vehicle as well as mission planning and execution. Reasoning about the vehicle capabilities and consequently the actions it can execute is continuous and occurs in real time. Vehicle component faults are incorporated into the reasoning process as a means of driving adaptive planning and execution. Adaptive planning is based on a Planning Domain Definition Language (PDDL) planner. Adaptive execution is prioritized over adaptive planning as mission planning can be very demanding in terms of computational resources. Changes in high level mission priorities are also addressed as part of the adaptive planning behaviour of the system. The main contribution of this thesis regarding persistently autonomous operations is an ontological framework that drives an adaptive behaviour for increasing persistent autonomy of AUVs in unexpected situations. That is, when vehicle component faults threaten to put the mission at risk and changes in high level mission priorities should be incorporated as part of decision making. Building maritime situation awareness for maritime security is a very difficult task. High volumes of information gathered from various sources as well as their efficient fusion taking into consideration any contradictions and the requirement for reliable decision making and (re)action under potentially multiple interpretations of a situation are the most prominent challenges. To address those challenges and help alleviate the burden from humans which usually undertake such tasks, this thesis is concerned with maritime situation awareness built with Markov Logic Networks (MLNs) that support humans in their decision making. However, commonly maritime situation awareness systems rely on human experts to transfer their knowledge into the system before it can be deployed. In that respect, a promising alternative for training MLNs with data is presented. In addition, an in depth evaluation of their performance is provided during which the significance of interpreting an unfolding situation in context is demonstrated. To the best of the author’s knowledge, it is the first time that MLNs are trained with data and evaluated using cross validation in the context of building maritime situation awareness for maritime security.
Visualization and Interaction with Medical Data in Immersive Environments

Immersive visualization techniques are just starting to see limited adoption in medical applications. e Visualization Group at the University of Bergen wish to expand its research efforts into such immersive visualization techniques. A new immersive environment installed at the University is meant to be utilized for this purpose. is thesis presents a solution which enables the use of VolumeShop in the immersive environments for presentation of volumetric data and for combination of 2D and 3D medical imaging modalities. VolumeShop is a rapid prototyping environment for visualization techniques, which is oen used by our visualization group. For general use of the immersive environment, motion tracking support needs to be added to VolumeShop. is motion tracking support can then be utilized to realize the immersive visualization pipeline. is pipeline computes correct perspective projection based on the user’s position. Additionally it implements intuitive gesture based interaction with the data using a handheld interaction device. A multimodal visualization pipeline is described and implemented, which enables the visualization of 2D+time ultrasound images combined with MRI volumes. To realize this pipeline, components for motion tracking of the ultrasound probe, for synchronization of the time offset between the motion tracking and the ultrasound series, picking landmarks in the ultrasound slice and MRI cross-section, and computation of the registration transformation which spatially unifies the two of the modalities. e result of this thesis is a general purpose motion tracking component as well as the two described pipelines.
Multiple feature fusion via covariance matrix for visual tracking

Aiming at the problem of complicated dynamic scenes in visual target tracking, a multi-feature fusion tracking algorithm based on covariance matrix is proposed to improve the robustness of the tracking algorithm. In the frame-work of quantum genetic algorithm, this paper uses the region covariance descriptor to fuse the color, edge and texture features. It also uses a fast covariance intersection algorithm to update the model. The low dimension of region covariance descriptor, the fast convergence speed and strong global optimization ability of quantum genetic algorithm, and the fast computation of fast covariance intersection algorithm are used to improve the computational efficiency of fusion, matching, and updating process, so that the algorithm achieves a fast and effective multi-feature fusion tracking. The experiments prove that the proposed algorithm can not only achieve fast and robust tracking but also effectively handle interference of occlusion, rotation, deformation, motion blur and so on.