Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic image caption generation for digital cultural emages collections

Recent years have witnessed considerable growth of the volume of digital collections, which has lead to an increasing demand for automated techniques that support the management, navigation and search of these collections. As machine learning techniques are advancing, it becomes feasible to automatically generate image captions. However, one of the main challenges that needs to be addressed is to create captions that include higher-level information, such as the event or location shown in an image. Recently, the automatic image caption generation problem has been formulated as a translation problem. However, state-of-the-art on image captioning models’ captions are limited to low-level description of the image itself. In this work, we assume that images and text naturally co-occur and explore the feasibility of including title information in a pretrained state-of-the-art image captioning model using OCLC’s CONTENTdm data. By using a combined objective function based on both title and image, we give a proof-of-concept for compressing image and title features with an autoencoder, so that can be used as input for a pretrained image caption generation model. Although the results are mixed, this thesis provides initial insights into the automatic generation of higher-level image captions.
Deep Learning for Well Data History Analysis

The rapid development of machine learning algorithms and the massive accumulation of well data from continuous monitoring has enabled new applications in the oil and gas industries. Data gathered from well sensors are a foundation of the oilfield digitization and data-driven analysis. Here, we describe a deep learning approach to predict the long-term well performance based on a moderate duration of well monitoring data. In this study, we first developed the data processing procedures for oilfield time series data and determined the proper selection of data sampling frequency, parameter combinations and data structures for deep learning models. Then we explored how Deep Learning (DL) models can be employed for well data analysis and how can we combine physics and DL models. Recurrent Neural Network (RNN) is a type of sequential DL model, which can be utilized for time series data analysis. This approach preserves preceding information and yields current response with memory of prior well behavior. Two candidate RNN models were tried to determine how well they were able to improve the accuracy and stability of well performance estimates. These two methods are Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM). In addition, a novel combination of RNN with Convolutional Neural Networks (CNNs), Long- and Short-term Time-series network (LSTNet), was also investigated. These various models were tested and compared based on the public production datasets from Volve Field. Both GRU and LSTM achieved higher accuracy in performance prediction compared to the simple RNN. In the case of frequent well shut-in and opening, the failure in capturing fast pressure responses and the extreme fluctuations with the simple RNN ultimately leads to high error. In contrast, LSTNet is more stable to frequent or significant well variations. With advanced deep learning structures, engineers can interpret long-term reservoir performance information from responses estimated by deep learning models, instead of performing costly well tests or shut-ins.
INTEGRATING DISRUPTIVE INNOVATION, COMPETITIVE ADVANTAGE, AND BUSINESS NETWORKING CAPABILITY ON SMALL BUSINESS PERFORMANCE IN INDONESIA

This study aimed at explaining the influence of disruptive innovation on business performance with competitive advantage as a mediator and networking capability as moderate. The tool of analysis used was Structural Equation Modelling (SEM). The sample size of this research was 120 owners and/or managers of Small and Medium Enterprises (SMEs) in Purwokerto areas. The result showed that disruptive innovation has a positive effect on both business performance and competitive advantage. The study supported the view that competitive advantage has a positive impact on business performance. Also, competitive advantage act as mediating variable on the relationship between disruptive innovation and business performance. Finally, the last hypothesis stated that networking as moderation variable of disruptive innovation to business performance was supported.
Collaborative Project Planning Using Large Format Displays

Large format displays’ popularity is constantly increasing in universities and workplaces due to their prices showing a constant decrease over time. Many studies have demonstrated their benefits on several kind of tasks, from navigation of 3D environments to complicated tasks that require high cognitive load, and these studies have demonstrated how they can increase productivity and the quality of work in multi-user collaborative environments. I am studying the general problem of using large format displays in a multi-user collabora- tive environments with the specific problem of university study course planning. The traditional approach is represented by a regular whiteboard that will be compared with a project plan- ning/scheduling software developed for large format displays. The purpose of this study is not only to confirm previous studies’ results that claim that the usage of large format displays in a multi-user collaborative environment can increase produc- tivity and work quality, but also to evaluate and understand how their usage influences users’ cooperation and collaboration while performing a complex collaborative task. We will study how the subjects actually interact, and how different is their interaction, both with tools and other subjects, compared to the traditional approach. I developed an application for project planning that runs on SAGE2, an open-source mid- dleware offering the possibility to exploit a large format display of any resolution and size. It also allows multiple users to control it at the same time, connecting to it with a regular web browser interface and a laptop, working in a shared environment. I will show four simple techniques to implement a general project planning application for large displays and I will show four different possible realizations using the same techniques, with a detailed overview on the one created for the user study. An innovative approach to collect data from the user study will be shown and explained in detail. Such approach improves and automatize the qualitative data analysis that must be conducted in similar studies. It includes space tracking of participants and automatic coding. Results show that the developed application is not missing any critical feature and it could be a good substitute of the traditional approach. Data suggests that collaboration behaviors could be influenced by such technology, and some slight differences on collaboration were found, but not enough to constitute statistical evidence. In a similar way, no statistical evidence was found about improvements in performance over classic whiteboard technology.
Design Methodologies using Multi-bit Flip-flops for Low Power

Merging 1-bit flip-flops into multi-bit flip-flops in the post-placement stage is one of the most effective techniques for minimizing clock power. In this dissertation, a new style of multi-bit flip-flop, called loosely coupled multi-bit flip-flop (LC-MBFF) is introduced. The merit of LC-MBFF is that the logically constituent 1-bit flip-flops in LC-MBFF can be physically apart, providing no need to set aside white space (i.e., no relocation) and relaxed timing check requirement. Utilizing LC-MBFFs, an multi-bit flip-flop allocation algorithm considering the routability and the clock tree, which fully explores the diverse allocation of LC-MBFF structures is also proposed to maximally reduce clock power consumption. Additionally, the proposed algorithm is extended to efficiently support the multibit flip-flop allocation for circuits with clock polarity assignment. Clock polarity assignment is a technique to mitigate power/ground noise by assigning different clock polarities to clock buffers. Experimental results with ISCAS89 and IWLS2005 benchmark circuits show that the proposed allocation algorithm using the newly designed multi-bit flip-flops is able to reduce the clock power consumed in the flip-flops by 8.51% on average while the best known multi-bit flip-flop allocation algorithm [7] reduces by 3.55%. Also it is shown that the extended noise-aware algorithm is effective in power/ground noise mitigation. In summary, this dissertation presents a new style of multi-bit flip-flop and allocation algorithms, to save clock power consumption and to mitigate power/ground noise. keywords: Flip-flop, Multi-bit, Power, Clock polarity assignment, Power/ground noise student number: 2014-30310
Secure Data Query Framework for Cloud and Fog Computing

Fog computing is mainly used to process a large amount of data produced by terminal devices. As fog nodes are the closest acquirers to the terminal devices, the processed data may be tampered with or illegally captured by some malicious nodes while the data is transferred or aggregated. When some applications need to require real-time process with high security, cloud service may sample some data from fog service to check final results. In this paper, we propose a secure data query framework for cloud and fog computing. We use cloud service to check queried data from fog network when fog network provides queried data to users. In the framework, cloud server pre-designates some data aggregation topology trees to fog network, and then fog network may acquire related data from fog nodes according to one of the pre-designated data aggregation trees. Additionally, some fog nodes are assigned as sampled nodes that can feed back related data to cloud server. Based on the security requirements of fog computing, we analyze the security of our proposed framework. Our framework not only guarantees the reliability of required data but also effectively protects data against man-in-the-middle attack, single node attack and collusion attack of malicious users. Also, the experiments show our framework is effective and efficient.
Construction and evaluation of an online environment to reduce off-topic messaging

ABSTRACT Online discussions have become more common as social network services have become more ubiquitous and complement various learning activities. However, studies investigating online discussions in recent years have shown that off-topic messaging has increased with the use of social network services. Thus, determining the design of a mechanism to reduce the frequency of off-topic messaging is an issue deserving attention. This study develops a Facebook-based system and employs two strategies (a filter reminder strategy and a self-reflection strategy) aiming to reduce off-topic messaging in comparative and empirical studies. The research questions are as follows: (a) Which strategy is more effective in reducing off-topic messaging? (b) What are the influences of the strategies on the patterns of students’ cognitive processes? and (c) Does this influence occur during discussions? The results indicate that the filter reminder strategy can not only reduce off-topic messaging but also elicit more diversified cognitive behaviors. Finally, based on the findings, this study provides suggestions for future research and advice regarding instruction.
An Integrated Turning Movements Estimation to Petri Net Based Road Traffic Modeling

The tremendous increase in the urban population highlights the need for more efficient transport systems and techniques to alleviate the increasing number of the resulting traffic-associated problems. Modeling and predicting road traffic flow are a critical part of intelligent transport systems (ITSs). Therefore, their accuracy and efficiency have a direct impact on the overall functioning. In this scope, a new approach for predicting the road traffic flow is proposed that combines the Petri nets model with a dynamic estimation of intersection turning movement counts to ensure a more accurate assessment of its performance. Thus, this manuscript extends our work by introducing a new feature, namely turning movement counts, to attain a better prediction of road traffic flow. A simulation study is conducted to get a better understanding of how predictive models perform in the context of estimating turning movements.
StackMold: Rapid Prototyping of Functional Multi-Material Objects with Selective Levels of Surface Details

We present StackMold, a DIY molding technique to prototype multi-material and multi-colored objects with embedded electronics. The key concept of our approach is a novel multi-stage mold buildup in which casting operations are interleaved with the assembly of the mold to form independent compartments for casting different materials. To build multi-stage molds, we contribute novel algorithms that computationally design and optimize the mold and casting procedure. By default, the multi-stage mold is fabricated in slices using a laser cutter. For regions that require more surface detail, a high-fidelity 3D-printed mold subsection can be incorporated. StackMold is an integrated end-to-end system, supporting all stages of the process: it provides a UI to specify material and detail regions of a 3D~object; it generates fabrication files for the molds; and it produces a step-by-step casting instruction manual.
Evaluating the Acceptability of Assistive Robots for Early Detection of Mild Cognitive Impairment

The employment of Social Assistive Robots (SARs) for monitoring elderly users represents a valuable gateway for at-home assistance. Their deployment in the house of the users can provide effective opportunities for early detection of Mild Cognitive Impairment (MCI), a condition of increasing impact in our aging society, by means of digitalized cognitive tests. In this work, we present a system where a specific set of cognitive tests is selected, digitalized, and integrated with a robotic assistant, whose task is the guidance and supervision of the users during the completion of such tests. The system is then evaluated by means of an experimental study involving potential future users, in order to assess its acceptability and identify key directions for technical improvements.
Different Shades of Greenwashing: Consumers’ Reactions to Environmental Lies, Half-Lies, and Organizations Taking Credit for Following Legal Obligations

Although corporate greenwashing is a widespread phenomenon, few studies have investigated its effects on consumers. In these studies, consumers were exposed to organizations that boldly lied about their green behaviors. Most greenwashing practices in real life, however, do not involve complete lies. This article describes a randomized 3 × 2 experimental study in the cruise industry investigating the effects of various degrees of greenwashing. Six experimental conditions were created based on behavioral-claim greenwashing (an organization telling the truth vs. its telling lies or half-lies) and motive greenwashing (an organization acting on its own initiative vs. its taking credit for following legal obligations). Dependent variables were three corporate reputation constructs: environmental performance, product and service quality, and financial performance. Compared to true green behavior, lies and half-lies had similar negative effects on reputation. Taking credit for following legal obligations had no main effect. Only in the case of true green behavior did undeservedly taking credit affect reputation negatively. Overall, the findings suggest that only true green behavior will have the desired positive effects on reputation.
Deep learning for image captioning: an encoder-decoder architecture with soft attention

Automatic image captioning, the task of automatically producing a natural-language description for an image, has the potential to assist those with visual impairments by explaining images using text-to-speech systems. However, accurate image captioning is a challenging task that requires integrating and pushing further the latest improvements at the intersection of computer vision and natural language processing fields This work aims at building an advanced model based on neural networks and deep learning for the automated generation of image captions.
Enterprise Architecture Best Practices in Large Corporations

Enterprise architecture (EA) is an integrated strategy, business, and information systems approach for analysis, governance, and information technology (IT) alignment. It is a comprehensive blueprint that requires the careful planning, documentation, and analysis of all the operations of an organization. Employing EA helps companies achieve strategic goals with the support of business activities and information systems. However, some large corporations avoid EA frameworks and methodologies owing to their implementation difficulties or the presence of conflicting frameworks and business needs. The goal of this paper is to increase large organizations’ awareness of enterprise architecture best practices (EABPs) and methods of EA framework implementation. Thus, this research has developed an EABP capability matrix to measure companies’ capacities to implement EABPs and provided lessons based on how 17 organizations implemented EABPs. Based on an analytical literature review, the developed matrix includes eight critical EABPs categorized under four themes: EA framework and methodology, strategic practices, business activities, and information systems. As practical and theoretical contributions: (1) This inclusive approach was not found in the EA literature as most past research focuses on only one of these themes. (2) The EA matrix can be used as a measurement matrix research methodology to measure the extent to which cases adopt EABPs, making it beneficial to EA researchers and practitioners. (3) EA practitioners can also use it to practically determine and rectify the weak points of EABPs, thus taking advantage of EA frameworks. The findings indicate that many large organizations implement EABPs as business-as-usual practices without EA frameworks and methodologies. However, those that adopt an EA framework use the open group architecture framework and rely heavily on enterprise resource planning in the implementation of EABPs.
Designing for learning with mobile and social media tools - A pragmatic approach

Over the last decade, mobile and social media (MSM) tools have been in a constant flux. A growing ecology of tools and affordances have enabled multiple types of user actions and abilities never witnessed or imagined before. Educators all around the world are actively exploring and investigating learning and teaching design and approaches to harness some of these opportunities for improved student learning outcomes. This paper discusses the findings from a PhD study that used a design-based research approach to investigate how MSM tools could be used to facilitate learner-driven and determined learning (heutagogy). A set of draft design principles was formulated to guide the development of a course-implemented and evaluated over two years. A summary of the findings from the study is discussed and a set of refined design principles is provided-capable of guiding educators in designing significant learning experiences using MSM tools.
The effect of changing training data on a fixed deep learning detection model

Within the lack of accurate data, for some computer vision applications, researchers usually use other pictures collected from different sources for the training. To know the effect of these added data, we compare the detection results of a customized dataset of objects, using the same detection model, while changing the training data fed into the network. For our work, we run the detection on images captured by the Microsoft Kinect sensor after training the network on different combinations of training data. The first part of the training data is captured by the Kinect itself, and the second is collected from several sources from the internet, referred to as collected images. We then change the distribution of these images between training and validation to feed them into the fixed training model. The results prove that this distribution of data can considerably affect training and detection results under the same model parameters. In addition, mixing the captured images with other collected ones can improve these results.
A survey of networked and Wi-Fi enabled practices to support disabled learners in museums

This article reports on a survey of network and Wi-Fi enabled technologies to support disabled learners in museums and similar cultural heritage environments. In this study, literature is classified using the Epistemological Model of Disability, classifying and defining methodologies and ontologies as it does so. Data was collected using a systematic literature review using six academic databases, which included peer-reviewed articles from a range of sources. The analysis addressed two research questions, and was developed in three phases: the first phase examined trends, definitions and methods of teaching and learning in cultural heritage; the second categorized genres that emerged in the first phase and discussed meta-levels; the third developed a critical discussion on teaching and learning models. The study had two principal findings: 1) museums have done much to make themselves part of the inclusion agenda, 2) more needs to be done to improve management strategies, increase agency and reduce practice using deficit models.
Impact of design patterns on software quality: a systematic literature review

The impact of design patterns on quality attributes has been extensively evaluated in studies with different perspectives, objectives, metrics, and quality attributes, leading to contradictive and hard to compare results. The authors' objective is to explain these results by considering confounding factors, practices, metrics, or implementation issues that affect quality. Furthermore, there is a lack of research that connects design patterns evaluations to patterns development studies. Accordingly, they also aim at providing an initiate on how patterns structure and implementation can be improved, to promote software quality. To achieve their goals, conducted a systematic literature review by searching the literature for related studies. The study covers the period between years 2000 and 2018. They identified 804 candidate papers. After applying inclusion and exclusion criteria, they were left with 50 primary studies. Their results show that documentation of patterns, size of pattern classes, and the scattering degree of patterns have clear impact on quality. In case studies, researchers used different metrics applied to different modules. Controlled experiments have major design differences. Reaching consensuses on the effect of patterns requires considering influencing factors, using unified metrics, and an agreement on what modules to measure. Studying how to improve patterns modularity is recommended for future research.