Miracle-dz/UniFAR-FTUs huggingface dataset:
Low Data Dialogue Act Classification for Virtual Agents during Debugging

by Andrew Edward Wood A “dialogue act” is a written or spoken action during a conversation. Dialogue acts are usually only a few words long, and are divided by researchers into a relatively small set (often less than 10) of dialogue act types, such as eliciting information, expressing an opinion, or making a greeting. Research interest into automatic classification of dialogue acts has grown recently due to the proliferation of Virtual Agents (VA) e.g. Siri, Cortana, Alexa. But unfortunately, the gains made into VA development in one domain are generally not applicable to other domains, since the composition of dialogue acts differs in different conversations. In this thesis, I target the problem of dialogue act classification for a VA assistant to software engineering repairing bugs in a low data setting. A problem in the SE domain is that very little sample data exists. Therefore, I present a transfer-learning approach to learn on a much larger dataset for general business conversations, and apply the knowledge to a manually created corpus of debugging conversations collected from 30 professional developers in a “Wizard of Oz” experiment and manually annotated with a predetermined dialogue act set. In experiments, we observe between 8% and 20% improvements over two key baselines. Additionally, I present a separate dialogue act classifier on the manually collected dataset that uses a manually discovered SE specific dialogue act set which achieves on average 69% precision and 50% recall over 5-fold cross validation.
Volunteer motivations in the Finnish fire service

Purpose This paper presents a large sample study of fire service volunteer motivations in Finland. The purpose of this paper is twofold: first, to explore the differences between initial motivations to begin volunteering vs motivations to continue volunteering; and second, to examine the differences in motivations as a function of demographic variables and geographic region of the fire brigade (rural vs urban). Design/methodology/approach An online survey was completed by 747 fire service volunteers throughout Finland. Findings The initial motivations for joining the brigade differed from those that motivated volunteers to continue in their roles. Significant differences between male and female volunteers’ initial motivations, as well as differences in motivations to continue volunteering as a function of age and region, were found. Research limitations/implications The findings support integrating a psychological, functional perspective with contextual and situational factors when considering volunteer motivations. Originality/value This study suggests a comprehensive understanding of fire service volunteer motivations and informs improved recruitment and retention practices.
Risk Factors for Peritonitis in a Pediatric Dialysis Program in Guatemala, 2011–2014

Background. Peritonitis is the most common complication of peritoneal dialysis but there is limited data regarding peritonitis related risk factors in developing countries. Objective. Describe the PD program at Foundation for Children with Kidney Disease (FUNDANIER), in Guatemala, and identify peritonitis related risk factors in these patients. Methods. This retrospective open cohort study included medical records from FUNDANIER during 2011 to 2014. Baseline demographics, treatment modalities, caregivers’ characteristics, and socioeconomic status were recorded. Results. Eighty-nine medical records were included with a treatment time of 1855 months. Median age of patients was 11.3 years (range 6–17). Median duration of PD therapy was 20.8 months (range 1–28). Sixty-eight peritonitis episodes were registered; forty-eight patients (54%) remained peritonitis-free. Median time to first peritonitis episode was 5 months (range 2–16). Peritonitis rate was one episode every 27 months or 0.44 episodes per patient-year. Peritonitis rate in patients with fair housing was 2.5 times higher than in those with good housing (CI = 1.0–5.2, ). Conclusion. Housing conditions are a relevant risk factor related to peritonitis. Strategies toward preventing peritonitis must consider housing status, establishing adequate follow-up in high-risk patients. Close monitoring of technique serves to overcome understaffing issues in this setting.
Effect of Different Concentrate Roughage Ratio on Growth Performance, Carcass Characterstics and Economics of Production of Beetal Kids under Stall feeding

One twenty days feeding trial was conducted on 12 Beetal male kids of 5–6 months age and 12.44±0.38 kg average body weight. Kids were divided in to three groups of 4 each in randomized block design and subjected to 3 feeding treatments having different roughage to concentrate ratio (T1 50:50, T2 60:40 and T3 40:60) as per NRC,1981. The roughage consisted of maize, bajra or mixture of maize and bajra along with groundnut straw, whereas concentrates were pellets and ground maize grain. The fortnightly body weight changes and daily feed intake were recorded. At the end of experiment two kids/treatment were randomly selected and slaughtered for studying the carcass characteristics, carcass yield and dressing percentage in T3 were significantly higher than in T1. However, net return per kid for T1 was highest followed by T3 and T2. Hence, it was concluded that Beetal kids under stall fed conditions on 50:50 roughage concentrate ratio showed optimal and economic growth performance.
Tobacco consumption and its association with various sociodemographic factors among females (15-49 years) residing in an urban slum of Pune, Maharashtra

Context: Tobacco consumption among females is on rise and is increasing at an alarming rate. This epidemic among the females is emerging as an important public health problem especially in slums. Aim: Study was conducted to find out association between sociodemographic factors and tobacco consumption. Settings and Design: Community-based cross-sectional study was carried out among females (15-49 years) in an urban slum of Pune during February 12 to April 13. Materials and Methods: Sampling frame was prepared from records of urban health center. Required sample size was selected by simple random method. Data collected using pretested validated structured questionnaire after obtaining informed consent. Statistical Analysis: Statistical analysis was performed using SPSS 20, and Chi-square test was used for determining association. Results: Of 305 females studied, 31.5% of them were currently consuming tobacco. Most common form of tobacco consumption was smokeless that is, Mishri application 55.2%. About 64% females started using tobacco before 20 years of age. Most of them (32%) consuming tobacco for increasing concentration at work. Tobacco consumption was found to be significantly more common among ever-married and Muslim females. Lack of will power and ignorance about the harmful effects of tobacco were major hindrances (51%) for quitting tobacco Conclusion: Tobacco consumption was found to be prevalent in almost one third of females (15-49 years) residing in an urban slum of Pune. Most of them used smokeless tobacco, mainly Mishri. Tobacco consumption was directly associated with age, marital status and religion.
Optimal Design of a DC–DC Boost Converter in Load Transient Conditions, Including Control Strategy and Stability Constraint

An optimization method is proposed to design a dc–dc boost converter, including the differential input filter and the associated control strategy, for embedded applications [aerospace, electric vehicle/hybrid electric vehicle (EV/HEV)], where high power density and high efficiency are strongly required. The problem is treated as a multiobjective optimization, whose purpose is to maximize compactness and efficiency of the converter. In order to find a tradeoff between the two objective functions (volume and power losses), a genetic algorithm was employed (NSGA-II) to generate the most convenient design solutions. The operating conditions includes both steady-state and load power transients of a constant power load (CPL). In order to comply with the specification on the output voltage admitted range in both conditions, the control strategy is discussed and included in the design procedure. The interaction of a differential input filter with a CPL may lead to serious instability issues. A constraint function in the algorithm excludes all the unstable and unfeasible design solutions. A high-efficiency converter ( $\eta =98.5\%$ ) was realized, and experimental tests prove that stability and design specifications are respected.
A multi-dimensional model of Enterprise Resource Planning critical success factors

ABSTRACT Recent literature on ERP implementation Critical Success Factors has focused on how different factors characterizing the context of the implementing firm, such size or culture, affect the relative importance of CSFs. Based on a systematic analysis of recent literature, this paper proposes a comprehensive model of contextual factors affecting the importance of ERP implementation CSFs. The proposed model answers the call for research on CSF to focus more on context determining CSF effectiveness. It also helps practitioners to identify important success factors in ERP implementations, calling for more research about specific contextual factors and CSFs in determining ERP implementation success.
Learning What to Remember: Strategies for Selective External Memory in Online Reinforcement Learning Agents

In realistic environments, intelligent agents must learn to integrate information from their past to inform present decisions. An agent’s immediate observations are often limited, and some degree of memory is necessary to complete many everyday tasks. However, an agent cannot remember everything it observes. The history of observations may be arbitrarily long, making it impractical to store and process. In this thesis, we will develop a novel method, called online policy gradient over a reservoir (OPGOR), for selecting what to remember from the stream of observation. We will also explore a number of alternative methods for handling this selective memory problem. OPGOR contrasts with recurrent neural networks (RNNs), which handle the task of selective memory by propagating information forward one step at a time. RNNs are very practical to a point but suffer on longer time horizons due to the difficulty of maintaining information over many steps with a mechanism based on one step updates. Furthermore, RNNs are generally trained by backpropagation through time (BPTT) which requires, at each time-step, compute time proportional to the length of the history. To handle this in practice, backpropagation is only performed over a fixed time window, further limiting its utility over long time horizons. OPGOR operates within the framework of external memory mechanisms for selective memory, which use explicit read and write operations instead of recurrent updates. An external memory consists of a fixed number of constant length vectors, or slots, in which relevant information can be written and later ii read. Such mechanisms give rise to three key questions: what to read from memory, what to write to memory, and what to drop from memory when something is written. External memory mechanisms have some advantages over RNNs for learning long-term dependencies, but in many cases they are still trained using BPTT, in which case they require similarly restrictive computation time. We will focus on external memory mechanisms which avoid the use of BPTT. Such mechanisms generate a vector, which we call a state variable, at each time-step and then, perhaps stochastically, decide whether to write it to memory, replacing an existing state variable in the process. External memory mechanisms that forgo BPTT often use a heuristic to decide which item to replace. Such heuristic replacement mechanisms can work well if the memory is very large but may fail if there are dependencies over much larger timescales than the
Development of a Product Platform for Ship Locks Using DSM Methods

During the coming decades, a significant number of ship locks in the Netherlands is due for replacement or renovation. In this respect, Rijkswaterstaat (RWS), the executive branch of the Dutch Ministry of Infrastructure and Water Management, is investigating the options while allowing for the necessary variety in lock configurations to meet lock location specific requirements. This paper presents the development of a product platform for locks to standardize selected lock components and to reduce the level of variety within the portfolio. Considering components and parameters, that are the main determinants in civil-mechanic lock design, the required solution freedom in choosing component types is determined to arrive at feasible lock configurations. Feasible configurations are determined for the locks in the RWS portfolio. A component and design process DSM analysis is carried out to arrive at a decision sequence for selecting component types to configure a lock.
Enabling disruptive technologies for higher education

Higher Education Institutions (HEIs) in the UK have invested significantly in technologies for learning and teaching, especially technologies designed to support learning and teaching such as Virtual Learning Environments, which are more or less universal. However, technologies provided by HEIs have not been universally successful in terms of adoption and usage. Meanwhile, academic community members use non-institutional technologies, or “disruptive technologies,” to support their learning and teaching. A number of researchers anticipated that the use of technology in learning and teaching would transform higher education. However, and to date, this has not happened. There is therefore a need to understand how non-institutional, disruptive technologies can be effectively incorporated into formal structures for supporting learning and teaching. In order to address this issue, this thesis set out to understand how HEIs in the UK can engage constructively with non-institutional technologies, using the concept of disruptive technologies as the primary analytical framework. The underlying aim was to establish whether non-institutional technologies can be effectively incorporated into HEIs systems for supporting learning and teaching. The thesis investigates these issues by carrying out a detailed analysis of practices of academic community members with non-institutional technologies used to support learning and teaching, over a period of two years. Data is gathered via surveys, interviews and observations. Study findings indicate that non-institutional technologies are used frequently to support learning and teaching in higher education, often in preference to institutional technologies. Study findings also indicate that non-institutional technologies can be effectively incorporated into learning and teaching if social and cultural practices around usage are clearly understood prior to incorporation.
Process-based quality control techniques in crowdsourcing(審査報告)

Crowdsourcing has become a popular approach to complete tasks that are tedious using manual methods or difficult for automatic ones. As crowdsourcing taps the capacities of humans, its possibilities are endless. However, the unpredictability of human behavior and the actuality of human error make it difficult to consistently achieve high-quality outcomes, posing quality control as a major challenge in crowdsourcing. Although many strategies have been proposed to optimize data quality, their effectiveness is dependent on each particular crowdsourcing application. To solve this, more techniques and experiments from which humans and algorithms can learn from are needed to be able to build a recommendation system that proposes quality management techniques depending on the attributes of the crowdsourcing application. In this dissertation, I approach quality management in crowdsourcing based on the sub-processes involved, specifically: task design, task deployment, and task assignment. I first experimented on factors affecting task design. In particular, I tested the effect of task complexity on a data extraction task and crowd type on a sentiment analysis task. Experiments show that there is no significant difference in the quality achieved from simple and more complex versions of a data extraction task and that the performance of paid unpaid workers are comparable in a sentiment analysis task. For task deployment, task deployment strategies were proposed along three dimensions: work structure, workforce organization, and work style. To semi-automatically implement these strategies in a crowdsourcing platform, a deployment tool was designed and developed. The effectiveness of the strategies when applied to text creation tasks were then studied and recommendations were drafted for both crowdsourcing researchers and practitioners. Finally, for task assignment, a fuzzy clustering-based method for building a personalized summary of tasks, also known as composite tasks, for crowd workers was validated. As observed from the experiments, personalization improves the workers’ overall experience and that diversifying tasks can improve the workers’ output quality. Author: Ria Mae Harina Borromeo Supervisor: Motomichi Toyama
Direct torque control of IM using PID controller

Direct torque control "DTC" technique is one of a high performance control system of an AC motor drive, which was proposed after the vector oriented control scheme during the resent 25 years. It has been developed rapidly for its concise system scheme, transient and dynamic performance. The DTC mechanism consists of voltage vector selection table, two hysteresis comparators and two estimators one for stator flux and another for electromagnetic torque. DTC is directly control torque and flux by using Voltage Source Inverter VSI, space vector and stator flux orientation and indirect speed regulated. A several control techniques can be used for improving the torque and flux performance. In this paper, the DTC with Proportional-Integral-Derivative (PID) controller used to improve the starting and dynamic performance of asynchronous motor AM, which gives good torque and flux response, best speed control and also minimize the unacceptable torque ripple. The mathematical model of DTC with PID controller of 3-phase induction motor IM are simulated under Matlab-Simulink. Therefore, the DTC based on PID controller has good performance of IM compared to classical DTC for starting, running state and also during change in load.
Cloud-to-cloud data transfer parallelization framework via spawning intermediate instances for scalable data migration

As enterprises are increasingly embracing the practice of multiple clouds federation, scalable data transfer between cloud datacenters is important from the standpoint of cloud consumers. Many existing works are done from the service provider perspective, requiring insights into the datacenter operations which are not available to the cloud consumer. In this dissertation, a data transfer framework that allows cloud consumers to circumvent the bandwidth limitation by spawning intermediate nodes and perform parallel transfer through many-tomany nodes is proposed. However, the effectiveness of such approach depends on many factors such as the time required to spawn new nodes, and bandwidth between the nodes. The objective of this work is to investigate the limitation and potential of the cloud-to-cloud parallel transfer (CPT). Firstly, all the components needed in the parallel data transfer is identified and modelled. Based on the transfer time and cost models, the circumstances where parallel transfer is worthy is identified. Then, a few optimizations are proposed, namely pipelining and network data piping to increase the data transfer throughput. Pipelining enables each stages of the parallel transfer to work concurrently while network data piping reduces the time spent on dividing files into chunks. Secondly, selected cloud Virtual Machines (VM) are benchmarked. Based on the observed behavior, pre-testing and VM-type selection techniques are proposed. Pre-testing utilized nodes top performing nodes while VM-type selection utilize suitable VM type and sizing. Thirdly, the CPT is implemented and tested on Amazon EC2. The adapted CPT for transfer between Hadoop clusters is also tested. The results showed that the transfer time of CPT is not only lesser than DistCp, but also has a lower cost – up to 8x in certain scenario.
What do these 5,599,881 parameters mean? An analysis of a speciﬁc LSTM music transcription model, starting with the 70,281 parameters of its softmax layer

A folk-rnn model is a long short-term memory network (LSTM) that generates music transcriptions. We have evaluated these models in a variety of ways – from statistical analyses of generated transcriptions, to their use in music practice – but have yet to understand how their behaviours precipitate from their parameters. This knowledge is essential for improving such models, calibrating them, and broadening their applicability. In this paper, we analyse the parameters of the softmax output layer of a speciﬁc model realisation. We discover some key aspects of the model’s local and global behaviours, for instance, that its ability to construct a melody is highly reliant on a few symbols. We also derive a way to adjust the output of the last hidden layer of the model to attenuate its probability of producing speciﬁc outputs.
Attitude toward Governmental Incentives on Childbearing and its Relationship with Fertility Preferences in Couples Attending Premarital Counseling Clinic in Health Centers in Mashhad

Background and purpose: Since last three decades a rapid reduction was seen in the total fertility rate in Iran. Therefore, the government’s policy on childbearing has changed and some incentives have been offered in 2012 to increase the rate of childbearing. This study aimed at determining the relationship between governmental incentives on childbearing and the attitude of couples attending premarital counseling clinics in health centers in Mashhad in 2013 towards fertility preferences. Materials and methods: The participants were 450 couples who referred to the health centers for premarital counseling. Demographic information was collected and the Miller’s Childbearing Questionnaire (1995) was used. Data was then analyzed using Pearson correlation and independent t-test. Results: The couple's desire for childbearing was calculated based on a numerical rating scale (1 to 10). The mean score for the desire for childbearing was 5.58 ± 2.88. The mean number of desired children was 2.37 ± 1.11and the mean of desired interval between marriage and the birth of first child was 31.01 ± 20.64 months. Inverse relationship was seen between the attitude toward governmental incentives on childbearing and the desire for childbearing and desired number of children (P<0.001). However, significant correlation was observed between the attitude toward governmental incentives on childbearing and the desired interval between marriage and the birth of first child (P<0.001). Conclusion: The results showed that the majority of couples were interested in having two children. Also, an increase was seen in the interval between marriage and the birth of first child. This study showed a positive effect of governmental incentives for childbearing in couples who tend to have lower fertility rate and delayed onset of childbearing.