Miracle-dz/UniFAR-FTUs huggingface dataset:
The DIARETDB1 Diabetic Retinopathy Database and Evaluation Protocol

Automatic diagnosis of diabetic retinopathy from digital fundus images has been an active research topic in the medical image processing community. The research interest is justified by the excellent potential for new products in the medical industry and significant reductions in health care costs. However, the maturity of proposed algorithms cannot be judged due to the lack of commonly accepted and representative image database with a verified ground truth and strict evaluation protocol. In this study, an evaluation methodology is proposed and an image database with ground truth is described. The database is publicly available for benchmarking diagnosis algorithms. With the proposed database and protocol, it is possible to compare different algorithms, and correspondingly, analyse their maturity for technology transfer from the research laboratories to the medical practice.
Comparative Performance of Arm and Farm on a Normalised Datasets

Association rule mining is basically used to generate association rules on a real life datasets. A well-known algorithm called apriori is used to generate the frequent pattern itemsets for a given transaction. Since real life dataset consist of nominal, continuous, integer attribute fields, to convert it into binary format some type of pre-processing has to be done on the dataset. In this paper, we had evaluated the performance of two algorithms that is ARM(Association Rule Mining) and FARM(Fuzzy Association Rule Mining) on the basis of generation time by supplying different support and confidence values.for data pre-processing ,two methods are used: discretisation and normalisation. Discretisation converts the range of possible values of continuous data into subranges which is identified by a unique integer label . It also convert values associated with instances to corresponding integer label. Normalisation process converts values of nominal data into corresponding integer labels.
On the automatic toBI accent type identification from data

This contribution faces the ToBI accent recognition problem with the goal of multiclass identiﬁcation vs. the more conservative Accent vs. No Accent approach. A neural network and a decision tree are used for automatic recognition of the ToBI accents in the Boston Radio Corpus. Multiclass classiﬁcation results show the difﬁculty of the problem and the impact of imbalanced classes. A study of the confusion/similarity between accent types, based on in-pair recognition rates, shows its impact on the overall performance. More expressive F0 contours parametrization techniques have been used to improve recognition rates.
THE EVOLUTION OF ENDOTHERMY: TESTING THE AEROBIC CAPACITY MODEL

One of the most important events in vertebrate evolution was the acquisition of endothermy, the ability to use metabolic heat production to elevate body temperature above environmental temperature. Several verbal models have been proposed to explain the selective factors leading to the evolution of endothermy. Of these, the aerobic capacity model has received the most attention in recent years. The aerobic capacity model postulates that selection acted mainly to increase maximal aerobic capacity (or associated behavioral abilities) and that elevated resting metabolic rate evolved as a correlated response. Here we evaluate the implicit evolutionary and genetic assumptions of the aerobic capacity model. In light of this evaluation, we assess the utility of phenotypic and genetic correlations for testing the aerobic capacity model. Collectively, the available intraspecific data for terrestrial vertebrates support the notion of a positive phenotypic correlation between resting and maximal rates of oxygen consumption within species. Interspecific analyses provide mixed support for this phenotypic correlation. We argue, however, that assessments of phenotypic or genetic correlations within species and evolutionary correlations among species (from comparative data) are of limited utility, because they may not be able to distinguish between the aerobic capacity model and plausible alternatives, such as selection acting directly on aspects of thermoregulatory abilities. We suggest six sources of information that may help shed light on the selective factors important during the evolution of high aerobic metabolic rates and, ultimately, the attainment of endothermy. Of particular interest will be attempts to determine, using a combination of mechanistic physiological and quantitative‐genetic approaches, whether a positive genetic correlation between resting and maximal rates of oxygen consumption is an ineluctable feature of vertebrate physiology.
AUTOMATED DISCOVERY IN ECONOMETRICS

Our subject is the notion of automated discovery in econometrics. Advances in computer power, electronic communication, and data collection processes have all changed the way econometrics is conducted. These advances have helped to elevate the status of empirical research within the economics profession in recent years, and they now open up new possibilities for empirical econometric practice. Of particular significance is the ability to build econometric models in an automated way according to an algorithm of decision rules that allow for (what we call here) heteroskedastic and autocorrelation robust (HAR) inference. Computerized search algorithms may be implemented to seek out suitable models, thousands of regressions and model evaluations may be performed in seconds, statistical inference may be automated according to the properties of the data, and policy decisions can be made and adjusted in real time with the arrival of new data. We discuss some aspects and implications of these exciting, emergent trends in econometrics.The first version of this paper was written in April 2004 for the 20th Anniversary Issue of Econometric Theory. Helpful comments by the co-editor, Oliver Linton, Benno Pötscher, Brendan Beare, and two referees on the first draft are gratefully acknowledged.
Scheduling Multiple Divisible Loads in a Multi-cloud System

In this paper, we propose a novel architecture of a multi-cloud system and investigate the problem of scheduling multiple divisible loads on this system. The scheduling problem takes into account the real-life constraints: the arbitrary release times, i.e., Ready times, of computing nodes, heterogeneous sizes and computation requirement of loads, and the network topology of the system based on dedicated links. We adopt the phase-based multi-round scheduling approach to design two scheduling strategies: a Static Scheduling Strategy (SSS) which assumes that the release times of computing nodes are predetermined and known, and a Dynamic Scheduling Strategy (DSS) which considers that the release times of computing nodes are unknown until they are released. The strategies are designed to achieve high utilization and load balance among computing nodes, thereby minimizing the total processing time of loads. Numerical studies and simulations were carried out to evaluate the performance of the proposed strategies. The results show that the proposed strategies outperform a baseline strategy which does not use the phase-based multi-round scheduling approach.
Access Region Expansion in the Concert Compiler

Concurrent Object Oriented Programming introduces signiicant challenges for a compiler. We examine one of these challenges, access region expansion, from two perspectives; compiler runtime performance and quality of access region expansion. The work was done within the Concert Compiler framework. We found that with clever modiications the compiler runtime performance could be signiicantly improved (4X). We also found that while there are inherent limitations to the quality of access region expansion, our implementation is overly restrictive. We conclude that further work in this topic needs to be done to fully understand the problem and deene an optimal solution.
Knee Design for a Bipedal Walking Robot Based on a Passive-Dynamic Walker

Passive-dynamic walkers are a class of robots that can walk down a ramp stably without actuators or control due to the mechanical dynamics of the robot. Using a passive-dynamic design as the basis for a powered robot helps to simplify the control problem and maximize energy efficiency compared to the traditional joint-angle control strategy. This thesis outlines the design of a knee for the robot known as Toddler, a passive-dynamic based powered walker built at the Massachusetts Institute of Technology. An actuator at the knee allows the robot to bend and straighten the leg, but a clutch mechanism allows the actuator to completely disengage so that the leg can swing freely. The clutch operates by using a motor to rotate a lead screw which engages or disengages a set of spur gears. Control of the knee is accomplished by utilizing the robot’s sensors to determine whether or not the knee should be engaged. The engagement signal is then fed through a simple motor control circuit which controls the motor that turns the lead screw. The knee design was successfully implemented on Toddler but more work is required in order to optimize his walking. In order to study the dynamics of walking with knees, we also built a copy of McGeer’s original passive walker with knees. Thesis Supervisor: Ernesto E. Blanco Title: Adjunct Professor of Mechanical Engineering
Duration Probabilistic Automata

Duration Probabilistic Automata (DPA) is a formalism for modelling concurrent execution of sequences of tasks. This formalism is well suited for scheduling problems such as job shop. We show how DPAs can be translated to other formalisms and how statistical modelchecking can be applied. The performance of these translations is tested using the tools Uppaal and PRISM. The results show that the Uppaal translation is the fastest of the two. Furthermore, the Uppaal translation was found viable for verifying DPAs that consist of 55 sequences of tasks with 55 tasks each.
Proactive insider threat detection through social media: the YouTube case

Insider threat is a major issue in cyber and corporate security. In this paper we study the psychosocial perspective of the insider via social media, Open Source Intelligence, and user generated content classification. Inductively, we propose a prediction method by evaluating the predisposition towards law enforcement and authorities, a personal psychosocial trait closely connected to the manifestation of malevolent insiders. We propose a methodology to detect users holding a negative attitude towards authorities. For doing so we facilitate the use of machine learning techniques and of a dictionary-based approach, so as to detect comments expressing negative attitude. Thus, we can draw conclusions over a user behavior and beliefs via the content the user generated within the limits a social medium. We also use an assumption free flat data representation technique in order to decide over the user's attitude. Furthermore, we compare the results of each method and highlight the common behavior manifested by the users. The demonstration is applied on a crawled community of users on YouTube.
Navigation and Visualization of Abstractions of Time-Oriented Clinical Data

We describe the methodology and architecture of a knowledge-based, interactive visualization system that enables physicians and medical support personnel to draw conclusions from heterogeneous time-oriented clinical data. Our system employs domain-specific ontologies to produce temporal and statistical abstractions of data, and also as the basis for semantically-based browsing and visualization. This builds on previous work in data mining, temporal reasoning, and information visualization, but offers fundamental advantages over any isolated approach, by leveraging each off the others. We performed an evaluation of a prototype, leading us to conclude that users can indeed use the system to perform such semantically-based browsing in a reasonable amount of time.
Secure managementinformation exchangeSaleem

This paper describes the design and implementation of a secure management protocol for the management of distributed applications. The protocol is a modiied use of the ISO CMIP protocol, with additional mechanisms and behaviour to provide the following security services: Mutual authentication of communicating parties. Both parties can prove to each other that they are who they claim to be by the exchange of signed credentials. The management information exchanged between the parties is protected from replay, mis-ordering, modiication, insertion and deletion of the PDUs. Conndentiality of the management PDUs. Only the communicating parties can read the information passed between them. The mechanism used also provides a level of back traac protection and perfect forward secrecy. In previous work we have implemented a public-key based system. Here, we present an experiment based on the use of a secret-key mechanism, for a faster, lightweight approach. The authentication mechanism makes use of the MD5 algorithm and the DES encryption standard. The PDU integrity mechanisms make use of a pseudo random number sequence for PDU numbering and the MD5 algorithm for generating unforgeable signatures for the PDUs.
USING CLASSROOM VIDEO TO IDENTIFY DEVELOPMENT OF TEACHER KNOWLEDGE

Teacher knowledge for teaching statistics through investigations at the primary school level can be analyzed using a framework based on a combination of statistical thinking components and teacher knowledge. This paper focuses on the question of whether growth of teacher knowledge occurs through teaching, and if so, what types of teacher knowledge. In this research, classroom video and stimulated recall interviews with the teachers provided the main data, both of which were analyzed using the framework for teacher knowledge for teaching statistics. Growth in some categories of teacher knowledge was identified, but not for other categories. Implications for teacher education are discussed.
Linking Heterogeneous Input Features with Pivots for Domain Adaptation

Sentiment classification aims to automatically predict sentiment polarity (e.g., positive or negative) of user generated sentiment data (e.g., reviews, blogs). In real applications, these user generated sentiment data can span so many different domains that it is difficult to manually label training data for all of them. Hence, this paper studies the problem of domain adaptation for sentiment classification where a system trained using labeled reviews from a source domain is deployed to classify sentiments of reviews in a different target domain. In this paper, we propose to link heterogeneous input features with pivots via joint non-negative matrix factorization. This is achieved by learning the domain-specific information from different domains into unified topics, with the help of pivots across all domains. We conduct experiments on a benchmark composed of reviews of 4 types of Amazon products. Experimental results show that our proposed approach significantly outperforms the baseline method, and achieves an accuracy which is competitive with the state-of-the-art methods for sentiment classification adaptation.
An Automated Approach for Software Bug Classification

Open source projects for example Eclipse and Fire fox have open source bug repositories. User reports bugs to these repositories. Users of these repositories are usually non-technical and cannot assign correct class to these bugs. Triaging of bugs, to developer, to fix them is a tedious and time consuming task. Developers are usually expert in particular areas. For example, few developers are expert in GUI and others are in java functionality. Assigning a particular bug to relevant developer could save time and would help to maintain the interest level of developers by assigning bugs according to their interest. However, assigning right bug to right developer is quite difficult for triager without knowing the actual class, the bug belongs to. In this research, we have classified the bugs in different labels on the basis of summary of the bug. Multinomial Naïve Bayes text classifier is used for classification purpose. For feature selection, Chi-Square and TFIDF algorithms were used. Using Naïve Bayes and Chi-square, we get average of 83 % accuracy.
Integration of Grey with Neural Network Model and Its Application in Data Mining

Because of Boundary types and geologic conditions, which possess random and obscure characteristics, groundwater heads vary with the conditions. The prediction of groundwater level is one of the main work of hydraulic government, which is predicted based on the history data and the relative influence factors. Therefore, prediction precision depends on the accuracy of history data. Data mining has provided a new method for analyzing massive, complex and noisy data. According to the complexity and ambiguity of groundwater system, a new integration of grey with neural network model is built to forecast groundwater heads, which were used to judge whether future groundwater heads were extraordinarily over the history range or not. This method overcomes the disadvantages which the grey method only predict the linear trend. The methods were used to analyze the random characteristics of groundwater heads in anyang city . The results indicate that the method is reliable, and reasonable
Enhancing the Interoperability of iSimp by Using the BioC Format

TCR engagement stimulates the activation of the protein kinase Raf-1. Active Raf-1 phosphorylates and activates the mitogen-activated protein (MAP) kinase / extracellular signal-regulated kinase kinase 1 (MEK1), which in turn phosphorylates and activates the MAP kinases / extracellular signal regulated kinases, ERK1 and ERK2. Raf-1 activity promotes IL-2 production in activated T lymphocytes. Therefore, we sought to determine whether MEK1 and ERK activities also stimulate IL-2 gene transcription. Expression of constitutively active Raf-1 or MEK1 in Jurkat T cells enhanced the stimulation of IL-2 promoter-driven transcription stimulated by a calcium ionophore and PMA, and together with a calcium ionophore the expression of each protein was sufficient to stimulate NF-AT activity. Expression of MEK1- interfering mutants inhibited the stimulation of IL-2 promoter- driven transcription and blocked the ability of constitutively active Ras and Raf-1 to costimulate NF-AT activity with a calcium ionophore. Expression of the MAP kinase-specific phosphatase, MKP-1, which blocks ERK activation, inhibited IL- 2 promoter and NF-AT-driven transcription stimulated by a calcium ionophore and PMA, and in addition, MKP-1 neutralized the transcriptional enhancement caused by active Raf-1 and MEK1 expression. We conclude that the MAP kinase signal transduction pathway consisting of Raf-1, MEK1, and ERK1 and ERK2 functions in the stimulation IL-2 gene transcription in activated T lymphocytes. Active Raf-1 phosphorylates MEK1. Active Raf-1 activates MEK1. MEK1 in turn phosphorylates ERK1. MEK1 in turn phosphorylates ERK2. MEK1 in turn activates ERK1. MEK1 in turn activates ERK2.
Validation of a touch screen tablet assessment of early literacy skills and a comparison with a traditional paper-based assessment

ABSTRACT Touch screen tablets are being increasingly used in schools for learning and assessment. However, the validity and reliability of assessments delivered via tablets are largely unknown. The present study tested the psychometric properties of a tablet-based app designed to measure early literacy skills. Tablet-based tests were also compared with traditional paper-based tests. Children aged 2–6 years (N = 99) completed receptive tests delivered via a tablet for letter, word, and numeral skills. The same skills were tested with a traditional paper-based test that used an expressive response format. Children (n = 35) were post-tested 8 weeks later to examine the stability of test scores over time. The tablet test scores showed high internal consistency (all α’s > .94), acceptable test-retest reliability (ICC range = .39–.89), and were correlated with child age, family SES, and home literacy teaching to indicate good predictive validity. The agreement between scores for the tablet and traditional tests was high (ICC range = .81–.94). The tablet tests provides valid and reliable measures of children’s early literacy skills. The strong psychometric properties and ease of use suggests that tablet-based tests of literacy skills have the potential to improve assessment practices for research purposes and classroom use.
Amorphous In–Ga–Zn–O Dual-Gate TFTs: Current–Voltage Characteristics and Electrical Stress Instabilities

We studied the electrical characteristics and electrical stress instabilities of amorphous In-Ga-Zn-O (-IGZO) dual-gate thin-film-transistors (DG TFTs). A threshold voltage of the bottom-gate (BG)-driven -IGZO DG TFTs showed a linear dependence on the top-gate (TG) voltage. The slope of this dependence is associated with the ratio of the TG to BG insulator capacitance. The BG-driven DG TFT showed linear field-effect mobility comparable to that of a single-gate (SG) TFT without the TG electrode and a smaller saturation field-effect mobility and a larger subthreshold swing in comparison to the SG TFT. These characteristics were explained by the BG-driven DG TFT model formulated by taking the TG bias effect into account. The TG interface showed worse stability under an electric bias stress in comparison to the BG interface. It was also found that a negative voltage applied to the TG improved the stability of the DG TFT under a constant-current stress. These observations suggest that the BG-driven -IGZO DG TFTs with the appropriate negative TG voltage applied can simultaneously show both normally off characteristics and higher stability than the SG TFTs.
Automatic Analysis and Annotation of Literary Texts

In this work a machine learning oriented perspective on computer aided support to literary analysis is presented. A representation of narrative phenomena is proposed and an automatic annotation model for such phenomena is trained on texts provided by a critic. As a short-term research task, we studied how the observable textual piece of evidence impact on the learning agent capabilities, over a specific literary work, i.e. ”Gli Indifferenti” by Alberto Moravia. Although preliminary, the results are good and confirm the viability of the approach for larger scale
Improved Background Removal through Polarisation in Vision-Based Tabletop Interface

This paper presents a novel method of enhancing computer assisted motor rehabilitation for brain injury patients by detailing a method of overcoming a significant technical constraint. The focus of our system is three dimen- sional(3D) tracking of upper limb movements on a horizontal tabletop system. We describe a new horizontal table- top system that utilises real-time stereo vision for 3D tracking of user interaction in the workspace. An overview of existing background removal techniques is presented, and their strengths and weaknesses discussed. We then present a novel approach that utilises polarisation of light for background removal. The system uses an LCD panel for the display device, which achieves a more portable system, and also allows for total removal of screen interference by utilising the polarising nature of LCD technology. Initial testing of this approach has proved highly successful. Key benefits are outlined.