Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of language learning front informant under various monotonicity constraints

Abstract The present paper deals with monotonic and dual monotonic language learning from positive and negative examples. The three notions of monotonicity reflect different formalizations of the requirement that the learner has to produce always better and better generalizations when fed more and more data on the concept to be learnt. The three versions of dual monotonicity describe the concept that the inference device has to produce exclusively specializations that fit better and better to the target language. We characterize strong-monotonic, monotonic, weak-monotonic, dual strong-monotonic, dual monotonic and dual weak-monotonic as well as finite language learning from positive and negative data in terms of recursively generable finite sets. Thereby, we elaborate a unifying approach to monotonic language learning by showing that there is exactly one learning algorithm which can perform any monotonic inference task.
Iris based authentication mechanism for e-Passports

E-Passports are widely issued all over the world, although they suffer from security flaws. Many guidelines were proposed to solve these security considerations, like the guideline of the International Civil Aviation Organization (ICAO), the Extended Access Control (EAC), and other solutions for the third e-Passport generation. In this paper, an iris based authentication mechanism for e-Passport is proposed and validated. This mechanism is based on Elliptic Curve Cryptography (ECC). The iris based cryptographic key regeneration scheme is used to obtain a stable input from biometric data which is used to generate the security parameters of the elliptic curve. Another important aspect of this work is that it is evaluated on a publicly available biometric database. The results show a good performance especially from a biometric point of view (False Acceptance Rate of 0.2% and False Rejection Rate of 3.6%) on a subset of the NIST-ICE database.
Stereotactic radiotherapy in neovascular age-related macular degeneration

AbstractStereotactic radiotherapy (SRT) is a new approach to treat neovascular age-related macular degeneration (nAMD). The INTREPID trial suggested that SRT could reduce the frequency of regular intravitreal injections (IVIs) with antivascular endothelial growth factor drugs, which are necessary to control disease activity. However, the efficacy of SRT in nAMD and resulting morphological changes have not been validated under real-life circumstances, an issue, which we would like to address in this retrospective analysis.Patients who met the INTREPID criteria for best responders were eligible for SRT. A total of 32 eyes of 32 patients were treated. Thereafter, patients were examined monthly for 12 months and received pro re nata IVI of aflibercept or ranibizumab. Outcome measures were: mean number of injections, best-corrected visual acuity, and morphological changes of the outer retina-choroid complex as well as patient safety.Mean number of IVI decreased by almost 50% during the 12 months after SRT compared to the year before, whereas visual acuity increased by one line (logMAR). Morphological evaluation showed that most changes affect outer retinal layers.Stereotactic radiotherapy significantly reduced IVI retreatment in nAMD patients under real-life circumstances. Therefore, SRT might be the first step to stop visual loss as a result of IVI undertreatment, which is a major risk.
Competition for forest fuels in Sweden

This thesis project focuses on the need for better tools for analyzing competition for forest fuels in Sweden, in particular how policy measures may influence the market and involved actors in different regions of Sweden. Raw materials from the forest have for long constituted important inputs in the Swedish forest industry. Today some of these raw materials are increasingly used as inputs also in bioenergy production, largely driven by bioenergy promoting policies, and there are signs of increased competition for forest fuels which may have various and uncertain effects on the industries in question. The thesis project explores the possibilities to adapt previously used forest sector equilibrium models into a new model suitable for analysis of Swedish forest fuel markets, called the Swedish Forest Sector Trade Model (SFSTM). The approach takes into account regional differences in supply and demand for 23 types of products produced and/or used by eight types of producers, plus consumers, in five different regions (four Swedish and one international). The project contributes with the novelty of an economic partial equilibrium model of the Swedish forest sector that includes regional trade and bioenergy production. After extensive data collection and fitting of the model setup according to Swedish circumstances, the model is programmed and solved in GAMS software. The model is calibrated and then tested as a tool for analysis by implementing scenarios that imply shifts in energy demand and introduction of a tax on forest fuels used in bioenergy production. The project results are primarily of interest in understanding the models functionality in a Swedish context, in particular with regards to the availability of regionalized data and related implications for the model setup. The thesis concludes in several key issues and recommendations on further steps to be taken if this type of model should continue to be developed in Sweden, such as; the need for better data on extraction and processing cost of logging residues, the need for better data on exogenous input costs in forest industries, and how the model results are affected by the choice of geographic scale of (trading) regions and related assumptions on transport cost functions.
Kernel-based distances for relational learning

In this paper we present a novel and general framework for kernel-based learning over relational schemata. We exploit the notion of foreign keys to perform the leap from a flat attribute-value representation to a structured representation that underlines relational learning. We define a new attribute type which builds on the notion of foreign keys that we call instance-set. It is shown that this more database oriented approach enables intuitive modeling of relational problems. We also define some kernel functions over relational schemata and adapt them so that they are used as a basis for a relational instance-based learning algorithm. We check the performance of our algorithm on a number of well known relational benchmark datasets.
Artificial Neural Network for Human Behavior Prediction through Handwriting Analysis

Handwriting Analysis or Graphology is a scientific method of identifying, evaluating and understanding personality through the strokes and patterns revealed by handwriting. Handwriting reveals the true personality including emotional outlay, fears, honesty, defenses and many others. Professional handwriting examiners called graphologist often identify the writer with a piece of handwriting. Accuracy of handwriting analysis depends on how skilled the analyst is. Although human intervention in handwriting analysis has been effective, it is costly and prone to fatigue. Hence the proposed methodology focuses on developing a tool for behavioral analysis which can predict the personality traits automatically with the aid of a computer without the human intervention. In this paper a method has been proposed to predict the personality of a person from the baseline, the pen pressure and the letter„t‟ as found in an individual‘s handwriting. These parameters are the inputs to the Artificial Neural Network which outputs the personality trait of the writer. The performance is measured by examining multiple samples.
Comparative intrauterine development and placental function of ART concepti: implications for human reproductive medicine and animal breeding.

BACKGROUND The number of children conceived using assisted reproductive technologies (ART) has reached >5 million worldwide and continues to increase. Although the great majority of ART children are healthy, many reports suggest a forthcoming risk of metabolic complications, which is further supported by the Developmental Origins of Health and Disease hypothesis of suboptimal embryo/fetal conditions predisposing adult cardiometabolic pathologies. Accumulating evidence suggests that fetal and placental growth kinetics are important features predicting post-natal health, but the relationship between ART and intrauterine growth has not been systematically reviewed. METHODS Relevant studies describing fetoplacental intrauterine phenotypes of concepti generated by in vitro fertilization (IVF), intracytoplasmic sperm injection (ICSI) and somatic cell nuclear transfer (SCNT) in the mouse, bovine and human were comprehensively researched using PubMed and Google Scholar. Intrauterine growth plots were created from tabular formatted data available in selected reports. RESULTS ART pregnancies display minor but noticeable alterations in fetal and placental growth curves across mammalian species. In all species, there is evidence of fetal growth restriction in the earlier stages of pregnancy, followed by significant increases in placental size and accelerated fetal growth toward the end of gestation. However, there is a species-specific effect of ART on birthweights, that additionally vary in a culture condition-, strain-, and/or stage at transfer-specific manner. We discuss the potential mechanisms that underlie these changes, and how they are affected by specific components of ART procedures. CONCLUSIONS ART may promote measurable alterations to intrauterine growth trajectory and placental function. Key findings include evidence that birthweight is not a reliable marker of fetal stress, and that increases in embryo manipulation result in more deviant fetal growth curves. Because growth kinetics in early life are particularly relevant to adult metabolic physiology, we advise more rigorous assessment of fetal growth and placental function in human ART pregnancies, as well as continued follow-up of ART offspring throughout post-natal life. Finally, strategies to minimize embryo manipulations should be adopted whenever possible.
Proteus: Exploiting Numerical Precision Variability in Deep Neural Networks

This work exploits the tolerance of Deep Neural Networks (DNNs) to reduced precision numerical representations and specifically, their recently demonstrated ability to tolerate representations of different precision per layer while maintaining accuracy. This flexibility enables improvements over conventional DNN implementations that use a single, uniform representation. This work proposes Proteus, which reduces the data traffic and storage footprint needed by DNNs, resulting in reduced energy and improved area efficiency for DNN implementations. Proteus uses a different representation per layer for both the data (neurons) and the weights (synapses) processed by DNNs. Proteus is a layered extension over existing DNN implementations that converts between the numerical representation used by the DNN execution engines and the shorter, layer-specific fixed-point representation used when reading and writing data values to memory be it on-chip buffers or off-chip memory. Proteus uses a novel memory layout for DNN data, enabling a simple, low-cost and low-energy conversion unit. We evaluate Proteus as an extension to a state-of-the-art accelerator [7] which uses a uniform 16-bit fixed-point representation. On five popular DNNs Proteus reduces data traffic among layers by 43% on average while maintaining accuracy within 1% even when compared to a single precision floating-point implementation. As a result, Proteus improves energy by 15% with no performance loss. Proteus also reduces the data footprint by at least 38% and hence the amount of on-chip buffering needed resulting in an implementation that requires 20% less area overall. This area savings can be used to improve cost by building smaller chips, to process larger DNNs for the same on-chip area, or to incorporate an additional three execution engines increasing peak performance bandwidth by 18%.
Model-based Virtual Environments for Collaboration University of Colorado at Boulder Model-based Virtual Environments for Collaboration

The simultaneous evolution of personal computing tools and networks has focused attention on the notion of harnessing computer technology to assist in human collaboration on group work. While personal productivity tool technology and use have reached a high level of sophistication, the most basic ideas for how computer technology should assist in collaboration across the network have not converged. The approaches range from ones where coordination of work is uniquely human-controlled, to those where the computer schedules all signiicant work in the group. On the one hand, proponents argue the system should provide its users with the most eeective form of coordination, while others argue the system absolutely must not appear to be dictatorial. Secondly, group work is usually based on trust and understanding among the team members { the more familiar the team members are with one another, the more eeectively they collaborate. If a new system removes or changes this underlying social model, the system will probably only be eeective if the users are willing to adapt their fundamental social behavior to t the system's implied model. In this paper, our premise is that it is risky to build computer systems depending on such social change for their success. Instead, we consider collaboration systems based on formal coordination models, yet which must be adaptable to existing social models in the organization. Our approach is to assume a class of distributed systems to support group work where part of the work is represented formally in the system environment, and the remainder is stored as informal knowledge. The system environment must go to great lengths to support extant social models by incorporating signiicant technology to accommodate informal communication within a context established by the formal model of the work.
University of Huddersfield Repository Data envelopment analysis and its application to the measurement of efficiency in higher education

The purpose of this paper is to examine the possibility of measuring efficiency in the context of higher education. The paper begins by exploring the advantages and drawbacks of the various methods for measuring efficiency in the higher education context. The ease with which data envelopment analysis (DEA) can handle multiple inputs and multiple outputs makes it an attractive choice of technique for measuring the efficiency of higher education institutions (HEIs), yet its drawbacks cannot be ignored. Thus a number of extensions to the methodology, designed to overcome some of the disadvantages, are presented. The paper ends with an application of DEA to a data set of more than 100 HEIs in England using data for the year 2000/01. Technical and scale efficiency in the English higher education sector appear to be high on average. The Pastor et al (2002) test for comparing nested DEA models is useful in reducing the full model to a smaller 'significant' set of inputs and outputs. Thus the quantity and quality of undergraduates, the quantity of postgraduates, expenditure on administration, and the value of interest payments and depreciation are significant inputs to, and the quantity and quality of undergraduate degrees, the quantity of postgraduate degrees and research are significant outputs in the English higher education production process. The possibility of differences in the production frontier (and hence the distribution of efficiencies) of three distinct groups of HEIs is explored using a test proposed by Charnes et al (1981), but no significant differences are found. Bootstrapping procedures, however, suggest that differences between the most and least efficient English HEIs are significant.
A MODEL OF SUCCESS FACTORS FOR E-GOVERNMENT ADOPTION - THE CASE OF POLAND

Ƞ This research focuses on the possible success factors of e-government in Poland. The purpose of this research was to identify success factors for adopting e-government in this country. The paper continues as follows. Firstly, the paper clarifies the concept of e-government and reviews success factors of e-government proposed by various authors in literature. Secondly, the success factors for adopting e-government in Poland are identified and the model of success factors is proposed. This model embraces the four categories of success factors. They are: economic, socio-cultural, technological and organizational. All the factors are considered in three stages: (1) ICTs access - reflecting technical and economic accessibilities of ICTs, (2) ICTs competences - reflecting competences and awareness related to the use of ICTs, (3) ICTs use - reflecting actual usage of ICTs. The paper concludes with a discussion on research findings, implications, and avenues for further research. This work contributes to extant research by showing a holistic approach to the success factors for adopting e-government, especially in transition economies.
AnAlysis of MAss-MediA Affected trAnsforMAtions of the dAnger concept

The article deals with a study of both short-term and long-term influence of massmedia upon a concept of danger (CD). Subjective concepts of danger (CD) were revealed by means of the authors “Assessment of Dangerous Situations” Questionnaire and a new classification of such concepts was developed. Demonstrated are differences in CD of men and women, as well as specific traits of CD transformations induced by news programs. The authors give advice on how to compile TV news programs in view of psychological mechanisms of perception.
Z-source inverter

This paper presents an impedance-source (or impedance-fed) power converter (abbreviated as Z-source converter) and its control method for implementing DC-to-AC, AC-to-DC, AC-to-AC, and DC-to-DC power conversion. The Z-source converter employs a unique impedance network (or circuit) to couple the converter main circuit to the power source, thus providing unique features that cannot be obtained in the traditional voltage-source (or voltage-fed) and current-source (or current-fed) converters where a capacitor and inductor are used respectively. The Z-source converter overcomes the conceptual and theoretical barriers and limitations of the traditional voltage-source converter (abbreviated as V-source converter) and current-source converter (abbreviated as I-source converter) and provides a novel power conversion concept. The Z-source concept can be applied to all DC-to-AC, AC-to-DC, AC-to-AC, and DC-to-DC power conversion. To describe the operating principle and control, this paper focuses on an example: a Z-source inverter for DC-AC power conversion needed in fuel cell applications. Simulation and experimental results are presented to demonstrate the new features.
Apoptosis through Bcl-2/Bax and Cleaved Caspase Up-Regulation in Melanoma Treated by Boron Neutron Capture Therapy

Boron neutron capture therapy (BNCT) is a binary treatment involving selective accumulation of boron carriers in a tumor followed by irradiation with a thermal or epithermal neutron beam. The neutron capture reaction with a boron-10 nucleus yields high linear energy transfer (LET) particles, alpha and 7Li, with a range of 5 to 9 µm. These particles can only travel very short distances and release their damaging energy directly into the cells containing the boron compound. We aimed to evaluate proliferation, apoptosis and extracellular matrix (ECM) modifications of B16F10 melanoma and normal human melanocytes after BNCT. The amounts of soluble collagen and Hsp47, indicating collagen synthesis in the ECM, as well as the cellular markers of apoptosis, were investigated. BNCT decreased proliferation, altered the ECM by decreasing collagen synthesis and induced apoptosis by regulating Bcl-2/Bax in melanoma. Additionally, BNCT also increased the levels of TNF receptor and the cleaved caspases 3, 7, 8 and 9 in melanoma. These results suggest that multiple pathways related to cell death and cell cycle arrest are involved in the treatment of melanoma by BNCT.
Antiangiogenic Steroids in Human Cancer Therapy

Despite advances in the early detection of tumors and in the use of chemotherapy, radiotherapy and surgery for disease management, the worldwide mortality from human cancer remains unacceptably high. The treatment of cancer may benefit from the introduction of novel therapies derived from natural products. Natural products have served to provide a basis for many of the pharmaceutical agents in current use in cancer therapy. Emerging research indicates that progressive growth and spread of many solid tumors depends, in part, on the formation of an adequate blood supply, and this process of tumor-associated angiogenesis is reported to have prognostic significance in several human cancers. This review focuses on the potential application in antitumor therapy of naturally-occurring steroids that target tumor-associated angiogenesis. Squalamine, a 7,24 dihydroxylated 24-sulfated cholestane steroid conjugated to a spermidine at position C-3, is known to have strong antiangiogenic activity in vitro, and it significantly disrupts tumor proliferation and progression in laboratory studies. Work on the interactions of squalamine with vascular endothelial cells indicate that it binds with cell membranes, inhibits the membrane Na+/H+ exchanger and may further function as a calmodulin chaperone. These primary actions appear to promote inhibition of several vital steps in angiogenesis, such as blockade of mitogen-induced actin polymerization, cell–cell adhesion and cell migration, leading to suppression of endothelial cell proliferation. Preclinical studies with squalamine have shown additive benefits in tumor growth delay when squalamine is combined with cisplatin, paclitaxel, cyclophosphamide, genistein or radiation therapy. This compound has also been assessed in early phase clinical trials in cancer; squalamine was found to exhibit little systemic toxicity and was generally well tolerated by treated patients with various solid tumor malignancies, including ovarian, non-small cell lung and breast cancers. Clinical trials with squalamine alone or combined with standard chemotherapies or other biologic therapies, including antiangiogenic agents, should be considered for selected cancer patients, and further study of the mechanism of action and bioactivity of squalamine is warranted.
An Improved Dynamic Spectrum Access Protocol Based on the Greedy Algorithm

In cognitive radio networks, media access control protocol decides the method how the nodes in the networks receive and send data by shared wireless channels. Whether the MAC protocol can use the limited channel effectively plays a very important role in the performance of the cognitive radio network. Focusing on designing and implementation an excellent MAC protocol in cognitive radio networks, this paper describes an improved dynamic spectrum access protocol based on the greedy algorithm. The simulation results shows it's good performance.
‘Bad hombres’? An examination of identities in U.S. media coverage of immigration

ABSTRACT When discussing immigration on the campaign trail in 2016, Republican presidential nominee Donald Trump used the term ‘bad hombres’ to criticise immigrants, and in particular Latino men. Using a novel dataset of images accompanying immigration news stories in three major news magazines from 2000–2010, we examine the extent to which press coverage helped set the stage for this sort of racialised and gendered association. We find overwhelming support for the ‘bad hombres’ hypothesis, demonstrating that in comparison to the true demographics of immigrants in the U.S., press portrayals significantly over-represent Latino men and under-represent all other immigrant groups. Our results are particularly important in light of research demonstrating that individuals’ political attitudes are influenced by the perceived ‘target group,’ or beneficiary, of a given policy, as well as scholarship demonstrating the media’s role in fostering a sense of ‘Latino threat. ’