Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical Modeling and Learning for Recognition-Based Handwritten Numeral String Segmentation

This paper proposes a recognition based approach to handwritten numeral string segmentation. We consider two classes: numeral strings segmented correctly or not. The feature vectors containing recognition information for numeral strings segmented correctly are assumed to be of the distribution of Gaussian mixture model (GMM). Based on this modeling, the recognition based segmentation is solved under the Max-Min posterior Pseudo-probabilities (MMP) framework of learning Bayesian classifiers. In the training phase, we use the MMP method to learn a posterior pseudo-probability measure function from positive samples and negative samples of numeral strings segmented correctly. In the process of recognition based segmentation, we generate all possible candidate segmentations of an input string through contour and profile analysis, and then compute the posterior pseudo-probabilities of being the numeral string segmented correctly for all the candidate segmentations. The candidate segmentation with the maximum posterior pseudo-probability is taken as the final result. The effectiveness of our approach is demonstrated by the experiments of numeral string segmentation and recognition on the NIST SD19 database.
9,400 years of cosmic radiation and solar activity from ice cores and tree rings

Understanding the temporal variation of cosmic radiation and solar activity during the Holocene is essential for studies of the solar-terrestrial relationship. Cosmic-ray produced radionuclides, such as 10Be and 14C which are stored in polar ice cores and tree rings, offer the unique opportunity to reconstruct the history of cosmic radiation and solar activity over many millennia. Although records from different archives basically agree, they also show some deviations during certain periods. So far most reconstructions were based on only one single radionuclide record, which makes detection and correction of these deviations impossible. Here we combine different 10Be ice core records from Greenland and Antarctica with the global 14C tree ring record using principal component analysis. This approach is only possible due to a new high-resolution 10Be record from Dronning Maud Land obtained within the European Project for Ice Coring in Antarctica in Antarctica. The new cosmic radiation record enables us to derive total solar irradiance, which is then used as a proxy of solar activity to identify the solar imprint in an Asian climate record. Though generally the agreement between solar forcing and Asian climate is good, there are also periods without any coherence, pointing to other forcings like volcanoes and greenhouse gases and their corresponding feedbacks. The newly derived records have the potential to improve our understanding of the solar dynamics and to quantify the solar influence on climate.
A WEB-BASED LEARNING ENVIRONMENT FOR IMPROVING PROGRAMMING STUDENTS ’ MENTAL MODELS

Recent research has found that many programming students often hold non-viable mental models of basic programming concepts such as assignment and object reference. To improve those students’ mental models, a constructivist-based learning model, integrating a cognitive conflict strategy with program visualisation, was proposed by the authors. In addition, a web-based learning environment has been developed to offer a practical tool for instructors and students to use the proposed learning model for teaching and learning. This paper describes this learning environment and also presents a preliminary study that was conducted to investigate the performance of this learning environment. The results of this study reveal that the learning environment is effective in helping students construct viable mental models of a relative simple concept, namely value assignment. The current aim of this work is to extend the environment to cover a number of key programming concepts and to make it available to fellow researchers and instructors for further investigation in their own teaching contexts.
Selective Editing Strategies for the U.S. Census Bureau Foreign Trade Data

The U.S. Census Bureau Foreign Trade Division (FTD) publishes monthly import and export statistics for the shipment of merchandise goods between foreign countries and the United States customs territories and foreign trade zones. The FTD publishes these statistics for over 17,000 import and 10,000 export commodity classifications. These data are not survey based, but collected from forms upon arrival or departure of merchandise goods. Data are edited and checked at every step of collection, processing, and tabulation. However, due to the limitations of the merchandise trade statistics program, monthly publication cells may still be subject to errors. We present score functions to rank edit failing records according to their potential impact on publication totals. In this report we present four separate score functions and the results of testing these score functions with the 2004 exports data.
Android Malware Analysis Platform

s—Malware for smartphones is a prominent threat to security , with Android leading the charge as a primary threat. To combat this, it is vital that the security field devote research into finding better methods of malware analysis and subsequently malware defense. As a result, initiative was taken to perform focused research on this subject in the form of an independent study. The purpose of the study is to develop a lab virtual environment for analyzing Android malware. The solution needs to be intuitive and centrally managed in order to be effective for a professor and their assistants. Work has begun on such a system, and the plan is to continue developing an environment until an adequate solution has been produced.
Kernel matrix approximation for parameters tuning of support vector regression

Parameters tuning is fundamental for support vector regression (SVR). Previous tuning methods mainly adopted a nested two-layer optimization framework, where the inner one solved a standard SVR for fixed hyper-parameters and the outer one adjusted the hyper-parameters, which directly led to high computational complexity. To solve this problem, we propose a kernel matrix approximation algorithm KMA-α based on Monte Carlo and incomplete Cholesky factorization. The KMA-α approximates a given kernel matrix by a low-rank matrix, which will be used to feed SVR to improve its performance and further accelerate the whole parameters tuning process. Finally, on the basis of the computational complexity analysis of the KMA-α, we verify the performance improvement of parameters tuning attributed to the KMA-α on benchmark databases. Theoretical and experimental results show that the KMA-α is a valid and efficient kernel matrix approximation algorithm for parameters tuning of SVR.
Framing, Motivated Reasoning, and Opinions about Emergent Technologies

How do individuals form opinions about new technologies? What role does factual information play? We address these questions by incorporating 2 dynamics, typically ignored in extant work: information competition and over-time processes. We present results from experiments on 2 technologies: carbon-nanotubes and genetically modified foods. We find that factual information is of limited utility—it does not have a greater impact than other background factors (e.g., values), it adds little power to newly provided arguments/frames (e.g., compared to arguments lacking facts), and it is perceived in biased ways once individuals form clear initial opinions (e.g., motivated reasoning). Our results provide insight into how individuals form opinions over time, and bring together literatures on information, framing, and motivated reasoning.
Gender in Domestic Programming: from Bricolage to Séances d'Essayage

Developments in ubiquitous computing mean that domestic appliances are increasingly programmable, providing new opportunities for end-user control and configuration. Unfortunately home programming, just as with end-user programming in professional contexts, is associated with stereotypically masculine learning styles. This is likely to result in future inequalities surrounding domestic technology. This paper summarises recent experimental evidence regarding the role of self-efficacy in learning through experimentation, demonstrates that similar genderlinked behaviour can be found in both domestic and professional contexts, and recommends a new approach to promoting such experimentation among women.
MAGIC

The performance of an IC degrades over its lifetime, ultimately resulting in IC failure. In this article, we present a hardware attack (called MAGIC) to maliciously accelerate NBTI aging effects in cores. In this attack, we identify the input patterns that maliciously age the pipestages of a core. We then craft a program that generates these patterns at the inputs of the targeted pipestage. We demonstrate the MAGIC-based attack on the OpenSPARC processor. Executing this program dramatically accelerates the aging process and degrades the processor’s performance by 10.92% in 1 month, bypassing existing aging mitigation and timing-error correction schemes. We also present two low-cost techniques to thwart the proposed attack.
Data-Oriented Parsing

Data-oriented parsing (DOP) is one of the leading paradigms in statistical natural language processing. In this volume, a collection of computational linguists offer a state-of-the-art overview of DOP, suitable for students and researchers in natural language processing and speech recognition as well as for computational linguistics. This handbook begins with the theoretical background of DOP and introduces the algorithms used in DOP as well as in other probabilistic grammar models. After surveying extensions to the basic DOP model, the volume concludes with a close study of the applications that use DOP as a backbone: speech understanding, machine translation, and language learning.
Email Folder Classification using Threads

While automatic classification of email is obviously a useful task to study, it is not obvious how to best utilize the rich metadata specific to email to improve the quality of the classification. In this paper, we propose a simple algorithm for using email threads to improve the precision of a personal email assistant’s automatic folder classification. We evaluate the approach on a large email dataset, and confirm the intuition that email threads can be used for high precision but low recall. Futhermore, we experimentally verify the intuition that using threads is hampered in its improvement of a classifier by the redundancy of their information.
Retinal vessel image segmentation based on correlational open active contours model

As the local contrast of retinal blood vessel is unstable, especially in unhealthy fundus images, an open active contour based method is presented for automated segmentation of retinal vessels. Firstly, we proposed a new initial parameters giving method and a pair of correlation open active contour model, based on these method, each vessel edge is modeled with an active contour which initiated by the corresponding boundary of Hessian vessel response; Secondly, to avoid the problem of snake from falling into the local minima, a local region pixel representation method named pixel's average intensity influence was proposed. Finally, a post-processing procedure based on context feature is introduced to filtering the non-vessel linear structures. The effectiveness of the proposed method was demonstrated through receiver operating characteristic analysis on the benchmarked dataset of DRIVE. The method achieves an average area under the receiver operating characteristic curve of 0.9543. Compared with some the state-of-the-art, the results of this method proposed show that our method is significantly better than the others. For example, the average accuracy, sensitivity and specificity achieved on the DRIVE are 0.9521, 0.7508, 0.9656, respectively.
MEAT: An Authoring Tool for Generating Adaptable Learning Resources

Mobile learning (m-learning) is a new trend in the e-learning field. The learning services in m-learning environments are supported by fundamental functions, especially the content and assessment services, which need an authoring tool to rapidly generate adaptable learning resources. To fulfill the imperious demand, this study proposes an authoring tool named Mobile E-learning Authoring Tool (MEAT) to produce adaptable learning contents and test items. In addition, the visualized course organization tool has also been provided to teachers to organize their teaching courses. All functionalities of the MEAT are designed according to the teachers’ feedback and their technological learning needs. To evaluate the MEAT, we have conducted an extensive comparison between the MEAT and other (adaptation) content authoring tools. The result indicates the MEAT is the only tool, which can produce adaptable contents and test items while supporting learning standard. In addition to technical comparison, the qualitative feedbacks from teachers and students are also shown in the evaluation section, and result points out the advantages and shortcomings of the MEAT. According to our findings, we have summarized some design principles for readers who are interested in designing elearning applications. It hopes our precious experiences can inspire readers to develop more valuable learning tools.
Fuzzy cloud service selection framework

Cloud computing is the latest computing paradigm that delivers hardware and software resources as virtualization enabled services. Recently, cloud service selection has emerged as an important research problem due to large number of cloud providers and their diverse service configurations, multiple selection criteria, and customer's fuzzy perception of Quality of Service (QoS). In this paper, we propose a novel fuzzy logic framework for cloud service selection based on the individual QoS criteria of customers. In this model, the necessary service configuration and run-time QoS data is collected from reliable sources such as monitoring services, customers' feedbacks, and certified cloud providers' information. The obtained results from conducted case study shows validity and applicability of the proposed framework.
A Novel Universal Neutralizing Monoclonal Antibody against Enterovirus 71 That Targets the Highly Conserved “Knob” Region of VP3 Protein

Hand, foot and mouth disease caused by enterovirus 71(EV71) leads to the majority of neurological complications and death in young children. While putative inactivated vaccines are only now undergoing clinical trials, no specific treatment options exist yet. Ideally, EV71 specific intravenous immunoglobulins could be developed for targeted treatment of severe cases. To date, only a single universally neutralizing monoclonal antibody against a conserved linear epitope of VP1 has been identified. Other enteroviruses have been shown to possess major conformational neutralizing epitopes on both the VP2 and VP3 capsid proteins. Hence, we attempted to isolate such neutralizing antibodies against conformational epitopes for their potential in the treatment of infection as well as differential diagnosis and vaccine optimization. Here we describe a universal neutralizing monoclonal antibody that recognizes a conserved conformational epitope of EV71 which was mapped using escape mutants. Eight escape mutants from different subgenogroups (A, B2, B4, C2, C4) were rescued; they harbored three essential mutations either at amino acid positions 59, 62 or 67 of the VP3 protein which are all situated in the “knob” region. The escape mutant phenotype could be mimicked by incorporating these mutations into reverse genetically engineered viruses showing that P59L, A62D, A62P and E67D abolish both monoclonal antibody binding and neutralization activity. This is the first conformational neutralization epitope mapped on VP3 for EV71.
On Verlinde-Like Formulas in c p,1 Logarithmic Conformal Field Theories

Two different approaches to calculate the fusion rules of the cp,1 series of logarithmic conformal field theories are discussed. Both are based on the modular transformation properties of a basis of chiral vacuum torus amplitudes, which contains the characters of the irreducible representations. One of these is an extension, which we develop here for a non-semisimple generalisation of the Verlinde formula introduced by Fuchs et al., to include fusion products with indecomposable representations. The other uses the Verlinde formula in its usual form and gets the fusion coefficients in the limit, in which the basis of torus amplitudes degenerates to the linear dependent set of characters of irreducible and indecomposable representations. We discuss the effects, which this linear dependence has on any result for fusion rules, which are calculated from these character’s modular transformation properties. We show that the two presented methods are equivalent. Furthermore we calculate explicit BPZ-like expressions for the resulting fusion rules for all p larger than 2.
Community resilience through entrepreneurship: the role of gender

Purpose This paper aims to contribute to entrepreneurship theorising by highlighting the salience of feminine caring positions in creating novel entrepreneurial roles and investigating how these roles contribute to community resilience. Using a critical feminist economics lens, alternative conceptualisations of the economy are expanded upon to reveal how an economic externality influences entrepreneurial discourse, gender roles and community resilience. Design/methodology/approach In this interpretive approach, empirical evidence is drawn from six months of intensive ethnographic research with 20 tourism handicraft micro-entrepreneurs in Crete and Epirus, Greece, in 2012 and hence in the context of a macroeconomic crisis. Ethnographic interviewing and participant observation are used as the methods to achieve the research objectives. Findings Thematic analysis is used to investigate how gender roles and entrepreneurial roles interact and how this interaction influences community resilience to an economic crisis. Using the critical theory to critique neoclassical economics interpretations of entrepreneurship, it becomes evident that politico-economic structures perpetuating feminised responsibility for social reproduction configure feminine entrepreneurial roles, and these roles have a positive effect on increasing community resilience. By conceptualising entrepreneurial involvement as being primarily for community gain, participants highlight how feminine entrepreneurial discourse differs from the neoclassical economics entrepreneurial discourse of entrepreneurial involvement being primarily for individual gain. Social implications This paper contributes to theoretical advancements on the role of gender in entrepreneurship and community resilience by investigating the entrepreneurs’ gendered responses to an exogenous shock. Providing insight into the role gender has in entrepreneurial adaptation and sustainable business practices means that new policies to combat social exclusion and promote rural development can be formulated. Originality/value The theoretical interplay between gender and entrepreneurship is investigated from a novel angle, that of critical feminist economics. The relationship between feminised interpretations of entrepreneurship and community resilience is brought to light, providing a unique insight into entrepreneurial resilience.
Organization of memory traces in the mammalian brain.

Perhaps the most fundamental issue in the broad field of neuronal substrates of learning and memory concerns the physical/biological mechanisms underlying long-term memory formation, storage, and retrieval in the mammalian brain. Considerable progress has been made in elucidating memory storage mechanisms in simpler invertebrate systems (Abrams et al 1991, Bergold et al 1990, Clark & Schuman 1991, Crow & Forrester 1990, Nelson & Alkon 1990), but similar advances have yet to be made in understanding the mammalian brain. As Lashley (1929) stressed so many years ago, the overriding problem for understanding memory mechanisms in the mammalian brain is localization of memory storage. Mechanisms of memory storage cannot be analyzed until the memory storage sites, whether localized or distributed, have been identified. Given the nature of the vertebrate brain, memories should, to some degree, be distributed over ensembles of neurons, but whether this is within a localized region, over several regions in a given structure, or over several structures, is not yet known for most forms of memory. Once the locus, or loci, of a given form of long-term memory has been determined, we should be able to analyze the mechanisms of memory storage. These will likely involve chemical/structural changes at synapses and possibly long-lasting changes in gene expression in the relevant neurons. Determining these mechanisms, however, will not inform us of what the memory is; the
Techno-economic Evaluation of FTTC/VDSL and FTTH Roll-Out Scenarios: Discounted Cash Flows and Real Option Valuation

Due to the increasing demand in data rates telecom operators are faced with the question of when to upgrade their access network infrastructure toward fiber-to-the-home (FTTH). In this paper a techno-economic study of fiber-to-the-cabinet/very high bit rate digital subscriber line (FTTC/VDSL) and FTTH deployments is performed. The economics and risks associated with the incumbent's decision to invest in dense urban and urban areas are analyzed using both discount cash flow (DCF) analysis and real options analysis (ROA). The results revealed that in the case of investment subsidization, the option value to expand in a later phase can significantly improve the financial metrics of the business cases. The analysis made is based on the ECOSYS techno-economic methodology and tool.
The accessibility of memory items in children's working memory

This thesis investigates the processes and systems that support recall in working memory. In particular it seeks to apply ideas from the adult-based dual-memory framework (Unsworth & Engle, 2007b) that claims primary memory and secondary memory are independent contributors to working memory capacity. These two memory systems are described as domain-general processes that combine control of attention and basic memory abilities to retain information. The empirical contribution comprises five experiments that specify how adults and children access, manage and report memory representations held in working memory. They provide a developmental perspective of the characteristics of these cognitive constructs. This thesis has combined traditional measures of primary and secondary memory (free recall) and methods used to classify individuals recall into the two independent systems, with new convergent paradigms in order to identify developmental trajectories of memory performance. The findings point towards qualitative and quantitative differences between how adults and children focus their recall from working memory. Primary and secondary memory capacities increase across childhood, but they seemingly develop at different rates. Between the ages of five- to ten-years children are reliant on the active maintenance of memory items within immediate memory, as controlled search and retrieval processes were far more demanding on children’s cognitive system. However, they did benefit from structured recall support and self-driven search processes, facilitating secondary memory. In addition, the experiments emphasised the impact of presentation modality on recall characteristics that are likely to be observed, and the susceptibility of information loss. Whilst auditory information reveals itself as highly accessible, it is also vulnerable to displacement and interference. In contrast visual representations appear to be more robust. Overall, the thesis will discuss the conceptual and empirical implications of whether the dual memory framework can help understand how working memory develops.