Miracle-dz/UniFAR-FTUs huggingface dataset:
I/S-Race: An iterative Multi-Objective Racing Algorithm for the SVM Parameter Selection Problem

Finding appropriate values for the parameters of an algorithm is an important and time consuming task. Recent studies have shown that racing algorithms can eectively handle this task. This paper presents a multi-objective racing algorithm called iterative S-Race (I/S-Race), which eciently addresses multi-objective model selection problems in the sense of Pareto optimality. We evaluate the I/S-Race for selecting parameters of SVMs, considering 20 widely-used classication datasets. The results revealed that the I/S-Race is an ecient and eective algorithm for au- tomatic model selection, when compared to a brute-force multi-objective selection approach and the S-Race algorithm.
Twenty‐nail dystrophy in a girl with incontinentia pigmenti

A 9‐year‐old girl with incontinentia pigmenti (IP) diagnosed soon after birth subsequently developed 20‐nail dystrophy at the age of 3 years. This persists unchanged 6 years later, and she has no other evidence of any other cutaneous disease, nor a family history of IP or nail dystrophy. Twenty‐nail dystrophy has been associated with a number of diseases including eczema, lichen planus, alopecia areata and psoriasis and a number of other congenital associations, but the association with IP appears to be unique.
Retrieval Performance for Malay Quran

Information Retrieval (IR) plays an important role in retrieving information related to the user’s query. IR relies on finding relevant data from a set of knowledge resources, such as the Quran. Finding information from the Quran can be based on metadata, indexing, or other content-based methods. The Quran is the most widely read book in the world and automating knowledge retrieval from this religious book in general is very challenging. One of the challenging tasks is when the Quran has been translated into various languages around the world by Muslim experts, such as Malay. This has led to the development of a number of search applications in multiple languages, which can retrieve knowledge based on keywords. Retrieving the knowledge from the Quran using keywords has several fundamental problems. In many cases, the searching cannot retrieve the relevant concept or knowledge and verses. Therefore this research investigated the existing problems related to the study of Quran and studies the current search techniques used in research on the Quran, especially the Malay Quran. This research will help researchers understand and learn the current scenario of research on the Quran.
A Model With Temperature-Dependent Exponent for Hot-Carrier Injection in High-Voltage nMOSFETs Involving Hot-Hole Injection and Dispersion

An improved hot-hole-involved interface-state generation model is proposed for hot-carrier injection (HCI) degradation in high-voltage (HV) nMOSFETs. This model is based on experiments over a wide range of temperatures, voltage conditions, simulation results, and the underlying physical mechanisms. The model provides a thorough picture of an HCI system in HV nMOSFETs, with hot-hole injection related to an additional maximum electric-field region. The hot-hole injection in HCI is assumed to introduce deeper localized hydrogen states in gate-oxide films than that in negative-bias temperature instabilities. This result facilitates the dispersive transport of hydrogen. Therefore, HCI degradation in HV transistors is explained within the framework of disorder-controlled hydrogen kinetics. The power-law model can successfully predict temperature dependences for HCI degradation.
A Comparative Study of Different Approaches for Tracking Communities in Evolving Social Networks

In real-world social networks, there is an increasing interest in tracking the evolution of groups of users and detecting the various changes they are liable to undergo. Several approaches have been proposed for this. In studying these approaches, we observed that most of them use a two-stage process. In the first stage, they run an algorithm to identify groups of users at each timestamp. In the second stage, a pairwise comparison based on a similarity measure is employed to track groups of users and detect changes they may undergo. While the majority of existing approaches use a two-stage process, they all run different algorithms to identify communities and rely on different similarity measures to track groups of users over time. Noting that the different approaches may perform differently depending on the dynamic social network under investigation, we decided to make a high level survey of some existing tracking approaches and then do a comparative analysis of some of them. In our analysis, we compared the algorithms in two main situations: (1) when groups of users do not overlap and (2) when the groups are overlapping. The study was done on three different testbeds extracted from the DBLP, Autonomous System (AS) and Yelp datasets.
An indexed full-text search method of printed document images with an M-tree

This paper describes an indexed full-text search method of printed document images for the occurrences of a specified character string image. It is based on N-gram-based indexing with an M-tree index structure. It is important to facilitate a full-text search method of historical letterpress printing collections to be able to deal with them. The proposed full-text search method is independent of difference of languages and fonts because it uses a pseudo-coding scheme that is based on the statistical features of character shapes. Conventional Word Spotting methods need a sequential scan of the whole document image and a matching calculation of the whole descriptor sequence of a document. The proposed N-gram-based indexing method accelerates the search process with an M-tree. Our method was evaluated in terms of its search time and of recall-precision curve for N-gram-based query strings. Our experiments demonstrated that the proposed approach achieves search times that are one hundred times faster improvement about search time.
Is This a Paraphrase? What Kind? Paraphrase Boundaries and Typology

A precise and commonly accepted definition of paraphrasing does not exist. This is one of the reasons that have prevented computational linguistics from a real success when dealing with this phenomenon in its systems and applications. With the aim of helping to overcome this difficulty, in this article, new insights on paraphrase characterization are provided. We first overview what has been said on paraphrasing from linguistics and the new lights shed on the phenomenon from computational linguistics. Under the light of the shortcomings observed, the paraphrase phenomenon is studied from two different perspectives. On the one hand, insights on paraphrase boundaries are set out analyzing paraphrase borderline cases and the interaction of paraphrasing with related linguistic phenomena. On the other hand, a new paraphrase typology is presented. It goes beyond a simple list of types and is embedded in a linguistically-based hierarchical structure. This typology has been empirically validated through corpus annotation and its application in the plagiarism-detection domain.
Tree-Based Virtual Backbone Construction in Mobile Ad Hoc Networks

The connected dominating set (CDS) has been extensively used for routing and broadcast in mobile ad hoc networks. While existing CDS protocols are successful in constructing CDS of small size, they either require localized information beyond immediate neighbors, lack the mechanism to properly handle nodal mobility, or involve lengthy recovery procedure when CDS becomes corrupted. In this paper, we introduce the tree-based CDS protocols, which first elect a number of initiators distributively and then utilize timers to construct a CDS from initiators with the minimum localized information. We demonstrate that our CDS protocols are capable of maintaining CDS in the presence of changes of network topology. Depending on the number of initiators, there are two versions of our tree-based CDS protocols. The Single-Initiator (SI) works the best when the network diameter is given. On the other hand, the Multi-Initiator version (MI) built on top of SI is a localized CDS protocol and possesses all of the advantages of SI. We evaluate our protocols by both the ns-2 simulation and an analytical model. Compared with the other known CDS protocols, the simulation results demonstrate that both SI and MI produce and maintain CDS of very competitive size. The analytical model shows the expected convergence time and the number of messages required by SI and MI in the construction of CDS, which match closely to our simulation results. This helps to establish the validity of our simulation. In addition to the two tree-based CDS protocols, we proposed the two post optimization techniques, Fast-Convergence Dominator Tree Construction (FC-DTC) and Extended Mobility Handling (EMH). FC-DTC significantly reduces the convergence time required by SI and MI, and EMH enables SI and MI to adapt to the topology changes more efficiently. The simulation and analytical studies demonstrate these extensions drastically improve the performance of SI and MI.
Avaliação econômica da produção de mudas de erva-mate

A avaliacao economica e importante para todos os investimentos, independente do faturamento, tamanho, estrutura, atividade e mercado. Diante do exposto, objetivou-se com presente trabalho realizar uma avaliacao economica de um viveiro florestal de medio porte destinado a producao de mudas de erva-mate. Os dados analisados foram coletados no municipio de Canoinhas (SC) e foram considerados os custos de implantacao, manutencao e operacao deste viveiro. O horizonte temporal utilizado foi vinte e cinco anos de investimento, considerando tres precos de mudas praticados na cidade e cinco taxas de juros para a analise de sensibilidade. Foram calculados o payback , razao receitas/custos, custo medio de producao, taxa interna de retorno (TIR) e valor presente liquido (VPL). O custo medio de producao observado foi baixo e foi inferior aos valores de comercializacao das mudas. Os tres precos das mudas comercializadas apresentaram resultados economicamente viaveis, porem, quando a muda e vendida com um maior preco, teve-se melhores resultados, como o payback mais curto e alta TIR. O aumento do preco de venda da muda aumentou a rentabilidade do projeto, enquanto que o aumento da taxa de juros aplicado diminuiu a rentabilidade.
Weighing the role of multi-criteria communities for recommender systems

Recommender systems RSs have been designed to deal with the information overload problem by providing users with personalised recommendations, and now are becoming increasingly popular. Most RSs are based on collaborative filtering which is a technique predicting users' preferences by using opinions of like-minded users through their ratings on items. Recently, context-aware recommender systems CARSs have been developed to exploit additionally contextual information such as time, place, weather and so forth for providing better recommendations. However, the majority of CARSs work on ratings as a unique criterion for building communities and ignore other available data. This paper focuses on integrating multi-criteria communities into CARSs to enhance the context-aware recommendation quality. The integration of multi-criteria communities could allow users to take advantage of different natures of communities rather than exploiting only single-criterion rating ones. The experiments show that our proposed method outperforms comparative context-aware approaches.
Broad antibiotic resistance profile of the subclass B3 metallo‐β‐lactamase GOB‐1, a di‐zinc enzyme

The metallo‐β‐lactamase (MBL) GOB‐1 was expressed via a T7 expression system in Escherichia coli BL21(DE3). The MBL was purified to homogeneity and shown to exhibit a broad substrate profile, hydrolyzing all the tested β‐lactam compounds efficiently. The GOB enzymes are unique among MBLs due to the presence of a glutamine residue at position 116, a zinc‐binding residue in all known class B1 and B3 MBL structures. Here we produced and studied the Q116A, Q116N and Q116H mutants. The substrate profiles were similar for each mutant, but with significantly reduced activity compared with that of the wild‐type. In contrast to the Q116H enzyme, which bound two zinc ions just like the wild‐type, only one zinc ion is present in Q116A and Q116N. These results suggest that the Q116 residue plays a role in the binding of the zinc ion in the QHH site.
Combining diagnostic test results to increase accuracy.

When multiple diagnostic tests are performed on an individual or multiple disease markers are available it may be possible to combine the information to diagnose disease. We consider how to choose linear combinations of markers in order to optimize diagnostic accuracy. The accuracy index to be maximized is the area or partial area under the receiver operating characteristic (ROC) curve. We propose a distribution-free rank-based approach for optimizing the area under the ROC curve and compare it with logistic regression and with classic linear discriminant analysis (LDA). It has been shown that the latter method optimizes the area under the ROC curve when test results have a multivariate normal distribution for diseased and non-diseased populations. Simulation studies suggest that the proposed non-parametric method is efficient when data are multivariate normal.The distribution-free method is generalized to a smooth distribution-free approach to: (i) accommodate some reasonable smoothness assumptions; (ii) incorporate covariate effects; and (iii) yield optimized partial areas under the ROC curve. This latter feature is particularly important since it allows one to focus on a region of the ROC curve which is of most relevance to clinical practice. Neither logistic regression nor LDA necessarily maximize partial areas. The approaches are illustrated on two cancer datasets, one involving serum antigen markers for pancreatic cancer and the other involving longitudinal prostate specific antigen data.
Dynamic Domain Specific Languages for Trust Models

We propose the development of a framework for the dynamic interpretation of Trust models, defined via a Domain Specific Language. A trust model usually defines abstractions, the interpretation of which change in conjunction with changes in the domain or changes in the context in which the program executes. In a scenario where trust model assumptions encoded in the DSL change, programmers must still work with the existing DSL, and therefore take more effort to describe their program or sometimes fail to specify their intent. In such changing circumstances a trust model risks becoming less effective and fit for purpose. We seek to develop an approach in which a trust model adapts to a changing environment by making the underlying DSL less restrictive, maintaining ¿exibility and adaptability to cope with changing or novel contexts without reducing the expressiveness of the abstractions used.
Mib1 modulates dynamin 2 recruitment via Snx18 to promote Dll1 endocytosis for efficient Notch signaling

Notch signaling regulates normal development and tissue homeostasis. Ligand endocytosis plays critical roles in Notch signaling activation. Endocytic proteins such as epsin and dynamin participate in Notch ligand activity by mediating Notch ligand endocytosis. The ubiquitin ligase Mib1 also plays essential roles in Notch signaling via Notch ligand ubiquitination. However, the molecular links between Mib1 and endocytic proteins have not been fully defined. Here, we show that Mib1 is involved in dynamin 2 recruitment to Dll1 and that Snx18, which interacts with dynamin 2, modestly regulates Dll1 endocytosis. Furthermore, the ubiquitin ligase activity of Mib1 is induced by Notch ligand–receptor interactions. Mib1 promotes the interaction between dynamin 2 and Snx18 in an ubiquitin ligase activity‐dependent manner. These results suggest that Mib1 modulates dynamin recruitment by regulating the interaction between Snx18 and dynamin 2, thereby helping to ensure the efficient signaling activity of Notch ligands.
Social Innovators and Developmental Stages

The past two decades have seen increased interest in social innovation as a leading source of change. While social innovation literature experienced vast growth, particularly in the Western world, there is limited research on the individuals engaged in it, especially concerning their leadership practice. The extant research focuses mostly on identifying traits and competencies exhibited by social innovators. These individuals are often thought of as exceptional people able to think systemically to identify and solve problems in novel ways. The field runs the risk of perpetuating the idea that to be a social innovator one must possess a specific list of impressive qualities and that possessing such qualities is sufficient to be a successful social innovator. Consequently, there is an incomplete picture, a one-dimensional portrait of social innovators. There is a need for comprehensive insight and knowledge regarding how they think, operate, and make meaning of their experiences. This study explored how social innovators make sense of their life and work, and how this meaning informs their leadership practice. Through qualitative interviews, the study investigated factors shaping participants’ journeys into social innovation, as well as emerging patterns concerning how they make meaning. The study identified the developmental stage of each participant through the use of constructive-developmental theory, a stage theory of adult development, and the administration of Cook-Greuter's instrument, Leadership Maturity Assessment for Professionals. The research examined relationships between participants’ assessed stages and the way they described themselves as social innovators. The results revealed that participants clustered around four developmental stages, with the majority within stages representing the societal expectations for most adults, particularly in professional settings. The analysis identified four critical factors shaping their social innovation journeys, as well as four strategies participants used to make meaning of their experiences. The study examines how these factors intersect and proposes recommendations for those interested in developing future social innovators. The findings of this study add a nuanced, more comprehensive understanding of social innovators. Perhaps the most significant contribution of the study is its potential to help reframe the conversation about who social innovators are beyond the current exceptional trait narrative.
An incremental, exploratory and transformational environment for lazy functional programming

Abstract Most programming environments for functional languages offer a single tool used to evaluate programs – either a batch compiler or an interpreter with a read-eval-print loop. This paper presents a programming environment that supports not only evaluation, but also a range of other programming activities including transformation. The environment is designed to encourage working in an incremental and exploratory style, avoiding constraints on the order in which things must be done yet guarenteeing security. What has already been done towards the development of a program automatically persists, as does information about what has yet to be done. For instance, new laws can be introduced as conjectures and used in program transformation, but full details of proof obligations and dependencies are maintained. The paper outlines the functional language supported by the environment, and uses an extended example to illustrate program construction, execution, tracing, modification and transformation.
A Bipartite Graph-Based Ranking Approach to Query Subtopics Diversification Focused on Word Embedding Features

Web search queries are usually vague, ambiguous, or tend to have multiple intents. Users have different search intents while issuing the same query. Understanding the intents through mining subtopics underlying a query has gained much interest in recent years. Query suggestions provided by search engines hold some intents of the original query, however, suggested queries are often noisy and contain a group of alternative queries with similar meaning. Therefore, identifying the subtopics covering possible intents behind a query is a formidable task. Moreover, both the query and subtopics are short in length, it is challenging to estimate the similarity between a pair of short texts and rank them accordingly. In this paper, we propose a method for mining and ranking subtopics where we introduce multiple semantic and content-aware features, a bipartite graphbased ranking (BGR) method, and a similarity function for short texts. Given a query, we aggregate the suggested queries from search engines as candidate subtopics and estimate the relevance of them with the given query based on word embedding and content-aware features by modeling a bipartite graph. To estimate the similarity between two short texts, we propose a Jensen-Shannon divergence based similarity function through the probability distributions of the terms in the top retrieved documents from a search engine. A diversified ranked list of subtopics covering possible intents of a query is assembled by balancing the relevance and novelty. We experimented and evaluated our method on the NTCIR-10 INTENT-2 and NTCIR-12 IMINE-2 subtopic mining test collections. Our proposed method outperforms the baselines, known related methods, and the official participants of the INTENT-2 and IMINE-2 competitions. key words: Subtopic Mining, Query Intent, Diversification, Word Embedding, Bipartite Graph
Sparse source imaging in electroencephalography with accurate field modeling

We have developed a new L1‐norm based generalized minimum norm estimate (GMNE) and have fully characterized the concept of sparseness regularization inherited in the proposed algorithm, which is termed as sparse source imaging (SSI). The new SSI algorithm corrects inaccurate source field modeling in previously reported L1‐norm GMNEs and proposes that sparseness a priori should only be applied to the regularization term, not to the data term in the formulation of the regularized inverse problem. A new solver to the newly developed SSI has been adopted and known as the second‐order cone programming. The new SSI is assessed by a series of simulations and then evaluated using somatosensory evoked potential (SEP) data with both scalp and subdural recordings in a human subject. The performance of SSI is compared with other L1‐norm GMNEs and L2‐norm GMNEs using three evaluation metrics, i.e., localization error, orientation error, and strength percentage. The present simulation results indicate that the new SSI has significantly improved performance in all evaluation metrics, especially in the metric of orientation error. L2‐norm GMNEs show large orientation errors because of the smooth regularization. The previously reported L1‐norm GMNEs show large orientation errors due to the inaccurate source field modeling. The SEP source imaging results indicate that SSI has the best accuracy in the prediction of subdural potential field as validated by direct subdural recordings. The new SSI algorithm is also applicable to MEG source imaging. Hum Brain Mapp 2008. © 2007 Wiley‐Liss, Inc.
Pitch as a medium: a new approach to psychophysical scaling.

Certain psychophysical continua have a primarily morphophoric function: they are the media in which patterns occur. The appropriate scale for any such medium is one that reflects the invariances preserved in transposition behavior. The results of two experiments designed to scale pitch by transposition methods lead to the conclusion that the musical scale, not the mel scale, describes the morphophoric function of pitch. However, an abrupt breaking point is evident at about 5,000 Hz, above which transposition behavior is erratic.