Miracle-dz/UniFAR-FTUs huggingface dataset:
Personalizing a Concept Similarity Measure in the Description Logic ELH with Preference Profile

Concept similarity measure aims at identifying a degree of commonality of two given concepts and is often regarded as a generalization of the classical reasoning problem of equivalence. That is, any two concepts are equivalent if and only if their similarity degree is one. However, existing measures are often devised based on objective factors, e.g. structural-based measures and interpretation-based measures. When these measures are employed to characterize similar concepts in an ontology, they may lead to unintuitive results. In this work, we introduce a new notion called concept similarity measure under preference profile with a set of formally defined properties in Description Logics. This new notion may be interpreted as measuring the similarity of two concepts under subjective factors (e.g. the agent's preferences and domain-dependent knowledge). We also develop a measure of the proposed notion and show that our measure satisfies all desirable properties. Two algorithmic procedures are introduced for top-down and bottom-up implementation, respectively, and their computational complexities are intensively studied. Finally, the paper discusses the usefulness of the approach to potential use cases.
Semi-Supervised Disfluency Detection

While the disfluency detection has achieved notable success in the past years, it still severely suffers from the data scarcity. To tackle this problem, we propose a novel semi-supervised approach which can utilize large amounts of unlabelled data. In this work, a light-weight neural net is proposed to extract the hidden features based solely on self-attention without any Recurrent Neural Network (RNN) or Convolutional Neural Network (CNN). In addition, we use the unlabelled corpus to enhance the performance. Besides, the Generative Adversarial Network (GAN) training is applied to enforce the similar distribution between the labelled and unlabelled data. The experimental results show that our approach achieves significant improvements over strong baselines.
Systematic Review with Network Meta-Analysis: Efficacy of Induction Therapy with a Second Biological Agent in Anti-TNF-Experienced Crohn's Disease Patients

Background and Aim Crohn's disease (CD) is a chronic inflammatory condition of the gastrointestinal tract with the potential to progress to a severe debilitating state. Treatment with biological agents is highly efficient, improving both short-term outcomes and long-term prognosis. Nonetheless, up to 60% of patients receiving biological therapy will exhibit nonresponse at some point. The optimal management of such patients is not clearly defined. Besides traditional anti-TNF agents (infliximab, adalimumab, and certolizumab), alternative biological therapies (natalizumab, vedolizumab, and ustekinumab) are currently available for the treatment of CD. Our aim was to analyze all available evidence regarding efficacy of a second biological in “biological-treatment-experienced” patients. Methods A systematic review of the literature was conducted using specific criteria for selecting relevant randomized clinical trials evaluating response to administration of secondary biological therapy in “anti-TNF-experienced” CD patients. Data from these studies was used to perform (a) traditional meta-analysis to ascertain the effect of secondary treatment versus placebo and (b) network meta-analysis to compare indirectly the efficacy of available biological agents. Results Out of initially 977 studies, only eight were included for analysis, providing a total of 1281 treated and 733 placebo-receiving CD patients. Treatment with a second biological was found to be superior to placebo for both induction of remission (OR 2.2, 95% CI 1.7 to 3) and response (OR 1.9, 95% CI 1.5 to 2.5), with global rates of 24% and 42%, respectively (placebo rate: 11% and 27%, p < 0.0001 for both). Indirect comparisons performed with network meta-analysis suggest no specific agent is clearly superior to others, with relatively better results observed for adalimumab in inducing disease remission. Conclusion In anti-TNF-experienced CD patients, secondary biological administration may be efficient, while no specific agent seems to outperform the others. Further research is needed to identify optimal management strategies for this challenging subset of patients.
Leveraging Meta-Embeddings for Bilingual Lexicon Extraction from Specialized Comparable Corpora

Recent evaluations on bilingual lexicon extraction from specialized comparable corpora have shown contrasted performance while using word embedding models. This can be partially explained by the lack of large specialized comparable corpora to build efficient representations. Within this context, we try to answer the following questions: First, (i) among the state-of-the-art embedding models, whether trained on specialized corpora or pre-trained on large general data sets, which one is the most appropriate model for bilingual terminology extraction? Second (ii) is it worth it to combine multiple embeddings trained on different data sets? For that purpose, we propose the first systematic evaluation of different word embedding models for bilingual terminology extraction from specialized comparable corpora. We emphasize how the character-based embedding model outperforms other models on the quality of the extracted bilingual lexicons. Further more, we propose a new efficient way to combine different embedding models learned from specialized and general-domain data sets. Our approach leads to higher performance than the best individual embedding model.
What drives global B2B e-commerce usage: an analysis of the effect of the complexity of trading system and competition pressure

ABSTRACT The purpose of this paper is to examine the conditions in which B2B e-commerce is most likely to diffuse widely at the country level. This paper examines the role of trading system complexity and pressure of competition on B2B EC diffusion by controlling Gross domestic product (GDP) per capita and Information and Communication Technology (ICT) infrastructure. Our sample comprises a pool of country-year dataset from 143 countries over a three-year period (2014–2016). The data were collected from Network Readiness Index Report and Global Competitiveness Report. The findings provide evidence that greater the complexity of trading relationships with the global economy and high level of pressure from the competition are the main forces for B2B use across countries. Consequently, the authors suggest that having a strong trade relationship with developed countries with matured B2B e-commerce practice is helpful in sustaining B2B e-commerce use across countries.
The Effects of Routine Outcome Monitoring (ROM) on Therapy Outcomes in the Course of an Implementation Process: A Randomized Clinical Trial

This study investigated the effects of the Partners for Change Outcome Management System (PCOMS) in adult outpatient treatment at a hospital-based mental health clinic. It also investigated whether the effects differed with the timing of the treatment within a 4-year implementation period, with clients’ initial distress levels, and between therapists. Adult clients (N = 170) were randomized to treatment as usual (TAU) or routine outcome monitoring (ROM). Twenty therapists provided therapy in both conditions. Therapy outcome was measured by the Behavior and Symptoms Identification Scale (BASIS-32). Data were analyzed in a series of multilevel models (MLMs). Clients in the ROM condition were 2.5 times more likely to demonstrate improvement than those in the TAU condition. Controlling for therapist variability, the overall effect size (ES) in favor of ROM was small (d = 0.26, p = .037). The superiority for ROM over TAU increased significantly over the duration of the study. ROM effects were not moderated by clients’ initial distress levels. Differences between therapists accounted for 9%–10% of the variability in outcomes, and there were no significant differences in ROM effects between therapists. ROM was associated with better treatment outcomes independent of clients’ initial distress levels. Clients treated later in the study benefitted more from ROM than those treated earlier.
Traditional Uses of Cannabinoids and New Perspectives in the Treatment of Multiple Sclerosis

Recent findings highlight the emerging role of the endocannabinoid system in the control of symptoms and disease progression in multiple sclerosis (MS). MS is a chronic, immune-mediated, demyelinating disorder of the central nervous system with no cure so far. It is widely reported in the literature that cannabinoids might be used to control MS symptoms and that they also might exert neuroprotective effects and slow down disease progression. This review aims to give an overview of the principal cannabinoids (synthetic and endogenous) used for the symptomatic amelioration of MS and their beneficial outcomes, providing new potentially possible perspectives for the treatment of this disease.
Sunitinib and Sorafenib Modulating Antitumor Immunity in Hepatocellular Cancer

Sorafenib and sunitinib are multiple tyrosine kinase inhibitors. Both of them have been approved by the US FDA in the treatment of patients with malignancies. In order to develop an effective and clinically useful chemoimmunotherapy modality against hepatocellular cancer (HCC), we investigate their tumoricidal and immune modulatory effect in the setting of HCC. In vitro experiments suggested that sunitinib and sorafenib both induced HCC cell apoptosis at an equivalent level, but stronger suppressive function to cell proliferation was detected in sorafenib. Correspondingly, treatment of tumor-bearing mice with sorafenib led to the suppression of tumor growth to a larger extent than sunitinib. Flow cytometry showed that treatment with sunitinib, not sorafenib, significantly reduced the frequency of regulatory T cells (Tregs) and myeloid-derived suppressive cells (MDSCs) in tumor-bearing mice; and allowed splenic lymphocytes to produce equivalent levels of IFN-γ and TNF-α in response to vaccination as that in wild type mice. This activation was not detected in control and sorafenib-treated tumor mice. In addition, treatment of tumor-bearing mice with sunitinib followed by adoptive transfer of tumor antigen-specific CD8+ T cells and immunization resulted in the additional suppression to tumor growth compared to sunitinib monotherapy. These results imply treatment with sunitinib, not sorafenib, is able to prevent tumor-induced immunotolerance and activate antitumorimmunity. Our data suggest that sunitinib may be a preferable chemotherapeutic agent to use in combination with immunotherapy for the treatment of HCC.
Association between breakfast consumption and educational outcomes in 9–11-year-old children

Abstract Objective Breakfast consumption has been consistently associated with health outcomes and cognitive functioning in schoolchildren. Evidence of direct links with educational outcomes remains equivocal. We aimed to examine the link between breakfast consumption in 9–11-year-old children and educational outcomes obtained 6–18 months later. Design Data on individual-level free school meal entitlement and educational outcomes (Statutory Assessment Tests (SATs) at Key Stage 2) were obtained via the SAIL databank and linked to earlier data collected on breakfast consumption. Multilevel modelling assessed associations between breakfast consumption and SATs. Setting Trial of the Primary School Free Breakfast Initiative in Wales. Subjects Year 5 and 6 students, n 3093 (baseline) and n 3055 (follow-up). Results Significant associations were found between all dietary behaviours and better performance in SATs, adjusted for gender and individual- and school-level free school meal entitlement (OR=1·95; CI 1·58, 2·40 for breakfast, OR=1·08; CI 1·04, 1·13 for healthy breakfast items). No association was observed between number of unhealthy breakfast items consumed and educational performance. Association of breakfast consumption with educational performance was stronger where the measure of breakfast consumption was more proximal to SATs tests (OR=2·02 measured 6 months prior to SATs, OR=1·61 measured 18 months prior). Conclusions Significant positive associations between self-reported breakfast consumption and educational outcomes were observed. Future research should aim to explore the mechanisms by which breakfast consumption and educational outcomes are linked, and understand how to promote breakfast consumption among schoolchildren. Communicating findings of educational benefits to schools may help to enhance buy-in to efforts to improve health behaviours of pupils.
SeVeN: Augmenting Word Embeddings with Unsupervised Relation Vectors

We present SeVeN (Semantic Vector Networks), a hybrid resource that encodes relationships between words in the form of a graph. Different from traditional semantic networks, these relations are represented as vectors in a continuous vector space. We propose a simple pipeline for learning such relation vectors, which is based on word vector averaging in combination with an ad hoc autoencoder. We show that by explicitly encoding relational information in a dedicated vector space we can capture aspects of word meaning that are complementary to what is captured by word embeddings. For example, by examining clusters of relation vectors, we observe that relational similarities can be identified at a more abstract level than with traditional word vector differences. Finally, we test the effectiveness of semantic vector networks in two tasks: measuring word similarity and neural text categorization. SeVeN is available at bitbucket.org/luisespinosa/seven.
Further Beneficial Effect of Hearing Aids on Speech Recognition Performance Besides Amplification: Importance of the Restoration of Symmetric Hearing.

OBJECTIVE To identify the improvement in auditory spectral resolution, temporal resolution, and speech recognition conferred with various hearing-aid (HA) technologies when the amplification effect of the HA is excluded, and to compare the beneficial effects depending on the restoration of symmetric hearing with different amplification settings in various hearing impairment conditions. STUDY DESIGN Prospective study. SETTING Tertiary referral center. SUBJECTS Sixty-two subjects who had used HAs for more than 3 months were divided into four groups: group 1, bilateral HAs (n = 28); group 2, unilateral HA in the better ear (n = 12); group 3, unilateral HA in the worse ear (n = 12); group 4, unilateral HA with symmetric hearing thresholds (n = 10). METHODS Aided performance and unaided performance were compared in each group. Four different psychoacoustic measurements were made: 1) spectral ripple discrimination; 2) temporal modulation detection; 3) speech recognition threshold in white noise; and 4) speech discrimination score (SDS) under quiet conditions. All test signals were presented by a loudspeaker located 1 m in front of the subject in a sound-attenuating booth, at their most comfortable levels, with or without HAs. RESULTS In group 1, the use of HAs significantly improved both SDS under quiet conditions and speech recognition in noise compared with the unaided condition. Group 2 showed better SDS under quiet conditions with HAs, and group 3 showed improved speech recognition in noise with HAs. In contrast, in group 4, no measurements differed with or without HAs. CONCLUSIONS These results demonstrate the benefits of HAs on speech perception in noise, in addition to their amplification effect, but only when HA restores symmetric hearing.
MEMD: A Diversity-Promoting Learning Framework for Short-Text Conversation

Neural encoder-decoder models have been widely applied to conversational response generation, which is a research hot spot in recent years. However, conventional neural encoder-decoder models tend to generate commonplace responses like “I don’t know” regardless of what the input is. In this paper, we analyze this problem from a new perspective: latent vectors. Based on it, we propose an easy-to-extend learning framework named MEMD (Multi-Encoder to Multi-Decoder), in which an auxiliary encoder and an auxiliary decoder are introduced to provide necessary training guidance without resorting to extra data or complicating network’s inner structure. Experimental results demonstrate that our method effectively improve the quality of generated responses according to automatic metrics and human evaluations, yielding more diverse and smooth replies.
Modeling with Recurrent Neural Networks for Open Vocabulary Slots

Dealing with ‘open-vocabulary’ slots has been among the challenges in the natural language area. While recent studies on attention-based recurrent neural network (RNN) models have performed well in completing several language related tasks such as spoken language understanding and dialogue systems, there has been a lack of attempts to address filling slots that take on values from a virtually unlimited set. In this paper, we propose a new RNN model that can capture the vital concept: Understanding the role of a word may vary according to how long a reader focuses on a particular part of a sentence. The proposed model utilizes a long-term aware attention structure, positional encoding primarily considering the relative distance between words, and multi-task learning of a character-based language model and an intent detection model. We show that the model outperforms the existing RNN models with respect to discovering ‘open-vocabulary’ slots without any external information, such as a named entity database or knowledge base. In particular, we confirm that it performs better with a greater number of slots in a dataset, including unknown words, by evaluating the models on a dataset of several domains. In addition, the proposed model also demonstrates superior performance with regard to intent detection.
Effect of Insulin-Induced Lipodystrophy on Glycemic Control among Children and Adolescents with Diabetes in Tikur Anbessa Specialized Hospital, Addis Ababa, Ethiopia

Background Lipodystrophy is one of the clinical complications of insulin injection that affects insulin absorption and leads to poor glycemic control. Objective To assess insulin-induced lipodystrophy and glycemic control. Methods A cross sectional study was done on 176 diabetic children and adolescents who inject insulin for a minimum of one year. First, anthropometric and clinical characteristics of the patients were recorded in questionnaire, and then observation and palpation techniques were used in assessing lipodystrophy. Result Out of the total 176 participants, 103 (58.5%) had insulin-induced lipodystrophy, of them 100 (97.1%) had lipohypertrophy and 3 (2.9%) had lipoatrophy. Being younger, failure to rotate the injection site every week and multiple reuse of insulin syringe had significant influence in development of insulin-induced lipohypertrophy. Lipohypertrophy in turn was associated with the use of higher dose of insulin and nonoptimal glycemic control. Conclusion Findings of this study revealed that in spite of using recombinant human insulin, the magnitude of the lipohypertrophy still remained high. Therefore, a routine workup of insulin-injecting patients for such complication is necessary, especially in the individuals who have a nonoptimal glycemic control.
Crystal Structure of the Labile Complex of IL-24 with the Extracellular Domains of IL-22R1 and IL-20R2

Crystal structure of the ternary complex of human IL-24 with two receptors, IL-22R1 and IL-20R2, has been determined at 2.15 Å resolution. A crystallizable complex was created by a novel approach involving fusing the ligand with a flexible linker to the presumed low-affinity receptor, and coexpression of this construct in Drosophila S2 cells together with the presumed high-affinity receptor. This approach, which may be generally applicable to other multiprotein complexes with low-affinity components, was necessitated by the instability of IL-24 expressed by itself in either bacteria or insect cells. Although IL-24 expressed in Escherichia coli was unstable and precipitated almost immediately upon its refolding and purification, a small fraction of IL-24 remaining in the folded state was shown to be active in a cell-based assay. In the crystal structure presented here, we found that two cysteine residues in IL-24 do not form a predicted disulfide bond. Lack of structural restraint by disulfides, present in other related cytokines, is most likely reason for the low stability of IL-24. Although the contact area between IL-24 and IL-22R1 is larger than between the cytokine and IL-20R2, calculations show the latter interaction to be slightly more stable, suggesting that the shared receptor (IL-20R2) might be the higher-affinity receptor.
Zero Pronoun Resolution with Attention-based Neural Network

Recent neural network methods for zero pronoun resolution explore multiple models for generating representation vectors for zero pronouns and their candidate antecedents. Typically, contextual information is utilized to encode the zero pronouns since they are simply gaps that contain no actual content. To better utilize contexts of the zero pronouns, we here introduce the self-attention mechanism for encoding zero pronouns. With the help of the multiple hops of attention, our model is able to focus on some informative parts of the associated texts and therefore produces an efficient way of encoding the zero pronouns. In addition, an attention-based recurrent neural network is proposed for encoding candidate antecedents by their contents. Experiment results are encouraging: our proposed attention-based model gains the best performance on the Chinese portion of the OntoNotes corpus, substantially surpasses existing Chinese zero pronoun resolution baseline systems.
Sprucing up the trees – Error detection in treebanks

We present a method for detecting annotation errors in manually and automatically annotated dependency parse trees, based on ensemble parsing in combination with Bayesian inference, guided by active learning. We evaluate our method in different scenarios: (i) for error detection in dependency treebanks and (ii) for improving parsing accuracy on in- and out-of-domain data.
The value of social search: Seeking collective personal experience in social Q&A

In this paper, we examine the value of social question-answering (Q&A) services as a platform for social search. We present a quasi-field study where we instructed 20 study participants to use a social Q&A service, Yahoo! Answers, for a period of one week, and interviewed them about their experience with Yahoo! Answers based on the questions (N=99) they posted to the site. The results indicate that participants turned to a social search system when they needed firsthand information, diverse perspectives, and others' value judgments. Participants also preferred social search systems over web search engines in situations where they could obtain tailored information, access original and non-popular information, filter out information, and interact with real people. Various strategies that participants employed to ensure that their questions would be likely to be answered were also identified. This study contributes to the field of information science by investigating a social Q&A service using the framework of social search from the information seeker's perspective. The results have implications for developers and designers of social search systems.