Miracle-dz/UniFAR-FTUs huggingface dataset:
From the patient to the population: Use of genomics for population screening

Genomic medicine is expanding from a focus on diagnosis at the patient level to prevention at the population level given the ongoing under-ascertainment of high-risk and actionable genetic conditions using current strategies, particularly hereditary breast and ovarian cancer (HBOC), Lynch Syndrome (LS) and familial hypercholesterolemia (FH). The availability of large-scale next-generation sequencing strategies and preventive options for these conditions makes it increasingly feasible to screen pre-symptomatic individuals through public health-based approaches, rather than restricting testing to high-risk groups. This raises anew, and with urgency, questions about the limits of screening as well as the moral authority and capacity to screen for genetic conditions at a population level. We aimed to answer some of these critical questions by using the WHO Wilson and Jungner criteria to guide a synthesis of current evidence on population genomic screening for HBOC, LS, and FH.
Theory in Emancipative Action: Aligning Action Research in Information Systems Education with Critical Social Research in Information Systems

As educators, we want to guide our students so that they develop to the best of their ability and are emancipated. As researchers in education, we often use action research. We use proven theories to guide our intervention to emancipate our students. Or do we? Recently, prominent information systems journals have published few papers in the field of information systems education. We demonstrate that the guidelines for action research from a critical social research perspective in information systems are not evident in action research studies in information systems education. The emancipative goals of pure critical social research and reliance on critical social theory to guide our intervention are lacking in these educational studies. Our aim is to provide alignment between educational action research in information systems and information systems research conducted from a critical social theory perspective. Our methodology is to explicitly propose phases of action research from a critical social research perspective, grounded both in information systems and education literature. Then, we demonstrate the value of this approach in a study on the improvement of a data warehousing module. We conclude that by using proven theories and reflecting on the presuppositions in a problem environment, the researcher is able to guide the development of students and the community.
Asymmetric Term Alignment with Selective Contiguity Constraints by Multi-Tape Automata

This article describes a HMM-based word-alignment method that can selectively enforce a contiguity constraint . This method has a direct application in the extraction of a bilingual terminological lexicon from a parallel corpus, but can also be used as a preliminary step for the extraction of phrase pairs in a Phrase-Based Statistical Machine Translation system. Contiguous source words composing terms are aligned to contiguous target language words. The HMM is transformed into a Weighted Finite State Transducer (WFST) and contiguity constraints are enforced by speciﬁc multi-tape WFSTs. The proposed method is especially suited when basic linguistic resources (morphological analyzer, part-of-speech taggers and term extractors) are available for the source language only.
Genome-wide association mapping of seed oligosaccharides in chickpea

Chickpea (Cicer arietinum L.) is one of the major pulse crops, rich in protein, and widely consumed all over the world. Most legumes, including chickpeas, possess noticeable amounts of raffinose family oligosaccharides (RFOs) in their seeds. RFOs are seed oligosaccharides abundant in nature, which are non-digestible by humans and animals and cause flatulence and severe abdominal discomforts. So, this study aims to identify genetic factors associated with seed oligosaccharides in chickpea using the mini-core panel. We have quantified the RFOs (raffinose and stachyose), ciceritol, and sucrose contents in chickpea using high-performance liquid chromatography. A wide range of variations for the seed oligosaccharides was observed between the accessions: 0.16 to 15.13 mg g-1 raffinose, 2.77 to 59.43 mg g-1 stachyose, 4.36 to 90.65 mg g-1 ciceritol, and 3.57 to 54.12 mg g-1 for sucrose. Kabuli types showed desirable sugar profiles with high sucrose, whereas desi types had high concentrations RFOs. In total, 48 single nucleotide polymorphisms (SNPs) were identified for all the targeted sugar types, and nine genes (Ca_06204, Ca_04353, and Ca_20828: Phosphatidylinositol N-acetylglucosaminyltransferase; Ca_17399 and Ca_22050: Remorin proteins; Ca_11152: Protein-serine/threonine phosphatase; Ca_10185, Ca_14209, and Ca_27229: UDP-glucose dehydrogenase) were identified as potential candidate genes for sugar metabolism and transport in chickpea. The accessions with low RFOs and high sucrose contents may be utilized in breeding specialty chickpeas. The identified candidate genes could be exploited in marker-assisted breeding, genomic selection, and genetic engineering to improve the sugar profiles in legumes and other crop species.
Prediction and predictability of global epidemics: the role of the airline transportation network

The systematic study of large-scale networks has unveiled the ubiquitous presence of connectivity patterns characterized by large scale hetero geneities and unbounded statistical fluctuations. These features affect dramatically the be havior of the diffusion processes occurring on networks, determining the ensuing statistical properties of their evolution pattern and dynamics. In this paper, we investigate the role of the large scale properties of the airline transportation network in determining the global evolution of emerging disease. We present a stochastic computational framework for the forecast of global epidemics that considers the complete world-wide air travel infrastructure complemented with census population data. We address two basic issues in global epidemic modeling: i) We study the role of the large scale properties of the airline transportation network in determining the global diffusion pattern of emerging diseases; ii) We evaluate the reliability of forecasts and outbreak scenarios with respect to the intrinsic stochasticit y of disease transmission and traffic flows. In order to address these issues we define a set of novel q uantitative measures able to characterize the level of heterogeneity and predictabil ity of the epidemic pattern. These
Retention Matters: Academic Libraries Leading the Way

ABSTRACT Academic libraries have always strived to be purposively involved in the university's mission attainment efforts. Today, the increase of retention levels is paramount, and, in many cases, it is at the center of higher education institution's strategic plans. For Andrews University, for example, the goal is to reach 80% in the next five years. In the United States, student retention efforts become even more critical due, in part, to declining enrollment numbers set off by soaring higher education costs. Academic librarians are aware of the library's role in the university's overall efforts to increase persistence rates and curtail attrition. Through a literature review, this article highlights how academic libraries are participating in this process. This study focuses on how academic libraries can be leaders in their Institutions by adopting a user-centered philosophy and services that will promote life-long learning, enhance students’ academic experience, and promote engagement, leading to retention.
CHATBOT FOR CONTINUOUS MOBILE LEARNING

The paper presents a cloud-based chatbot application for delivering educational content using a continuous mobile learning paradigm. The microlessons can be accessed by the learners in their available time slots, such as breaks or commuting. Another aim of the system is to target a specific group, the so-called “digital illiterates”, that have problems accessing typical e-Learning systems, as web applications. A knowledge base for basic computer access skills was implemented and showcased
Understanding alternatives in data analysis activities

Data workers are non-professional data scientists who engage in data analysis activities as part of their daily work. In this position paper, we share past and on-going work to understand data workers’ sense-making practices. We use multidisciplinary approaches to explore their human-tool partnerships. We introduce our current research on the role of alternatives in data analysis activities. Finally, we conclude with open questions and research directions.
Markov modeling of discrete fading channels using birth‐death channels

Finite‐state Markov channels (FSMCs) are widely employed to model the memory of time‐correlated discrete fading channels (DFCs). This work proposes a special class of FSMCs, denoted birth‐death channels (BDCs), to model the statistical behavior of the DFC. The number of states that generates accurate BDCs is estimated for a broad range of fading environments. Next, we evaluate the performance of error‐correcting codes over the BDC. A statistic of interest for this analysis is the P(m,n), the probability the channel generates up to m errors in a block of length n. It also proposed a combinatorial method to evaluate the probability P(m,n) for the BDC. The study of the statistical description of this channels has applications in modeling real channels and in designing coded systems for channels with memory.
Multi-objective linear fractional inventory model with possibility and necessity constraints under generalised intuitionistic fuzzy set environment

This study presented a multi-objective linear fractional inventory (LFI) problem with generalised intuitionistic fuzzy numbers. In modelling, the authors have assumed the ambiances where generalised trapezoidal intuitionistic fuzzy numbers (GTIFNs) used to handle the uncertain information in the data. Then, the given multi-objective generalised intuitionistic fuzzy LFI model was transformed into its equivalent deterministic linear fractional programming problem by employing the possibility and necessity measures. Finally, the applicability of the model is demonstrated with a numerical example and the sensitivity analysis under several parameters is investigated to explore the study.
An investigation of the effect of boundary conditions on the mechanical characteristics of an energy storing and returning prosthetic foot subject to non-destructive testing

It has been recognised that that the mechanical characteristics of Energy Storing and Returning (ESR) prosthetic running feet are not well understood and inconsistent static rating results have been reported elsewhere. The hypothesis that the inconsistent results are due to the varying foot mounting methods used during non-destructive testing was investigated. An ESR prosthetic running foot was rigidly mounted to the load cell of a hydraulic test machine while the metatarsal region of the foot contacted the machine bed. The friction between the foot and the machine bed was varied to create different mounting conditions. For each mounting condition, the foot was displaced vertically and force-displacement data was collected to compare the measured peak force and efficiency of the foot. It is shown that the mounting method affects both the peak measured force (1.2-2.83kN) and efficiency (71-97%) of the foot under test. A novel mounting strategy was then proposed and assessed to overcome the limitations of the previously used mounting methods. The new mounting method produced a linear spring rate across the entire displacement leading to an efficiency of 99.4% and peak measured force of 1.71kN which was in agreement with previously collected data during amputee running. It is concluded that the inconsistencies in reported mechanical characteristics of Energy Storing and Returning prosthetic feet are due to the varying foot mounting methods used during non-destructive testing. A novel foot mounting method has been shown to overcome the limitations of the previous research. Further investigation is needed to fully understand the effect of the prostheses mechanical properties on running performance.
Using Mobile Vocabulary Learning Apps as Aids to Knowledge Retention : Business Vocabulary Acquisition

In recent years, mobile technologies have begun to offer pedagogical affordances that promote meaningful learning, especially in vocabulary acqui sition, both inside and outside the classroom (Chai , Wong, & King, 2016; Duman, Orhon, & Gedik, 2014; Li u, 2016). Vocabulary knowledge is perhaps the most important component for foreign and second lan gu ge learners in higher education surroundings (Chen & Chung, 2008; Huckin, Haynes, & Coady, 1993) , as vocabulary is one of the building blocks of a language (Nation, 2001). Previous researchers have found that second and foreign language learners struggle with learning and memorizing vocabulary (O xford, 1990; Schmitt, 2010). Consequently, it is vital to provide learners with an effective learnin g tool to facilitate their vocabulary acquisition. Mobile assisted language learning has proven itself a capable educational tool to aid the retention of newly acquired language using ubiquitous learning ( Chinnery, 2006; Hwang & Chang, 2011; Hwang & Tsai, 2011). Among the various mobile assisted lang u ge learning technologies available, smartphones have perhaps the highest potential for enhancing th e educational process and creating interesting, motivating, stimulating and unconventional learning opportunities (Cui & Bull, 2005). This has led to a drastic increase in available vocabulary applicatio ns that may help foreign and second language learne rs to acquire vocabulary knowledge. It is with this ba ckground that we developed the in-house mobile application, Excel@EnglishPolyU, specifically to help Hong Kong University student s with business vocabulary. This article is a preliminary attempt to examine th effects of the app to enhance undergraduate students’ knowledge retention of business vocabular y of different difficulty levels through extended ubiquitous learning opportunities. The guiding ques tions are:
Extracting activity patterns from taxi trajectory data: a two-layer framework using spatio-temporal clustering, Bayesian probability and Monte Carlo simulation

ABSTRACT Global positioning system (GPS) data generated from taxi trips is a valuable source of information that offers an insight into travel behaviours of urban populations with high spatio-temporal resolution. However, in its raw form, GPS taxi data does not offer information on the purpose (or intended activity) of travel. In this context, to enhance the utility of taxi GPS data sets, we propose a two-layer framework to identify the related activities of each taxi trip automatically and estimate the return trips and successive activities after the trip, by using geographic point-of-interest (POI) data and a combination of spatio-temporal clustering, Bayesian inference and Monte Carlo simulation. Two million taxi trips in New York, the United States of America, and ten million taxi trips in Shenzhen, China, are used as inputs for the two-layer framework. To validate each layer of the framework, we collect 6,003 trip diaries in New York and 712 questionnaire surveys in Shenzhen. The results show that the first layer of the framework performs better than comparable methods published in the literature, while the second layer has high accuracy when inferring return trips.
Detecting Topic-Oriented Speaker Stance in Conversational Speech

Being able to detect topics and speaker stances in conversations is a key requirement for developing spoken language understanding systems that are personalized and adaptive. In this work, we explore how topic-oriented speaker stance is expressed in conversational speech. To do this, we present a new set of topic and stance annotations of the CallHome corpus of spontaneous dialogues. Speciﬁcally, we focus on six stances—positivity, certainty, surprise, amusement, interest, and comfort—which are useful for characterizing important aspects of a conversation, such as whether a conversation is going well or not. Based on this, we investigate the use of neural network models for automatically detecting speaker stance from speech in multi-turn, multi-speaker contexts. In particular, we examine how performance changes depending on how input feature representations are constructed and how this is related to dialogue structure. Our experiments show that incorporating both lexical and acoustic features is beneﬁcial for stance detection. However, we observe variation in whether using hierarchical models for encoding lexical and acoustic information improves performance, suggesting that some aspects of speaker stance are expressed more locally than others. Overall, our ﬁndings highlight the importance of modelling interaction dynamics and non-lexical content for stance detection.
MTES: An Intelligent Trust Evaluation Scheme in Sensor-Cloud-Enabled Industrial Internet of Things

As an enabler for smart industrial Internet of Things (IoT), sensor cloud facilitates data collection, processing, analysis, storage, and sharing on demand. However, compromised or malicious sensor nodes may cause the collected data to be invalid or even endanger the normal operation of an entire IoT system. Therefore, designing an effective mechanism to ensure the trustworthiness of sensor nodes is a critical issue. However, existing cloud computing models cannot provide direct and effective management for the sensor nodes. Meanwhile, the insufficient computation and storage ability of sensor nodes makes them incapable of performing complex intelligent algorithms. To this end, mobile edge nodes with relatively strong computation and storage ability are exploited to provide intelligent trust evaluation and management for sensor nodes. In this article, a mobile edge computing-based intelligent trust evaluation scheme is proposed to comprehensively evaluate the trustworthiness of sensor nodes using probabilistic graphical model. The proposed mechanism evaluates the trustworthiness of sensor nodes from data collection and communication behavior. Moreover, the moving path for the edge nodes is scheduled to improve the probability of direct trust evaluation and decrease the moving distance. An approximation algorithm with provable performance is designed. Extensive experiments validate that our method can effectively ensure the trustworthiness of sensor nodes and decrease the energy consumption.
Modelling text meta-properties in automated text scoring for non-native English writing

Automated text scoring (ATS) is the task of automatically scoring a text based on some given grading criteria. This thesis focuses on ATS in the context of free-text writing exams aimed at learners of English as a foreign language (EFL). The benefit of an ATS system is primarily to provide instant and consistent feedback to language learners, and service reliability also forms a crucial part of an ATS system. Based on previous work, we investigated only partially explored meta-properties in text and integrated them into a machine learning based ATS model across multiple datasets: • In most previous work, the proposed models implicitly assume that texts produced by learners in an exam are written independently. However, this is not true for the exams where learners are required to compose multiple texts. We hence explicitly instructed our model which texts are written by the same learner, which boosts model performance in most cases. • We used three intra-exam properties within the same exam including prompt, genre and task as a starting point, and we showed that explicitly modelling these properties via frustratingly easy domain adaptation (FEDA) (Daume III, 2007) can positively affect model performance in some cases. Furthermore, modelling multiple intra-exam properties together is better than modelling any single property individually or no property in four out of five test sets. • We studied how to utilise and combine learners’ responses from multiple writing exams. We also proposed a new variant of the transfer-learning ATS model which mitigates the drawbacks of previous work. This variant first builds a ranking model across multiple datasets via FEDA, and the ranking score of each text predicted by the ranking model is used as an extra feature in the baseline model. This variants gives improvement compared to a baseline model on the development sets in terms of root-mean-square error. Furthermore, the transfer-learning model utilising multiple datasets tuned on each development set is always better than the baseline model on the corresponding test set. • We found that different datasets favour different meta properties. We therefore combined all the models looking at different meta properties together using ensemble learning. Compared to the baseline model, the combined model has a statistically significant improvement on all the test sets in terms of root-meansquare error based on a permutation test.
Comparative Analysis of Performance of Controllers in Software Defined Networks using Mininet

—This Software Defined Networks, the latest trend in networking architecture aims to provide agile and flexible networks. It is one of the most highlighted research areas in networking. As the number of nodes increases in the network it is difficult or sometime impossible to handle all the nodes which are connected to same or different network in different scenarios. This was one of the limitations in the traditional networking. This research work aims to analyze and test the capability of the controllers to come up with a solution to this issue. The performance analysis is done based on scalability and throughput on different number of nodes and varying topology scenarios. From among the multiple existing controllers that provide Software Defined Networks functionalities, two of the best choices-the Beacon Controller and the Floodlight Controller are used along with Mininet for performance analysis using simulation.
An Early PDP Connectionist Model for Describing the Behavioral Phenomenon

The focus of modern neuroscience on cognitive processes has relegated to behavior the epiphenomenal status of neural processing and the difficulties generated by this interpretation have encouraged the use of computational models. However, the implementation based on inferred cognitive constructs has been inefficient. The objective of this work was to review the concept of behavior by a selectionist approach and propose a connectionist computational model that operates integrally with its neurophysiological bases. The behavioral phenomenon was functionally defined and described at different levels of analysis. Functional levels make it possible to understand why behavioral phenomena exist, while topographic levels describe how morphophysiological mechanisms implement the response. The connectionist notions of PDP ANNs formalizes the proposal. The model stands out for contextualizing neural processing as part of the response, addressing the behavioral phenomenon as a whole that needs to be explained in its most different levels of analysis.