Miracle-dz/UniFAR-FTUs huggingface dataset:
An analysis of quiz in MOOC

Quiz is an assessment component in Massive Open Online Course (MOOC) for assessing knowledge of the learner and to provide feedback to help them in constructing their own learning path. MOOC is offered by the institutions on an existing platform — with or without customization, or a new platform may be developed; in all cases, there is a need to specify the features required for quiz in MOOC. In this paper, we present a checklist of features for quiz incorporated in MOOC, from the learner's perspective. The use-case based approach has been followed for identifying the features of quiz in MOOC. The checklist helps during requirement specification of quiz in MOOC, as the provider can select the desired features from the checklist.
Sport Expertise: The Role of Precise Timing of Verbal-Analytical Engagement and the Ability to Detect Visual Cues

This study proposed that relative timing of high-alpha (10–12 Hz) left (T3) and right (T4) cortical temporal electroencephalographic (EEG) power levels would differentiate performance groups in a reactive sport such as cricket batting. The time course of EEG event-related alpha synchronisation (ERS) and desynchronisation was investigated in two groups (eight skilled and ten less skilled) of right-handed cricket batsmen whilst viewing projected video footage of a bowler delivering a randomised series of 24 deliveries repeated 10 times (total of 240 deliveries). Ball release from the bowler's hand was used as the corresponding reaction cue. Participants were instructed to press one of two buttons on a keypad to identify in-swingers or out-swingers. T3 ERS was significantly greater in skilled batsmen from approximately 1500 ms prior to ball release, but differences reduced close to ball release, reaching nonsignificance by 250 ms. There was no significant difference in T4 between the groups. This study uniquely highlights that the relative timing of the T3 high-alpha ERS state appears to differentiate batting skill groups in a reactive task.
Low-dose angiostatic tyrosine kinase inhibitors improve photodynamic therapy for cancer: lack of vascular normalization

Photodynamic therapy (PDT) is an effective clinical treatment for a number of different cancers. PDT can induce hypoxia and inflammation, pro‐angiogenic side effects, which may counteract its angio‐occlusive mechanism. The combination of PDT with anti‐angiogenic drugs offers a possibility for improved anti‐tumour outcome. We used two tumour models to test the effects of the clinically approved angiostatic tyrosine kinase inhibitors sunitinib, sorafenib and axitinib in combination with PDT, and compared these results with the effects of bevacizumab, the anti‐VEGF antibody, for the improvement of PDT. Best results were obtained from the combination of PDT and low‐dose axitinib or sorafenib. Molecular analysis by PCR revealed that PDT in combination with axitinib suppressed VEGFR‐2 expression in tumour vasculature. Treatment with bevacizumab, although effective as monotherapy, did not improve PDT outcome. In order to test for tumour vessel normalization effects, axitinib was also applied prior to PDT. The absence of improved PDT outcome in these experiments, as well as the lack of increased oxygenation in axitinib‐treated tumours, suggests that vascular normalization did not occur. The current data imply that there is a future for certain anti‐angiogenic agents to further improve the efficacy of photodynamic anti‐cancer therapy.
A learning method for association between vision and ego-motion which is Capable of Adapting to Arbitrary Image Distortion

The present study considers how the function of vision can be constructed from the experiences of a robot. Specifically, a learning method is proposed for the association between vision and ego-motion. A novel concept of a time evolution operator on sensory inputs is introduced. The time variation of sensory inputs is obtained when the operator is applied to the current inputs. Association between the time evolution operator and the egomotion is learned. Once the learning has been performed, ego-motion can be inferred from the operator constructed from sensory inputs. The main feature of this method is that it does not use any knowledge of image construction obtained by optics and so is flexible with respect to arbitrary image distortion. This flexibility is confirmed by a computer simulation. The proposed method may be considered a method of recovery of structure and motion from motion that remains nearly unaffected by arbitrary image distortion.
Optimizing multiple objectives in collaborative filtering

This paper is about the utility of making personalized recommendations. While it is important to accurately predict the target user's preference, in practice the accuracy should not be the only concern; a useful recommender system needs to consider the user's utility or satisfaction of fulfilling a certain information seeking task. For example, recommending popular items (products) is unlikely to result in more gain than discovering insignificant ("long tail") yet liked items because the popular ones might be already known to the user. Equally, recommending items that are out of stock would be frustrating for both the user and system if the system is employed to discover items to purchase. Thus, it is important to have a flexible recommendation framework that takes into account additional recommendation goals meanwhile minimizing the performance loss in order to provide greater adjustability and a better user experience. To achieve this, in this paper, we propose a general recommendation optimization framework that not only considers the predicted preference scores (e.g. ratings) but also deals with additional operational or resource related recommendation goals. Using this framework we demonstrate through realistic examples how to expand existing rating prediction algorithms by biasing the recommendation depending on other external factors such as the availability, profitability or usefulness of an item. Our experiments on real data sets demonstrate that this framework is indeed able to cope with multiple objectives with minor performance loss.
The Durham/UKST Galaxy Redshift Survey – II. The field galaxy luminosity function

We present the results for the galaxy luminosity function as estimated from the Durham/UKST Galaxy Redshift Survey. This survey is magnitude-limited to bJ∼17, contains ∼2500 galaxies sampled at a rate of one-in-three, and surveys a ∼4×106(h−1 Mpc)3 volume of space. The maximum-likelihood parameters for a standard Schechter luminosity function are estimated to be M*bJ=−19.72±0.09, α=−1.14±0.08 and φ*=(1.2±0.2)×10−2(h3 Mpc−3). Attempting to correct for the scatter in the observed magnitudes leads to a flatter faint-end slope, α=−1.04±0.08, which, combined with the different luminosity function shape, causes a higher normalization to be estimated, φ*=(1.7±0.3)×10−2(h3 Mpc−3). Neither of these parametric functions provides a good formal fit to the non-parametric estimate of the luminosity function. A comparison with galaxy luminosity functions from other redshift surveys shows good agreement, and the shape of the luminosity function now appears to be well defined down to MbJ≃−17. There are some discrepancies between the different surveys for galaxies fainter than this absolute magnitude. However, our estimate agrees well with that from the APM–Stromlo Galaxy Redshift Survey and we measure a fairly flat faint-end slope.
Obesity and the development of insulin resistance and impaired fasting glucose in black and white adolescent girls: a longitudinal study.

OBJECTIVE Age at onset of type 2 diabetes has decreased during the past 20 years, especially in black women. Studies of factors associated with insulin resistance and hyperglycemia in preadolescent and adolescent populations are essential to understanding diabetes development. RESEARCH DESIGN AND METHODS The National Heart, Lung, and Blood Institute (NHLBI) Growth and Health Study (NGHS) is a 10-year cohort study of the development of obesity in black and white girls. Two NGHS centers examined the associations of obesity, puberty, and race with fasting insulin, glucose, and homeostasis model assessment of insulin resistance (HOMA-IR; a calculated index of insulin resistance) measures at 9-10 years of age (baseline) and 10 years later. RESULTS Black girls had greater baseline and year-10 BMI than white girls, with a greater 10-year incidence of obesity. BMI-insulin correlations were positive in both black and white girls at both visits, but insulin remained higher in black girls after controlling for BMI. In black girls, insulin and HOMA-IR were higher in the prepubertal period (before the emergence of racial differences in BMI), increased more during puberty, and decreased less with its completion. Baseline BMI predicted year-10 glucose and the development of impaired fasting glucose (IFG) in black girls. In white girls, the rate of BMI increase during follow-up predicted these outcomes. The 10-year incidence of diabetes in black girls was 1.4%. CONCLUSIONS Black-white differences in insulin resistance are not just a consequence of obesity, but precede the pubertal divergence in BMI. The development of IFG appears to be a function of the rate of increase of BMI in white girls and early obesity in black girls.
On the channel capacity of the residential power circuit used as a digital communications medium

Based on the results of an extensive measurement campaign executed in the city of Amsterdam, the Netherlands, a channel model for the residential power line channel (RPLC) used as a digital communications channel is presented. From this model bounds for the channel capacity of such a channel used under European regulations are calculated. It is concluded that it is indeed theoretically possible to use the RPLC to solve the local loop problem in a second, fixed line telephone network.
Positional language modeling for extractive broadcast news speech summarization

Extractive summarization, with the intention of automatically selecting a set of representative sentences from a text (or spoken) document so as to concisely express the most important theme of the document, has been an active area of experimentation and development. A recent trend of research is to employ the language modeling (LM) approach for important sentence selection, which has proven to be effective for performing extractive summarization in an unsupervised fashion. However, one of the major challenges facing the LM approach is how to formulate the sentence models and estimate their parameters more accurately for each text (or spoken) document to be summarized. This paper extends this line of research and its contributions are three-fold. First, we propose a positional language modeling framework using different granularities of position-specific information to better estimate the sentence models involved in summarization. Second, we also explore to integrate the positional cues into relevance modeling through a pseudo-relevance feedback procedure. Third, the utilities of the various methods originated from our proposed framework and several well-established unsupervised methods are analyzed and compared extensively. Empirical evaluations conducted on a broadcast news summarization task seem to demonstrate the performance merits of our summarization methods.
Is the GDPR and Its Right to Data Portability a Major Enabler of Citizen Science?

Abstract Citizen science is an emerging trend with an ever greater number of adherents. It involves the collection and contribution of large amounts of data by private individuals for scientific research. Often such data will concern the individuals themselves and will be collected through processes of self monitoring. This phenomenon has been greatly influenced by the Internet of Things (IoT) and the connectivity of a wide range monitoring devices through the internet. In collecting such data use will often be made of the services of various commercial organisations, for example that offer cloud storage services. The possibility of data portability is extremely important in citizen science as it allows individuals (or data subjects) to be able move their data from one source to another (i. e. to new areas of scientific research). This article explores the limits and possibilities that legal rights to data portability offer, in particular the new right as outlined by the European Union’s General Data Protection Regulation. In doing so this article will look at where this right (and how it operates in the international legal context) is able to facilitate the phenomenon of citizen science.
Robotica: a Mathematica package for robot analysis

Robotica is a computer aided design package for robotic manipulators developed in the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign. It encapsulates over 30 functions into a Mathematica package allowing efficient symbolic and numeric calculation of kinematic and dynamic equations for multi-degree-of-freedom manipulators. An X-Windows front end that utilizes the interprocess communication features of Mathematica 2.1 has also been created for ease of use. This paper describes the most important features of the package and how they are used. <<ETX>>
MDA-9/Syntenin regulates differentiation and angiogenesis programs in head and neck squamous cell carcinoma

Little is known about the molecular pathways regulating poor differentiation and invasion of head and neck squamous cell carcinoma (HNSCC). In the present study, we aimed to determine the role of MDA-9/Syntenin, a metastasis associated molecule in HNSCC tumorigenesis. Elevated MDA-9/Syntenin expression was evident in 67% (54/81) primary HNSCC tumors (p=0.001-0.002) and 69% (9/13) pre-neoplastic tissues (p=0.02-0.03). MDA-9/Syntenin overexpression was associated with the stage (p=0.001), grade (p=0.001) and lymph node metastasis (p=0.0001). Silencing of MDA-9/Syntenin in 3 poorly differentiated HNSCC cell lines induced squamous epithelial cell differentiation, disrupted angiogenesis and reduced tumor growth in vitro and in vivo. We confirmed SPRR1B and VEGFR1 as the key molecular targets of MDA-9/Syntenin on influencing HNSCC differentiation and angiogenesis respectively. MDA-9/Syntenin disrupted SPRR1B expression interacting through its PDZ1 domain and altered VEGFR1 expression in vitro and in vivo. VEGFR1 co-localized with MDA-9/Syntenin in HNSCC cell lines and primary tumor. Downregulation of growth regulatory molecules CyclinD1, CDK4, STAT3, PI3K and CTNNB1 was also evident in the MDA-9/Syntenin depleted cells, which was reversed following over-expression of MDA-9/Syntenin in immortalized oral epithelial cells. Our results suggest that early induction of MDA-9/Syntenin expression influences HNSCC progression and should be further evaluated for potential biomarker development.
Gene Expression Profile of Colon Mucosa after Cytotoxic Insult in wt and Apc-Mutated Pirc Rats: Possible Relation to Resistance to Apoptosis during Carcinogenesis

Apc-mutated Pirc rats, spontaneously developing intestinal tumours, are resistant to 1,2-dimethylhydrazine- (DMH-) induced colon apoptosis. To understand this phenomenon, we analyzed the expression of genotoxic stress-related genes Mgmt, Gsta1, and Gstp1 in the colon of wt and Pirc rats in basal conditions and 24 h after DMH; plasmatic oxidant/antioxidant status was also evaluated. After DMH, Mgmt expression was increased in both genotypes but significantly only in wt rats; Gsta1 expression was significantly increased in both genotypes. Gstp1 expression did not vary after DMH but was lower in Pirc rats. Moreover, for each genotype, we studied by microarray technique whole gene expression profile after DMH. By unsupervised cluster analysis, 28 genes were differentially modulated between the two genotypes. Among them were interferon-induced genes Irf7, Oas1a, Oasl2, and Isg15 and the transcription factor Taf6l, overexpressed in DMH-treated wt rats and unchanged in Pirc rats. RT-PCR confirmed their overexpression in DMH-treated wt rats and showed a slighter variation in DMH-treated Pirc rats. Taken together, despite a blunted induction of Irf7, Oas1a, and Mgmt, defective apoptosis in Pirc rats 24 h after DMH is not mirrored by major differences in gene expression compared with wt rats.
Advanced Thermal Field Sensitivity Analysis of Power Cables

An advanced finite-element based technique is described in this paper for accurate sensitivity evaluation of power cables. The technique is applied to a practical three 132 kV XLPE underground cable system in the Saudi power system with multi-layer non-homogenous soil. The perturbed finite element sensitivity evaluation model accommodates most of the thermal parameters and the boundary conditions. In this work, the effect of multilayered thermal conductivities and boundary parameters variations on the actual cable system ampacity is investigated. Also, a comparison is held of the obtained results with the conventional finite element approach in order to show the applicability and usefulness of the developed methodology for the purpose of exploiting the operating parameter variations effects in straight sensitivity manner without repeating the thermal field analysis for such parameter changes.
CBPMAC/TFC: A Wireless Sensor Network MAC Protocol for Systems with Burst and Periodic Signals

Aiming at the application scene with both burst signals and periodic signals, this paper proposes a cluster-based wireless sensor network MAC protocol combining TDMA, FDMA with CSMA/CA - CBPMAC/TFC(a cluster-based wireless sensor network MAC protocol for the system with burst and periodic signals/based on TDMA, FDMA, CSMA/CA). It adopts TDMA mechanism in the clusters, and assigns time-slots for periodic signals and burst signals respectively. In the periodic signal time- slot, only one child node sends its periodic signal, while in the burst signal time-slots, all the child nodes share the channel according to CSMA/CA mechanism. Among different clusters, FDMA mechanism is adopted. Different cluster works at different frequency channel, so they can work simultaneously. For the requirement of synchronization, a cluster-head-based relative time synchronization mechanism is proposed. In addition, the alternation strategy of cluster heads and the disposal strategy of malfunctions are introduced. Analysis and test results show that the protocol could meet the system requirements in reliability, real-time response and energy efficiency.
Transparent to whom? No algorithmic accountability without a critical audience

ABSTRACT Big data and data science transform organizational decision-making. We increasingly defer decisions to algorithms because machines have earned a reputation of outperforming us. As algorithms become embedded within organizations, they become more influential and increasingly opaque. Those who create algorithms may make arbitrary decisions in all stages of the ‘data value chain’, yet these subjectivities are obscured from view. Algorithms come to reflect the biases of their creators, can reinforce established ways of thinking, and may favour some political orientations over others. This is a cause for concern and calls for more transparency in the development, implementation, and use of algorithms in public- and private-sector organizations. We argue that one elementary – yet key – question remains largely undiscussed. If transparency is a primary concern, then to whom should algorithms be transparent? We consider algorithms as socio-technical assemblages and conclude that without a critical audience, algorithms cannot be held accountable.
A Lower Bound on the Bayesian MSE Based on the Optimal Bias Function

A lower bound on the minimum mean-squared error (MSE) in a Bayesian estimation problem is proposed in this paper. This bound utilizes a well-known connection to the deterministic estimation setting. Using the prior distribution, the bias function which minimizes the Cramer-Rao bound can be determined, resulting in a lower bound on the Bayesian MSE. The bound is developed for the general case of a vector parameter with an arbitrary probability distribution, and is shown to be asymptotically tight in both the high and low signal-to-noise ratio (SNR) regimes. A numerical study demonstrates several cases in which the proposed technique is both simpler to compute and tighter than alternative methods.
Genetic Association of MMP10, MMP14, and MMP16 with Dental Caries

Matrix metalloproteinases (MMPs), which degrade extracellular proteins as part of a variety of physiological processes, and their inhibitors have been implicated in the dental caries process. Here we investigated 28 genetic variants spanning the MMP10, MMP14, and MMP16 genes to detect association with dental caries experience in 13 age- and race-stratified (n = 3,587) samples from 6 parent studies. Analyses were performed separately for each sample, and results were combined across samples by meta-analysis. Two SNPs (rs2046315 and rs10429371) upstream of MMP16 were significantly associated with caries in an individual sample of white adults and via meta-analysis across 8 adult samples after gene-wise adjustment for multiple comparisons. Noteworthy is SNP rs2046315 (p = 8.14 × 10−8) association with caries in white adults. This SNP was originally nominated in a genome-wide-association study (GWAS) of dental caries in a sample of white adults and yielded associations in a subsequent GWAS of surface level caries in white adults as well. Therefore, in our study, we were able to recapture the association between rs2046315 and dental caries in white adults. Although we did not strengthen evidence that MMPs 10, 14, and 16 influence caries risk, MMP16 is still a likely candidate gene to pursue.
Uma geografia temporal do encontro

The integration between spatial and social networks is fundamental to new approaches on cities as systems of interaction. In this paper, we focus on the spatial and temporal conditions of encounters as a key condition for the formation of social networks. Drawing on classic approaches such as Freeman’s concept of segregation as ‘restriction on contact’, Hagerstrand’s time-geography, and recent explorations of social media locational data, we analysed the space-time structure of potential encounters latent in the urban trajectories of people with different income levels in Rio de Janeiro, Brazil. This approach allows us to estimate trajectories examining spatiotemporal positions in tweets, and assess spaces of potential encounter and levels of social diversity on the streets. Finally, we discuss the utility and limitations of an approach developed to grasp how clusters of encounters between groups with different income levels are produced.
Reranking Models in Fine-grained Opinion Analysis

We describe the implementation of reranking models for fine-grained opinion analysis -- marking up opinion expressions and extracting opinion holders. The reranking approach makes it possible to model complex relations between multiple opinions in a sentence, allowing us to represent how opinions interact through the syntactic and semantic structure. We carried out evaluations on the MPQA corpus, and the experiments showed significant improvements over a conventional system that only uses local information: for both tasks, our system saw recall boosts of over 10 points.
Modeling Uncertainty and Inconsistency in Ontology-Based Context-Awareness Computing

The ubiquitous city is an urban application based on ubiquitous and context-aware computing. Therefore, context- aware systems for the ubiquitous city need to construct multiple ontologies which conceptualize complicate structure of heterogeneous contexts of the city. In this paper, we focus on uncertainty and inconsistency caused by integration of two or more ontologies for interpreting sensor data, inferring contexts, and applying appropriate rules. We define concepts of uncertainty and inconsistency in context-awareness computing and provide the method for detecting and resolving uncertainty and inconsistency through building an example context, flood context ontology.