Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the Efficiency of Inclusion Dependency Detection

The detection of all inclusion dependencies (INDs) in an unknown dataset is at the core of any data profiling effort. Apart from the discovery of foreign key relationships, INDs can help perform data integration, integrity checking, schema (re-)design, and query optimization. With the advent of Big Data, the demand increases for efficient INDs discovery algorithms that can scale with the input data size. To this end, we propose S-indd++ as a scalable system for detecting unary INDs in large datasets. S-indd++ applies a new stepwise partitioning technique that helps discard a large number of attributes in early phases of the detection by processing the first partitions of smaller sizes. S-indd++ also extends the concept of the attribute clustering to decide which attributes to be discarded based on the clustering result of each partition. Moreover, in contrast to the state-of-the-art, S-indd++ does not require the partition to fit into the main memory- which is a highly appreciable property in the face of the ever growing datasets. We conducted an exhaustive evaluation of S-indd ++ by applying it to large datasets with thousands attributes and more than 266 million tuples. The results show the high superiority of S-indd++ over the state-of-the-art. S-indd++ reduced up to 50~% of the runtime in comparison with Binder, and up to 98~% in comparison with S-indd.
Stillness Moves: Exploring Body Weight-Transfer Learning in Physical Training for Tai-Chi Exercise

Body weight-transfer plays an important role in many exercises. The correlation of the body posture, movement, and weight-transfer will mutually affect the trainee to do well in performances such as Tai-Chi exercise. According to the traditional way of learning Tai-Chi, we proposed Stillness Moves, a physical training system for Tai-Chi, which captures and records users' skeleton movement and weight-transfer information for offering real-time and summary visual feedback. Based on above, we provide a gradual learning program in physical training, which combines body movement and weight-transfer learning. We evaluated our system and compared the performance without and with weight-transfer guidance in the user study. The result demonstrated that weight-transfer guidance is beneficial for trainee learning the Tai-Chi moves. For difficult moves, the trainee should learn the weight-transfer first, then, learning the body movement.
CrowdMerge: Achieving Optimal Crowdsourcing Quality Management by Sequent Merger

Quality management of worker responses is an increasingly important issue in crowdsourcing, which can be a fundamental reference for workload allocation. However, most of the existing solutions to worker response evaluation meet with significant challenges. Part of them are only able to obtain locally optimal results with low accuracy, while the others have to pay a high price to achieve global optimality. In order to tackle these challenges, this paper proposes a solution to worker quality management which is able to obtain optimal evaluation results close to the globally one with acceptable computational overhead. It is proved to be globally optimal results under certain conditions. The basic idea of the approach is to calculate the similarities among the workers according to which the workers can be clustered gradually. The unknown answers (or outputs) of the tasks are predicted from the results obtained by the clustered workers which will further help with the similarity calculation and the clustering among the remaining workers. A framework and detailed schemes are presented, and simulation experiments are conducted to validate the efficacy of the approach.
Research on Role Mining Algorithms in RBAC

While role mining algorithms have been proposed in recent years, but there still lacks a more comprehensive study to compare and analyze these algorithms. These role mining algorithms have been evaluated when they were proposed, but different datasets and evaluation criteria have been used in the evaluation. In this paper, we use a comprehensive evaluation framework to compare these role mining algorithms. We use real datasets to compare six algorithms from several aspects such as weighted structural comp-lexity(WSC), number of generated roles and operating efficiency. Our results illustrate the advantages and disadvantages of these algorithms.
Multi-model Databases and Tightly Integrated Polystores: Current Practices, Comparisons, and Open Challenges

One of the most challenging issues in the era of Big Data is the Variety of the data. In general, there are two solutions to directly manage multi-model data currently: a single integrated multi-model database system or a tightly-integrated middleware over multiple single-model data stores. In this tutorial, we review and compare these two approaches giving insights on their advantages, trade-offs, and research opportunities. In particular, we dive into four key aspects of technology for both types of systems, namely (1) theoretical foundation of multi-model data management, (2) storage strategies for multi-model data, (3) query languages across models, and (4) query evaluation and its optimization. We provide a comparison of performance for the two approaches and discuss related open problems and remaining challenges. The slides of this tutorial can be found at http://udbms.cs. helsinki.fi/? tutorials/CIKM2018.
Optical Graphene-Based Biosensor for Nucleic Acid Detection; Influence of Graphene Functionalization and Ionic Strength

A main challenge for optical graphene-based biosensors detecting nucleic acid is the selection of key parameters e.g. graphenic chemical structure, nanomaterial dispersion, ionic strength, and appropriate molecular interaction mechanisms. Herein we study interactions between a fluorescein-labelled DNA (FAM-DNA) probe and target single-stranded complementary DNA (cDNA) on three graphenic species, aiming to determine the most suitable platform for nucleic acid detection. Graphene oxide (GO), carboxyl graphene (GO-COOH) and reduced graphene oxide functionalized with PEGylated amino groups (rGO-PEG-NH2, PEG (polyethylene glycol)) were dispersed and characterized by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The influence of ionic strength on molecular interaction with DNA was examined by fluorescence resonance energy transfer (FRET) comparing fluorescence intensity and anisotropy. Results indicated an effect of graphene functionalization, dispersion and concentration-dependent quenching, with GO and GO-COOH having the highest quenching abilities for FAM-DNA. Furthermore, GO and GO-COOH quenching was accentuated by the addition of either MgCl2 or MgSO4 cations. At 10 mM MgCl2 or MgSO4, the cDNA induced a decrease in fluorescence signal that was 2.7-fold for GO, 3.4-fold for GO-COOH and 4.1-fold for rGO-PEG-NH2. Best results, allowing accurate target detection, were observed when selecting rGO-PEG-NH2, MgCl2 and fluorescence anisotropy as an advantageous combination suitable for nucleic acid detection and further rational design biosensor development.
Contrasting Search as a Learning Activity with Instructor-designed Learning

The field of Search as Learning addresses questions surrounding human learning during the search process. Existing research has largely focused on observing how users with learning-oriented information needs behave and interact with search engines. What is not yet quantified is the extent to which search is a viable learning activity compared to instructor-designed learning. Can a search session be as effective as a lecture video - our instructor-designed learning artefact - or learning? To answer this question, we designed a user study that pits instructor-designed learning (a short high-quality video lecture as commonly found in online learning platforms) against three instances of search, specifically (i) single-user search, (ii) search as a support tool for instructor-designed learning, and, (iii) collaborative search. We measured the learning gains of 151 study participants in a vocabulary learning task and report three main results: (i) lecture video watching yields up to 24% higher learning gains than single-user search, (ii) collaborative search for learning does not lead to increased learning, and (iii) lecture video watching supported by search leads up to a 41% improvement in learning gains over instructor-designed learning without a subsequent search phase.
Value-Aware Resampling and Loss for Imbalanced Classification

Existing1 machine learning methods usually treat training samples equally, and their performance degrades significantly when facing imbalanced training data. This paper introduces Value-Aware Resampling and Loss (VARL) to tackle the imbalanced classification problem, where high-value samples play a more important role than those of low-value samples in the model training process. Specifically, the training value of each training sample is assessed according to its predicted probability of ground truth label, and then training samples are resampled to produce a balanced training set, at last the model training is further boosted by using an instance-level value-aware loss function. To conduct fair comparisons among different methods, we compile 13 datasets for imbalanced classification. Experiments demonstrate that our proposed method can effectively measure the training value of training samples, and achieve superior performance in imbalanced classification compared with several existing methods.
MicroRNAs as Biomarkers of B-cell Lymphoma

B-cell lymphomas represent a diverse group of neoplasms classified primarily by histopatholgy and are often challenging to accurately diagnose. Despite having been recognized less than 20 years ago, microRNAs (miRNAs) have emerged as one of the most promising class of cancer molecular biomarkers and are particularly attractive as they can be readily detected in formalin-fixed paraffin-embedded biopsy material and biological fluids such as blood. Many of the identified B-cell lymphoma miRNA biomarkers also play crucial regulatory roles in normal B-cell development. Below we consider the identity, function, and biomarker potential of miRNAs in B-cell lymphoma and most importantly the barriers that remain to be overcome if they are really to become part of routine clinical practice.
Ready for Use: Subject-Independent Movement Intention Recognition via a Convolutional Attention Model

Brain-Computer Interface (BCI) enables human to communicate with and intuitively control an external device through brain signals. Movement intention recognition paves the path for developing BCI applications. The current state-of-the-art in EEG based BCI usually involves subject-specific adaptation before ready to use. However, the subject-independent scenario, in which a well-trained model is directly applied to new subjects without any pre-calibration, is particularly desired yet rarely explored. In order to fill the gap, we present a Convolutional Attention Model (CAM) for EEG-based human movement intention recognition in the subject-independent scenario. The convolutional network is designed to capture the spatio-temporal features of EEG signals, while the integrated attention mechanism is utilized to focus on the most discriminative information of EEG signals during the period of movement imagination while omitting other less relative parts. Experiments conducted on a real-world EEG dataset containing 55 subjects show that our model is capable of mining the underlying invariant EEG patterns across different subjects and generalizing to unseen subjects. Our model achieves better performance than a series of state-of-the-art and baseline approaches.
HES: Highly Efficient and Scalable Technique for Matching Regex Patterns

Several security devices use signature based detection engine to detect malicious activities through the internet. The main challenge of this scenario is to keep up with the increase of line speed. On one hand, regular expression (regex) patterns allow security analysts to express more complicated attacks. On the other hand, they make pattern matching procedure much more costly. Several finite automata based techniques have been proposed to speed up the matching procedure. However, they are still impractical in the real world, due to their high spatial or temporal complexity. In this paper, a novel technique, called HES, is proposed to handle tens of thousands regex patterns, with minimum space limitation. The experimental results over several rule sets including Snort and Bro, as two leading open source intrusion detection systems, as well as random regex patterns, reveals us HES matched patterns significantly faster than DFA, as one of the fastest state-of-the-art techniques. In addition, the HES storage requirement is close to NFA, which leads as one of the most compact method. These results proved that HES can be used in the real world, as a signature based matching engine, and gives us the power to use more regex patterns.
Dissimilarity Representation Learning for Generalized Zero-Shot Recognition

Generalized zero-shot learning (GZSL) aims to recognize any test instance coming either from a known class or from a novel class that has no training instance. To synthesize training instances for novel classes and thus resolving GZSL as a common classification problem, we propose a Dissimilarity Representation Learning (DSS) method. Dissimilarity representation is to represent a specific instance in terms of its (dis)similarity to other instances in a visual or attribute based feature space. In the dissimilarity space, instances of the novel classes are synthesized by an end-to-end optimized neural network. The neural network realizes two-level feature mappings and domain adaptions in the dissimilarity space and the attribute based feature space. Experimental results on five benchmark datasets, i.e., AWA, AWA$_2$, SUN, CUB, and aPY, show that the proposed method improves the state-of-the-art with a large margin, approximately 10% gain in terms of the harmonic mean of the top-1 accuracy. Consequently, this paper establishes a new baseline for GZSL.
Sensitivity and positive predictive value of the registration of self-expanding metal stent treatment for obstructive colorectal cancer in two Danish nationwide registries

Background Randomized controlled trials have not provided clear evidence for the use of self-expanding metal stents (SEMS) for colonic cancer obstruction. Existing observational research mainly originates from highly specialized single-center settings with limited generalizability. The conduct of population-based nationwide studies is possible by using Danish medical databases. However, the quality of the coding of SEMS procedures in these databases is unclear. Methods From March 1, 2010 through December 31, 2013, we compared the registration of SEMS procedures among obstructive colorectal cancer patients in the Danish National Patient Register (DNPR) and the Danish Colorectal Cancer Group (DCCG) database to the registration in a prospective SEMS database (the reference standard). Results Ninety-three patients were included in the reference standard for the evaluation of DNPR data. In the DNPR, only two patients were incorrectly registered (positive predictive value [PPV]=98%, 95% CI: 92%–100%) whereas six patients were not captured by the DNPR (sensitivity =94%, 95% CI: 87%–98%). For the evaluation of the DCCG database, the reference standard included 54 patients. Only two patients in the DCCG database were incorrectly recorded (PPV =95%, 95% CI: 82%–99%), whereas 19 patients were not captured by the DCCG database (sensitivity =65%, 95% CI: 51%–77%). Conclusion We found high PPV and sensitivity of SEMS coding in the DNPR, supporting the use of these data in future research. The PPV of SEMS data in the DCCG database was high, but the sensitivity was low, suggesting that data on SEMS treatment from this database should be used with caution.
HRAM: A Hybrid Recurrent Attention Machine for News Recommendation

Popular methods for news recommendation which are based on collaborative filtering and content-based filtering have multiple drawbacks. The former method does not account for the sequential nature of news reading and suffers from the problem of cold-start, while the latter, suffers from over-specialization. In order to address these issues for news recommendation we propose a Hybrid Recurrent Attention Machine (HRAM). HRAM consists of two components. The first component utilizes a neural network for matrix factorization. While in the second component, we first learn the distributed representation of each news article. We then use the historical data of the user in a sequential manner and feed it to an attention-based recurrent layer. Finally, we concatenate the outputs from both these components and use further hidden layers in order to make predictions. In this way, we harness the information present in the user reading history and boost it with the information available through collaborative filtering for providing better news recommendations. Extensive experiments over two real-world datasets show that the proposed model provides significant improvement over the state-of-the-art.
Disk-based Matrix Completion for Memory Limited Devices

More and more data need to be processed or analyzed within mobile devices for efficiency or privacy reasons, but performing machine learning tasks with large data within the devices is challenging because of their limited memory resources. For this reason, disk-based machine learning methods have been actively researched, which utilize storage resources without holding all the data in memory. This paper proposes D-MC2, a novel disk-based matrix completion method that (1) supports incremental data update (i.e., data insertion and deletion) and (2) spills both data and model to disk when necessary; these functionalities are not supported by existing methods. First, D-MC2 builds a two-layered index to efficiently support incremental data update; there exists a trade-off relationship between model learning and data update costs, and our two-layered index simultaneously optimizes the two costs. Second, we develop a window-based stochastic gradient descent (SGD) scheduler to efficiently support the dual spilling; a huge amount of disk I/O is incurred when the size of model is larger than that of memory, and our new scheduler substantially reduces it. Our evaluation results show that D-MC2 is significantly more scalable and faster than other disk-based competitors under the limited memory environment. In terms of the co-optimization, D-MC2 outperforms the baselines that only optimize one of the two costs up to 48x. Furthermore, the window-based scheduler improves the training speed 12.4x faster compared to a naive scheduler.
COPD assessment test and FEV1: do they predict oxygen uptake in COPD?

Background Chronic obstructive pulmonary disease (COPD) manifests itself in complex ways, with local and systemic effects; because of this, a multifactorial approach is needed for disease evaluation, in order to understand its severity and impact on each individual. Thus, our objective was to study the correlation between easily accessible variables, usually available in clinical practice, and maximum aerobic capacity, and to determine models for peak oxygen uptake (VO2peak) estimation in COPD patients. Subjects and methods Individuals with COPD were selected for the study. At the first visit, clinical evaluation was performed. During the second visit, the volunteers were subjected to the cardiopulmonary exercise test. To determine the correlation coefficient of VO2peak with forced expiratory volume in 1 second (FEV1) (% pred.) and the COPD Assessment Test score (CATs), Pearson or Spearman tests were performed. VO2 at the peak of the exercise was estimated from the clinical variables by simple and multiple linear regression analyses. Results A total of 249 subjects were selected, 27 of whom were included after screening (gender: 21M/5F; age: 65.0±7.3 years; body mass index: 26.6±5.0 kg/m2; FEV1 (% pred.): 56.4±15.7, CAT: 12.4±7.4). Mean VO2 peak was 12.8±3.0 mL⋅kg−1⋅min−1 and VO2peak (% pred.) was 62.1%±14.9%. VO2peak presented a strong positive correlation with FEV1 (% pred.), r: 0.70, and a moderate negative correlation with the CATs, r: -0.54. In the VO2peak estimation model based on the CAT (estimated VO2peak =15.148− [0.185× CATs]), the index explained 20% of the variance, with estimated error of 2.826 mL⋅kg−1⋅min−1. In the VO2peak estimation model based on FEV1 (estimated VO2peak =6.490+ [0.113× FEV1]), the variable explained 50% of the variance, with an estimated error of 2.231 mL⋅kg−1⋅min−1. In the VO2peak estimation model based on CATs and FEV1 (estimated VO2peak =8.441− [0.0999× CAT] + [0.1000× FEV1]), the variables explained 55% of the variance, with an estimated error of 2.156 mL⋅kg−1⋅min−1. Conclusion COPD patients’ maximum aerobic capacity has a significant correlation with easily accessible and widely used clinical variables, such as the CATs and FEV1, which can be used to estimate peak VO2.
Extended criteria donor lung reconditioning with the organ care system lung: a single institution experience

Lung transplantation is a life‐saving procedure limited by donor's availability. Lung reconditioning by ex vivo lung perfusion represents a tool to expand the donor pool. In this study, we describe our experience with the OCS™ Lung to assess and recondition extended criteria lungs. From January 2014 to October 2016, of 86 on‐site donors evaluated, eight lungs have been identified as potentially treatable with OCS™ Lung. We analyzed data from these donors and the recipient outcomes after transplantation. All donor lungs improved during OCS perfusion in particular regarding the PaO2/FiO2 ratio (from 340 mmHg in donor to 537 mmHg in OCS) leading to lung transplantation in all cases. Concerning postoperative results, primary graft dysfunction score 3 at 72 h was observed in one patient, while median mechanical ventilation time, ICU, and hospital stay were 60 h, 14 and 36 days respectively. One in‐hospital death was recorded (12.5%), while other two patients died during follow‐up leading to 1‐year survival of 62.5%. The remaining five patients are alive and in good conditions. This case series demonstrates the feasibility and value of lung reconditioning with the OCS™ Lung; a prospective trial is underway to validate its role to safely increase the number of donor lungs.
Social Status and Communication Behavior in an Evolving Social Network

The degree to which individuals can exert influence on propagation of information and opinion dynamics in online communities is highly dependent on their social status. Therefore, there is a high demand for identifying influential users in a community by predicting their social position in that community. Moreover, understanding how people with various social status behave, can shed light on the dynamics of interaction in social networks. In this paper, I study an evolving online social network originated from an online community for university students and I tackle the problem of forecasting users' social status, represented as their PageRank, based on frequency of recurring temporal sequences of observed behavior, i.e. behavioral motifs. I show that individuals with different values of PageRank exhibit different behavior even in early weeks since the online community's inception and it is possible to forecast future PageRank values given frequency of behavioral motifs with high accuracy.