Miracle-dz/UniFAR-FTUs huggingface dataset:
HDFD - A High Deformation Facial Dynamics Benchmark for Evaluation of Non-Rigid Surface Registration and Classification

Objects that undergo non-rigid deformation are common in the real world. A typical and challenging example is the human faces. While various techniques have been developed for deformable shape registration and classification, benchmarks with detailed labels and landmarks suitable for evaluating such techniques are still limited. In this paper, we present a novel facial dynamic dataset HDFD which addresses the gap of existing datasets, including 4D funny faces with substantial non-isometric deformation, and 4D visual-audio faces of spoken phrases in a minority language (Welsh). Both datasets are captured from 21 participants. The sequences are manually landmarked, with the spoken phrases further rated by a Welsh expert for level of fluency. These are useful for quantitative evaluation of both registration and classification tasks. We further develop a methodology to evaluate several recent non-rigid surface registration techniques, using our dynamic sequences as test cases. The study demonstrates the significance and usefulness of our new dataset --- a challenging benchmark dataset for future techniques.
Moving Objects Analytics: Survey on Future Location & Trajectory Prediction Methods

The tremendous growth of positioning technologies and GPS enabled devices has produced huge volumes of tracking data during the recent years. This source of information constitutes a rich input for data analytics processes, either offline (e.g. cluster analysis, hot motion discovery) or online (e.g. short-term forecasting of forthcoming positions). This paper focuses on predictive analytics for moving objects (could be pedestrians, cars, vessels, planes, animals, etc.) and surveys the state-of-the-art in the context of future location and trajectory prediction. We provide an extensive review of over 50 works, also proposing a novel taxonomy of predictive algorithms over moving objects. We also list the properties of several real datasets used in the past for validation purposes of those works and, motivated by this, we discuss challenges that arise in the transition from conventional to Big Data applications. CCS Concepts: Information systems > Spatial-temporal systems; Information systems > Data analytics; Information systems > Data mining; Computing methodologies > Machine learning Additional Key Words and Phrases: mobility data, moving object trajectories, trajectory prediction, future location prediction.
Joint Distributed Representation of Text and Structure of Semi-Structured Documents

Majority of textual data over web is in the form of semi-structured documents. Thus, structural skeleton of such documents plays important role in determining the semantics of the data content. Presence of structure sometimes allows us to write simple rules to extract such information, but it may not be always possible due to flexibility in the structure and the frequency with which such structures are altered. In this paper, we propose a joint modeling of text and the associated structure to effectively capture the semantics of the semi-structure documents. The model simultaneously learns the dense continuous representation for word tokens and the structure associated with them. We utilize the context of structures for projection such that similar structures containing semantically similar topics are close to each other in vector space. We explore two semantic text mining tasks over web data to test the effectiveness of our representation viz., document similarity, and table semantic component identification. In context of traditional rule-based approaches, both these tasks demand rich, domain-specific knowledge sources, homogeneous schema for the documents, and rules that capture the semantics. On the other hand, our approach is unsupervised and resource conscious in nature. Despite of working without knowledge resources and large training data, it performs at par with state-of-the-art rule based and other unsupervised approaches.
Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning

Gaussian process regression is a machine learning approach which has been shown its power for estimation of unknown functions. However, Gaussian processes suffer from high computational complexity, as in a basic form they scale cubically with the number of observations. Several approaches based on inducing points were proposed to handle this problem in a static context. These methods though face challenges with real-time tasks and when the data is received sequentially over time. In this paper, a novel online algorithm for training sparse Gaussian process models is presented. It treats the mean and hyperparameters of the Gaussian process as the state and parameters of the ensemble Kalman filter, respectively. The online evaluation of the parameters and the state is performed on new upcoming samples of data. This procedure iteratively improves the accuracy of parameter estimates. The ensemble Kalman filter reduces the computational complexity required to obtain predictions with Gaussian processes preserving the accuracy level of these predictions. The performance of the proposed method is demonstrated on the synthetic dataset and real large dataset of UK house prices.
Content Driven Enrichment of Formal Text using Concept Definitions and Applications

Formal text is objective, unambiguous and tends to have complex sentence construction intended to be understood by the target demographic. However, in the absence of domain knowledge it is imperative to define key concepts and their relationship in the text for correct interpretation for general readers. To address this, we propose a text enrichment framework that identifies the key concepts from input text, highlights definitions and fetches the definition from external data sources in case the concept is undefined. Beyond concept definitions, the system enriches the input text with concept applications and a pre-requisite concept graph that showcases the inter-dependency within the extracted concepts. While the problem of learning definition statements is attempted in literature, the task of learning application statements is novel. We manually annotated a dataset for training a deep learning network for identifying application statements in text. We quantitatively compared the results of both application and definition identification models with standard baselines. To validate the utility of the proposed framework for general readers, we report enrichment accuracy and show promising results.
Multicore architecture and cache optimization techniques for solving graph problems

With the advent of era of Big Data and Internet of Things, there has been an exponential increase in the availability of large data sets. These data sets require in-depth analysis that provides intelligence for improvements in methods for academia and industry. Majority of the data sets are represented and available in the form of graphs. Therefore, the problem at hand is to address solving graph problems. Since the data sets are large, the time it takes to analyze the data is significant. Hence, in this paper, we explore techniques that can exploit existing multicore architecture to address the issue. Currently, most Central Processing Units have incorporated multicore design; in addition, co-processors such as Graphics Processing Units have large number of cores that can used to gain significant speedup. Therefore, in this paper techniques to exploit the advantages of multicore architecture is studied.
Scikit-Multiflow: A Multi-output Streaming Framework

Scikit-multiflow is a multi-output/multi-label and stream data mining framework for the Python programming language. Conceived to serve as a platform to encourage democratization of stream learning research, it provides multiple state of the art methods for stream learning, stream generators and evaluators. scikit-multiflow builds upon popular open source frameworks including scikit-learn, MOA and MEKA. Development follows the FOSS principles and quality is enforced by complying with PEP8 guidelines and using continuous integration and automatic testing. The source code is publicly available at this https URL.
Fully Unsupervised Convolutional Learning for Fast Image Retrieval

In this paper, we propose a fully unsupervised method for optimizing the Convolutional Neural Network (CNN) representations towards image retrieval. To accomplish this goal, we obtain the feature representations from the activations of the convolutional layers of a CNN pretrained model, and then we adapt it to a lightweight fully convolutional network, which allows for producing compact image representations, reducing the storage requirements. Subsequently, we retrain the weights of the convolutional layers in order to produce more efficient compact image descriptors, which improve the retrieval performance both in terms of time and precision, in a fully unsupervised fashion. The experimental evaluation on three publicly available image retrieval datasets indicates the effectiveness of the proposed method in learning more efficient representations for the retrieval task, outperforming other unsu-pervised CNN-based retrieval techniques, as well as conventional hand-crafted feature-based approaches in all the used datasets.
Bandits with Side Observations: Bounded vs. Logarithmic Regret

We consider the classical stochastic multi-armed bandit but where, from time to time and roughly with frequency $\epsilon$, an extra observation is gathered by the agent for free. We prove that, no matter how small $\epsilon$ is the agent can ensure a regret uniformly bounded in time. More precisely, we construct an algorithm with a regret smaller than $\sum_i \frac{\log(1/\epsilon)}{\Delta_i}$, up to multiplicative constant and loglog terms. We also prove a matching lower-bound, stating that no reasonable algorithm can outperform this quantity.
Foreign English Accent Adjustment by Learning Phonetic Patterns

State-of-the-art automatic speech recognition (ASR) systems struggle with the lack of data for rare accents. For sufficiently large datasets, neural engines tend to outshine statistical models in most natural language processing problems. However, a speech accent remains a challenge for both approaches. Phonologists manually create general rules describing a speaker's accent, but their results remain underutilized. In this paper, we propose a model that automatically retrieves phonological generalizations from a small dataset. This method leverages the difference in pronunciation between a particular dialect and General American English (GAE) and creates new accented samples of words. The proposed model is able to learn all generalizations that previously were manually obtained by phonologists. We use this statistical method to generate a million phonological variations of words from the CMU Pronouncing Dictionary and train a sequence-to-sequence RNN to recognize accented words with 59% accuracy.
VButton: Practical Attestation of User-driven Operations in Mobile Apps

More and more malicious apps and mobile rootkits are found to perform sensitive operations on behalf of legitimate users without their awareness. Malware does so by either forging user inputs or tricking users into making unintended requests to online service providers. Such malware is hard to detect and generates large revenues for cybercriminals, which is often used for committing ad/click frauds, faking reviews/ratings, promoting people or business on social networks, etc. We find that this class of malware is possible due to the lack of practical and robust means for service providers to verify the authenticity of user-driven operations (i.e., operations supposed to be performed, or explicitly confirmed, by a user). We design and build the VButton system to fill this void. Our system introduces a class of attestation-enabled app UI widgets (called VButton UI). Developers can easily integrate VButton UI in their apps to allow service providers to verify that a user-driven operation triggered by a VButton UI is indeed initiated and intended by a real user. Our system contains an on-device Manager, and a server-side Verifier. Leveraging ARM TrustZone, our system can attest operation authenticity even in the presence of a compromised OS. We have implemented the VButton system on an ARM development board as well as a commercial off-the-shelf smartphone. The evaluation results show that the system incurs negligible overhead.
In-database analytics with ibmdbpy

The increasing size of the available data and database volumes represents a real challenge for the data management community. In general, current approaches in data mining require the data to be first extracted from an underlying database. From a practical point of view, this presents many drawbacks. In this short article, we present a possible solution to bridge the gap between data repositories and end user analysis. We demonstrate the interestingness of this approach with ibmdbpy, an open source Python interface developed by IBM for database administration and data analytics.
String Matching: Communication, Circuits, and Learning

String matching is the problem of deciding whether a given $n$-bit string contains a given $k$-bit pattern. We study the complexity of this problem in three settings. Communication complexity. For small $k$, we provide near-optimal upper and lower bounds on the communication complexity of string matching. For large $k$, our bounds leave open an exponential gap; we exhibit some evidence for the existence of a better protocol. Circuit complexity. We present several upper and lower bounds on the size of circuits with threshold and DeMorgan gates solving the string matching problem. Similarly to the above, our bounds are near-optimal for small $k$. Learning. We consider the problem of learning a hidden pattern of length at most $k$ relative to the classifier that assigns 1 to every string that contains the pattern. We prove optimal bounds on the VC dimension and sample complexity of this problem.
Enhancing Session-Based Recommendations through Sequential Modeling

Recommender systems typically determine the items they should recommend by learning models of user-preferences. Most often, those preferences are modeled as static and independent of context. In real life however, users consider items in sequence: TV series are watched episode by episode and accessories are chosen after the main appliance. Unfortunately, since sequences are more complex to model, they are often not taken into account. We developed an efficient sequence-modeling approach based on Bayesian Variable-order Markov Models and combined it with an existing content-based system, the Ontology Filtering. We tested this approach through live evaluations on two e-commerce sites. It dramatically increased performance, more than doubling the CTR and strongly increasing recommendation-mediated sales. These tests also confirm that the technique works efficiently and reliably in a production setting.
Exploration of nurses’ perception about professionalism in home care nursing in Iran: a qualitative study

Background Professionalism is one of the basic concepts of home care nursing, as a growing model of community-based nursing. This phenomenon results from the nurse-patient interaction in home and community environments with multiple dimensions. Objective The purpose of this study was to explore the professional experiences of home care nurses in Iran. Methods In this study, the qualitative content analysis method was used based on the 2004 Graneheim & Lundman approach. The study was conducted between September 2016 and September 2017 in Khorasan Razavi province, Iran. Participants were selected by Purposeful Sampling. The main method of data collection was semi-structured interviews with 21 nurses who had home-based care experience. Eventually, after reaching saturation, the data were analyzed in-depth. Results In total, 3 main categories and 11 subcategories were extracted. Generally, nurses considered the concept of professionalism in home care nursing by attention to basic values, social capital, and maintaining quality and standards of care. Conclusion Professionalism in home care nursing is one of today’s challenges in the health system of the country, and professionalism requires more attention and conditions for its prosperity.
Investigating the Decision-Making Behavior of Maximizers and Satisficers in the Presence of Recommendations

Psychological theory distinguishes between maximizing and satisficing decision-making styles. Maximizers tend to explore more or all alternatives when making a choice, while satisficers evaluate options until they find one that is good enough. There is limited research that examines how the existence of a recommender influences the choice process and decisions of different types of decision-makers. We report the results of a controlled study, in which we monitored the choice process of participants when provided with automated recommendations and different types of additional information regarding available options. Our analyses show that none of the differences that were expected based on the literature manifested itself in the experiment. Maximizers neither inspected more items, nor invested more time to study them. Instead, like satisficers, they mostly picked one of the top-ranked items recommended by the system, which emphasizes the value of recommenders in particular for maximizers, who would otherwise face a more challenging decision problem. The analysis of the preferences of participants over different types of additional information revealed that highlighting key pros and cons was perceived as particularly helpful for the maximizers, an insight that can be used for the design of explanation approaches for recommenders.
Quantifying the Effects of Ethanol and Temperature on the Fitness Advantage of Predominant Saccharomyces cerevisiae Strains Occurring in Spontaneous Wine Fermentations

Different Saccharomyces cerevisiae strains are simultaneously or in succession involved in spontaneous wine fermentations. In general, few strains occur at percentages higher than 50% of the total yeast isolates (predominant strains), while a variable number of other strains are present at percentages much lower (secondary strains). Since S. cerevisiae strains participating in alcoholic fermentations may differently affect the chemical and sensory qualities of resulting wines, it is of great importance to assess whether the predominant strains possess a “dominant character. ” Therefore, the aim of this study was to investigate whether the predominance of some S. cerevisiae strains results from a better adaptation capability (fitness advantage) to the main stress factors of oenological interest: ethanol and temperature. Predominant and secondary S. cerevisiae strains from different wineries were used to evaluate the individual effect of increasing ethanol concentrations (0-3-5 and 7% v/v) as well as the combined effects of different ethanol concentrations (0-3-5 and 7% v/v) at different temperature (25–30 and 35°C) on yeast growth. For all the assays, the lag phase period, the maximum specific growth rate (μmax) and the maximum cell densities were estimated. In addition, the fitness advantage between the predominant and secondary strains was calculated. The findings pointed out that all the predominant strains showed significantly higher μmax and/or lower lag phase values at all tested conditions. Hence, S. cerevisiae strains that occur at higher percentages in spontaneous alcoholic fermentations are more competitive, possibly because of their higher capability to fit the progressively changing environmental conditions in terms of ethanol concentrations and temperature.
Inference of Big-Five Personality Using Large-scale Networked Mobile and Appliance Data

We present the first large-scale (9270-user) study of data from both mobile and networked appliances for Big-Five personality inference. We correlate aggregated behavioral and physical health features with personalities, and perform binary classification using SVM and Decision Tree. We find that it is possible to infer each Big-Five personality at accuracies of 75% from this dataset despite its size and complexity (mix of mobile and appliance) as prior methods offer similar accuracy levels. We would like to achieve better accuracies and this study is a first step towards seeing how to model such data.
Lightitude

In this paper, we propose an indoor positioning system, Lightitude, which utilizes the already existed, uneven indoor light intensity distribution established by densely deployed indoor lights as the medium. As common indoor lights cannot act as landmarks due to lack of unique features (e.g., unique intensity or flicker frequency), systems that exploiting the received light intensity (RLI) usually impose strong constraints on user's motion and make ideal assumptions about the indoor environment. Different from these approaches, we first propose a realistic light intensity model to reconstruct the RLI distribution given any motion (position, orientation) of the receiver, thus RLI collected with every motion of the receiver could be used for positioning. Then we design a particle-filter-based positioning module, which harnesses user's natural mobility to eliminate the ambiguity of a single RLI. Experiment results show that Lightitude achieves mean accuracy 1.93m and 1.98m in an office (720m2) and a library (960m2) respectively. Lightitude is still robust against interferences like sunlight, shading of human-body and several user behaviors.
Self-Regulation, Knowledge, Experience: Which User Characteristics are Useful for Predicting Video Engagement?

The use of videos in education has attracted considerable research attention. However, in order to gain the most benefits, learners need to actively engage with videos. It is an important, yet challenging, task to disentangle the relation between engagement with videos and learning, and at the same time to take into account relevant individual differences in order to offer personalised support. In this paper we investigate the question: "Can user characteristics relating to self-regulation, knowledge, and experience be leveraged for predicting user engagement with videos? ". Our results show that users' domain knowledge and self-regulation abilities can inform overall engagement prediction (inactive, passive and constructive learners), which makes them useful for adaptation and personalisation.
iSynchronizer: A Tool for Extracting, Integration and Analysis of MovieLens and IMDb Datasets

The growing popularity of e-commerce has ignited the interest of the research community in e-commerce application research and development. For this purpose, variety of applications and resources such as MovieLens and IMDb datasets have been utilized, which present a rich database of items (movies), their attributes and ratings. It is a common practice to utilize both MovieLens and IMDb datasets to produce richer dataset about items and user-generated information such as tags and ratings for conducting research in recommender systems domain. Moreover, the combined dataset contains contextual feature represented by timestamps. However, dealing with a big chunk of the IMDb dataset and its integration with MovieLens dataset remains a challenge due to the various issues such as resource constraints and broken links. To address these issues, a more complex procedure has to be adopted for the integration of both datasets. Hence, this paper introduces iSynchronizer, which is an open-source tool for synchronizing MovieLens and IMDb datasets autonomously with minimum effort. Moreover, the tool includes code to generate a matrix of item features and user preferences over item features from the raw integrated dataset.