Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of physical activity and dietary behaviours in relation to the body mass index in a national sample of Iranian children and adolescents: CASPIAN Study.

OBJECTIVE To examine the relation of dietary and physical activity (PA) patterns with the body mass index (BMI), and the associations between these patterns among children. METHODS A representative sample of 21 111 school students aged 6-18 years was selected by multistage random cluster sampling from 23 provinces in the Islamic Republic of Iran. PA and dietary pattern were assessed by self-administered validated questionnaires. FINDINGS Fruit and vegetables, dairy products and snacks (salty, fatty or sweet) had a similar consumption frequency of approximately twice a day. The type of fat most frequently consumed was hydrogenated solid fat (consumed by 73.8% of families). The PA level was significantly higher among boys than girls, in rural than in urban residents, and in intermediate students than high-school students. Among boys, the frequency of consumption of vegetables and plant proteins (R(2) = 0.46); and among girls, the frequency of consumption of dairy products and fruits, as well as high PA level had a significant inverse association with BMI (R(2) = 0.57). Among boys, the low frequency of consumption of fruits, the time spent on PA and the energy expenditure; and among girls, the time spent on PA and the energy expenditure, had significant relationships with overweight. When controlling for covariates, PA levels had significant relationships with the frequency of consumption of all food groups. CONCLUSION Unhealthy lifestyles make Iranian young people prone to chronic diseases later in life. When examining their health benefits, the interrelationship of dietary and PA behaviours should be considered.
Sutherland, Olga and Peräkylä, Anssi and Elliott, Robert (2014) Conversation analysis of the two-chair self-soothing task in emotion-

Despite an increasing recognition of the relevance and significance of self-compassion processes, little research has explored interventions that seek to enhance these in therapy. In this study, we used conversation analysis to examine the compassionate self-soothing task of emotion-focused therapy involving two-chair work, with seven clients. The analysis yielded a detailed description of interactional practices and processes involved in the accomplishment of self-soothing, drawing on Goffman’s concept of the participation frame. In this article we show how therapists and clients collaborate to move from the ordinary frame of therapeutic conversation to a self-soothing frame and back again. Furthermore, we show that in this movement between the frames, they make use of a number interactional practices: therapists' instructions to clients, specific ways of sequencing actions in interaction, explanations and justification of the importance of the self-soothing task, pronouns as a way to distinguish among addressees (e.g., clients versus soothing agents), corrections of clients’ talk, and response tokens (hm mm, yeah, good). These practices are used to help clients accomplish selfsoothing in the form of self-praise, disclosing caring, and offering of helpful advice.
A density-based data cleaning approach for deduplication with data consistency and accuracy

Data cleaning is a critical part of the data transformation stage in data warehousing where the extracted data from relational databases are usually unclean. This may affect critical tasks in different organizations such as data analysis and decision making. Current techniques of data cleaning generally deal with one or two quality aspects. The techniques assume the availability of master data, or that users are involved in data cleaning such as manually placing confidence scores that represent the correctness of the values of data. In this paper, we present a uniform framework and algorithms to integrate data deduplication with inconsistent data repairing and discovering of the accurate values in data. We utilize the embedded density information in data to fix errors based on data density where tuples that are close to each other are packed together. We present a weight model to assign confidence scores that are based on the density of data. The assignments are automated and no user is involved in the process. We consider the inconsistent data in terms of violations with respect to a set of functional dependencies (FDs), as these violations are common in practice. We present a cost model for data repairing that is based on the weight model. We experimentally verify the quality and the scalability of our algorithms. We use synthetic and real datasets.
LIG-MRIM at NTCIR-12 Lifelog Semantic Access Task

This paper describes the participation of the LIG-MRIM research team to the Lifelog Semantic Access sub-task of the NTCIR-12 (2016). Our approach mainly relies on mapping the query terms to visual concepts computed on the Lifel-ogs images according to two separated learning schemes. A post-processing is then performed if the topic is related to temporal, location or activity information associated with the images. The results obtained are promising for a first participation to such a task, with event-based MAP above 29% and an event-based nDCG value close to 39%. Team Name MRIM Subtasks Lifelog Semantic Access Task
A Cost Adjusting Method for Increasing Customers' Sentiment Classification Performance

 Abstract—The internet could be a perfect platform for spreading the electronic word of mouth (e-WOM). Consumers not only heavily depended comments regarding the products or services in social media to make their purchase decisions. The negative product reviews could cause a negative impact on business products. When online reviews increase, inevitably there will produce imbalanced class data, in which the amount of positive comments (negative comments) is far larger than the number of negative comments (positive comments). When training a classifier using this kind of imbalanced data, it'll lead to a higher accuracy for determining the majority example, but an unacceptable error for classifying the minority examples. However, in the domain of sentiment classification, the available works didn't discuss this issue to solve the imbalanced comments. Therefore, this study aims to find the best combination from the cost adjustment, under-sampling, and over-sampling methods based on support vector machines (support vector machines, SVM) to improve the classification performance of imbalanced semantic comments. A comparative analysis of the experimental results will be provided to evaluation these methods. In addition, we use a real online travel site reviews as the case study to verify the effectiveness of the methods.
Tangible nanocomposites with diverse properties for heart valve application

Abstract Cardiovascular disease claims millions of lives every year throughout the world. Biomaterials are used widely for the treatment of this fatal disease. With the advent of nanotechnology, the use of nanocomposites has become almost inevitable in the field of biomaterials. The versatile properties of nanocomposites, such as improved durability and biocompatibility, make them an ideal choice for various biomedical applications. Among the various nanocomposites, polyhedral oligomeric silsesquioxane-poly(carbonate-urea)urethane, bacterial cellulose with polyvinyl alcohol, carbon nanotubes, graphene oxide and nano-hydroxyapatite nanocomposites have gained popularity as putative choices for biomaterials in cardiovascular applications owing to their superior properties. In this review, various studies performed utilizing these nanocomposites for improving the mechanical strength, anti-calcification potential and hemocompatibility of heart valves are reviewed and summarized. The primary motive of this work is to shed light on the emerging nanocomposites for heart valve applications. Furthermore, we aim to promote the prospects of these nanocomposites in the campaign against cardiovascular diseases.
Indoor 3D object model obtained using data fusion from laser sensor and digital camera on a mobile robot

This paper proposes a framework to build a 3D model for an indoor object from partial data using a mobile robot. The purpose of the paper is to obtain photorealistic 3D models for objects from raw mobile laser scanning data and digital camera information (to improve the appearance information). The proposed method integrates and analyses the relationships between the visual data and raw mobile laser scanning data. This method is capable to recover the complete density range and the shape of the investigated object. In order to obtain 3D mapping using the laser sensor system (called Lidar), this is positioned to a vertical scan orientation and the tilt actuator provides a rotation movement to the Lidar in order to perform a 3D scan. In this paper, the mobile system acquires 3D Lidar Data only while it is stationary. A 3D map of an object is created by acquiring overlapping scans from multiple locations and viewpoints using local co-ordinates for each scan and merging the scan into a single global co-ordinate system. Video registration is used to texture the scan points in order to provide photo realistic 3D maps. Some techniques are also implemented to improve the resolution of the Lidar Scan with video data. Experiments on real-world data are given to illustrate the suitability of this approach.
Short term-Long term dual model Background Subtractor

Background subtraction is the most common technique to segment moving objects in a video sequence. We propose a data driven background subtraction method which uses two background models for each pixel: a long term model and a short term model. The long term model captures the long term evolution of the background while the short term model adapts quickly to rapidly changing background conditions like swaying tree leaves or camera jitter. Each model comprises of a collection of previously observed pixel values. Two segmentation maps are generated based on whether the current pixel value finds the required number of matches with the samples in the corresponding model. The final segmentation mask is obtained as an intersection of the two. Evaluation tests on the public CDnet dataset shows improved performance compared to popular methods.
Detection of Aspens Using High Resolution Aerial Laser Scanning Data and Digital Aerial Images

The aim was to use high resolution Aerial Laser Scanning (ALS) data and aerial images to detect European aspen (Populus tremula L.) from among other deciduous trees. The field data consisted of 14 sample plots of 30 m × 30 m size located in the Koli National Park in the North Karelia, Eastern Finland. A Canopy Height Model (CHM) was interpolated from the ALS data with a pulse density of 3.86/m2, low-pass filtered using Height-Based Filtering (HBF) and binarized to create the mask needed to separate the ground pixels from the canopy pixels within individual areas. Watershed segmentation was applied to the low-pass filtered CHM in order to create preliminary canopy segments, from which the non-canopy elements were extracted to obtain the final canopy segmentation, i.e. the ground mask was analysed against the canopy mask. A manual classification of aerial images was employed to separate the canopy segments of deciduous trees from those of coniferous trees. Finally, linear discriminant analysis was applied to the correctly classified canopy segments of deciduous trees to classify them into segments belonging to aspen and those belonging to other deciduous trees. The independent variables used in the classification were obtained from the first pulse ALS point data. The accuracy of discrimination between aspen and other deciduous trees was 78.6%. The independent variables in the classification function were the proportion of vegetation hits, the standard deviation of in pulse heights, accumulated intensity at the 90th percentile and the proportion of laser points reflected at the 60th height percentile. The accuracy of classification corresponded to the validation results of earlier ALS-based studies on the classification of individual deciduous trees to tree species.
Real-time Localization for Wireless Sensor Networks with multiple beacon transmissions

This paper proposes and analyses a localization mechanism for Wireless Sensor Networks. The requirement of the proposed mechanism is the use of a fixed small number of beacon transceivers which will be equipped with GPS functionality and able to transmit a gradient of power levels. The several power levels serve to identify the distance by means of power without using the Received Signal Strength Indicator (RSSI). The location estimation is then completed using linear algebra. Due to wide range of applications, the method utilizes Quality of Service (QoS) parameters promising an optimized appliance for each case. This new localization method is fully scalable, power aware and capable to be deployed both in networks with movable or stationary nodes.
Estimating the bispectrum of the Very Small Array data

We estimate the bispectrum of the Very Small Array data from the compact and extended configuration observations released in 2002 December, and compare our results with those obtained from Gaussian simulations. There is a slight excess of large bispectrum values for two individual fields, but this does not appear when the fields are combined. Given our expected level of residual point sources, we do not expect these to be the source of the discrepancy. Using the compact configuration data, we put an upper limit of 5400 on the value of fNL, the non-linear coupling parameter, at 95 per cent confidence. We test our bispectrum estimator using non-Gaussian simulations with a known bispectrum, and recover the input values.
A signal constellation for pilotless communications over Wiener phase noise channels

In this contribution, we propose a signal constellation for the the phase noise channel which does not require pilots thus increasing the effective information rate in a communication system. This constellation does not present rotational symmetry thus enabling decoding algorithms such as SPA, to converge without the use of pilots. We will provide Bit Error Rate (BER) simulations which show the superiority of this constellation over standard MPSK with pilots. Moreover, we will provide a method to analyze any arbitrary signal constellation and provide a figure of merit for its performance when iterative decoding algorithms are used.
Access Security Based on Bayesian Trust Network

In this paper, a novel access control scheme based on Bayesian trust network is developed to address to the security services in P2P environments. The trust mechanism is constructed with Bayesian network model, which could describe precise trust degree of the P2P node responding to various operation or access. The model of access control is proposes with Bayesian trust network of P2P members, and dynamically manages the access permission according to the Bayesian trust network of subject and object.
IT Service Management from a Perspective of Small and Medium Sized Companies

Most of the existing information security management studies focus on information security of large organizations preventing security threats. However, a security management perspective of a small and medium-sized (SME) company is relatively different. Small and medium sized companies are at a greater risk because they cannot afford expensive tools. They lack process improvement capabilities and effort that can be spent on security management is limited. Due to limited budget, they cannot invest heavily in the IT service infrastructure and IT manpower. They become easy targets for cyber criminals and they can be easily victimized as they lack the resources compared to larger companies. Malicious internet content also becomes one of the major security concerns. Therefore, this study aims to answer the research problem: How small and mediumsized organizations perform IT security management. The main contribution of the paper is to capture information security management challenges from an SME's viewpoint and provide solutions to tackle these challenges. This paper aims to improve information security management of SMEs. From IT Service management perspective, the case study research approach was utilized in the study.
Lexical Acquisition with WordNet and the Mikrokosmos Ontology

This paper discusses an approach to augmenting a lexicon for knowledge-based machine translation (KBMT) with information derived from WordNet. The Mikrokosmos project at NMSU's Computing Research Laboratory has concentrated on the creation of the Spanish and Japanese lexicons, so the English lexicon is less developed. We investigated using WordNet as a means to automate p0rtions of the English lexicon development. Several heuristics axe used to find the WordNet synonym sets corresponding to the concepts in the Mikrokosmos language-independent ontology. Two of these heuristics exploit the WordNet is-a hierarchy: one performs hierarchical matching of both taxonomies, and the other computes similarity based on frequency of defining words and their ancestors in a corpus. The result is a lexicon acquisition tool that produces plausible lexical mappings from English words into the Mikrokosmos ontology. Initial performance results are included, which indicate good accuracy in the mappings.
Design of $F$-Band Transition From Microstrip to Ridge Gap Waveguide Including Monte Carlo Assembly Tolerance Analysis

This paper describes the design and realization of a transition from a microstrip line to a ridge gap waveguide operating between 95 and 115 GHz. The study includes simulations, measurements, and a Monte Carlo analysis of the assembly tolerances. The purpose of this tolerance study is to identify the most critical misalignments that affect the circuit performance and to provide guidelines about the assembly tolerance requirements for the proposed transition design.
Edge-based representation and recognition for surgically altered face images

Basically, plastic surgery procedure introduces skin texture variations between images of the same person (intra-face) thereby making recognition more difficult than in normal scenario. Since the shape of significant face features such as eyes, nose, eyebrow and mouth remains unchanged even after plastic surgery, edge-based recognition methods can be employed. This paper employs an edge-based Gabor feature representation approach for the recognition of surgically altered faces. We use the edge information, which is dependent on the shapes of the significant face features, to address the problems of texture variations due to plastic surgery procedures. To ensure that the edge information richly captures significant features of the face and with little or no false edges, a simple illumination normalization step(s) is proposed prior to edge information extraction. Then, the Gabor wavelet is applied on the edge image, which accentuates on the uniqueness of the significant features for discriminating amongst different persons. Experimental results on plastic surgery database (Rhytidectomy, Rhinoplasty and Blepharoplasty) shows that the proposed method performs significantly well in comparison to existing plastic surgery, face recognition methods reported in the literature.
Empowering Leadership Behavior and Perceived Insider Status: The Moderating Role of Organization-based Self-esteem

Perceived insider status(PIS) is defined as the extent to which an employee perceives him/herself as an insider in a particular organization which has exhibited positive correlation with organizational commitment,task performance and organizational citizenship behavior in previous studies.Underpinned by the organizational investment model,the objective of the research is to develop a process model linking empowering leadership behavior to organizational citizenship behavior(OCB) with the mediating role of perceived insider status(PIS). The moderating effects of organization-based self-esteem(OBSE) on the relationship between PIS and OCB,as well as on the mediating role of PIS are also investigated.We tested this model with a dataset from a sample of 150 subordinate-supervisor dyads from the People's Republic of China.Two versions of questionnaires were mailed to the supervisors and subordinates respectively.We asked all the participants to complete the questionnaires and return them directly to the researchers in postage-paid envelopes that we provided to them.Participation in the study was voluntary,and confidentiality was ensured.After deleting unmatched dyads,we obtained 146 dyads of subordinate and supervisor in the final data set.Mediated and moderated regression was performed.Results showed that empowering leadership behavior had significantly positive effect on PIS.OBSE moderated the relation between PIS and OCB,and also moderated the mediating role of PIS.Furthermore,PIS fully mediated the relation between empowering leadership behavior and OCB.Our results demonstrated the importance of investing in employees to enhance the feeling of social inclusion.Research implications and future research directions are also discussed.
Scalable audio separation with light Kernel Additive Modelling

Recently, Kernel Additive Modelling (KAM) was proposed as a unified framework to achieve multichannel audio source separation. Its main feature is to use kernel models for locally describing the spectrograms of the sources. Such kernels can capture source features such as repetitivity, stability over time and/or frequency, self-similarity, etc. KAM notably subsumes many popular and effective methods from the state of the art, including REPET and harmonic/percussive separation with median filters. However, it also comes with an important drawback in its initial form: its memory usage badly scales with the number of sources. Indeed, KAM requires the storage of the full-resolution spectrogram for each source, which may become prohibitive for full-length tracks or many sources. In this paper, we show how it can be combined with a fast compression algorithm of its parameters to address the scalability issue, thus enabling its use on small platforms or mobile devices.
HDAC4 Reduction: A Novel Therapeutic Strategy to Target Cytoplasmic Huntingtin and Ameliorate Neurodegeneration

HDAC4 histone deacetylase is found to associate with huntingtin in a polyQ-length dependent manner. Reduction of HDAC4 levels in mouse models of Huntington's disease (HD) delays cytoplasmic aggregation in the brain and improves the molecular pathology of HD, providing a potential new therapeutic target.
Modeling and improving security of a local disk system for write-intensive workloads

Since security is of critical importance for modern storage systems, it is imperative to protect stored data from being tampered with or disclosed. Although an increasing number of secure storage systems have been developed, there is no way to dynamically choose security services to meet disk requests' flexible security requirements. Furthermore, existing security techniques for disk systems are not suitable to guarantee desired response times of disk requests. We remedy this situation by proposing an adaptive strategy (referred to as AWARDS) that can judiciously select the most appropriate security service for each write request, while endeavoring to guarantee the desired response times of all disk requests. To prove the efficiency of the proposed approach, we build an analytical model to measure the probability that a disk request is completed before its desired response time. The model also can be used to derive the expected value of disk requests' security levels. Empirical results based on synthetic workloads as well as real I/O-intensive applications show that AWARDS significantly improves overall performance over an existing scheme by up to 358.9% (with an average of 213.4%).