Miracle-dz/UniFAR-FTUs huggingface dataset:
Salt Intake of Children and Adolescents: Influence of Socio-Environmental Factors and School Education

(1) Background: The aim of this study was to investigate the salt consumption by children and adolescents from the Silesian Province (Poland), taking into account the region’s dietary traditions and the age of the students+. (2) Methods: 300 students aged 10–18 from different types of schools were enrolled in the study and divided into groups in terms of school, sex, and the state of their nutrition. A survey questionnaire about dietary habits, including the frequency and serving size with respect to 12 salty products, was used. On the basis of the frequency and the amount of consumed products, as well as the data on salt content, the amount of total daily intake of salt was estimated. (3) Results: The mean daily intake of salt by children and adolescents was 1.083 g (0.433 g of sodium); children aged 10–12 consumed the highest amount of salt (1.296 g/day) compared to pupils aged 13–15 (1.131 g of sodium) and adolescents aged 16–18 (0.863 g/day). (4) Conclusions: With age, as a result of various factors, the consumption of salt declines. The parents’ impact and the familial socio-environmental factors begin to wane, and other factors start to have influence, e.g., school education of a healthy lifestyle and health behavior of peers.
A Hidden Markov Model for Collaborative Filtering

We present a method to make personalized recommendations when user preferences change over time. Most of the works in the recommender systems literature have been developed under the assumption that user preference has a static pattern. However, this is a strong assumption especially when the user is observed over a long period of time. With the help of a dataset on employees’ blog reading behavior, we show that users’ product selection behaviors change over time. We propose a hidden Markov model to correctly interpret the users’ product selection behaviors and make personalized recommendations. The user preference is modeled as a hidden Markov sequence. A variable number of product selections of different types by each user in each time period requires a novel observation model. We propose a Negative Binomial mixture of Multinomial to model such observations. This allows us to identify stable global preferences of users and to track individual users through these preferences. We evaluate our model using three real world datasets with different characteristics. They include data on employee blog reading behavior inside a firm, users’ movie rating behavior at Netflix, and users’ music listening behavior collected through last.fm. We compare the recommendation performance of the proposed model with that of a number of collaborative filtering algorithms and a recently proposed temporal link prediction algorithm. We find that the proposed HMM based collaborative filter performs as well as the best among the alternative algorithms when the data is sparse or static. However, it outperforms the existing algorithms when the data is less sparse and the user preference is changing. We further examine the performances of the algorithms using simulated data with different characteristics and highlight the scenarios where it is beneficial to use a dynamic model to generate product recommendation.
Energy-Efficient BWP Configuration for Multi-Slice Users

Fifth Generation (5G) mobile networks introduce the concept of slicing to ensure isolation among the various supported heterogeneous services. The User Equipment (UE) can be connected to multiple slices simultaneously. Additionally, the notion of a Bandwidth Part (BWP) was also instigated to reduce power consumption. A BWP is a small chunk of the bandwidth scanned by the UE to retrieve its service data. Therefore, a UE connected to multiple services can be configured with multiple BWPs each associated with a given service. Such UEs find themselves scanning multiple BWPs, which can be time consuming and highly energy intensive. Hence, it is paramount to study the appropriate choice of the BWP configuration from an energy-efficiency perspective for multi-slice users depending on their battery level. In this paper, two energy-efficient BWP selection solutions are proposed for users connected to multiple slices. The first solution is based on a centralized approach where UEs are stirred optimally to the best BWP configuration, while the second solution relies on a user-centric distributed approach using non-cooperative game theory. The proposed schemes take into account the users’ battery level and their sojourn time in the network as well as the scanned BWP size. Both solutions are compared with one another and against the legacy solution. Intensive simulation results demonstrate the efficiency of our proposition in terms of users’ energy efficiency and quality of service.
When Does Retargeting Work? Timing Information Specicity

Firms can now serve personalized recommendations to consumers who return to their website, based on their earlier browsing history. At the same time, online advertising has greatly advanced in its use of external browsing data across the web to target internet ads appropriately. ‘Dynamic Retargeting’ integrates these two advances by using information from internal browsing data to improve internet advertising on external websites. Consumers who previously visited the rms’ website are shown ads that reect the specic products they have looked at before on the rm’s own website
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition

From the Publisher: This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing.
Real Time Event Monitoring with Trident

Building a scalable, fault-tolerant stream mining system that deals with realistic data volumes presents unique challenges. Consider-able work is being done to make the development of such systems simpler, creating high level abstractions on top of existing systems. Many of the technical barriers can be eliminated by adopting a state-of-the-art inter-face, such as the Trident API for Storm. We describe a stream mining tool, based on Trident, for monitoring breaking news events on Twitter, which can be extended quickly and scaled easily.
Accuracy performance analysis of multimodal biometrics

Since biometrics may be used to ensure that a person accessing information is authorized to do so, interest in biometrics for information assurance has increased recently. New biometric applications are constantly being announced while at the same time new spoofing technology is being developed to defeat them. One approach to overcoming the problem of spoofing is the use of multimodal biometric fusion. Most current research is focused on overcoming the deficiencies of a single biometric trait or reducing the false acceptance rate, both without any emphasis on the false rejection rate. Multimodal biometric fusion combines measurements from different biometric traits to enhance the strengths and diminish the weaknesses of the individual measurements. We investigate two types of errors associated with biometrics. The accuracy is analyzed for multimodal biometric systems utilizing two commonly used fusion rules. The purpose of the study is to provide a theoretical evaluation for the false acceptance and the false rejection rates to improve the accuracy as well as the convenience of biometric applications.
A general inference network based architecture for multimedia information retrieval

Bayesian inference networks have found application in probabilistic information retrieval in the context of textual documents. The paper outlines an architecture whereby an inference network retrieval engine can be applied to multimedia retrieval. Initially, the system was developed to support the content based retrieval of images, however extensions are described so that retrieval of more general multimedia data may be performed.
An Introduction to Bayesian Inference Via Variational Approximations: Supplemental Notes

1.1 The Tractability-Fit Tradeoff in Variational Approximations The goal of a variational approximation is to approximate a posterior, p(β|Y ) by making an approximating distribution, q(β), as close as possible to the true posterior (Bishop, 2006). We search over the space of approximating distributions in order to find the particular distribution with the minimum KL-divergence with the actual posterior. Formally, we search over the set of approximating distributions q(β) to minimize
Team Description Paper : HuroEvolutionTN Humanoid Robot for RoboCup 2017 Humanoid League

HuroEvolutionTN is a teen size humanoid robot that was made at National Taiwan University of Science and Technology. In this paper a teen-size humanoid robot, named HuroEvolutionTN, is developed for the purpose of participating in the RoboCup soccer gamers. The HuroEvolution is 95 centimeter in height and 12 kilogram in weight, and it is simply configured with 21 degrees of freedom, where 12 degrees of freedom are used for two lower limbs, 6 degrees of freedom are used for two arms, two degrees of freedom are used for the head camera, and one degree of freedom is used for the waist. Each leg is designed as a parallel mechanism structure to reduce the backlash effects of gear motors, as well as to reduce the uses of gear motors. The HuroEvolutionTN is capable of omni-walking with respect to different locomotion parameters. Moreover, a specialized turning locomotion is also generated to change the robot’s heading based on a limited degrees of freedom of the leg structure. The image recognition and localization approaches are also applied for navigating the HuroEvolutionTN to finish the match in competitions.
AIS Electronic Library (AISeL)

This paper critically reviews the literature relating to the SISP in global organizations. It considers IS strategic benefits, requirements, and drivers as evaluation criteria for SISP process. Classification of the IS strategic benefits will be developed from academic literature and published case studies as well as IS strategic requirements and drivers. With this in mind, the planning team must identify how it can group the requirements and drivers of an organization. One way of doing so may be achieved by understanding the benefits that can be obtained by IS for the organization, thereby leading to a categorization of requirements against benefits being created to understand which requirements are needed for obtaining specific benefits. The result of this paper is taxonomies of SISP benefits, requirements, drivers, and techniques for global organization. This classification can benefit the evaluation of IS strategic planning processes to support decision-makers through the planning process.
Arrigo, Francesca and Higham, Desmond J. (2017) Sparse

Time sliced networks describing human-human digital interactions are typically large and sparse. This is the case, for example, with pairwise connectivity describing social media, voice call or physical proximity, when measured over seconds, minutes or hours. However, if we wish to quantify and compare the overall time-dependent centrality of the network nodes, then we should account for the global flow of information through time. Because the time-dependent edge structure typically allows information to diffuse widely around the network, a natural summary of sparse but dynamic pairwise interactions will generally take the form of a large dense matrix. For this reason, computing nodal centralities for a time-dependent network can be extremely expensive in terms of both computation and storage; much more so than for a single, static network. In this work, we focus on the case of dynamic communicability, which leads to broadcast and receive centrality measures. We derive a new algorithm for computing time-dependent centrality that works with a sparsified version of the dynamic communicability matrix. In this way, the computation and storage requirements are reduced to those of a sparse, static network at each time point. The new algorithm is justified from first principles and then tested on a large scale data set. We find that even with very stringent sparsity requirements (retaining no more than ten times the number of nonzeros in the individual time slices), the algorithm accurately reproduces the list of highly central nodes given by the underlying full system. This allows us to capture centrality over time with a minimal level of storage and with a cost that scales only linearly with the number of time points. We also describe and test three variants of the proposed algorithm that require fewer parameters and achieve a further reduction in the computational cost.
Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data

scenario is akin to the magnetic furnace model proposed by Axford and McKenzie (14–16) and to ideas invoking reconnection of mesoscale loops (38, 39). We adopt from the furnace model the idea that reconnection plays a major role, as it will release plasma, set free magnetic energy, and produce Alfvén waves. However, our model of the nascent solar wind is intrinsically 3-D, and the magnetic field geometry is derived empirically. The plasma is accelerated in the funnel above a critical height of 5 Mm but originates below from the neighboring loops. The initial heating of the solar wind plasma is achieved in the side loops. ESA PRODEX program (Swiss contribution). SUMER, EIT, and MDI are instruments on SOHO, an ESA and NASA mission. We thank the teams of MDI and EIT for providing the magnetic field data and the context image. Machine learning was applied for the automated derivation of causal influences in cellular signaling networks. This derivation relied on the simultaneous measurement of multiple phosphorylated protein and phospholipid components in thousands of individual primary human immune system cells. Perturbing these cells with molecular interventions drove the ordering of connections between pathway components, wherein Bayesian network computational methods automatically elucidated most of the traditionally reported signaling relationships and predicted novel interpathway network causalities, which we verified experimentally. Reconstruction of network models from physiologically relevant primary single cells might be applied to understanding native-state tissue signal-ing biology, complex drug actions, and dysfunctional signaling in diseased cells. Extracellular cues trigger a cascade of information flow, in which signaling molecules become chemically, physically, or location-ally modified; gain new functional capabilities ; and affect subsequent molecules in the cascade, culminating in a phenotypic cellular response. Mapping of signaling pathways typically has involved intuitive inferences arising from the aggregation of studies of individual pathway components from diverse experimental systems. Although pathways are often conceptualized as distinct entities responding to specific triggers, it is now understood that in-terpathway cross-talk and other properties of networks reflect underlying complexities that cannot be explained by the consideration of individual pathways or model systems in isolation. To properly understand normal cellular responses and their potential disregulation in disease, a global multivariate approach is required (1). Bayesian networks (2), a form of graphical models, have been proffered as a promising framework for modeling complex systems such as cell signaling cascades, because they can represent probabilistic dependence relationships among multiple interacting components …
Communications of the Association for Information Systems

: Drawing on notions of power and the social construction of risk, we build new theory to understand the persistence of shadow systems in organizations. From a single case study in a mid-sized savings bank, we derive two feedback cycles that concern shifting power relations between business units and central IT associated with shadow systems. A distant business-IT relationship and changing business needs can create repeated cost and time pressures that make business units draw on shadow systems. The perception of risk can trigger an opposing power shift back through the decommissioning and recentralization of shadow systems. However, empirical findings suggest that the weakening tendency of formal risk-management programs may not be sufficient to stop the shadow systems cycle spinning if they fail to address the underlying causes for the emergence of shadow systems. These findings highlight long-term dynamics associated with shadow systems and pose “risk” as a power-shifting construct.
An Eecient Communication Strategy for Finite Element Methods on the Connection Machine Cm-5 System an Eecient Communication Strategy for Finite Element Methods on the Connection Machine Cm-5 System

The objective of this paper is to propose communication procedures suitable for unstructured nite element solvers implemented on distributed-memory parallel computers such as the Connection Machine CM-5 system. First, a data-parallel implementation of the recursive spectral bisection (RSB) algorithm proposed by Pothen et al. is presented. The RSB algorithm is associated with a node renumber-ing scheme which improves data locality of reference. Two-step gather and scatter operations taking advantage of this data locality are then designed. These communication primitives make use of the indirect addressing capability of the CM-5 vector units to achieve high gather and scatter bandwidths. The performance of the proposed communication strategy is illustrated on large-scale three-dimensional uid dynamics problems.
Gene prediction in metagenomic fragments based on the SVM algorithm

Metagenomic sequencing is becoming a powerful method to explore various environmental organisms without isolation and cultivation. Genomic sequences data generated by this technology is growing explosively while numerous computational methods for analysis are still urgently in need. One of the first and most important processes is exhaustive gene prediction. As short and anonymous DNA fragments, assembly of metagenomic sequences usually has not a fixed end point to obtain complete genomes and moreover is often not available. This situation makes the annotation more complicated than in complete genomes. Here, we present a newly developed SVM-based algorithm which comprises a supervised universal model and a data-specific novel model. It utilizes entropy density profiles of codon usage, translation initiation signal scoring and open read frame length for model training. Tests on fixed-length artificial shotgun sequences of 700 bp showed a sensitivity of 94.7% and a specificity of 94.9% on average, which indicate that our method has the totally higher performance than the best of current gene prediction methods. Thousands of additional genes are predicted when applied to two metagenomic samples from human gut community. Furthermore, compared to other gene predictors, our algorithm predicts the most potential novel genes.
Exploiting Inaccurate A Priori Knowledge in Robot Exploration

Exploration is a task in which autonomous mobile robots incrementally discover features of interest in initially unknown environments. Most of the current exploration approaches ignore prior knowledge about the environments that have to be explored. However, in some practical cases, such knowledge could be available. In this paper, we present a method that includes a priori knowledge in an exploration strategy that selects the next best locations the robot should reach in partially explored indoor environments by exploiting the (possibly inaccurate) knowledge of their � oor plans
Communications in Statistics - Simulation and Computation

In bioinformatics application, the estimation of the starting and ending points of drop-down in the longitudinal data is important. One possible approach to estimate such change times is to use the partial spline model with change points. In order to use estimate change time, the minimum operator in terms of a smoothing parameter has been widely used, but we showed that the minimum operator causes large MSE of change point estimates. In this paper, we proposed the summation operator in terms of a smoothing parameter, and our simulation study showed that the summation operator gives smaller MSE for estimated change points than the minimum one. We also applied the proposed approach to the experiment data, blood ﬂow during photodynamic cancer therapy.
Romanian Rural Lodgings: How Many Survived Over a Decade? A Preliminary Study Focused on the Rural Localities Hosting 10 or More Accommodation Units

To the best of our knowledge, no previous study exits on the survival of tourist accommodation units and/or on the economic entities related to these accommodations in Romania. Therefore no such study exists in relation with the rural accommodation units. Through the present study we try to make a small step in filling the research gap regarding the survival of extant rural accommodation units in a developing country, Romania. The findings show an overall simple survival rate (SSR) of 38.21%. The existence of tourist attractions (spa/mountain resorts and World Heritage Sites) improve the extant lodgings SSR, while exceptions exist in the counties of Sibiu, Neamt, Suceava, Cluj and Harghita. The dominant surviving accommodation units are the rural pensions. The owners/operators of the survivor lodgings are mainly individual enterprises, though between 2005 and 2016 the number of operators registered as LLCs increased. Indirectly, the findings also imply that most of the survivor lodgings can be considered lifestyle enterprises.