Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Process Network Implementation of Ray-Tracing Application on Heterogeneous Multi-Core Systems

As more mobile devices are equipped with multi-core CPUs and are required to execute many compute-intensive multimedia applications, it is important to optimize the systems, considering the underlying parallel hardware architecture. In this paper, we implement and optimize ray-tracing application tailored to a given mobile computing platform with multiple heterogeneous processing elements. In this paper, a lightweight ray-tracing application is specified and implemented in Kahn process network (KPN) model-of-computation, which is known to be suitable for the description of real-time applications. We take an open-source C/C++ implementation of ray-tracing and adapt it to KPN description in the Distributed Application Layer framework. Then, several possible configurations are evaluated in the target mobile computing platform (Exynos 5422), where eight heterogeneous ARM cores are integrated. We derive the optimal degree of parallelism and a suitable distribution of the replicated tasks tailored to the target architecture.
Modelling and synthesis of optimal adders under left-to-right input arrival

In this paper parallel adders with minimum delay and minimum complexity under left-to-right sequential input arrival are investigated. The delay is the result time after the arrival of the last input digits which are the least significant ones. An analytical model of the complexity is established and a synthesis algorithm is described. The algorithm is implemented on a powerful multiview layout synthesis tool called GenOptim. This CAD tool outputs a set of different descriptions including: a netlist view, a layout view, a VHDL behaviour view and finally a test view including functional and structural test patterns with a very high fault coverage.
Reducing Wind Power Forecast Error Based on Machine Learning Algorithms and Producers Merging

This paper proposes forecasting methods for Wind Power Plants` (WPPs`) generation based on Machine Learning Algorithms. In order to increase the precision of generation forecasts from WPPs further, the methodology is introduced of organising WPP owners in a so-called "ECO balance group". The described theoretical bases have been applied to the WPPs in the Croatian transmission power system. Deviation calculations were made for the forecasted and realised generation of WPPs in 2015, when the proposed methods were not effective, and for 2019 when these methods had already been applied.
Future of Education in Industry 4.0

With the developments in technology and innovation, the manufacturing, workforce, training, and educational systems were affected. Facing the fourth industrial revolution, academics are researching the possible changes that might arise in education and skills of the future workforce. As the workplace develops, new competencies will surface. With this context in mind, the authors initiated this research. A detailed questionnaire was prepared as a pilot study to comprehend students' views on the use of technology in classrooms and its impact on their learning experience and engagement. Knowledge of their views allowed the authors to draw inferences as to the skills and competencies of future students and whether they would match Industry 4.0. Furthermore, a gap analysis was conducted, whereby the existing situation at a Canadian higher educational institution was compared to the desired situation, and recommendations were put forward.
Chinese Word Sense Disambiguation Based on Beam Search

Research on word sense disambiguation (WSD) is of great importance in natural language processing. In this paper, a new method based on beam search algorithm for Chinese WSD is proposed. By mining potential knowledge between phrase and semantic category in a sentence, this approach can construct its semantic network. It searches an optimal semantic category sequence from a Chinese sentence's semantic network with beam search algorithm, so that correct meanings of ambiguous words can be found from the optimal sequence. Experiments show that a better WSD performance is gotten.
Analyzing the Interaction of Vortex and Gas–Liquid 
Interface Dynamics in Fuel Spray Nozzles by Means 
of Lagrangian-Coherent Structures (2D)

Predictions of the primary breakup of fuel in realistic fuel spray nozzles for aero-enginecombustors by means of the SPH method are presented. Based on simulations in 2D, novel insightsinto the fundamental effects of primary breakup are established by analyzing the dynamics ofLagrangian-coherent structures (LCSs). An in-house visualization and data exploration platformis used in order to retrieve fields of the finite-time Lyapunov exponent (FTLE) derived from theSPH predictions aiming at the identification of time resolved LCSs. The main focus of this paperis demonstrating the suitability of FTLE fields to capture and visualize the interaction between thegas and the fuel flow leading to liquid disintegration. Aiming for a convenient illustration at a highspatial resolution, the analysis is presented based on 2D datasets. However, the method and theconclusions can analoguosly be transferred to 3D. The FTLE fields of modified nozzle geometriesare compared in order to highlight the influence of the nozzle geometry on primary breakup, whichis a novel and unique approach for this industrial application. Modifications of the geometry areproposed which are capable of suppressing the formation of certain LCSs, leading to less fluctuationof the fuel flow emerging from the spray nozzle.
Intervention Design Elements Are Associated with Frontline Health Workers’ Performance to Deliver Infant and Young Child Nutrition Services in Bangladesh and Vietnam

ABSTRACT Background Frontline health workers (FLWs) are needed for delivering interventions at scale to reduce maternal and child undernutrition, but low- and middle-income countries often face inadequate FLW performance. Objectives We examined whether and how intervention design elements such as training, supervision, and mass media improved FLW performance in delivering nutrition services. Methods Survey data were collected in 2010 and 2014 as part of impact evaluations of Alive & Thrive (A&T) interventions to improve infant and young child feeding (IYCF) practices in Bangladesh and Vietnam. FLWs in A&T intensive (A&T-I) areas received specialized IYCF training, job aids, and regular supportive supervision. Those in non-intensive (A&T-NI) areas received standard government training and supervision. There was mass media exposure in both areas. Multiple regression was used to test differences in exposure to intervention design elements and performance outcomes between the 2 program areas. Path analyses were conducted to examine the paths from exposure to performance outcomes measured at FLW and end-user levels. Results Compared to FLWs in A&T-NI areas, those in A&T-I areas had higher scores in training (by 1.3–3.6 of 10 points), supportive supervision (0.3–3.5 points), and mass media exposure (0.3–3.5 points). These intervention design elements were significantly associated with FLW knowledge and motivation, which in turn improved service delivery. FLW-level performance outcomes contributed to improving end-user-level outcomes such as higher service received (β = 0.12–1.04 in Bangladesh and 0.11–0.96 in Vietnam) and maternal knowledge (β = 0.12–0.17 in Bangladesh and 0.04–0.21 in Vietnam). Conclusions Training, supervision, and mass media exposure can be implemented at large scale and contribute to improved FLW service delivery by enhancing knowledge and motivation, which in turn positively influence mother's service utilization and IYCF knowledge. Training, supervision, and mass media to enhance service provision should be considered when designing interventions. This trial was registered at clinicaltrials.gov as NCT01678716 (Bangladesh) and NCT01676623 (Vietnam).
A Neurocognitive Mechanism for Precision of Visual Working Memory Representations

Author(s): Xie, Weizhen | Advisor(s): Zhang, Weiwei | Abstract: Human memories do not always precisely correspond to exceedingly rich contents in the external environment. This variability of internal representations, especially in working memory (WM) – a system that maintains a small amount of information over a short time period at the service of other mental activities, sets an important functional limit in human cognition. However, neurocognitive mechanisms underlying this WM precision bottleneck remain unclear. One class of theories attributes WM precision to noisy sustained neural activity that supports WM retention (i.e., neural noise hypothesis). Another class of theories maintains that WM retention and precision are supported by independent neural mechanisms. Specifically, WM precision and its manifestation as a certain level of noise in sustained neural activity may be supported by pattern separation, a computation potentially implemented in the hippocampus to orthogonalize similar memories into non-overlapping representations. This pattern separation hypothesis is preliminarily supported by 3 lines of evidence in 4 experiments of the current dissertation. First, in Experiment 1, observers with better pattern separation performance in a behavioral task tend to have higher precision in both WM and long-term memory (LTM), in contrast to a lack of significant association between pattern separation behavioral performance and the probability of successful remembering in either WM or LTM. Second, using functional Magnetic Resonance Imaging (fMRI), Experiment 2 shows that the hippocampus, along with several other regions in a distributed neural network, increases activity as the task demand on WM precision increases. This hippocampal sensitivity to WM precision task demand seems to be primarily driven by the DG/CA3 subfield – where pattern separation most likely occurs – in Experiment 3 using high-resolution fMRI. Third, Experiment 4 further demonstrates that the hippocampal DG/CA3, during WM delay period, retains decodable item-specific information, which further predicts activity in the visual cortices, potentially linking pattern separation to sensory recruitment for precise visual WM representations. Overall, these findings support a novel hippocampal pattern separation mechanism for WM precision, which is central to the ongoing debate on the nature of WM storage limitations. Articulating this potential mechanism may provide a better understanding of compromised mental clarity, manifested as reduced memory precision, in clinical populations such as schizophrenia and pathological aging, etc.
The IPIPAN Team Participation in the Check-Worthiness Task of the CLEF2019 CheckThat! Lab

This paper describes the participation of the IPIPAN team at the CLEF-2019 CheckThat! Lab focused on automatic identification and verification of claims. We participated in Task 1 oriented on assessing the check-worthiness of claims in political debate by identifying and ranking, which sentences should be prioritized for fact-checking. We proposed a logistic regression-based classifier using features such as vector representation of sentences, Part-of-Speech (POS) tags, named entities, and sentiment scores. In the official evaluation, our best performing run was ranked 3 out of 12 teams.
Computer-Aided Software Engineering (CASE) Tool for Big Data and IoT Architecture

Building a new Business Intelligence (BI) architectures for big data and Internet of Things (IoT) is a complex task. It aims to satisfy the needs of decision makers by taking into consideration several constraints and requirements (volume, velocity, and variety). This can influence the decision of choosing the most appropriate tools, techniques, and technologies in the data flow management for IoT solutions. With the aim of helping the decision makers to control their analytical needs and to achieve the business goals, we propose a Computer-Aided Software Engineering (CASE) tool that supports the design of BI for IoT architecture (BIIoT) based on knowledge. It recommends existing technologies which fit well with the scalability of the business requirements and verifies the data flow in the proposed architecture. Thus, the BIIoT tool is validated through a use case related to agriculture service provisioning.
The Decisive Factor of Negtive Scope in Chinese Singular-Topic Paratactic Sentences

This paper studies several sentences regarded as having scope ambiguity of negation by Yuan Yu-lin(2000) and puts forward the view that the decisive factor of negative scope in Chinese paratactic sentences with a same topic is semantic.The several clauses following the negative word closely are all in the negative scope if they are similar semantically.If the clause within which having a negative word is semantically contrary to the following clauses,the negative scope is confined to the clause itself.
A Method of Mining Key Accounts from Internet Pyramid Selling Data

Internet pyramid selling causes great harm and has difficulty in obtaining evidence. As most of internet pyramid selling often uses virtual coins to complete cash settlement, criminals can exchange virtual coins by controlling visual accounts to transfer funds illegally. The purpose of the work is to mine the key accounts and build evidence chain of personnel and funds. In order to deal with a large number of transaction data, we characterize the virtual currency trading data, which is divided into seven transaction characteristics. The key accounts are these outliers which have too prominent trading behaviour. So we use positive samples to train One Class Support Vector Machine (OCSVM) classification to find trading behaviours’ boundary and detect outliers in classification. Then, the correlation between member’s information and pyramid selling hierarchical relationship can also be obtained by further linking the trading behaviour of key accounts. Finally, we can screen out the virtual accounts directly controlled by the pyramid selling organization, analyse the flow of funds, investigate and obtain evidence on amount of money involved in pyramid selling. The experimental results show that the classification model cannot only establish the normal model for account transfer behaviour, but also effectively identify abnormal transfer behaviour, thus improving efficiency of investigation and evidence collection for economic investigation departments.
Word Distance Approach for Celebrity Profiling

This paper describes and evaluates a model for Celebrity Profiling 2019 dataset. The training data set contain 33,836 celebrities’ text with 50 different languages. The task was to create a model for this textual complex dataset which predict gender (male, female, nonbinary), fame (star, superstar, rising), occupation (sports, performer, creator, professional, manager, science, politics, religious) and birthyear (1940-2011) of celebrity. We use word distance features as input to different classifiers for different aspects (gender, fame, occupation and birthyear) of celebrity to create models. Results showed that word distance-based features outperformed the PAN baseline results.
Enumerative Combinatorics: Volume 1

Richard Stanley's two-volume basic introduction to enumerative combinatorics has become the standard guide to the topic for students and experts alike. This thoroughly revised second edition of Volume 1 includes ten new sections and more than 300 new exercises, most with solutions, reflecting numerous new developments since the publication of the first edition in 1986. The material in Volume 1 was chosen to cover those parts of enumerative combinatorics of greatest applicability and with the most important connections with other areas of mathematics. The four chapters are devoted to an introduction to enumeration (suitable for advanced undergraduates), sieve methods, partially ordered sets, and rational generating functions. Much of the material is related to generating functions, a fundamental tool in enumerative combinatorics. In this new edition, the author brings the coverage up to date and includes a wide variety of additional applications and examples, as well as updated and expanded chapter bibliographies. Many of the less difficult new exercises have no solutions so that they can more easily be assigned to students. The material on P-partitions has been rearranged and generalized; the treatment of permutation statistics has been greatly enlarged; and there are also new sections on q-analogues of permutations, hyperplane arrangements, the cd-index, promotion and evacuation, and differential posets.
Inductive Databases and Condensed Representations for Data Mining

Knowledge discovery in databases and data mining aim at semiautomatic tools for the analysis of large data sets. It can be argued that several data mining tasks consist of locating interesting sentences from a given logic that are true in the database. Then the task of the user/analyst is to is to query this set, the theory of the database. This view gives rise to the concept of of inductive databases, i.e., databases that in addition to the data contain also inductive generalizations about the data. We describe a rough framework for inductive databases, and consider also condensed representations, data structures that make it possible to answer queries about the inductive database approximately correctly and reasonably efficiently.
Negative Plant-Soil Feedback Driven by Re-assemblage of the Rhizosphere Microbiome With the Growth of Panax notoginseng

There is a concerted understanding of the accumulation of soil pathogens as the major driving factor of negative plant-soil feedback (NPSF). However, our knowledge of the connection between plant growth, pathogen build-up and soil microbiome assemblage is limited. In this study, significant negative feedback between the soil and sanqi (Panax notoginseng) was found, which were caused by the build-up of the soil-borne pathogens Fusarium oxysporum, F. solani, and Monographella cucumerina. Soil microbiome analysis revealed that the rhizospheric fungal and bacterial communities were changed with the growth of sanqi. Deep analysis of the phylum and genus levels corroborated that rhizospheric fungal Ascomycota, including the soil-borne pathogens F. oxysporum, F. solani, and especially M. cucumerina, were significantly enriched with the growth of sanqi. However, the bacteria Firmicutes and Acidobacteria, including the genera Pseudomonas, Bacillus, Acinetobacter and Burkholderia, were significantly suppressed with the growth of sanqi. Using microbial isolation and in vitro dual culture tests, we found that most isolates derived from the suppressed bacterial genera showed strong antagonistic ability against the growth of sanqi soil-borne pathogens. Interestingly, inoculation of these suppressed isolates in consecutively cultivated soil could significantly alleviate NPSF. In summary, sanqi growth can suppress antagonistic bacteria through re-assemblage of the rhizosphere microbiome and cause the accumulation of soil-borne pathogens, eventually building negative feedback loops between the soil and plants.
Stable reconstruction of generalized impedance boundary conditions

We are interested in the identification of a generalized impedance boundary condition from the far fields created by one or several incident plane waves at a fixed frequency. We focus on the particular case where this boundary condition is expressed with the help of a second-order surface operator: the inverse problem then amounts to retrieve the two functions λ and μ that define this boundary operator. We first derive a global Lipschitz stability result for the identification of λ or μ from the far field for bounded piecewise constant impedance coefficients and give a new type of stability estimate when inexact knowledge of the boundary is assumed. We then introduce an optimization method to identify λ and μ, using in particular an H1-type regularization of the gradient. Finally, we show some numerical results in two dimensions, including a study of the impact of various parameters, and by assuming either an exact knowledge of the shape of the obstacle or an approximate one.
A Cross-Domain Decision Support System to Optimize Building Maintenance

Operations and maintenance optimization are primary issues in Facility Management (FM). Moreover, the increased complexity of the digitized assets leads Facility Managers to the adoption of interdisciplinary metrics that are able to measure the peculiar dynamics of the asset-service system. The aim of this research concerns the development of a cross-domain Decision Support System (DSS) for maintenance optimization. The algorithm underpinning the DSS enables the maintenance optimization through a wiser allocation of economic resources. Therefore, the primary metric encompassed in the DSS is a revised version of the Facility Condition Index (FCI). This metric is combined with an index measuring the service life of the assets, one measuring the preference of the owner and another measuring the criticality of each component in the asset. The four indexes are combined to obtain a Maintenance Priority Index (MPI) that can be employed for maintenance budget allocation. The robustness of the DSS has been tested on an office building in Italy and provided good results. Despite the proposed algorithm could be included in a wider Asset Management system employing other metrics (e.g., financial), a good reliability in the measurement of cross-domain performance of buildings has been observed.
Discovery of Subject of Science and Technology Policy based on LDA Model

With the continuous increase of science and technology policy, how to find valuable information from the massive scientific and technological policies has becoming an urgent problem to be solved. Thus, this paper proposes a subject discovery method for large scale science and technology policy set. Based on LDA subject model for science and technology policy document subject modeling, this approach extracts time and geographical labels of science and technology policy, computes intensity of subjects under different time and geographical conditions, and obtains important subjects and analysis of theme change trend of the intensity of subjects under condition constraints. Experimental results demonstrate that this method can excavate and analyze the subjects form large scale science and technology policies quickly and effectively.
Evaluating CoAP End to End Security for Constrained Wireless Sensor Networks

In the past few years, Internet Engineering Task Force (IETF) has standardized "lightweight" IPv4/IPv6 protocol variations for Wireless Sensor Network (WSN) on resource constrained devices. In the above approaches, security is considered a strong requirement, however, adapting IP oriented security protocols (like the IETF Data-gram Transport Layer Protocol DTLS) to actual WSN security enabled implementations requires tradeoffs between performance, energy consumption and security level. In this paper, the above tradeoffs are explored for the latest version of the widely accepted Contiki OS (version 3.x) when DTLS security is enabled for the IETF supported Constrained Application Protocol (CoAP). More specifically, the DTLS framework is integrated in the Contiki 3 CoAP stack for 2 different cipher suites and the performance in comparison with non secure CoAP applications is evaluated through simulation, in terms of speed, memory overhead and energy consumption for various WSN clientserver network environment. Finally, tradeoff and recommendation for balanced performance versus security are proposed.
Advertising Copy Testing in Print Media

Copy testing continues to be an important, controversial, expensive and involving area of advertising research. This article reviews four categories of print copy testing methods: learning based measures, affective measures, choice measures and physiological measures. Emphasis is given to how well each measure relates to possible advertising objectives and how reliable, sensitive and valid each measure is. Additional topics covered include whether to test only parts of, or the entire ad and context effects.