Miracle-dz/UniFAR-FTUs huggingface dataset:
AN NMF-Based Community Detection Method Regularized with Local and Global Information

Community identification is an important task in complex network analysis. Recently, Nonnegative Matrix Factorization (NMF) has been successfully used as an effective tool to discover community structures due to its powerful interpretability property. The main issues of this model are requiring a prior knowledge about the community structure and weak stability of the solution. To tackle these issues, in this paper a novel NMF-based method is proposed by incorporating both local and global information to the community identification process. The proposed method first, is mapped the graph into a data space using linear sparse coding. Then a novel metric is proposed to identify community centers that are used to form micro-communities. These micro-communities are further used to identify positive and negative edges that considered in global information regularization term. The local information of the graph is also used in the objective function our NMF-based model. Finally, the objective function is solved to form final communities. The experimental results on three real-world networks denote the superiority of proposed method compared to several well-known and state-of-the-art methods.
Learning to recommend diverse items over implicit feedback on PANDOR

In this paper, we present a novel and publicly available dataset for online recommendation provided by Purch1. The dataset records the clicks generated by users of one of Purch's high-tech website over the ads they have been shown for one month. In addition, the dataset contains contextual information about offers such as offer titles and keywords, as well as the anonymized content of the page on which offers were displayed. Then, besides a detailed description of the dataset, we evaluate the performance of six popular baselines and propose a simple yet effective strategy on how to overcome the existing challenges inherent to implicit feedback and popularity bias introduced while designing an efficient and scalable recommendation algorithm. More specifically, we propose to demonstrate the importance of introducing diversity based on an appropriate representation of items in Recommender Systems, when the available feedback is strongly biased.
Monitoring population levels of physical activity and sedentary time in Norway across the lifespan

The pandemic of physical inactivity is recognized globally but there is a scarcity of studies employing valid and reliable assessment methods of physical activity (PA) across the lifespan. The purpose of this study is to provide a comprehensive description of objectively measured PA, sedentary time, and prevalence of meeting PA recommendations, in a population‐based sample of Norwegian children, adolescents and adults.
Modelling local phase of images and textures with applications in phase denoising and phase retrieval

The Fourier magnitude has been studied extensively, but less effort has been devoted to the Fourier phase, despite its well-established importance in image representation. Global phase was shown to be more important for image representation than the magnitude, whereas local phase, exhibited in Gabor filters, has been used for analysis purposes in detecting image contours and edges. Neither global nor local phase has been modelled in closed form, suitable for Bayesian estimation. In this work, we analyze the local phase of textured images and propose a local (Markovian) model for local phase coefficients. This model is Gaussian-mixture-based, learned from the graph representation of images, based on their complex wavelet decomposition. We demonstrate the applicability of the model in restoration of images with noisy local phase and in image retrieval, where we show superior performance to the well-known hybrid input-output (HIO) method. We also provide a framework for application of the model in a general setup of image processing.
Identification of sieve elements and companion cell protoplasts by a combination of brightfield and fluorescence microscopy

Premise of the Study Phloem‐limited diseases are becoming increasingly pervasive, threatening the existence of crops worldwide. Studies of phloem diseases are complicated by the inaccessibility of the phloem tissue. Phloem cells are located deep inside the plant body, are interspersed with other cell types, are among the smallest cells in the plant kingdom, and make up a small percentage of the total cell population in a plant. These properties make phloem cells difficult to investigate. Methods We used leaf midrib protoplasts and a combination of organelle‐specific dyes including Neutral Red (acidic compartments), MitoTracker Green (mitochondria), Hoechst 3342 (nucleus), and chloroplast autofluorescence. We examined the protoplasts under light and fluorescence microscopy. Results When observed using brightfield and fluorescence microscopy, sieve element protoplasts were distinguished by the lack of vacuole and a nucleus, and by containing various mitochondria. Companion cells showed a dense cytoplasm and various small vacuoles. They also revealed their characteristic large nucleus and abundant mitochondria. Discussion We present evidence that a combination of organelle‐specific dyes and autofluorescence allows for the identification of sieve elements and companion cell protoplasts from citrus leaf tissue.
Cardiomyocytes and Macrophages Discourse on the Method to Govern Cardiac Repair

In response to pathophysiological stress, the cardiac tissue undergoes profound remodeling process that incorporates the elimination of dying resident cells, compensatory hypertrophy of functional cardiomyocytes, growth and remodeling of the vascular compartment and formation of a fibrotic scar. Accumulating evidences indicate that cardiac remodeling is, at least in part, controlled by a complex crosstalk between cardiomyocytes and macrophages. The strategic location of abundant macrophages to the proximity of cardiomyocytes suggest that they could regulate the fate of cardiomyocytes in the injured heart. As such, macrophages appear as critical support cells for cardiomyocytes and play central roles in cardiac hypertrophy, fibrosis and remodeling. Notably, the cardiac tissue expands heterogeneous population of cardiac macrophages through local proliferation of resident macrophage as well as recruitment and differentiation of blood-derived monocytes. It has also been suggested that cardiac-resident macrophages display distinct functional properties from that of monocyte-derived macrophages in cardiac tissue. Furthermore, macrophages are an overflowing source of biological entities with non-canonical roles on cardiac conduction or cardiomyocyte proliferation by regulating action potential diffusion or cardiac cell cycle reentry. Alternatively, stressed cardiomyocytes can trigger the release of a broad repertoire of instructive signals that can regulate macrophage number, skew their phenotype and therefore direct their beneficial or deleterious actions. In this review, we highlight recent discoveries describing how the intricate dialogue between cardiomyocytes and macrophages can shape the deleterious or healing signaling mechanisms in the injured cardiac tissue.
Feature specific analysis of a deep convolutional neural network for ageing classification

Deep learning is a sub-field of machine learning with their models being vaguely inspired by the communication patterns and information processing of a nervous system. In a deep learning model each layer of the network, which is build up of multiple neurons, transforms its input data into slightly more abstract and composite abstractions. Within a convolutional neural network (CNN), the first layer of neurons may be transform its raw input pixels to encoded edges, with the next layer encoding arrangements of edges, with the next layer more abstract features such as eyes and noses. However, locating which exact neurons in each layer are looking for each abstract feature is still a hot area for research. Here we show a novel way to pinpoint each neuron in the hidden layers that is searching for a specific feature that is believed to play a pivotal role in the classification task. We found that by removing the feature of interest from the original image and running this through the CNN, each neurons activation map can be compared to the activation generated by the original image using t-SNE. This allowed us to successfully located the individual neurons in the network that are significantly effected by the removal of this feature and hence changing the classification. This method allows us to get a better understanding of what the network has learnt and how important this learnt information is when coming to the final classification.
LED Driver Based on Boost Circuit and LLC Converter

In order to reduce the size and cost of a medium-power light-emitting diode (LED) driver, a novel single-stage LED driver integrating a boost converter with a half-bridge LLC resonant converter has been proposed. This topology is composed of a boost power factor correction circuit operating in a discontinuous conduction mode and an isolated LLC circuit unit with soft-switching characteristics. The proposed LED driver only adopted two switch components, thus benefiting from control simple and without employing an extra switch driver and a control circuit. The operating principle and characteristics of the proposed LED driver are analyzed in detail. In addition, experimental results of a laboratory prototype for supplying a 100-W/70-V LED lighting systems from 220-V/50-Hz ac line voltage are presented to verify well the correctness of the theoretical analysis and parameters’ design.
A Novel Framework to Represent Documents using a Semantically-grounded Graph Model

As an increasing number of text-based documents, whose complexity increases in turn, are available over the Internet, it becomes obvious that handling such documents as they are, i.e. in their original natural-language based format, represents a daunting task to face up for computers. Thus, some methods and techniques have been used and refined, throughout the last decades, in order to transform the digital documents from the full text version to another suitable representation, making them easier to handle and thus helping users in getting the right information with a reduced algorithmic complexity. One of the most spread solution in document representation and retrieval has consisted in transforming the full text version into a vector, which describes the contents of the document in terms of occurrences patterns of words. Although the wide adoption of this technique, some remarkable drawbacks have been soon pointed out from the researchers’ community, mainly focused on the lack of semantics for the associated terms. In this work, we use WordNet as a generalist linguistic database in order to enrich, at a semantic level, the document representation by exploiting a label and properties based graph model, implemented in Neo4J. This work demonstrates how such representation allows users to quickly recognize the document topics and lays the foundations for cross-document relatedness measures that go beyond the mere word-centric approach.
Measurement of Bandwidth and FFT of a low frequency modulated signal in a Phase locked VCO

This paper presents the experimental analysis of a low frequency phase locked Voltage controlled Oscillator. The measurement of bandwidth and FFT curves of low frequency signal in range of 500 Hz to 1.5 KHz is presented. The resistors and capacitors are varied to lock the phase at desired frequency By using minimal circuit components the proposed phase locked VCO works. Further FFT of the output signal is obtained to show the validity of proposed phase locked VCO experimentally.
Force Steadiness During a Cognitively Challenging Motor Task Is Predicted by Executive Function in Older Adults

Motor performance and cognitive function both decline with aging. Older adults for example are usually less steady for a constant-force task than young adults when performing low-intensity contractions with limb muscles. Healthy older adults can also show varying degrees of cognitive decline, particularly in executive function skills. It is not known, however, whether age-related changes in steadiness of low-force tasks and cognitive function are independent of one another. In this study, we determined if executive function skills in aging are associated with the steadiness during a low-force muscle contraction performed with and without the imposition of a cognitive challenge. We recruited 60 older adults (60–85 years old, 34 women, 26 men) and 48 young adults (19–30 years old, 24 women, 24 men) to perform elbow flexor muscle contractions at 5% of maximal voluntary contraction (MVC) force in the presence and absence of a difficult mental-math task (counting backward by 13 from a four-digit number). Force steadiness was quantified as the coefficient of variation (CV) of force and executive function was estimated with the Trail-making Test part A and B. The cognitive challenge increased the CV of force (i.e., decreased force steadiness) with greater changes in older adults than young adults (5.2 vs. 1.3%, respectively, cognitive challenge × age: P < 0.001). Older adults were 35% slower in both parts A and B of the Trail-making Test (P < 0.001), and to eliminate the effects of age and education on this variable, all further analyses were performed with the age-corrected z-scores for each individual using established normative values. Hierarchical regression models indicated that decreased force steadiness during a cognitive challenge trial was in part, explained by the performance in the Trail-making Test part A and B in older (r = 0.53 and 0.50, respectively, P < 0.05), but not in young adults (P > 0.05). Thus, healthy community-dwelling older adults, who have poorer executive function skills, exhibit reduced force steadiness during tasks when also required to perform a high cognitive demand task, and are likely at risk of reduced capacity to perform daily activities that involve cognitively challenging motor tasks.
Semantic Representation of Information by Ontological Networks to Improve Knowledge Management in Higher Education

: Institutions of Higher Education (IHE) seek to respond to new ways of conceiving and projecting higher education embodied in the proﬁle of a professional. Faced with this challenge, educational models focus mainly on the theoretical-practical references of critical training, constructivism and collaborative learning. Although there are several knowledge management solutions in the ﬁeld of higher education, which have managed to formalize the organizational structure of academic institutions in ontologies, so far none of these proposals divides and makes explicit the development that the actors of the ecosystem (students and educators) take over time. Our proposal is to construct a semantic representation of the academic ecosystem by implementing an ontological network that allows managing the knowledge generated in a more efﬁcient way. This architecture is intended to be used as an instrument to support the academic body and for the centralization of information. To achieve our objective, we carry out a reengineering process of relevant institutional documents, such as the academic record of the students, in each of their facets of learning. We have interviewed specialists in the area and reuse academic domain ontologies to form a consistent knowledge base.
Effects of Preoperative Enteral Nutrition on Postoperative Recent Nutritional Status in Patients with Siewert II and III Adenocarcinoma of Esophagogastric Junction after Neoadjuvant Chemoradiotherapy

Abstract Objective: To study the effects of preoperative enteral nutrition (EN) on postoperative recent nutritional status (PRNS) in patients with Siewert II and III adenocarcinomas of esophagogastric junction (AEG) after neoadjuvant chemoradiotherapy (NCRT). Methods: A total of 66 patients with resectable AEG (Siewert II and III) were randomly divided into two groups. The trial group accepted oral nutrition supplementation (ONS) for 7 days before surgery while the control not. Results: Nutrition indexes were higher in trial group after surgery whereas the opposite was true for the diamine oxidase (DAO) and d-lactate (P < 0.05). The rate of malnutrition and nutritional risk became lower in trial group on the 8th day after surgery (P < 0.05). Injury levels of intestinal mucosa were more severe among control group. The recent prognosis was better in trial group. For patients with or without nutritional risks at admission, the PRNS and recent prognosis were improved by preoperative EN. Logistic regression analysis suggested that preoperative EN could be an independent protective factor of PRNS. Conclusions: Preoperative EN may improve the PRNS and recent prognosis of patients with Siewert II and III AEG after NCRT.
NEXUS Network: Connecting the Preceding and the Following in Dialogue Generation

Sequence-to-Sequence (seq2seq) models have become overwhelmingly popular in building end-to-end trainable dialogue systems. Though highly efficient in learning the backbone of human-computer communications, they suffer from the problem of strongly favoring short generic responses. In this paper, we argue that a good response should smoothly connect both the preceding dialogue history and the following conversations. We strengthen this connection by mutual information maximization. To sidestep the non-differentiability of discrete natural language tokens, we introduce an auxiliary continuous code space and map such code space to a learnable prior distribution for generation purpose. Experiments on two dialogue datasets validate the effectiveness of our model, where the generated responses are closely related to the dialogue context and lead to more interactive conversations.
Infrastructure Cost Evaluation of Intra-Data Centre Networks

In this paper we present an evaluation of the infrastructure costs of five intra-data centre network architectures, namely, leaf-spine, fat-tree, hybrid fat-tree, Facebook 4-Post and a new Facebook fabric. A bottom-up cost calculation approach is used whereby all network architectures are designed so as to meet target oversubscription ratios. From the resulting designs the infrastructure costs are calculated as the sum of the switching and cabling costs. The results show that the leaf-spine network provides the lowest costs. Besides, for all architectures, the leaf (or top of the rack) switches are the dominant cost drivers.
Supporting SMEs towards adopting mixed reality : A training concept to bring the reality-virtuality continuum into application

The vision of Industry 4.0 follows a human centred approach in cyber-physical production systems. For this vision, human machine interfaces are required. A promising concept is the implementation of natural user interfaces. Especially the merging of real and virtual worlds, often referred to as mixed reality, is anticipated to be a future game changer. Yet manufacturing companies, especially small and medium sized companies, are facing the challenge to embrace the potential of these technologies early on and turn them into productive applications. To address this challenge, this paper proposes a training concept, putting the reality-virtuality continuum in focus. The modular, holistic training concept is presented based on mixed reality and technology adoption strategies. Finally, an evaluation of the first trainings is presented.
Person-Job Fit

Person-Job Fit is the process of matching the right talent for the right job by identifying talent competencies that are required for the job. While many qualitative efforts have been made in related fields, it still lacks quantitative ways of measuring talent competencies as well as the job’s talent requirements. To this end, in this article, we propose a novel end-to-end data-driven model based on a Convolutional Neural Network (CNN), namely, the Person-Job Fit Neural Network (PJFNN), for matching a talent qualification to the requirements of a job. To be specific, PJFNN is a bipartite neural network that can effectively learn the joint representation of Person-Job fitness from historical job applications. In particular, due to the design of a hierarchical representation structure, PJFNN can not only estimate whether a candidate fits a job but also identify which specific requirement items in the job posting are satisfied by the candidate by measuring the distances between corresponding latent representations. Finally, the extensive experiments on a large-scale real-world dataset clearly validate the performance of PJFNN in terms of Person-Job Fit prediction. Also, we provide effective data visualization to show some job and talent benchmark insights obtained by PJFNN.
Performance Optimization of Self-represented Systems Supporting Run-time Meta-modeling

Fully self-representing architecture, which supports parallel evolution of meta-models and models when the system running, was proposed by integrating runtime meta-modeling and run-time modeling approaches in current research. Secondly, we focused on the study of the unified structure method, which was put forward to represent in-memory objects at different levels of abstraction and can significantly simplify caching mechanism. After that, a 3-level caching system based on web application was proposed in current research as well. In the end, the availability of the proposed approach was verified from aspects of performance analysis and practical application.
Inferring geometric constraints in human demonstrations

This paper presents an approach for inferring geometric constraints in human demonstrations. In our method, geometric constraint models are built to create representations of kinematic constraints such as fixed point, axial rotation, prismatic motion, planar motion and others across multiple degrees of freedom. Our method infers geometric constraints using both kinematic and force/torque information. The approach first fits all the constraint models using kinematic information and evaluates them individually using position, force and moment criteria. Our approach does not require information about the constraint type or contact geometry; it can determine both simultaneously. We present experimental evaluations using instrumented tongs that show how constraints can be robustly inferred in recordings of human demonstrations.
Demographic Variables in Knowledge Sharing Behavior among IT Engineers in Taiwan

The purpose of this study was to investigate demographic variables in knowledge sharing. The study conducted a quantitative study, and the participants were IT engineers. Random sampling was used. The questionnaires were sent to 25 IT companies by mails. The cover of the questionnaire explained the purposes of the study. A self-addressed stamped envelope was enclosed. 250 copies of questionnaires were sent and 84 copies were returned. The response rate was 33.6%. The data was analyzed by the independent t-test and One-way ANOVA in IBM SPSS 22. The findings of the study show (1) knowledge sharing behavior differs in terms of education level, (2) knowledge sharing behavior has significant influence in terms of profession, and (3) knowledge sharing behavior does not have significant influence in gender, age group, position grade, job tenure, workplace, marital status, and number of children.