Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of health promotion program with collaborative approach on physical activity of overweight school-age girls

Background: Nowadays, due to ineffectiveness of interventions in the prevention of obesity in school-age children, collaborative approach in order to prevent and control the obesity seems necessary. Aim: to assess the impact of health promotion programs with collaborative approach on physical activity levels of overweight school-age girls. Methods: This quasi-experimental and pretest/post-test study was conducted among 80 overweight female students which were randomly selected. Data were collected via activity checklist. In the test groups, health promotion programs were administered in 3 stages of assessment, supportive planning and evaluation in 6 to 8 one-hour sessions for four consecutive weeks (2 sessions per week for students, parents and teachers), activities suitable with children’s condition 3 sessions per week, and evaluation for 3 months. Level of children's activity in both groups were evaluated by the child and parents before and 3 months after intervention and analyzed by SPSS software version 16, independent and paired t-tests, and chi-square. Results: Before the intervention, the two groups were similar in terms of age, place of birth, history of disease, nutrition, parental age, education, occupation and income, and duration of exercise. After the intervention, both groups had significant differences in duration of exercise (P<0.001). Conclusion: Health promotion programs with collaboration of children, parents and teachers could increase exercise duration in overweight children.
Automated Permission Model Generation for Securing SDN Control-Plane

An important consideration in software-defined networks (SDNs), is that one SDN application, through a bug or API misuse, can break an entire SDN. While previous works have tried to mitigate such concerns by implementing access control mechanisms (permission models) for an SDN controller, they commonly require serious manual efforts in creating a permission model. Moreover, they do not support flexible permission models, and they are often tightly coupled with a specific SDN controller. To address such limitations, we introduce an automated permission generation and verification system called VOGUE. A distinguishing aspect of VOGUE is that it automatically generates flexible permission models and yet is completely separated from the SDN controller implementation. To demonstrate the feasibility of our approach, we implement a prototype, evaluate its completeness and soundness, and examine its performance. In addition, to show the effectiveness of VOGUE, we demonstrate its use cases and security impact to SDN in the context of popular SDN controllers.
Flexible Hierarchical System of Automatic Voltage Control

The subsystem for optimal control of voltage and reactive power of EPS is developed. The proposed solution uses state of art methods for state estimation, forecasting and dynamic optimization. A new architecture of an artificial neural network is proposed – a neuro-analytical network. Algorithms are proposed that allow reliable combination of classical automatic control methods and methods using machine learning. The proposed methodology is designed for use in a real power system for automatic voltage control.
Statistical QoS Provisioning Over Uncertain Shared Spectrums in Cognitive IoT Networks: A Distributionally Robust Data-Driven Approach

With the soaring wireless traffic for Internet of Things (IoT), spectrum shortage becomes an extremely serious problem, leading to the paradigm shift in spectrum usage from an exclusive mode to a sharing mode. However, how to guarantee the quality of service (QoS) when using the shared spectrum is not straightforward due to its uncertain availability. In this paper, from a session-based view, we propose a metric to evaluate how much data can be delivered via a shared band during a session period, named probabilistic link capacity (PLC), which offers us an effective way to guarantee the QoS statistically. Different from most existing works where the distributional information is assumed exactly known, we develop a distributionally robust (DR) data-driven approach to estimate the value of the PLC based on the first and second order statistics. Two cases are considered that the statistics are exact or uncertain with estimation errors. For each case, to calculate the DR-PLC, we formulate it into a semidefinite programming problem based on the worst-case of conditional-value-at-risk. With the proposed metric, we further design a service-based spectrum-aware data transmission scheme, which allows us to efficiently use different kinds of spectrums to satisfy the diverse IoT service requirements.
Mapping high-performance RNNs to in-memory neuromorphic chips

The increasing need for compact and low-power computing solutions for machine learning applications has triggered significant interest in energy-efficient neuromorphic systems. However, most of these architectures rely on spiking neural networks, which typically perform poorly compared to their non-spiking counterparts in terms of accuracy. In this paper, we propose a new adaptive spiking neuron model that can be abstracted as a low-pass filter. This abstraction enables faster and better training of spiking networks using back-propagation, without simulating spikes. We show that this model dramatically improves the inference performance of a recurrent neural network and validate it with three complex spatio-temporal learning tasks: the temporal addition task, the temporal copying task, and a spoken-phrase recognition task. We estimate at least 500x higher energy-efficiency using our models on compatible neuromorphic chips in comparison to Cortex-M4, a popular embedded microprocessor.
Arabic Speaking Material Design Using Content and Language Integrated Learning (CLIL)

The aim of this study is to develop Arabic speaking skill materials model based on Content Language Integrated Learning at Jakarta State University. Students have low Arabic speaking ability. So far, the material used in the lesson emphasizes the memorization of dialogues and sentence patterns as one of the main characteristics of the lingual audio method. This research was done in Arabic Program of Language and Art Faculty of Jakarta State University. The research method used the Research and Development (R & D). The procedure of research used Borg and Gall model which has the steps of this study are; preliminary step, a development step, and evaluation step. The data ware took by observation, questionnaire, interview, and experiment. The research finding shows that the development of CLIL based Arabic speaking material using Qur’an, Hadits, Fiqih and Tsaqofah Islamiah is an effective model for improving learning to speak Arabic. It can be seen from the perception of students to the mode.
Depression and Physical Illness: Depression and adherence to medical advice

Depression has emerged as one of the few promising predictors of treatment adherence among medical patients. This chapter discusses the research evaluating the relationship between depression and adherence to medical regimens. It considers methodological issues in the assessment of adherence and depression. The chapter reviews the empirical studies that have examined whether depressed individuals have poorer treatment adherence. Several studies have addressed adherence in patients infected with human immunodeficiency virus (HIV). The chapter focuses on the relationship between depression and adherence and identifies key studies that address the question of whether adherence mediates the relationship between depression and health outcome. There are two predominant approaches to measuring depression: interview and self-report. Research on depression and adherence is increasing rapidly. It is important that future research on this topic be designed with greater methodological sophistication.
A spectrum of approaches to health information interaction: From avoidance to verification

People respond to illness in a range of ways, and take different approaches to engaging with health information throughout the course of their illness. This study describes and explains the variety of approaches to health information interactions made by patients on hemodialysis. Ethnographic observations (156 hours) were conducted in three hemodialysis clinics, and semistructured interviews about health information were held with 28 patients. Demographic data were collected. Data were analyzed qualitatively. We found a spectrum of five approaches to health information: avoiders, who close themselves off from health information; receivers, who encounter information in the dialysis clinic but do not seek it out; askers, who only pose questions about health to their healthcare providers but otherwise do not seek; seekers, who actively look for health information both in and out of the clinic; and verifiers, who seek information and triangulate it among multiple sources. Trust in healthcare providers and coping sociality differed across approaches. The findings indicate that health information should be provided to patients using strategies tailored to their preferences and existing approaches to information interaction.
Joint Precoding Spatial and Rotating Symbol Modulation for Physical-Layer Security

In traditional generalized precoding-aided spatial modulation (GPSM), the mappings of the receive antenna combination index and the constellation symbol are performed independently. In this letter, we propose a joint GPSM and rotating symbol modulation for physical-layer security in wireless communications, which rotates conventional amplitude/phase modulation symbols according to the receive antenna combination index that is unknown to the eavesdropper. Both theoretical and numerical results for the bit error rate and secrecy rate indicate that the eavesdropper can neither successfully recover the spatial information bits nor symbol information bits, which validate that the proposed scheme can significantly enhance the physical-layer security of the considered communication system.
CRITICAL THINKING ABILITY IN NURSING STUDENTS AND CLINICAL NURSES

Authorities of nursing believe that the ability to think critically by nurses play an important role in providing individual care, solving problems and making complex decisions. Therefore, critical thinking ability is to be established during scientific experiences in nursing students and to be promoted in employed nurses. Determining and comparing critical thinking abilitys in first and last term baccalaureate nursing students and clinical nurses, a descriptive - comparative study was carried out in which 172 contributors, selected with stratified random sampling method filled up self reporting questionnaires. Findings revealed that 98.3% of clinical nurses, 94% of last term students and 93% of first term students were weak in critical thinking abilities, besides, there were significantly differences between nursing students and clinical nurses in average scores of inference (p<0.0001), interpretation (p=0.026), evaluation of arguments (p<00.01), and also, total scores of critical thinking abilities. In other words, differentiated aspects of critical thinking ability had higher levels in student nurses. Furthermore, we did not find significant differences between students and clinical nurses from the point of diagnosis of assumption and deduction abilities. To Conclude, regarding findings, although last term students had been involved in a 4 years nursing education curriculum, they were the same with the first term students from the point of their critical thinking abilities, and also, inspite of 2 years of clinical nursing experiences in nurses, their critical thinking abilities were significantly lower than students. So, revising present educational strategies and applying active learning method, both in theory and practice seems to be critical, and regarding clinical nurses, participating in problem solving and decision making situations is of great importance, and for both groups individual patient care with the basis of nursing process will promoted knowledge, attitude, skills and motivation, which all will promote critical thinking abilities consequently.
Transmission and Reception Alignment for User Equipment Energy Efficiency in LTE Networks

Energy efficiency has become an essential component in the design of communication protocols, especially for battery-enabled user equipment (UE) devices such as smartphones. In this letter, an energy-efficient UE packet scheduling scheme for LTE/LTE-Advanced cellular networks is proposed. It achieves energy savings by promoting the simultaneous occurrence of transmissions and receptions. Simulation results show that this approach improves the energy efficiency of the device by increasing the number of simultaneous transmissions and receptions; the network performance, however, suffers little impact.
A deep learning-based quality assessment model of collaboratively edited documents: A case study of Wikipedia

Wikipedia is becoming increasingly critical in helping people obtain information and knowledge. Its leading advantage is that users can not only access information but also modify it. However, this presents a challenging issue: how can we measure the quality of a Wikipedia article? The existing approaches assess Wikipedia quality by statistical models or traditional machine learning algorithms. However, their performance is not satisfactory. Moreover, most existing models fail to extract complete information from articles, which degrades the model’s performance. In this article, we first survey related works and summarise a comprehensive feature framework. Then, state-of-the-art deep learning models are introduced and applied to assess Wikipedia quality. Finally, a comparison among deep learning models and traditional machine learning models is conducted to validate the effectiveness of the proposed model. The models are compared extensively in terms of their training and classification performance. Moreover, the importance of each feature and the importance of different feature sets are analysed separately.
UAV and Piecewise Convex Approximation Assisted Localization With Unknown Path Loss Exponents

In this correspondence, we investigate unmanned aerial vehicle (UAV)-base stations (BSs) assisted and received signal strength (RSS) based mobile station (MS) localization. A practical air-to-ground path loss model is utilized, where the path loss exponent (PLE) varies with the elevation angle and altitude of UAV, and the accurate PLE estimate is often difficult to obtain. With unknown and unequal PLEs for different UAVs, the UAVs assisted localization problem becomes nonlinear and non-convex, which cannot be solved by the existing methods. We propose a piecewise convex approximation aided localization (PCAL) approach to convert the localization problem into linear and convex, without requiring the knowledge of PLE. The proposed PCAL approach with unknown and unequal PLEs achieves much higher accuracy than the existing methods which require perfectly known and equal PLE, due to its higher robustness against shadowing. In addition, a grid search aided ambiguity elimination (GSAE) method, which is more effective than the state-of-the-art methods, is proposed to determine the final MS localization estimate based on multiple tentative estimates derived from PCAL. The effectiveness of PCAL is also verified by the Cramer-Rao lower bound (CRLB) derived.
What makes online micro blog content go viral : a study on Sina Weibo

10 DECLARATION . 12 COPYRIGHT STATEMENT . 14 ACKNOWLEDGEMENT . 16 CHAPTER 1 . 17 INTRODUCTION . 17 1.1Introduction . 17 1.2 Research Background . 18 1.3 Literature Review . 19 1.3. 1 Literature Review of Content . 19 1.3. 2 Literature Review of Publisher . 20 1.3. 3 Literature Review of Context . 21 1.4 Context . 21 1.4. 1 Facebook . 22 1.4. 2 Twitter . 23 1.4. 3 WhatsApp . 24 1.4. 4 RenRen.com . 25 1.4. 5 Sina Weibo . 26 1.4. 6 WeChat . 29 1.5 Aim and objectives . 30 1.6 Justification . 31 1.6. 1 Justification of academic aspects . 31 1.6. 2 Justification of context aspects . 32 1.7 Methodology . 33 1.8 Overview of all chapters . 33 1.8. 1 Overview of Chapter 1 . 33 1.8. 2 Overview of Chapter 2 . 33 1.8. 3 Overview of Chapter 3 . 34 1.8. 4 Overview of Chapter 4 . 34 1.8. 5 Overview of Chapter 5 . 34 1.8. 6 Overview of Chapter 6 . 35 1.8. 7 Overview of Chapter 7 . 35
Long-Acting b 2 -Agonist Monotherapy vs Continued Therapy With Inhaled Corticosteroids in Patients With Persistent Asthma

Author Affiliations and Financial Disclosures are listed at the end of this article. Corresponding Author and Reprints: Stephen C. Lazarus, MD, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143-0111 (e-mail: lazma@itsa.ucsf.edu). Context Long-acting b2-agonists are prescribed for patients with persistent asthma and are sometimes used without inhaled corticosteroids (ICSs). No evidence exists, however, to support their use as monotherapy in adults with persistent asthma.
The case for classes and instances - a response to representing instances: the case for reengineering conceptual modelling grammars

ABSTRACT In “Representing instances: The case for reengineering conceptual modelling grammars”, Lukyanenko et al. (2019) argue that conceptual modelling has been biased towards a focus on knowledge about general phenomena (classes) rather than about specific instances. While we agree that more attention needs to be paid to instances, we critically reflect on their underlying assumptions about instances and classes. Lukyanenko et al. (2019) assume that instances are mainly material things, and also assume that class-based modelling typically requires that class definitions include comprehensive attribute structures that are expected to be stable over time. Based on these assumptions, they conclude that classes are not needed for modelling instances. As an alternative to these assumptions, we suggest that instances can be viewed as language constructs, i.e., as objects that may be anything that is uniquely referred to and identified in human communication. Based on this assumption, we introduce an identity-oriented view of classes, implying that classes are required for modelling objects (instances). We agree with Lukyanenko et al. (2019) that a reengineering of conceptual modelling grammars is required. This reengineering would benefit from approaches such as an identity-oriented view of classes and a class-instance modelling grammar.
Diffusion on Social Media Platforms: A Point Process Model for Interaction among Similar Content

Abstract Social media platforms disseminate a massive volume of user-generated content, some of which convey similar and overlapping information. We study how the diffusion of a given piece of content (called a cascade) is influenced by the diffusion of other cascades carrying similar content (called parallel cascades). We theorize that the diffusion of a cascade can be inhibited or amplified by that of parallel cascades containing similar content. To study this phenomenon, we formulate a generalized version of the self-exciting point process model and showcase a novel approach to evaluating the parallel diffusion of similar social media content. We estimate the model using Twitter data. We observe that, on average, the diffusion of a cascade is inhibited by the concurrent diffusion of parallel cascades with similar content. We further identify an asymmetry among content producers as the diffusion of content contributed by those with larger networks is more likely to be amplified by the diffusion of similar content. Our study underscores the importance of accounting for content similarity as failing to do so may overestimate assessments of a cascade’s diffusion. Our results also suggest that smaller, individual social media content contributors should avoid publishing repetitive content and channel their efforts towards developing novel content, while this is not a concern for larger content contributors.
Query Expansion With Local Conceptual Word Embeddings in Microblog Retrieval

Since the length of microblog texts, such as tweets, is strictly limited to 140 characters, traditional Information Retrieval techniques suffer from the vocabulary mismatch problem severely and cannot yield good performance in the context of microblogosphere. To address this critical challenge, in this paper, we focus on the use of local conceptual word embeddings for enhance microblog retrieval effectiveness. In particular, we propose a novel <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq1-2945764.gif"/></alternatives></inline-formula>-Nearest Neighbor (<inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq2-2945764.gif"/></alternatives></inline-formula>NN) based Query Expansion (QE) algorithm to generate words from local word embeddings to expand the original query, which leads to better understanding of the information need. Besides, in order to further satisfy users’ real-time information need, we incorporate temporal evidences into the expansion algorithm, which can boost recent tweets in the retrieval results with respect to a given topic. Experimental results on the official TREC Twitter corpora demonstrate the significant superiority of our approach over baseline methods.
Video Face Clustering With Self-Supervised Representation Learning

Characters are a key component of understanding the story conveyed in TV series and movies. With the rise of advanced deep face models, identifying face images may seem like a solved problem. However, as face detectors get better, clustering and identification need to be revisited to address increasing diversity in facial appearance. In this paper, we propose unsupervised methods for feature refinement with application to video face clustering. Our emphasis is on distilling the essential information, identity, from the representations obtained using deep pre-trained face networks. We propose a self-supervised Siamese network that can be trained without the need for video/track based supervision, that can also be applied to image collections. We evaluate our methods on three video face clustering datasets. Thorough experiments including generalization studies show that our methods outperform current state-of-the-art methods on all datasets. The datasets and code are available at https://github.com/vivoutlaw/SSIAM.
Domestic Violence in the African American Community

This article discusses intimate partner violence (IPV) as a major public health issue for women, in particular, African American women. The intersection of IPV and institutional racism doubly victimizes African American women as they try to break out of the cycle of violence. The research shows that IPV in the African American community is more common and violent than in the Caucasian community due to structural, cultural community, and situational contexts, overshadowed by institutional racism, that effect the romantic relationships of African American men and women. Research shows that the anger, hatred, and frustrations of African American men, caused by institutional racism, are being displaced onto their wives and lovers. Suggestions for intervention are presented.