Miracle-dz/UniFAR-FTUs huggingface dataset:
Family Nursing: Research, Theory, and Practice

I. INTRODUCTORY CONCEPTS. 1. Introduction to the Family. 2. Family Nursing: Focus, Evolution, and Goals. II. THEORETICAL FOUNDATIONS OF FAMILY NURSING. 3. Theoretical Foundations for Family Nursing. 4. Structural-Functional Theory. 5. Family Developmental Theory. 6. Systems Theory. III. FAMILY NURSING PRACTICE. 7. The Family Nursing Process. 8. Family Identifying Data: Sociocultural Assessment and Intervention. 9. Family Environmental Data. 10. Family Communication Patterns and Processes. 11. Family Power and Decision-Making. 12. Family Role Structure. 13. Family Values. 14. The Family Affective Function. 15. The Family Socialization Function. 16. The Family Health Care Function. 17. Family Stress, Coping and Adaptation. IV. CULTURAL DIVERSITY AMONG FAMILIES. 18. The Latino Family. 19. The African-American Family. 20. The Asian-American Family. APPENDICES. A. The Friedman Family Assessment Model (Long Form). B. The Friedman Family Assessment Model (Short Form). C. Case Study of the O'Shea Family. D. Family Nursing Process Example: The O'Shea Family. E. Answers to Vignette and Study Questions. References. Glossary. Index.
Tree-structured Deep Neural Networks by Learning to Semantically Split

Tree-structured Deep Neural Networks by Learning to Semantically Split Juyong Kim Department of Computer Science and Engineering College of Engineering Seoul National University We propose a novel deep neural network that is both lightweight and effectively structured for model parallelization. Our network, which we name as SplitNet, automatically learns to split the network weights into either a set or a hierarchy of multiple groups that use disjoint sets of features, by learning both the class-to-group and feature-to-group assignment matrices along with the network weights. This produces a tree-structured network that involves no connection between branched subtrees of semantically disparate class groups. SplitNet thus greatly reduces the number of parameters and required computations, and is also embarrassingly model-parallelizable at test time, since the evaluation for each subnetwork is completely independent except for the shared lower layer weights that can be duplicated over multiple processors, or assigned to a separate processor. We validate our method with two different deep network models (ResNet and AlexNet) on three datasets (CIFAR-100, ILSVRC 2012 and ImageNet-22K) for image classification, on which our method obtains networks with significantly reduced number of parameters while achieving comparable or superior accuracies over original full deep networks, and accelerated test speed with multiple GPUs. Especially, we achieved state-of-the-art accuracy on the ImageNet-22K with SplitNet based on ResNet-50. Codes available
Deep Reinforcement Learning Based Intelligent User Selection in Massive MIMO Underlay Cognitive Radios

Cognitive radio (CR) and massive multiple-input multiple-output (MIMO) have attracted much interest recently due to the amazing ability to accommodate more users and improve spectrum utilization. This paper investigates the QoS-aware user selection approach for massive MIMO underlay cognitive radio. Two main CR scenarios are considered: 1) the channel state information (CSI) of the cross channels are available at the secondary base station (SBS), and 2) any CSI of cross-network is unavailable at SBS. For the former, we develop the low-complexity increase-user-with-minimum-power algorithm (IUMP) and decrease-user-with-maximum-power algorithm (DUMP) which both can address the problem of user selection with power allocation. However, the CSI is typically not available in practice. To address the intractable issue, we propose a deep reinforcement learning-based approach, which can enable the SBS to realize efficient and intelligent user selection. The simulation results show that the IUMP and DUMP algorithms have obvious performance advantages over traditional user selection methods. In addition, results also verify that our constructed neural network can efficiently learn the optimal user selection policy in the unknown dynamic environment with fast convergence and high success rate.
Toward Multifunctional and Wearable Smart Skins with Energy‐Harvesting, Touch‐Sensing, and Exteroception‐Visualizing Capabilities by an All‐Polymer Design

An all‐polymer design to realize multifunctional and wearable smart skin devices with energy‐harvesting, touch‐sensing, and exteroception‐visualizing capabilities is reported. The design process of the skin, material selection principles, and performance under different mechanical manipulations are systematically elaborated. Stretchable hydrogel electrodes are sandwiched by a triboelectric layer and a ZnS phosphor–embedded mechanoluminescent layer, while the mechanical stability is ensured by an in situ assembly configuration. Regarding the performance of the three capabilities, first, the smart skin can function as a triboelectric nanogenerator and delivers an output peak voltage of 180 V and a power density of 625 µW cm−2 with superb cyclic stability throughout 10 000 continuous contact–separation cycles. Second, the skin can detect a pressure limit of 0.58 kPa with a sensitivity of 0.23 kPa−1, where the location detection can be achieved by applying a pixel configuration to the device. Third, a light‐emitting response can be stimulated to allow for visualization of the magnitude and location of an applied force. Remarkably, the as‐developed smart skin exhibits robust vitality without degradation of functionality, even when the device is subjected to a high strain of 700%.
Performance Analysis of the Segment Transfer Rate of TCP-UEM

UsingTCP(TransmissionControlProtocol)inwirelessnetworkscanaffectitsperformanceduetoits lack of ability to identify packets losses properly, causing the triggering of its congestion control mechanism. Some TCP variants were proposed to improve this control, being TCP-UEM one of them. This variant allows the evaluation of the link reliability in wireless networks in time intervals, keeping the end-to-end semantics. TCP-UEM was implemented in FreeBSD OS and its performance with relation to segment transfer rate (in Mbps) was compared to two other variants, TCP-NEWRENO and TCP-CUBIC. This paper describes TCP-UEM, discusses results of the tests and the statistical analysis that were carried out using two scenarios. For each scenario, 30 samples of 30 seconds of execution time with different loss rates were collected. The results showed that TCP-UEM presented a good performance, achieving a performance higher than the other two variants in the majority of the tests, with different loss rates.
DPAM: A Demand-Based Page-Level Address Mappings Algorithm in Flash Memory for Smart Industrial Edge Devices

Edge computing brings data storage closer to the location where it is needed. Therefore, the edge devices, especially smart industrial edge devices, require higher storage systems. NAND flash memory has the advantages of small size, high speed, and strong shock resistance, which is widely used in various storage systems, providing a good choice for edge devices. NAND flash has unique physical characteristics, such as “out-of-place updates” and “prewrite erasure,” therefore, the traditional address mapping methods require improvement. This article presents a novel demand-based page-level address mapping algorithm called DPAM. The goal of DPAM is to provide efficient address translation by using a smaller address mapping table. Due to the high service cost of block-level address mapping and hybrid address mapping, a page-level address mapping scheme is proposed. The algorithm is implemented and tested on the flash simulation platform FlashSim. The results indicate that our algorithm provides improvements of 7.11% for the hit ratio and 7% for the number of block erasures compared with other approaches.
Novel methods for distributed acoustic sensing data

In this thesis, we propose novel methods for analysing nonstationary, multivariate time series, focusing in particular on the problems of classification and imputation within this context. Many existing methods for time series classification are static, in that they assign the entire series to one class and do not allow for temporal dependence with the signal. In the first part of this thesis, we propose a computationally efficient extension of an existing dynamic classification method to the online setting. Dependence within the series is captured by adopting the multivariate locally stationary wavelet (mvLSW) framework and the signal is classified at each time point into one of a number of known classes. We apply the method to multivariate acoustic sensing data in order to detect anomalous regions and evaluate the results against alternative methods in the literature. The second part of this thesis considers imputation in multivariate locally stationary time series containing missing values. We first introduce a method for estimating the local wavelet spectral matrix that can be used in the presence of missingness. We then propose a novel method for imputing missing values that uses the local auto and cross-covariance functions of a mvLSW process to perform one step-ahead forecasting and backcasting. The performance of this nonstationary imputation approach is then assessed against competitor methods for simulated examples and a case study involving a dataset from a Carbon Capture and Storage facility. The software that implements this imputation scheme is also described, together with examples of the R package functionality.
Approach towards an ideal envelope shape design for energy efficiency and low carbon emission

The building envelope shape is the most salient design characteristic and has a significant influence on energy consumption during the post-occupancy service life and carbon emission. However, during the conceptual design phase, envelope shape finding is defined without considering the energy performance during post-occupancy service life and sustainable characteristics (i.e. low carbon emission). In addition, there is no suitable method for designers to make such calculations. To bridge the post-occupancy service life in efficiency, this research developed an ideal envelope shape finding approach to facilitate the conceptual design phase. The steady-state principle has been used to predict the thermal flow and energy impact on the aspect ratio of various shapes, and compactness. Integrated dynamic simulation and particle swarm optimization method were used to identify the optimal and sub-optimal combinations of envelope shapes for energy consumption and carbon emission. The findings of this research provide a benchmark of energy consumption characteristics of envelope shape and a cut-off range for low carbon emission envelope design. This is one of the simplified design approach facelift the conventional design process to predict post-occupancy energy performances and carbon emission impact.
User-Driven Geolocated Event Detection in Social Media

Event detection is one of the most important research topics in social media analysis. Despite this interest, few researchers have addressed the problem of identifying geolocated events in an unsupervised way, and none includes user interests during the process. In this paper, we tackle the problem of local event detection from social media data. We present a method to automatically identify events by evaluating the burstiness of hashtags in a geographical area and a time interval, and at the same time integrating user feedback. We devise two algorithms to discover user-driven events. The first one relies on an exact enumeration process, while the other directly samples the space of events. In our empirical study, we provide evidence that geolocated events cannot be detected by non location-aware methods. We also show that our methods (i) outperform by a factor of two to several orders of magnitude state-of-the-art methods designed to discover geolocated events, (ii) are more robust to noise, and (iii) produce high quality events with respect to user interests.
Unifying Task-Oriented Knowledge Graph Learning and Recommendation

Incorporating knowledge graphs (KGs) into recommender systems (knowledge-aware recommendation) to improve the recommendation accuracy and explainability has attracted considerable research efforts. However, existing methods largely assume that KGs are complete when transferring knowledge from them, which may lead to suboptimal performance for those KGs, can be hardly complete in real-life scenarios. In this paper, we present a robustly co-learning model (RCoLM) that takes the incompleteness nature of KGs into consideration when incorporating them into recommendation. The RCoLM aims at transferring knowledge between recommendation task and knowledge graph completion (KG completion) task by utilizing a transfer learning model. An earlier version of this paper appeared in KDD 2019. This version is an extension of the previous submission and two major innovations are presented here. At first, distinct from previous knowledge-aware recommendation methods, which mainly focus on transferring knowledge from KGs to item recommendations, the RCoLM attempts to exploit user-item interactions from recommendations for KG completion, and unifies the two tasks in a joint model for mutual enhancements. Second, the RCoLM provides a general task-oriented negative sampling strategy on KG completion task, which further improves the adaptive ability of the proposed algorithm and plays an essential role for obtaining superior performance in various sub-tasks of the KG completion. The extensive experiments on two real-world public datasets demonstrate that RCoLM outperforms not only state-of-the-art knowledge-aware recommendation methods but also existing KG completion methods.
Pose-based multisource networks using convolutional neural network and long short-term memory for action recognition

Abstract. Pose-based action recognition has aroused increasing attention for its broad application prospects and excellent performance. Though the pose-based action recognition methods have been significantly advanced, pose-based action recognition remains a challenging task for various human action categories and subtle changes in human poses. To solve those problems, we propose pose-based multisource networks. First, human pose features are extracted from the raw video, followed by a filtration. Then, using a convolutional neural network (CNN) and long short-term memory (LSTM), the extracted pose sequence is fed into the proposed multisource networks. Subsequently, the CNN-based spatial model processes the relative position in each frame, and the LSTM-based temporal model is built to learn the temporal correlation of pose sequence. Afterward, the temporal model contains three sublevels to fully exploit the subtle information in the temporal domain. Finally, the experimental results verify the effectiveness of the proposed approach on SUB-JHMDB, MPII Cooking Activities, SYSU 3D Human-Object Interaction, and NTU RGB+D.
Saccade Information Based Directional Heat Map Generation for Gaze Data Visualization

Heat map is an important tool for eye tracking data analysis and visualization. It is very intuitive to express the area watched by observer, but ignores saccade information that expresses gaze shift. Based on conventional heat map generation method, this paper presents a novel heat map generation method for eye tracking data. The proposed method introduces a mixed data structure of fixation points and saccades, and considers heat map deformation for saccade type data. The proposed method has advantages on indicating gaze transition direction while visualizing gaze region. key words: eye tracking, gaze analysis, heat map, visualization
Designing Digital Technology for Service Work: Systematic and Participatory Approach

Digital technology is dramatically changing our life, industry, and society. Recent evolution of digital technology is expected to significantly impact service work, such as healthcare and social welfare. Meanwhile, an insufficient understanding of service work and workers' needs leads to the failure of technology development and integration. To make digital technology better fit to values and work practices of service workers, various studies have been conducted, especially in the field of participatory design, interaction design, and service design. However, the contribution of engineering design to this topic is still limited. This paper proposes an engineering design approach to design digital technology for better integration in service work. This study combines a modelling method to represent specifications of digital technology and the participatory approach in alignment with the changing service work and workers' values. A case study of the mobile application system for elderly-care professionals is introduced to exemplify the effectiveness of the proposed method. This study intends to foster innovation of service work through digitalization.
Deep Learning Research and Development Platform: Characterizing and Scheduling with QoS Guarantees on GPU Clusters

Deep learning (DL) has been widely adopted in various domains of artificial intelligence (AI), achieving dramatic developments in industry and academia. Besides giant AI companies, numerous small and medium-sized enterprises, institutes, and universities (EIUs) have focused on the research and development (R&D) of DL. Considering the high cost of datacenters and high performance computing (HPC) systems, EIUs prefer adopting off-the-shelf GPU clusters as a DL R&D platform for multiple users and developers to process diverse DL workloads. In such scenarios, the scheduling of multiple DL tasks on a shared GPU cluster is both significant and challenging in terms of efficiently utilizing limited resources. Existing schedulers cannot predict the resource requirements of diverse DL workloads, leading to the under-utilization of computing resources and a decline in user satisfaction. This paper proposes GENIE, a QoS-aware dynamic scheduling framework for a shared GPU cluster, which achieves users’ QoS guarantee and high system utilization. In accordance with an exhaustive characterization, GENIE analyzes the key factors that affect the performance of DL tasks and proposes a prediction model derived from lightweight profiling to estimate the processing rate and response latency for diverse DL workloads. Based on the prediction models, we propose a QoS-aware scheduling algorithm to identify the best placements for DL tasks and schedule them on the shared cluster. Experiments on a GPU cluster and large-scale simulations demonstrate that GENIE achieves a QoS-guarantee percentage improvement of up to 67.4 percent and a makespan reduction of up to 28.2 percent, compared to other baseline schedulers.
DNS rule-based schema to botnet detection

ABSTRACT Botnets are considered a serious issue today. They have several negative economic impacts as well. Such impacts are affecting organizations and individuals. Recent botnets–such as Zeus and Citadel’s Conficker–use the Domain Name System (DNS) to avoid detection. These botnets use the DNS server to prevent the network administrator from locating and shutting down the C&C servers. Therefore, this paper proposes a DNS rule-based approach for Botnet Detection (DNS-BD) that can improve the accuracy of DNS traffic-based detection of botnets. This approach is based on DNS query and response behaviours; it aims to detect any abnormal DNS query and response behaviours by applying the proposed DNS query and response rules. The result showed that the proposed approach can detect the botnet with an accuracy of 99.35% and a false-positive rate of 0.25%. A comparison with well-known DNS-based approaches evaluates the effectiveness of the proposed approach. It has been concluded that the approach proposed outperforms other approaches that can be implemented as part of any anti-viruses IDS product.
Automatic Detection of Screams and Shouts in the Metro

Ce travail s’appuie sur les principes de la reconnaissance de motifs sonores et de la modelisation statistique pour proposer un systeme capable de reconnaitre et detecter automatiquement des cris de personnes a l’interieur d’un metro. Utilisant des enregistrements provenant de reconstitutions de scenes d’agressions dans une rame de metro Parisien en fonctionnement, nous avons estime des modeles statistiques issus de trois architectures de reseaux de neurones differentes (DNN, CNN et RNN/LSTM). Ces modeles ont ete appris sur 3 categories de sons a reconnaitre dans un premier temps (cris, parole, et bruit environnant), puis sur des categories introduisant des informations specifiques au deplacement de la rame de metro (afin d’apporter une information contextuelle supplementaire), considerant soit les evenements sonores isoles soit le flux audio continu. Les resultats obtenus montrent que le modele le plus efficace est le modele RNN/LSTM qui permet de mieux prendre en compte la structure temporelle des evenements sonores. La reconnaissance des trois categories cris, parole et bruit de fond est probante, independamment du reste de l’environnement sonore, mais l’ajout d’information contextuelle permet d’ameliorer les taux de reconnaissance. Nous concluons que le manque de donnees est un facteur limitant, qui pourrait etre attenue en utilisant l’apprentissage par transfert, consistant a utiliser des reseaux plus complexes pre-appris sur des donnees differentes, ou des techniques d’augmentation, consistant a accroitre la taille de la base de donnees en creant des donnees artificielles a partir de celles existantes.
Semantic and Logic Modeling of Disaster Simulation for Multi-agent Systems

—Disaster management is a complex collaborative process involving several stakeholders from different domains and requiring preparation for designing action plans. Computer simulation of such collaborative process allows globally assessing the efficiency of such preparation. Studies have shown that multi-agent systems (MAS) are well suited for identifying an optimal strategy or potential issues in the context of one or several action plans. Thus, our approach relies on a MAS for simulating action plans. We consider combining such an approach with Semantic Web technologies, in order to define the conceptual simulation model according to the preparation model of disaster management. The knowledge base is expressed through two different ontologies (semDM to model the preparation results and semMAS to model the simulation) that will be discussed in this paper. On top of these knowledge models, domain-specific constraints allow for checking consistency, and logic rules are used to define the semMAS modeling according to semDM.
Digital Implementation of Bio-Inspired Spiking Neuronal Networks

Spiking Neural Network as the third generation of artificial neural networks offers a promising solution for future computing, prosthesis, robotic and image processing applications. This thesis introduces digital designs and implementations of building blocks of a Spiking Neural Networks (SNNs) including neurons, learning rule, and small networks of neurons in the form of a Central Pattern Generator (CPG) which can be used as a module in control part of a bio-inspired robot. The circuits have been developed using Verilog Hardware Description Language (VHDL) and simulated through Modelsim and compiled and synthesised by Altera Qurtus Prime software for FPGA devices. Astrocyte as one of the brain cells controls synaptic activity between neurons by providing feedback to neurons. A novel digital hardware is proposed for neuron-synapseastrocyte network based on the biological Adaptive Exponential (AdEx) neuron and Postnov astrocyte cell model. The network can be used for implementation of large scale spiking neural networks. Synthesis of the designed circuits shows that the designed astrocyte circuit is able to imitate its biological model and regulate the synapse transmission, successfully. In addition, synthesis results confirms that the proposed design uses less than 1% of available resources of a VIRTEX II FPGA which saves up to 4.4% of FPGA resources in comparison to other designs.