Miracle-dz/UniFAR-FTUs huggingface dataset:
A REVIEW OF CORD TRACTION IN THE THIRD STAGE OF LABOUR FROM 1963 TO 1969

Cord traction, as a method of management of the third stage of labour, was introduced to The Royal North Shore Hospital of Sydney in 1963. A controlled investigation of 713 consecutive cases was carried out in which cord traction was compared with abdominal manipulation. In the controlled investigation, the manual removal rate was 1·8% with abdominal manipulation and 0·8% with cord traction. There was no statistical difference in mean blood loss, post‐partum hæmorrhage rate or duration of the third stage. The post‐partum hæmorrhage and manual removal rates during the years 1964 to 1968 after the Introduction of cord traction and the oxytocic drug “Syntometrine” were compared with those obtained in the period 1958–1963. During the 10 years to 1968, the post‐partum hæmorrhage rate changed from 4·3% before the introduction of cord traction and “Syntometrine” to 1·9% after their introduction. A somewhat similar change occurred in relation to the manual removal rate, the respective figures being 1·8% and 1·1%.
Exploration of algorithms and implementations for efficient 3D-HEVC depth map encoding

3D High Efficiency Video Coding (3D-HEVC) standard was the pioneer standard to use depth map coding along with texture coding to achieve high compression ratio and quality in 3D videos. Depth map coding introduced new tools requiring more computational effort compared to texture coding, implying new challenges to encode 3D videos efficiently, especially when the encoding is performed on embedded systems or requiring real-time processing. This Thesis proposes algorithms and implementations to improve the depth map coding in the 3D-HEVC standard. The main contributions are divided into three parts. (i) Reduction of intra-frame coding effort, where four new algorithms were designed based on a deep analysis of the time distribution and modes used in the coding process. (ii) Enhancement of bipartition modes, exploring (a) the encoding effort control of the bipartition modes, (b) the parallelism for Intra_Wedge mode, (c) the compression of patterns for wedgelet storage reduction, and d) the hardware design for the bipartition modes decoding. (iii) Reduction of inter-frame coding effort, where three new algorithms were designed to speed up the coding of Pand B-frames. The set of algorithms and techniques developed can be applied individually or combined to improve the use of computational resources when encoding depth maps.
Convolutional Neural Network with Expert Knowledge for Hyperspectral Remote Sensing Imagery Classification

The recent interest in artificial intelligence and machine learning has partly contributed to an interest in the use of such approaches for hyperspectral remote sensing (HRS) imagery classification, as evidenced by the increasing number of deep framework with deep convolutional neural networks (CNN) structures proposed in the literature. In these approaches, the assumption of obtaining high quality deep features by using CNN is not always easy and efficient because of the complex data distribution and the limited sample size. In this paper, conventional handcrafted learning-based multi features based on expert knowledge are introduced as the input of a special designed CNN to improve the pixel description and classification performance of HRS imagery. The introduction of these handcrafted features can reduce the complexity of the original HRS data and reduce the sample requirements by eliminating redundant information and improving the starting point of deep feature training. It also provides some concise and effective features that are not readily available from direct training with CNN. Evaluations using three public HRS datasets demonstrate the utility of our proposed method in HRS classification.
Distributed Logless Atomic Durability with Persistent Memory

Datacenter operators have started deploying Persistent Memory (PM), leveraging its combination of fast access and persistence for significant performance gains. A key challenge for PM-aware software is to maintain high performance while achieving atomic durability. The latter typically requires the use of logging, which introduces considerable overhead with additional CPU cycles, write traffic, and ordering requirements. In this paper, we exploit the data multiversioning inherent in the memory hierarchy to achieve atomic durability without logging. Our design, LAD, relies on persistent buffering space at the memory controllers (MCs)---already present in modern CPUs---to speculatively accumulate all of a transaction's updates before they are all atomically committed to PM. LAD employs an on-chip distributed commit protocol in hardware to manage the distributed speculative state each transaction accumulates across multiple MCs. We demonstrate that LAD is a practical design relying on modest hardware modifications to provide atomically durable transactions, while delivering up to 80% of ideal---i.e., PM-oblivious software's---performance.
Memory-Related Encoding-Specificity Paradigm: Experimental Application to the Exercise Domain

The Encoding-Specificity Paradigm indicates that memory recall will be superior when contextual factors are congruent between memory encoding and memory retrieval. However, unlike other contextual conditions (e.g., verbal context, mental operations, global feature context, mood dependency, and physical operations), this paradigm has nearly been ignored in the exercise domain. Thus, the purpose of this study was to examine the Encoding-Specificity Paradigm in the context of exercise and rest conditions. 24 young adults (age: M = 21 years) completed a within-subject, counterbalanced experiment involving four laboratory visits, including 1) R-R (rest-rest) condition, 2) R-E (rest-exercise) condition, 3) E-R (exercise-rest) condition, or 4) E-E (exercise-exercise) condition. The exercise bout included a 15-minute moderate-intensity walk on a treadmill. Memory recall was assessed via a 15 word-list task. Memory recall was greater for R-R (8.71 ± 3.1) versus R-E (7.46 ± 2.8), and similarly, for E-E (8.63 ± 2.7) versus E-R (8.21 ± 2.7). The mean word recall for the congruent and incongruent conditions, respectively, was 8.67 (2.4) and 7.83 (2.4). There was a statistically significant condition effect (F = 5.02; P = .03; partial η² = .18). This experiment provides direct support for the Encoding-Specificity Paradigm in the exercise domain.
Design and Fabrication of Smart Home With Internet of Things Enabled Automation System

Home automation systems have attracted considerable attention with the advancement of communications technology. A smart home (SH) is an Internet of Things (IoT) application that utilizes the Internet to monitor and control appliances using a home automation system. Lack of IoT technology usage, unfriendly user interface, limited wireless transmission range, and high costs are the limitations of existing home automation systems. Therefore, this study presents a cost-effective and hybrid (local and remote) IoT-based home automation system with a user-friendly interface for smartphones and laptops. A prototype called IoT@HoMe is developed with an algorithm to enable the monitoring of home conditions and automate the control of home appliances over the Internet anytime and anywhere. This system utilizes a node microcontroller unit (NodeMCU) as a Wi-Fi-based gateway to connect different sensors and updates their data to Adafruit IO cloud server. The collected data from several sensors (radio-frequency identification, ultrasonic, temperature, humidity, gas, and motion sensors) can be accessed via If This Then That (IFTTT) on users’ devices (smartphones and/or laptops) over the Internet regardless of their location. A set of relays is used to connect the NodeMCU to homes under controlled appliances. The designed system is structured in a portable manner as a control box that can be attached for monitoring and controlling a real house. The proposed IoT-based system for home automation can easily and efficiently control appliances over the Internet and support home safety with autonomous operation. IoT@HoMe is a low cost and reliable automation system that reduces energy consumption and can notably provide convenience, safety, and security for SH residents.
Fair Data Allocation and Trajectory Optimization for UAV-Assisted Mobile Edge Computing

This letter investigates fairness-aware task data allocation and trajectory optimization in unmanned aerial vehicles (UAV)-assisted mobile edge computing (MEC) systems, where a fixed-wing UAV is used as a flying computing server to receive task data of mobile terminals (MTs). Under the fairness consideration, we aim to minimize the maximum energy consumption among all MTs. Despite the non-convexity of the original formulated joint optimization problem, we transform the problem into two convex sub-problems by introducing auxiliary variables, and solve them jointly by proposing an iterative algorithm. Simulation results show that the proposed algorithm can effectively reduce the maximum energy consumption among all MTs.
Not “cured by a verdict”

The literature dealing with the effect of legal settlement of compensation claims on disability and return to gainful employment is reviewed. The literature does not support the view that patients invariably become symptom‐free and resume work within months of the finalisation of their claims. Up to 75% of those injured in compensable accidents may fail to return to gainful employment two years after legal settlement. Factors associated with a poor prognosis include older age at settlement, low back injury, and loss of libido.
Navigational Acts and Discourse: Fostering Learner Agency in Computer-Assisted Language Learning.

: Fostering learner agency is a primary goal of Integrative Computer Assisted Language Learning, a type of CALL (Warschauer, 1996) that encompasses networked learning and multimedia, including hypermedia. Although navigation has been a focus of attention in some more established CALL scenarios such as Intelligent Computer Assisted Language learning (ICALL) systems in second language acquisition (SLA) research, much less focus has been on more emerging CALL scenarios such as augmented reality, amongst others. Therefore, if learner agency is to be fostered, identifying how it manifests is important. This study focuses on ‘directional agency’ (Knight, Barberà and Appel, 2017) which is agency exercised in relation to navigational acts during language learning tasks. Using our own case study data as a starting point (where learners carry out navigation as part of the tasks), the paper draws attention to the somatic acts of navigation while using spoken language as part of the multimodal experiences learners can face. A specific literature review was carried out on other current and emerging CALL scenarios that were considered as cases in order to survey the presence of intentional navigational acts and talk, explore and understand it as a phenomenon in the field and to refine directional agency as a construct. Results suggest that directional agency is present across other CALL scenarios and task types; directional agency can be shared across learners and technological features; there are multiple forms of navigation including embodied navigation and whole or partial human body navigation that can occurr in the same task. Navigational acts can accompany learner-learner talk and they can also form part of learner-computer ‘talk’. Learners and computers can act as “semiotic initiators and responders” (Coffin and Donohue, 2014), resembling sequential turn-taking of talk so that both learners and digital technologies can be understood as potential actors in the task discourse.
Distributed Multiple Access for the Uplink of Multi-cell OFDMA Networks

It is expected that wireless data services with diverse QoS requirements will account for most of the traffic in the future 4G networks. Current centralized scheduling requires a substantial amount of overhead when the number of wireless devices is very large in order to collect users’ CSI (Channel State Information). We explore a distributed scheduling method for the next-generation cellular networks, in which the scheduling decisions are performed jointly by the wireless devices and the BSs in order to reduce the complexity at the BSs and to reduce the protocol overhead. We propose a distributed multi-channel multiple access protocol for the uplink data transfer in TDD (Time-Division Duplex) mode for a multi-cell network. The protocol may be used as a low-overhead scheduling solution for delay-tolerant services especially with large numbers of devices, and can coexist with the legacy centralized scheduling schemes.
Towards the optimisation of spatial electrocutaneous display parameters for sensory substitution

A spatial electrocutaneous display for sensory substitution is being designed. The display will be used to feed back sensory signals from transfemoral prostheses to amputees. The aim is to improve their walking performance. Two experiments have been performed to optimise the display parameters for dynamic, cyclic signals, which are common during walking. From these experiments it became clear that healthy subjects were capable of discerning artificially generated disturbed knee angle patterns from undisturbed patterns. This shows that the electrocutaneous display has the capability to feed back signals from a transfemoral prosthesis.
View-Based 3-D CAD Model Retrieval With Deep Residual Networks

In industrial enterprises, effective retrieval and reuse of three-dimensional (3-D) computer-aided design (CAD) models could greatly save time and cost in new product development and manufacturing. Consequently, this article proposes a novel view-based approach for 3-D CAD model retrieval enabled by deep learning. This article constructs a multiview model dataset in industrial domain that collects solid and line views of database models. Since views contain rich information for differentiating these models, the problem of model retrieval is defined as a view recognition problem. Then, the extended deep residual networks (ResNets) are successfully trained to facilitate the model retrieval. With the learned networks, engineers could take a group of views, an engineering drawing, or even a hand-drawn sketch that represents their query intents as input and acquire the relevant 3-D CAD models and embedded knowledge for product lifecycle reuse. The experimental results demonstrate the effectiveness and efficiency of the approach.
Digitalization and Energy Consumption

The rationale behind this study is premised on the importance of long-term projections of the demand for energy commodities as applied to the national and regional energy and economic security and policy-making that aims at improving the quality of life of the population. There is a wide range of tools and models available to facilitate the studies and projections of the fuel and energy demand. That said, ongoing changes in inner workings and development externalities of the economy and the society in general call for bringing them up to date. Today, we are witnessing dramatic developments and the ever increasing adoption of information and communication technologies that will ultimately pervade all facets of human life. Digital technologies enjoy widespread application in the final energy consumption sectors: they are employed to control industrial processes, improve work performance and safety, and identify the directions to be pursued to enhance energy saving. As of now, we still lack a clear understanding of the impact digitalization will have on the structure of the demand for energy commodities. On the one hand, intelligent control fosters energy saving, on the other hand, digital technologies themselves, as they require the processing of a great bulk of data, consume energy as well. The development of the methods that serve as a backbone of long-term projections of the demand for energy commodities has to properly account for the dynamics of the adoption of digital technologies across all economic sectors on a par with the effect they have on the structure of the demand for energy commodities. We propose one of the many possible approaches to estimate the effect of digitalization on electricity consumption in the tertiary sector. The approach is based on studying the interactions between the growth of electricity consumption and the growth of the number of connected devices. Furthermore, we present our tentative estimates of the additional growth of the demand for electricity in Russia for 2030 2035, caused by the accelerated development of the electric car market. Keywords—digitalization, energy demand, modeling, long-term forecasting, households, service industry, per capita energy consumption
A leader’s approaches to fostering a culture of knowledge sharing in an information organization

Purpose The purpose of this paper is to look at organizational culture and knowledge sharing, and to explore how a leader fosters a culture of knowledge sharing in an information organization. Design/methodology/approach The literature survey is used. It indicates that little is known about how to foster a culture of knowledge sharing from a leadership perspective in an information organization. Findings This study finds that the main approaches that a leader need to use to foster a culture of knowledge sharing are to set the mission, short-term, middle-term and long-term goals and objectives of fostering a culture of knowledge sharing, master as many leadership styles as possible, adjust and choose a leadership style that is appropriate to fostering a culture of knowledge sharing in a given situation, lead by example, develop messaging, make a communication plan, reward and recognize knowledge-sharing behaviors and make knowledge management fun. Research limitations/implications The limitation is that this study is solely focused on the literature survey and opinions. Practical implications This paper provides a useful overview of the approaches used to foster a culture of knowledge sharing in an information organization. Originality/value The views, approaches and suggestions will be useful and valuable to improve the success of knowledge sharing in information organizations in the digital age.
A Hybrid Ensemble Model for Corporate Bankruptcy Prediction Based on Feature Engineering Method

The bankruptcy of manufacturing corporates is an important factor affecting economic stability. Corporate bankruptcy has become a hot research topic mainly through financial data analysis and prediction. With the development of data science and artificial intelligence, machine learning technology helps researchers improve the accuracy and robustness of classification models. Ensemble learning, with its strong predictive power and robustness, plays an important role in machine learning and binary classification prediction. In this study, we proposed a bankruptcy classification model combining feature engineering method and ensemble learning method, Synthetic Minority Oversampling Technique (SMOTE) imbalanced data learning algorithm is applied to generate balanced dataset, multi-interval discretization filter is applied to enhance the interpretability of the features and ensemble learning method is applied to get an accurate and objective prediction. To demonstrate the validity and performance of the proposed model, we conducted comparative experiments with ten other baseline classifiers, proving that SMOTE imbalanced learning algorithm and feature engineering method with multi-interval discretization was effective. The comparative experiment results show that the ensemble learning method has a good effect on improving the performance of the proposed model. The final results show that the proposed model has achieved better performance and robustness than other baseline classifiers in terms of classification accuracy, F-measure and Area under Curve (AUC).
Comparing Data-Driven Methods for Extracting Knowledge from User Generated Content

This study aimed to compare two techniques of business knowledge extraction for the identification of insights related to the improvement of digital marketing strategies on a sample of 15,731 tweets. The sample was extracted from user generated content (UGC) from Twitter using two methods based on knowledge extraction techniques for business. In Method 1, an algorithm to detect communities in complex networks was applied; this algorithm, in which we applied data visualization techniques for complex networks analysis, used the modularity of nodes to discover topics. In Method 2, a three-phase process was developed for knowledge extraction that included the application of a latent Dirichlet allocation (LDA) model, a sentiment analysis (SA) that works with machine learning, and a data text mining (DTM) analysis technique. Finally, we compared the results of each of the two techniques to see whether or not the results yielded by these two methods regarding the analysis of companies’ digital marketing strategies were mutually complementary.
An A3AOs Method of Software Tools Integration in the Complex System Development

Companies commonly face cost pressures due to the increasing industry rivalry and competition. Software tools in this regard has become more and more popular in assisting the complex systems development. However, the lack of a proper system development process that integrates the usage of software tools will hinder the company efficiency. This is especially visible as each engineer may use software tools differently. In the context of complex system developments, this research investigates the optimization of the system development process by integrating software tools. We propose an architecture of a new system development process using A3 Architectural Overviews. Based on a real‐life case study and quick‐prototyping techniques, the architecture evolves into an optimized system development process that can increase efficiency, quality and consistency for the case company.
Dynamic Channel Bonding Algorithm for Densely Deployed 802.11ac Networks

To meet the rising demand of mobile data, IEEE 802.11ac was recently developed to offer better network performance than previous IEEE 802.11 standards, representing the 5G Wi-Fi technology. One of the new technologies added to IEEE 802.11ac is a dynamic bandwidth channel access scheme that supports channel bonding (CB) with an up to 160 MHz bandwidth. However, the use of CB increases the number of stations sharing the same medium, resulting in an increased collision rate and severe interference caused by overlapping sub-channels. This problem is further aggravated in densely deployed wireless networks where the service ranges of access points (APs) are likely to be highly overlapping. To resolve these issues, we present an analytical throughput model that considers the effect of both collisions and interference. With this analytical model, we formulate the CB problem to optimize the throughput along with the traffic demand and propose a genetic algorithm for solving the problem. Further, a dynamic channel bonding algorithm (named DCB) is proposed to solve the problem with lower complexity. Extensive simulation results demonstrate that our proposed algorithm provides a higher performance than other recent dynamic CB algorithms with respect to the throughput achieved for the traffic demand.
A TAM Framework to Evaluate UsersÃ¢ÂÂ Perception towards OnlineElectronic Payments

Technology Acceptance Model (TAM) is capable of explaining user behavior across a broad range of end-user computing technologies and user populations. In its original form TAM has been extensively incorporated as a methodology to measure attitude towards technology adoption from users in multiple domains, as well as within financial domain. TAM variations have also been proposed and applied for measuring users’ attitude towards adoption of several IT based services. In this paper we demonstrate a revised TAM model for measuring users’ attitude towards online electronic payments adoption. We present the model developed, as well as initial results from a relevant survey at a Greek bank’s target users group.