Adding imperative programming to the pattern calculus

University of Technology, Sydney. Dept. of Software Engineering.By focusing on data and flow control, imperative languages provide a finely grained and efficient mechanism for directly manipulating state and memory. By focusing on functions, polymorphism increases the modularity and reusability of programs. The pattern calculus gives a new account of polymorphism over arbitrary datatypes which has been used as the foundation for building the functional language FISh2. The power of the new polymorphism is not limited to a functional setting and it can be extended into an imperative setting. The main contribution of this thesis is to expand the pattern calculus with imperative features and implement this within a version of FISh2. Two approaches are developed in expanding the calculus to imperative programming based on two setting: functional and imperative. Based on a functional setting, updatable locations are given separate location types; while based on an imperative setting, locations and their values share the same types. In both approaches, structured locations can be defined in the same way the calculus defines structured data. Hence, generic functions on locations can be defined by pattern-matching on (location) constructors. In that way, the power of the combination exceeds that of the boundary of functional or imperative alone. In particular, with the generic assignment function, we have a new approach on memory management which performs inplace update whenever it is reasonable to do so. Similar ideas could be used to extend the power of parametric polymorphism to parallel programming. To illustrate the approach, a key problem is addressed in detail, namely, distributing a data structure over a network of processors

A closed-form of cooperative detection probability using EGC-based soft decision under Suzuki fading

© 2017 IEEE. In cooperative spectrum sensing based on energy detection, several researchers have concluded that Soft Decision has better detection performance than Hard Decision. In this paper, we focus on Equal Gain Combining (EGC)-based soft decision under Suzuki fading which is a composite Rayleigh-lognormal fading. We use Moment-Generating function (MGF) to approximate Probability Density Function (PDF) of power sum of received signals at Fusion Center. Then we propose a novel method to evaluate cooperative detection performance under the effect of i.i.d Suzuki fading by using Gauss-Hermite approximation and MGF matching. Finally, we compare the results of EGC-based Soft Decision with those of Hard Decision

Energy Management and Time Scheduling for Heterogeneous IoT Wireless-Powered Backscatter Networks

© 2019 IEEE. In this paper, we propose a novel approach to jointly address energy management and network throughput maximization problems for heterogeneous IoT low-power wireless communication networks. In particular, we consider a low-power communication network in which the IoT devices can harvest energy from a dedicated RF energy source to support their transmissions or backscatter the signals of the RF energy source to transmit information to the gateway. Different IoT devices may have dissimilar hardware configurations, and thus they may have various communications types and energy requirements. In addition, the RF energy source may have a limited energy supply source which needs to be minimized. Thus, to maximize the network throughput, we need to jointly optimize energy usage and operation time for the IoT devices under different energy demands and communication constraints. However, this optimization problem is non-convex due to the strong relation between energy supplied by the RF energy source and the IoT communication time, and thus obtaining the optimal solution is intractable. To address this problem, we study the relation between energy supply and communication time, and then transform the non-convex optimization problem to an equivalent convex-optimization problem which can achieve the optimal solution. Through simulation results, we show that our solution can achieve greater network throughputs (up to five times) than those of other conventional methods, e.g., TDMA. In addition, the simulation results also reveal some important information in controlling energy supply and managing low-power IoT devices in heterogeneous wireless communication networks

Moody Man: Improving creative teamwork through dynamic affective recognition

While a significant part of communication in the workplace is now happening online, current platforms don’t fully support socio-cognitive nonverbal communication, which hampers the shared understanding and creativity of virtual teams. Given text-based communication being the main channel for virtual collaboration, we propose a novel solution leveraging an AI-based, dynamic affective recognition system. The app provides live feedback about the affective content of the communication in Slack, in the form of a visual representation and percentage breakdown of the ‘sentiment’ (tone, emoji) and main ‘emotion states’ (e.g. joy, anger). We tested the usability of the app in a quasi-experiment with 30 participants from diverse backgrounds, linguistic analysis and user interviews. The findings show that the app significantly increases shared understanding and creativity within virtual teams. Emerged themes included impression formation assisted by affective recognition, supporting long-term relationships development; identified challenges related to transparency and emotional complexity detected by AI

Effect of ciprofloxacin dosages on the performance of sponge membrane bioreactor treating hospital wastewater

© 2018 Elsevier Ltd This study aimed to evaluate treatment performance and membrane fouling of a lab-scale Sponge-MBR under the added ciprofloxacin (CIP) dosages (20; 50; 100 and 200 µg L−1) treating hospital wastewater. The results showed that Sponge-MBR exhibited effective removal of COD (94–98%) during the operation period despite increment of CIP concentrations from 20 to 200 µg L−1. The applied CIP dosage of 200 µg L−1 caused an inhibition of microorganisms in sponges, i.e. significant reduction of the attached biomass and a decrease in the size of suspended flocs. Moreover, this led to deteriorating the denitrification rate to 3–12% compared to 35% at the other lower CIP dosages. Importantly, Sponge-MBR reinforced the stability of CIP removal at various added CIP dosages (permeate of below 13 µg L−1). Additionally, the fouling rate at CIP dosage of 200 µg L−1 was 30.6 times lower compared to the control condition (no added CIP dosage)

Some algorithms to solve a bi-objectives problem for team selection

In real life, many problems are instances of combinatorial optimization. Cross-functional team selection is one of the typical issues. The decision-maker has to select solutions among (kh) solutions in the decision space, where k is the number of all candidates, and h is the number of members in the selected team. This paper is our continuing work since 2018; here, we introduce the completed version of the Min Distance to the Boundary model (MDSB) that allows access to both the "deep" and "wide" aspects of the selected team. The compromise programming approach enables decision-makers to ignore the parameters in the decision-making process. Instead, they point to the one scenario they expect. The aim of model construction focuses on finding the solution that matched the most to the expectation. We develop two algorithms: one is the genetic algorithm and another based on the philosophy of DC programming (DC) and its algorithm (DCA) to find the optimal solution. We also compared the introduced algorithms with the MIQP-CPLEX search algorithm to show their effectiveness

Arrhythmia Detection Using Convolutional Neural Models

Our main goal was studying the effectiveness of transfer learning using 2D CNNs. For this task, we generated spectrograms from ECG segments that were fed to a CNN to automatically extract features. These features are classified by a MLP into arrhythmic or normal rhythm segments, achieving 90% accuracy.Nuestra meta principal consistió en estudiar la efectividad de la transferencia de aprendizaje en el uso de CNNs 2D. Para ello, generamos espectrogramas, a partir de segmentos de electrocardiogramas, que sirvieron como entrada de una CNN para extraer automáticamente sus características. Estas características son clasificadas por un MLP para discernir entre segmentos arrítmicos o normales, obteniendo una precisión del 90%

Impact of COVID-19 on Air Quality in Hanoi and Ho Chi Minh City, Vietnam

Vietnam has had one of the fastest growing economies in Asia over the years. However, the COVID-19 pandemic has proven to be a major hindrance to this growth as the country’s GDP plummeted significantly. Air pollution can further amplify the impact of the pandemic since residents exposed to high levels of pollution are likely to increasingly suffer from respiratory illnesses, such as asthma. This paper investigates the impact of COVID-19 on air quality and how air quality can influence the spread of the virus. Finally, the paper proposes suitable machine learning practices for predicting air quality, based on historical trends, using spatial and temporal data

Detection of gait initiation Failure in Parkinson's disease based on wavelet transform and Support Vector Machine

© 2017 IEEE. Gait initiation Failure (GIF) is the situation in which patients with Parkinson's disease (PD) feel as if their feet get 'stuck' to the floor when initiating their first steps. GIF is a subtype of Freezing of Gait (FOG) and often leads to falls and related injuries. Understanding of neurobiological mechanisms underlying GIF has been limited by difficulties in eliciting and objectively characterizing such gait phenomena in the clinical setting. Studies investigating the effects of GIF on brain activity using EEG offer the potential to study such behavior. In this preliminary study, we present a novel methodology where wavelet transform was used for feature extraction and Support Vector Machine for classifying GIF events in five patients with PD and FOG. To deal with the large amount of EEG data, a Principal Component Analysis (PCA) was applied to reduce the data dimension from 15 EEG channels into 6 principal components (PCs), retaining 93% of the information. Independent Component Analysis using Entropy Bound Minimization (ICA-EBM) was applied to 6 PCs for source separation with the aim of improving detection ability of GIF events as compared to the normal initiation of gait (Good Starts). The results of this analysis demonstrated the correct identification of GIF episodes with an 83.1% sensitivity, 89.5% specificity and 86.3% accuracy. These results suggest that our proposed methodology is a promising non-invasive approach to improve GIF detection in PD and FOG

PRIMARY EDUCATION IN VIETNAM AND PUPIL ONLINE ENGAGEMENT

This paper focuses on exploring the disparities in social awareness and use of the Internet between urban and rural school children in the North of Vietnam. Design/methodology/approach: A total of 525 pupils, aged 9 to 11 years old, randomly selected from 7 urban and rural schools, who are Internet users, participated in the study and consented to responding to a questionnaire adapted from an equivalent European Union (EU) study. A comparative statistical analysis of the responses was then carried out, using IBM SPSS v21, which consisted of a descriptive analysis, an identification of personal self-development opportunities, as well as issues related to pupils’ digital prowess and knowledge of Internet use, and Internet safety, including parental engagement in their offspring’s online activities. Findings: The study highlights the fact that children from both the urban and rural regions of the North of Vietnam mostly access to the Internet from home, but with more children in the urbanized areas accessing it at school than their rural counterparts. Although children from the rural areas scored lower on all the Internet indicators, such as digital access and online personal experience and awareness, there was no disparity in awareness of Internet risks between the two sub-samples. It is noteworthy that there was no statistically significant gender difference towards online activities that support self-development. In relation to safe Internet usage, children are likely to seek advice from their parents, rather than through teachers or friends. However, they are not yet provided with an effective safety net while exposing themselves to the digital world. Originality/value: Although the Vietnamese national curriculum on the Computer Science subject does not explicitly cover the use of the Internet and its related aspects, the majority of children who took part in this study claimed to have used the Internet in their learning activities. This emphasises the urgent need for the MoE and educators in the country to not only improve ICT facilities in schools, but also to revise the Computer Science curriculum in order to (a) provide a supportive environment for learning development and (b) collectively advocate the dynamics of Internet use in order to ensure safe access and use by the children