Panel Discussion on“ The Role of Computer Science in University Development Strategy”

At the end of the keynotes during the second Global Forum of Development of Computer Science,a panel discussion was held to encourage further discussion on ways for universities to adapt to the rapidly changing computer science field.Five deans of top computer science departments participated,including the moderator.The discussions were guided along three topics,namely the role of computer science departments in universities today,the nature of computer science as a fundamental discipline or an applied one,and computer science education.Out of these topics,the panelists mainly focused on the interdisciplinary nature of computer science in teaching,research,and industry.The panelists agreed about ways to prepare for the interdisciplinary future,for example by establishing new research centers,introducing projectbased curricula,and collaborating with industry while keeping the campus vibrant.They also pointed out that universities may be under equipped for preparing future professionals to keep up with rapid new advances,especially in machine learning and artificial intelligence.

Phytochemically rich dietary components and the risk of colorectal cancer:A systematic review and meta-analysis of observational studies

BACKGROUND Personalized nutrition and protective diets and lifestyles represent a key cancer research priority.The association between consumption of specific dietary components and colorectal cancer(CRC)incidence has been evaluated by a number of population-based studies,which have identified certain food items as having protective potential,though the findings have been inconsistent.Herein we present a systematic review and meta-analysis on the potential protective role of five common phytochemically rich dietary components(nuts,cruciferous vegetables,citrus fruits,garlic and tomatoes)in reducing CRC risk.AIM To investigate the independent impact of increased intake of specific dietary constituents on CRC risk in the general population.METHODS Medline and Embase were systematically searched,from time of database inception to January 31,2020,for observational studies reporting CRC incidence relative to intake of one or more of nuts,cruciferous vegetables,citrus fruits,garlic and/or tomatoes in the general population.Data were extracted by two independent reviewers and analyzed in accordance with the Meta-analysis of Observational Studies in Epidemiology(MOOSE)and Preferred Reporting Items for Systematic Review and Meta-analysis(PRISMA)reporting guidelines and according to predefined inclusion/exclusion criteria.Effect sizes of studies were pooled using a random-effects model.RESULTS Forty-six studies were identified.CRC risk was significantly reduced in patients with higher vs lower consumption of cruciferous vegetables[odds ratio(OR)=0.90;95%confidence interval(CI):0.85-0.95;P<0.005],citrus fruits(OR=0.90;95%CI:0.84-0.96;P<0.005),garlic(OR=0.83;95%CI:0.76-0.91;P<0.005)and tomatoes(OR=0.89;95%CI:0.84-0.95;P<0.005).Subgroup analysis showed that this association sustained when looking at case-control studies alone,for all of these four food items,but no significant difference was found in analysis of cohort studies alone.Nut consumption exhibited a similar trend,but overall results were not significant(OR=0.72;95%CI:0.50-1.03;P<0.07;I2=90.70%).Putative anticarcinogenic mechanisms are proposed using gene-set enrichment analysis of gene/protein perturbations caused by active compounds within each food item.CONCLUSION Increased cruciferous vegetable,garlic,citrus fruit and tomato consumption are all inversely associated with CRC risk.These findings highlight the potential for developing precision nutrition strategies for CRC prevention.

Model-guided design of a high performance and durability Ni nanofiber/ceria matrix solid oxide fuel cell electrode

Mixed ionic electronic conductors(MIECs)have attracted increasing attention as anode materials for solid oxide fuel cells(SOFCs)and they hold great promise for lowering the operation temperature of SOFCs.However,there has been a lack of understanding of the performance-limiting factors and guidelines for rational design of composite metal-MIEC electrodes.Using a newly-developed approach based on 3 D-tomography and electrochemical impedance spectroscopy,here for the first time we quantify the contribution of the dual-phase boundary(DPB)relative to the three-phase boundary(TPB)reaction pathway on real MIEC electrodes.A new design strategy is developed for Ni/gadolinium doped ceria(CGO)electrodes(a typical MIEC electrode)based on the quantitative analyses and a novel Ni/CGO fiber-matrix structure is proposed and fabricated by combining electrospinning and tape-casting methods using commercial powders.With only 11.5 vol%nickel,the designer Ni/CGO fiber-matrix electrode shows 32%and 67%lower polarization resistance than a nano-Ni impregnated CGO scaffold electrode and conventional cermet electrode respectively.The results in this paper demonstrate quantitatively using real electrode structures that enhancing DPB and hydrogen kinetics are more efficient strategies to enhance electrode performance than simply increasing TPB.

Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems:A Review

Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.Typical applications span from computational fluid dynamics(CFD)to geoscience and climate systems.Recently,much effort has been given in combining DA,UQ and machine learning(ML)techniques.These research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive guide.This paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of applications.This review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems.Therefore,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice versa.Some exciting perspectives of this rapidly developing research field are also discussed.Index Terms-Data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).

Towards efficient deep neural network training by FPGA-based batch-level parallelism

Training deep neural networks(DNNs)requires a significant amount of time and resources to obtain acceptable results,which severely limits its deployment in resource-limited platforms.This paper proposes DarkFPGA,a novel customizable framework to efficiently accelerate the entire DNN training on a single FPGA platform.First,we explore batch-level parallelism to enable efficient FPGA-based DNN training.Second,we devise a novel hardware architecture optimised by a batch-oriented data pattern and tiling techniques to effectively exploit parallelism.Moreover,an analytical model is developed to determine the optimal design parameters for the DarkFPGA accelerator with respect to a specific network specification and FPGA resource constraints.Our results show that the accelerator is able to perform about 10 times faster than CPU training and about a third of the energy consumption than GPU training using 8-bit integers for training VGG-like networks on the CIFAR dataset for the Maxeler MAX5 platform.

Computer Science at University 2.0: What the Future Holds and How We Can Shape It

Alongside the development of computer science,many fields like engineering,finance and the natural sciences increasingly leverage computer science techniques to facilitate their own evolution.As a result,students now need to have a good grasp of computer science skills in order to keep pace with the new forms of working in these other fields.Therefore,higher education need to change to support this transformation.Computer Science 2.0 refers to this new way of educating that facilitates these interdisciplinary connections.This creates exciting opportunities for students and staff to interact across disciplines in teaching,research,and transfer to create the so-called University 2.0.

IC Cloud: Enabling Compositional Cloud

Cloud computing has attracted great interest from both academic and industrial communities. Different paradigms, architectures and applications based on the concept of cloud have emerged. Although many of them have been quite successful, efforts are mainly focusing on the study and implementation of particular setups. However, a generic and more flexible solution for cloud construction is missing. In this paper, we present a composition-based approach for cloud computing （compositional cloud） using Imperial College Cloud （IC Cloud） as a demonstration example. Instead of studying a specific cloud computing system, our approach aims to enable a generic framework where wrious cloud computing architectures and implementation strategies can be systematically studied. With our approach, cloud computing providers/adopters are able to design and compose their own systems in a quick and flexible manner. Cloud computing systems will no longer be in fixed shapes but will be dynamic and adjustable according to the requirements of different application domains.

ECC:Edge Collaborative Caching Strategy for Differentiated Services Load-Balancing

Due to the explosion of network data traffic and IoT devices,edge servers are overloaded and slow to respond to the massive volume of online requests.A large number of studies have shown that edge caching can solve this problem effectively.This paper proposes a distributed edge collaborative caching mechanism for Internet online request services scenario.It solves the problem of large average access delay caused by unbalanced load of edge servers,meets users’differentiated service demands and improves user experience.In particular,the edge cache node selection algorithm is optimized,and a novel edge cache replacement strategy considering the differentiated user requests is proposed.This mechanism can shorten the response time to a large number of user requests.Experimental results show that,compared with the current advanced online edge caching algorithm,the proposed edge collaborative caching strategy in this paper can reduce the average response delay by 9%.It also increases the user utility by 4.5 times in differentiated service scenarios,and significantly reduces the time complexity of the edge caching algorithm.

A Rigorous Architectural Approach to Adaptive Software Engineering

The engineering of distributed adaptive software is a complex task which requires a rigorous approach. Software architectural （structural） concepts and principles are highly beneficial in specifying, designing, analysing, constructing and evolving distributed software. A rigorous architectural approach dictates formalisms and techniques that are compositional, components that are context independent and systems that can be constructed and evolved incrementally. This paper overviews some of the underlying reasons for adopting an architectural approach, including a brief ＂rational history＂ of our research work, and indicates how an architectural model can potentially facilitate the provision of self-managed adaptive software system.

A Creative Approach to Reducing Ambiguity in Scenario-based Software Architecture Analysis

In software engineering, a scenario describes an anticipated usage of a software system. As scenarios are useful to understand the requirements and functionalities of a software system, the scenario-based analysis is widely used in various tasks, especially in the design stage of software architectures. Although researchers have proposed various scenario-based approaches to analyse software architecture, there are still limitations in this research field, and a key limitation is that scenarios are typically not formally defined and thus may contain ambiguities. As these ambiguities may lead to defects, it is desirable to reduce them as many as possible. In order to reduce ambiguity in scenario-based software architecture analysis, this paper introduces a creative computing approach to scenario-based software requirements analysis. Our work expands this idea in three directions. Firstly, we extend an architecture description language(ADL)-based language – Breeze/ADL to model the software architecture. Secondly, we use a creative rule – combinational rule(CR) to combine the vector clock algorithm for reducing the ambiguities in modelling scenarios. Then, another creative rule – transformational rule(TR) is employed to help to transform our Breeze/ADL model to a popular model – unified modelling language(UML) model. We implement our approach as a plugin of Breeze, and illustrate a running example of modelling a poetry to music system in our case study.Our results show the proposed creative approach is able to reduce ambiguities of the software architecture in practice.

Distortion minimization for multimedia transmission in NOMA HAP-UAV integrated aerial access networks

Aerial access networks have been envisioned as a promising 6G solution to enhance the ground communication systems in both coverage and capacity. To better utilize the spectrum and fully explore different channel characteristics, this paper constructs an integrated network comprising the High Altitude Platform(HAP) and Unmanned Air Vehicles(UAVs) with the NonOrthogonal Multiple Access(NOMA) technology. In order to improve the transmission quality of images and videos, a power management scheme is proposed to minimize the distortion of the transmissions from the HAP and UAVs to the terminals. The power control is formulated as a non-convex problem constrained by the maximal transmit power and the minimal terminal rate requirements. The variable substitution and the first-order Tailor’s expansion is used to transform it into a sequence of convex problems, which are subsequently solved through the gradient projection method. Simulation demonstrates the signal distortion and error rate improvement achieved by the proposed algorithm.

Effective Variational Data Assimilation in Air-Pollution Prediction

Numerical simulations are widely used as a predictive tool to better understand complex air flows and pollution transport on the scale of individual buildings,city blocks,and entire cities.To improve prediction for air flows and pollution transport,we propose a Variational Data Assimilation(VarDA)model which assimilates data from sensors into the open-source,finite-element,fluid dynamics model Fluidity.VarDA is based on the minimization of a function which estimates the discrepancy between numerical results and observations assuming that the two sources of information,forecast and observations,have errors that are adequately described by error covariance matrices.The conditioning of the numerical problem is dominated by the condition number of the background error covariance matrix which is ill-conditioned.In this paper,a preconditioned VarDA model is presented,it is based on a reduced background error covariance matrix.The Empirical Orthogonal Functions(EOFs)method is used to alleviate the computational cost and reduce the space dimension.Experimental results are provided assuming observed values provided by sensors from positions mainly located on roofs of buildings.

Collaborative Defense with Multiple USVs and UAVs Based on Swarm Intelligence

Modern defense systems are developing towards systematization.intellectualization and automation,which include the collaborative defense system on the sea between multiple unmanned surface vehicles(USVs)and unmanned aerial vehicles(UAVs).UAVs can fly in high altitude and collect marine environment information on patrolling.Furthermore,UAVs can plan defense paths for USVs to intercept intruders with full-assignment or reassignment strategies aiming at maximum overall benefits.Thus,we propose dynamic overlay reconnaissance algor计hm based on genetic idea(GI-DORA)to solve the problem of multi-UAV multi-station reconnaissance.Moreover,we develop continuous particle swarm optimization based on obstaele dimension(OD-CPSO)to optimize defense path of USVs to intercept intruders.In addition,under the designed defense constraints,we propose dispersed particle swarm optimization based on mutation and crossover(MC-DPSO)and real-time batch assignment algorithm(RTBA)in emergency for formulating combat defense mission assignment strategy in different scenarios.Finally,we illus trate the feasibility and effectiveness of the proposed met hods.

Inferring Functional Connectivity in fMRI Using Minimum Partial Correlation

Functional connectivity has emerged as a promising approach to study the functional organisation of the brain and to define features for prediction of brain state. The most widely used method for inferring functional connectivity is Pearson＇s correlation, but it cannot differentiate direct and indirect effects. This disadvantage is often avoided by computing the partial correlation between two regions controlling all other regions, but this method suffers from Berkson＇s paradox. Some advanced methods, such as regularised inverse covariance, have been applied. However, these methods usually depend on some parameters. Here we propose use of minimum partial correlation as a parameter-free measure for the skeleton of functional connectivity in functional magnetic resonance imaging （flVIRI）. The minimum partial correlation between two regions is the minimum of absolute values of partial correlations by controlling all possible subsets of other regions. Theoretically, there is a direct effect between two regions if and only if their minimum partial correlation is non-zero under faithfulness and Gaussian assumptions. The elastic PC-algorithm is designed to efficiently approximate minimum partial correlation within a computational time budget. The simulation study shows that the proposed method outperforms others in most cases and its application is illustrated using a resting-state fMRI dataset from the human connectome project.

Large-scale Data Collection and Analysis via a Gamified Intelligent Crowdsourcing Platform

In this contribution, we present iHEARu-PLAY, an online, multi-player platform for crowdsourced database collection and labelling, including the voice analysis application (VoiLA), a free web-based speech classification tool designed to educate iHEARu-PLAY users about state-of-the-art speech analysis paradigms. Via this associated speech analysis web interface, in addition, VoiLA encourages users to take an active role in improving the service by providing labelled speech data. The platform allows users to record and upload voice samples directly from their browser, which are then analysed in a state-of-the-art classification pipeline. A set of pre-trained models targeting a range of speaker states and traits such as gender, valence, arousal, dominance, and 24 different discrete emotions is employed. The analysis results are visualised in a way that they are easily interpretable by laymen, giving users unique insights into how their voice sounds. We assess the effectiveness of iHEARu-PLAY and its integrated VoiLA feature via a series of user evaluations which indicate that it is fun and easy to use, and that it provides accurate and informative results.

Time-in-action RL

The authors propose a novel reinforcement learning(RL)framework,where agent behaviour is governed by traditional control theory.This integrated approach,called time-in-action RL,enables RL to be applicable to many real-world systems,where underlying dynamics are known in their control theoretical formalism.The key insight to facilitate this integration is to model the explicit time function,mapping the state-action pair to the time accomplishing the action by its underlying controller.In their framework,they describe an action by its value(action value),and the time that it takes to perform(action time).An action-value results from the policy of RL regarding a state.Action time is estimated by an explicit time model learnt from the measured activities of the underlying controller.RL value network is then trained with embedded time model to predict action time.This approach is tested using a variant of Atari Pong and proved to be convergent.