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This study addresses an important issue of efficiency of national health care systems and develops an appropriate stochastic frontier gender-specific health efficiency model where the inefficiency term is modeled as a linear function of relevant explanatory variables.We used the latest data available on life expectancy as health outcomes and number of doctors, number of nurses, total health expenditure, GDP in prices as the inputs.Using the likelihood-ratio test, Translog stochastic frontier gender-specific health efficiency model is found an appropriate in Malaysia.From the result, total health expenditure is found significant and positively impact on life expectancy that indicates an increase in total health expenditures is expected to higher the overall health outcome measured by life expectancy while GDP in prices is observed unexpected to have a negative effect on life expectancy but significant. The population density in Malaysia is recorded to reduce the inefficiency on life expectancy and the total fertility rate is noticed unexpected to increase the inefficiency. The number of government hospitals, number of beds, and demographic rates like crude birth rate, crude death rate, infant mortality rate, and maternal mortality rate are found to reduce the inefficiency on life expectancy in good health in Malaysia.The average health efficiency for both male and female was 0.9321 and 0.9946, indicating that on an average, 93.2 percent for male and 99.4 percent for female of the health outcome potentials are realized by country Malaysia.Year-wise gender-specific health efficiency periodically fluctuates during the period of investigation.The study recommends that governments improve not only health care expenditure but also factors affecting health other than health care to reduce the burden on health-care facilities and reduce the burden of disease in Malaysia.

A stable theory that will fit with decision making in E-Government is a major point for establishing the EGovernment platform.This would contribute to lessen the gap generated from ICT system application with users, and reduce the gap between the government and citizens.However, the Egovernment platform is facing many items which obstruct its application.One of these items is the gap between the public sector and the decision making in the e-government.This study will contribute to lessen the gap generated from ICT system applications with users, and reduce the gap between the government and public.This paper focuses on three major steps. The first step is the theories related with the decision making in the E-government, the second step is a stable theory with the decision making in E-government, and the third step is the Social Construction of Technology Theory (SCOT).This research attempts to suggest and discuss the possible solutions to develop the decision making model in the E-government by depending on the SCOT theory. The results of this article are as follows: This study has realised the necessity to develop a suitable model, which could support the current case of a decision making system in the Iraqi e-government, and generally, this study has realised the necessity to support the citizens' participation in the decision making of the e-government.

Pseudo MV/LV (Medium/Low Voltage) transformer loads are usually used as partial inputs to the Distribution System State Estimation (DSSE) in MV systems. Such pseudo load can be represented by the aggregation of Smart Metering (SM) data. This follows the government restriction that Distribution Network Operators (DNOs) can only use aggregated SM data. Therefore, we assess the subsequent performance of DSSE, which shows the impact of this restriction - it affects the voltage angle estimation significantly. The possibilities for improving the DSSE accuracy under this restriction are further studied. First, two strategies that can potentially relax this restriction's impact are studied: the correlations among pseudo loads' errors are taken into account in the DSSE process; a power loss estimation method is proposed. Second, the investments (i.e., either adding measurement devices or increasing the original devices' accuracy) for the satisfactory DSSE results are assessed. All these are for addressing DNOs' concerns on this restriction.

When investing in cyber security resources, information security managers have to follow effective decisionmaking strategies. We refer to this as the cyber security investment challenge.In this paper, we consider three possible decision support methodologies for security managers to tackle this challenge. We consider methods based on game theory, combinatorial optimisation, and a hybrid of the two. Our modelling starts by building a framework where we can investigate the effectiveness of a cyber security control regarding the protection of different assets seen as targets in presence of commodity threats. As game theory captures the interaction between the endogenous organisation's and attackers' decisions, we consider a 2-person control game between the security manager who has to choose among different implementation levels of a cyber security control, and a commodity attacker who chooses among different targets to attack. The pure game theoretical methodology consists of a large game including all controls and all threats. In the hybrid methodology the game solutions of individual control-games along with their direct costs (e.g. financial) are combined with a Knapsack algorithm to derive an optimal investment strategy. The combinatorial optimisation technique consists of a multi-objective multiple choice Knapsack based strategy. To compare these approaches we built a decision support tool and a case study regarding current government guidelines. The endeavour of this work is to highlight the weaknesses and strengths of different investment methodologies for cyber security, the benefit of their interaction, and the impact that indirect costs have on cyber security investment. Going a step further in validating our work, we have shown that our decision support tool provides the same advice with the one advocated by the UK government with regard to the requirements for basic technical protection from cyber attacks in SMEs.

The approach to manpower policy in most Malaysian universities appears to be guided by the traditional method of putting the right number of people in the right place at the right time or arranging for suitable number of people to be allocated to various jobs usually in a hierarchical structure.The technique has been practiced for years.This traditional method is deficit in the sense that it neither offers computational tools that will enable administrators to determine possible line of action to be taken nor provide tools to generate alternative policies and strategies.The objective of this study was to design a planning model for projecting university faculty employment under alternative policy suggested by the government. The planning model was developed using Markov chain technique.Two scenarios were considered in the study; scenario 1 was based on historical data pattern between year 1999 – 2014 and scenario 2 was based on RMK 9 policies.The results showed the possibility average length of stay by position and probability of loss for both scenarios. The greater impact is expected for average length of stay of senior lecturers compared to other faculty position considering the new policy.

Several studies have shown how to approximately predict public opinion, such as in political elections, by analyzing user activities in blogging platforms and on-line social networks. The task is challenging for several reasons. Sample bias and automatic understanding of textual content are two of several non trivial issues. In this work we study how Twitter can provide some interesting insights concerning the primary elections of an Italian political party. State-of-the-art approaches rely on indicators based on tweet and user volumes, often including sentiment analysis. We investigate how to exploit and improve those indicators in order to reduce the bias of the Twitter users sample. We propose novel indicators and a novel content-based method. Furthermore, we study how a machine learning approach can learn correction factors for those indicators. Experimental results on Twitter data support the validity of the proposed methods and their improvement over the state of the art.

Identify how many variables both constants and the dynamics deliverables within a change project is likely to pose the needs for constant review in delivery and ordering priority of when the variables are indeed available. Change leaders motivate themselves by increasing the pace of change within the overall change plan. The focus on few constants and more variables could help in understanding the trend for more opportunities, more changes, shorter lifespan, and less stability in planning and implement change. This paper aims at a practical framework for successful change planning and implementation by focussing on the need for an agile approach in implementing change. It introduces the reader to the evolving concept of agile change management thinking. It explores some approaches to plotting and implementing change and gives practical tips embedded in a pocket guide for implementing change. Concepts are part of critical analysis of the significant benefits to change in society, government and organisations.

We are recently experiencing an unprecedented explosion of available data from the Web, sensors readings, scientific databases, government authorities and more. Such datasets could benefit from the introduction of rule sets encoding commonly accepted rules or facts, application- or domain-specific rules, commonsense knowledge etc. This raises the question of whether, how, and to what extent knowledge representation methods are capable of handling huge amounts of data for these applications. In this paper, we consider inconsistency-tolerant reasoning in the form of defeasible logic, and analyze how parallelization, using the MapReduce framework, can be used to reason with defeasible rules over huge datasets. We extend previous work by dealing with predicates of arbitrary arity, under the assumption of stratification. Moving from unary to multi-arity predicates is a decisive step towards practical applications, e.g. reasoning with linked open (RDF) data. Our experimental results demonstrate that defeasible reasoning with millions of data is performant, and has the potential to scale to billions of facts.

The Korean government funded 12.8 billion USD to 652 research and development (R&D) projects supported by 20 ministries in 2019. Every year, various organizations are supported to conduct R&D projects focusing on selected core technologies by evaluating emerging technologies which industries are planning to develop. To manage the whole cycle of national R&D projects, information sharing on national R&D projects is very essential. The blockchain technology is considered as a core solution to share information reliably and prevent forgery in various fields. For efficient management of national R&D projects, we enhance and analyse the Perfect Sharing Project (PSP)-Platform based on a new blockchain-based platform for information sharing and forgery prevention. It is a shared platform for national ICT R&D projects management with excellent performance in preventing counterfeiting. As a consensus algorithm is very important to prevent forgery in blockchain, we survey not only architectural aspects and examples of the platform but also the consensus algorithms. Considering characteristics of the PSP-Platform, we adopt an atomic proof (POA) consensus algorithm as a new consensus algorithm in this paper. To prove the validity of the POA consensus algorithm, we have conducted experiments. The experiment results show the outstanding performance of the POA consensus algorithm used in the PSP-Platform in terms of block generation delay and block propagation time.

In the UK both the Bank of England and the Financial Conduct Authority have recently carried out experiments using new digital technology for regulatory purposes. The idea is to replace rules written in natural legal language with computer code and to use artificial intelligence for regulatory purposes. This new way of designing regulatory rules is in line with the UK government's vision for the country to become a global leader in digital technology. It is also reflected in the FCA's business plan. The article reviews the technology and the advantages and disadvantages of combining the technology with regulatory law. It then informs the discussion from a broader perspective. It analyses regulatory technology through criteria developed in the mainstream regulatory debate. It contributes to that debate by anticipating problems that will arise as the technology evolves. In addition, the hope is to assist the government in avoiding mistakes that have occurred in the past and creating a better system from the start.

The Electronic Visualisation and the Arts London 2022 Conference (EVA London 2022) is co-sponsored by the Computer Arts Society (CAS) and BCS, the Chartered Institute for IT, of which the CAS is a Specialist Group. Of course, this has been a difficult time for all conferences, with the Covid-19 pandemic. For the first time since 2019, the EVA London 2022 Conference is a physical conference. It is also an online conference, as it was in the previous two years. We continue with publishing the proceedings, both online, with open access via ScienceOpen, and also in our traditional printed form, for the second year in full colour. Over recent decades, the EVA London Conference on Electronic Visualisation and the Arts has established itself as one of the United Kingdom's most innovative and interdisciplinary conferences. It brings together a wide range of research domains to celebrate a diverse set of interests, with a specialised focus on visualisation. The long and short papers in this volume cover varied topics concerning the arts, visualisations, and IT, including 3D graphics, animation, artificial intelligence, creativity, culture, design, digital art, ethics, heritage, literature, museums, music, philosophy, politics, publishing, social media, and virtual reality, as well as other related interdisciplinary areas. The EVA London 2022 proceedings presents a wide spectrum of papers, demonstrations, Research Workshop contributions, other workshops, and for the seventh year, the EVA London Symposium, in the form of an opening morning session, with three invited contributors. The conference includes a number of other associated evening events including ones organised by the Computer Arts Society, Art in Flux, and EVA International. As in previous years, there are Research Workshop contributions in this volume, aimed at encouraging participation by postgraduate students and early-career artists, accepted either through the peer-review process or directly by the Research Workshop chair. The Research Workshop ...

In this work we present a thorough quantitative analysis of information consumption patterns of qualitatively different information on Facebook. Pages are categorized, according to their topics and the communities of interests they pertain to, in a) alternative information sources (diffusing topics that are neglected by science and main stream media); b) online political activism; and c) main stream media. We find similar information consumption patterns despite the very different nature of contents. Then, we classify users according to their interaction patterns among the different topics and measure how they responded to the injection of 2788 false information (parodistic imitations of alternative stories). We find that users prominently interacting with alternative information sources ? i.e. more exposed to unsubstantiated claims ? are more prone to interact with intentional and parodistic false claims

With the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to have diversity in LCS selection for the same task. However, this diversity in sensor types adds complexity to appropriate sensor selection for monitoring tasks. In addition, LCS devices are often associated with low confidence in terms of sensing accuracy because of the complexities in sensing principles and the interpretation of monitored data. From the data analytics point of view, data quality is a major concern as low-quality data more often leads to low confidence in the monitoring systems. Therefore, any applications on building monitoring systems using LCS devices need to focus on two main techniques: sensor selection and calibration to improve data quality. In this paper, data-driven techniques were presented for sensor calibration techniques. To validate our methodology and techniques, an air quality monitoring case study from the Bradford district, UK, as part of two European Union (EU) funded projects was used. For this case study, the candidate sensors were selected based on the literature and market availability. The candidate sensors were narrowed down into the selected sensors after analysing their consistency. To address data quality issues, four different calibration methods were compared to derive the best-suited calibration method for the LCS devices in our use case system. In the calibration, meteorological parameters temperature and humidity were used in addition to the observed readings. Moreover, we uniquely considered Absolute Humidity (AH) and Relative Humidity (RH) as part of the calibration process. To validate the result of experimentation, the Coefficient of Determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) were compared for both AH and RH. The experimental results showed that calibration with AH has better performance as compared with RH. The experimental results showed the selection and calibration techniques that can be used in designing similar LCS based monitoring systems.

Arguing that questions of power expressed through aesthetic form are too often left out of current approaches to digital culture, this article revives the modernist aesthetic category of glamour in order to analyze contemporary forms of platformed cultural production. Through a case study of popular feminism, the article traces the ways in which glamour, defined as a beguiling affective force linked to promotional capitalist logics, suffuses digital content, metrics, and platforms. From the formal aesthetic codes of the ubiquitous beauty and lifestyle Instagram feeds that perpetuate the beguiling promise of popular feminism, to the enticing simplicity of online metrics and scores that promise transformative social connection and approbation, to the political economic drive for total information awareness and concomitant disciplining, predicting and optimizing of consumer-citizens, the article argues that the ambivalent aesthetic of glamour provides an apt descriptor and compelling heuristic for digital cultural production today.

The blockchain technology promises to transform finance, money and even governments. However, analyses of blockchain applicability and robustness typically focus on isolated systems whose actors contribute mainly by running the consensus algorithm. Here, we highlight the importance of considering trustless platforms within the broader ecosystem that includes social and communication networks. As an example, we analyse the flash-crash observed on 21st June 2017 in the Ethereum platform and show that a major phenomenon of social coordination led to a catastrophic cascade of events across several interconnected systems. We propose the concept of "emergent centralisation" to describe situations where a single system becomes critically important for the functioning of the whole ecosystem, and argue that such situations are likely to become more and more frequent in interconnected socio-technical systems. We anticipate that the systemic approach we propose will have implications for future assessments of trustless systems and call for the attention of policy-makers on the fragility of our interconnected and rapidly changing world.