Improving Public Administration Performance through Electronic Government Applications

Electronic Government applications have been the focus of hundreds of local and national government administrations all over the world during the past decade. The emphasis of most of these applications lies in their effort to improve the experience of the user in interacting with public administration services and to minimise waiting times in completing transactions public services and citizens. Early applications were relying mainly on the speed and simplicity of submitting a request by the user while most of the work beyond the web based interaction was carried out as in the era before the introduction of the web based applications. The benefits from such endeavours have been short lived as citizens are looking for real enhancements in they way public administration serves their needs and responds to their requests. The authors argue that for e-government applications to succeed changes would have to be effected in the way public administration organizes itself and how it utilizes information management systems to respond to user / citizen requirements including and addressing the goals of all stakeholders involved. Currently the number of successful applications to that end is quite low when compared to the projects implemented so far. The authors propose steps that would maintain the focus of future implementations in doing so

A switching multi-level method for the long tail recommendation problem

Recommender systems are decision support systems that play an important part in generating a list of product or service recommendations for users based on the past experiences and interactions. The most popular recommendation method is Collaborative Filtering (CF) that is based on the users’ rating history to generate the recommendation. Although, recommender systems have been applied successfully in different areas such as e-Commerce and Social Networks, the popularity bias is still one of the challenges that needs to be further researched. Therefore, we propose a multi-level method that is based on a switching approach which solves the long tail recommendation problem (LTRP) when CF fails to find the target case. We have evaluated our method using two public datasets and the results show that it outperforms a number of bases lines and state-of-the-art alternatives with a further reduce of the recommendation error rates for items found in the long tail

Can e-Government Systems Bridge the Digital Divide?

Electronic Government systems are often seen as panacea in the remedy of all failings of governance. With a history span of almost two decades, e-government implementations have often reached dead ends and have regularly failed to deliver the promise that the governments that have initiated them have made to their citizens. Despite an abundance of development models and best case scenarios identified in literature, e-government services are continually failing to attract the citizens and to capture their trust and faith. The main reason quoted for such failures is the lack of innovation and inclusivity in the way a service is designed and delivered. The digital divide is the major risk of marginalizing sectors of society or even whole continents due to lack of access to web based services. In the developing world it is mainly the lack of, or poor infrastructure that maintains and often widens the divide, while in the developed world it is lack of skills and difficulty of accessing services that leads citizens to abandon their efforts in using services online. Whatever the reason that leads to non-access of services the effect is similar and those citizens that fall victim to it are increasingly consumed into the trap of the digital divide. Efforts and initiatives to address the divide have primarily focused on building the infrastructure and providing access to the web. However, the quality and accessibility of online services is quite often then reason why citizens distance themselves from web-based services and the internet in total. This paper attempts to explore the shortfall in criteria for evaluating a government’s efforts in planning, implementing and delivering services that address the operational requirements of efficient government, but equally cater for the needs of the citizens as end users of the service

Variational restricted Boltzmann machines to automated anomaly detection

Data-driven methods are implemented using particularly complex scenarios that reflect in-depth perennial knowledge and research. Hence, the available intelligent algorithms are completely dependent on the quality of the available data. This is not possible for real-time applications, due to the nature of the data and the computational cost that is required. This work introduces an Automatic Differentiation Variational Inference (ADVI) Restricted Boltzmann Machine (RBM) to perform real-time anomaly detection of industrial infrastructure. Using the ADVI methodology, local variables are automatically transformed into real coordinate space. This is an innovative algorithm that optimizes its parameters with mathematical methods by choosing an approach that is a function of the transformed variables. The ADVI RBM approach proposed herein identifies anomalies without the need for prior training and without the need to find a detailed solution, thus making the whole task computationally feasible. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature

An explainable semi-personalized federated learning model

Training a model using batch learning requires uniform data storage in a repository. This approach is intrusive, as users have to expose their privacy and exchange sensitive data by sending them to central entities to be preprocessed. Unlike the aforementioned centralized approach, training of intelligent models via the federated learning (FEDL) mechanism can be carried out using decentralized data. This process ensures that privacy and protection of sensitive information can be managed by a user or an organization, employing a single universal model for all users. This model should apply average aggregation methods to the set of cooperative training data. This raises serious concerns for the effectiveness of this universal approach and, therefore, for the validity of FEDL architectures in general. Generally, it flattens the unique needs of individual users without considering the local events to be managed. This paper proposes an innovative hybrid explainable semi-personalized federated learning model, that utilizes Shapley Values and Lipschitz Constant techniques, in order to create personalized intelligent models. It is based on the needs and events that each individual user is required to address in a federated format. Explanations are the assortment of characteristics of the interpretable system, which, in the case of a specified illustration, helped to bring about a conclusion and provided the function of the model on both local and global levels. Retraining is suggested only for those features for which the degree of change is considered quite important for the evolution of its functionality. © 2022 - IOS Press. All rights reserved

The importance of biometric sensor continuous secure monitoring

The security of a biometric information system depends partially on the ability of the biometric information sensor to authenticate itself securely to the processing centre it usually interacts with, thus ensuring that the securely transmitted biometric data has not been constructed by an attacker. The biometric sensors of a biometric information system are often physically exposed to potential adversaries who may manipulate them and therefore compromise the security of the system. Such attacks can be detected by continuous and secure sensor monitoring. The proposed scheme is based on the Kerberos protocol for dealing with sensor authentication issues. Using authenticated control/data packets its functionality has been extended to provide secure sensor monitoring, which can help in detecting physical attacks on the sensor itself. © 2008 IEEE