Effect of acceptable use policy on employee computer use : case of Sri Lankan software development organizations

It is a known fact that some employees misuse the organizational computers to do their personal work such as sending emails, surfing the Internet, chatting, playing games. These activities not only waste productive time of employees but also bring a risk factor to the organization. This affects organizations in the software industry very much as almost all of their employees are connected to the Internet throughout them day./ By introducing an Acceptable Use Policy (AUP) for an organization, it is believed that the computer misuse by its employees could be reduced. In many countries Acceptable Use Policies are used and they have been studied with various perspectives. In Sri Lankan context research on these areas are scarce. This research explored the situation in Sri Lanka with respect to AUPs and their effectiveness./ A descriptive study was carried out to identify the large and medium scale software development organizations that had implemented computer usage guidelines for employees. A questionnaire was used to gather information regarding employee’s usual computer usage behavior. Stratified random sampling was employed to draw a representative sample from the population./ Majority of the organizations have not employed a written guideline on acceptable use of work computers. The study results did not provide evidence to conclude that the presence or non presence of an AUP has a significant difference in computer use behaviors of employees. A significant negative correlation was observed between level of awareness about AUP and misuse. Access to the Internet and organizational settings were identified as significant factors that influence employee computer misuse behavior.

A survey of data mining techniques for social media analysis

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors.

Fast adaptive real-time classification for data streams with concept drift

An important application of Big Data Analytics is the real-time analysis of streaming data. Streaming data imposes unique challenges to data mining algorithms, such as concept drifts, the need to analyse the data on the fly due to unbounded data streams and scalable algorithms due to potentially high throughput of data. Real-time classification algorithms that are adaptive to concept drifts and fast exist, however, most approaches are not naturally parallel and are thus limited in their scalability. This paper presents work on the Micro-Cluster Nearest Neighbour (MC-NN) classifier. MC-NN is based on an adaptive statistical data summary based on Micro-Clusters. MC-NN is very fast and adaptive to concept drift whilst maintaining the parallel properties of the base KNN classifier. Also MC-NN is competitive compared with existing data stream classifiers in terms of accuracy and speed.

Connectivity recovery in epidemic membership protocols

Epidemic protocols are a bio-inspired communication and computation paradigm for extreme-scale network system based on randomized communication. The protocols rely on a membership service to build decentralized and random overlay topologies. In a weakly connected overlay topology, a naive mechanism of membership protocols can break the connectivity, thus impairing the accuracy of the application. This work investigates the factors in membership protocols that cause the loss of global connectivity and introduces the first topology connectivity recovery mechanism. The mechanism is integrated into the Expander Membership Protocol, which is then evaluated against other membership protocols. The analysis shows that the proposed connectivity recovery mechanism is effective in preserving topology connectivity and also helps to improve the application performance in terms of convergence speed.

The internet, information architecture and community memory

This article reviews current technological developments, particularly Peer-to-Peer technologies and Distributed Data Systems, and their value to community memory projects, particularly those concerned with the preservation of the cultural, literary and administrative data of cultures which have suffered genocide or are at risk of genocide. It draws attention to the comparatively good representation online of genocide denial groups and changes in the technological strategies of holocaust denial and other far-right groups. It draws on the author's work in providing IT support for a UK-based Non-Governmental Organization providing support for survivors of genocide in Rwanda.

Extending the human nervous system through Internet implants - Experimentation and impact

It is now possible to directly link the human nervous system to a computer and thence onto the Internet. From an electronic and mental viewpoint this means that the Internet becomes an extension of the human nervous system (and vice versa). Such a connection on a regular or mass basis will have far reaching effects for society. In this article the authors discuss their own practical implant self-experimentation, especially insofar as it relates to extending the human nervous system. Trials involving an intercontinental link up are described. As well as technical aspects of the work, social, moral and ethical issues, as perceived by the authors, are weighed against potential technical gains. The authors also look at technical limitations inherent in the co-evolution of Internet implanted individuals as well as the future distribution of intelligence between human and machine.

This is me: digital identity and reputation on the Internet

The chapter reports on the ‘This Is Me’ project, that aimed to help students and the wider public to be aware of the impact that online material, particularly that on the Internet, has on their identity and reputation. The chapter explores practical aspects of Digital Identity, relating to issues such as employability, relationships and even death. For example, understanding the impact a photograph posted on a social networking website might have for different groups of people, ranging from friends or parents to future employers. As part of the ‘This is Me’ project stories were collected from students and others about Digital Identity matters, a grounded methodological approach based on action research was used to establish issues related to Digital Identity particularly relevant to those in academia. Drawing from these issues, resources were developed to help inform and educate people about how they can understand and control their own Digital Identity. A number of these resources are presented here, along with reflections on how they are used and can be adapted.

Imbalanced functional link between executive control network and reward network explain the online-game seeking behaviors in Internet gaming disorder

Literatures have shown that Internet gaming disorder (IGD) subjects show impaired executive control and enhanced reward sensitivities than healthy controls. However, how these two networks jointly affect the valuation process and drive IGD subjects' online-game-seeking behaviors remains unknown. Thirty-five IGD and 36 healthy controls underwent a resting-states scan in the MRI scanner. Functional connectivity (FC) was examined within control and reward network seeds regions, respectively. Nucleus accumbens (NAcc) was selected as the node to find the interactions between these two networks. IGD subjects show decreased FC in the executive control network and increased FC in the reward network when comparing with the healthy controls. When examining the correlations between the NAcc and the executive control/reward networks, the link between the NAcc - executive control network is negatively related with the link between NAcc - reward network. The changes (decrease/increase) in IGD subjects' brain synchrony in control/reward networks suggest the inefficient/overly processing within neural circuitry underlying these processes. The inverse proportion between control network and reward network in IGD suggest that impairments in executive control lead to inefficient inhibition of enhanced cravings to excessive online game playing. This might shed light on the mechanistic understanding of IGD.

How reliable is internet-based self-reported identity, socio-demographic and obesity measures in European adults?

In e-health intervention studies, there are concerns about the reliability of internet-based, self-reported (SR) data and about the potential for identity fraud. This study introduced and tested a novel procedure for assessing the validity of internet-based, SR identity and validated anthropometric and demographic data via measurements performed face-to-face in a validation study (VS). Participants (n = 140) from seven European countries, participating in the Food4Me intervention study which aimed to test the efficacy of personalised nutrition approaches delivered via the internet, were invited to take part in the VS. Participants visited a research centre in each country within 2 weeks of providing SR data via the internet. Participants received detailed instructions on how to perform each measurement. Individual’s identity was checked visually and by repeated collection and analysis of buccal cell DNA for 33 genetic variants. Validation of identity using genomic information showed perfect concordance between SR and VS. Similar results were found for demographic data (age and sex verification). We observed strong intra-class correlation coefficients between SR and VS for anthropometric data (height 0.990, weight 0.994 and BMI 0.983). However, internet-based SR weight was under-reported (Δ −0.70 kg [−3.6 to 2.1], p < 0.0001) and, therefore, BMI was lower for SR data (Δ −0.29 kg m−2 [−1.5 to 1.0], p < 0.0001). BMI classification was correct in 93 % of cases. We demonstrate the utility of genotype information for detection of possible identity fraud in e-health studies and confirm the reliability of internet-based, SR anthropometric and demographic data collected in the Food4Me study.