From Big Data To Knowledge – Good Practices From Industry

Recent advancements in data gathering technologies have led to the rise of a large amount of data through which useful insights and ideas can be derived. These data sets are typically too large to process using traditional data processing tools and applications and thus known in the popular press as ‘big data’. It is essential to extract the hidden meanings in the available data sets by aggregating big data into knowledge, which may then positively contribute to decision making. One way to engage in data-driven strategy is to gather contextual relevant data on specific customers, products, and situations, and determine optimised offerings that are most appealing to the target customers based on sound analytics. Corporations around the world have been increasingly applying analytics, tools and technologies to capture, manage and process such data, and derive value out of the huge volumes of data generated by individuals. The detailed intelligence on consumer behaviour, user patterns and other hidden knowledge that was not possible to derive via traditional means could now be used to facilitate important business processes such as real-time control, and demand forecasting. The aim of our research is to understand and analyse the significance and impact of big data in today’s industrial environment and identify the good practices that can help us derive useful knowledge out of this wealth of information based on content analysis of 34 firms that have initiated big data analytical projects. Our descriptive and network analysis shows that the goals of a big data initiative are extensible and highlighted the importance of data representation. We also find the data analytical techniques adopted are heavily dependent on the project goals

Fuzzy spectral and spatial feature integration for classification of nonferrous materials in hyperspectral data

Hyperspectral data allows the construction of more elaborate models to sample the properties of the nonferrous materials than the standard RGB color representation. In this paper, the nonferrous waste materials are studied as they cannot be sorted by classical procedures due to their color, weight and shape similarities. The experimental results presented in this paper reveal that factors such as the various levels of oxidization of the waste materials and the slight differences in their chemical composition preclude the use of the spectral features in a simplistic manner for robust material classification. To address these problems, the proposed FUSSER (fuzzy spectral and spatial classifier) algorithm detailed in this paper merges the spectral and spatial features to obtain a combined feature vector that is able to better sample the properties of the nonferrous materials than the single pixel spectral features when applied to the construction of multivariate Gaussian distributions. This approach allows the implementation of statistical region merging techniques in order to increase the performance of the classification process. To achieve an efficient implementation, the dimensionality of the hyperspectral data is reduced by constructing bio-inspired spectral fuzzy sets that minimize the amount of redundant information contained in adjacent hyperspectral bands. The experimental results indicate that the proposed algorithm increased the overall classification rate from 44% using RGB data up to 98% when the spectral-spatial features are used for nonferrous material classification

Semantic Communications for Wireless Sensing: RIS-aided Encoding and Self-supervised Decoding

Semantic communications can reduce the resource consumption by transmitting task-related semantic information extracted from source messages. However, when the source messages are utilized for various tasks, e.g., wireless sensing data for localization and activities detection, semantic communication technique is difficult to be implemented because of the increased processing complexity. In this paper, we propose the inverse semantic communications as a new paradigm. Instead of extracting semantic information from messages, we aim to encode the task-related source messages into a hyper-source message for data transmission or storage. Following this paradigm, we design an inverse semantic-aware wireless sensing framework with three algorithms for data sampling, reconfigurable intelligent surface (RIS)-aided encoding, and self-supervised decoding, respectively. Specifically, on the one hand, we propose a novel RIS hardware design for encoding several signal spectrums into one MetaSpectrum. To select the task-related signal spectrums for achieving efficient encoding, a semantic hash sampling method is introduced. On the other hand, we propose a self-supervised learning method for decoding the MetaSpectrums to obtain the original signal spectrums. Using the sensing data collected from real-world, we show that our framework can reduce the data volume by 95% compared to that before encoding, without affecting the accomplishment of sensing tasks. Moreover, compared with the typically used uniform sampling scheme, the proposed semantic hash sampling scheme can achieve 67% lower mean squared error in recovering the sensing parameters. In addition, experiment results demonstrate that the amplitude response matrix of the RIS enables the encryption of the sensing data

Putting Youth at the epicenter of Governance, Peace and Security in Africa

Press Release: Accra, 29 October 2019: The Youth for Peace Africa (Y4P) Program of the African Union Commission, managed by its Peace and Security Department, reviewed and validated the draft Continental Framework on Youth, Peace and Security and the findings of the Study on the Roles and Contributions of Youth to peace and security in Africa, in Accra, Ghana, from 22 to 25 October 2019

The Philosophy of Taking Conspiracy Theories Seriously

During the last few decades, the proliferation of interest in conspiracy theories became a widespread phenomenon in our culture, and also in academia. In this piece, I review a new book on the topic of conspiracy theory theory (that is-the theory of conspiracy theories) Taking Conspiracy Theories Seriously, edited by M R. X. Dentith. To contextualize the review, I first turn to the '90s, to see what sparked current interest in conspiracy theories within the field of analytic philosophy. I then critically asses the current limitations of social epistemology, as a field. Among other things, I show how accepted assumptions in social epistemology cause cross-disciplinary disagreements with other social sciences, present the dilemma of trivializing whistle-blowers, and discuss few neglected roles technologies play in belief formation

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

MazeSuite is a complete toolset to prepare, present and analyze navigational and spatial experiments1. MazeSuite can be used to design and edit adapted virtual 3D environments, track a participants' behavioral performance within the virtual environment and synchronize with external devices for physiological and neuroimaging measures, including electroencephalogram and eye tracking

Why People Continue to Use Social Networking Services: Developing a Comprehensive Model

Social networking (SNW) services such as Facebook and MySpace are growing exponentially. While many people are spending an increasing amount of their time on the services, others use them minimally or discontinue use after a short period of time. This research is asking the question: What are the salient factors influencing individuals to continue using and extending the range of SNW services after their initial acceptance? This research recognizes that long-term viability and the eventual success of these services depends on continued usage rather than initial acceptance, and usage continuance of SNW services at the individual level is fundamental to the survival of many social technology-empowered businesses and organizations. We look to the Expectation-Confirmation Model of information systems (IS) continuance and a series of social theories as the underlying theoretical foundations. We develop the Usage Continuance Model of SNW Services to investigate continued usage behavior and enduring impacts of SNW services. The model proposes that usage continuance behavior of SNW services is a joint function of individuals’ perceptions of (1) intrinsic flow experience of SNW services, expected instrumentality of SNW services in managing and improving informational and relational values, and social influence as the outgrowth of social capital, and (2) costs in informational risks and participative efforts of time and social exchanges. The joint function is moderated by individuals’ use history of SNW features. The proposed model and hypotheses offer a comprehensive framework for empirically extending the IS continuance research to the ever pervasive SNW context