Yes, I know this IoT Device Might Invade my Privacy, but I Love it Anyway! A Study of Saudi Arabian Perceptions

The Internet of Things (IoT) ability to monitor our every move raises many privacy concerns. This paper reports on a study to assess current awareness of privacy implications of IoT devices amongst Saudi Arabians. We found that even when users are aware of the potential for privacy invasion, their need for the convenience these devices afford leads them to discount this potential and to ignore any concerns they might initially have had. We then conclude by making some predictions about the direction the IoT field will take in the next 5-7 years, in terms of privacy invasion, protection and awareness

New Areas of Contributions and New Addition of Security

Open Journal of Big Data (OJBD) (www.ronpub.com/ojbd) is an open access journal, which addresses the aspects of Big Data, including new methodologies, processes, case studies, poofs-of-concept, scientific demonstrations, industrial applications and adoption. This editorial presents two articles published in the first issue of the second volume of OJBD. The first article is about the investigation of social media for the public engagement. The second article looks into large-scale semantic web indices for six RDF collation orders. OJBD has an increasingly improved reputation thanks to the support of research communities. We will set up the Second International Conference on Internet of Things, Big Data and Security (IoTBDS 2017), in Porto, Portugal, between 24 and 26 April 2017. OJBD is published by RonPub (www.ronpub.com), which is an academic publisher of online, open access, peer-reviewed journals

Energy Consumption of a Hexspider Robot-o as Function of Footwear and Underground: Experimental Investigations

In this paper, the previously designed and implemented spiderino robot-o, or a robot used to act in a swarm for educational purposes, is investigated regarding his energy-consuming properties. An experimental setup consists of a platform inside which the spiderino robot-o can freely move with a fixed random movement program. Furthermore, the robot-o feet were equipped with footwear made of materials with different friction coefficients, non-equipped or plastics, equipped with cork, equipped with rubber, and the underground was chosen to be stone, wood, plastics, and with regard to floor inclination for all variants flat and for the stone variant also inclined by 10%. The results show a characteristic curve in energy consumption according to a slightly different behaviour concerning the material combinations of footwear and floor, which adapts energy consumption physically due to the specific footwear-underground combination

The Concept of Identifiability in ML Models

Recent research indicates that the machine learning process can be reversed by adversarial attacks. These attacks can be used to derive personal information from the training. The supposedly anonymising machine learning process represents a process of pseudonymisation and is, therefore, subject to technical and organisational measures. Consequently, the unexamined belief in anonymisation as a guarantor for privacy cannot be easily upheld. It is, therefore, crucial to measure privacy through the lens of adversarial attacks and precisely distinguish what is meant by personal data and non-personal data and above all determine whether ML models represent pseudonyms from the training data

Online predicting conformance of business process with recurrent neural networks

Conformance Checking is a problem to detect and describe the differences between a given process model representing the expected behaviour of a business process and an event log recording its actual execution by the Process-aware Information System (PAIS). However, such existing conformance checking techniques are offline and mainly applied for the completely executed process instances, which cannot provide the real-time conformance-oriented process monitoring for an on-going process instance. Therefore, in this paper, we propose three approaches for online conformance prediction by constructing a classification model automatically based on the historical event log and the existing reference process model. By utilizing Recurrent Neural Networks, these approaches can capture the features that have a decisive effect on the conformance for an executed case to build a prediction model and then use this model to predict the conformance of a running case. The experimental results on two real datasets show that our approaches outperform the state-of-the-art ones in terms of prediction accuracy and time performance

A Multi-core architecture for a hybrid information system

This paper demonstrates our proposed Multi-core architecture for a hybrid information system (HIS) with the related work, system design, theories, experiments, analysis and discussion presented. Different designs on clusters, communication between different types of chips and clusters and network queuing methods have been described. Our aim is to achieve quality, reliability and resilience and to demonstrate it, our emphasis is on latency with messages communicated in our system – understand how it happens, what can trigger its increase, and then experiment with different types of focuses, including under Store-and-Forward Flow Control method, Wormhole flow control method, cluster size and message size to get a better understanding. Our analysis allows us to reduce latency and avoid its sharp increase. We justify our research contributions, particularly in the area of “traffic analysis and management” and “performance analysis of transmission control” of the HIS systems