In the 1960"s and 1970', when most of these unfinished essays were written, I was a free thinking architecture student who perceived a fundamental disconnect within diverse institutions, including Architecture and Education and Money - Banking. I had an intuitive feeling that architecture is more than a visual aesthetic, rather a holistic social experience. The everyday objects with which we interact are an integral part of our social disposition and social wellbeing. The attempt is to develop a method for understanding some previously largely ignored aspects of environmental design – the role of Common Objects as a communication facilitator.- Eric M. Lee
This analytical paper asks, does the One-China policy shape the People’s Republic of China’s foreign policy? This paper begins by briefly defining the One-China policy and situating it in the respective histories of China and its current incarnation as the People’s Republic of China (PRC). Then, after untangling the often muddled classifications of soft, sharp, and hard power, the question is interrogated in the context of each class of power (Nye, 2004; Nye, 2011; Nye, 2018; Raby, 2019; Walker & Ludwig, 2017). This analytical essay concludes that the PRC does employ predominantly sharp and hard power strategies that are heavily influenced by the One-China policy.
El movimiento relativo de un punto respecto de un observador no solo depende de la velocidad relativa del movimiento. La dirección de esta velocidad juega un papel fundamental en la deformación del tiempo. En este estudio, se ha cuantificado el efecto de la dirección sobre el factor de intervalos de tiempo.
Small office home office networks have become a target for many threat actors, hackers and cyber attackers and hence there is an urgent need to secure the network from such attackers. Most small office home office network users do not see the need to provide enough security to their networks because they assume no one is going to hack them forgetting that the biggest threat of our small home networks today comes from the outside. The challenge of misconfiguration of routers, firewalls and default configurations in our small home networks renders the network vulnerable to attacks such as DDos , phishing attacks , virus and other network attacks hence the need to implement a detection algorithm to help identify flaws in the pattern of the small office network. It turns out that about 75% of existing approaches focused on intrusion detection in 802.11 wireless networks of a SOHO and not the entire network. These approaches do not efficiently secure the network entirely leaving the rest prone to attacks can occur with or without the internet. This paper proposes to add another layer of security to the other preventive measures in a SOHO network by designing, implementing and testing a supervised neural network algorithm to identify attacks on the small home network and also to send a notification to users to keep them informed of the activities on their network. The supervised neural network algorithm will have a dataset representing both attacks and non-attacks which will be used in the training phase. The system should be able to detect and identify the various attacks and anomalies when they occur on the network and help keep the users informed.
The current rapid growth in the computer and internet development has ushered in numerous cybersecurity challenges which are constantly evolving with time. The current cybersecurity solutions are no longer optimal in tackling these emerging cyber threats and attacks. This paper proposes the creation of a cybersecurity dataset to be used for a hybrid machine learning (ML) approach of supervised and unsupervised learning for an effective intrusion detection system. The proposed model entails a five-stage process which starts at the setup of a simulated network environment of network attacks to generate a dataset which feeds into the data normalization stage and then to data dimension reduction stage using the principal component analysis as a feature extraction method after which the data of reduced dimension is clustered using the k-Means method to bring about a new data set with fewer features. This new dataset is afterward classified using the enhanced support vector machine (ESVM). The proposed model is expected to provide a high-quality dataset and an efficient intrusion detection system in terms of intrusion detection accuracy of 99.5%, short train time of 3seconds and a low false-positive rate of 0.4%.
This research focuses on two fundamental aspects of hot spot policing that have been widely neglected by previous scholarly research. These aspects include the adequate concentration of crime at a smaller geographical unit to be considered a crime hot spot, and the cost-benefit implication of focusing limited police resources on such a smaller place in an effort to prevent criminal activities. Substantial limitations in call-t- service data from police departments raise concern on the purported concentration of crime at places that warrant such strategy in the first place. We will examine data from the Cincinnati Police Department and propose guidelines on adopting a threshold when designating places as crime hot spots, using time and cost-benefit analysis as key determinants.
