Ontology and the Foundations of Mathematics

This Element looks at the problem of inter-translation between mathematical realism and anti-realism and argues that so far as realism is inter-translatable with anti-realism, there is a burden on the realist to show how her posited reality differs from that of the anti-realist. It also argues that an effective defence of just such a difference needs a commitment to the independence of mathematical reality, which in turn involves a commitment to the ontological access problem – the problem of how knowable mathematical truths are identifiable with a reality independent of us as knowers. Specifically, if the only access problem acknowledged is the epistemological problem – i.e. the problem of how we come to know mathematical truths – then nothing is gained by the realist notion of an independent reality and in effect, nothing distinguishes realism from anti-realism in mathematics.

International Order and the Access Problem of the Defeated Hegemon: the Case of Russia in the Post-Cold War Era

It has been evident that Russia as the heir of Soviet foreign policy, could neither achieve to integrate herself into the international order, nor could the international order achieve to find a solution to Russian foreign policy identity quest. As long as Russia cannot find a stable and permanent status for herself in the world politics, her foreign policy will signify a permanent instability on the behalf of the international order. The current hegemonic international order is far from residing technical capabilities in terms of satisfying Russia’s foreign policy expectations, because it is unprecedentedly rigid in terms of allowing or refusing the incorporation of hegemonic power like Russia. While it cannot return to multipolarity, it could not set a community based international order either. Since the current international order was founded upon liberal anti-Soviet values, it entered into a lightness of exposing Russia to make clear-cut choices in her foreign policy. As much as the current international order was founded upon liberal anti-Soviet values, its demands from the new members would much higher that especially Russia would not easily adapt herself to.

Quasar: Easy Machine Learning for Biospectroscopy

Data volumes collected in many scientific fields have long exceeded the capacity of human comprehension. This is especially true in biomedical research where multiple replicates and techniques are required to conduct reliable studies. Ever-increasing data rates from new instruments compound our dependence on statistics to make sense of the numbers. The currently available data analysis tools lack user-friendliness, various capabilities or ease of access. Problem-specific software or scripts freely available in supplementary materials or research lab websites are often highly specialized, no longer functional, or simply too hard to use. Commercial software limits access and reproducibility, and is often unable to follow quickly changing, cutting-edge research demands. Finally, as machine learning techniques penetrate data analysis pipelines of the natural sciences, we see the growing demand for user-friendly and flexible tools to fuse machine learning with spectroscopy datasets. In our opinion, open-source software with strong community engagement is the way forward. To counter these problems, we develop Quasar, an open-source and user-friendly software, as a solution to these challenges. Here, we present case studies to highlight some Quasar features analyzing infrared spectroscopy data using various machine learning techniques.

A Non-Interactive Attribute-Based Access Control Scheme by Blockchain for IoT

As an important method of protecting data confidentiality in the Internet of Things (IoT), access control has been widely concerned. Because attribute-based access control mechanisms are dynamic, it is not only suitable to solve the dynamic access problem in IoT, but also to deal with the dynamic caused by node movement and access data change. The traditional centralized attribute-based access control mechanism has some problems: due to the large number of devices in IoT, the central trusted entity may become the bottleneck of the whole system. Moreover, when a central trusted entity is under distributed denial-of-service (DDoS) attack, the entire system may crash. Blockchain is a good way to solve the above problems. Therefore, we developed a non-interactive, attribute-based access control scheme that applies blockchain technology in IoT scenarios by using PSI technology. In addition, the attributes of data user and data holder are hidden, which protects the privacy of both parties’ attributes and access policy. Furthermore, the experimental results indicate that our scheme has high efficiency.

Channel Access-Based Joint Optimization of AoI and SINR under Attack: Game Theory and Distributed Approach

This paper focuses on the joint optimization of the Age of Information (AoI) and Signal to Interference plus Noise Ratio- (SINR-) oriented channel access problem under attack in the Wireless Sensor Networks (WSNs). Firstly, to overcome the uncertain, dynamic, and incomplete information constrains, an active probability model and a controlling channel model are proposed for the sensors and the receiving end, respectively. Secondly, to ensure the AoI and SINR of the data generated by the sensors when transmitted under attack, one utility function based on average AoI and SINR is defined. Then, considering the distributed feature of the channel access process, the joint optimization problem is formulated under the game theory structure. Then, a distributed learning algorithm is proposed to reach the Nash Equilibrium (NE) of the game. Finally, simulation results have verified the correctness and effectiveness of the proposed method.

Video Stream Session Migration Method Using Deep Reinforcement Learning in Cloud Computing Environment

In the resource scheduling of streaming Media Edge Cloud (MEC), in order to balance the cost and load of migration, this paper proposes a video stream session migration method based on deep reinforcement learning in cloud computing environment. First, combined with the current popular OpenFlow technology, a novel MEC architecture is designed, which separates streaming media service processing in application layer from forwarding path optimization in network layer. Second, taking the state information of the system as the attribute feature, the session migration is calculated, and gradient reinforcement learning is combined with in-depth learning and deterministic strategy for video stream session migration to solve the user request access problem. The experimental results show that the method has a better request access effect, can effectively improve the request acceptance rate, and can reduce the migration cost, while shortening the running time.

Determinants of abortion among youth 15–24 in Ethiopia: A multilevel analysis based on EDHS 2016

Introduction Determinants of the magnitude of abortion among women of diverse social and economic status, particularly in Africa poorly understood because of the missing information in most countries. In this study, we addressed abortion and its determinants among youth women of 15–24 ages to provide clear direction for policymaking in Ethiopia. Methods We examined the 2016 Ethiopian demographic health survey data downloaded from the EDHS website after obtaining permission on abortion among 15–24 age women. We applied bivariate and multilevel binary logistic regression. Community and Individual level abortion predictors passed through a three-level binary logistic regression analysis where we used p-value <0.05 and adjusted odds ratios (AOR) with 95% confidence intervals (CI). Result The abortion among the youth population in this study was 2.5%. Factors associated with pregnancy were age group 20–24 2.5(1.6–3.8), youth with one birth 0.65(0.44–0.96), youth with 2–5 births 0.31(0.18–0.55), age ≥18 0.50(0.33–0.76), married 38(17–84), divorced 20(7–55), birth in the last five years 0.65(0.44–0.96), middle wealth youth 1.7(1.0.4–2.8), being in Amhara0.31(0.11–0.85), and 0.30(0.12–0.77). Conclusion Less abortion occurred in economically poor youths. It is a noble finding; however, the access problem might lead to the result. We observed more abortions in age <18years; those have not given birth until the data collection date. It portrays forth clear policy direction for politicians and all other stakeholders to intervene in the problem. The analysis also showed abortion increased with age. It shows that as age increased, youths disclose abortion which is rare at an early age, and again given an essential clue for the next interventions. The fact in this study is both age and marriage affected abortion similarly. It might be because of various culture-related perceptions where it is not appropriate for an unmarried woman to appear with any pregnancy outcome as the reason behind the decreased number of abortions at a younger age. Thus, more attention is required during implementation for unmarried and lower age youth regardless of the magnitude of the abortion.

Access Control in NB-IoT Networks: A Deep Reinforcement Learning Strategy

The Internet of Things (IoT) is a key enabler of the digital mutation of our society. Driven by various services and applications, Machine Type Communications (MTC) will become an integral part of our daily life, over the next few years. Meeting the ITU-T requirements, in terms of density, battery longevity, coverage, price, and supported mechanisms and functionalities, Cellular IoT, and particularly Narrowband-IoT (NB-IoT), is identified as a promising candidate to handle massive MTC accesses. However, this massive connectivity would pose a huge challenge for network operators in terms of scalability. Indeed, the connection to the network in cellular IoT passes through a random access procedure and a high concentration of IoT devices would, very quickly, lead to a bottleneck. The latter procedure needs, then, to be enhanced as the connectivity would be considerable. With this in mind, we propose, in this paper, to apply the access class barring (ACB) mechanism to regulate the number of devices competing for the access. In order to derive the blocking factor, we formulated the access problem as a Markov decision process that we were able to solve using one of the most advanced deep reinforcement learning techniques. The evaluation of the proposed access control, through simulations, shows the effectiveness of our approach compared to existing approaches such as the adaptive one and the Proportional Integral Derivative (PID) controller. Indeed, it manages to keep the proportion of access attempts close to the optimum, despite the lack of accurate information on the number of access attempts.