Generative Adversarial Networks for Mitigating Biases in Machine Learning Systems

In this paper, we propose a new framework for mitigating biases in machine learning systems. The problem of the existing mitigation approaches is that they are model-oriented in the sense that they focus on tuning the training algorithms to produce fair results, while overlooking the fact that the training data can itself be the main reason for biased outcomes. Technically speaking, two essential limitations can be found in such model-based approaches: 1) the mitigation cannot be achieved without degrading the accuracy of the machine learning models, and 2) when the data used for training are largely biased, the training time automatically increases so as to find suitable learning parameters that help produce fair results. To address these shortcomings, we propose in this work a new framework that can largely mitigate the biases and discriminations in machine learning systems while at the same time enhancing the prediction accuracy of these systems. The proposed framework is based on conditional Generative Adversarial Networks (cGANs), which are used to generate new synthetic fair data with selective properties from the original data. We also propose a framework for analyzing data biases, which is important for understanding the amount and type of data that need to be synthetically sampled and labeled for each population group. Experimental results show that the proposed solution can efficiently mitigate different types of biases, while at the same time enhancing the prediction accuracy of the underlying machine learning model

A Distributed Approach for Networked Flying Platform Association with Small Cells in 5G+ Networks

The densification of small-cell base stations in a 5G architecture is a promising approach to enhance the coverage area and facilitate the ever increasing capacity demand of end users. However, the bottleneck is an intelligent management of a backhaul/fronthaul network for these small-cell base stations. This involves efficient association and placement of the backhaul hubs that connects these small-cells with the core network. Terrestrial hubs suffer from an inefficient non line of sight link limitations and unavailability of a proper infrastructure in an urban area. Seeing the popularity of flying platforms, we employ here an idea of using networked flying platform (NFP) such as unmanned aerial vehicles (UAVs), drones, unmanned balloons flying at different altitudes, as aerial backhaul hubs. The association problem of these NFP-hubs and small-cell base stations is formulated considering backhaul link and NFP related limitations such as maximum number of supported links and bandwidth. Then, this paper presents an efficient and distributed solution of the designed problem, which performs a greedy search in order to maximize the sum rate of the overall network. A favorable performance is observed via a numerical comparison of our proposed method with optimal exhaustive search algorithm in terms of sum rate and run-time speed.Comment: Submitted to IEEE GLOBECOM 2017, 7 pages and 4 figure

Mutations in Epigenetic Modifiers in Myeloid Malignancies and the Prospect of Novel Epigenetic-Targeted Therapy

In the recent years, the discovery of a series of mutations in patients with myeloid malignancies has provided insight into the pathogenesis of myelodysplastic syndromes (MDSs), myeloproliferative neoplasms (MPNs), and acute myeloid leukemia (AML). Among these alterations have been mutations in genes, such as IDH1/2, TET2, DNMT3A, and EZH2, which appear to affect DNA and/or histone lysine methylation. Large clinical correlative studies are beginning to decipher the clinical importance, prevalence, and potential prognostic significance of these mutations. Additionally, burgeoning insight into the role of epigenetics in the pathogenesis of myeloid malignancies has prompted increased interest in development of novel therapies which target DNA and histone posttranslational modifications. DNA demethylating agents have been demonstrated to be clinically active in a subset of patients with MDS and AML and are used extensively. However, newer, more specific agents which alter DNA and histone modification are under preclinical study and development and are likely to expand our therapeutic options for these diseases in the near future. Here, we review the current understanding of the clinical importance of these newly discovered mutations in AML and MDS patients. We also discuss exciting developments in DNA methyltransferase inhibitor strategies and the prospect of novel histone lysine methyltransferase inhibitors

Stages of Imam al-Ash’ari thought in theology

Imam al-Ash’ari was the founder and leading figure of the Asha’irah sect of the Ahl al-Sunnah wa al-Jama’ah. His approach is often used as a main reference by Muslims since the time of its appearance until now concerning questions of faith.Some other schools of thought try to claim that the holding of Imam al-Ash’ari doctrine coincides with their beliefs. Imam al-Ash’ari is said to go through three stages of thought before he returned to the Ahl al-Sunnah wa al-Jama’ah at the end of his life to follow Imam Ahmad bin Hanbal the leadership of Salaf. Therefore,anyone who does not abide by the final stand of Imam al-Ash'ari, they are not considered as Asha’irah followers for conflicting Imam al-Ash’ari and Salaf scholars as recognized the best of generations. To assure the issues, this article is put forward to reexamine of the truth of the allegations either Imam al-Ash’ari has gone through three stages of thought or otherwise. Through document analysis method based on the works of Imam al-Ash’ari and his followers, the study found that Imam al-Ash’ari only used two stages of thought; Mu’tazilah initially and Ahl al-Sunnah wa al-Jama’ah in the end. Imam al-Ash’ari also used the tafwid Salaf and Khalaf interpretation methods especially in interacting with mutashabihat texts throughout his stay with the Ahl al-Sunnah wa al-Jama’ah. The study also identified that the disagreements among Asha’irah when determining the thoughts of Imam al-Ash’ari is due to their uncertainty about the exact date of his works written

Recent advances in the treatment of acute myeloid leukemia

Acute myeloid leukemia (AML) is a disorder with significant molecular and clinical heterogeneity. Although there have been clear advances in the identification of somatic genetic and epigenetic alterations present in the malignant cells of patients with AML, translating this knowledge into an integrated view with an impact on the clinical treatment of AML has been slower to evolve. Recent clinical advances in the treatment of AML include studies demonstrating the benefit of dose-intense daunorubicin therapy in induction chemotherapy for patients of any age. We also review use of the DNA methyltransferase inhibitor azacitidine for treatment of AML in elderly patients as well as a study of global patterns of DNA methylation in patients with AML. Lastly, we review a recent assessment of the role of allogeneic hematopoietic stem cell transplantation in AML in first complete remission

Game-Theoretic Foundations for Forming Trusted Coalitions of Multi-Cloud Services in the Presence of Active and Passive Attacks

The prominence of cloud computing as a common paradigm for offering Web-based services has led to an unprecedented proliferation in the number of services that are deployed in cloud data centers. In parallel, services' communities and cloud federations have gained an increasing interest in the recent past years due to their ability to facilitate the discovery, composition, and resource scaling issues in large-scale services' markets. The problem is that the existing community and federation formation solutions deal with services as traditional software systems and overlook the fact that these services are often being offered as part of the cloud computing technology, which poses additional challenges at the architectural, business, and security levels. The motivation of this thesis stems from four main observations/research gaps that we have drawn through our literature reviews and/or experiments, which are: (1) leading cloud services such as Google and Amazon do not have incentives to group themselves into communities/federations using the existing community/federation formation solutions; (2) it is quite difficult to find a central entity that can manage the community/federation formation process in a multi-cloud environment; (3) if we allow services to rationally select their communities/federations without considering their trust relationships, these services might have incentives to structure themselves into communities/federations consisting of a large number of malicious services; and (4) the existing intrusion detection solutions in the domain of cloud computing are still ineffective in capturing advanced multi-type distributed attacks initiated by communities/federations of attackers since they overlook the attacker's strategies in their design and ignore the cloud system's resource constraints. This thesis aims to address these gaps by (1) proposing a business-oriented community formation model that accounts for the business potential of the services in the formation process to motivate the participation of services of all business capabilities, (2) introducing an inter-cloud trust framework that allows services deployed in one or disparate cloud centers to build credible trust relationships toward each other, while overcoming the collusion attacks that occur to mislead trust results even in extreme cases wherein attackers form the majority, (3) designing a trust-based game theoretical model that enables services to distributively form trustworthy multi-cloud communities wherein the number of malicious services is minimal, (4) proposing an intra-cloud trust framework that allows the cloud system to build credible trust relationships toward the guest Virtual Machines (VMs) running cloud-based services using objective and subjective trust sources, (5) designing and solving a trust-based maxmin game theoretical model that allows the cloud system to optimally distribute the detection load among VMs within a limited budget of resources, while considering Distributed Denial of Service (DDoS) attacks as a practical scenario, and (6) putting forward a resource-aware comprehensive detection and prevention system that is able to capture and prevent advanced simultaneous multi-type attacks within a limited amount of resources. We conclude the thesis by uncovering some persisting research gaps that need further study and investigation in the future

Thermal decomposition kinetics of sodium carboxymethyl cellulose: Model-free methods

Thermal analysis techniques such as thermogravimetric analysis (TGA) have been widely used because they provide rapid quantitative determination of various processes under isothermal or non-isothermal conditions. It allows the estimation of effective kinetic and thermodynamic parameters for various decomposition and thermal reactions. In this article, thermal degradation of sodium carboxymethyl cellulose (SMC) is investigated by means of dynamic thermogravimetric/derivative thermogravimetry (TG/DTG) in helium atmosphere with the flow rate 100 mL/min at the heating rate of 10-30 °C/min until the furnace wall temperature reached 700 °C. The non-isothermal degradation of SMC found to be taking place occurred major one step and minor two steps. Using a non-isothermal kinetic method based on a TGA data, kinetic parameters (Eand ln A) are calculated by Kissinger-Akahira-Sunose (KAS), Flynn-Wall-Ozawa (FWO) and Friedman methods. The results of studied polymer demonstrated that E and ln A is varied with function of conversion (α), which is in good agreement with literature data

SELF ORGANIOZING FUZZY CONTROLLER FOR A NONLINEAR TIME VARYING SYSTEM

This paper proposes a self organizing fuzzy controller as an enhancement level of the fuzzy controller. The adjustment mechanism provides explicit adaptation to tune and update the position of the output membership functions of the fuzzy controller. Simulation results show that this controller is capable of controlling a non-linear time varying system so that the performance of the system improves so as to reach the desired state in a less number of samples