arXiv subjects

Arslan Munir

Publications and source records attributed to Arslan Munir.

30 records · Page 2Linked to original sources

arXiv2018

Mitigation of Policy Manipulation Attacks on Deep Q-Networks with Parameter-Space Noise

Recent developments have established the vulnerability of deep reinforcement learning to policy manipulation attacks via intentionally perturbed inputs, known as adversarial examples. In this work, we propose a technique for mitigation of such attacks based on addition of noise to the parameter space of deep reinforcement learners during training. We experimentally verify the effect of parameter-space noise in reducing the transferability of adversarial examples, and demonstrate the promising performance of this technique in mitigating the impact of whitebox and blackbox attacks at both test and training times.

cs.LG↗

arXiv2018

A Psychopathological Approach to Safety Engineering in AI and AGI

The complexity of dynamics in AI techniques is already approaching that of complex adaptive systems, thus curtailing the feasibility of formal controllability and reachability analysis in the context of AI safety. It follows that the envisioned instances of Artificial General Intelligence (AGI) will also suffer from challenges of complexity. To tackle such issues, we propose the modeling of deleterious behaviors in AI and AGI as psychological disorders, thereby enabling the employment of psychopathological approaches to analysis and control of misbehaviors. Accordingly, we present a discussion on the feasibility of the psychopathological approaches to AI safety, and propose general directions for research on modeling, diagnosis, and treatment of psychological disorders in AGI.

cs.AI↗

arXiv2018

A Design Space Exploration Methodology for Parameter Optimization in Multicore Processors

The need for application-specific design of multicore/manycore processing platforms is evident with computing systems finding use in diverse application domains. In order to tailor multicore/manycore processors for application specific requirements, a multitude of processor design parameters have to be tuned accordingly which involves rigorous and extensive design space exploration over large search spaces. In this paper, we propose an efficient methodology for design space exploration. We evaluate our methodology over two search spaces - small and large, using a cycle-accurate simulator (ESESC) and a standard set of PARSEC and SPLASH-2 benchmarks. For the smaller design space, we compare results obtained from our design space exploration methodology with results obtained from fully exhaustive search. The results show that solution quality obtained from our methodology are within 1.35% - 3.69% of the results obtained from fully exhaustive search while only exploring 2.74% - 3% of the design space. For larger design space, we compare solution quality of different results obtained by varying the number of tunable processor design parameters included in the exhaustive search phase of our methodology. The results show that including more number of tunable parameters in the exhaustive search phase of our methodology greatly improves solution quality.

cs.DC↗

arXiv2018

Selecting Microarchitecture Configuration of Processors for Internet of Things

The Internet of Things (IoT) makes use of ubiquitous internet connectivity to form a network of everyday physical objects for purposes of automation, remote data sensing and centralized management/control. IoT objects need to be embedded with processing capabilities to fulfill these services. The design of processing units for IoT objects is constrained by various stringent requirements, such as performance, power, thermal dissipation etc. In order to meet these diverse requirements, a multitude of processor design parameters need to be tuned accordingly. In this paper, we propose a temporally efficient design space exploration methodology which determines power and performance optimized microarchitecture configurations. We also discuss the possible combinations of these microarchitecture configurations to form an effective two-tiered heterogeneous processor for IoT applications. We evaluate our design space exploration methodology using a cycle-accurate simulator (ESESC) and a standard set of PARSEC and SPLASH2 benchmarks. The results show that our methodology determines microarchitecture configurations which are within 2.23%-3.69% of the configurations obtained from fully exhaustive exploration while only exploring 3%-5% of the design space. Our methodology achieves on average 24.16x speedup in design space exploration as compared to fully exhaustive exploration in finding power and performance optimized microarchitecture configurations for processors.

eess.SP↗

arXiv2018

Technology in Hospitality Industry: Prospects and Challenges

The leisure and hospitality industry is one of the driving forces of the global economy. The widespread adoption of new technologies in this industry over recent years has fundamentally reshaped the way in which services are provided and received. In this paper, we explore some of the state-of-the-art technologies currently employed in the hospitality industry and how they are improving guest experiences and changing the hospitality service platform. We also envision some potential future hospitality services we can expect as the Internet of things (IoT) technology keeps growing. We recognize that the technological backbone of many hospitality establishments needs to be overhauled in order to facilitate the changing landscape of technology in the modern world. We discuss some fundamental challenges that need to be overcome to institute a lasting future-proof solution for the hospitality industry. We also touch upon the problems these challenges pose for guests and hospitality service providers (HSP).

cs.DC↗

arXiv2017

Whatever Does Not Kill Deep Reinforcement Learning, Makes It Stronger

Recent developments have established the vulnerability of deep Reinforcement Learning (RL) to policy manipulation attacks via adversarial perturbations. In this paper, we investigate the robustness and resilience of deep RL to training-time and test-time attacks. Through experimental results, we demonstrate that under noncontiguous training-time attacks, Deep Q-Network (DQN) agents can recover and adapt to the adversarial conditions by reactively adjusting the policy. Our results also show that policies learned under adversarial perturbations are more robust to test-time attacks. Furthermore, we compare the performance of $ε$-greedy and parameter-space noise exploration methods in terms of robustness and resilience against adversarial perturbations.

cs.AI↗

arXiv2017

A Comparative Quantitative Analysis of Contemporary Big Data Clustering Algorithms for Market Segmentation in Hospitality Industry

The hospitality industry is one of the data-rich industries that receives huge Volumes of data streaming at high Velocity with considerably Variety, Veracity, and Variability. These properties make the data analysis in the hospitality industry a big data problem. Meeting the customers' expectations is a key factor in the hospitality industry to grasp the customers' loyalty. To achieve this goal, marketing professionals in this industry actively look for ways to utilize their data in the best possible manner and advance their data analytic solutions, such as identifying a unique market segmentation clustering and developing a recommendation system. In this paper, we present a comprehensive literature review of existing big data clustering algorithms and their advantages and disadvantages for various use cases. We implement the existing big data clustering algorithms and provide a quantitative comparison of the performance of different clustering algorithms for different scenarios. We also present our insights and recommendations regarding the suitability of different big data clustering algorithms for different use cases. These recommendations will be helpful for hoteliers in selecting the appropriate market segmentation clustering algorithm for different clustering datasets to improve the customer experience and maximize the hotel revenue.

cs.DB↗

arXiv2017

Analysis of Big Data Maturity Stage in Hospitality Industry

Big data analytics has an extremely significant impact on many areas in all businesses and industries including hospitality. This study aims to guide information technology (IT) professionals in hospitality on their big data expedition. In particular, the purpose of this study is to identify the maturity stage of the big data in hospitality industry in an objective way so that hotels be able to understand their progress, and realize what it will take to get to the next stage of big data maturity through the scores they will receive based on the survey.

cs.CY↗

arXiv2017

Models and Framework for Adversarial Attacks on Complex Adaptive Systems

We introduce the paradigm of adversarial attacks that target the dynamics of Complex Adaptive Systems (CAS). To facilitate the analysis of such attacks, we present multiple approaches to the modeling of CAS as dynamical, data-driven, and game-theoretic systems, and develop quantitative definitions of attack, vulnerability, and resilience in the context of CAS security. Furthermore, we propose a comprehensive set of schemes for classification of attacks and attack surfaces in CAS, complemented with examples of practical attacks. Building on this foundation, we propose a framework based on reinforcement learning for simulation and analysis of attacks on CAS, and demonstrate its performance through three real-world case studies of targeting power grids, destabilization of terrorist organizations, and manipulation of machine learning agents. We also discuss potential mitigation techniques, and remark on future research directions in analysis and design of secure complex adaptive systems.

eess.SY↗

arXiv2017

IFCIoT: Integrated Fog Cloud IoT Architectural Paradigm for Future Internet of Things

We propose a novel integrated fog cloud IoT (IFCIoT) architectural paradigm that promises increased performance, energy efficiency, reduced latency, quicker response time, scalability, and better localized accuracy for future IoT applications. The fog nodes (e.g., edge servers, smart routers, base stations) receive computation offloading requests and sensed data from various IoT devices. To enhance performance, energy efficiency, and real-time responsiveness of applications, we propose a reconfigurable and layered fog node (edge server) architecture that analyzes the applications' characteristics and reconfigure the architectural resources to better meet the peak workload demands. The layers of the proposed fog node architecture include application layer, analytics layer, virtualization layer, reconfiguration layer, and hardware layer. The layered architecture facilitates abstraction and implementation for fog computing paradigm that is distributed in nature and where multiple vendors (e.g., applications, services, data and content providers) are involved. We also elaborate the potential applications of IFCIoT architecture, such as smart cities, intelligent transportation systems, localized weather maps and environmental monitoring, and real-time agricultural data analytics and control.

cs.DC↗

arXiv2017

Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks

Deep learning classifiers are known to be inherently vulnerable to manipulation by intentionally perturbed inputs, named adversarial examples. In this work, we establish that reinforcement learning techniques based on Deep Q-Networks (DQNs) are also vulnerable to adversarial input perturbations, and verify the transferability of adversarial examples across different DQN models. Furthermore, we present a novel class of attacks based on this vulnerability that enable policy manipulation and induction in the learning process of DQNs. We propose an attack mechanism that exploits the transferability of adversarial examples to implement policy induction attacks on DQNs, and demonstrate its efficacy and impact through experimental study of a game-learning scenario.

cs.LG↗

arXiv2016

Phase distance mapping: a phase-based cache tuning methodology for embedded systems

Networked embedded systems typically leverage a collection of low-power embedded systems (nodes) to collaboratively execute applications spanning diverse application domains (e.g., video, image processing, communication, etc.) with diverse application requirements. The individual networked nodes must operate under stringent constraints (e.g., energy, memory, etc.) and should be specialized to meet varying application requirements in order to adhere to these constraints. Phase-based tuning specializes system tunable parameters to the varying runtime requirements of different execution phases to meet optimization goals. Since the design space for tunable systems can be very large, one of the major challenges in phase-based tuning is determining the best configuration for each phase without incurring significant tuning overhead (e.g., energy and/or performance) during design space exploration. In this paper, we propose phase distance mapping, which directly determines the best configuration for a phase, thereby eliminating design space exploration. Phase distance mapping applies the correlation between the characteristics and best configuration of a known phase to determine the best configuration of a new phase. Experimental results verify that our phase distance mapping approach, when applied to cache tuning, determines cache configurations within 1 % of the optimal configurations on average and yields an energy delay product savings of 27 % on average.

cs.AR↗