Molecular Memory with Atomically-Smooth Graphene Contacts

We report the use of bilayer graphene as an atomically-smooth contact for nanoscale devices. A two-terminal Bucky ball (C60) based molecular memory is fabricated with bilayer graphene as a contact on the polycrystalline nickel electrode. Graphene provides an atomically-smooth covering over an otherwise rough metal surface. The use of graphene additionally prohibits the electromigration of nickel atoms into the C60 layer. The devices exhibit a low-resistance state in the first sweep cycle and irreversibly switch to a high resistance state at 0.8-1.2 V bias. The reverse sweep has a hysteresis behavior as well. In the subsequent cycles, the devices retain the high-resistance state, thus making it write-once read-many memory (WORM). The ratio of current in low-resistance to high-resistance state is lying in 20-40 range for various devices with excellent retention characteristics. Control sample without the bilayer graphene shows random hysteresis and switching.Comment: 13 pages and 4 figure

Exploring the human factors in moral dilemmas of autonomous vehicles

Given the widespread popularity of autonomous vehicles (AVs), researchers have been exploring the ethical implications of AVs. Researchers believe that empirical experiments can provide insights into human characterization of ethically sound machine behaviour. Previous research indicates that humans generally endorse utilitarian AVs; however, this paper explores an alternative account of the discourse of ethical decision-making in AVs. We refrain from favouring consequentialism or non-consequential ethical theories and argue that human moral decision-making is pragmatic, or in other words, ethically and rationally bounded, especially in the context of intelligent environments. We hold the perspective that our moral preferences shift based on various externalities and biases. To further this concept, we conduct three Amazon Mechanical Turk studies, comprising 479 respondents to investigate factors, such as the “degree of harm,” “level of affection,” and “fixing the responsibility” that influences people’s moral decision-making. Our experimental findings seem to suggest that human moral judgments cannot be wholly deontological or utilitarian and offer evidence on the ethical variations in human decision-making processes that favours a specific moral framework. The findings also offer valuable insights for policymakers to explore the overall public perception of the ethical implications of AV as part of user decision-making in intelligent environments

AI in Assisting the Elderly and People with Disabilities

The focus of this research is to magnify those technologies that have been developed and that need more modification in their make. We will disclose some machines that have a great impact on the lives elderly and people with disabilities. As we know that artificial intelligence has advanced our life and now we can take advantage of it by using machines though that is related to defense or related to our daily life goods buying robots. These machines are not very common to everybody but we need to do it as these assist more than a human being to elder or disable persons. We also need to invest in these kinds of projects that can be fruitful to human beings. As it is clear that there is no sufficient human resources exist that can assist the elderly and people with disabilities. So ICTs are expected to play its part in assisting those people. In this age, 3D printers making better and better prosthetic for those in need. In the future we will reach a level that will make regular body parts inferior and before we know it the cyborg age will be upon us by this 3D technology. Also in the labs around the world bioengineering have begun to print prototype body parts like ears, noses, artificial bones and skin, even an entire face

Factional federalism, state capacity, and fiscal constraints: Pakistan's COVID-19 challenges

As Covid-19 spreads across Pakistan, Hassan Javid, Sameen M. Ali, and Umair Javed (Lahore University of Management Sciences, Pakistan) explain how the country's ability to effectively deal with the virus will be impeded by tensions between the central and provincial governments, a lack of state capacity, and fiscal constraints

Management of IoT systems for urban services

Urban Cities are growing exponentially and with them the need for efficient management of city resources. SOA (Service Oriented Architecture) and IoT (Internet of Things) provides promising solutions for this issue. SOA brings interoperability, reusability and dynamic discovery. Whereas, IoT provide next evolution on the Internet by allowing physical real-world objects to connect to the internet through unique identifiers. Arrowhead Project has developed a framework to provide collaborative automation by enabling embedded devices to interact with each other. This project uses SOA to target five different domains. These domains include Smart Building and Infrastructure, Energy Production, Virtual Market of Energy, Production, and Electro-Mobility. In the thesis, three applications were developed. Light Simulator was developed to demonstrate the working principle of the IoT enabled Light devices. Meter application was developed to monitor and manage IoT enabled systems. Finally, Energy Consumption service was developed as a pilot service for Arrowhead Project. This service was deployed on Arrowhead and it calculates the energy consumed by different real-time objects. Furthermore, since Arrowhead framework follows all principles of SOA, reusability of above services can provide composability to develop further applications

A Multi-armed Bandit Approach to Online Spatial Task Assignment

Spatial crowdsourcing uses workers for performing tasks that require travel to different locations in the physical world. This paper considers the online spatial task assignment problem. In this problem, spatial tasks arrive in an online manner and an appropriate worker must be assigned to each task. However, outcome of an assignment is stochastic since the worker can choose to accept or reject the task. Primary goal of the assignment algorithm is to maximize the number of successful assignments over all tasks. This presents an exploration-exploitation challenge; the algorithm must learn the task acceptance behavior of workers while selecting the best worker based on the previous learning. We address this challenge by defining a framework for online spatial task assignment based on the multi-armed bandit formalization of the problem. Furthermore, we adapt a contextual bandit algorithm to assign a worker based on the spatial features of tasks and workers. The algorithm simultaneously adapts the worker assignment strategy based on the observed task acceptance behavior of workers. Finally, we present an evaluation methodology based on a real world dataset, and evaluate the performance of the proposed algorithm against the baseline algorithms. The results demonstrate that the proposed algorithm performs better in terms of the number of successful assignments

Efficient task assignment for spatial crowdsourcing: A combinatorial fractional optimization approach with semi-bandit learning

Spatial crowdsourcing has emerged as a new paradigm for solving problems in the physical world with the help of human workers. A major challenge in spatial crowdsourcing is to assign reliable workers to nearby tasks. The goal of such task assignment process is to maximize the task completion in the face of uncertainty. This process is further complicated when tasks arrivals are dynamic and worker reliability is unknown. Recent research proposals have tried to address the challenge of dynamic task assignment. Yet the majority of the proposals do not consider the dynamism of tasks and workers. They also make the unrealistic assumptions of known deterministic or probabilistic workers’ reliabilities. In this paper, we propose a novel approach for dynamic task assignment in spatial crowdsourcing. The proposed approach combines bi-objective optimization with combinatorial multi-armed bandits. We formulate an online optimization problem to maximize task reliability and minimize travel costs in spatial crowdsourcing. We propose the distance-reliability ratio (DRR) algorithm based on a combinatorial fractional programming approach. The DRR algorithm reduces travel costs by 80% while maximizing reliability when compared to existing algorithms. We extend the DRR algorithm for the scenario when worker reliabilities are unknown. We propose a novel algorithm (DRR-UCB) that uses an interval estimation heuristic to approximate worker reliabilities. Experimental results demonstrate that the DRR-UCB achieves high reliability in the face of uncertainty. The proposed approach is particularly suited for real-life dynamic spatial crowdsourcing scenarios. This approach is generalizable to the similar problems in other areas in expert systems. First, it encompasses online assignment problems when the objective function is a ratio of two linear functions. Second, it considers situations when intelligent and repeated assignment decisions are needed under uncertainty

Flag-Verify-Fix: Adaptive Spatial Crowdsourcing leveraging Location-based Social Networks

This paper introduces the flag-verify-fix pattern that employs spatial crowdsourcing for city maintenance. The patterns motivates the need for appropriate assignment of dynamically arriving spatial tasks to a pool for workers on the ground. The assignment is aimed at maximizing the coverage of tasks spread over spatial locations; however, the coverage depends of willingness of workers to perform tasks assigned to them. We introduce the maximum coverage assignment problem that formulates two design issues of dynamic assignment. The quantity issue determines the number of worker required for a task and selection issue determines the set of workers. We propose an adaptive algorithm that uses location diversity based on a location-based social network to address the quantity issue and employs Thompson sampling for selecting the workers by learning their willingness. We evaluate the performance of the proposed algorithm in terms of coverage and number of assignments using real world datasets. The results show that our proposed algorithm achieves 30%-50% more coverage than the baseline algorithms, while requiring less workers per task

A Capability Requirements Approach for Predicting Worker Performance in Crowdsourcing

Abstract—Assigning heterogeneous tasks to workers is an important challenge of crowdsourcing platforms. Current ap-proaches to task assignment have primarily focused on content-based approaches, qualifications, or work history. We propose an alternative and complementary approach that focuses on what capabilities workers employ to perform tasks. First, we model various tasks according to the human capabilities required to perform them. Second, we capture the capability traces of the crowd workers performance on existing tasks. Third, we predict performance of workers on new tasks to make task routing decisions, with the help of capability traces. We evaluate the ef-fectiveness of our approach on three different tasks including fact verification, image comparison, and information extraction. The results demonstrate that we can predict worker’s performance based on worker capabilities. We also highlight limitations and extensions of the proposed approach. Keywords—microtask, taxonomy, crowdsourcing, performance I