11,074 research outputs found
Towards the Safety of Human-in-the-Loop Robotics: Challenges and Opportunities for Safety Assurance of Robotic Co-Workers
The success of the human-robot co-worker team in a flexible manufacturing
environment where robots learn from demonstration heavily relies on the correct
and safe operation of the robot. How this can be achieved is a challenge that
requires addressing both technical as well as human-centric research questions.
In this paper we discuss the state of the art in safety assurance, existing as
well as emerging standards in this area, and the need for new approaches to
safety assurance in the context of learning machines. We then focus on robotic
learning from demonstration, the challenges these techniques pose to safety
assurance and indicate opportunities to integrate safety considerations into
algorithms "by design". Finally, from a human-centric perspective, we stipulate
that, to achieve high levels of safety and ultimately trust, the robotic
co-worker must meet the innate expectations of the humans it works with. It is
our aim to stimulate a discussion focused on the safety aspects of
human-in-the-loop robotics, and to foster multidisciplinary collaboration to
address the research challenges identified
A Vision of Collaborative Verification-Driven Engineering of Hybrid Systems
Abstract. Hybrid systems with both discrete and continuous dynamics are an important model for real-world physical systems. The key challenge is how to ensure their correct functioning w.r.t. safety requirements. Promising techniques to ensure safety seem to be model-driven engineering to develop hybrid systems in a well-defined and traceable manner, and formal verification to prove their correctness. Their combination forms the vision of verification-driven engineering. Despite the remarkable progress in automating formal verification of hybrid systems, the construction of proofs of complex systems often requires significant human guidance, since hybrid systems verification tools solve undecidable problems. It is thus not uncommon for verification teams to consist of many players with diverse expertise. This paper introduces a verification-driven engineering toolset that extends our previous work on hybrid and arithmetic verification with tools for (i) modeling hybrid systems, (ii) exchanging and comparing models and proofs, and (iii) managing verification tasks. This toolset makes it easier to tackle large-scale verification tasks.
Fast Differentially Private Matrix Factorization
Differentially private collaborative filtering is a challenging task, both in
terms of accuracy and speed. We present a simple algorithm that is provably
differentially private, while offering good performance, using a novel
connection of differential privacy to Bayesian posterior sampling via
Stochastic Gradient Langevin Dynamics. Due to its simplicity the algorithm
lends itself to efficient implementation. By careful systems design and by
exploiting the power law behavior of the data to maximize CPU cache bandwidth
we are able to generate 1024 dimensional models at a rate of 8.5 million
recommendations per second on a single PC
USING FILTERS IN TIME-BASED MOVIE RECOMMENDER SYSTEMS
On a very high level, a movie recommendation system is one which uses data about the user, data about the movie and the ratings given by a user in order to generate predictions for the movies that the user will like. This prediction is further presented to the user as a recommendation. For example, Netflix uses a recommendation system to predict movies and generate favorable recommendations for users based on their profiles and the profiles of users similar to them. In user-based collaborative filtering algorithm, the movies rated highly by the similar users of a particular user are considered as recommendations to that user. But usersâ preferences vary with time, which often affects the efficacy of the recommendation, especially in a movie recommendation system. Because of the constant variation of the preferences, there has been research on using time of rating or watching the movie as a significant factor for recommendation. If time is considered as an attribute in the training phase of building a recommendation model, the model might get complex. Most of the research till now does this in the training phase, however, we study the effect of using time as a factor in the post training phase and study it further by applying a genre-based filtering mechanism on the system. Employing this in the post training phase reduces the complexity of the method and also reduces the number of irrelevant recommendations
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Efficient Memory-Protected Integration of Add-On Software Subsystems in Small Embedded Automotive Applications
Current innovations in the automotive industry
evolve mainly in the electronics and software domain. This leads
to an increasing integration of additional software subsystems
into already existing electronic control units (ECUs) to cope with
the raised amount and complexity of present ECUs in modern
high-end vehicles. This paper discusses different approaches
which are required to integrate such add-on software subsystems
in an isolated memory domain, and considers particularly the
special needs of small embedded systemsâincluding the limited
hardware support. Special focus is brought to the efficient detection
of malicious memory accesses, as well as the benefits of
a thereupon possible and adaptable failure-handling strategy.
All investigations are based on a developed memory-protection
framework which has been tailored to the special needs of a sample
vehicle dynamics control system. Its usage allows the combination
of. integrating additional subsystems without reducing the main
applicationâs availability
To Index or Not to Index: Optimizing Exact Maximum Inner Product Search
Exact Maximum Inner Product Search (MIPS) is an important task that is widely
pertinent to recommender systems and high-dimensional similarity search. The
brute-force approach to solving exact MIPS is computationally expensive, thus
spurring recent development of novel indexes and pruning techniques for this
task. In this paper, we show that a hardware-efficient brute-force approach,
blocked matrix multiply (BMM), can outperform the state-of-the-art MIPS solvers
by over an order of magnitude, for some -- but not all -- inputs.
In this paper, we also present a novel MIPS solution, MAXIMUS, that takes
advantage of hardware efficiency and pruning of the search space. Like BMM,
MAXIMUS is faster than other solvers by up to an order of magnitude, but again
only for some inputs. Since no single solution offers the best runtime
performance for all inputs, we introduce a new data-dependent optimizer,
OPTIMUS, that selects online with minimal overhead the best MIPS solver for a
given input. Together, OPTIMUS and MAXIMUS outperform state-of-the-art MIPS
solvers by 3.2 on average, and up to 10.9, on widely studied
MIPS datasets.Comment: 12 pages, 8 figures, 2 table
A new approach to collaborative frameworks using shared objects
Multi-user graphical applications currently require the creation of a set of interface objects to maintain each participating display. The concept of shared objects allows a single object instance to be used in multiple contexts concurrently. This provides a novel way of reducing collaborative overheads by requiring the maintenance of only a single set of interface objects. The paper presents the concept of a shared-object collaborative framework and illustrates how the concept can be incorporated into an existing object-oriented toolkit
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