The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Motion planning classically concerns the problem of accomplishing a goal configuration while avoiding obstacles. However, the need for more sophisticated motion planning methodologies, taking temporal aspects into account, has emerged. To address this issue, temporal logics have recently been used to formulate such advanced specifications. This paper will consider Signal Temporal Logic in combination...
We present SIRUM: a system for Scalable Informative RUle Mining from multi-dimensional data. Informative rules have recently been studied in several contexts, including data summarization, data cube exploration and data quality. The objective is to produce a small set of rules (patterns) over the values of the dimension attributes that provide the most information about the distribution of a numeric...
An analytics process is subjective to the perspective of the analyst. This paper presents a learning approach that models the process of how an analyst conducts analytics. The approach is applied in the context of correlation analysis for production yield optimization. The benefit is demonstrated by showing that learning from resolving a yield issue for one automotive product line can help resolve...
A generalized sequential probability ratio test (GSPRT) is a classical algorithm for binary sequential hypothesis testing. Though it is well-studied in the literature, there has been no optimal design of this test due to the difficulty of choosing its thresholds. In this paper we formulate the binary sequential hypothesis testing as an optimization problem. The latter is non-convex, and finding a...
Recent MPEG video compression standards are still block-based: blocks of pixels are sequentially coded using spatial or temporal prediction schemes. For each block, a vector of coding parameters has to be selected. In order to limit the complexity of this decision, independence between blocks is assumed, and coding parameters are locally optimized to maximize the coding efficiency. Few studies have...
There exist several coverage-based approaches to reduce time and resource costs of test execution. While these methods are well-investigated and evaluated for smaller to medium-size projects, we faced several challenges in applying them in the context of a very large industrial software project, namely SAP HANA. These issues include: varying effectiveness of algorithms for test case selection/prioritization,...
Many embedded processors do not support floating-point arithmetic in order to comply with strict cost and power consumption constraints. But, they generally provide support for SIMD as a mean to improve performance for little cost overhead. Achieving good performance when targeting such processors requires the use of fixed-point arithmetic and efficient exploitation of SIMD data-path. To reduce time-to-market,...
Mutation testing is widely considered as a high-end test criterion due to the vast number of mutants it generates. Although many efforts have been made to reduce the computational cost of mutation testing, its scalability issue remains in practice. In this paper, we introduce a novel method to speed up mutation testing based on state infection information. In addition to filtering out uninfected test...
Tracking many vehicles in wide coverage aerial imagery is crucial for understanding events in a large field of view. Most approaches aim to associate detections from frame differencing into tracks. However, slow or stopped vehicles result in long-term missing detections and further cause tracking discontinuities. Relying merely on appearance clue to recover missing detections is difficult as targets...
Group activity recognition from videos is a very challenging problem that has barely been addressed. We propose an activity recognition method using group context. In order to encode both single-person description and two-person interactions, we learn mappings from highdimensional feature spaces to low-dimensional dictionaries. In particular the proposed two-person descriptor takes into account geometric...
A semantics-preserving transformation by Komondoor and Horwitz has been shown to be most effective in the elimination of type-3 clones. The two original algorithms for realizing this transformation, however, are not as efficient as the related (slice-based) transformations. We present an asymptotically-faster algorithm that implements the same transformation via bidirectional reachability on a program...
The design and implementation of static analyses that disambiguate pointers has been a focus of research since the early days of compiler construction. One of the challenges that arise in this context is the analysis of languages that support pointer arithmetics, such as C, C++ and assembly dialects. This paper contributes to solve this challenge. We start from an obvious, yet unexplored, observation:...
As the number of cores increases, cache-based memory hierarchy is becoming a major problem in terms of the scalability and energy consumption. Software-managed scratchpad memories (SPM) is a scalable alternative to caches, but the benefit comes at the cost of explicit management of data. For instance, an instruction SPM needs a code management techniques to load code blocks to the SPM. This paper...
Multimedia Broadcast Multicast Service (MBMS)1 is an efficiency cellular strategy for shared content delivery, widely popular in Long Term Evolution (LTE) networks. Typical multimedia IP packets possess redundantly large headers, relying on Robust Header Compression (ROHC) for efficiency boost. This work investigates the adaptation of ROHC schemes for MBMS. We formulate and solve the optimization...
Cooperative co-evolutionary algorithms (CCEAs) conduct high-efficiency problem solving by decomposing a given problem into a number of separate subcomponents, which terms the divide-and-conquer manner. In this paper, the dynamic multi-population framework was incorporated into the CCEAs to continuously search multiple optima of the subcomponents, so as to compensate the lost information induced by...
Decision makers tend to define their optimization problems as multi-objective optimization problems. Generating the whole nondominated set is often unrealistic due to its size but also because most of these points are irrelevant to the decision maker. Another approach consists in obtaining preference information and integrating it a priori, in order to reduce the size of the nondominated set and have...
This paper proposes a novel case-based reasoning (CBR) approach to support the intelligent management of energy resources in a residential context. The proposed approach analyzes previous cases of consumption reduction in houses, and determines the amount that should be reduced in each moment and in each context, in order to meet the users' needs in terms of comfort while minimizing the energy bill...
Heterogeneous events, which are defined as events connecting strongly-typed objects, are ubiquitous in the real world. We propose a HyperEdge-Based Embedding (Hebe) framework for heterogeneous event data, where a hyperedge represents the interaction among a set of involving objects in an event. The Hebe framework models the proximity among objects in an event by predicting a target object given the...
Multiple instance learning (MIL) is a form of weakly supervised learning for problems in which training instances are arranged into bags, and a label is provided for whole bags but not for individual instances. Most proposed MIL algorithms focus on bag classification, but more recently, the classification of individual instances has attracted the attention of the pattern recognition community. While...
ASTRO-DF is a class of adaptive sampling algorithms for solving simulation optimization problems in which only estimates of the objective function are available by executing a Monte Carlo simulation. ASTRO-DF algorithms are iterative trust-region algorithms, where a local model is repeatedly constructed and optimized as iterates evolve through the search space. The ASTRO-DF class of algorithms is...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.