Beyond performance: Feature awareness in personalized interfaces

Personalized graphical user interfaces have the potential to reduce visual complexity and improve interaction efficiency by tailoring elements such as menus and toolbars to better suit an individual user's needs. When an interface is personalized to make useful features more accessible for a user's current task, however, there may be a negative impact on the user's awareness of the full set of available features, making future tasks more difficult. To assess this tradeoff we introduce awareness as an evaluation metric to be used in conjunction with performance. We then discuss three studies we have conducted, which show that personalized interfaces tradeoff awareness of unused features for performance gains on core tasks. The first two studies, previously published and presented only in summary, demonstrate this tradeoff by measuring awareness using a recognition test of unused features in the interface. The studies also evaluated two different types of personalized interfaces: a layered interfaces approach and an adaptive split menu approach. The third study, presented in full, focuses on adaptive split menus and extends results from the first two studies to show that different levels of awareness also correspond to an impact on performance when users are asked to complete new tasks. Based on all three studies and a survey of related work, we outline a design space of personalized interfaces and present several factors that could affect the tradeoff between core task performance and awareness. Finally, we provide a set of design implications that should be considered for personalized interfaces.     

Makespan minimization of multi-slot just-in-time scheduling on single and parallel machines

The paper addresses the problem of multi-slot just-in-time scheduling. Unlike the existing literature on this subject, it studies a more general criterion—the minimization of the schedule makespan rather than the minimization of the number of slots used by schedule. It gives an O(nlog ² n)-time optimization algorithm for the single machine problem. For arbitrary number of m>1 identical parallel machines it presents an O(nlog n)-time optimization algorithm for the case when the processing time of each job does not exceed its due date. For the general case on m>1 machines, it proposes a polynomial time constant factor approximation algorithm.     

Hybrid phenol biosensor based on modified phenoloxidase electrode

Electrolytic deposition has been widely used to immobilize biomacromolecules, and it is always the most important factor to preserve or even increase an activity of the immobilized protein. We report here simple and rather universal method for the highly efficient immobilization of laccase for amperometric biosensing. Laccase from Cerrena unicolor has been successfully immobilized (electrolytic deposition) on the surface of thin, ordered polythiophene films (3-methylthiophene/3-thiopheneacetic acid/N-heptyl-3,6-bis(2-thiophene)carbazole). Two different compounds capable of mediating laccase-catalyzed reactions have been tested by cyclic voltammetry. They exhibited quasi-reversible electrodic behaviour with formal redox potentials ranging from 68 and 918 mV (E⁰vs. SCE). The immersion of the laccase-coated electrode in solution with substrate generated large catalytic currents easily recorded by cyclic voltammetry at low potential scan rates. Considering the fact, that immobilization strategy showed high efficiency, obtained results suggest that method for phenoloxidase immobilization has a great potential of enabling high throughput fabrication of bioelectronics’ devices.     

Is Evolution Algorithmic?

In Darwin’s Dangerous Idea, Daniel Dennett claims that evolution is algorithmic. On Dennett’s analysis, evolutionary processes are trivially algorithmic because he assumes that all natural processes are algorithmic. I will argue that there are more robust ways to understand algorithmic processes that make the claim that evolution is algorithmic empirical and not conceptual. While laws of nature can be seen as compression algorithms of information about the world, it does not follow logically that they are implemented as algorithms by physical processes. For that to be true, the processes have to be part of computational systems. The basic difference between mere simulation and real computing is having proper causal structure. I will show what kind of requirements this poses for natural evolutionary processes if they are to be computational.     

TSV constraints related to temperature excursion, pressure during molding, materials used and handling loads

The current paper focuses on several mechanical aspects of a waferlevel packaging approach using a direct face-to-face Chip-to-Wafer (C2W) bonding of a MEMS device on an ASIC substrate wafer. Requirements of minimized inherent stress from packaging and good decoupling from forces applied in manufacturing and application are discussed with particular attention to the presence of through-silicon vias (TSV) in the substrate wafer. The paper deals with FEM analysis of temperature excursion, pressure during molding, materials used and handling load influence on mechanical stress within the TSV system and on wafer level, which can be large enough to disintegrate the system.     

A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems

In a cloud computing paradigm, energy efficient allocation of different virtualized ICT resources (servers, storage disks, and networks, and the like) is a complex problem due to the presence of heterogeneous application (e.g., content delivery networks, MapReduce, web applications, and the like) workloads having contentious allocation requirements in terms of ICT resource capacities (e.g., network bandwidth, processing speed, response time, etc.). Several recent papers have tried to address the issue of improving energy efficiency in allocating cloud resources to applications with varying degree of success. However, to the best of our knowledge there is no published literature on this subject that clearly articulates the research problem and provides research taxonomy for succinct classification of existing techniques. Hence, the main aim of this paper is to identify open challenges associated with energy efficient resource allocation. In this regard, the study, first, outlines the problem and existing hardware and software-based techniques available for this purpose. Furthermore, available techniques already presented in the literature are summarized based on the energy-efficient research dimension taxonomy. The advantages and disadvantages of the existing techniques are comprehensively analyzed against the proposed research dimension taxonomy namely: resource adaption policy, objective function, allocation method, allocation operation, and interoperability.     

A modular approach to learning manipulation strategies from human demonstration

Object manipulation is a challenging task for robotics, as the physics involved in object interaction is complex and hard to express analytically. Here we introduce a modular approach for learning a manipulation strategy from human demonstration. Firstly we record a human performing a task that requires an adaptive control strategy in different conditions, i.e. different task contexts. We then perform modular decomposition of the control strategy, using phases of the recorded actions to guide segmentation. Each module represents a part of the strategy, encoded as a pair of forward and inverse models. All modules contribute to the final control policy; their recommendations are integrated via a system of weighting based on their own estimated error in the current task context. We validate our approach by demonstrating it, both in a simulation for clarity, and on a real robot platform to demonstrate robustness and capacity to generalise. The robot task is opening bottle caps. We show that our approach can modularize an adaptive control strategy and generate appropriate motor commands for the robot to accomplish the complete task, even for novel bottles.     

A learning-based colour image segmentation with extended and compact structural tensor feature representation

In this paper a novel Tensor-Based Image Segmentation Algorithm (TBISA) is presented, which is dedicated for segmentation of colour images. A purpose of TBISA is to distinguish specific objects based on their characteristics, i.e. shape, colour, texture, or a mixture of these features. All of those information are available in colour channel data. Nonetheless, performing image analysis on the pixel level using RGB values, does not allow to access information on texture which is hidden in relation between neighbouring pixels. Therefore, to take full advantage of all available information, we propose to incorporate the Structural Tensors as a feature extraction method. It forms enriched feature set which, apart from colour and intensity, conveys also information of texture. This set is next processed by different classification algorithms for image segmentation. Quality of TBISA is evaluated in a series of experiments carried on benchmark images. Obtained results prove that the proposed method allows accurate and fast image segmentation.