Towards coordinated multirobot missions for lunar sample collection in an unknown environment

In this paper, the recent results of the space project IMPERA are presented. The goal of IMPERA is the development of a multirobot planning and plan execution architecture with a focus on a lunar sample collection scenario in an unknown environment. We describe the implementation and verification of different modules that are integrated into a distributed system architecture. The modules include a mission planning approach for a multirobot system and modules for task and skill execution within a lunar use-case scenario. The skills needed for the test scenario include cooperative exploration and mapping strategies for an unknown environment, the localization and classification of sample containers using a novel approach of semantic perception, and the skill of transporting sample containers to a collection point using a mobile manipulation robot. Additionally, we present our approach of a reliable communication framework that can deal with communication loss during the mission. Several modules are tested within several experiments in the domain of planning and plan execution, communication, coordinated exploration, perception, and object transportation. An overall system integration is tested on a mission scenario experiment using three robots.

An ERP-client benefit-oriented maintenance taxonomy

The worldwide installed base of enterprise resource planning (ERP) systems has increased rapidly over the past 10 years now comprising tens of thousands of installations in large- and medium-sized organizations and millions of licensed users. Similar to traditional information systems (IS), ERP systems must be maintained and upgraded. It is therefore not surprising that ERP maintenance activities have become the largest budget provision in the IS departments of many ERP-using organizations. Yet, there has been limited study of ERP maintenance activities. Are they simply instances of traditional software maintenance activities to which traditional software maintenance research findings can be generalized? Or are they fundamentally different, such that new research, specific to ERP maintenance, is required to help alleviate the ERP maintenance burden? This paper reports a case study of a large organization that implemented ERP (an SAP system) more than three years ago. From the case study and data collected, we observe the following distinctions of ERP maintenance: (1) the ERP-using organization, in addition to addressing internally originated change-requests, also implements maintenance introduced by the vendor; (2) requests for user-support concerning the ERP system behavior, function and training constitute a main part of ERP maintenance activity; and (3) similar to the in-house software environment, enhancement is the major maintenance activity in the ERP environment, encompassing almost 64% of the total change-request effort. In light of these and other findings, we ultimately: (1) propose a clear and precise definition of ERP maintenance; (2) conclude that ERP maintenance cannot be sufficiently described by existing software maintenance taxonomies; and (3) propose a benefits-oriented taxonomy, that better represents ERP maintenance activities. Three salient dimensions (for characterizing requests) incorporated in the proposed ERP maintenance taxonomy are: (1) who is the maintenance source? (2) why is it important to service the request? and (3) what––whether there is any impact of implementing the request on the installed module(s)?

ICEPOLE: High-speed, hardware-oriented authenticated encryption

This paper introduces our dedicated authenticated encryption scheme ICEPOLE. ICEPOLE is a high-speed hardware-oriented scheme, suitable for high-throughput network nodes or generally any environment where specialized hardware (such as FPGAs or ASICs) can be used to provide high data processing rates. ICEPOLE-128 (the primary ICEPOLE variant) is very fast. On the modern FPGA device Virtex 6, a basic iterative architecture of ICEPOLE reaches 41 Gbits/s, which is over 10 times faster than the equivalent implementation of AES-128-GCM. The throughput-to-area ratio is also substantially better when compared to AES-128-GCM. We have carefully examined the security of the algorithm through a range of cryptanalytic techniques and our findings indicate that ICEPOLE offers high security level.

Improving robot vision models for object detection through interaction

We propose a method for learning specific object representations that can be applied (and reused) in visual detection and identification tasks. A machine learning technique called Cartesian Genetic Programming (CGP) is used to create these models based on a series of images. Our research investigates how manipulation actions might allow for the development of better visual models and therefore better robot vision. This paper describes how visual object representations can be learned and improved by performing object manipulation actions, such as, poke, push and pick-up with a humanoid robot. The improvement can be measured and allows for the robot to select and perform the `right' action, i.e. the action with the best possible improvement of the detector.

Injury narrative text classification using factorization model

Narrative text is a useful way of identifying injury circumstances from the routine emergency department data collections. Automatically classifying narratives based on machine learning techniques is a promising technique, which can consequently reduce the tedious manual classification process. Existing works focus on using Naive Bayes which does not always offer the best performance. This paper proposes the Matrix Factorization approaches along with a learning enhancement process for this task. The results are compared with the performance of various other classification approaches. The impact on the classification results from the parameters setting during the classification of a medical text dataset is discussed. With the selection of right dimension k, Non Negative Matrix Factorization-model method achieves 10 CV accuracy of 0.93.

Decision boundary setting and classifier combination for text classification

This thesis presents a promising boundary setting method for solving challenging issues in text classification to produce an effective text classifier. A classifier must identify boundary between classes optimally. However, after the features are selected, the boundary is still unclear with regard to mixed positive and negative documents. A classifier combination method to boost effectiveness of the classification model is also presented. The experiments carried out in the study demonstrate that the proposed classifier is promising.

Classification of activities of daily living of individual with limb loss

The monitoring of the actual activities of daily living of individuals with lower limb amputation is essential for an evidence-based fitting of the prosthesis, more particularly the choice of components (e.g., knees, ankles, feet)[1-4]. The purpose of this presentation was to give an overview of the categorization of the load regime data to assess the functional output and usage of the prosthesis of lower limb amputees has presented in several publications[5, 6]. The objectives were to present a categorization of load regime and to report the results for a case.

Performance dispersion for evidence-based classification of stationary throwers

Background There is a need for better understanding of the dispersion of classification-related variable to develop an evidence-based classification of athletes with a disability participating in stationary throwing events. Objectives The purposes of this study are (A) to describe tools designed to comprehend and represent the dispersion of the performance between successive classes, and (B) to present this dispersion for the elite male and female stationary shot-putters who participated in Beijing 2008 Paralympic Games. Study design Retrospective study Methods This study analysed a total of 479 attempts performed by 114 male and female stationary shot-putters in three F30s (F32-F34) and six F50s (F52-F58) classes during the course of eight events during Beijing 2008 Paralympic Games. Results The average differences of best performance were 1.46±0.46 m for males between F54 and F58 classes as well as 1.06±1.18 m for females between F55 and F58 classes. The results demonstrated a linear relationship between best performance and classification while revealing two male Gold Medallists in F33 and F52 classes were outliers. Conclusions This study confirms the benefits of the comparative matrices, performance continuum and dispersion plots to comprehend classification-related variables. The work presented here represents a stepping stone into biomechanical analyses of stationary throwers, particularly on the eve of the London 2012 Paralympic Games where new evidences could be gathered.

Centroid training to achieve effective text classification

Traditional text classification technology based on machine learning and data mining techniques has made a big progress. However, it is still a big problem on how to draw an exact decision boundary between relevant and irrelevant objects in binary classification due to much uncertainty produced in the process of the traditional algorithms. The proposed model CTTC (Centroid Training for Text Classification) aims to build an uncertainty boundary to absorb as many indeterminate objects as possible so as to elevate the certainty of the relevant and irrelevant groups through the centroid clustering and training process. The clustering starts from the two training subsets labelled as relevant or irrelevant respectively to create two principal centroid vectors by which all the training samples are further separated into three groups: POS, NEG and BND, with all the indeterminate objects absorbed into the uncertain decision boundary BND. Two pairs of centroid vectors are proposed to be trained and optimized through the subsequent iterative multi-learning process, all of which are proposed to collaboratively help predict the polarities of the incoming objects thereafter. For the assessment of the proposed model, F1 and Accuracy have been chosen as the key evaluation measures. We stress the F1 measure because it can display the overall performance improvement of the final classifier better than Accuracy. A large number of experiments have been completed using the proposed model on the Reuters Corpus Volume 1 (RCV1) which is important standard dataset in the field. The experiment results show that the proposed model has significantly improved the binary text classification performance in both F1 and Accuracy compared with three other influential baseline models.

Fuzzy-DL perception for multi-robot systems

For future planetary robot missions, multi-robot-systems can be considered as a suitable platform to perform space mission faster and more reliable. In heterogeneous robot teams, each robot can have different abilities and sensor equipment. In this paper we describe a lunar demonstration scenario where a team of mobile robots explores an unknown area and identifies a set of objects belonging to a lunar infrastructure. Our robot team consists of two exploring scout robots and a mobile manipulator. The mission goal is to locate the objects within a certain area, to identify the objects, and to transport the objects to a base station. The robots have a different sensor setup and different capabilities. In order to classify parts of the lunar infrastructure, the robots have to share the knowledge about the objects. Based on the different sensing capabilities, several information modalities have to be shared and combined by the robots. In this work we propose an approach using spatial features and a fuzzy logic based reasoning for distributed object classification.

Towards robust automatic affective classification of images using facial expressions for practical applications

Affect is an important feature of multimedia content and conveys valuable information for multimedia indexing and retrieval. Most existing studies for affective content analysis are limited to low-level features or mid-level representations, and are generally criticized for their incapacity to address the gap between low-level features and high-level human affective perception. The facial expressions of subjects in images carry important semantic information that can substantially influence human affective perception, but have been seldom investigated for affective classification of facial images towards practical applications. This paper presents an automatic image emotion detector (IED) for affective classification of practical (or non-laboratory) data using facial expressions, where a lot of “real-world” challenges are present, including pose, illumination, and size variations etc. The proposed method is novel, with its framework designed specifically to overcome these challenges using multi-view versions of face and fiducial point detectors, and a combination of point-based texture and geometry. Performance comparisons of several key parameters of relevant algorithms are conducted to explore the optimum parameters for high accuracy and fast computation speed. A comprehensive set of experiments with existing and new datasets, shows that the method is effective despite pose variations, fast, and appropriate for large-scale data, and as accurate as the method with state-of-the-art performance on laboratory-based data. The proposed method was also applied to affective classification of images from the British Broadcast Corporation (BBC) in a task typical for a practical application providing some valuable insights.

Evaluation of object detection proposals under condition variations

Object detection is a fundamental task in many computer vision applications, therefore the importance of evaluating the quality of object detection is well acknowledged in this domain. This process gives insight into the capabilities of methods in handling environmental changes. In this paper, a new method for object detection is introduced that combines the Selective Search and EdgeBoxes. We tested these three methods under environmental variations. Our experiments demonstrate the outperformance of the combination method under illumination and view point variations.

A determinism and desire: Some neurological processes in perceiving the design object

The environment moderates behaviour using a subtle language of ‘affordances’ and ‘behaviour-settings’. Affordances are environmental offerings. They are objects that demand action; a cliff demands a leap and binoculars demand a peek. Behaviour-settings are ‘places;’ spaces encoded with expectations and meanings. Behaviour-settings work the opposite way to affordances; they demand inhibition; an introspective demeanour in a church or when under surveillance. Most affordances and behaviour-settings are designed, and as such, designers are effectively predicting brain reactions. • Affordances are nested within, and moderated by behaviour-settings. Both trigger automatic neural responses (excitation and inhibition). These, for the best part cancel each other out. This balancing enables object recognition and allows choice about what action should be taken (if any). But when excitation exceeds inhibition, instinctive action will automatically commence. In positive circumstances this may mean laughter or a smile. In negative circumstances, fleeing, screaming or other panic responses are likely. People with poor frontal function, due to immaturity (childhood or developmental disorders) or due to hypofrontality (schizophrenia, brain damage or dementia) have a reduced capacity to balance excitatory and inhibitory impulses. For these people, environmental behavioural demands increase with the decline of frontal brain function. • The world around us is not only encoded with symbols and sensory information. Opportunities and restrictions work on a much more primal level. Person/space interactions constantly take place at a molecular scale. Every space we enter has its own special dynamic, where individualism vies for supremacy between the opposing forces of affordance-related excitation and the inhibition intrinsic to behaviour-settings. And in this context, even a small change–the installation of a CCTV camera can turn a circus to a prison. • This paper draws on cutting-edge neurological theory to understand the psychological determinates of the everyday experience of the designed environment.