Abstract
Convolution Neural Network (CNN)-based object detection models have achieved unprecedented accuracy in challenging detection tasks. However, existing detection models (detection heads) trained on 8-bits/pixel/channel low dynamic range (LDR) images are unable to detect relevant objects under lighting conditions where a portion of the image is either under-exposed or over-exposed. Although this issue can be addressed by introducing High Dynamic Range (HDR) content and training existing detection heads on HDR content, there are several major challenges, such as the lack of real-life annotated HDR dataset(s) and extensive computational resources required for training and the hyper-parameter search. In this paper, we introduce an alternative backwards-compatible methodology to detect objects in challenging lighting conditions using existing CNN-based detection heads. This approach facilitates the use of HDR imaging without the immediate need for creating annotated HDR datasets and the associated expensive retraining procedure. The proposed approach uses HDR imaging to capture relevant details in high contrast scenarios. Subsequently, the scene dynamic range and wider colour gamut are compressed using HDR to LDR mapping techniques such that the salient highlight, shadow, and chroma details are preserved. The mapped LDR image can then be used by existing pre-trained models to extract relevant features required to detect objects in both the under-exposed and over-exposed regions of a scene. In addition, we also conduct an evaluation to study the feasibility of using existing HDR to LDR mapping techniques with existing detection heads trained on standard detection datasets such as PASCAL VOC and MSCOCO. Results show that the images obtained from the mapping techniques are suitable for object detection, and some of them can significantly outperform traditional LDR images.

Abstract
The present work aims at developing a smart dental implant meant to restore the proprioceptive control of the masticatory muscle activity, in consequence of the loss of natural teeth. When periodontal afferent information is not available, the control of the occlusal forces is impaired and the capacity of regulating the masticatory force on a certain tooth or teeth is affected. The active implant being proposed detects the force exerted on teeth and proportionally generates stimuli to send that information to the brain in order to restore the neurobiological mechanisms associated to the masticatory sensory-motor function. After the description of the physiological and biomechanical aspects related to the loss of teeth and masticatory function, details are provided on the force sensing, processing and stimuli generation circuits included in the active implant being proposed. Preliminary simulation results that illustrate the implant functionality are presented.

Abstract

Abstract
Purpose Elevating the human experience (HX) through research collaborations is the purpose of this article. ServCollab facilitates and supports service research collaborations that seek to reduce human suffering and improve human well-being. Design/methodology/approach To catalyze this initiative, the authors introduce ServCollab's three human rights goals (serve, enable and transform), standards of justice for serving humanity (distributive, procedural and interactional justice) and research approaches for serving humanity (service design and community action research). Research implications ServCollab seeks to advance the service research field via large-scale service research projects that pursue theory building, research and action. Service inclusion is the first focus of ServCollab and is illustrated through two projects (transformative refugee services and virtual assistants in social care). This paper seeks to encourage collaboration in more large-scale service research projects that elevate the HX. Practical implications ServCollab seeks to raise the aspirations of service researchers, expand the skills of service research teams and build mutually collaborative service research approaches that transform human lives. Originality/value ServCollab is a unique organization within the burgeoning service research community. By collaborating with service researchers, with service research centers, with universities, with nonprofit agencies and with foundations, ServCollab will build research capacity to address large-scale human service system problems. ServCollab takes a broad perspective for serving humanity by focusing on the HX. Current business research focuses on the interactive roles of customer experience and employee experience. From the perspective of HX, such role labels are insufficient concepts for the full spectrum of human life.

Abstract
With the implementation of micro grids and smart grids, new business models able to cope with the new opportunities are being developed. Virtual Power Players are a player that allows aggregating a diversity of entities, to facilitate their participation in the electricity markets and to provide a set of new services promoting generation and consumption efficiency, while improving players’ benefits. The elastic behavior of the demand consumption jointly used with other available resources such as distributed generation (DG) can play a crucial role for the success of smart grids. This paper proposes methodologies to develop strategic remuneration of aggregated consumers with demand response participation, this model uses a clustering algorithm, applied on values that were obtained from a scheduling methodology of a real Portuguese distribution network with 937 buses, 20310 consumers and 548 distributed generators. The normalization methods and clustering methodologies were applied to several variables of different consumers, which creates sub-groups of data according to their correlations. The clustering process is evaluated so that the number of data sub-groups that brings the most added value for the decision-making process is found, according to players characteristics. © Springer Nature Switzerland AG 2020.

Abstract
Machine learning, a subfield of artificial intelligence, has been widely used to automate tasks usually performed by humans. Some applications of these techniques are understanding network traffic behavior, predicting it, classifying it, fixing its faults, identifying malware applications, and preventing deliberate attacks. The goal of this work is to use machine learning algorithms to classify, in separate procedures, the errors of the network, their causes, and possible fixes. Our application case considers the WiBACK wireless system, from which we also obtained the data logs used to produce this paper. WiBACK is a collection of software and hardware with auto-configuration and self-management capabilities, designed to reduce CAPEX and OPEX costs. A principal components analysis is performed, followed by the application of decision trees, k nearest neighbors, and support vector machines. A comparison between the results obtained by the algorithms trained with the original data sets, balanced data sets, and the principal components data is performed. We achieve weighted F1-score between 0.93 and 0.99 with the balanced data, 0.88 and 0.96 with the original unbalanced data, and 0.81 and 0.89 with the Principal Components Analysis. © 2020, Springer Nature Switzerland AG.