A wide range of multimedia services is expected to be offered for mobile users via various wireless access networks. Even the integration of Cloud Computing in such networks does not support an adequate Quality of Experience (QoE) in areas with high demands for multimedia contents. Fog computing has been conceptualized to facilitate the deployment of new services that cloud computing cannot provide, particularly those demanding QoE guarantees. These services are provided using fog nodes located at the network edge, which is capable of virtualizing their functions/applications. Service migration from the cloud to fog nodes can be actuated by request patterns and the timing issues. To the best of our knowledge, existing works on fog computing focus on architecture and fog node deployment issues. In this article, we describe the operational impacts and benefits associated with service migration from the cloud to multi-tier fog computing for video distribution with QoE support. Besides that, we perform the evaluation of such service migration of video services. Finally, we present potential research challenges and trends.

Depression is the most incapacitating disease worldwide, and it has an alarming comorbidity rate with anxiety. The use of social networks to expose personal difficulties has enabled works on the automatic identification of specific mental conditions, particularly depression. In spite of many solutions proposed for the automatic recognition of depression, fewer exist for anxiety and its comorbidity with depression. In this paper, we propose DAC Stacking, a solution that leverages stacking ensembles and Deep Learning (DL) to automatically identify depression, anxiety, and their comorbidity, using data extracted from Reddit. The stacking is composed of single-label binary classifiers, that either distinguish between specific disorders and control users (experts), or between pairs of target conditions (differentiating). A meta-learner explores these base classifiers as a context for reaching a multi-label decision. We assessed alternative ensemble topologies, exploring roles for base models, DL architectures, and word embeddings. All base classifiers and ensembles outperformed the baselines for depression and anxiety (f-measures near 0.79). The ensemble topology with the best performance (Hamming Loss of 0.29 and Exact Match Ratio of 0.46) combines base classifiers of three DL architectures, and includes expert and differentiating base models. The analysis of the influential classification features according to SHAP revealed the strengths of our solution and provided insights on the challenges for the automatic classification of the addressed mental conditions.

Battlefield networking (BN) considers each network entity as a network in itself, composed of several communication technologies usually interconnected by multiple satellites. In this context, we must consider the intrinsic properties of military contexts to apply SDN in BN to get more flexibility and programmability in network-centric operations. For instance, BN considers several communication technologies, as well as the distinct application requirements on the traffic transmitted over BN in terms of QoS and security features in dynamic environments, where flexible network-centric operations are mandatory. In this article, we define an SDBN architecture that adapts SDN technology to BN, enabling SDNbased applications, methods, and policies. To the best of our knowledge, the proposed architecture in this article is the first one aimed at integrating BN and SDN into dynamic and heterogeneous network-centric environments.

Depression has become a public health issue, and the high comorbidity rate with anxiety worsens the clinical picture. Early identification is crucial for decisions on the proper line of treatment. The use of social networks to expose personal difficulties has enabled works on the automatic identification of specific mental conditions, particularly depression. This paper explores deep learning techniques to develop an ensemble stacking classifier for the automatic identification of depression, anxiety, and their comorbidity, using a self-diagnosed dataset extracted from Reddit. At the lowest level, binary classifiers make predictions about specific disorders, outperforming all baseline models. A meta-learner explores these weak classifiers as a context for reaching a multi-label decision, achieving a Hamming Loss of 0.29 and Exact Match Ratio of 0.47. We performed a qualitative analysis using SHAP, which confirmed the relationship between the influential features and symptoms of these disorders.