Fractional Skipping: Towards Finer-Grained Dynamic CNN Inference

While increasingly deep networks are still in general desired for achieving state-of-the-art performance, for many specific inputs a simpler network might already suffice. Existing works exploited this observation by learning to skip convolutional layers in an input-dependent manner. However, we argue their binary decision scheme, i.e., either fully executing or completely bypassing one layer for a specific input, can be enhanced by introducing finer-grained, "softer" decisions. We therefore propose a Dynamic Fractional Skipping (DFS) framework. The core idea of DFS is to hypothesize layer-wise quantization (to different bitwidths) as intermediate "soft" choices to be made between fully utilizing and skipping a layer. For each input, DFS dynamically assigns a bitwidth to both weights and activations of each layer, where fully executing and skipping could be viewed as two "extremes" (i.e., full bitwidth and zero bitwidth). In this way, DFS can "fractionally" exploit a layer's expressive power during input-adaptive inference, enabling finer-grained accuracy-computational cost trade-offs. It presents a unified view to link input-adaptive layer skipping and input-adaptive hybrid quantization. Extensive experimental results demonstrate the superior tradeoff between computational cost and model expressive power (accuracy) achieved by DFS. More visualizations also indicate a smooth and consistent transition in the DFS behaviors, especially the learned choices between layer skipping and different quantizations when the total computational budgets vary, validating our hypothesis that layer quantization could be viewed as intermediate variants of layer skipping. Our source code and supplementary material are available at \link{https://github.com/Torment123/DFS}

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Aerial Object Detection using Learnable Bounding Boxes

Current methods in computer vision and object detection rely heavily on neural networks and deep learning. This active area of research is used in applications such as autonomous driving, aerial imaging, defense and surveillance. State-of-the-art object detection methods rely on rectangular shaped, horizontal/vertical bounding boxes drawn over an object to accurately localize its position. Such orthogonal bounding boxes ignore object pose, resulting in reduced object localization, and limiting downstream tasks such as object understanding and tracking. To overcome these limitations, this research presents object detection improvements that aid tighter and more precise detections. In particular, we modify the object detection anchor box definition to firstly include rotations along with height and width and secondly to allow arbitrary four corner point shapes. Further, the introduction of new anchor boxes gives the model additional freedom to model objects which are centered about a 45-degree axis of rotation. The resulting network allows minimum compromises in speed and reliability while providing more accurate localization. We present results on the DOTA dataset, showing the value of the flexible object boundaries, especially with rotated and non-rectangular objects

Protocol design and optimization for QoS provisioning in wireless mesh networks

Wireless Mesh Network (WMN) has been recognized as a promising step towards the goal of ubiquitous broadband wireless Internet access. By exploiting the state-of-the-art radio and multi-hop networking technologies, mesh nodes in WMN collaboratively form a stationary wireless communication backbone. Data between clients and the Internet is routed through a series of mesh nodes via one or multiple paths. Such a mesh structure enables WMN to provide clients high-speed Internet access services with a less expensive and easier-to-deployment wireless infrastructure comparing to the wired counterparts. Due to the unique characteristics of WMN, existing protocols and schemes designed for other wellstudied wireless networks, such as Wi-Fi and Mobile Ad-hoc Network (MANET), are not suitable for WMN and hence cannot be applied to WMN directly. Therefore, novel protocols specifically designed and optimized forWMNare highly desired to fully exploit the mesh architecture. The goal is to provide high-level Quality-of-Service (QoS) to WMN clients to enable a rich portfolio of wireless and mobile applications and scenarios. This dissertation investigates the following important issues related to QoS provisioning in WMN: high throughput routing between WMN clients and the Internet, fairness provisioning among WMN clients and network-level capacity optimization. We propose innovative solutions to address these issues and improve the performance, scalability and reliability of WMN. In addition, we develop CyMesh, a multi-radio multi-channel (MRMC) wireless mesh network testbed, to evaluate the capacity and performance of WMN in real world environments. Extensive simulation (using the QualNet simulator) and experimental (over the CyMesh testbed) results demonstrate the effectiveness of the designed protocols. In particular, we learn that the system capacity of WMN can be improved significantly by exploiting the MRMC network architecture and the antenna directionality of radios equipped on mesh nodes, and our proposed fulfillment based fairness is a reasonable notion for fair service provisioning among WMN clients. Moreover, we report the encountered problems, key observations and learned lessons during the design and deployment of CyMesh, which may serve as a valuable resource for future MRMC WMN implementations

Application-Aware Network Traffic Management in MEC-Integrated Industrial Environments

The industrial Internet of things (IIoT) has radically modified industrial environments, not only enabling novel industrial applications but also significantly increasing the amount of generated network traffic. Nowadays, a major concern is to support network-intensive industrial applications while ensuring the prompt and reliable delivery of mission-critical traffic flows concurrently traversing the industrial network. To this end, we propose application-aware network traffic management. The goal is to satisfy the requirements of industrial applications through a form of traffic management, the decision making of which is also based on what is carried within packet payloads (application data) in an efficient and flexible way. Our proposed solution targets multi-access edge computing (MEC)-integrated industrial environments, where on-premises and off-premises edge computing resources are used in a coordinated way, as it is expected to be in future Internet scenarios. The technical pillars of our solution are edge-powered in-network processing (eINP) and software-defined networking (SDN). The concept of eINP differs from INP because the latter is directly performed on network devices (NDs), whereas the former is performed on edge nodes connected via high-speed links to NDs. The rationale of eINP is to provide the network with additional capabilities for packet payload inspection and processing through edge computing, either on-premises or in the MEC-enabled cellular network. The reported in-the-field experimental results show the proposal feasibility and its primary tradeoffs in terms of performance and confidentiality

KISS: Stochastic Packet Inspection Classifier for UDP Traffic

This paper proposes KISS, a novel Internet classifica- tion engine. Motivated by the expected raise of UDP traffic, which stems from the momentum of Peer-to-Peer (P2P) streaming appli- cations, we propose a novel classification framework that leverages on statistical characterization of payload. Statistical signatures are derived by the means of a Chi-Square-like test, which extracts the protocol "format," but ignores the protocol "semantic" and "synchronization" rules. The signatures feed a decision process based either on the geometric distance among samples, or on Sup- port Vector Machines. KISS is very accurate, and its signatures are intrinsically robust to packet sampling, reordering, and flow asym- metry, so that it can be used on almost any network. KISS is tested in different scenarios, considering traditional client-server proto- cols, VoIP, and both traditional and new P2P Internet applications. Results are astonishing. The average True Positive percentage is 99.6%, with the worst case equal to 98.1,% while results are al- most perfect when dealing with new P2P streaming applications

A clinician’s guide to understanding and critically appraising machine learning studies: a checklist for Ruling Out Bias Using Standard Tools in Machine Learning (ROBUST-ML)

Developing functional machine learning (ML)-based models to address unmet clinical needs requires unique considerations for optimal clinical utility. Recent debates about the rigours, transparency, explainability, and reproducibility of ML models, terms which are defined in this article, have raised concerns about their clinical utility and suitability for integration in current evidence-based practice paradigms. This featured article focuses on increasing the literacy of ML among clinicians by providing them with the knowledge and tools needed to understand and critically appraise clinical studies focused on ML. A checklist is provided for evaluating the rigour and reproducibility of the four ML building blocks: data curation, feature engineering, model development, and clinical deployment. Checklists like this are important for quality assurance and to ensure that ML studies are rigourously and confidently reviewed by clinicians and are guided by domain knowledge of the setting in which the findings will be applied. Bridging the gap between clinicians, healthcare scientists, and ML engineers can address many shortcomings and pitfalls of ML-based solutions and their potential deployment at the bedside