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Accurate object detection requires correct classification and high-quality localization. Currently, most of the single shot detectors (SSDs) conduct simultaneous classification and regression using a fully convolutional network. Despite high efficiency, this structure has some inappropriate designs for accurate object detection. The first one is the mismatch of bounding box classification, where the classification results of the default bounding boxes are improperly treated as the results of the regressed bounding boxes during the inference. The second one is that only one-time regression is not good enough for high-quality object localization. To solve the problem of classification mismatch, we propose a novel reg-offset-cls (ROC) module including three hierarchical steps: the regression of the default bounding box, the prediction of new feature sampling locations, and the classification of the regressed bounding box with more accurate features. For high-quality localization, we stack two ROC modules together. The input of the second ROC module is the output of the first ROC module. In addition, we inject a feature enhanced (FE) module between two stacked ROC modules to extract more contextual information. The experiments on three different datasets (i.e., MS COCO, PASCAL VOC, and UAVDT) are performed to demonstrate the effectiveness and superiority of our method. Without any bells or whistles, our proposed method outperforms state-of-the-art one-stage methods at a real-time speed. The source code is available at https://github.com/JialeCao001/HSD.

Numerical weather prediction (NWP) data possess internal inaccuracies, such as low NWP wind speed corresponding to high actual wind power generation. This study is intended to reduce the negative effects of such inaccuracies by proposing a pure data-selection framework (PDF) to choose useful data prior to modeling, thus improving the accuracy of day-ahead wind power forecasting. Briefly, we convert an entire NWP training dataset into many small subsets and then select the best subset combination via a validation set to build a forecasting model. Although a small subset can increase selection flexibility, it can also produce billions of subset combinations, resulting in computational issues. To address this problem, we incorporated metamodeling and optimization steps into PDF. We then proposed a design and analysis of the computer experiments-based metamodeling algorithm and heuristic-exhaustive search optimization algorithm, respectively.Experimental results demonstrate that (1) it is necessary to select data before constructing a forecasting model; (2) using a smaller subset will likely increase selection flexibility, leading to a more accurate forecasting model; (3) PDF can generate a better training dataset than similarity-based data selection methods (e.g., K-means and support vector classification); and (4) choosing data before building a forecasting model produces a more accurate forecasting model compared with using a machine learning method to construct a model directly.

Future mobile devices are anticipated to perceive, understand and react to the world on their own by running multiple correlated deep neural networks locally on-device. Yet the complexity of deep models needs to be trimmed down to fit in mobile storage and memory. Previous studies squeeze the redundancy within a single model. In this work, we reduce the redundancy across multiple models. We propose Multi-Task Zipping (MTZ), a framework to merge correlated, pre-trained deep neural networks for cross-model compression. Central in MTZ is a layer-wise neuron sharing and incoming weight updating scheme that induces a minimal change in the error function. MTZ inherits information from eachmodel and demands light retraining to re-boost the accuracy of individual tasks. MTZ supports typical network layers and applies to inference tasks with different input domains. Evaluations show that MTZ can fully merge the hidden layers of two VGG-16 network. Moreover, MTZ can effectively merge nine residual networks for diverse inference tasks and models for different input domains. Withthe joint model merged by MTZ, the latency to switch between these tasks on memory-constrained devices is reduced by 8.71.

Model-based design is an important method of addressing problems associated with designing complex control systems. For complex dynamic systems in the presence of uncertainties, the modeling process from the first principles becomes extremely tedious and simplification in mechanism and parameter measurement may result in model inaccuracy. On the contrary, machine learning has the characteristic of fitting complicated equations, which makes it widely used in the research of model identification. However, it only brings a black-box model where the design schemes based on an analytical model cannot be applied. In this article, a simple and novel scheme for modeling and control of robotic manipulators is proposed; without prior knowledge, a dynamic model in an analytical form is obtained from artificially excited training data using the symbolic regression technique, and then, a controller is designed based on the dynamic model. Due to the ingenious experimental design, on one hand, the amount of training data is far less than the system identification method by machine learning. On the other hand, a decoupling feature is used in the model that greatly simplifies controller design. The experimental results on two-degree of freedom (DOF) and 6-DOF robotic manipulator simulators verify that the scheme is feasible and effective.

With the dawn of 5G network, a new set of requirements for site spectrum monitoring, cellular planning are emerging, all of which are relying on fine-grained signal map. Although with significant importance, the traditional signal map construction could be time-consuming and labor-intensive. The state-of-the arts usually employ crowdsourcing scheme and matrix completion algorithm to solve the dilemma. However, the crowdsourcing scheme usually suffers from uneven distributed and inadequate participants. To this end, in this paper, we study how to effectively reconstruct and update the signal map in the case of partially measured signal maps with smaller cost and propose a GAN-based active signal map reconstruction method (GSMAC). Our method is mainly innovative in two parts: GSMC, GAN-based signal map construction, and ACS, an active crowdsourcing scheme. Specifically, GSMC can effectively update the signal map with only a small number of observations while updating the signal map online. ACS consists of a reinforce learning-based active query mechanism which quantitatively evaluates the most valuable measurement site for reconstruction. The simulation results and real implemented data-driven experiments demonstrate the advantages and effectiveness of our approach in both accuracy and cost.

Compressed sensing has been identified as a good candidate for user detection in grant-free non-orthogonal multiple access (NOMA) by exploiting the inherent sparsity of user activity. However, most of the existing works do not fully utilize the temporal correlation in NOMA and rely heavily on the unrealistic assumption that the number of active users is known in advance. To address these issues, we propose a temporal correlation enhanced multiuser detection scheme to achieve efficient and practical multiuser detection. Firstly, using 1 bit memory to piggyback the information whether the active users still have data to transmit, the base station can realize whether the active users in current time slot will turn to be silent or remain active. Then, to make explicit use of the temporal correlation of active user sets, a cross validation based adaptive subspace pursuit (CVASP) algorithm, is developed by utilizing the reported information on prior active users. The proposed CVASP is a highly practical algorithm that does not require any prior knowledge of the user sparsity level, as the cross validation technique could properly determine the stopping condition. Extensive simulation results demonstrate that the proposed mechanism could achieve a superier performance without requiring any prior knowledge.

This article presents a quick overview of conventional precoding and beamforming techniques in the cellular networks and proposes a novel coordinated beamforming technique for coordinated multipoint (CoMP) networks, which is based upon signal-to-leakage-plus-noise ratio (SLNR) metric. The proposed scheme has reduced computational cost and offers improved performance compared to the conventional multi-cell precoding schemes in the noise-dominated environments. Simulation results reveal that proposed scheme offers a spectrally efficient, low complexity solution with improved average user throughput and sum rate in the CoMP enabled networks.

In order to make redundant robot manipulators (RRMs) track the complex time-varying trajectory, the motion-planning problem of RRMs can be converted into a constrained time-varying quadratic programming (TVQP) problem. By using a new punishment mechanism-combined recurrent neural network (PMRNN) proposed in this article with reference to the varying-gain neural-dynamic design (VG-NDD) formula, the TVQP problem-based motion-planning scheme can be solved and the optimal angles and velocities of joints of RRMs can also be obtained in the working space. Then, the convergence performance of the PMRNN model in solving the TVQP problem is analyzed theoretically in detail. This novel method has been substantiated to have a faster calculation speed and better accuracy than the traditional method. In addition, the PMRNN model has also been successfully applied to an actual RRM to complete an end-effector trajectory tracking task.

Connected and autonomous vehicles are expected to revolutionize transportation systems in the near future. Nevertheless, full autonomy still requires further development that will only be achieved through a high level of cooperation and coordination among vehicles. For the safe execution of several autonomous functions, vehicles will need to frequently communicate their current location to the Road Side Units (RSUs). However, due to information aging, it is expected that the delays from the time a message is generated until the location of the vehicle is updated will induce a tracking error. Thus, there is a tradeoff between the frequency of location updates and tracking error. Further, in order to reduce the queue-induced delays present in the system, the number of messages can be reduced by employing an event-triggered communication scheme. In this work, a family of optimization problems is proposed, utilizing the event-triggered algorithm, in order to determine the optimal triggering threshold. Both single and multiple RSU cases are considered in several simulation scenarios, demonstrating that the proposed optimization framework derives the optimal tracking accuracy. In the case of multiple RSUs, results are further optimized by assigning roads (and in turn vehicles) to RSUs if coverage areas of multiple RSUs overlap.

In this article, the exponential synchronization control issue of reaction-diffusion neural networks (RDNNs) is mainly resolved by the sampling-based event-triggered scheme under Dirichlet boundary conditions. Based on the sampled state information, the event-triggered control protocol is updated only when the triggering condition is met, which effectively reduces the communication burden and saves energy. In addition, the proposed control algorithm is combined with sampled-data control, which can effectively avoid the Zeno phenomenon. By thinking of the proper Lyapunov-Krasovskii functional and using some momentous inequalities, a sufficient condition is obtained for RDNNs to achieve exponential synchronization. Finally, some simulation results are shown to demonstrate the validity of the algorithm.

Deep reinforcement learning is confronted with problems of sampling inefficiency and poor task migration capability. Meta-reinforcement learning (meta-RL) enables meta-learners to utilize the task-solving skills trained on similar tasks and quickly adapt to new tasks. However, meta-RL methods lack enough queries toward the relationship between task-agnostic exploitation of data and task-related knowledge introduced by latent context, limiting their effectiveness and generalization ability. In this article, we develop an algorithm for off-policy meta-RL that can provide the meta-learners with self-oriented cognition toward how they adapt to the family of tasks. In our approach, we perform dynamic task-adaptiveness distillation to describe how the meta-learners adjust the exploration strategy in the meta-training process. Our approach also enables the meta-learners to balance the influence of task-agnostic self-oriented adaption and task-related information through latent context reorganization. In our experiments, our method achieves 10%-20% higher asymptotic reward than probabilistic embeddings for actor-critic RL (PEARL).

Detecting highly-overlapped objects in crowded scenes remains a challenging problem, especially for one-stage detector. In this paper, we extricate YOLOv4 from the dilemma in a crowd by fine-tuning its detection scheme, named YOLO-CS. Specifically, we give YOLOv4 the power to detect multiple objects in one cell. Center to our method is the carefully designed joint prediction scheme, which is executed through an assignment of bounding boxes and a joint loss. Equipped with the derived joint-object augmentation (DJA), refined regression loss (RL) and Score-NMS (SN), YOLO-CS achieves competitive detection performance on CrowdHuman and CityPersons benchmarks compared with state-of-the-art detectors at the cost of little time. Furthermore, on the widely used general benchmark COCO, YOLO-CS still has a good performance, indicating its robustness to various scenes.

Compared to conventional microgrids (MGs) with pre-defined and static topology, dynamic MGs feature varying electric boundaries and enhanced operational resiliency. This paper evaluates the operation stability of inverter-based dynamic MGs in terms of fulfilling the requested MGs topology change and associated network reconfiguration, which falls into the category of large-signal stability analysis. A complete nonlinear state-space model of inverter-based dynamic MGs is derived, where coordinated controls among inverters along with the effect of communication delays have been considered. In response to reconfiguration demands, an evaluation scheme is developed to determine whether the system could perform seamless topology variation without losing synchronism. Specifically, the system’s stability region is quantified by the domain of attraction (DoA) around the desired operating point using Takagi-Sugeno fuzzy modeling (T-S multimodeling). The large-signal stability of a multi-bus inverter-based dynamic MG with both interface inverters and sectionalizing switches have been analyzed. The stability regions estimated under different scenarios of topology variation have been validated using time-domain simulation in MATLAB/Simulink. The developed evaluation scheme is used to quantify the effects of control gain designs and communication delays on system large-signal stability.

With the massive deployment of the Internet of things (IoT) devices, many data analysis applications emerge for the large amount of data accumulated by IoT. Federated learning (FedL) on IoT devices is an appealing mode to train a precise data analysis model. However, existing federated learning schemes either take expensive computation costs (e.g., public key cryptographic operations) or a large number of interactions among participants. Obviously, these schemes are unsuitable for IoT devices due to the limited computational and communication resources. In this work, we propose a lightweight privacy-preserving federated learning scheme for IoT devices. To protect the privacy of individual local data, we add masks to intervening parameters. An effective secret sharing scheme is adopted to ensure that masks can be eliminated accurately. Considering that FedL involves multiple iterations and mask generation for each iteration costs a large number of interactions among users for privacy guarantee, we also design a secure mask reusing mechanism for large-scale FedL tasks. We prove that our scheme is secure against the honest-but-curious model. In addition, we also expand our scheme to deal with the collusion attack. Extensive experiments on real IoT devices demonstrate the accuracy and efficiency of our work.

Theories can be represented as statistical models for empirical testing. There is a vast literature on model selection and multimodel inference that focuses on how to assess which statistical model, and therefore which theory, best fits the available data. For example, given some data, one can compare models on various information criterion or other fit statistics. However, what these indices fail to capture is the full range of counterfactuals. That is, some models may fit the given data better not because they represent a more correct theory, but simply because these models have more fit propensity - a tendency to fit a wider range of data, even nonsensical data, better. Current approaches fall short in considering the principle of parsimony (Occam’s Razor), often equating it with the number of model parameters. Here we offer a toolkit for researchers to better study and understand parsimony through the fit propensity of Structural Equation Models. We provide an R package (ockhamSEM) built on the popular lavaan package. To illustrate the importance of evaluating fit propensity, we use ockhamSEM to investigate the factor structure of the Rosenberg Self-Esteem Scale.

The huge amount of data produced in the 6G networks not only brings new challenges to the reliability and efficiency of mobile devices but also drives rapid development of new storage techniques. With the benefits of fast access speed and high reliability, NAND flash memory has become a promising storage solution for the 6G networks. In this paper, we investigate a protograph-coded bit-interleaved coded modulation with iterative detection and decoding (BICM-ID) utilizing irregular mapping (IM) in the NAND flash-memory systems. First, we propose an enhanced protograph-based extrinsic information transfer (EPEXIT) algorithm to facilitate the analysis of protograph codes in the IM-BICM-ID systems. With the use of EPEXIT algorithm, a simple design method is conceived for the construction of a family of high-rate protograph codes, called irregular-mapped accumulate-repeat-accumulate (IMARA) codes, which possess excellent decoding thresholds and linear-minimum-distance-growth property. Furthermore, motivated by the voltage-region iterative gain characteristics of IM-BICM-ID systems, a novel read-voltage optimization scheme is developed to acquire accurate read-voltage levels, thus minimizing the decoding thresholds (in dB) of protograph codes. Analyses and simulations indicate that the proposed IMARA-aided IM-BICM-ID scheme and read-voltage optimization scheme remarkably improve the convergence and decoding performance of flash-memory systems. Thus, the proposed protograph-coded IM-BICM-ID can be viewed as a reliable and efficient storage solution for the new-generation mobile networks, such as Internet of Vehicles.

Non-Euclidean property of graph structures has faced interesting challenges when deep learning methods are applied. Graph convolutional networks (GCNs) can be regarded as one of the successful approaches to classification tasks on graph data, although the structure of this approach limits its performance. In this work, a novel representation learning approach is introduced based on spectral convolutions on graph-structured data in a semisupervised learning setting. Our proposed method, COnvOlving cLiques (COOL), is constructed as a neighborhood aggregation approach for learning node representations using established GCN architectures. This approach relies on aggregating local information by finding maximal cliques. Unlike the existing graph neural networks which follow a traditional neighborhood averaging scheme, COOL allows for aggregation of densely connected neighboring nodes of potentially differing locality. This leads to substantial improvements on multiple transductive node classification tasks.

The future communication technologies like 6G are capable to provide higher mobility and better quality of service requirements to Internet of Things (IoT). To ensure mobility, the 6G technologies need more reliable and scalable solutions, which are capable to integrate large scale heterogeneous IoT networks. In a heterogeneous environment, seamless mobility along with the demands of IP addresses requires a proxy mobile IPv6 (PMIPv6) protocol that provides cost-effective solutions in next generation IoT networks. The PMIPv6 has been exploited for resource efficiency in IoT-enabled next generation networks. In this paper, we have proposed a demand-based resource-efficient location-aware PMIPv6 extension for seamless mobility in the next generation IoT networks. The proposed approach efficiently utilizes the network resources using location information and received signal strength (RSS). This solution enhances the performance of PMIPv6 protocol in terms of signaling cost, and load on network entities. Furthermore, mathematical models are derived in terms of signaling cost, load distribution. The proposed solution is compared with the existing RSS-based PMIPv6 extension protocols. The results show that the proposed scheme enhances the performance and is a resource-friendly for the next generation large scale IoT networks.

This paper introduces a new modular cascaded multilevel inverter (MLI) which is composed of a cascaded connection of submodules (SMs). The combination of basic cells (BCs) with polarity changer H-Bridge produces the SMs, which is the basis of the introduced inverter. Owing to its high modularity, the proposed inverter can be extended to reach higher voltage levels. Hence, two extensions are suggested: 1) Extension based on cascading more BCs within each SM, and 2) Extension based on cascading more SMs. Using a set of algorithms, the magnitude of DC voltage sources is determined to synthesize the proper AC side voltage waveform. Furthermore, the proposed topology is optimized in different ways to achieve the desired purposes, such as maximizing the number of voltage levels, minimizing the number of switches and DC sources, and providing the least total standing voltage on the switches. Therefore, in each of the mentioned optimization goals, the optimal structure is obtained. A prototype of the presented MLI is defined and its performance is verified through several simulation studies in MATLAB/Simulink and experimental tests. Besides, a comprehensive comparison is made with the best performance of the existing alternatives to demonstrate the merits and superiorities of the proposed MLI.

One of the combinatorial optimization problems is Knapsack problem, which aims to maximize the benefit of objects whose weight not exceeding the capacity of knapsack. This paper introduces artificial bee colony algorithm to select a subset of project and represented by knapsack problem to put the best investment plan which achieve the highest profits within a determined costs, this plan is one of the applications of the financial field. The result from the proposed algorithm implemented by matlab (8.3) show the ability to find best solution with precisely and rapidity compared to genetic algorithm http://dx.doi.org/10.25130/tjps.23.2018.039

Policy-based chameleon hash (PCH) is one of the techniques used for rewriting transaction-level data stored in blockchains. This technique integrates the access policy of the attribute-based encryption (ABE) in the transactions and only allows users with attributes set satisfying the access policy to modify the transactions. However, some operations in the PCH-based rewritable blockchain solution require high computational cost which may impact the performance of user systems, especially on resource-constrained devices. To solve this problem, we propose an online/offline rewritable blockchain with auditable outsourced computation (OO-RB-AOC) scheme. We utilize the ring signature to ensure the credibility of multiple attribute authorities, and adopt the online/offline technique for generating the hash of the rewritable transaction. In addition, expensive computations (e.g., pairings) required for rewriting the transactions can be outsourced to the clouds. The users can rest assured that the computations from the clouds are correct with the audit mechanism of the proposed scheme. On the other hand, the proposed scheme offers a desirable feature for commercial application where the clouds can limit the number of outsourced requests according to the subscription of the users. We also prove the security of the proposed OO-RB-AOC scheme. Finally, we present a theoretical comparison and experimental analysis of the proposed scheme.

The increasing popularity of remote Cloud File Sharing (CFS) has become a major concern for privacy breach of sensitive data. Aiming at this concern, we present a new resource sharing framework by integrating enterprise-side Attribute-Based Access Control/eXtensible Access Control Markup Language (ABAC/XACML) model, client-side Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme, and cloud-side CFS service. The framework workflow is provided to support the encrypted-file writing and reading algorithms in accordance with ABAC/XACML-based access policy and attribute credentials. However, an actual problem of realizing this framework is that policy matrix, derived from access policy, seriously affects the performance of existing CP-ABE from Lattice (CP-ABE-L) schemes. To end it, we present an optimal generation algorithm of Small Policy Matrix (SPM), which only consists of small elements, and generates an all-one reconstruction vector. Based on SPM, the improved CP-ABE-L scheme is proposed to reduce the cumulative errors to the minimum. Furthermore, we give the optimal estimation of system parameters to implement a valid Error Proportion Allocation (EPA). Our experiments indicate that our scheme has short size of parameters and enjoys efficient computation and storage overloads. Thus, our new framework with optimization methods is conducive to enhancing the security and efficiency of remote work on CFS.

We study the problem of multi-user dynamic searchable symmetric encryption (DMUSSE) where a data owner stores its encrypted documents on an untrusted remote server and wishes to selectively allow multiple users to access them by issuing keyword search queries. Specifically, we consider the case where some of the users may be corrupted and colluding with the server to extract additional information about the dataset (beyond what they have access to). We provide the first formal security definition for the dynamic setting as well as forward and backward privacy definitions. We then propose SE, the first provably secure DMUSSE scheme and instantiate it in two versions, one based on oblivious data structures and one based on update queues, with different performance trade-offs. Furthermore, we extend SE to support verifiability of results. To achieve this, users need a secure digest initially computed by the data owner and changed after every update. We efficiently accommodate this, without relying on a trusted third party, by adopting a blockchain-based approach for the digests dissemination and deploy our schemes over the permissioned Hyperledger Fabric blockchain. We prototype both versions and experimentally evaluate their practical performance, both as stand-alone systems and running on top of Hyperledger Fabric.

Dye-Sensitized Solar Cells (DSSCs) has been extensively explored as a third generation solar cell due to their simple fabrication procedures, better performance especially under low-light intensities, aesthetic features, etc. DSSCs provide an efficient and simple effectuated technology for prospective energy demands. Computing the experimental data in a high performance platform is really an appreciable task for better understanding and accessibility for photovoltaic technology. In this research paper, we fabricate the proposed DSSC, process the experimental data, as an alternative method we compute the experimental data and obtain I-V and IPCE characteristics by programming in MATLAB.

We propose a flexible low complexity design (FLCD) of coded distributed computing (CDC) with empirical evaluation on Amazon Elastic Compute Cloud (Amazon EC2). CDC can expedite MapReduce like computation by trading increased map computations to reduce communication load and shuffle time. A main novelty of FLCD is to utilize the design freedom in defining map and reduce functions to develop asymptotic homogeneous systems to support varying intermediate values (IV) sizes under a general MapReduce framework. Compared to existing designs with constant IV sizes, FLCD offers greater flexibility in adapting to network parameters and significantly reduces the implementation complexity by requiring fewer input files and shuffle groups. The FLCD scheme is the first proposed low-complexity CDC design that can operate on a network with an arbitrary number of nodes and computation load. We perform empirical evaluations of the FLCD by executing the TeraSort algorithm on an Amazon EC2 cluster. This is the first time that theoretical predictions of the CDC shuffle time are validated by empirical evaluations. The evaluations demonstrate a 2.0 to 4.24x speedup compared to conventional uncoded MapReduce, a 12% to 52% reduction in total time, and a wider range of operating network parameters compared to existing CDC schemes., 13 pages, 4 figures

We evaluate the accuracy of model selection and associated short-run forecasts using beta autoregressive moving average ( β ARMA ) models, which are tailored for modeling and forecasting time series that assume values in the standard unit interval, ( 0 , 1 ) , such as rates, proportions, and concentration indices. Different model selection strategies are considered, including one that uses data resampling. Simulation evidence on the frequency of correct model selection favors the bootstrap-based approach. Model selection based on information criteria outperforms that based on forecasting accuracy measures. A forecasting analysis of the proportion of stored hydroelectric energy in South Brazil is presented and discussed. The empirical evidence shows that model selection based on data resampling typically leads to more accurate out-of-sample forecasts.

The report illustrates a stepwise execution for system for an electric drive having a control system using the software ‘Simulink’ on MATLAB. The induction motor (IM) presented in the report is constructed after an circuit in the stator reference frame that is actually based on space vector theory. The control system for motor is prepared via the (indirect) vector control (field oriented control) which is used for the rotor flux reference frame. It makes use of a converter. The prepared simulink model can be potentially implemented as model for illustrating electrical motors. Especially in terms of schooling where the output is centered and focused on getting introduced to new things by doing them and where the outcome is to get introduced to new things by the help of a practical real life approach. The following application thus correlates the space vector theory and practice.

Weakly supervised object detection (WSOD aims to train object detectors by using only image-level annotations. Many recent works on WSOD adopt multiple instance detection networks (MIDN, which usually generate a certain number of proposals and regard proposal classification as a latent model learning within image classification. However, these methods tend to detect salient object, salient object parts and clustered objects due to lack of instance-level annotations during training. Thus a core issue is how to guarantee that the network learn as many objects with precise bounding boxes as possible. In this paper, we address this issue by exploiting the potential of proposal scores during training. We propose an adaptive instance refinement (AIR framework with three novel designs, which can be integrated with MIDN into a single network. Specifically, adaptive instance mining attempts to discover all positive instances according to the score distribution of proposals and their spatial similarity. Adaptive score modulation dynamically adjusts proposal scores to make the network focus more on instances with different difficulties in different training iterations. Adaptive knowledge refinement distills important information from all previous stages by the weighted average of proposal scores. The experimental results on the PASCAL VOC 2007 and 2012 benchmarks and the MS COCO benchmark demonstrate that AIR significantly improves the performance of the original MIDN and achieves the state-of-the-art results.

Workflow scheduling in cloud environments has become a significant topic in both commercial and industrial applications. However, it is still an extraordinarily challenge to generate effective and economical scheduling schemes under the deadline constraint especially for the large scale workflow applications. To address the issue, this paper investigates the cloud workflow scheduling problem with the aim of minimizing the whole cost of workflow execution whereas maintaining its execution time under a predetermined deadline. A novel knowledge-based adaptive discrete water wave optimization (KADWWO) algorithm is developed based on the problem-specific knowledge of cloud workflow scheduling. In the proposed KADWWO, a discrete propagation operator is designed based on the idle time knowledge of hourly-based cost model to adaptively explore the huge search space. The adaptive refraction operator is employed to avoid stagnation and expand the available resource pool. Meanwhile, the dynamic grouping based breaking operator is designed to exploit the excellent block structure knowledge of task allocation scheme and corresponding resource to intensify the local region and accelerate convergence. Extensive simulation experiments on the well-known scientific workflow demonstrate that the KADWWO approach outperforms several recent state-of-the-art algorithms.

Fusion and interaction of multimodal features are essential for video question answering. Structural information composed of the relationships between different objects in videos is very complex, which restricts understanding and reasoning. In this paper, we propose a quaternion hypergraph network (QHGN) for multimodal video question answering, to simultaneously involve multimodal features and structural information. Since quaternion operations are suitable for multimodal interactions, four components of the quaternion vectors are applied to represent the multimodal features. Furthermore, we construct a hypergraph based on the visual objects detected in the video. Most importantly, the quaternion hypergraph convolution operator is theoretically derived to realize multimodal and relational reasoning. Question and candidate answers are embedded in quaternion space, and a Q&A reasoning module is creatively designed for selecting the answer accurately. Moreover, the unified framework can be extended to other video-text tasks with different quaternion decoders. Experimental evaluations on the TVQA dataset and DramaQA dataset show that our method achieves state-of-the-art performance.

Musculoskeletal simulations are useful in biomechanics to investigate the causes of movement disorder, to estimate non-measurable physiological quantities or to study the optimality of human movement. We introduce Bioptim, an easy-to-use Python framework for biomechanical optimal control, handling musculoskeletal models. Relying on algorithmic differentiation and the multiple shooting formulation, Bioptim interfaces nonlinear solvers to quickly provide dynamically consistent optimal solutions. The software is both computationally efficient (C++ core) and easily customizable, thanks to its Python interface. It allows to quickly define a variety of biomechanical problems such as motion tracking/prediction, muscle-driven simulations, parameters optimization, multiphase problems, etc. It is also intended for real-time applications such as moving horizon estimation and model predictive control. Six contrasting examples are presented, comprising various models, dynamics, objective functions and constraints. They include data-driven simulations (i.e., a multiphase muscle driven gait cycle and an upper-limb real-time moving horizon estimation of muscle forces) and predictive simulations (i.e., a muscle-driven pointing task, a twisting somersault with a quaternion-based model, a position controller using external forces, and a multiphase torque-driven maximum-height jump motion).

Constrained operations for autonomous vehicles have been extensively studied in the literature over the recent years. However, to the best of the authors knowledge, all of the existing works address only constant or time-varying constraint functions. In this work, we study path-dependent constraint requirements, which explicitly depend on the path parameter, instead of depending on the time variable directly. This approach is more practical in reality, where the constraint requirements are often shaped by the environment boundaries. From the system users perspectives, it is also much easier to define constraint functions based on the path parameter. A modified version of the universal barrier function is used in the analysis of pathdependent constraint requirements. We show that under the proposed novel control scheme, the vehicles line-of-sight distance and angle error terms can converge into small sets near the equilibrium with a fixed-time convergence rate, while the pathdependent constraint requirements are satisfied at all time. A simulation and an experiment study further demonstrate the efficacy of the proposed scheme.

Smart grid is a typical application of industrial cyber-physical systems (ICPS) in the electric power industry. Due to the exposure to different kinds of uncertainties and unpredictable faults, how to reliably assess the short-term voltage stability (SVS) of smart grids to prevent the occurrence of large-scale blackouts is still of primary concern. To tackle this challenging problem, this article develops a novel machine learning scheme to achieve accurate and interpretable online SVS assessment in two steps. First, it utilizes time-series shapelet transform to extract key dynamics and convert the postfault time series into flat features. Second, it designs a graph convolutional network (GCN) to incorporate these features with topology information for SVS assessment. The GCN explores the spatial-temporal dynamics of power system via graph convolution and introduces a system layer to derive the final assessment result. Compared with conventional methods, this novel scheme makes full use of the spatial-temporal information in SVS dynamics, resulting in higher assessment accuracy and stronger adaptability. Besides, it is capable of discovering certain valuable underlying rules and patterns related to SVS. Test results on the IEEE 39-bus system and real-world Guangdong Power Grid in South China verify the effectiveness of the proposed scheme.

As the backbone of Industry 4.0, industrial cyber-physical systems (ICPSs) that are geographically dispersed, federated, cooperative, and security-critical systems become the center of interest from both industry and academia. In ICPS, there are huge amounts of devices, such as sensors and actuators, which are embedded and networked together to improve the performance of real-time monitoring and control. Reliability and temperature are two important concerns of these embedded and networked devices in ICPS due to their stringent requirement of reliable execution and long lifespan. In this article, we study the problem of maximizing soft-error reliability of CPU- and GPU-integrated embedded platforms deployed in ICPS under the temperature constraint. To speed up the estimation of soft-error rate (SER) and temperature, we train an artificial neural network (ANN) that is able to quickly and accurately derive the system's SER and temperature. To solve the temperature-constrained reliability optimization problem, we propose a feedback control-based task scheduling scheme that adaptively determines the number of tasks admitted in the system and the number of replicas for the admitted tasks. We perform a series of simulation experiments to verify the efficacy of our scheme. The experimental results demonstrate that: 1) the estimated SER and temperature derived by our ANN-based method are very close to the ground-truth data and 2) our proposed feedback control-based task scheduling method can improve system reliability by up to 184.2% with a lower peak temperature when compared with one baseline and two state-of-the-art methods.

To reduce the cost of human and material resources and improve the collaborations among medical systems, research laboratories and insurance companies for healthcare researches and commercial activities, electronic medical records (EMRs) have been proposed to shift from paperwork to friendly shareable electronic records. To take advantage of EMRs efficiently and reduce the cost of local storage, EMRs are usually outsourced to the remote cloud for sharing medical data with authorized users. However, cloud service providers are untrustworthy. In this paper, we propose an efficient, secure, and flexible EMR sharing system by introducing a novel cryptosystem called dual-policy revocable attribute-based encryption and tamper resistance blockchain technology. Our proposed system enables EMRs to be shared at a fine-grained level and allows data users to detect any unauthorized manipulation. Moreover, the key generation center can revoke malicious users without affecting the honest users. We provide the formal security model as well as the concrete scheme with security analysis. The experimental simulation and experimental analysis of our proposed scheme demonstrate that our proposed system has superior performances to the most relevant solutions.

In this research paper, CBIR algorithm development using RGB histogram-based block contour method to improve the retrieval performance is presented along with the simulation results. CBIR to extract the visual content of an image automatically like color, texture, size, direction, distance, nearness or its shape using HSV colour RGB histogram & block contour methods is proposed here in this paper. The work presented in this paper is a new methodology of the picture retrievals by the combination of different parameters of the query image with the help of RGB histograms & the block contours. Simulations are carried out in the Matlab environment and the simulation results are observed. The CBIR based system is going to be tested using different types of test images for varieties & finally the nearest test outcomes will be got & be tabulated neatly. Here, in this proposed work using different sizes of the blocks & the no. of characteristics & the time obtained for extracting those realistic parameters could be found out & also other parameters such as the recalling, precision, characteristics were calculated and compared with existing systems. The precision and recall are used to calculate the performance of the designed system. The results presented gives precision values & the values of the recall parameter of the developed & existing CBIR systems, which shows the efficacy of the proposed methodology that is superior. Here, the observation is – the developed methodology will be highly unique, its performance will be more superior to the existing systems. Finally, to conclude, it could be iterated that this contributory work was developed to show about the RGB histogram & block contour concept involvement to extract the different features of the images and carry out the performance of system (retrieval). Finally, the results of the simulation gives the improved performance of the system (retrieval) over the others, thus showing the profoundness or the effectiveness of the research work done.

Embedded SoC System verification have become chief challenge in recent years due to increase in complex architectures and software’s. Even though SoC caters the needs for implementing the complex architectures, verification still remains crucial in terms of the accuracy, time computation and most importantly complex methodologies. Several methodologies were proposed, one such is Universal Verification Methodology (UVM), which occupies the major role in the SoC verification, UVM needs brighter light of research for the better improvisation in terms of the testing features and time of computation. Hence HUM (Hybrid Universal Methodology) was proposed with idea of extending the UVM by integrating the Fault Injection Systems along with Intelligent Predictors. The proposed tool has been tested with the IoTWireless Transceivers algorithms. The algorithms are targeted for the ARM architectures and results proved to be more vital.

UVM is the most important mechanism in the field of SoC Verification. But complex characteristics, implementation difficulties in UVM still persist which leads to at most challenging part for the improvisation. To solve this issue, a new verification methodology called VEDA (Verification Environment for IP Architectures) which integrates python as the verification language is proposed. The powerful features of Python are used to bring the most versatile environment for verification and to make the UVM as the user friendly mechanism. The paper proposes the verification mechanism for the different IP’s which plays asignificant role in all IoT applications. Further the proposed method is being compared to existing UVM and also other existing python HDL tools and proved to be more advantages when compared to the existing methodologies.

The “Quantum Dot Cellular Automata” or QCA is mainly used to design and simulate ‘3D’ structures for area-occupation, latency and total power reduction of any electronic circuit. At present, this “Nano-technology world” requires less-complex, small-size, high-frequency, fault-tolerant, low-power devices to increase the component-density in a single die. QCA designs are the well-known and optimum alternative of CMOS designs due to its above mentioned advantages. Further, reversible gates can also be formed based on QCA design. Reversible logic not only reduce the complexity of the design but also it can increase the information storage possibility by saving the power and energy. It is capable of reducing the leakage power (‘KTln2’ amount of dissipated energy for every bit can be ignored due to this logic). Cost of any device depends on the delay, power and occupied-area of the device which can be decreased by reversible multilayer (‘3D’) QCA structures. So, this proposed technique has an ability to provide the design for a cost-effective electronic device with good scalability and fault-tolerance. In the present work, we have simulated of an “Arithmetic and Logic Unit” in “QCA Designer” and “Xilinx” software. The first software is used due to the above advantages and here we apply reversible logic (reversible TSG gate with reversible AND-OR gate). This ALU is capable of performing one arithmetic (addition of two inputs) and three logical operations (AND, OR and XOR operations of two inputs) at a time. The TSG gate is used here as a full-adder with two input XOR operation and The reversible AND-OR gate is utilized for getting the AND result and OR result of two inputs. This design is also required a “4:1 MUX” which helps to select the operations properly. Schematic designs of proposed design with calculated values of delay and total power (for different room-temperature) are achieved in our work by using ‘Xilinx’ simulator. The used Verilog coding of our formed-design is also implemented in a “FPGA Board” (“Spartan 3E”) for hardware verification. Our presented circuitry can also continue its functionalities at high-temperature.

Deep learning methods have achieved significant progress in the presence of correctly annotated datasets in instance segmentation. However, object classes in large-scale datasets are sometimes ambiguous, which easily causes confusion. Besides, limited experience and knowledge of annotators can lead to mislabeled object semantic classes. To solve this issue, a novel method is proposed in this paper, which considers different roles of noisy class labels in different sub-tasks. Our method is based on two basic observations: firstly, the foreground-background annotation of a sample is correct even though its class label is noisy. Secondly, symmetric loss benefits the model robustness to noisy labels but harms the learning of hard samples, while cross entropy loss is the opposite. Based on the two basic observations, in the foreground-background sub-task, cross entropy loss is used to fully exploit correct gradient guidance. In the foreground-instance sub-task, symmetric loss is used to prevent incorrect gradient guidance provided by noisy class labels. Furthermore, we apply contrastive self-supervised loss to update features of all foreground, to compensate for insufficient guidance provided by partially correct labels especially in the highly noisy setting. Extensive experiments conducted with three popular datasets (i.e., Pascal VOC, Cityscapes and COCO) have demonstrated the effectiveness of our method in a wide range of noisy class label scenarios.

The challenging problem in the traffic system is lane detection. This Lane detection which attracts the computer vision community’s attention. For computer vision and machine learning techniques, the Lane detection which acts as the multi-feature detection problem. Many machine learning techniques are used for lane detection. Driver support system is one of the most important features in the modern vehicles to ensure the safety of the driver and decrease the vehicle accidents on road. Road Lane detection is the most challenging task and complex tasks now-a-days. Road localization and relative position between vehicle and roads which also includes with this. But in this journal, we propose a new method. Here, an on- board camera to be used which is looking outwards are presented here in this work. This proposed technique which can be used for all types of roads like painted, unpainted, curved, straight roads etc in different weather conditions. No need for camera calibration and coordination of the transform, may be any changing illumination situation, shadow effects, various road types. No representation for speed limits. This includes that the system acquires the front view using a camera mounted on the vehicles and detect the Lane by applying the code from the Python Programming process. This proposed system does not require any more information about roads. This system which demonstrates a robust performance for Lane detection.

This paper highlighted on a new improvement of the salp swarm algorithm (SSA) that is taken from the behavior of salp fishes. This shows a promising result, however it suffered from some drawbacks that limited its ability. The proposed improvement of SSA is called SSA 2. In this paper the design system where the ZigBee protocol received temperature, humidity and gas from the sensors. The base station collects received data and sends it to platform in order to process these data via SSA2. This system enables the ability to monitor and track any fault that may be occurring. A new term has been developed to make the algorithm more targeted in finding the optimal answer. Moreover, the calculation results and simulation results in Matlab shows great enhancement in the accuracy and precision of the results at very fast speed for convergence compared with the original salp swarm algorithm (SSA).

The small signal stability analysis has got much importance for a single machine infinite bus system (SMIB). Conventional controllers are not smart to give the faster response. Hence advanced and intelligent controllers like Fractional order PID (FOPID) and State space analysis methods are used to obtain the better responses. In this article, corresponding SMIB system is analyzed using simulation studies and are carried out in MATLAB 2017a.

In this paper, a new dataset, the driver emotion facial expression (DEFE) dataset for drivers spontaneous emotions analysis is introduced. The dataset includes facial expression recordings from 60 participants during driving. After watching a selected video-audio clip to elicit a specic emotion, each participant completed the driving tasks in the same driving scenario and rated his/her emotional responses during the driving processes from the aspects of dimensional emotion method and discrete emotion method. The study also conducted classication experiments to recognize the scales of arousal, valence, dominance, as well as the emotion category and intensity to establish baseline results for the proposed dataset. Furthermore, this paper compared emotion recognition results difference through facial expressions between dynamic driving and static life scenarios. The results showed signicant differences in the AUs presence of facial expressions between dynamic driving and static life scenarios, Therefore, to accurately recognize the drivers emotions to establish a reliable emotion-aware human-machine interaction system, thereby improving driving safety and comfort, publishing a human emotion dataset specically for the driver is necessary. The proposed dataset will be publicly available so that researchers worldwide can use it to develop and examine their driver emotion analysis methods.

In cloud data sharing systems, Searchable Encryption (SE) schemes ensure data confidentiality with retrieving, but it faces several issues in practice. First, most of the previous Ciphertext-Policy Attribute-Based Keyword Search (CP-ABKS) systems enable users to initiate search requests with a single keyword, which results in many inaccurate results to be returned, thereby wasting computing and bandwidth resources. Second, untrusted cloud servers may return a small portion of incomplete search results to compress communication overhead. Besides, most CP-ABKS schemes only support an unshared multi-owner setting, which incurs a large amount of computational and storage overhead. Furthermore, when the keyword space is a polynomial, most of the previous schemes suffer from offline keyword guessing attacks. To address these issues, we focus on a multi-keyword search scheme which supports the verification of search results without losing efficiency by combining Ciphertext Policy Attribute-Based Encryption (CP-ABE) technology under the shared multi-owner mechanism. We show the security of our scheme, which achieves selective security against offline keyword guessing attacks and guarantees the unforgeability of signatures. The comparison of experimental results illustrates that our scheme is effective and enjoys superior functionalities than the most relevant solutions.