Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Navigation Control Design for Biomedical Untethered Microrobot with Network-induced Delays

In this paper, the optimal navigation control design for biomedical untethered microrobot is comprehensively investigated in discrete-time domain with stochastic network-induced delays. First, the error dynamics of the microrobot tracking location and velocity are analyzed based on the 3D-based microrobot navigation modeling. Then, the optimal navigation optimization problem is formulated to regulate the microrobot to achieve the target reference trajectory, and a two-step control algorithm is proposed by using a backward recursion method. In particular, for each sampling interval, the optimal control gain is iteratively derived off-line and the control strategy can be calculated on-line in a real-time fashion.
Hop-by-Hop Congestion Measurement and Practical Active Queue Management in NDN

Content replication and name-based routing in Named Data Network (NDN) naturally lead to connectionless multi-source and multipath transmissions. Traditional congestion control designed for end-to-end connections cannot well fit this architecture. Explicit congestion notification (ECN) can better support NDN because congestion is detected where it occurs and ECN can timely notify the traffic initiator of congestion. NDN can be deployed as an overlay protocol (sharing the underlying devices with other protocols), which means the congestion may also occur at an underlying device (e.g. a switch). In this case, the NDN nodes cannot access the queue or other link status at a remote underlying device for congestion detection. A promising ECN scheme must be able to detect congestion happening anywhere (at an NDN node or an underlying device) without using underlying link information. This paper proposes Hop-byHop Congestion Measurement (HbHCM) and Practical Active Queue Management (PAQM) to enable detecting congestion and generating ECN at NDN nodes via monitoring the change of transmission delays. HbHCM measures the transmission delay at the hop level and PAQM converts the delay to ECN signals to notify consumers. We compared HbHCM + PAQM with two milestone solutions (router-label and ECN-based). The simulation results show that HbHCM + PAQM can accurately detect congestion, improve bandwidth utilisation and better support multipath transmission, no need to rely on route or link information.
Face Identification System Using Convolutional Neural Network for Low Resolution Image

This research aims to determine the performance of face identification on closed circuit television (CCTV) cameras. There are two data classifications used, namely training data and testing data. The training data use the CASIA-Webface dataset. Meanwhile, the testing data consists of two data, namely the source data and the target data. The source data are form of photos taken using a Digital Single Lens Reflex (DSLR) camera, while the target data use video data taken with CCTV. The source data consists of 10 IDs, each of them has 1 image for each size, so the total images used in the source data are 50 IDs. While the target data are 20 IDs, each of them has 20 face images with low resolution characteristics, less light and face capture not parallel to CCTV. This research uses Convolutional Neural Network (CNN) method with ResNet50 architecture, ArcFace as a loss function in the training process and Cosine Similarity for the face identification process. ResNet50 and ArcFace use an embedding size of 512 and in the training process, ArcFace’s scale and margin parameters are 64 and 0.5. The results indicate differences in accuracy, True Positive Rate (TPR) and False Positive Rate (FPR) from the face identification process between the image sizes used and the respective IDs in the source data. The method used had the highest performance for face image identification scenarios of 128 x 128 pixels with accuracy and FPR reached 99.30% and 0.02%. From the TPR, the method used had high performance at size of 512 x 512 pixels namely 91.50%.
Deep Reinforcement Learning based Node Pairing Scheme in Edge-chain for IoT Applications

Nowadays, the Internet of Things $({I}{o}{T})$ is playing an important role in our life. This inevitably generates mass data and requires a more secure transmission. As blockchain technology can build trust in a distributed environment and ensure the data traceability and tamper resistance, it is a promising way to support ${I}{o}{T}$ data transmission and sharing. In this paper, edge computing is considered to provide adequate resources for end users to offload computing tasks in the blockchain enabled ${I}{o}{T}$ system, and the node pairing problem between end users and edge computing servers is researched with the consideration of wireless channel quality and the service quality. From the perspective of the end users, the objective optimization is designed to maximize the profits and minimize the payments for completing the tasks and ensuring the resource limits of the edge servers at the same time. The deep reinforcement learning (DRL) method is utilized to train an intelligent strategy, and the policy gradient based node pairing (PG-NP) algorithm is proposed. Through a deep neural network, the well-trained policy matched the system states to the optimal actions. The REINFORCE algorithm with baseline is applied to train the policy network. According to the training results, as the comparison strategies are $\max$-credit, $\max$-SINR, random and $\max$-resource, the PG-NP algorithm performs about 57% better than the second-best method. And testing results show that PGNP also has a good generalization ability which is negatively correlated with the training performance to a certain extend.
State and faults estimation via Artificial Neural Networks

This paper deals with the state and fault estimation for non linear systems modeled using the Takagi Sugeno approach. An artificial neural network with unknown inputs is used in the objective of estimate state and faults affecting the system. Firstly, the problem of state estimation is considered. In second step, the proposed approach is extended to the actuator fault estimations. The proposed method is applied to an academic example to show its efficiency.
Deep Dynamic Clustering of Spam Reviewers using Behavior-Anomaly-based Graph Embedding

Online reviews have become an increasingly important factor in the purchase decision of a customer. However, many spammers write deceptive reviews to alter the credibility of a product/service. Often than not, these spammers exhibit group behavior, which can be exploited to differentiate them from authentic reviewers. Such behaviors are found in spammers working together as well as with crowdsourced review manipulators. The existing graph-based spammer detection approaches do not capture the dynamic and nonlinear relationship between the users. This paper aims to address this issue by introducing a method to use a deep structure embedding approach that preserves highly nonlinear structural information along with the dynamic aspects of user reviews to identify and cluster the spam users. It is worth mentioning that, in the experiment with real datasets, our method captures about 92% of all spam reviewers using an unsupervised learning approach.
A Consequent-Pole Magnetic-Geared Machine With Axially Embedded Permanent Magnets for Hybrid Electric Vehicle

This paper reveals the fundamental reason for the asymmetrical issue in magnetic-geared machines (MGMs) based on flux modulation theory. The analysis indicates that the magnetic circuits of three-phase windings in MGMs are inherently asymmetrical. This asymmetrical issue is even more severe in conventional consequent-pole MGMs (CP-MGMs) because of the more distorted magnetic field. Hence, to address the asymmetrical issue, a novel structure featuring enhanced harmonic elimination capability is proposed in this paper. Consequently, torque density can also be improved. In particular, the proposed CP-MGM employs modular structure and axially embedded permanent magnets (PMs) to achieve symmetrical back electromotive force (EMF) waveforms and improved torque capability, respectively. To further improve the electromagnetic performance of the proposed CP-MGM, the PM arc ratio and flux modulator width ratio are analytically designed, which provides a general design guideline for CP-MGMs. To illustrate the merits of the proposed CP-MGM, a few other MGMs are included for a fair comparison based on finite element analysis (FEA). Results show the proposed CP-MGM can achieve more symmetrical back EMF waveforms and lower torque ripple, as well as lower PM consumption and higher torque density, as compared with its MGM counterparts.
Minimizing NOMA Resource Usage for Cellular IoT Networks through Distributed Source Coding

Many research endeavors to optimize the performance of NOMA (non-orthogonal multiple access) have focused on mobile broadband applications, where transmissions typically occur in the downlink with full-buffered traffic and the primary performance metric is the sum date rates. Machine-type communications, however, are distinctly different, where transmissions typically occur in the uplink and traffic load from individual machines is low with short payloads. To optimize NOMA performance for resource-constrained cellular IoT (Internet of Things) networks, in this paper we investigate a problem to minimize total radio resource used by all IoT devices for sending the collected data to the base station. By employing distributed source coding (DSC) between the two nodes in a NOMA pair, we first solve the resource optimization problem of a two-node pair through joint data and power allocation. With the calculated resource usage of two nodes in OMA and NOMA, we then construct a weighted graph and investigate maximum weight matching for pairing IoT devices in the network with minimum total resource usage. We compare different pairing algorithms for solving the problem while striking a good balance between complexity and optimality. Simulation results show that DSC can achieve significant performance gain for NOMA resource usage compared to the baseline approaches in cellular IoT networks.
A Privacy-Preserving Location-Aware and Traffic Order-Based Route Collection Scheme in VANETs

Collecting driving routes is effective in predicting traffic patterns and alleviating traffic jams. However, due to the sensitivity of the location information, drivers are usually reluctant to share their route information. Although some efforts have been made to address this challenge, most of them either do not consider traffic order issues or fall short of achieving practical efficiency. In this paper, we propose an efficient and privacy-preserving route collection scheme, named EPRC, to solve the above-mentioned problems. The main idea of EPRC is to perform location-aware and traffic order-based route aggregation on drivers’ encrypted data using super-increasing sequences and a homomorphic encryption cryptosystem. The proposed scheme achieves better computation and communication efficiency by reducing computational complexity and communication overhead from $O(M)$ to $O(1)$, where M denotes the number of road segments. Security analysis demonstrates the privacy of an individual driver’s route is preserved under standard cryptographic assumptions. Performance evaluations via implementing EPRC on mobile devices and systems show EPRC’s efficiency in terms of computation and communication costs.
A Framework to Preserve User Privacy for Machine Learning as a Service

Suffered from the contradiction between the limited capacity of local devices and large size of DNN models, a practical solution is transferring the heavy computational tasks from the local to the server side such as cloud. However, the untrusted server naturally requires all the user data to train neural networks and infer results, which causes the asset loss of the local and raises serious privacy concerns on user’s sensitive information. To solve this problem in scenarios of machine learning as a service, we propose a general framework to balance the user privacy, model accuracy and training efficiency, simultaneously. Specifically, our representative subset selection algorithm takes the training value of data into account, selecting the most representative subset from the training data, in order to mitigate the loss of data assets, lower down the transmission overhead from the local to the server and lessen the training burden on the server at the same time. We also design a noisy representation transformation algorithm applying on the features extracted by neural networks to further perturb the data within the selected representative subset. Extensive experiments demonstrate that our framework can run locally with little sacrifice on the computation resource. It can not only protect private data before uploading, but also promote the training efficiency of servers.
Journey to MARS: Interplanetary Coding for relieving CDNs

The amount of consumer data transmitted over the internet will increase in the future. At the moment, this traffic is mainly handled by Content Delivery Networks (CDN) via Hypertext Transfer Protocol (HTTP). However, this server-based approach has the disadvantage that the servers themselves and their connections to the Internet are under a high load, which makes them a bottleneck. Therefore, new approaches for efficient content distribution are sought. One outstanding approach is the Interplanetary File System (IPFS), the synthesis of various successful Peer-to-Peer (P2P) approaches. Theoretically, the load on the server can be reduced by distributing the data to several nodes. To transmit data optimally, however, a high degree of coordination between nodes is necessary. Research on other content distribution schemes has shown that the cooperation effort can be avoided by using Random Linear Network Coding (RLNC). This has not been used in IPFS so far. In this work we present Multi Access Recoding System (MARS), a protocol which combines IPFS with RLNC. Simulations on the Interplanetary Testbed (IPTB) have shown that MARS is able to reduce the server load and download time by up to 50% compared to a transmission using only a single server and up to 45% compared to the same setup without RLNC. Even without coordination, this only causes an additional network load of 30%. In addition, MARS improves the download time by at least 15% even for very few peer nodes or peers with little storage.
Controller design of DC/DC buck converter based on feedback linearization with integrator

The paper presents the feedback linearization technique of voltage mode DC/DC buck converter. The nonlinear state-space model is presented and has been stabilized by pole placement technique and finally the voltage control of output has been realized by integral control. The designed controller has been simulated by MATLAB. Results are shown that the developed controller has good dynamic and static behavior.
Meeting Stringent QoS Requirements in IIoT-based Scenarios

The Industrial Internet of Things (IIoT) provides automation solutions for industrial processes through the interconnection of different sensors, actuators and robotic devices to the Internet, enabling for the automation of manufacturing processes through Factory Automation. However, IIoT processes are often critical, and require very high Quality of Service (QoS) to work properly, as well as network scalability and flexibility. Fog computing, a paradigm that brings computation and storage devices closer to the edge of the network to enhance QoS, as well as Software-Defined Networking (SDN), which enables for network scalability and flexibility, can be integrated into IIoT architectures in the form of fog nodes that integrate both, computation resources and SDN capabilities, to meet these needs. However, the QoS of the IIoT system depends on the placement of these fog nodes, creating a need to obtain placements that optimize QoS in order to meet the requirements by minimizing the latency between the fog nodes and the IIoT devices that consume their services. In this paper, this fog node placement problem is formalized and solved by means of Mixed Integer Programming. We also show relevant experimental results of our formulation and analyze its performance.
Continual Information Cascade Learning

Modeling the information diffusion process is an essential step towards understanding the mechanisms driving the success of information. Existing methods either exploit various features associated with cascades to study the underlying factors governing information propagation, or leverage graph representation techniques to model the diffusion process in an end-to-end manner. Current solutions are only valid for a static and fixed observation scenario and fail to handle increasing observations due to the challenge of catastrophic forgetting problems inherent in the machine learning approaches used for modeling and predicting cascades. To remedy this issue, we propose a novel dynamic information diffusion model CICP (Continual Information Cascades Prediction). CICP employs graph neural networks for modeling information diffusion and continually adapts to increasing observations. It is capable of capturing the correlations between successive observations while preserving the important parameters regarding cascade evolution and transition. Experiments conducted on real-world cascade datasets demonstrate that our method not only improves the prediction performance with accumulated data but also prevents the model from forgetting previously trained tasks.
Reliability-Aware Service Function Chain Backup Protection Method

Network Function Virtualization (NFV) decouples network functions from hardware, improves the flexibility of resource allocation, and enhances network scalability. Any failures of software and hardware in an NFV environment will result in the interruption of Service Function Chains (SFCs). As one of the key technologies of 5G, NFV has more stringent delay and reliability requirements for services. This paper takes software and hardware failures into account, and proposes a reliability-aware service function chain backup protection (RABP) method to meet SFCs’ high reliability and low latency demands. First, we formulate a mixed-integer linear programming model to maximize the revenue-to-cost ratio. Then, we propose a heuristic algorithm for suboptimal solutions. SFC deployment is divided into two stages: primary virtual network function (VNF) deployment and backup VNF deployment. The reliability enhancement and delay optimization deployment method is presented to deploy primary VNFs. A breadth-first search method is used to consolidate primary VNFs as much as possible to improve reliability of SFCs and reduce transmission delay. When deploying backup VNFs, a resource-efficient backup selection method is presented to reduce backup resource consumption while meeting reliability demands. Finally, experiment results show that the proposed RABP method not only has the best acceptance ratio and long-term average revenue to cost ratio, but also reduces backup resource consumption and transmission delay.
FAST-RAM: A Fast AI-assistant Solution for Task Offloading and Resource Allocation in MEC

As one of the key concepts in the 5G network, MEC can support the latency-sensitive and compute-intensive services by widely deploying computing and storage capacity to the base stations at the network edge. Because these services are sensitive to latency, the joint optimization problem of task offloading and resource allocation needs to be solved in a short time. In this paper, we propose a Fast AI-assistant Solution for Task Offloading and Resource Allocation in MEC (FAST-RAM), which can directly solve the joint optimization problem leveraging a deep neural network. FAST-RAM can produce the offloading policy and resource allocation scheme in milliseconds. Meantime, our solution has near-optimal performance and sufficient feasibility under different network environments.
Combination of Convolutional Neural Network and Gated Recurrent Unit for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to identify views and sentiment polarities towards a given aspect in reviews. Compared with general sentiment analysis, ABSA can provide more detailed and complete information. Recently, ABSA has become an important task for natural language understanding and has attracted considerable attention from both academic and industry fields. The sentiment polarity of a sentence is not only decided by its content but also has a relatively significant correlation with the targeted aspect. For this reason, we propose a model for aspect-based sentiment analysis which is a combination of Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU), utilizing the local features generated by CNN and the long-term dependency learned by GRU. Extensive experiments have been conducted on datasets of hotels and cars, and results show that the proposed model achieves excellent performance in terms of aspect extraction and sentiment classification. Experiments also demonstrate the great domain expansion capability of the model.
Fixed-Point Sampling Strategy for Estimation on Current Measurement Errors in IPMSM Drives

Due to the unbalanced three-phase currents and the torque ripples and speed fluctuations to the drive caused by the current sampling errors of three-phase current sensors and corresponding signal processing circuits, the estimation on current sampling errors is of great importance to eliminate these adverse effects. The observer-based methods, however, present two defects: first, they could increase the computational burden to the drives, thus limiting their practical applications; second, their high dependence on the system speed or position feedback information might degrade the estimation accuracy. To solve these problems, this article proposed a fixed-point sampling method of lower branch flows and three phase-currents, which adopts the relations between the measured currents, rather than any complex algorithm, to estimate the current sampling errors. To coordinate with the proposed strategy, a fixed-point sampling scheme was accordingly rolled out. Compared with the observer-based methods, as a fixed-point one, much applicable to actual digital controllers, it could reduce the computational burden of the drive. Meanwhile, its effectiveness was verified by experimental results on a 1.5-kW IPMSM prototype, which showed accurate estimated current sampling errors and the effectively eliminated torque ripples and speed fluctuations.
Reliable and Robust Weakly Supervised Attention Networks for Surface Defect Detection

Automated surface-anomaly detection of industrial products using deep learning is a critical task to the digitalization and intelligence of the manufacturing industry. In recent years, with the rapid development of artificial intelligence techniques, deep learning has been successfully applied to this task. However, the current defect detection methods based on deep learning usually adopt strong supervised learning strategy such as object bounding box or pixel-level labels to predict the location of defects, which leads to the performance of the algorithm depends on the number of data provided and the quality of the annotations. Therefore, how to significantly reduce the cost of data annotation without reducing the performance of the model, that is a challenging and urgent task. As such, this paper proposes a weakly supervised attention network designed for surface defect detection. It can simultaneously predict both the location and probability of defects only by using image-level labels. Experimental results also demonstrate that the proposed method is able to learn on a small number of surface defect data, and can accurately realize automatic evaluation of defect detection, showing great potential for industrial application.