Reviewerly/exHarmony huggingface dataset:
Heuristic smoothing ant colony optimization with differential information for the traveling salesman problem

The traveling salesman problem (TSP) is an NP complete problem with potential applications. Thus far, numerous approaches have been proposed to solve the TSP including exact, heuristic and metaheuristic methods. Among them, the ant colony optimization (ACO) algorithm, belonging to metaheuristic methods, has proven to be an efficient method for solving the TSP. However, the phenomena of rapid convergence to local optima and unsatisfactory computational accuracy distinctly limit the performance of ACO. This study therefore proposes a novel ACO algorithm (HSDACO) to compensate for these shortcomings. In the HSDACO algorithm, the involvement of heterogeneous population automation at each iteration generates better candidate solutions with maintaining parameter diversity. Then, the implementation of three smoothing techniques with the 2-Opt method guarantees the effective assessment of candidate solutions in favor of higher quality. Next, the introduction of a differential information updating mechanism, using differential edge information obtained from the candidate solutions, enables the evaporation operation of the pheromone trail to obtain more reasonable guidance. Subsequently, the design of evolutionary state estimation and adjustment is used to monitor the population under correct guidance and regulate the evolutionary state effectively and efficiently. Finally, the HSDACO algorithm is evaluated on public TSP benchmark instances and compared with the other state-of-the-art algorithms. Experimental results show that the proposed HSDACO achieves substantial improvement for mid-scale TSP instances and overwhelming advantages for small-scale TSP instances, in terms of solution accuracy and convergence speed.
Bankruptcy-evolutionary games based solution for the multi-agent credit assignment problem

Multi-agent Credit Assignment (MCA) problem is considered as one of the critical challenges in developing Multi-Agent Reinforcement Learning (MARL). The MCA problem addressed how to distribute global reward, which is received by the Multi-Agent System (MAS) owing to an interaction with the environment among the agents. In this paper, a two-step method is proposed to solve the MCA using the bankruptcy and Evolutionary Games (EG). The first phase turns the MCA problem into a bankruptcy game through introducing the constraint of Maximum Performance Power (MPP). In this game, the agents act as players through a mixed strategy. The game's outcome is the each agent's share of the global reward. An instance of credit assignment is extracted by determining each agent's share from the global reward. Using the mixed strategy leads to generate a lot of credit assignment instances. Therefore, finding the best instance of credit assignment is challenging. To select the best one in the second step, the EG is applied. In other words, the second step turns the MCA problem into an EG; the players are considered as the instances of credit assignment extracted from the first step. The proposed method was evaluated in an operational environment which is called Multi-score Puzzle (MsP) as compared to the state-of-the-art algorithms such as COMA, VDN, SQDDPG, ranking, dynamic and history-based methods. Simulation results indicated the better performance of the proposed method in terms of group learning rate, confidence, expertness, certainty and correctness. Efficiency was the only criterion in which the proposed method underperformed other methods.
High-Speed DSP Pipelining and Retiming techniques for Distributed-Arithmetic RNS-based FIR Filter Design

Digital FIR Filters plays a major role in many signal processing applications. Generally, these filters are designed with multipliers and adders to find the filter output. This paper acquaints how to reduce the complexity of higher order FIR filter by using performance optimization techniques like retiming and pipelining. The filter’s throughput, energy efficiency, and latency, as well as the complexity of its technology, all need to be improved. By adopting pipelining technique, the arithmetic processes of addition and multiplication are separated. The break addition procedure is retimed. The architecture of Pipelining and Retiming with m-tap filters and n-bit word lengths were designed. The smallest delay achieved by the proposed distributed arithmetic-based FIR Filter with pipelining was 2.564ns for a 4tap implementation receiving an 8bit input, while the largest delay achieved was 56.04ns for a 64-tap implementation receiving a 32-bit word length. Delays as low as 0.68ns for a 4-tap implementation receiving an 8-bit input and as high as 4.53ns for a 64tap implementation receiving a 32bit word length have been achieved by using the suggested distributed arithmetic-based FIR Filter with retiming approach. Delay has been reduced by 73.2% for 4tap with 8bit input and by 91.9% for 64tap with 32bit word length compared to the pipelining approach.
Data-Driven Adaptive Prediction of Cloud Resource Usage

Abstract Predicting computing resource usage in any system allows optimized management of resources. As cloud computing is gaining popularity, the urgency of accurate prediction is reduced as resources can be scaled on demand. However, this may result in excessive costs, and therefore there is a considerable body of work devoted to cloud resource optimization which can significantly reduce the costs of cloud computing. The most promising methods employ load prediction and resource scaling based on forecast values. However, prediction quality depends on prediction method selection, as different load characteristics require different forecasting mechanisms. This paper presents a novel approach that incorporates data-driven adaptation of prediction algorithms to generate short- and long-term cloud resource usage predictions and enables the proposed solution to readjust to different load characteristics as well as both temporary and permanent usage changes. First, preliminary tests were performed that yielded promising results – up to 36% better prediction quality. Subsequently, a fully autonomous, multi-stage optimization solution was proposed. The proposed approach was evaluated using real-life historical data from various production servers. Experiment results demonstrate 9.28% to 80.68% better prediction quality when compared to static algorithm selection.
UAV penetration mission path planning based on improved holonic particle swarm optimization

To meet the requirements of safety, concealment, and timeliness of trajectory planning during the unmanned aerial vehicle (UAV) penetration process, a three-dimensional path planning algorithm is proposed based on improved holonic particle swarm optimization (IHPSO). Firstly, the requirements of terrain threat, radar detection, and penetration time in the process of UAV penetration are quantified. Regarding radar threats, a radar echo analysis method based on radar cross section (RCS) and the spatial situation is proposed to quantify the concealment of UAV penetration. Then the structure-particle swarm optimization (PSO) algorithm is improved from three aspects. First, the conversion ability of the search strategy is enhanced by using the system clustering method and the information entropy grouping strategy instead of random grouping and constructing the state switching conditions based on the fitness function. Second, the unclear setting of iteration numbers is addressed by using particle spacing to create the termination condition of the algorithm. Finally, the trajectory is optimized to meet the intended requirements by building a predictive control model and using the IHPSO for simulation verification. Numerical examples show the superiority of the proposed method over the existing PSO methods.
A Variation-Aware Ternary True Random Number Generator Using Magnetic Tunnel Junction at Subcritical Current Regime

In this article, a variation-aware ternary true random number generator (TTRNG) is proposed and simulated using stochastic switching of the magnetic tunnel junction (MTJ) in the current below critical current and carbon nanotube field-effect transistors (CNTFET). The proposed TTRNG does not need any converters and produces ternary random numbers directly. The stochastic behavior of the MTJ ensures the generation of a truly random sequence. At the same time, the CNTFET threshold voltage adjustability through flat band voltage allows the implementation of ternary logic circuits. A ternary post-processing block is also designed to ensure the equality of the ratio of the numbers generated by the proposed TTRNG, even with the process and voltage variations. Circuit-level simulations performed using the HSPICE tool indicate the correct operation of the proposed TTRNG circuit. Moreover, statistical simulations also authenticate that the ternary sequence generated by the proposed TTRNG is of excellent quality, even in the presence of significant process and voltage variations. The proposed TTRNG can be used in various applications, including cryptography and Monte Carlo simulations. Also, the proposed circuit was redesigned and simulated using FinFET technology as an available technology for fabricating integrated circuits.
Time Minimization in Hierarchical Federated Learning

Federated Learning is a modern decentralized machine learning technique where user equipments perform machine learning tasks locally and then upload the model parameters to a central server. In this paper, we consider a 3-layer hierarchical federated learning system which involves model parameter exchanges between the cloud and edge servers, and the edge servers and user equipment. In a hierarchical federated learning model, delay in communication and computation of model parameters has a great impact on achieving a predefined global model accuracy. Therefore, we formulate a joint learning and communication optimization problem to minimize total model parameter communication and computation delay, by optimizing local iteration counts and edge iteration counts. To solve the problem, an iterative algorithm is proposed. After that, a time-minimized UE-to-edge association algorithm is presented where the maximum latency of the system is reduced. Simulation results show that the global model converges faster under optimal edge server and local iteration counts. The hierarchical federated learning latency is minimized with the proposed UE-to-edge association strategy.
A compositional approach to creating architecture frameworks with an application to distributed AI systems

Artificial intelligence (AI) in its various forms finds more and more its way into complex distributed systems. For instance, it is used locally, as part of a sensor system, on the edge for low-latency high-performance inference, or in the cloud, e.g. for data mining. Modern complex systems, such as connected vehicles, are often part of an Internet of Things (IoT). This poses additional architectural challenges. To manage complexity, architectures are described with architecture frameworks, which are composed of a number of architectural views connected through correspondence rules. Despite some attempts, the definition of a mathematical foundation for architecture frameworks that are suitable for the development of distributed AI systems still requires investigation and study. In this paper, we propose to extend the state of the art on architecture framework by providing a mathematical model for system architectures, which is scalable and supports co-evolution of different aspects for example of an AI system. Based on Design Science Research, this study starts by identifying the challenges with architectural frameworks in a use case of distributed AI systems. Then, we derive from the identified challenges four rules, and we formulate them by exploiting concepts from category theory. We show how compositional thinking can provide rules for the creation and management of architectural frameworks for complex systems, for example distributed systems with AI. The aim of the paper is not to provide viewpoints or architecture models specific to AI systems, but instead to provide guidelines based on a mathematical formulation on how a consistent framework can be built up with existing, or newly created, viewpoints. To put in practice and test the approach, the identified and formulated rules are applied to derive an architectural framework for the EU Horizon 2020 project “Very efficient deep learning in the IoT” (VEDLIoT) in the form of a case study.
Synchronous Medical Image Augmentation framework for deep learning-based image segmentation

Various deep learning (DL) models are widely applied in medical image analysis, and their performance depends on the scale and diversity of available training data. However, medical images often suffer from difficulty in data acquisition, imbalance in sample categories, and high cost of labeling. In addition, most image augmentation approaches mainly focus on image synthesis only for classification tasks, and rarely consider the synthetic image-label pairs for image segmentation tasks. In this paper, we focus on the medical image augmentation for DL-based image segmentation and the synchronization between augmented image samples and their labels. We design a Synchronous Medical Image Augmentation (SMIA) framework, which includes two modules based on stochastic transformation and synthesis, and provides diverse and annotated training sets for DL models. In the transform-based SMIA module, for each medical image sample and its tissue segments, a subset of SMIA factors with a random number of factors and stochastic parameter values are selected to simultaneously generate augmented samples and the paired tissue segments. In the synthesis-based SMIA module, we randomly replace the original tissues with the augmented tissues using an equivalent replacement method to synthesize new medical images, which can well maintain the original medical implications. DL-based image segmentation experiments on bone marrow smear and dermoscopic images demonstrate that the proposed SMIA framework can generate category-balanced and diverse training data, and have a positive impact on the performance of the models.
L-GhostNet: Extract Better Quality Features

A lightweight image recognition model, L-GhostNet based on improved GhostNet, is proposed to address the problems of extensive computation and high storage cost of deep convolutional neural networks. The model incorporated learning group convolution and improved CA into GhostNet to reduce the calculation and number of parameters and improve the flexibility of the network. At the same time, the pruning ratio in the learning group convolution is increased to control the end time of pruning in the whole process; the improved CA uses a fully connected layer to replace the convolutional layer, which can make the connection between the two dimensions tighter and increase the flexibility of the model. Experiments on datasets in various fields, such as grape leaf recognition, gesture recognition, face recognition, rice recognition, and CIFAR-10, show that L-GhostNet has slightly improved accuracy, reduced computation by more than 44%, decreased the number of parameters by more than 33%, and improved FPS by 26% on all datasets compared to GhostNet. Compared with other commonly used lightweight network models, MobileNets and ShuffleNets, it has the best overall performance with the lowest FLOPs, highest accuracy, and fewer parameters on all datasets at the same level of FLOPs.
AttenFace: A Real Time Attendance System Using Face Recognition

The current approach to marking attendance in colleges is tedious and time consuming. I propose AttenFace, a standalone system to analyze, track and grant attendance in real time using face recognition. Using snapshots of class from live camera feed, the system identifies students and marks them as present in a class based on their presence in multiple snapshots taken throughout the class duration. Face recognition for each class is performed independently and in parallel, ensuring that the system scales with number of concurrent classes. Further, the separation of the face recognition server from the back-end server for attendance calculation allows the face recognition module to be integrated with existing attendance tracking software like Moodle. The face recognition algorithm runs at 10 minute intervals on classroom snapshots, significantly reducing computation compared to direct processing of live camera feed. This method also provides students the flexibility to leave class for a short duration (such as for a phone call) without losing attendance for that class. Attendance is granted to a student if he remains in class for a number of snapshots above a certain threshold. The system is fully automatic and requires no professor intervention or any form of manual attendance or even camera set-up, since the back-end directly interfaces with in-class cameras. AttenFace is a first-of-its-kind one-stop solution for face-recognition-enabled attendance in educational institutions that prevents proxy, handling all aspects from students checking attendance to professors deciding their own attendance policy, to college administration enforcing default attendance rules.
Part-Aware Framework for Robust Object Tracking

The local parts of the target are vitally important for robust object tracking. Nevertheless, existing excellent context regression methods involving siamese networks and discrimination correlation filters mostly represent the target appearance from the holistic model, showing high sensitivity in scenarios with partial occlusion and drastic appearance changes. In this paper, we address this issue by proposing a novel part-aware framework based on context regression, which simultaneously considers the global and local parts of the target and fully exploits their relationship to be collaboratively aware of the target state online. To this end, the spatial-temporal measure among context regressors corresponding to multiple parts is designed to evaluate the tracking quality of each part regressor by solving the imbalance among global and local parts. The coarse target locations provided by part regressors are further aggregated by treating their measures as weights to refine the final target location. Furthermore, the divergence of multiple part regressors in each frame reveals the interference degree of background noise, which is quantified to control the proposed combination window functions in part regressors to adaptively filter redundant noise. Besides, the spatial-temporal information among part regressors is also leveraged to assist in accurately estimating the target scale. Extensive evaluations demonstrate that the proposed framework help many context regression trackers achieve performance improvements and perform favorably against state-of-the-art methods on the popular benchmarks: OTB, TC128, UAV, UAVDT, VOT, TrackingNet, GOT-10k, LaSOT.
Mask-Aware Pseudo Label Denoising for Unsupervised Vehicle Re-Identification

As a significant part of Intelligent Transportation System (ITS), vehicle Re-Identification (Re-ID) aims to retrieve all target vehicle images captured from non-overlapping cameras. Though the Re-ID methods based on supervised learning have achieved rapid progress, they are still difficult to be applied in real scenarios due to the domain bias between the training set and real scenarios. Recently, methods based on unsupervised learning have been proposed to address the problem of domain bias by exploring techniques of pseudo-label generation. However, these methods suffer from pseudo-label noise. To solve this problem, we propose the Mask-Aware Pseudo Label Denoising framework (MAPLD) consisting of three key components, i.e., Mask-Aware Feature Extraction (MAFE), Adaptive Threshold Neighborhood Consistency (ATNC), and Compact Loss (CL). Firstly, the MAFE is proposed to improve the distinguishability of feature representation and widen the gap in feature space among vehicles with different IDs. Next, the ATNC is introduced to filter out pseudo-label noise of hard negative samples by comparing the image ID of the samples in their neighborhood set i.e., neighborhood consistency. Moreover, the threshold of neighborhood consistency is adaptively adjusted according to feature similarity ranking, which is robust to hyper-parameter variation. Finally, consisting of regression term and compact term, the CL is designed to drive the cluster more compact and alleviate the impact of outliers of hard positive samples. Extensive experiments on VeRi-776 and VeRi-Wild datasets demonstrate that MAPLD can generate reliable pseudo-labels and achieve superior performance in unsupervised target-only and unsupervised domain adaptation tasks.
Feature Alignment FPN for Oriented Object Detection in Remote Sensing Images

As the basic and essential component of most object detectors, a feature pyramid network (FPN) can effectively extract multiscale features to recognize objects at different scales. Nevertheless, due to the cross-scale fusion and upsampling operations in FPN, current detectors still suffer from information loss and feature misalignment. To alleviate these problems, a flow-guided upsampling module (FGUM) and a multifeature attention module (MFAM) are proposed to improve the FPN. Specifically, FGUM uses a novel flow warp in the upsampling operation to align features, resulting in better cross-scale fusion. At the same time, the MFAM module fully considers the integrity of multiscale features and reduces the aliasing effect by optimizing the weight of each level of features. To verify the effectiveness of the improved FPN, it is applied to three state-of-the-art models for experiments, and all of them achieve better detection accuracy compared to the original FPN.
Chaotic encryption of real-time ECG signal in embedded system for secure telemedicine

Security in telemedicine has been of great interest in recent years due to the personal and confidential information that is used, since it is sent or stored through insecure channels, which generates a security problem such in telemetry, where biomedical signals (ECG, EEG, SpO2) are used for diagnosis of diseases. In this work, a chaos-based cryptographic algorithm is proposed using two-dimensional Badola map implemented in an embedded system based on a 32-bit microcontroller to provide confidentiality of biomedical signals such as the electrocardiogram for applications in low-cost secure embedded systems in telemedicine for monitoring or diagnosis. The electrocardiogram (ECG) signal is acquired in real-time through a medical ECG module, which allows us to measure the ECG signal directly of the patient in real-time. In addition, we propose to use a simple non-linear function over the ECG signal to increase considerably the sensitivity at plain biosignal in the encryption process. In experimental results, several security analyzes are presented such as key space, secret key sensitivity, clear signal sensitivity, histograms, correlation coefficient and encryption time. The results show that the proposed chaotic encryption algorithm is resistant against common attacks and it can be used in secure low-cost embedded systems for telemedicine applications.