Reviewerly/exHarmony huggingface dataset:
Exploration and Exploitation in Federated Learning to Exclude Clients with Poisoned Data

Federated Learning (FL) is one of the hot research topics, and it utilizes Machine Learning (ML) in a distributed manner without directly accessing private data on clients. How-ever, FL faces many challenges, including the difficulty to obtain high accuracy, high communication cost between clients and the server, and security attacks related to adversarial ML. To tackle these three challenges, we propose an FL algorithm inspired by evolutionary techniques. The proposed algorithm groups clients randomly in many clusters, each with a model selected randomly to explore the performance of different models. The clusters are then trained in a repetitive process where the worst performing cluster is removed in each iteration until one cluster remains. In each iteration, some clients are expelled from clusters either due to using poisoned data or low performance. The surviving clients are exploited in the next iteration. The remaining cluster with surviving clients is then used for training the best FL model (i.e., remaining FL model). Communication cost is reduced since fewer clients are used in the final training of the FL model. To evaluate the performance of the proposed algorithm, we conduct a number of experiments using FEMNIST dataset and compare the result against the random FL algorithm. The experimental results show that the proposed algorithm outperforms the baseline algorithm in terms of accuracy, communication cost, and security.
Proof-of-Concept of Network Key Management Using Lattice-Based Cryptography

With the ever-increasing use of large-scale IoT networks in different sectors of the industry, it has become critical to realise seamless and secure communication between devices in the network. Realising secure group communication in the IoT requires solving the problem of group-key establishment. In this work, we solve the problem by designing a new lattice-based Key Encapsulation Mechanism (KEM) for resource-constrained devices that enable the distribution of a symmetric key or any other data between all the devices in a given network. This is achieved by coupling multiple private keys to a unique public key. Moreover, we present a proof-of-concept implementation based on the GGH algorithm. The results show it is feasible to use lattice-based cryptography to allow for seamless and secure group communications within a decentralised IoT network. It has been bench-marked against other common post-quantum constructs and proven to be more practical with respect to memory consumption and security, although considerably slower due to lack of optimisation in the implementation.
A green-aware optimization strategy for virtual machine migration in cloud data centers

Migrating Virtual Machines (VM) is a useful tool for managing computing resources, load balancing, reducing energy consumption and facilitating fault management in cloud computing. In this paper, we propose a green energy-aware VM migration approach aiming to increase the use of green energy of the data center taking into account the characteristics of the VMs and the available resources of the data stores. In essence, we define an energy aware optimization problem in hybrid energy-powered data centers. The objective is to extend the existing Dynamic Resource Scheduling (DRS) of the virtualization solution in order to integrate other environmental variables. The obtained results based on an experimental study carried out in the pedagogic data center of the IT department of our university have shown that the energy efficiency can be managed by reassigning VMs to the data-center hosts.
Research on Cancer Diagnosis Method Based on LightGBM-Gridsearchcv

Cancer has become a non-negligible problem that threatens human health in today's society. The traditional methods of cancer diagnosis usually use cell morphology, histopathology and other methods. Nowadays, the use of machine learning technology to predict cancer has become a new actionable way. This paper proposes the use of machine learning algorithms to assist breast cancer diagnosis, using a variety of algorithms such as LightGBM, Random Forests (RF), Support Vector Machines (SVM), Linear SVM, K-Nearest Neighbor (KNN), and combined with grid search algorithms, respectively constructed intelligent predictive and diagnostic models for malignant breast cancer. Finally, using the breast cancer data set of the University of Wisconsin (WCBD) hospital to conduct experiments, a classification model based on LightGBM-Gridsearchcv is proposed. Compared with the models, the LightGBM-Gridsearchcv model has a recognition accuracy of 95.9% for malignant cancer cases. The machine learning method has put forward a new research idea and method for the diagnosis of breast cancer, and provided a research direction for the promotion of intelligent medical treatment, which has very important practical significance and application value.
Fast Person Detector with Efficient Multi-level Contextual Block for Supporting Assistive Robot

The robotic demand a vision method to work in real-time on embedded devices. Besides, an assistive robot requires person detection, which is widely used to help automatically interact with the user. This work presents a fast real-time person detection (Fast-PdNet) to localize human areas implemented on a Jetson Nano. This device has been commonly used as an embedded system and is suitable for synchronizing sensors and actuators. The proposed architecture contains layers of Convolutional Neural Network consisting of two main modules: backbone and detection. An efficient extractor module with a multi-level contextual block is employed to extract the spatial features quickly. It avoids high-cost computing to distinguish interest features of the human body and background features. The lightweight learning attention selects suspected specific features area without generating excessive parameters. The end-to-end training was conducted on MS COCO 2017 to generate efficiently weighted models. The Fast-PdNet achieves competitive performance with other light detectors evaluated on the MS COCO 2017, PASCAL VOC 2007, and 2012 datasets. Moreover, this detector can run 35 frames per second when working in real-time on Jetson Nano.
Global Resource Management in the ELASTIC Architecture

Smart systems increasingly demand the processing of a massive amount of data generated by heterogeneous and distributed data sources. Due to the inherent cyber-physical nature of these systems, many applications require that this processing respects a set of non-functional requirements (such as timeliness, or energy-efficiency). To cope with this challenge, edge-cloud architectures need to provide flexible mechanisms to support varying processing needs, whilst guaranteeing the minimum level of quality of service required by these smart applications. This paper addresses this challenge in the context of the ELASTIC software architecture, which has been developed integrating responsive data-in-motion (edge computing) and latent data-at-rest analytics (cloud computing) into a single solution, satisfying extreme-scale analytics' performance requirements. The paper focuses on how the architecture fulfils the non-functional properties inherited from the applications, namely real-time and energy-efficiency, whilst ensuring the performance of the software architecture.
Discovering traces between textual requirements and logical models in the functional design of Printed Circuit Boards

Traceability management is a critical activity in the engineering process and management of critical systems to know why a system artefact exists and to perform certain operations such as change impact analysis or consistency checking. In this context, the process to create traces is usually done manually by engineers. Although some tools can suggest traces between system artefacts based on some metadata, the reality shows that the information embedded in the whole set of system artefacts is not completely exploited. That is why, in this work, authors propose a traceability function to discover traces between system artefacts (requirements and logical models) based on the semantic representation of the artefact content. Then, this semantic representation is used to implement a trace engine based on the topological comparison of the underlying conceptual graphs. This functionality is implemented on top of the Arrowhead framework creating a toolchain comprising 3 tools (MSExcel, Capella and the Traceability Studio). A case study is also conducted in the field of Printed Circuit Board (PCB) design to assess both the technological feasibility of the approach and the quality of the discovery process (73% of precision) between 11 logical models and 66 textual requirements.
Adaptive N-Step Technique for Real-Time Anomaly Detection in Smart Manufacturing

Sensors installed at various assembly lines are becoming basic building blocks of industry 4.0 smart manufacturing environment. The data collected by sensors can be utilised to provide recommendation engine, automate the manufacturing process and also spot anomalies. Of these, automatic anomaly detection is a very significant task which detects misleading observations, data points, and/or events that deviate from the intended behaviour. The traditional approach to anomaly detection involves the use of more efficient and convoluted techniques to achieve higher accuracy. However, these techniques typically require much larger time and hence, is not suitable for real-time applications. In this work, an adaptive N-step anomaly detection technique is proposed wherein the number of steps (or modules) in the detection technique is based on the outlier percentage in the manufacturing process. A modular approach with techniques such as Density Based Spatial Clustering for Applications with Noise (DBSCAN), Isolation Forest (IF), Local Outlier Filter (LOF), etc., are used for automatic anomaly detection. Notably, the output of one step is fed as input to the next step, thereby making use of the knowledge gained in the previous step. The proposed adaptive approach results in >99% accuracy even when the outlier population is around 25%. Such high accuracy in real-time anomaly detection would serve as an important step towards having a Digital Twin model for smart manufacturing environment.
Coordination value in Agile software development

Background: Agile software projects involve a high degree of coordination between project members to manage complexity and frequent change. There is a need to understand what coordinating mechanisms are valuable for project and team coordination. Coordination mechanisms, such as meetings and Slack, can foster a smooth workflow but also fragment work by interrupting the focused work of developers. Objective: This study aimed to investigate valuable coordination mechanisms and how they can be balanced against the need for uninterrupted work periods. Method: We conducted 30 interviews and observed 109 meetings in five companies using agile software development methods. We used coordination-dependency mapping to identify valuable coordination mechanisms. Results: Valuable coordination mechanisms included instant messaging tools, daily stand-up meetings, boards, open work area, Scrum of Scrums, bug crush days, BizDev meetings, and Make it Happen meetings. Conclusion: We advise companies to identify valuable coordination mechanisms using coordination-dependency mapping and then to bundle, schedule, and substitute these coordination mechanisms to reduce interruptions to development work.
Collaboration tool choices and use in remote software teams

As a result of the COVID-19 pandemic, more software teams than ever find themselves working in a remote model with team members separated by location, timezone, and working hours. This working model is expected to persist post-pandemic as companies explore the benefits of hybrid working. Software teams have always been reliant on tools to help them build software. Now they find themselves wholly reliant on tools to help them collaborate online. Surprisingly, there has been little research to date on which collaboration tools are used, how they are chosen, how they are used, and what challenges are faced when using such tools. This short paper offers emerging findings from an ongoing study in which we are interviewing software professionals about these questions. The insights are preliminary in that we are still conducting additional interviews beyond the ones reported here, yet some common themes are already emerging. Among others, we highlight the following: choice of tools made by the teams is opportunistic; teams adapt existing collaboration practices to utilize the tools better when working remotely; and a persistent problem exists of being unable to find information across chats, emails, and documents.
Multi-scale Attention-based Individual Character Network for Handwritten Signature Verification

Offline handwritten signature verification dictates a neural network to learn from training signature features and tell whether a tested signature is genuine or not. One common idea is that the author first input a reference signature and a tested signature of their original and gray-inverse version. After a couple of convolution and attention, each two of them are concatenated together. With the strategy that all the outcomes should be same, the model is trained to focus more on the signature stroke features than on color. In this paper, the author proposes a novel Siamese network to extract signature stroke features. The database the author uses contains a large scale and challenging signature from reference writers and sophisticated calligraphers. To improve the performance of the model, the author pre-processes all input signatures. The network reaches an accuracy of 82% on SigComp2011 dataset.
Aircraft Target Detection Based on Attention Mechanism and Faster R-CNN

Aircraft, as a special strategic target, has high value in both civil and military use, and it is especially important to achieve intelligent scientific aircraft target detection compared to traditional methods. Therefore, an aircraft detection method based on attention mechanism and Faster R-CNN target detection algorithm is proposed in this paper. The RSOD dataset is selected, and the VOC format dataset is used for pre-processing and data enhancement. The Faster R-CNN model is constructed, which uses ResNet50 as the backbone network for feature extraction and also adds the attention mechanism in the backbone network; the region proposal is obtained after convolving the feature extraction results using the RPN(Region Proposal Network); the prediction results are finally obtained by pooling using ROI Pooling. The experimental results show that the model can have good recognition effect and provide help for the intelligent recognition of aircraft.
Research on end-to-end binocular stereo matching algorithm based on image segmentation

Binocular stereo matching will lead to low texture and repeated texture due to uneven illumination and occlusion, and the matching accuracy will be reduced. To solve this problem, the image preprocessing methods of Gaussian filter preprocessing and super-pixel segmentation are adopted, and the processed image area is used as the input of the real-time end-to-end stereo matching algorithm stereonet network to assist the stereo matching algorithm to estimate the parallax. This method can expand the stereo disparity estimation under the low resolution features, effectively reduce the memory consumption, improve the running speed, and improve the recognition of the edge region of the disparity map. The algorithm in this paper is verified by the standard data of the Middlebury test platform. The results show that the algorithm in this paper can achieve a relatively accurate result in a fast time in the ill-conditioned region, which has certain engineering significance for the realization of real-time stereo matching algorithm.
FYCFNet: Vehicle and Pedestrian Detection Network based on Multi-model Fusion

Vision-based solutions for target detection in autonomous driving are very much about the accuracy of detection. A correct or incorrect detection may cause or avoid a traffic accident. Therefore, in this paper, to further improve the detection accuracy of vision schemes, we propose a multi-model fusion network: Fusion Network with YoloV5 and CBNEet Faster-RCNN (FYCFNet) that fuses a one-stage target detection model and a two-stage model, which consists of three parts: the first part is a single-stage YOLOV5 [1] detection model, the second part is a Faster-RCNN [2] with CBNet-V2 [3] as the backbone, and the third part is the post-fusion head of weighted boxes fusion. We tested the performance of this network and compared it with other mainstream networks, and verified that the network achieves a very impressive accuracy improvement.
Image Mosaic algorithm based on improved AKAZE

In the process of image Mosaic, traditional image matching algorithms have high time complexity, too many low-quality redundant feature points extracted, and feature descriptors are not accurate enough to describe key points, leading to low matching accuracy and low Mosaic efficiency. An image registration algorithm based on improved AKAZE was proposed. First, phase correlation method is used to estimate the global coarse displacement motion parameters of matched images and calculate the coincidence range between images. Then, AKAZE algorithm is used for feature detection, and Opponent-LATCH color feature descriptor with color information is introduced to describe the feature points. Finally, KNN and Bidirectional matching principle is used for rough matching, and PROSAC algorithm is used for fine matching. Experimental results show that the proposed algorithm has better robustness and better matching accuracy and running time than traditional algorithms.
Lightweight detection method based on improved YOLOv4

In view of the large amount of model parameters and large amount of calculation when YOLOv4 target detection model is applied to unmanned vehicles, it is difficult to be applied to embedded platform or mobile terminal. In this paper, an improved YOLOv4 model was proposed, which used the MobileNetv1 as the backbone network of YOLOv4. The depth separable convolution was used to replace the ordinary convolution in the enhanced feature extraction network structure PANET of YOLOv4. By introducing the ECA attention mechanism and using DIOU_NMS post-processing strategy, the detection accuracy of the model was improved. The improved YOLOv4 model was tested on the PASCAL VOC data set. The results showed that the number of parameters was one-fifth of the original, the model size was reduced by 193M, the mAP was reduced by 0.56%, and the fps was increased by 20.4. It can meet the requirements of real-time detection of unmanned vehicles.
Aero-Optical Image and Video Restoration Based on Mean Filter and Adversarial Network

Target detection, recognition, and tracking technology has been widely concerned with the development of artificial intelligence. However, images or videos taken from a long distance could be easily affected by atmospheric turbulence so degradation often occur. With the aim of providing reliable data for further processing, a new method is proposed to obtain sharp image and video from those with aero-optical effect. Firstly, both the degradation and reconstruction models are established. Secondly, mean filter is applied to remove the additive noise in the shooting process of infrared camera. Then, combined with the pyramid structure, the generated countermeasure network based on MobileNet-v2 is used to restore the blurred image without noise. Finally, experiments with the evaluation indexes including Peak-Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), and Kernelized Correlation Filters (KCF) are carried out to demonstrate the significant improvements over competitive algorithms.
A study of a federated learning framework based on the interstellar file system and blockchain: Private Blockchain Federated Learning

Federated learning is a scalable machine learning paradigm in which a large group of individuals collaborates to build a high-quality machine learning model. This paper proposes Private Blockchain Federated Learning (PriChainFL). It is a federated learning method that combines blockchain and IPFS, in response to the fact that federated learning is still vulnerable to attacks such as data poisoning and member inference attacks. It also reduces the storage cost of blockchain.
An Improved Lightweight Method of Convolutional Neural Network for Face Recognition

With the continuous development of convolutional neural networks, more hardware resources are required. Lightweight convolutional neural networks show their own advantages in this aspect. In this paper, three kinds of lightweight convolutional neural networks with different layers are designed based on DenseNet’s fully connected network structure, and training tests are carried out on self-built face database. Test results show that, within limits to increase the depth of the network can strengthen the training speed of network convergence, 7 layers network parameters increased by 88% compared with 4 layers, but the training convergence rate is increased by 3 times, also proved that the whole connections of network structure on the lightweight convolution neural network is feasible, and provide the method to the lightweight convolution neural network performance improvement.
Exploring a Computer Vision and Artificial Intelligence-based Approach to Sit-and-reach Distance Measurement

The key technique of sit-and-reach distance measurement in this study mainly consists of two stages: the first stage is the initial calibration of the test site, including camera calibration, identification and calibration of the test site identification points and finger key points; the second stage is the distance measurement stage, including the calculation of the coordinates of the finger tip to the scale projection point of the suspended distance measurement and the calculation of the conversion distance of the projection point. After the validity test of ranging, the error was 0. 148cm with standard deviation of 0.118, maximum value of 0.495, and minimum value of 0.002 for 90 experiments, which proved that the research results had high ranging accuracy. Since this study uses a common webcam, the method is easy to be widely used.
Multi angle location and identification method of suspension insulators based on R2CNN algorithm

With the continuous development of smart grids, power inspections have become intelligent and sophisticated. This paper proposes a method based on inclined boxes for the automatic position recognition and diagnosis of suspension insulators under a visible light channel. The rotational region convolutional neural networks (R2CNN) algorithm is used to extract the features of large sample images of suspension insulators, and the model is trained to identify and select insulated devices in any direction. The open-source TensorFlow software is used as the identification tool and is combined with related tuning strategies to optimize the model during the training process. The final model’s recognition accuracy was 89.73%. The results prove that this method overcomes the limitations of using axis-aligned boxes for detection, which can provide more accurate position information for diagnoses of suspension insulators. The model has strong robustness in the changing environment, and has certain innovation value and engineering significance.