Reviewerly/exHarmony huggingface dataset:
Solving multiple travelling salesman problem through deep convolutional neural network

Abstract The multiple travelling salesman problem (mTSP) is a classical optimisation problem that is widely applied in various fields. Although the mTSP was solved using both classical algorithms and artificial neural networks, reiteration is inevitable for these methods when presented with new samples. To meet the online and high‐speed logistics requirements deploying new information technology, the iterative algorithm may not be reliable and timely. In this study, a deep convolutional neural network (DCNN)‐based solution method for mTSP is proposed, which can establish the mapping between the parameters and the optimal solutions directly and avoid the use of iterations. To facilitate the DCNN in establishing a mapping, an image representation that can transfer the mTSP from an optimisation problem into a computer vision problem is presented. While maintaining the excellent quality of the results, the efficiency of the solution achieved by the proposed method is much higher than that of the traditional optimisation method after training. Meanwhile, the method can be applied to solve the mTSP under different constraints after transfer learning.
The Development of Image Classification Algorithms Based on CNNs

Image classification is a method to process the given images by picking up the distinct features of different kinds of images to distinguish different targets in the image. The classification of images analyzes the given images quantitatively by using computers and divides the image or areas contained in the image into several categories instead of explaining them by human’s visualization. Image classification is an important research direction in computer vision technology and the basis for other applications such as image detection, behavior analysis, and object tracking. With the arrival of big data and the improvement of computer power, Deep Learning (DL) has swept the world, and the Convolutional Neural Network (CNN) image classification method has broken down the limitations of traditional image classification methods and has become a currently mainstream image classification algorithm. Thereinto, some typical architectures e.g., MobileNet, ResNet and VGG are attracted a lot of attention. This paper will review the development of the image classification and introduce some typical CNNs.
News Short Text Classification Based on Bert Model and Fusion Model

Text classification task is one of the most fundamental tasks in NLP, and the classification of short news text could be the basis for many other tasks. In this paper, we applied a fusion model combining Bert and TextRNN with some modified details to expect higher accuracy of text classification. We used the THUCNews as dataset which consists of two columns one for news text and the other for numbers. The original dataset was seperated into three parts: training set, validation set and test set. Besides, we used BERT model which contains two pre-training tasks and TextRNN model which refers to the use of RNN to solve text classification problems. We trained these two models in parallel, and then the optimal Bert and TextRNN models obtained through training and parameter tuning are added with a fully-connected layer to receive the final results by weighting the efficiency of Bert and TextRNN. The fusion model solves the problem of over-fitting and under-fitting of a single model, and helps to obtain a model with better generalization performance. The experimental results show the sharp change in loss and accuracy as well as the final accuracy of the BERT model. The precision, recall-rate and F1-score are also evaluated in this paper. The accuracy of fusion model of BERT and TextRNN is much better than single Bert model and has a gap to 1.76%.
Building XAI-Based Agents for IoT Systems

The technological maturity of AI solutions has been consistently increasing over the years, expanding its application scope and domains. Smart home systems have evolved to act as proactive assistants for their residents, autonomously detecting behavioral patterns, inferring needs, and making decisions pertaining to the management and control of various home subsystems. The implementation of explainable AI (XAI) solutions in this challenging domain can improve user experience and trust by providing clear and understandable explanations of the system’s behavior. The article discusses the increasing importance of explainable artificial intelligence (XAI) in smart home systems, which are becoming progressively smarter and more accessible to end-users, and presents an agent-based approach for developing explainable Internet of things (IoT) systems and an experiment conducted at the Centre of Real Time Computer Systems at the Kaunas University of Technology. The proposed method was adapted to build an explainable, rule-based smart home system for controlling light, heating, and ventilation. The results of this study serve as a demonstration of the feasibility and effectiveness of the proposed theoretical approach in real-world scenarios.
DP-AMI-FL: Secure Framework for Machine Learning-based AMI Applications

Machine Learning (ML) algorithms have shown quite promising applications in smart meter data analytics enabling intelligent energy management systems for the Advanced Metering Infrastructure (AMI). One of the major challenges in developing ML applications for the AMI is to preserve user privacy while allowing active end-users participation. This paper addresses this challenge and proposes Differential Privacy-enabled AMI with Federated Learning (DP-AMI-FL), framework for ML-based applications in the AMI. This framework provides two layers of privacy protection: first, it keeps the raw data of consumers hosting ML applications at edge devices (smart meters) with Federated Learning (FL), and second, it obfuscates the ML models using Differential Privacy (DP) to avoid privacy leakage threats on the models posed by various inference attacks. The framework is evaluated by analyzing its performance on a use case aimed to improve Short-Term Load Forecasting (STLF) for residential consumers having smart meters and home energy management systems. Extensive experiments demonstrate that the framework when used with Long Short-Term Memory (LSTM) recurrent neural network models, achieves high forecasting accuracy while preserving users data privacy.
An Unsupervised Similarity-based Method for Estimating Behind-the-Meter Solar Generation

Accurate knowledge of solar generation in power distribution systems provides great values to utilities for efficient and reliable distribution system operation. However, many solar PV resources are installed behind-the-meter (BTM), and as a result only the net load measurements are available to the utilities. In this paper, a high-performance method for disaggregating BTM solar generation traces from net load traces is developed. The algorithm takes the net load data measured by smart meters and other widely available environmental measurements (e.g., publicly monitored solar irradiance and temperature) as inputs, and disaggregates the net load traces into BTM solar generation and load traces. Notably, the proposed method does not rely on any separately metered data of BTM solar generation. Rather, in a fully unsupervised fashion, the proposed method effectively exploits the self-similarity and cross-customer similarity of customer loads to achieve accurate BTM solar disaggregation. The developed unsupervised method is evaluated on two real-world smart meter data sets collected from New York and Texas, and exhibits very high performance that closely approaches the ideal performance bound from supervised learning.
Understanding the Challenges When 3D Semantic Segmentation Faces Class Imbalanced and OOD Data

3D semantic segmentation (3DSS) is an essential process in the creation of a safe autonomous driving system. However, deep learning models for 3D semantic segmentation often suffer from the class imbalance problem and out-of-distribution (OOD) data. In this study, we explore how the class imbalance problem affects 3DSS performance and whether the model can detect the category prediction correctness, or whether data is ID or OOD. For these purposes, we conduct two experiments using four representative 3DSS models and five trust scoring methods, and conduct both a confusion and feature analysis of each class. Furthermore, a data augmentation method for the 3D LiDAR dataset is proposed to create a new dataset based on SemanticKITTI and SemanticPOSS, called AugKITTI. We propose the wPre metric and TSD for a more in-depth analysis of the results, and follow are proposals with an insightful discussion. Based on the experimental results, we find that: 1) classes are not only imbalanced in their data size but also in the basic properties of each semantic category; 2) intraclass diversity and interclass ambiguity make class learning difficult and greatly limit the models’ performance, creating the challenges of semantic and data gaps; 3) trust scores are unreliable for classes whose features are confused with other classes. For 3DSS models, those misclassified ID classes and OODs may also be given high trust scores, making the 3DSS predictions unreliable, and leading to the challenges in judging 3DSS result trustworthiness. All of these outcomes point to several research directions for improving the performance and reliability of the 3DSS models used for real-world applications.
FaceIDP: Face Identification Differential Privacy via Dictionary Learning Neural Networks

In big-data era, large amount of facial images could be used to breach the face identification system, which demands effective Face IDentification Differential Privacy (FaceIDP) of the facial images for widespread adoption of the face identification technique. In this paper, to our best knowledge, we take the first step to systematically study an effective important FaceIDP approach via the help of Dictionary Learning (DL) for secure releasing of facial images. First, a Dictionary Learning neural Network (DLNet) has been developed and trained with the facial images database, to learn the common dictionary basis of the facial image database. Then, the coding coefficients of the facial images are obtained. After that, the sanitizing noise is added to the coding coefficients, which obfuscates the facial feature vector that is used to identify a user’s identification. We have also proved that the FaceIDP is ε-differentially private. More importantly, optimal noise scale parameters have been obtained via the Lagrange Multiplier (LM) method to achieve better data utility for a given privacy budget ε. Finally, substantial experiments have been conducted to validate the efficiency of the FaceIDP with two real-life facial image databases.
Distributed Stochastic Algorithm Based on Enhanced Genetic Algorithm for Path Planning of Multi-UAV Cooperative Area Search

Multiple unmanned aerial vehicle (Multi-UAV) cooperative area search is an important and effective means of intelligence acquisition and disaster rescue. Search path planning is a critical factor to improve multi-UAV search performance. Aiming at the search inefficiency resulting from insufficient cooperation between UAVs in existing researches, we present a novel distributed real-time search path planning method based on distributed model predictive control (DMPC) framework. Firstly, we formulate the overall search objective function in finite time domain, considering not only repeated searches, but also maintenance of connectivity and collision avoidance between UAVs. Secondly, we decompose the overall search objective function to establish a distributed constrained optimization problem (DCOP) model, so that all UAVs optimize the overall search objective by interacting with neighbors. Thirdly, aiming at the problem of falling into the local optima in existing algorithms, distributed stochastic algorithm based on enhanced genetic algorithm (DSA-EGA) is proposed to solve the established DCOP model. We design a point crossover operator and introduce anytime local search (ALS) framework that stores the global optimal solution explored. Finally, the simulation results of different benchmark problems demonstrate that the proposed DSA-EGA outperforms other state-of-the-art algorithms in terms of the quality of solution. The simulation results of cooperative area search problems illustrate that the established DCOP model improves the search efficiency by 7.7%, and DSA-EGA improves the search efficiency by 4.3% at least. In addition, we also verify that our method has high scalability.
UbiNN: A Communication Efficient Framework for Distributed Machine Learning in Edge Computing

Deployment of distributed machine learning at the edge is conducive to reducing latency and protecting privacy associated with transmitting data back to the cloud. Nonetheless, as machine learning models scale, bandwidth resources are limited and heterogeneity in data collection from heterogeneous edge devices is visible, resulting in low model accuracy and high communication overhead. In this paper, a novel distributed deep learning framework called ubiquitous neural network (UbiNN) is proposed to improve communication efficiency without affecting the accuracy of the local neural network model at the edge or the global neural network model in the cloud. As for the accuracy of the neural network model, a common dataset with a small portion of insensitive data is constructed for training the neural network model, and its accuracy is enhanced by a new algorithm based on knowledge distillation and covariance computation (KDCC). Experimental results demonstrate that the test accuracy of UbiNN is extremely close to the centralized machine learning and non-federated learning scheme DDNN and up to 18.74% better than that of other classic federated learning schemes when using public datasets such as MNIST, CIFAR-10, and REUTERS-21578. Meanwhile, communication overheads are substantially reduced in terms of data transmission volume and latency.
Joint Image-to-Image Translation for Traffic Monitoring Driver Face Image Enhancement

The real traffic monitoring driver face (TMDF) images are with complex multiple degradations, which decline face recognition accuracy in real intelligent transportation systems (ITS). This paper is the first to propose joint image-to-image (I2I) translation to enhance TMDF images of ITS. First, as TMDF images are without corresponding clear ones, identity preserving is critical for TMDF images under unpaired I2I translation. This paper proposes a fast diagonal symmetry pattern (FDSP) to preserve identity structure under unpaired I2I translation. Second, FDSP is introduced into CycleGAN to form FDSP-CG, which aims to learn the degradation mapping (i.e., FDSP-CG-d) from the clarity domain to the degradation domain. FDSP-CG-d can generate massive degradation/clarity image pairs for paired I2I translation training. Third, this paper proposes the dual residual block (DRB) to strengthen Pix2pix for rich face detail features learning (i.e., DRB-P2P), which learns the enhancement mapping from the degradation image to its clear version under paired I2I translation. Finally, the experiments on TMDF (i.e., the brevity name of the face database collected from real ITS) and Chinese famous face (CFF) databases, as well as CelebA and MegaFace databases, indicate that the proposed method can efficiently enhance TMDF images whose degradation variations are learned by FDSP-CG.
NASA+: Neural Architecture Search and Acceleration for Multiplication-Reduced Hybrid Networks

Multiplication is arguably the most computation-intensive operation in modern deep neural networks (DNNs), limiting their extensive deployment on resource-constrained devices. Thereby, pioneering works have handcrafted multiplication-free DNNs, which are hardware-efficient but generally inferior to their multiplication-based counterparts in task accuracy, calling for multiplication-reduced hybrid DNNs to marry the best of both worlds. To this end, we propose a Neural Architecture Search and Acceleration (NASA) framework for the above hybrid models, dubbed NASA+, to boost both task accuracy and hardware efficiency. Specifically, NASA+ augments the state-of-the-art (SOTA) search space with multiplication-free operators to construct hybrid ones, and then adopts a novel progressive pretraining strategy to enable the effective search. Furthermore, NASA+ develops a chunk-based accelerator with novel reconfigurable processing elements to better support searched hybrid models, and integrates an auto-mapper to search for optimal dataflows. Experimental results and ablation studies consistently validate the effectiveness of our NASA+ algorithm-hardware co-design framework, e.g., we can achieve up to 65.1% lower energy-delay-product with comparable accuracy over the SOTA multiplication-based system on CIFAR100. Codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/GATECH-EIC/NASA</uri> .
A Systematic Analysis, Review of Data Encryption Technology and Security Measures in IoT, Big Data and Cloud

Information is currently a more important resource than ever for any firm we can imagine. Sensor frameworks, IoT, distributed computing, and information investigations are only a couple of the new advancements and improvements that have made it conceivable to gather information all the more generally, proficiently, and successfully. Information security and protection, then again, are fundamental for information to be utilized to its most extreme potential. Notwithstanding the way that information security and protection have been concentrated on widely in the course of the most recent decade, we presently face new and troublesome information security and protection concerns. Nonetheless, there is a higher risk of illicit access, information spillage, touchy data disclosure, and security break therefore. Despite the fact that there have been a few explorations on information security and security assurance, deliberate reviews regarding the matter in distributed storage frameworks are as yet inadequate. This paper inspects the difficulties, advantages, and downsides of existing information security and protection arrangements openly distributed computing. In particular, First, we’ll go through the nuts and bolts of IoT, distributed computing, and information investigation. Second, we go into the issues and prerequisites of information security and security assurance in distributed storage frameworks in extraordinary profundity. Third, a rundown of information encryption innovation and insurance approaches is given. At long last, we go north of a couple of open information security research regions for distributed storage.
Frail Older Healthcare Tracking System

By measuring self-health and giving professionals data for diagnostic testing and better patient advice, remote health monitoring devices assist individuals in managing and monitoring their highest priorities and earlier. Our research intends to create a prototype for geriatric health monitoring utilizing sensors and alarm messages sent to physicians or families in the event that vital signs are aberrant. A Tracking system, an Arduino Nano, a node MCU, and a display are among the elements of this smart monitoring system. Other sensors include a pulse/heartbeat detector, a GSR for activity recognition, a temperature, an inertial transmitter for detection and tracking, and a gyroscope for step measuring. The Arduino receives data from the sensors. The microcontroller receives such sensor readings from the Arduino and transmits them. The sensor readings may then be saved in the cloud thanks to the data transmission from the nodeMCU. These data may also be shown graphically using MATLAB on Thing speak, a cloud-based platform. The vitals graph on the Cloud server has been presented using the monitoring device’s precise readings of the vitals, which have been successfully programmed to send out alert message within abnormal ranges of values (smart monitoring). The limitation of our study is that the size of device is larger than a portable one and it has to be fitted into a shorter enclosure for the comfort of the patient in addition to monitoring their patient’s condition.By always wearing the gadget, you can maintain your health. However, a more refined version of this study could make it easier for patients to monitor and control their health.
Data Plane Layer Modification of SDN Architecture with The Help of Blockchain

Network architectures are improving, as are the environments they serve. Recently, there has been a lot of research interest in Software-Defined Networking. The reason of its attention is because of its flexibility, manageability, scalability. And its affects can’t be avoided throughout the networking world. As well as the advantages of the visibility of the network are discussed. In SDN, all network traffic patterns are managed by a centralized network that manages, secures, and optimizes network resources. In this literature, most recent approaches centered on changing SDN architecture using blockchain technology. Blockchain is now seen as one of the key developments in technology. In order to protect their exchanges, many applications may rely on the blockchain. Since the launch of blockchain technology, many security flaws have been eliminated. SDN based on Blockchain has the potential to change the lifestyle of people in Some field will continue to have an impact in many places because of its great influence on many businesses or sectors, and what it can do. While blockchain technologies can provide us with more reliable and convenient features, Services, safety issues, and concerns are also important topics to consider in this innovative approach. A collection of conclusions and conclusions, by categorizing the current work, Proposals for potential directions for study are discussed.
A Critical Analysis on the Security Attacks and Relevant Countermeasures using ML

In Side Channel Analysis (SCA), machine learning algorithms have shown their efficacy, offering several upgrades over the well-established profiling procedure of Template Attacks. A design concept and method that may be successfully utilised as a foundation for adding SCA safeguards on the scalar Elliptic Cryptography (ECC) integrators is provided, with a focus on the necessity to limit their impact on embedded systems. The deconstruction of the round computations of the Scalar Multiplication (SM) and Lafayette Power Ladder (MPL) algorithms into the highlighted finite site activities and their reorganisation into single phase operation sets form the foundation of the suggested design approach. We demonstrate how sophisticated SCA measures may be quickly implemented, focused on randomising the projective values of the ECC point outputs from the MPL round, utilising the suggested design technique as a foundation. A roadmap for the assault is presented, and numerous simple ML-based SCAs are conducted in order to assess the approach and associated SCA responses. The suggested roadmap makes the assumption that attacker would have limited funds and won't gain access to a large amount of leaks trails in order to launch Deep Learning assaults. When SCA remedies are included into the suggested design method, The performance of the trained models shows a strong resilience to ML-based SCAs.