Reviewerly/exHarmony huggingface dataset:
DLMHS: Flow‐based intrusion detection system using deep learning neural network and meta‐heuristic scale

Summary The technological growth of the internet gained applications in various areas of human life like banking, public networking, online transactions, and electronic trade. The exponential growth of these services leads to increase of network traffic, and results increase in the number of possible network attacks. The researchers proposed various methodologies to address these from decades. It is evident from the literature that machine learning techniques, ANN methods, and Meta‐Heuristic approaches have gained significant reputation in handling security attacks and these methods depend on features of requests for extracting the knowledge. It is observed that the exponential growth in the volume of transactions in network traffic contains diversified behavior and exhibits deviation in feature values. Hence, it is required to consider the associability of transactions and its feature values. In this paper, a unique set of flow level features is defined to extract the knowledge from the traffic. The diversified behavior of the traffic is addressed with ensemble classifiers with distribution similarity and drift detection. The proposed model derives a threshold value using feature impact scale for the classifiers defined in the ensemble and analyzes the flow characteristics based on the threshold. The Deep Learning Neural Network (DLNN) is used for attack detection at flow level to validate the traffic as normal or attack. The experimental study is carried out with NSL‐KDD benchmark datasets to analyze the statistical parameters. Furthermore, the proposed model (DLMHS) is compared with existing models described in literature.
A Deep Autoencoder With Novel Adaptive Resolution Reconstruction Loss for Disentanglement of Concepts in Face Images

Among the different categories of natural images, face images are very important because of their broad range of applications. One challenging topic of face processing by computers is extracting information related to only specific concepts from face images without the help of labels. In this article, we propose a deep autoencoder model for extracting facial concepts based on their scales. A novel adaptive resolution (AR) reconstruction loss is introduced for training the autoencoder model. With the help of this new reconstruction loss, the deep autoencoder model is able to receive a real face image and compute its representation vector, which not only makes it possible to reconstruct the input image faithfully but also separates the concepts related to specific scales. We demonstrate that the autoencoder trained using the AR reconstruction loss is able to outperform benchmark models in generating faithful and high-quality reconstructions of real face images and is able to successfully transfer the facial concepts associated with a specific scale from one input image to another.
Abalone Age Prediction Using Machine Learning

Abalone is a marine snail found in the cold coastal regions. Age is a vital characteristic that is used to determine its worth. Currently, the only viable solution to determine the age of abalone is through very detailed steps in a laboratory. This paper exploits various machine learning models for determining its age. A comprehensive analysis of various machine learning algorithms for abalone age prediction is performed which include, backpropagation feed-forward neural network (BPFFNN), K-Nearest Neighbors (KNN), Naive Bayes, Decision Tree, Random Forest, Gauss Naive Bayes, and Support Vector Machine (SVM). In addition, five different optimizers were also tested with BPFFNN to evaluate their effect on its performance. Comprehensive experiments were performed using our data set.
Ontology-Driven Lexicographic Systems

The article considers the special class of ontology-driven lexicographic systems. The principles of their formation and use for processing large numbers of network documents are described. The rules of interaction with network information resources based on transformation of text documents into an interactive format are presented. The rules of formation of ontological systems that implement document structuring functions, the information display functions and functional management are described. The process of formation of monodiciplinary and multidisciplinary L-systems is described. The principles of formation of semiotically and semantically organized information environment are described. The definition of lexicographic effect is given. The hyperproperty of the inductance of a set of elementary information units (EIU) is presented. The flexibility of ontologies is described, which makes them a convenient mechanism for describing almost any data structure, so in addition to the actual EIU and their descriptions (i.e. information processed by the system). An ontology-driven system architecture is defined for a class of lexicographic systems. This architecture implements procedures of taxonomy-based management, which allows dynamic change of the set of available functions, interfaces, and so on. The operations that implement transdisciplinary integration of arbitrary documents in active modes of interaction are defined.
Numerical method for finite-size security analysis of quantum key distribution

Quantum key distribution (QKD) enables secure information exchange with untrusted quantum links between remote communication parties. A central task in the studies of QKD protocols is security analysis, which aims at deriving the length of the final key such that it is secure regardless of the eavesdropper's computational power. In literature, the security analysis has been done both analytically and numerically. Compared to analytical methods which tend to require techniques specific to each QKD protocol, numerical ones are more general since they can be directly applied to many QKD protocols with little adaptation. However, current numerical methods are carried out based on some assumptions such as working in the asymptotic limit and collective attacks from eavesdroppers. In this work, we remove these assumptions and develop a numerical finite-size security analysis against general attacks for a large class of QKD protocols. We also give an example of applying the method to the recent phase-matching QKD protocol with a simplified protocol design. Our result shows that the finite-size key rate can surpass the linear key-rate bound with a practical QKD system operated for only several hours.
Proof of Chance: A Lightweight Consensus Algorithm for the Internet of Things

This article is to propose a consensus algorithm, called Proof of Chance (PoCh), which is designed for the industrial Internet of Things (IIoT). The PoCh protocol is designed to be scalable and extensible, with a controllable conformance delay and low hardware and computation requirements. To reach a consensus, PoCh uses chance rather than computing power: “if condition <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{1}$</tex-math></inline-formula> , I am a candidate; if condition <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula> , I am the miner.” During every consensus iteration, the condition <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{1}$</tex-math></inline-formula> is updated, and a single miner is chosen using condition <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula> . Those conditions are randomized without the node generating any value and without assigning any weight to such value. The fault tolerance of PoCh is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$5f/3 + 1$</tex-math></inline-formula> , meaning that PoCh can successfully achieve consensus as long as more than 40% of nodes are functioning properly, compared to 50% in the Proof of Stake (PoS) protocol.
Multi-level landmark-guided deep network for face super-resolution

Recent years deep learning-based methods incorporating facial prior knowledge for face super-resolution (FSR) are advancing and have gained impressive performance. However, some important priors such as facial landmarks are not fully exploited in existing methods, leading to noticeable artifacts in the resultant SR face images especially under large magnification. In this paper, we propose a novel multi-level landmark-guided deep network (MLGDN) for FSR. More specifically, to fully exploit the dependencies between low and high resolution images and to reduce network parameters as well as capture more reliable feature representation, we introduce a recursive back-projection network with a particular feedback mechanism for coarse-to-fine FSR. Furthermore, we incorporate an attention fusion module in the front of backbone network to strengthen face components and a feature modulation module to refine features in the middle of backbone network. By this way, the facial landmarks extracted from face images can be fully shared by the modules in different levels, which benefit to produce more faithful facial details. Both quantitative and qualitative performance evaluations on two benchmark databases demonstrate that the proposed MLGDN can achieve more impressive SR results than other state-of-the-art competitors. Code will be available at https://github.com/zhuangcheng31/MLG_Face.git/.
An end-to-end identity association network based on geometry refinement for multi-object tracking

In multi-target tracking, object interactions and occlusions are two significant factors that affect tracking performance. To settle this, we propose an identity association network (IANet) that integrates the geometry refinement network (GRNet) and the identity verification (IV) module to perform data association and reason the mapping between the detections and tracklets. In our data association process, the object drifts caused by object interactions are suppressed effectively by encoding the direction and velocity of objects to refine the geometric position of tracklets. The tracklets with refined geometric information are further utilized in the IV module to achieve a sufficient encoding of multivariate spatial cues including both appearance and geometry information, which defeats the misleading impacts of interactions and occlusions dramatically in multi-object tracking. The extensive experiments and comparative evaluations have demonstrated that our proposed method can significantly outperform many state-of-the-art methods on benchmarks of 2D MOT2015, MOT16, MOT17, MOT20, and KITTI by using public detection and online settings.
Current Status, Requirements, and Challenges of Blockchain Application in Land Registry

The land registry system is one of the crucial aspects of any government. Proper and reliable land management is essential for economic growth and governance. The traditional land registry model lacks many essential requirements and mired with a different type of malpractices and security concerns. Blockchain technology has been proposed as a possible solution to counter these malpractices and security concerns. Even though there are many advantages of using blockchain in the land registry, still there are some concerns and challenges that need to be reviewed and addressed for successful blockchain implementation in the land registry. This paper reviews the current state of blockchain implementation in the land registry and the existing project being implemented in different countries. It further highlights the requirements, implications and challenges for successfully implementing blockchain in the land registry system. This paper provides a future direction for new implementations.
Discrete-time dynamic graph echo state networks

Relations between entities evolving through discrete time-steps can be represented by discrete-time dynamic graphs. Examples include hourly interactions between social network users or daily infection spreading. In this paper, we present Dynamic Graph Echo State Network (DynGESN), a reservoir computing model for the efficient processing of discrete-time dynamic temporal graphs. We prove a sufficient condition for the echo state property, which ensures that graph embeddings are independent of initial conditions, and we briefly analyze reservoir dynamics. DynGESN is compared against temporal graph kernels (TGKs) on twelve graph classification tasks, and against ten different end-to-end trained temporal graph convolutional networks (TGNs) on four vertex regression tasks, since TGKs are limited to graph-level tasks. Compared to TGKs that need to hold the entire history of vertex interactions, our model provides a vector encoding for the dynamic graph that is updated at each time-step without requiring training. Experiments show that our model achieves accuracy in line with TGKs that have comparable computational complexity, while still offering space and time requirements better suited to scale to large-size data. Moreover, DynGESN overall achieves superior or on par accuracy with respect to TGNs, while improving efficiency by up ten times on inference and training time.
Train Me If You Can: Decentralized Learning on the Deep Edge

The end of Moore’s Law aligned with data privacy concerns is forcing machine learning (ML) to shift from the cloud to the deep edge. In the next-generation ML systems, the inference and part of the training process will perform at the edge, while the cloud stays responsible for major updates. This new computing paradigm, called federated learning (FL), alleviates the cloud and network infrastructure while increasing data privacy. Recent advances empowered the inference pass of quantized artificial neural networks (ANNs) on Arm Cortex-M and RISC-V microcontroller units (MCUs). Nevertheless, the training remains confined to the cloud, imposing the transaction of high volumes of private data over a network and leading to unpredictable delays when ML applications attempt to adapt to adversarial environments. To fill this gap, we make the first attempt to evaluate the feasibility of ANN training in Arm Cortex-M MCUs. From the available optimization algorithms, stochastic gradient descent (SGD) has the best trade-off between accuracy, memory footprint, and latency. However, its original form and the variants available in the literature still do not fit the stringent requirements of Arm Cortex-M MCUs. We propose L-SGD, a lightweight implementation of SGD optimized for maximum speed and minimal memory footprint in this class of MCUs. We developed a floating-point version and another that operates over quantized weights. For a fully-connected ANN trained on the MNIST dataset, L-SGD (float-32) is 4.20× faster than the SGD while requiring only 2.80% of the memory with negligible accuracy loss. Results also show that quantized training is still unfeasible to train an ANN from the scratch but is a lightweight solution to perform minor model fixes and counteract the fairness problem in typical FL systems.
Semi-supervised regression with manifold: A Bayesian deep kernel learning approach

Semi-supervised learning (SSL) aims at utilizing the vast unlabeled data to help the supervised training. While existing SSL methods have shown promising results on image classification tasks, most of them rely on the cluster assumption that does not apply to image regression tasks. In this paper, we address the under-studied semi-supervised image regression problem, of which the outputs are continuous values instead of categorical distributions. To tackle this challenging task, we propose an algorithm, called ManiDKL, with the idea that the prediction function should be smooth with respect to the intrinsic manifold of data distribution and behave similarly on both labeled and unlabeled data. In particular, we propose a framework that implements the Tikhonov regularization with generative manifold learning to ensure manifold smoothness of regression function and also reduces the problem to kernel learning. Then a semi-supervised non-parametric Bayesian based deep kernel learning algorithm is proposed, in which unlabeled data are incorporated through posterior regularization. We show the effectiveness of ManiDKL with extensive experiments. It shows that ManiDKL performs comparatively with state-of-the-art SSL image classification methods. Most importantly, we show the superiority of ManiDKL over all existing SSL regression methods on public image datasets.
A Novel Approach Exploiting Machine Learning to Detect SQLi Attacks

The increasing use of Information Technology applications in the distributed environment is increasing security exploits. Information about vulnerabilities is also available on the open web in an unstructured format that developers can take advantage of to fix vulnerabilities in their IT applications. SQL injection (SQLi) attacks are frequently launched with the objective of exfiltration of data typically through targeting the back-end server organisations to compromise their customer databases. There have been a number of high profile attacks against large enterprises in recent years. With the ever-increasing growth of online trading, it is possible to see how SQLi attacks can continue to be one of the leading routes for cyber-attacks in the future, as indicated by findings reported in OWASP. Various machine learning and deep learning algorithms have been applied to detect and prevent these attacks. However, such preventive attempts have not limited the incidence of cyber-attacks and the resulting compromised database as reported by (CVE) repository. In this paper, the potential of using data mining approaches is pursued in order to enhance the efficacy of SQL injection safeguarding measures by reducing the false-positive rates in SQLi detection. The proposed approach uses CountVectorizer to extract features and then apply various supervised machine-learning models to automate the classification of SQLi. The model that returns the highest accuracy has been chosen among available models. Also a new model has been created PALOSDM (Performance analysis and Iterative optimisation of the SQLI Detection Model) for reducing false-positive rate and false-negative rate. The detection rate accuracy has also been improved significantly from a baseline of 94% up to 99%.
An Individual-Difference-Aware Model for Cross-Person Gaze Estimation

We propose a novel method on refining cross-person gaze prediction task with eye/face images only by explicitly modelling the person-specific differences. Specifically, we first assume that we can obtain some initial gaze prediction results with existing method, which we refer to as InitNet, and then introduce three modules, the Validity Module (VM), Self- Calibration (SC) and Person-specific Transform (PT) module. By predicting the reliability of current eye/face images, VM is able to identify invalid samples, e.g. eye blinking images, and reduce their effects in modelling process. SC and PT module then learn to compensate for the differences on valid samples only. The former models the translation offsets by bridging the gap between initial predictions and dataset-wise distribution. And the later learns more general person-specific transformation by incorporating the information from existing initial predictions of the same person. We validate our ideas on three publicly available datasets, EVE, XGaze, and MPIIGaze dataset. We demonstrate that our proposed method outperforms the SOTA methods significantly on all of them, e.g. respectively 21.7%, 36.0%, and 32.9% relative performance improvements. We are the winner of the GAZE 2021 EVE Challenge and our code can be found here https://github.com/bjj9/EVE_SCPT.
Recognition of Bird Nests on Power Transmission Lines in Aerial Images Based on Improved YOLOv4

Bird nests on transmission line towers pose a serious threat to the safe operation of power systems. Exploring an effective method to detect bird nests taken by drone inspection is crucial. However, the images taken by drones have problems such as drastic changes in the size of the object, occlusion of the object, and inconsistency in the characteristics of the object in relation to the background. The original YOLOv4 model has difficulty solving these problems. Therefore, this article improves the original YOLOv4 model by adding a Swin transformer block to its backbone network, fusing the attention mechanism into the neck of the original model, implementing classification and regression tasks for head decoupling, and using an anchor-free frame strategy and the SimOTA sample allocation method. The improved model was trained and tested on a bird nest dataset, and the detection accuracy reached 88%. Finally, the method was compared and evaluated against Faster R-CNN, RetinaNet, SSD, and the original YOLOv4, four of the other mainstream object detection models. The results showed that the accuracy obtained by the algorithm was better than the other models; the algorithm could effectively detect difficult objects such as multiple angles, occlusions, and small objects, and the detection speed could meet the real-time requirements.
Attribute-based access control scheme for data sharing on hyperledger fabric

With the popularization of the IoT and big data, the application value of data has become increasingly prominent. Therefore, it is particularly important to ensure the legitimate use of privacy data in the process of data sharing through access control. Traditional schemes usually use a trusted central organization to implement access control. However, a fully trusted central organization does not exist, and the access process of centralized access control is also opaque. In order to solve these problems, in this paper, we propose a decentralized attribute-based and fine-grained access control scheme for data sharing on Hyperledger Fabric. Firstly, the encrypted data is stored on the InterPlanetary File System (IPFS) and the returned hash is saved in the blockchain, by virtue of the immutability of blockchain. Secondly, a decentralized fine-grained attribute-based access control through smart contracts to prevent unauthorized users from accessing data resource. Thirdly, the linear integer secret sharing algorithm is used to achieve symmetric key sharing among multiple attributes to ensure the security of the key. Finally, the security analysis of the proposed scheme and the performance evaluation using the Hyperledger Caliper tool show the feasibility and effectiveness of the new scheme.