Back to grammar: Using grammatical error correction to automatically assess L2 speaking proficiency

Abstract: In an interconnected world where English has become the lingua franca of culture, entertainment, business, and academia, the growing demand for learning English as a second language (L2) has led to an increasing interest in automatic approaches for assessing spoken language proficiency. In this regard, mastering grammar is one of the key elements of L2 proficiency. In this paper, we illustrate an approach to L2 proficiency assessment and feedback based on grammatical features using only publicly available data for training and a small proprietary dataset for testing. Specifically, we implement it in a cascaded fashion, starting from learners’ utterances, investigating disfluency detection, exploring spoken grammatical error correction (GEC), and finally using grammatical features extracted with the spoken GEC module for proficiency assessment. We compare this grading system to a BERT-based grader and find that the two systems have similar performances when using manual transcriptions, but their combinations bring significant improvements to the assessment performance and enhance validity and explainability. Instead, when using automatic transcriptions, the GEC-based grader obtains better results than the BERT-based grader. The results obtained are discussed and evaluated with appropriate metrics across the proposed pipeline.


Aggregating knowledge and collaborative information for sequential recommendation

Abstract: Sequential recommendation aims to predict users’ future activities based on their historical interaction sequences. Various neural network architectures, such as Recurrent Neural Networks (RNN), Graph Neural Networks (GNN), and self-attention mechanisms, have been employed in the tasks, exploring multiple aspects of user preferences, including general interests, short-term interests, long-term interests, and item co-occurrence patterns. Despite achieving good performance, there are still limitations in capturing complex user preferences. Specifically, the current structures of RNN, GNN, etc., only capture item-level transition relations while neglecting attribute-level transition relations. Additionally, the explicit item relations are studied using item co-occurrence modules, but they cannot capture the implicit item-item relations. To address these issues, we propose a knowledge-augmented Gated Recurrent Unit (GRU) to improve the short-term user interest module and adopt a collaborative item aggregation method to enhance the item co-occurrence module. Additionally, our long-term interest module utilizes a bitwise gating mechanism to select historical item features significant to users’ current preferences. We extensively evaluate our model on three real-world datasets alongside competitive methods, demonstrating its effectiveness in top K sequential recommendation.


Multi-aspect multilingual and cross-lingual parliamentary speech analysis

Abstract: Parliamentary and legislative debate transcripts provide an informative insight into elected politicians’ opinions, positions, and policy preferences. They are interesting for political and social sciences as well as linguistics and natural language processing (NLP) research. While exiting research studied individual parliaments, we apply advanced NLP methods to a joint and comparative analysis of six national parliaments (Bulgarian, Czech, French, Slovene, Spanish, and United Kingdom) between 2017 and 2020. We analyze emotions and sentiment in the transcripts from the ParlaMint dataset collection, and assess if the age, gender, and political orientation of speakers can be detected from their speeches. The results show some commonalities and many surprising differences among the analyzed countries.


MeFiNet: Modeling multi-semantic convolution-based feature interactions for CTR prediction

Abstract: Extracting more information from feature interactions is essential to improve click-through rate (CTR) prediction accuracy. Although deep learning technology can help capture high-order feature interactions, the combination of features lacks interpretability. In this paper, we propose a multi-semantic feature interaction learning network (MeFiNet), which utilizes convolution operations to map feature interactions to multi-semantic spaces to improve their expressive ability and uses an improved Squeeze & Excitation method based on SENet to learn the importance of these interactions in different semantic spaces. The Squeeze operation helps to obtain the global importance distribution of semantic spaces, and the Excitation operation helps to dynamically re-assign the weights of semantic features so that both semantic diversity and feature diversity are considered in the model. The generated multi-semantic feature interactions are concatenated with the original feature embeddings and input into a deep learning network. Experiments on three public datasets demonstrate the effectiveness of the proposed model. Compared with state-of-the-art methods, the model achieves excellent performance (+0.18% in AUC and -0.34% in LogLoss VS DeepFM; +0.19% in AUC and -0.33% in LogLoss VS FiBiNet).


Dynamic optimization based on quantum computation-A comprehensive review

Abstract: Solving dynamic optimization problems (DOPs) induced by time-varying optimization objective functions and constraints is challenging. Quantum computation has received considerable attention to accelerate the solution of such optimization problems in recent years, and quantum optimization algorithms have been developed. Yet, there are no comprehensive review papers on quantum optimization algorithms for DOPs. This paper presents a critical review of dynamic optimization based on quantum computing. A brief overview of dynamic optimization problems is first given. The existing quantum optimization algorithms are introduced in detail, including QPSO, QEA, QAA, QGA, QNN, etc. The principles of different quantum algorithms and their improved forms are presented and discussed. The optimization results obtained by different quantum optimization algorithms prove the superiority of quantum algorithms over traditional optimization algorithms for DOPs. In the near future, quantum computing applied to traditional intelligent algorithms can still be the platform for innovating new algorithms. The dynamic optimization based on quantum learning algorithms needs further research. The ultimate combination of quantum algorithms and quantum computers is still lacking. Developments in this area may show powerful capabilities to solve dynamic optimization problems.


HSB based reversible data hiding using sorting and pairwise expansion

Abstract: Reversible data hiding (RDH) continues to advance in its applicability across diverse fields, including medical imaging, military applications, and cloud computing. In recent times, there has been a proliferation of RDH methods that focus on embedding in the higher significant bit (HSB) plane, as opposed to the least significant bit (LSB), in order to leverage the greater correlation, thereby achieving a larger embedding capacity (EC). This paper introduces a novel and efficient RDH approach that utilizes sorting and pairwise expansion. The proposed method initiates by partitioning the original image into HSB and LSB planes and then converting the HSB plane into a chessboard pattern. Subsequently, pixels are organized in ascending order based on their predicted values, forming groups of 1 × 3 pixels. The embedding process occurs in two passes: the first employs a difference expansion-based RDH, while the second pass employs pairwise expansion to minimize distortion. This method accomplishes an expanded EC while preserving the quality of the resulting image. Experimental results also demonstrate that the proposed approach maintains a significant lead of approximately 0.5 dB over the best-performing existing methods, underscoring its superior embedding efficiency.


Investigating FAIR data principles compliance in horizon 2020 funded Agri-food and rural development multi-actor projects

Abstract: The agri-food and rural development sector in Europe is undergoing digitalization, and therefore becoming increasingly data-dependent. However, limited experience in implementing Findable, Accessible, Interoperable, and Reusable (FAIR) data principles has restricted knowledge sharing and reusability. This study aims to investigate the existing FAIR Data Management practices in the agri-food and rural development sector by assessing the FAIRness of project outputs from recent H2020-funded Multi-Actor Projects (MAPs). We conducted a FAIRness assessment of project outputs using both semi-automatic and manual framework, and we also carried out a comprehensive review of the data sharing practices among selected MAPs. Of the investigated MAPs, <10% have achieved FAIR compliance and applied FAIR data management practices. The measured FAIRness of project products, including journal articles, conference papers, and books, is higher than that of other product types such as videos, audios, and presentations. The study highlights the critical need for standardizing the adoption of FAIR data principles and data management practices across both the agri-food and rural development sector but also across bureaucratic data management throughout Europe more widely. Such a change would facilitate broader utilization and re-use of MAPs outputs and results, enhancing decision-making and agri-food and rural development practices.


GST-based polarization approach to picking P-and S-waves arrival time for high-speed rail early warning system

Abstract: The first arrival time of seismic P- and S- waves affects the determination of the beginning of an earthquake by the Earthquake Early Warning System for High-speed Rail (HSREEWs). Accordingly, we propose an approach to picking P- and S-waves arrival time for HSREEWs. This approach can treat the continuous seismic signals in real-time without prior knowledge of whether an earthquake occurs. Firstly, we replace the fixed time window of the S transform (ST) with the dynamic window of the generalized S transform (GST) to make the necessary time of signal processing more flexible. Meanwhile, the GST extends the sequential signal to the time-frequency domain (TF), and combines the TF polarization to construct four three-component polarization filters. Accordingly, the P-wave and S-wave can be separated from the seismic waveform by those filters, which effectively avoids the phenomenon that the S-wave phase is difficult to determine because of the interference of S-wave overlapping in the coda region. Secondly, based on the instantaneous frequency of seismic signal, we construct a new STA/LTA (GSPA-STA/LTA) with the characteristic function (CF) of constrained TF spectrum to pick the first arrivals of the P- and S-waves, and provide the corresponding automatic picking algorithm. Finally, we analyze 504 seismic events larger than Ms40 near the Japanese Tohoku Shinkansen to comprehensively verify the effectiveness of our approach. Since the P-wave and S-wave are first separated, the subsequent process just has to identify the first arrival times of their respective components. The approach has a good practical value in maintaining the safe operation of high-speed rail under earthquake, which meets the industry demands for speed, scale and accuracy in HSREEWs.


Secure Summation: Capacity Region, Groupwise Key, and Feasibility

Abstract: The secure summation problem is considered, where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K users, each holds an input, wish to compute the sum of their inputs at a server securely, i.e., without revealing any information beyond the sum even if the server may collude with any set of up to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T users. First, we prove a folklore result for secure summation - to compute 1 bit of the sum securely, each user needs to send at least 1 bit to the server, each user needs to hold a key of at least 1 bit, and all users need to hold collectively some key variables of at least <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K − 1 bits. Next, we allow any arbitrary group of users to share an independent key and any arbitrary group of users to collude with the server. For such a general groupwise key and colluding user setting, we show that secure summation is feasible if and only if the hypergraph, where each node is a user and each edge is a group of users sharing the same key, is connected after removing the nodes corresponding to any colluding set of users and their incident edges. Finally, we focus on the symmetric groupwise key setting, where every group of G users share an independent key. We show that for symmetric groupwise keys with group size <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G , if <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G = 1 or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G > <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K − <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T , the secure summation problem is not feasible; else 1 < <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G ≤ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K − <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T , to compute 1 bit of the sum securely, each user needs to send at least 1 bit to the server and the size of each groupwise key is at least ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K − <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T − 1)/( K−T G ) bits.


Zero-order fuzzy neural network with adaptive fuzzy partition and its applications on high-dimensional problems

Abstract: The number of fuzzy rules in a fuzzy neural network usually depends on the grid-type partition or clustering-based partition. However, the number of grids or clusters needs to be set in advance and the initial number of fuzzy sets on each dimension is usually the same. This usually does not fit the real situation (for example, when forecasting the weather, various features such as temperature, humidity, wind direction, wind speed, etc., may have different numbers of fuzzy sets). In this paper, a novel zero-order fuzzy neural network with an adaptive fuzzy partition (AdFPFNN) is proposed, which does not need to preset the number of fuzzy sets and owns various numbers of fuzzy sets on different dimensions. The trick is to use the mean shift algorithm to perform adaptive clustering for each feature individually. For common fuzzy neural networks, dealing with high-dimensional problems is challenging work on account of the “fuzzy rule curse” and “computation underflow”. To overcome these issues, an improved version of AdFPFNN is devised to tackle high-dimensional problems, called PaCoAMF-based AdFPFNN. It adopts two techniques to overcome the “fuzzy rule curse” and “computation underflow”. One is the construction of a partially combined fuzzy rule base, which can generate rules that are adequate but do not grow exponentially with the number of features. Another is the proposal of an adaptive membership function that can guarantee the elimination of numerical underflow due to the product T-norm. Without feature selection or other dimensional reduction methods, the proposed PaCoAMF-based AdFPFNN can be directly used to solve high-dimensional problems. Simulation results on one constructed dataset and 13 real-world datasets (including 5 high-dimensional problems) confirm the effectiveness of the proposed models.


SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing

Abstract: There is a growing interest in the distributed optimization framework that goes under the name of Federated Learning (FL). In particular, much attention is being turned to FL scenarios where the network is strongly heterogeneous in terms of communication resources (e.g., bandwidth) and data distribution. In these cases, communication between local machines (agents) and the central server (Master) is a main consideration. In this work, we present SHED, an original communication-constrained Newton-type (NT) algorithm designed to accelerate FL in such scenarios. SHED is by design robust to non independent identically distributed (non i.i.d.) data distributions, handles heterogeneity of agents’ communication resources (CRs), only requires sporadic Hessian computations, and achieves global asymptotic super-linear convergence. This is possible thanks to an incremental strategy, based on eigendecomposition of the local Hessian matrices, which exploits (possibly) outdated second-order information. SHED is thoroughly validated on real datasets by assessing (i) the number of communication rounds required for convergence, (ii) the overall amount of data transmitted and (iii) the number of local Hessian computations. For all these metrics, SHED shows superior performance against state-of-the art techniques like BFGS, GIANT and FedNL.


Dealing with Drift of Adaptation Spaces in Learning-based Self-Adaptive Systems Using Lifelong Self-Adaptation

Abstract: Recently, machine learning (ML) has become a popular approach to support self-adaptation. ML has been used to deal with several problems in self-adaptation, such as maintaining an up-to-date runtime model under uncertainty and scalable decision-making. Yet, exploiting ML comes with inherent challenges. In this article, we focus on a particularly important challenge for learning-based self-adaptive systems: drift in adaptation spaces. With adaptation space, we refer to the set of adaptation options a self-adaptive system can select from to adapt at a given time based on the estimated quality properties of the adaptation options. A drift of adaptation spaces originates from uncertainties, affecting the quality properties of the adaptation options. Such drift may imply that the quality of the system may deteriorate, eventually, no adaptation option may satisfy the initial set of adaptation goals, or adaptation options may emerge that allow enhancing the adaptation goals. In ML, such a shift corresponds to a novel class appearance, a type of concept drift in target data that common ML techniques have problems dealing with. To tackle this problem, we present a novel approach to self-adaptation that enhances learning-based self-adaptive systems with a lifelong ML layer. We refer to this approach as lifelong self-adaptation . The lifelong ML layer tracks the system and its environment, associates this knowledge with the current learning tasks, identifies new tasks based on differences, and updates the learning models of the self-adaptive system accordingly. A human stakeholder may be involved to support the learning process and adjust the learning and goal models. We present a general architecture for lifelong self-adaptation and apply it to the case of drift of adaptation spaces that affects the decision-making in self-adaptation. We validate the approach for a series of scenarios with a drift of adaptation spaces using the DeltaIoT exemplar.


Two-stage GNN-based fraud detection with camouflage identification and enhanced semantics aggregation

Abstract: Graph neural network (GNN) has got lots of attention recently in the fraud detection task due to its message-passing mechanism, which can aggregate neighbors feature in the information network. While promising, currently most GNN-based fraud detectors fail to identify camouflage caused by fraud with graph structure information and embed entity in deep layer with rich semantics effectively. To solve these problems, we propose a two-stage GNN-based approach with camouflage identification and enhanced semantics aggregation (CIES-GNN) for fraud detection. In the proposed approach, camouflage is identified by reconstructing subgraphs with both node feature and structure information. Detailedly, hidden edges between fraudsters are found by reconstructing a dense subgraph of fraudster nodes, and redundant connections between benign nodes and fraudster nodes are eliminated by reconstructing subgraphs in a label-balance distribution. Moreover, to embed entity in deep layer without semantics obfuscation, the node information is embedded in an enhanced semantics aggregation module, which fuses higher-order information in intra-relation and aggregates semantics in inter-relation respectively. Experiments on two real-world datasets demonstrate that the proposed CIES-GNN outperforms state-of-the-art baselines.


Entity recognition method for airborne products metrological traceability knowledge graph construction

Abstract: The airborne system, as one of the complex and extensive subsystems of an aircraft, primarily performs critical flight assurance functions. The quality of its components has a direct impact on the aircraft's safety and reliability. Metrology documents comprehensively document the performance parameters throughout the entire product life cycle. The Metrological Traceability Knowledge Graph (MTKG) for airborne products offers decision support to engineers engaged in metrological tasks, ensuring the continuous high quality of the products. This paper introduces an entity recognition method for airborne product metrological traceability knowledge graph construction. First, the ontology for MTKG is developed. Next, a fine-tuned multi-network model is proposed. Named entities in the field of metrology are recognized through three stages: word vector representation, sentence feature extraction, and optimal label assignment. Meanwhile, active learning methods are incorporated to reduce the expense of data annotation. The proposed model is validated using an actual metrology corpus, and the experimental results demonstrate its superior performance compared to the other four baseline methods. Finally, the MTKG is developed using this approach, offering engineers intelligent applications, including metrological traceability analysis and traceability path reasoning within the process of product metrology. This enhances the metrology capabilities of airborne products and demonstrates the extensive potential of knowledge graphs in metrology.


Safe multi-agent drone control using control barrier functions and acceleration fields

Abstract: The objective of this article is to develop real-time controllers for positioning multi-agent autonomous platforms (specifically drones) without any collisions. The framework is divided in two controllers. The first, based on control barrier functions and quadratic programming, is an acceleration field, that computes an acceleration for each position and velocity of the system that guides the agents towards the goal while avoiding obstacles and inter-collisions. The second controls the agents’ total thrusts and angular velocities to track the previously-defined acceleration profiles. We show formal guarantees for both controllers, and simulation and experimental studies are used to show the efficacy of the proposed approach.


Interpretable hierarchical belief rule base expert system for complex system modeling

Abstract: Belief rule base (BRB) expert system has been widely used in complex system modeling. Readability and consistency are the primary characteristics and advantages of BRB. However, these two characteristics may be lost in BRB establishment and optimization. Aiming at this problem, a novel hierarchical BRB (H-BRB) expert system is proposed. To quantify the interpretability of BRB, the definition of interpretability of BRB is proposed from the readability and the consistency. Two strategies are proposed to construct the H-BRB expert system with good readability. A new optimization method is proposed to guarantee the consistency of H-BRB, including a new optimization mechanism, a new objective function with interpretability constraints, and a modified optimization algorithm. With all the above, an H-BRB expert system with good readability and consistency can be established. A case study of the health state assessment of the aerospace relay is implemented to verify the effectiveness of the proposed method.


Supervised adaptive similarity consistent latent representation hashing

Abstract: Cross–modal hashing has attracted significant attention in multimedia data similarity given its appealing computational cost and retrieval performance. Supervised hashing benefits from the auxiliary learning of a similarity matrix, which is usually predefined by inner product features or category labels. However, a predefined similarity matrix fails to reflect the real similarity relationship between image–text pairs. In addition, existing methods fix the weights to a value or update them by introducing sensitive dataset–related hyper–parameters. To overcome these problems, we propose a method to perform supervised adaptive similarity consistent latent representation hashing (SCLRH) that adaptively learns the similarity matrix during hashing learning. In SCLRH, we assume that multimodal data are observed and reconstructed from different perspectives of a common consistent latent representation. Instead of using a predefined similarity matrix, SCLRH adaptively learns this matrix to reflect the underlying manifold structure and describes the fine–grained similarity between consistent latent representations. In addition, SCLRH introduces a self–weighted learning strategy to update the weights based on the contributions of different modalities without involving additional hyper–parameters. Experimental results on three benchmark datasets demonstrate the superiority of the proposed SCLRH for cross–modal retrieval.


Surface Noise

Abstract: Abstract Chapter 5 analyzes the cyclic surface noise patterns heard on early recording formats including wax cylinders and gramophone discs. The sound of recording and playback machines is theorized alongside the composition being recording, treating the resultant totality as one music. An analogy is made between surface noise and a Renaissance illustration of a sighting grid by Fludd. Six modes of interaction are defined between perceived meter and the pulse patterns of surface noise: (1) performance-centricity; (2) narrative asynchrony; (3) ekphrastic (non)coincidence; (4) expressive synchrony; (5) metaphoric development; and (6) surface noise-centricity. The modes are illustrated in recordings by Simpson singing Bonds, Caruso singing Donizetti, Granados and Arensky recording their own music, Krushelnytska singing Lysenko, and a re-recording of the historic Brahms cylinder. The deterioration of the cylinder is related to a letter in which Rilke voiced his concerns about a tarnished daguerreotype of his father.


Figleaves, Dogwhistles, and Falsehood

Abstract: Abstract In this chapter, I explore the role that figleaves and dogwhistles play in the spread of falsehood and wildly implausible conspiracy theories. I begin from the Norm of Truthfulness, a woolly norm that is widely accepted, which functions in a manner similar to the Norm of Racial Equality. I show how dogwhistles and figleaves are used to provide deniability for violations of this norm. This allows blatantly false claims to remain circulation, and provides cover for political figures who might otherwise see their careers ended by their support for wild falsehoods.


Introduction

Abstract: Abstract This chapter introduces the main research question of the book: how have blatant racism and wildly implausible conspiracy theories been mainstreamed in our political culture? The chapter acknowledges that racism and political lies are far from new. Yet it argues that there is something new in the prevalence of blatant racism and obvious lies in our current political discourse. Moreover, it argues that this is dangerous in ways that should motivate us to seek a fuller understanding. This introduction sets out the basic elements of the response that the book will make, including (1) a focus on divisions among the public; (2) particular attention to manipulable groups; (3) the linguistic devices of dogwhistles and figleaves; (4) and the rise of authoritarianism. It also offers a chapter-by-chapter overview of the book.


A closer look at branch classifiers of multi-exit architectures

Abstract: Multi-exit architectures consist of a backbone and branch classifiers that offer shortened inference pathways to reduce the run-time of deep neural networks. In this paper, we analyze different branching patterns that vary in their allocation of computational complexity for the branch classifiers. Constant-complexity branching keeps all branches the same, while complexity-increasing and complexity-decreasing branching place more complex branches later or earlier in the backbone respectively. Through extensive experimentation on multiple backbones and datasets, we find that complexity-decreasing branches are more effective than constant-complexity or complexity-increasing branches, which achieve the best accuracy-cost trade-off. We investigate a cause by using knowledge consistency to probe the effect of adding branches onto a backbone. Our findings show that complexity-decreasing branching yields the least disruption to the feature abstraction hierarchy of the backbone, which explains the effectiveness of the branching patterns.


Synchronization of fuzzy-chaotic systems with Z-controller in secure communication

Abstract: Soft Computing approaches may be effectively used for the aim to improve security of communication systems. Recently, we proposed a technique for fuzzy-chaotic masking signals generation. In this paper we developed this approach further. It is proposed a procedure for receiver and transmitter synchronization by using fuzzy chaotic systems (fuzzy Lorentz systems). The receiver system utilizes a fuzzy controller based on the use of Z-numbers in If-Then rules. The transmitter system is designed in form of a classical fuzzy system. The fuzzy controller is used to synchronize the fuzzy Lorentz system-based receiver and transmitter at a minimal error.


Online controls and the protest- repression nexus in Iran

Abstract: Abstract This chapters seeks to understand the strategy and impact of Internet shutdowns in an authoritarian context outside of armed conflict. It studies the mass protests that spread across Iran in November 2019 to investigate how Iranian security forces engaged in violent repression during the regime-ordered nationwide shutdown of the Internet. It combines insights from NGO and news reports, network measurement data on the Internet shutdown, and data on censorship circumvention to show how the Internet shutdown was accompanied by mass repression intended to cover up violence by security forces and motivate witnesses and bystanders to self-censor, even once the Internet was turned back on.


Deep learning for nano-photonic materials – The solution to everything!?

Abstract: Deep learning is currently being hyped as an almost magical tool for solving all kinds of difficult problems that computers have not been able to solve in the past. Particularly in the fields of computer vision and natural language processing, spectacular results have been achieved. The hype has now infiltrated several scientific communities. In (nano-) photonics, researchers are trying to apply deep learning to all kinds of forward and inverse problems. A particularly challenging problem is for instance the rational design of nanophotonic materials and devices. In this opinion article, I will first discuss the public expectations of deep learning and give an overview of the quite different scales at which actors from industry and research are operating their deep learning models. I then examine the weaknesses and dangers associated with deep learning. Finally, I’ll discuss the key strengths that make this new set of statistical methods so attractive, and review a personal selection of opportunities that shouldn’t be missed in the current developments.


Curricular-balanced long-tailed learning

Abstract: The real-world data distribution is essentially long-tailed, which poses a significant challenge to the deep model. Classification models minimizing cross-entropy loss struggle to classify the tail classes, although cross-entropy training is successful on balanced data. We reveal that minimizing cross-entropy loss under long-tailed distribution leads to the Tail Collapse phenomenon, which fundamentally limits the performance of neural networks. To correct the optimization behavior of cross-entropy training, we propose a new Curricular Balanced Loss (CurB Loss) to alleviate the imbalance. The CurB loss has two factors: the re-weighting factor and the curriculum learning factor. We design the re-weighting factor based on the margin-based training that can theoretically reach the optimums of networks. Then, we incorporate the idea of Curriculum Learning into the re-weighting loss in an adaptive manner. We design the curriculum learning factor to make the model gradually emphasize the hard classes. The empirical results demonstrate the complementary of the two factors. Our method outperforms previous state-of-the-art methods by 0.9%, 2.7%, 1.2% on CIFAR10-LT, CIFAR-100-LT and ImageNet-LT, demonstrating the effectiveness of CurB Loss for long-tailed visual recognition.


Soft Hybrid Knowledge Distillation against deep neural networks

Abstract: Traditional knowledge distillation approaches are typically designed for specific tasks, as they primarily distilling deep features from intermediate layers of a neural network, generally with ingeniously designed knowledge representations, which increase the difficulty of model development and interpretation. In contrast, we empirically show that a soft logits knowledge distillation is enough to significantly narrow the teacher–student model performance gap. In this paper, the Soft Hybrid Knowledge Distillation (SHKD) is proposed to generate representative features from the input and output location of models, and applicable to various teacher and student architectures. Specifically, the additional mixup task is first applied to the same batch of training images, which enriches the high-order feature information transferred by the model. And then, a top-k guided specification filter is added to the teacher model, to reduce the bias induced throughout the knowledge transfer process. Finally, the SHKD utilizes the same and different teacher–student model pairs of the deep neural network, which can be easily adjusted to the computational resources available in the current world. Extensive experiments are conducted to examine the efficiency, and show that the SHKD outperforms state-of-the-art knowledge distillation methods. With the SHKD, the high performance of models will be maintained, and the gap between the teacher model and the student model can be reduced, making the construction of deep neural networks more convenient to the unknown scenarios. The source code is available at https://github.com/lambett/SHKD.


Hi-PART: Going Beyond Graph Pooling with Hierarchical Partition Tree for Graph-Level Representation Learning

Abstract: Graph pooling refers to the operation that maps a set of node representations into a compact form for graph-level representation learning. However, existing graph pooling methods are limited by the power of the Weisfeiler–Lehman (WL) test in the performance of graph discrimination. In addition, these methods often suffer from hard adaptability to hyper-parameters and training instability. To address these issues, we propose Hi-PART, a simple yet effective graph neural network (GNN) framework with Hi erarchical Par tition T ree (HPT). In HPT, each layer is a partition of the graph with different levels of granularities that are going toward a finer grain from top to bottom. Such an exquisite structure allows us to quantify the graph structure information contained in HPT with the aid of structural information theory. Algorithmically, by employing GNNs to summarize node features into the graph feature based on HPT’s hierarchical structure, Hi-PART is able to adequately leverage the graph structure information and provably goes beyond the power of the WL test. Due to the separation of HPT optimization from graph representation learning, Hi-PART involves the height of HPT as the only extra hyper-parameter and enjoys higher training stability. Empirical results on graph classification benchmarks validate the superior expressive power and generalization ability of Hi-PART compared with state-of-the-art graph pooling approaches.


Analysis of tabular data based on graph neural network using supervised contrastive loss

Abstract: In many applications in the industry, tabular data are the most commonly used data type. Tabular data have the advantage of being easy to understand and interpret. Machine learning algorithms are mainly used to deduce a mapping function that maps input data to output in tabular data. Machine learning methods for dealing with tabular data are classified into two categories: similarity-based approach and feature-based approach. The feature-based approach explores a mapping function by clarifying the relationships between features, while the similarity-based approach uses similarities of observations to explore a mapping function. Both aspects have their pros and cons. Feature-based models are easy to understand and intuitive to use and deploy but generally cannot utilize the relationships between observations. Similarity-based models are most suited for exploiting the relationships among observations, but their availability is usually limited. In order to take advantage of both aspects, we propose an algorithm to combine feature-based and similarity-based approaches using a graph neural network. Additionally, to complement the shortcomings of cross-entropy loss, the proposed method applies supervised contrastive learning. Through supervised contrastive learning, the proposed method boosts the efficiency of learning the generalized representation for each class. Experimental results show that the proposed method provides more precise results for classification tasks, implying that it may improve the generalization capability.


Is This a Violation? Learning and Understanding Norm Violations in Online Communities

Abstract: Using norms to guide and coordinate interactions has gained tremendous attention in the multi-agent community. However, new challenges arise as the interest moves towards dynamic socio-technical systems, where human and software agents interact, and interactions are required to adapt to human's changing needs. For instance, different agents (human or software) might not have the same understanding of what it means to violate a norm (e.g., what characterizes hate speech), or their understanding of a norm might change over time (e.g., what constitutes an acceptable response time). The challenge is to address these issues by learning the meaning of a norm violation from limited interaction data. For this, we use batch and incremental learning to train an ensemble of classifiers. Ensemble learning and data-sampling handle the imbalanced class distribution of the interaction stream. At the same time, the training approaches use different strategies to ensure that the ensemble models reflect the latest community view on the meaning of norm violation. Batch learning uses weight assignment, while incremental learning continuously updates the ensemble models as community members interact. Here, we extend our previous work by creating a different balance strategy for online learning and integrating interpretability to understand norm violations. Additionally, we evaluate the proposed approaches in the context of Wikipedia article edits, where interactions revolve around editing articles, and the norm in question is prohibiting vandalism. Lastly, we conduct ablation studies to compare the ensemble's performance against a single model approach and to examine the behavior of two data sampling techniques. Results indicate that the different machine learning frameworks can learn the meaning of a norm violation in a setting with data imbalance and concept drift, although with significant differences.


Multi-modal anchor adaptation learning for multi-modal summarization

Abstract: In this paper, we focus on analyzing the relationship between the input of source text and source image, and then through the integration and generalization of the multi-modal information (e.g., texts and images), outputs the multi-modal summarization including text and image. However, existing multi-modal summarization methods face several challenges: (1) Different modalities exist in different semantic spaces, and expressing the multi-modal information of source modalities in a similar semantic representation space is crucial. (2) For the result of text summarization, it is crucial to explore how to capture both the differences and similarities within the source modalities, which can reduce redundancy to improve the quality of text summarization. In order to overcome the challenge above, Multi-Modal Anchor Adaptation Learning for Multi-Modal Summarization (MA-Sum) has been proposed. Specifically, MA-Sum employs a novel and highly efficient image anchor selection method, which selects the object sample containing the richest image information as the image anchor. Simultaneously, it carefully selects the text sentence closely intertwined with the image semantics to serve as the language anchor. Therefore, the multi-modal anchor can be seen as a bridge for multi-modal alignment to alleviate the semantic gap between textual and visual. Moreover, based on the distance between the anchors and the semantic information in the respective modal, the positive and negative semantic information of each modal will be distinguished. Based on negative semantic information, the counterfactual learning mechanism is constructed to optimize the result of multi-modal summarization. Finally, the process of multi-modal features interaction is optimized by image summary which is chosen by using multi-modal anchors. According to the experimental results, compared with the state-of-art multi-modal summarization, our proposed MA-Sum can be optimized in terms of summarization consistency and completeness, so as to obtain the optimal multi-modal summarization metric.


Spatial unity for the apperception engine

Abstract: We develop a logical approach for computational agents to spatially explore their environment, extending on the Kant-inspired logical unification methods of the Apperception Engine. Evaluating models of the Regional Connection Calculus as Alexandroff Topologies, we axiomatise connectedness and unity of space. We further define dimensionality for tolerance spaces and prove that locally verifiable properties guarantee global consistency of space as the existence of isomorphisms to grids, turbands or tori. Finally, we provide a generally competent and explainable computational agent that unifies intuition in space, the Spatial Apperception Engine.


BDACD: Blockchain-based decentralized auditing supporting ciphertext deduplication

Abstract: Public auditing enables data owners to entrust a third-party auditor (TPA) to perform auditing tasks periodically. To resist malicious TPAs, a plethora of blockchain-based public auditing mechanisms have been proposed. However, existing schemes cannot effectively mitigate single point of failure and collusion between TPAs and miners. These schemes are burdened by significant wastage of storage resources due to the presence of redundant data. In this paper, we propose a blockchain-based decentralized auditing scheme supporting ciphertext deduplication (BDACD). BDACD leverages a decentralized blockchain, which takes over the role traditionally played by a centralized TPA. A decentralized autonomous organization is incorporated to enhance resistance against collusion attacks. By integrating advanced ciphertext deduplication techniques, BDACD effectively mitigates storage overhead. Furthermore, we introduce a novel concept, T-Merkle hash tree, along with a corresponding search algorithm. This innovation significantly enhances blockchain storage utilization and enables efficient half-interval searches within a block. To ensure accountability and fairness, a smart contract is employed to establish an arbitration mechanism. This mechanism serves a dual purpose: penalizing any malevolent actions by cloud servers and providing adequate compensation to data owners whose data integrity has been compromised. Detailed security analysis and performance evaluation demonstrate BDACD has desirable security and efficiency.


T2FM: A novel hashtable based type-2 fuzzy frequent itemsets mining

Abstract: Association rule mining (ARM) is an important research issue in the field of data mining that aims to find relations among different items in binary databases. The conventional ARM algorithms consider the frequency of the items in binary databases, which is not sufficient for real time applications. In this paper, a novel hash table based Type-2 fuzzy mining algorithm (T2FM) with an efficient pruning strategy is presented for discovering multiple fuzzy frequent itemsets from quantitative databases. The algorithm employs a hash table based structure for efficient storage and retrieval of item/itemset which reduces the search efficiency to O(1) or constant time. Previously, type-2 based Apriori and FP-growth based fuzzy frequent itemsets mining were proposed, which required large amounts of computation and a greater number of candidate generation and processing. Meanwhile, the proposed approach reduces a huge amount of computation by finding the common keys before the actual intersection operation takes place. An efficient pruning strategy is proposed to avoid unpromising candidates in order to speed up the computations. Several experiments are carried out to verify the efficiency of the approach in terms of runtime and memory for different minimum support threshold and the results show that the designed approach provides better performance compared to the state-of-the-art algorithms.


Constraint-based high utility mobile trajectory pattern mining for security systems

Abstract: In security system, high utility pattern mining of a large number of users mobile trajectories is helpful to analyze user behavior patterns, and enhance the internal prevention of the security system.Currently, the frequent pattern mining for mobile trajectory in security systems do not take into account the differences in the safety levels between staying points and the frequency of their occurrence. Existing high utility mobile trajectory pattern mining methods are unable to discover staying points that meet specific user requirements. To solve these problems, a constraint-based high utility mobile trajectory mining algorithm is proposed (names as HUIM-ILC-ACO). It takes into account the user’s stay time, stay frequency, and the safety level of each stopping point and calculates the utility value of each staying point by incorporating these factors through weighted computation. Based on this, the algorithm combines ant colony optimization with length constraints and item constraints to construct a method for mining high utility mobile trajectory patterns that better align with user interests. Experimental results on real datasets and a target mobile trajectory RFID dataset show that proposed algorithm is efficient in terms of runtime and pattern quantity, and it can effectively mine a pattern set that is closer to user interests.


Multi-scale cross-layer feature interaction GAN for underwater image enhancement

Abstract: Due to the intricate underwater environment, underwater images frequently exhibit issues such as low contrast, detail loss, and color distortion, which significantly affect underwater operations. To alleviate these concerns and improve image quality, multi-scale cross-layer feature interaction GAN for underwater image enhancement model (MCFIGAN) is proposed in this paper. In the proposed model, we have designed a cross-layer feature interaction module (CLFIM) that fosters the fusion and transmission of features at various levels. This approach aims to extract more comprehensive feature information and enhance the robustness of the network model. Additionally, we have constructed a multi-scale feature module (MSFEM) to extract features at different levels and scales, enabling the model to gain a more comprehensive understanding of the object. By utilizing CLFIM and MSFEM, the generator is capable of producing a generated image that contains a wealth of feature information. Then, an enhanced discriminator module (EDM) is designed to improve the model's ability to identify generated images, thereby encouraging the generator network to generate more realistic underwater images. Finally, several training sessions utilizing multiple joint losses yield underwater images with characteristics of high contrast and rich detail. Through simulation experiments conducted on EUVP dataset and UIEB dataset, our proposed model is found to be superior to related models in terms of visual effect and quantitative index, particularly in contrast enhancement, detail recovery, and color restoration.


A machine-learning based approach to predict facies associations and improve local and regional stratigraphic correlations

Abstract: The geological record has challenged stratigraphers through time. Many depositional, tectonic and paleobiological events require stratigraphic positioning to determine temporal relationships among such events. This task is complicated and challenging, especially in sedimentary succession with scarcity or lack of paleontological content bearing biostratigraphic value and radiometric ages. Therefore, subjective personal criteria adopted during correlations and field mapping activities make stratigraphic correlations more complex and confusing. New methodological approaches are necessary to test human expertise in recognizing stratigraphic units with environmental significance and to contribute to stratigraphic correlations based on quantitative data. We used cores obtained by coal drilling campaigns during the 1970s and 1980s on the southern border of the Paraná Basin, southern Brazil, to generate a quantitative database. Data obtained from gamma spectrometry in the cores of Carboniferous to Permian age measured total count (cps), potassium (K), uranium (U) and thorium (Th). We used machine learning (ML) to predict facies associations and to confront the quantitative database with the three facies associations mapped through time based on qualitative geological mapping criteria. The k-nearest neighbors (KNN) algorithm reached maximum accuracy values of 86.0%% and f1-score of 90.0%, 73.0% and 91.0% for facies associations 1, 2 and 3, respectively, using exponential moving average and normalized data. This KNN-based method using gamma spectrometric data opened new possibilities to perform local and regional stratigraphic correlations using quantified data. New tests must be performed to improve this promising correlation method in other regional settings and related basins.


A Relation Embedding Assistance Networks for Multi-hop Question Answering

Abstract: Multi-hop Knowledge Graph Question Answering aims at finding an entity to answer natural language questions from knowledge graphs. When humans perform multi-hop reasoning, people tend to focus on specific relations across different hops and confirm the next entity. Therefore, most algorithms choose the wrong specific relation, which makes the system deviate from the correct reasoning path. The specific relation at each hop plays an important role in multi-hop question answering. Existing work mainly relies on the question representation as relation information, which cannot accurately calculate the specific relation distribution. In this article, we propose an interpretable assistance framework that fully utilizes the relation embeddings to assist in calculating relation distributions at each hop. Moreover, we employ the fusion attention mechanism to ensure the integrity of relation information and hence to enrich the relation embeddings. The experimental results on three English datasets and one Chinese dataset demonstrate that our method significantly outperforms all baselines. The source code of REAN will be available at https://github.com/2399240664/REAN


Transfer learning approach to reduce similar IOT sensor data for industrial applications

Abstract: The Industrial Internet of Things (IIoT) is a developing technology that has the potential to advance industrial intelligence, achieve targeted effectiveness, and lower manufacturing costs. Data fusion, a procedure that enables the collection, analysis, and processing of the enormous amounts of Internet of Things (IoT) information created by industrialized machinery and applications, is essential to IIoT's advancement and improvement of industrial products and applications. A real-time, efficient, and private information image fusion mechanism is required by the IIoT. Moreover, the current efforts call for such training of multiple systems in data analysis that also fall short of meeting real-time IIoT needs. The ineffectiveness of internal defenses and the challenge of finding the right balance between system stability and data confidentiality, however, undermine the efficiency and privacy preservation of a data fusion process. In this paper, we propose a Transfer learning-based Secure Data Fusion (TSDF) for the IIoT to address the aforementioned issues. In addition to the proposed network, we developed and deployed a defect estimation method that makes use of sensor data fusion to evaluate the manufacturing plant's operational state. The efficiency outcomes of evaluating the conceptual model are included in the original study, including a comparison to a conventional deep neural network structure. The study's findings demonstrate that TSDF may provide privacy, preserving information fusion in diverse IIoT application environments while reaching higher system capacity and low latency.


Sea-Pix-GAN: Underwater image enhancement using adversarial neural network

Abstract: In the last decade, the exploration of deep-sea ecosystems has surged, offering exciting prospects for discovering untapped resources such as medical drugs, food and energy sources, and renewable energy products. Consequently, research in underwater image processing has witnessed substantial growth. However, underwater imaging poses significant challenges, particularly without sophisticated, specialized cameras. Traditional cameras are impacted by absorption and scattering in the aquatic environment, producing hazy images with a blue–green tint. This phenomenon holds implications for marine research and other disciplines that rely on underwater imaging. While hardware advancements have been made over the years, image processing remains a valuable, cost-effective, and practical approach for underwater enhancement. Despite the existence of state-of-the-art techniques for underwater enhancement and restoration, their performance is often inconsistent. While some methods excel in contrast restoration, color restoration remains a pervasive challenge. In this paper, we introduce Sea-Pix-GAN, a Generative Adversarial Network (GAN)-based model that addresses these issues in underwater image enhancement. We redefine the problem as an image-to-image translation task and tailor the objective and loss functions to achieve color, content, and style transfer. The model is trained on a large dataset of underwater scenes, encompassing the diverse color dynamics of underwater subjects. Sea-Pix-GAN demonstrates promising results in restoring color, contrast, texture, and saturation. To validate its effectiveness, we compare the performance of Sea-Pix-GAN quantitatively based on metrics like PSNR, SSIM, and UIQM and qualitatively against several existing techniques.


Monocular endoscopy images depth estimation with multi-scale residual fusion

Abstract: Monocular depth estimation plays a fundamental role in clinical endoscopy surgery. However, the coherent illumination, smooth surfaces, and texture-less nature of endoscopy images present significant challenges to traditional depth estimation methods. Existing approaches struggle to accurately perceive depth in such settings. To overcome these challenges, this paper proposes a novel multi-scale residual fusion method for estimating the depth of monocular endoscopy images. Specifically, we address the issue of coherent illumination by leveraging image frequency domain component space transformation, thereby enhancing the stability of the scene's light source. Moreover, we employ an image radiation intensity attenuation model to estimate the initial depth map. Finally, to refine the accuracy of depth estimation, we utilize a multi-scale residual fusion optimization technique. To evaluate the performance of our proposed method, extensive experiments were conducted on public datasets. The structural similarity measures for continuous frames in three distinct clinical data scenes reached impressive values of 0.94, 0.82, and 0.84, respectively. These results demonstrate the effectiveness of our approach in capturing the intricate details of endoscopy images. Furthermore, the depth estimation accuracy achieved remarkable levels of 89.3 % and 91.2 % for the two models' data, respectively, underscoring the robustness of our method. Overall, the promising results obtained on public datasets highlight the significant potential of our method for clinical applications, facilitating reliable depth estimation and enhancing the quality of endoscopy surgical procedures.


Efficient Syndrome Decoder for Heavy Hexagonal QECC via Machine Learning

Abstract: Error syndromes for heavy hexagonal code and other topological codes such as surface code have typically been decoded by using Minimum Weight Perfect Matching– (MWPM) based methods. Recent advances have shown that topological codes can be efficiently decoded by deploying machine learning (ML) techniques, in particular with neural networks. In this work, we first propose an ML-based decoder for heavy hexagonal code and establish its efficiency in terms of the values of threshold and pseudo-threshold for various noise models. We show that the proposed ML-based decoding method achieves ~ 5 × higher values of threshold than that for MWPM. Next, exploiting the property of subsystem codes, we define gauge equivalence for heavy hexagonal code, by which two distinct errors can belong to the same error class. A linear search-based method is proposed for determining the equivalent error classes. This provides a quadratic reduction in the number of error classes to be considered for both bit flip and phase flip errors and thus a further improvement of ~ 14% in the threshold over the basic ML decoder. Last, a novel technique based on rank to determine the equivalent error classes is presented, which is empirically faster than the one based on linear search.


Residual networks for text-independent speaker identification: Unleashing the power of residual learning

Abstract: The human voice, a dynamic signal, conveys valuable information for speaker identification, encompassing gender, age, emotions, and language. In the biometrics industry, identifying voices in real-time amidst diverse accents, tones, and noisy backgrounds is a challenging task. Voice biometry, a complex aspect of speaker identification, is gaining importance in various applications, such as user authentication, attendance systems, forensics, and banking operations, as it eliminates the need for traditional credentials like cards or passwords. Recent advancements in Human–Computer Interaction technology have made conversational tasks technically feasible. Deep Neural Learning approaches, especially Convolutional Deep Neural Networks (CDNN), have emerged as a powerful tool in the field of speech processing, surpassing traditional Speaker Identification methods. This paper introduces a novel approach using 1-Dimensional Convolutional Residual Blocks for audio classification and Speaker Identification, specifically focusing on speaker recognition from spoken Hindi language. The proposed Residual architecture significantly enhances speaker identification, even in low Signal Noise Ratio environments, achieving an impressive accuracy rate of 86.02%. This outperforms traditional Gaussian Mixture Model (GMM) and Feed Forward Back-propagation Network (FFBN) model for the same set of speakers. Future research directions may explore the classification of audio and speaker identification using various acoustic features derived from speech signals.


Investigation on evaluation of education effect based on deep learning algorithm

Abstract: With the rapid development of society today, in the context of the impact of education on personal future development, people have increasingly attached importance to education. Various types of online and offline education institutions have mushroomed. However, the factors that affect the effectiveness of education are numerous and complex. As a very important part of early childhood learning, preschool education should receive more attention. The deep learning algorithm can reasonably classify and plan the factors affecting preschool education through massive data calculations to ensure the reasonable allocation of resources and save the time and energy costs wasted in allocating educational resources. The educational effectiveness of preschool education institutions and organizations can be reasonably evaluated. This article applied deep learning algorithms to the evaluation of preschool education effectiveness. Data related to the effectiveness of preschool education in preschool education institutions using convolutional neural networks and not using algorithms were compared. The experimental results showed that the expert recognition rates of the convolutional neural network algorithm and traditional manual measurement data were 98.8% and 92.55%, respectively. At the time level of teaching quality estimation, five algorithms and 100 sets of relevant experimental data were compared. In terms of the accuracy of feature search, the average accuracy of the convolutional neural network algorithm was 97.96%, while the accuracy of the traditional manual search was 53.9%. Therefore, the application of the convolutional neural network algorithm in deep learning to the evaluation of preschool education effectiveness was more efficient and efficient and could improve the evaluation effect from various aspects.


Exploring the user guidance for more accurate building segmentation from high-resolution remote sensing images

Abstract: In recent years, the computer vision domain has witnessed a surge of interest in interactive object segmentation, an area of study that seeks to expedite the annotation process for pixel-wise segmentation tasks through user guidance. Despite this growing focus, existing methods mainly focus on a single type of pre-annotation and neglect the quality of boundary prediction, which significantly influences subsequent manual adjustments to segmentation boundaries. To address these limitations, we introduce a novel end-to-end network to facilitate more precise building segmentation using diverse types of user guidance. In our proposed method, a centroid map is generated to provide foreground prior information crucial to the subsequent segmentation procedure, and the boundary correction module automatically refines the segmentation mask from existing segmentation networks. Extensive experiments on two popular building extraction datasets demonstrate that our method outperforms all existing approaches given various user guidance (bounding boxes, inside-outside points, or extreme points), achieving the IoU scores of over 95% on SpaceNet-Vegas dataset and over 93% on Inria-building dataset. The remarkable performance of our method further demonstrates its immense potential to alleviate the labor-intensive annotation process associated with remote sensing datasets. The code of our proposed method is available at https://github.com/StephenDHYang/UGBS-pytorch.


GCDN-Net: Garbage classifier deep neural network for recyclable urban waste management

Abstract: The escalating waste volume due to urbanization and population growth has underscored the need for advanced waste sorting and recycling methods to ensure sustainable waste management. Deep learning models, adept at image recognition tasks, offer potential solutions for waste sorting applications. These models, trained on extensive waste image datasets, possess the ability to discern unique features of diverse waste types. Automating waste sorting hinges on robust deep learning models capable of accurately categorizing a wide range of waste types. In this study, a multi-stage machine learning approach is proposed to classify different waste categories using the "Garbage In, Garbage Out" (GIGO) dataset of 25,000 images. The novel Garbage Classifier Deep Neural Network (GCDN-Net) is introduced as a comprehensive solution, adept in both single-label and multi-label classification tasks. Single-label classification distinguishes between garbage and non-garbage images, while multi-label classification identifies distinct garbage categories within single or multiple images. The performance of GCDN-Net is rigorously evaluated and compared against state-of-the-art waste classification methods. Results demonstrate GCDN-Net's excellence, achieving 95.77% accuracy, 95.78% precision, 95.77% recall, 95.77% F1-score, and 95.54% specificity when classifying waste images, outperforming existing models in single-label classification. In multi-label classification, GCDN-Net attains an overall Mean Average Precision (mAP) of 0.69 and an F1-score of 75.01%. The reliability of network performance is affirmed through saliency map-based visualization generated by Score-CAM (class activation mapping). In conclusion, deep learning-based models exhibit efficacy in categorizing diverse waste types, paving the way for automated waste sorting and recycling systems that can mitigate costs and processing times.


Differentially Private Enhanced Permissioned Blockchain for Private Data Sharing in Industrial IoT

Abstract: The integration of permissioned blockchain such as Hyperledger fabric (HF) and Industrial internet of Things (IIoT) has opened new opportunities for interdependent supply chain partners to improve their performance through data sharing and coordination. The multichannel mechanism, private data collection and querying mechanism of HF enable private data sharing, transparency, traceability, and verification across the supply chain. However, the existing querying mechanism of HF needs further improvement for statistical data sharing because the query is evaluated on the original data recorded on the ledger. As a result, it gives rise to privacy issues such as leaking of business secrets, tracking of resources and assets, and disclosing of personal information. Therefore, we solve this problem by proposing a differentially private enhanced permissioned blockchain for private data sharing in the context of supply chain in IIoT which is known as (EDH-IIoT). First, we integrate differential privacy into the chaincode (smart contract) of HF which evaluates the query and adds a calibrated noise into it. Second, we propose an algorithm to efficiently utilize ϵ through reuse of the privacy budget for the repeated queries. Third, we also propose an algorithm to track the privacy budget (ϵ) and avoid the degrade of privacy preservation in case of multiple queries on the same portion of the ledger's data. Furthermore, the reuse and tracking of ϵ enables the data owner to ensure that ϵ does not exceed the threshold which is the maximum privacy budget (ϵt). Finally, we model two privacy attacks namely linking attack and composition attack to evaluate and compare privacy preservation, and the efficiency of reuse of ϵ with the default chaincode of HF and traditional differential privacy model, respectively. The results confirm that EDH-IIoT obtains an accuracy of 97% in the shared data for ϵ = 1, and a reduction of 35.96% in spending of ϵ.


Hybrid ML-MDKL feature subset selection and classification technique accompanied with rat swarm optimizer to classify the multidimensional breast cancer mammogram image

Abstract: Breast cancer is a pathological condition characterized by the abnormal proliferation of cells inside the breast tissue. The biggest challenge at the moment is determining the most optimal subset from large datasets. Numerous methodologies have been presented for the purpose of selecting the most suitable subset from datasets with high dimensions. However, the outcomes have proven to be insufficient in effectively addressing extensive quantities of multidimensional datasets. This publication presents a proposal for an efficient approach of selecting numerous feature subsets and performing classification on Multidimensional Datasets (MDD). In this study, the Moment Invariant Wavelet Feature Extraction (MI-WFE) approach is employed for the purpose of feature extraction. The New Adaptive Hybrid Levy Flight based Cuckoo Search Optimization Algorithm is employed to identify the most pertinent subset of features. This study presents a novel approach, referred to as H-RS-LVCSO, which aims to enhance the local search capability and optimization speed of the algorithm. This is achieved through the hybridization of the Rat Swarm Optimizer with the levy flight based cuckoo search optimization technique. In this study, it is suggested a hybridized approach for classifying breast cancer, utilizing a Multilayer Multiple Deep Kernel Learning (ML-MDKL) Classifier in conjunction with the standard Support Vector Machine (SVM) Classifier. The simulation of the suggested method is conducted using the MATLAB software. The approach under consideration is evaluated in comparison to two pre-existing methods. The suggested method demonstrates an accuracy improvement of 10.58% compared to existing methods. Furthermore, it outperforms these methods by 17.5% in terms of accuracy. The proposed method exhibits a precision improvement of 6.52% and 14.23% compared to the existing methods.


Knowledge-guided pre-training and fine-tuning: Video representation learning for action recognition

Abstract: Video-based action recognition is an important task in the computer vision community, aiming to extract rich spatial–temporal information to recognize human actions from videos. Many approaches adopt self-supervised learning in large-scale unlabeled datasets and exploit transfer learning in the downstream action recognition task. Though much progress has been made for action recognition with video representation learning, two main issues remain for most existing methods. Firstly, the pre-training with self-supervised pretext tasks usually learns neutral and not much informative representations for the downstream action recognition task. Secondly, the valuable learned knowledge from large-scaled pre-training datasets will be gradually forgotten in the fine-tuning stage. To address such issues, in this paper, we propose a novel video representation learning method with knowledge-guided pre-training and fine-tuning for action recognition, which incorporates external human parsing knowledge for generating informative representation in the pre-training, and preserves the pre-trained knowledge in the fine-tuning stage to avoid catastrophic forgetting via self-distillation. Our model, with contributions from the external human parsing knowledge, video-level contrastive learning, and knowledge preserving self-distillation, achieves state-of-the-art performance on two popular benchmarks, i.e., UCF101 and HMDB51, verifying the effectiveness of the proposed method.


Facial attribute editing method combined with parallel GAN for attribute separation

Abstract: Facial attribute editing encounters problems of incorrect changes to face regions and artifacts in generated images. We propose a facial attribute editing method combined with parallel GAN for attribute separation. First, the method integrates the U2-net encoder and Trans-GAN decoder as a model encoder to extract and generate facial spatial information effectively. Second, RGB images and semantic mask images are used to train a parallel generator and discriminator respectively. Semantic consistency loss is introduced to ensure that the two branches have consistent semantic output and achieve the effect of convergence in the same direction parallel generator and discriminator. The proposed model, trained on the CelebAMask-HQ original dataset and verified by the CelebA dataset, adopts the separation of the face mask image and the background mask image, to improve the correct rate of face attribute editing. Compared with existing facial attribute editing methods, the proposed method is capable of balancing attribute editing ability and details preservation ability. It can accurately edit the target attribute area and greatly improve the quality of facial images.


Relevance Feedback with Brain Signals

Abstract: The Relevance Feedback (RF) process relies on accurate and real-time relevance estimation of feedback documents to improve retrieval performance. Since collecting explicit relevance annotations imposes an extra burden on the user, extensive studies have explored using pseudo-relevance signals and implicit feedback signals as substitutes. However, such signals are indirect indicators of relevance and suffer from complex search scenarios where user interactions are absent or biased. Recently, the advances in portable and high-precision brain-computer interface (BCI) devices have shown the possibility to monitor user’s brain activities during search process. Brain signals can directly reflect user’s psychological responses to search results and thus it can act as additional and unbiased RF signals. To explore the effectiveness of brain signals in the context of RF, we propose a novel RF framework that combines BCI-based RF with pseudo-relevance signals and implicit signals to improve the performance of document re-ranking. The experimental results on the user study dataset show that incorporating brain signals leads to significant performance improvement in our RF framework. Besides, we observe that brain signals perform particularly well in several hard search scenarios, especially when implicit signals as feedback are missing or noisy. This reveals when and how to exploit brain signals in the context of RF.


Automated modeling of nonlinear systems using fuzzy modular neural network

Abstract: Modular neural networks (MNNs) have garnered substantial attention in the field of nonlinear system modeling. However, even though MNNs require fewer hyperparameters due to their hierarchical structure compared to traditional NNs, determining the optimal module arrangement remains challenging. To address these issues, a novel approach named fuzzy modular neural networks (FMNN) is introduced. This method employs conditional fuzzy clustering and incremental radial basis function (RBF) neural networks to automatically construct sub-modules within the MNN framework. The resultant sub-modules are chosen utilizing a distance-based fuzzy integrative strategy, effectively diminishing the necessity for manual intervention. To showcase the superiority of the FMNN approach, a series of experiments are carried out employing three benchmark examples. These experiments encompass a comparison of modeling accuracy against other extensively employed neural network models. The experimental findings illustrate that FMNN surpasses alternative neural network models in terms of model precision.


BNDCNet: Bilateral nonlocal decoupled convergence network for semantic segmentation

Abstract: The perceptual scope of deep convolutional neural networks is inherently confined to a local scale due to the inherent limitations of convolution operations. This confinement subsequently hampers a comprehensive understanding of intricate scenes. In response, an innovative approach called Bilateral Non-Local Decoupled Convergence Network (BNDCNet) is proposed to facilitate contextual interaction of global information. The proposed module decouples the information from the input feature map and uses a bilateral non-local architecture for system processing. This strategy facilitates inter-pixel interaction and aggregates global information. An important aspect of our approach involves the computation of adaptive convolutional channel weights for the feature map. This innovation greatly improves the efficacy and performance of the model. The proposed method achieved high performance on the competitive scene-parsing datasets CamVid, Cityscapes, and KITTI, and thus demonstrated effectiveness and generality. Code has been released: https://github.com/Mantee0810/BNDC.


Improved DWT and IDWT architectures for image compression

Abstract: In the recent era, a rapid development in the field of image processing has been observed. One of the important applications in image processing is compression. Several wavelet transform based image compression techniques have already been introduced. In this paper, Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT) based improved image compression and decompression techniques have been proposed by incorporating a scaling factor. The DWT and IDWT algorithms are implemented using folded architecture. To reduce the usages of hardware resources, a multiplier is recursively used. Image compression and decompression schemes based on proposed DWT and IDWT architectures are tested using four different image databases. The proposed technique provides better results in terms of bits per pixel, compression ratio, mean square error, peak-signal-to-noise ratio, normalized correlation coefficient and structural similarity index. FPGA based synthesis has been performed using Xilinx Vivado Synthesis tool in terms of slice LUTs, slice registers, clock frequency, delay and power. The synthesis results show that proposed DWT and IDWT architectures are amenable for image compression and decompression applications.


Distance metric-based learning for long-tail object detection

Abstract: Despite the recent success of general object detection, almost all models perform unsatisfactorily on long-tail datasets. The main cause of performance degradation is the imbalance in the number of positive samples between categories. The traditional approaches can lead to distortion of the classification feature space, which in turn can seriously affect the classification ability of the network. To address the above issues, we propose a novel distance metric-based learning approach for long-tail object detection (LTDL) in this paper. Specifically, we directly use the feature space as the optimization target, thus allowing clearer decision boundaries between classes. In order to optimize the decision boundary, we adjust the intra-class and inter-class distances by Margin Module (MAM). Meanwhile, to further exploit the information provided by the dataset, we introduce supervised information of labels for distance weighting using the Semantic Module (SEM). In addition, to protect the learning of tail samples and optimize the classifier, we propose a Distance-based Equilibrium Loss (DEL). Extensive experiments conducted on the LVIS benchmark have demonstrated the strength of our proposed approach. The experimental results show that our method improves the baseline by 2.9% AP. And our best model can outperform almost all other representative methods.


On the linearity of the periods of subtraction games

Abstract: A subtraction game is an impartial combinatorial game involving a finite set S of positive integers. The nim-sequence GS associated with this game is ultimately periodic. In this paper, we study the nim-sequence GS∪{c} where S is fixed and c varies. We conjecture that there is a multiple q of the period of GS, such that for sufficiently large c, the pre-period and period of GS∪{c} are linear in c if c modulo q is fixed. We prove it in several cases. We also give new examples with period 2 inspired by this conjecture.


Evolutionary feature selection based on hybrid bald eagle search and particle swarm optimization

Abstract: Feature selection is a complicated multi-objective optimization problem with aims at reaching to the best subset of features while remaining a high accuracy in the field of machine learning, which is considered to be a difficult task. In this paper, we design a fitness function to jointly optimize the classification accuracy and the selected features in the linear weighting manner. Then, we propose two hybrid meta-heuristic methods which are the hybrid basic bald eagle search-particle swarm optimization (HBBP) and hybrid chaos-based bald eagle search-particle swarm optimization (HCBP) that alleviate the drawbacks of bald eagle search (BES) by utilizing the advantages of particle swarm optimization (PSO) to efficiently optimize the designed fitness function. Specifically, HBBP is proposed to overcome the disadvantages of the originals (i.e., BES and PSO) and HCBP is proposed to further improve the performance of HBBP. Moreover, a binary optimization is utilized to effectively transfer the solution space from continuous to binary. To evaluate the effectiveness, 17 well-known data sets from the UCI repository are employed as well as a set of well-established algorithms from the literature are adopted to jointly confirm the effectiveness of the proposed methods in terms of fitness value, classification accuracy, computational time and selected features. The results support the superiority of the proposed hybrid methods against the basic optimizers and the comparative algorithms on the most tested data sets.


Compositional data analysis (CoDA) and geochemical signatures of the terminal complex aquifer in an arid zone (northeastern Algeria)

Abstract: The Biskra region, one of the arid regions in North-East Algeria, has known remarkable agricultural development, necessitating additional mobilization of groundwater resources, which are almost the only source of water in the region. In the long term, this heavy mobilization may threaten the quantity and quality of groundwater. This study was carried out to gain a better understanding of groundwater quality in the Terminal Complex (TC) aquifer of the Biskra region, to determine the hydrogeochemical processes controlling water mineralization, and further to investigate the suitability of the groundwater for consumption. For this purpose, multivariate statistical analysis, based on compositional data analysis (CoDA), including robust PCA, cluster analysis (HCA), ilr-ion diagram, and correlation analysis as well as graphical approaches, was carried out on the hydrochemical results of forty-four (44) groundwater samples analysed by standard methods. The results showed that most groundwater parameters exceeded WHO-recommended safe limits for drinking water. Robust CoDA revealed three distinct processes controlling groundwater hydrochemistry: the first “carbonate link” [NO3−, HCO3−, pH] at the northern boundary, which was progressively replaced by the second link [Ca2+, Mg2+, SO42−], and the third “mineralization link” [Na+, Cl−, K+, EC, TDS], located on the Outaya, Biskra, Sidi Okba, and El-Houche axes. In addition, around 93% of samples were within the geochemical zone [SO42− +Cl− >CO32-- HCO3−], while only 7% fell within the [CO32− - HCO3−> SO42− + Cl−] zone, with the following chemical facies dominating: Ca–SO4 (52.27%), Ca–Mg–SO4 (22.72%), Na–Cl (20.45%), and Ca–HCO3 (4.56%). The modified water quality index (WQI) revealed five quality classes: excellent (9.10%), good (11.36%), poor (65.90%), very poor (11.36%), and unsuitable quality (2.28%), with an apparent deterioration in water quality from north to south, following the same direction of groundwater flow. Finally, the use of CoDA with isometric log-ratio plotting, for the first time in the study area, has enabled a more thorough and reliable assessment of the processes controlling groundwater quality, which is valuable for rationalizing pumping and can help decision-makers to plan current and future water resource management practices.


Detecting multiple moving objects for unusual crowd activities detection in video surveillance system

Abstract: Unusual crowd activity detection is a challenging problem in surveillance video applications because feature extraction is difficult process in crowded scenes. The main objective of this research work is to detect unusual crowd activities and to detect unusual splits of moving objects. Various methods have been employed to address these challenges. However, there is still a lack of appropriate handling of this problem due to frames having occlusion, noise, and congestion. This paper proposes a novel clustering approach to detect unusual crowd activities. The proposed method consists of five phases including foreground extraction, foreground enhancement, foreground estimation, clustering crowds, and the Unusual Crowd Activities (UCA) model. The UCA model can find unusual crowd activities and unusual splits of moving objects using the Laplacian Matrix formulation. Two public datasets viz. PETS 2009 and UMN dataset are used for evaluating the proposed methodology. To estimate the effectiveness of the proposed work, several unusual event detection methods are compared with the proposed work results. The experimental results revealed that the proposed method gives better results than the existing methods.


SDGIN: Structure-aware dual-level graph interactive network with semantic roles for visual dialog

Abstract: Visual Dialog aims to answer an appropriate response based on a multi-round dialog history and a given image. Existing methods either focus on semantic interaction, or implicitly capture coarse-grained structural interaction (e.g., pronoun co-references). The fine-grained and explicit structural interaction feature for dialog history is seldom explored, resulting in insufficient feature learning and difficulty in capturing precise context. To address these issues, we propose a structure-aware dual-level graph interactive network (SDGIN) that integrates verb-specific semantic roles and co-reference resolution to explicitly capture context structural features for discriminative and generative tasks in visual dialog. Specifically, we create a novel structural interaction graph that injects syntactic knowledge priors into dialog by introducing semantic role labeling that imply which words are sentence stems. Furthermore, considering the single perspective limitation of previous algorithms, we design a dual-perspective mechanism that learns fine-grained token-level context structure features and coarse-grained utterance-level interactions in parallel. It possess an elegant view to explore precise context interactions, realizing the mutual complementation and enhancement of different granularity features. Experimental results show the superiority of our proposed approach. Compared to other task-specific models, our SDGIN outperforms previous models and achieves a significant improvement on the benchmark dataset VisDial v1.0.


Sensor fault diagnosis and correction for data center cooling system using hybrid multi-label random Forest and Bayesian Inference

Abstract: The measurement biases in working sensors significantly hampers the effective operation and control of cooling systems in the data center. However, previous studies only focus on sensor fault diagnosis or sensor bias correction, neglecting the simultaneous detection, diagnosis and correction of sensor faults. This study proposed a novel method, combining Multi-Label Random Forest and Bayesian Inference (HMLRF-BI), to simultaneously detect, diagnose and correct sensor faults. Case studies were conducted involving eight single sensors and six multiple sensor fault cases in the Computer Room Air Handler (CRAH) to comprehensively evaluate the diagnostic and correction performance of the proposed method. Simulation results demonstrate that the method accurately diagnoses sensor fault types with a diagnostic accuracy of 97.42 % and an F1 score exceeding 97.90 %. Moreover, the proposed method also performs well in single/multiple sensor bias correction scenarios, with a correction accuracy exceeding 96.81 % and 97.10 %, respectively. Furthermore, a novel cycle mechanism is proposed, which utilized the MLRF fault diagnosis model to re-diagnose the corrected fault sensor to complete the closed-loop cycle of the HMLRF-BI method with an overall accuracy of 96.17 %. This study successfully filled the knowledge gap in the simultaneous detection, diagnosis and correction of sensor faults in data center CRAH, providing a comprehensive solution to restore sensor measurement performance, which is a promising application method.


Deep Contrastive Clustering via Hard positive sample Debiased

Abstract: Deep graph clustering aims to reveal the underlying information of the graph and provide accurate embedding for the node clustering task, in which contrastive learning plays an important role. However, the commonly used contrastive loss function incorrectly classifies elements outside the diagonal of the cross view as negative samples, which contain a large number of positive sample pairs. In order to overcome the above problems, we propose a new deep graph contrastive clustering method, which combines hard positive sample debiasing and sample pair weighting, and improves the recognition ability of the network by removing the potential positive sample pairs and hard sample pair weighting in the negative sample pair of the loss function. More specifically, we developed a symmetric graph neural network to encode node representations. Using two sets of node representations, the correctness of negative cases is increased by clustering to generate high-confidence pseudo-label pairs for labels and confidence. The similarity distribution differences are weighted by adapting to different dataset samples to improve the sample recognition ability. To verify the efficacy of our DCHD, we compare it to existing state-of-the-art methods for node clustering tasks on six real-world datasets. Overall, the experimental results show that our proposed method outperforms current state-of-the-art graph clustering methods.


Fragility, robustness and antifragility in deep learning

Abstract: We propose a systematic analysis of deep neural networks (DNNs) based on a signal processing technique for network parameter removal, in the form of synaptic filters that identifies the fragility, robustness and antifragility characteristics of DNN parameters. Our proposed analysis investigates if the DNN performance is impacted negatively, invariantly, or positively on both clean and adversarially perturbed test datasets when the DNN undergoes synaptic filtering. We define three filtering scores for quantifying the fragility, robustness and antifragility characteristics of DNN parameters based on the performances for (i) clean dataset, (ii) adversarial dataset, and (iii) the difference in performances of clean and adversarial datasets. We validate the proposed systematic analysis on ResNet-18, ResNet-50, SqueezeNet-v1.1 and ShuffleNet V2 x1.0 network architectures for MNIST, CIFAR10 and Tiny ImageNet datasets. The filtering scores, for a given network architecture, identify network parameters that are invariant in characteristics across different datasets over learning epochs. Vice-versa, for a given dataset, the filtering scores identify the parameters that are invariant in characteristics across different network architectures. We show that our synaptic filtering method improves the test accuracy of ResNet and ShuffleNet models on adversarial dataset when only the robust and antifragile parameters are selectively retrained at any given epoch, thus demonstrating applications of the proposed strategy in improving model robustness.


Texture and motion aware perception in-loop filter for AV1

Abstract: Lossy compression introduces artifacts, and many conventional in-loop filters have been adopted in the AV1 standard to reduce these artifacts. Researchers have explored deep learning-based filters to remove artifacts in the compression loop. However, the high computational complexity of CNN-based filters remains a challenge. In this paper, a Texture- and Motion-Aware Perception (TMAP) in-loop filter is proposed to addresses this issue by selectively applying CNNs to texture-rich and high-motion regions, while utilizing non-learning methods to detect these regions. The proposed method introduces a new CNN structure, the Dense-Dual-Field Network (DDFN), which leverages a larger receptive field to enhance the quality of reconstructed frames by incorporating more contextual information. Furthermore, to improve perceptual quality, a novel loss function integrating wavelet-based perceptual information is presented. Experimental results demonstrate the superiority of our proposed models over other lightweight CNN models, and the effectiveness of the perceptual loss function is validated using the VMAF metric.


Machine learning based software effort estimation using development-centric features for crowdsourcing platform

Abstract: Crowd-Sourced software development (CSSD) is getting a good deal of attention from the software and research community in recent times. One of the key challenges faced by CSSD platforms is the task selection mechanism which in practice, contains no intelligent scheme. Rather, rule-of-thumb or intuition strategies are employed, leading to biasness and subjectivity. Effort considerations on crowdsourced tasks can offer good foundation for task selection criteria but are not much investigated. Software development effort estimation (SDEE) is quite prevalent domain in software engineering but only investigated for in-house development. For open-sourced or crowdsourced platforms, it is rarely explored. Moreover, Machine learning (ML) techniques are overpowering SDEE with a claim to provide more accurate estimation results. This work aims to conjoin ML-based SDEE to analyze development effort measures on CSSD platform. The purpose is to discover development-oriented features for crowdsourced tasks and analyze performance of ML techniques to find best estimation model on CSSD dataset. TopCoder is selected as target CSSD platform for the study. TopCoder’s development tasks data with development-centric features are extracted, leading to statistical, regression and correlation analysis to justify features’ significance. For effort estimation, 10 ML families with 2 respective techniques are applied to get broader aspect of estimation. Five performance metrices (MSE, RMSE, MMRE, MdMRE, Pred (25) and Welch’s statistical test are incorporated to judge the worth of effort estimation model’s performance. Data analysis results show that selected features of TopCoder pertain reasonable model significance, regression, and correlation measures. Findings of ML effort estimation depicted that best results for TopCoder dataset can be acquired by linear, non-linear regression and SVM family models. To conclude, the study identified the most relevant development features for CSSD platform, confirmed by in-depth data analysis. This reflects careful selection of effort estimation features to offer good basis of accurate ML estimate.


A content-based recommender system using stacked LSTM and an attention-based autoencoder

Abstract: Recommender systems (RS) are popular in many areas, such as movies, music, news, books, research articles, search queries, and social tagging. Proprietary recommender systems are used by e-commerce websites like eBay, Amazon, and Alibaba to better match customers with products they are likely to purchase. This study suggests a recommender system that combines stacked long short-term memory (LSTM) and an attention-based autoencoder. This system would be used in a self-supervised learning paradigm, and the Amazon product datasets were used to run simulations. The results showed that the proposed method is more accurate, uses less computing power, and can be used on a large scale. No matter how big the data is, it can capture low-dimensional fixed latent representations and use the bare minimum of information already in the items, whether they are new or old. Several evaluation metrics show that the method works and that the cold start problem has been solved.


Dynamic ε-Multilevel Hierarchy Constraint Optimization With Adaptive Boundary Constraint Handling Technology

Abstract: Real-world optimization problems are often difficult to solve because of the complexity of the objective function and the large number of constraints that accompany it. To solve such problems, we propose Adaptive Dynamic ε-Multilevel Hierarchy Constraint Optimization (εMHCO). Firstly, we propose the dynamic constraint tolerance factor ε which can change dynamically with the feasible ratio and the number of iterations in the current population. This ensures a reasonable proportion of virtual feasible solutions in the population. Secondly, we propose adaptive boundary constraint handling technology (ABCHT). It can reshape the current individual position adaptively according to the size of constraint violation and increase the diversity of the population. Finally, we propose multi-level hierarchy optimization, whose multiple population structure is beneficial to solve real-world constraint optimization problems (COPs). To validate and analyze the performance of εMHCO, numerical experiments are conducted on the latest real-world test suite CEC’2020, which contains a set of 57 real-world COPs, and compared with four state-of-the-art algorithms. The results show that εMHCO is significantly superior to, or at least comparable to the state-of-the-art algorithms in solving real-world COPs. Meanwhile, the effectiveness and feasibility of εMHCO are verified on the real-world problem of the pipeline inner detector speed control.