Reviewerly/exHarmony huggingface dataset:
Improved Seagull Optimization Algorithm Combined with an Unequal Division Method to Solve Dynamic Optimization Problems

The numerical solution of the dynamic optimization problem is often sought for chemical processes, but the discretization of control variables is a difficult problem. Firstly, based on the analysis of the seagull optimization algorithm, this paper introduces the cognitive part in the process of a seagull’s attack behavior to make the group approach the best position. Secondly, the algorithm adds the mechanism of natural selection, where the fitness value is used to sort the population, and the best half is used to replace the worst half, so as to find out the optimal solution. Finally, the improved seagull optimization algorithm (ISOA) is combined with the unequal division method to solve dynamic optimization problems. The feasibility of the method is verified by three practical examples of dynamic optimization in chemical industry.
Anomaly detection in autonomous electric vehicles using <scp>AI</scp> techniques: A comprehensive survey

Abstract The next wave in smart transportation is directed towards the design of renewable energy sources that can fuel automobile sector to shift towards the autonomous electric vehicles (AEVs). AEVs are sensor‐driven and driverless that uses artificial intelligence (AI)‐based interactions in Internet‐of‐vehicles (IoV) ecosystems. AEVs can reduce carbon footprints and trade energy with peer AEVs, smart grids (SG), and roadside units (RSUs). It supports green transportation vision. However, the sensor information, energy units, and user data are exchanged through open channels, and thus, are susceptible to various security and privacy attacks. Thus, AEVs can be remotely operated and directed by malicious entities that can propagate false updates to the peer nodes in IoV environment. This can cause the failure of components, congestion, as well as the entire disruption of IoV network. Globally researchers and security analysts have addressed solutions that pertain to specific security requirements, but still, the detection and classification of malicious AEVs is a widely studied topic. Malicious AEVs exhibit an anomaly behavior that differentiates them from normal AEVs, and thereby, the detection of anomalous AEVs and classification of anomaly type is required. Motivated from the aforementioned facts, the survey presents a systematic outlook of AI techniques in anomaly detection of AEVs. A solution taxonomy is proposed based on research gaps in the existing surveys, and the evaluation metrics for AI‐based anomaly detection are discussed. The open challenges and issues in AI deployments are discussed and a case study is presented on anomaly classification through a weighted ensemble technique. Thus, the proposed survey is designed to guide the manufacturing industry, AI practitioners, and researchers worldwide to formulate and design accurate and precise mechanisms to detect anomalies.
Human evaluations of machine translation in an ethically charged situation

Despite the immense influence of machine translation (MT) on cross-cultural communication worldwide, little is known about end users’ predispositions toward MT. Our online experiment ( N = 284) compares people’s perceptions of MT and human translation in an ethically charged situation, in which the translation serves an immigrant worker in an interaction defined by power imbalance. Using hierarchical linear regression, we found that an otherwise identical translation was evaluated differently when it was attributed to MT or human translation. Results reveal that translators and non-translators alike exhibit a negative bias toward the MT product when asked to assess its accuracy and reliability, its ability to convey cultural and emotional otherness, and its potential effectiveness in helping the disadvantaged immigrant in need of the translation. We also demonstrate how lower evaluations of the MT product lead to a stronger wish to intervene in the translation by introducing changes to the original message. Our results suggest that predispositions toward MT must be taken into account in any consideration of MT-mediated communication, as these predispositions may shape the communicative act itself.
Optimal Qubit Assignment and Routing via Integer Programming

We consider the problem of mapping a logical quantum circuit onto a given hardware with limited 2-qubit connectivity. We model this problem as an integer linear program, using a network flow formulation with binary variables that includes the initial allocation of qubits and their routing. We consider several cost functions: an approximation of the fidelity of the circuit, its total depth, and a measure of cross-talk, all of which can be incorporated in the model. Numerical experiments on synthetic data and different hardware topologies indicate that the error rate and depth can be optimized simultaneously without significant loss. We test our algorithm on a large number of quantum volume circuits, optimizing for error rate and depth; our algorithm significantly reduces the number of CNOTs compared to Qiskit’s default transpiler SABRE [ 19 ] and produces circuits that, when executed on hardware, exhibit higher fidelity.
DMSANet: Dual Multi Scale Attention Network

Attention mechanism of late has been quite popular in the computer vision community. A lot of work has been done to improve the performance of the network, although almost always it results in increased computational complexity. In this paper, we propose a new attention module that not only achieves the best performance but also has lesser parameters compared to most existing models. Our attention module can easily be integrated with other convolutional neural networks because of its lightweight nature. The proposed network named Dual Multi Scale Attention Network (DMSANet) is comprised of two parts: the first part is used to extract features at various scales and aggregate them, the second part uses spatial and channel attention modules in parallel to adaptively integrate local features with their global dependencies. We benchmark our network performance for Image Classification on ImageNet dataset, Object Detection and Instance Segmentation both on MS COCO dataset.
Optimal temporospatial strategy selection approach to moving target defense: A FlipIt differential game model

Decision-making on defense against moving targets largely focuses on selection of spatial strategies, which can lack consideration of optimal defense timing, lack comprehensive defense effectiveness, and affect the execution of a defense strategy. Current selection methods for defense timing are based on the decision-making model of a discrete time series structure, and they present problems such as inadequate real-time performance for continuous decision-making. Based on the dynamic temporospatial confrontation characteristics of network attack and defense, this paper proposes an improved moving target defense model based on the multidimensional transition of exploration-attack-detection surfaces, analyzes the characteristics of attack-defense games for moving target defense (MTD) and temporospatial strategies, and describes the characteristics of stealth interaction based on the FlipIt MTD system security state evolution. We use the game to analyze continuous network attack-defense processes to build an MTD temporospatial decision-making model. We quantify the attacker's and defender's utilities, design a saddle-point strategy solution, and develop an optimal temporospatial defense strategy selection algorithm. We use application examples and numerical analysis results to show that the model and algorithm are accurate and effective.
A neighborhood information utilization fireworks algorithm and its application to traffic flow prediction

A Fireworks Algorithm (FWA) is a novel intelligent optimization algorithm inspired by fireworks explosion, and it has been shown to be superior or competitive in various fields. Current FWA generates sparks without considering neighborhood information. This work improves sparks generation process by utilizing neighborhood information, and thus a neighborhood information utilization fireworks algorithm (NiFWA) is proposed. Specifically, fireworks are first divided into different subpopulations at different stages, and each subpopulation is considered as a whole, as well as it is also a neighborhood of other subpopulations. Then, a firework in a subpopulation generates its offspring by incorporating neighborhood information. Experimental results show that the proposed method is superior to FWA and its variants, competitive to state-of-the-art methods on CEC2013, CEC2017, three real world problems from CEC2010. Finally, NiFWA is used to assist kernel extreme learning machine in predicting the traffic flow.
Adaptive Transfer Learning on Graph Neural Networks

Graph neural networks (GNNs) is widely used to learn a powerful representation of graph-structured data. Recent work demonstrates that transferring knowledge from self-supervised tasks to downstream tasks could further improve graph representation. However, there is an inherent gap between self-supervised tasks and downstream tasks in terms of optimization objective and training data. Conventional pre-training methods may be not effective enough on knowledge transfer since they do not make any adaptation for downstream tasks. To solve such problems, we propose a new transfer learning paradigm on GNNs which could effectively leverage self-supervised tasks as auxiliary tasks to help the target task. Our methods would adaptively select and combine different auxiliary tasks with the target task in the fine-tuning stage. We design an adaptive auxiliary loss weighting model to learn the weights of auxiliary tasks by quantifying the consistency between auxiliary tasks and the target task. In addition, we learn the weighting model through meta-learning. Our methods can be applied to various transfer learning approaches, it performs well not only in multi-task learning but also in pre-training and fine-tuning. Comprehensive experiments on multiple downstream tasks demonstrate that the proposed methods can effectively combine auxiliary tasks with the target task and significantly improve the performance compared to state-of-the-art methods.
PDLHR: Privacy-Preserving Deep Learning Model With Homomorphic Re-Encryption in Robot System

The robot system is a significant application that has attracted great attention, and deep learning is a powerful feature extraction technology that has achieved significant breakthroughs in many fields, especially in robot systems. However, the required massive dataset for a deep learning model in a robot system easily leads to privacy leakage. There have been few reports on privacy-preserving deep learning models, and none on multikeys, in robot systems. Existing privacy-preserving deep learning schemes in multiple keys have low efficiency and high interactions in non-robotic environments. To address these issues, this article proposes a privacy-preserving deep learning model with homomorphic re-encryption (PDLHR) and secure calculation tools in a robot system. The proposed re-encryption scheme is based on the Bresson-Catalano-Pointcheval (BCP) cryptosystem, which solves the multiple keys question, keeps the homomorphic nature, and is more simplified than the existing re-encryption scheme based on the BCP cryptosystem. The secure calculation tools are designed to realize efficient ciphertext computations. Compared to the previous work, PDLHR decreases the interactions in the decryption process, improves the ciphertext training efficiency, and preserves the privacy of input data, training model, and inference results. Security analysis and performance evaluations demonstrate that the proposed scheme realizes security, efficiency, and effectiveness with low communication and computation costs.
End-to-End Domain Adaptive Attention Network for Cross-Domain Person Re-Identification

Person re-identification (re-ID) remains challenging in a real-world scenario, as it requires a trained network to generalise to totally unseen target data in the presence of variations across domains. Recently, generative adversarial models have been widely adopted to enhance the diversity of training data. These approaches, however, often fail to generalise to other domains, as existing generative person re-identification models have a disconnect between the generative component and the discriminative feature learning stage. To address the on-going challenges regarding model generalisation, we propose an end-to-end domain adaptive attention network to jointly translate images between domains and learn discriminative re-id features in a single framework. To address the domain gap challenge, we introduce an attention module for image translation from source to target domains without affecting the identity of a person. More specifically, attention is directed to the background instead of the entire image of the person, ensuring identifying characteristics of the subject are preserved. The proposed joint learning network results in a significant performance improvement over state-of-the-art methods on several challenging benchmark datasets.
Automatic Determination of Harassment in Social Network Using Machine Learning

Globally the number of Internet users are very high, and the majority of the users are youngsters. They will be participating in many activities in social networks like twitter, Facebook, etc. With lightning speed of Internet, everyone can explore the information of unknown. As a result, much more cyber-based crimes and harassments are raising day by day. Artificial intelligence can bring out solution for such issues. Lot of research has been taken place for the identification of online harassments through comments and messages posted over the platforms. Sometimes context of the statement matters for judging the comment. We propose a mechanism for identifying online harassment based on context by using one of the familiar online platform called Twitter. For this research work, we have used few machine learning algorithms.
Sensor Fusion for Drone Detection

With the rapid development of commercial drones, drone detection and classification have emerged and grown recently. Drone detection works to detect unmanned aerial vehicles (UAVs). Usually, systems for drone detection utilize a combination of one or more sensors and some methodology. Many unique technologies and methods are used to detect drones. However, each type of technology offers its benefits and limitations. Most approaches use computer vision or machine learning, but one methodology that has not been given much attention is Sensor Fusion. Sensor Fusion has less uncertainty than most methods, making it suitable for drone detection. In this paper, we propose an artificial neural network-based detection system that uses a deep neural network (DNN) to process the RF data and a convolutional neural network (CNN) to process image data. The features from CNNs and DNNs are concatenated and input into another DNN, which outputs a single prediction score of drone presence. Our model achieved a validation accuracy of 75% that shows the feasibility of a sensor fusion based technique for drone detection.
Demystifying Attestation in Intel Trust Domain Extensions via Formal Verification

In August 2020, Intel asked the research community for feedback on the newly offered architecture extensions, called Intel Trust Domain Extensions (TDX), which give more control to Trust Domains (TDs) over processor resources. One of the key features of these extensions is the remote attestation mechanism, which provides a unified report verification mechanism for TDX and its predecessor Software Guard Extensions (SGX). Based on our experience and intuition, we respond to the request for feedback by formally specifying the attestation mechanism in the TDX using ProVerif's specification language. Although the TDX technology seems very promising, the process of formal specification reveals a number of subtle discrepancies in Intel's specifications that could potentially lead to design and implementation flaws. After resolving these discrepancies, we also present fully automated proofs that our specification of TD attestation preserves the confidentiality of the secret and authentication of the report by considering the state-of-the-art Dolev-Yao adversary in the symbolic model using ProVerif. We have submitted the draft to Intel, and Intel is in the process of making the changes.
How Blockchain Innovation could affect the Audit Profession: A Qualitative Study

Blockchain is transforming not only the way of recording, processing and storing financial transactions and information, but also the way audit firms can practice their profession. The purpose of this article is to examine how this technology will affect the audit profession. Based on a qualitative study carried out on a sample of 17 auditors, this research shows that this technology could affect audit firms at six key levels. Blockchain will allow an auditor to (1) save time and improve the efficiency of their audit, (2) favor an audit covering the whole population instead of an audit based on sampling techniques, (3) focus the audit on testing controls rather than testing transactions, (4) set up a continuous audit process, (5) play a more strategic audit role and (6) develop new advisory services. The results underline the need for the establishment of a clear and coherent legislative system and new audit standards, allowing auditors to embed this technology and enhance audit practices. JEL Codes: M42
Channel Autoencoder for Wireless Communication: State of the Art, Challenges, and Trends

To tackle the sub-optimization problem of the conventional block structure communication systems, recently, a novel concept named end-to-end communication system that can optimize the whole system jointly has been proposed. A channel autoencoder (AE) is one of the methods, which regards the wireless communication system as an AE along with a channel model. In this article, we present a comprehensive overview of the recent advancements of channel AEs, whose practicability mainly depends on the robustness of the impairments in actual channels. Among existing works, assuming the imperfect channel models before training or constructing a communication system without channel models are both viable methods to deal with channel impairments. Therefore, we divide the channel AEs into two categories, model-assumed and model-free channel AEs, for each of which a universal structure is investigated, namely radio transformer network and gradient generation network, respectively. Then their performance is compared extensively, and the open research issues are discussed in the end to provide some directions for future study.
Joint Detection and Classification of RF Signals Using Deep Learning

With the rapid expansion of wireless technologies, monitoring and regulating the Radio Frequency (RF) spectrum usage becomes more important than ever. In this paper, we present a Deep Learning (DL) based approach to analyze the RF spectrum by detecting, localizing, and classifying active signals in RF frequency bands. We represent the radio signals in wideband spectrograms and formulate the signal detection and classification problem as an object detection task related to the computer vision field. To this end, You Only Look Once (YOLO), a state-of-the-art object detector, is adapted and optimized to detect, localize, and classify signals in spectrograms. For the experimental evaluation of YOLO as a signal detector, a rich dataset was simulated, consisting of diverse signals modulated with digital and analog modulation schemes and transmitted over channels with realistic propagation conditions. Our proposed method achieves an Average Precision (AP) of almost 87% and an average Intersection over Union (IoU) of 90%, thus demonstrating significant potential for analyzing RF spectral activity with high accuracy.
Hierarchical Topology-Based Cluster Representation for Scalable Evolutionary Multiobjective Clustering

Evolutionary multiobjective clustering (MOC) algorithms have shown promising potential to outperform conventional single-objective clustering algorithms, especially when the number of clusters k is not set before clustering. However, the computational burden becomes a tricky problem due to the extensive search space and fitness computational time of the evolving population, especially when the data size is large. This article proposes a new, hierarchical, topology-based cluster representation for scalable MOC, which can simplify the search procedure and decrease computational overhead. A coarse-to-fine-trained topological structure that fits the spatial distribution of the data is utilized to identify a set of seed points/nodes, then a tree-based graph is built to represent clusters. During optimization, a bipartite graph partitioning strategy incorporated with the graph nodes helps in performing a cluster ensemble operation to generate offspring solutions more effectively. For the determination of the final result, which is underexplored in the existing methods, the usage of a cluster ensemble strategy is also presented, whether k is provided or not. Comparison experiments are conducted on a series of different data distributions, revealing the superiority of the proposed algorithm in terms of both clustering performance and computing efficiency.
Joint structured pruning and dense knowledge distillation for efficient transformer model compression

In this paper, we develop a novel Joint Model Compression (referred to as JMC) method by combining structured pruning and dense knowledge distillation techniques to significantly compress original large language model into a deep compressed shallow network. In particular, a new Direct Importance-aware Structured Pruning (referred as DISP) approach is proposed to structurally prune the redundant structures in the Transformer networks directly based on the corresponding parameter matrices in the model. Besides, a Dense Knowledge Distillation (referred to as DKD) method is developed with a many-to-one layer mapping strategy to leverage more comprehensive layer-wise linguistic knowledge for the distillation. Further, the proposed structured pruning and dense knowledge distillation are integrated together to perform the joint compression, which enables us to achieve a significant compression without sacrificing model accuracy. The extensive experimental results across four NLP tasks on seven datasets demonstrate its effectiveness and superiority to the baselines, while maintaining similar performance to original large model with further remarkable benefits for inference-time speedup and memory efficiency.