Reviewerly/exHarmony huggingface dataset:
A Surrogate-Assisted Evolutionary Algorithm for Seeking Multiple Solutions of Expensive Multimodal Optimization Problems

Surrogate-assisted evolutionary algorithms for expensive optimization problems have gained considerable attention in recent years. In many real-world optimization problems, we may face expensive optimization problems with multiple optimal solutions. Locating multiple optima for such expensive problems is qualitatively challenging. This study proposes a surrogate-assisted differential evolution based on region decomposition to seek multiple optima for expensive multimodal optimization problems. In this study, we have designed three major components: 1) the adaptive region decomposition, 2) the multilayer perceptron-based global surrogate, and 3) the self-adaptive gradient descent-based local search. First, the improved adaptive region decomposition detects promising subregions at the beginning phase, and continuously discards inferior subregions successively. Second, the multilayer perceptron-based surrogate and self-adaptive gradient-based mutation work in a collaborative manner on distinct sub-populations to seek multiple optimal solutions. Overall, an attempt has been made to solve expensive multimodal optimization problems. Systematic experiments on 20 test functions show the encouraging and promising performance of our proposed approach.
On the Geometry of Fixed Points and Discontinuity

Recently, there has been a considerable effort to obtain new solutions to the Rhoades' open problem on the existence of contractive mappings that admit discontinuity at the fixed point. An extended version of this problem is also stated using a geometric approach. In this paper, we obtain new solutions to this extended version of the Rhoades' open problem. A related problem, the fixed-circle problem (resp. fixed-disc problem) is also studied. Both of these problems are related to the geometric properties of the fixed point set of a self-mapping on a metric space. Furthermore, a new result about metric completeness and a short discussion on the activation functions used in the study of neural networks are given. By providing necessary examples, we show that our obtained results are effective.
Enabling Efficient Privacy-Preserving Spatiotemporal Location-Based Services for Smart Cities

GPS-enabled Internet of Things devices, which can obtain the location and temporal information of installed objects to promote Location Based Service, are completely changing our lives. In recent years, the confidentiality and privacy of personal data have attracted widespread attention, especially when outsourcing to third-party providers. To achieve both the confidentiality and availability of outsourced data, various Dynamic Searchable Symmetric Encryption (DSSE) schemes have been proposed. However, existing solutions are limited either in terms of security or efficiency. To address this challenge, we propose a Secure and Efficient Search over Encrypted Spatio-Temporal Data (SES-ESTD) scheme that utilizes constrained pseudo-random function and enhanced asymmetric scalar-product preserving encryption. Our scheme not only achieves high retrieval efficiency but also ensures forward security and content privacy. We provide a formal security analysis to prove SES-ESTD is forward secure and content private. Furthermore, extensive experiments indicate that SES-ESTD incurs lower computation and storage overheads compared to other schemes. Most importantly, SES-ESTD achieves millisecond-level retrieval for millions of data points and provides a retrieval speed that is 2.85 times faster than existing forward secure spatio-temporal DSSE schemes.
ASO-DKELM: Alpine skiing optimization based deep kernel extreme learning machine for elderly stroke detection from EEG signal

Stroke is considered the third leading cause of mortality followed by cancer and heart disease worldwide. Stroke detection in advance is the only procedure to avoid health risks while it necessitates continuous monitoring and observation of subjects. Many research works already exist in the medical field to detect diseases in advance using machine learning techniques. However, their detection results are limited by several biasing factors such as more training time and imprecise detection. So a novel stroke detection system using the Deep Kernel Extreme Learning Machine based Alpine Skiing (DKELM-AS) approach is proposed to predict the probability of subjects being affected by stroke disease. The electroencephalogram (EEG) signals of normal and stroke patients are collected and utilized to investigate the proposed approach. The raw data are preprocessed and the features are extracted based on frequency ranges using the Fast Hartley Transform (FHT) technique. Finally, the proposed DKELM-AS approach classifies them as stroke-affected EEG or normal EEG based on the precursor symptoms obtained from EEG signals. The experimental results reveal that the proposed DKELM-AS approach achieves an overall accuracy rate of about 95.2% in stroke detection based on the precursor symptoms of EEG signals.
Novel Robust Parcel-Size Classification Using mmWave Radar

A novel robust package-height estimation and parcel-size classification scheme is proposed in this work. A pertinent prototype system consisting of a millimeter-wave radar, a shipment track, and a stepping motor has been built for proof of concept. Three new schemes using the range-profile data produced by the mmWave radar, namely, the detection based-on cumulative distribution (DBCD) scheme, the detection based-on peak-value clustering (DBPC) scheme, and the DBCD–DBPC hybrid scheme are introduced here. For benchmark evaluation of the proposed new system, two practical scenarios, namely, the undisturbed scenario (no people is close to the system) and the disturbed scenario (some people is close to the system), are tested. This new system can overcome the serious drawback of the existing camera-assisted systems, which have to rely on sufficient lighting conditions, and operate normally even in complete darkness. Meanwhile, the proposed new system can accommodate parcels/packages in arbitrary orientations, which cannot be allowed by the current camera-assisted systems, during the parcel-size measurements. According to the test experiments, the proposed DBCD–DBPC hybrid scheme can reach up to a very high average classification accuracy of 96.4% for both undisturbed and disturbed scenarios. The proposed novel package-height estimation and parcel-size classification technique in this work can be very useful for intelligent inventory management and smart logistics in the future.
Anchor-Free Multi-UAV Detection and Classification Using Spectrogram

The advancements in unmanned aerial vehicle (UAV) technology have brought immense convenience to society. However, unauthorized UAVs pose a serious threat to personal privacy, public safety, and aviation security. Therefore, accurate UAV detection and classification are crucial. Moreover, with the increased popularity of UAVs, the likelihood of multiple UAVs appearing in the same area simultaneously has also dramatically increased. Recent studies demonstrate that object detectors, such as FasterRCNN and YOLO, can be used to detect and classify multiple UAVs based on spectrograms. To our best knowledge, the object detectors are directly used to classify UAV without considering the characteristics of the UAV signal spectrogram, which results in a decrease in recognition performance. In this paper, we analyze the characteristics of the UAV signal spectrogram in detail and conclude two problems, i.e., prior anchor mismatch and cross-domain detection, hindering the implementation of object detector for UAV recognition. To solve prior anchor mismatch, we propose an anchor-free detector based on keypoint and design a novel keypoints matching algorithm to improve recognition performance. To solve cross-domain detection, we propose an adversarial learning based data adaptation method, which can generate domain-independent and domain-aligned features. Finally, the experiments adopt practical spectrogram and synthetic spectrogram to verify the superiority of the proposed anchor-free detector and the effectiveness of the proposed data adaptation method.
Recommendations with minimum exposure guarantees: A post-processing framework

Relevance-based ranking is a popular ingredient in recommenders, but it frequently struggles to meet fairness criteria because social and cultural norms may favor some item groups over others. For instance, some items might receive lower ratings due to some sort of bias (e.g. gender bias). A fair ranking should balance the exposure of items from advantaged and disadvantaged groups. To this end, we propose a novel post-processing framework to produce fair, exposure-aware recommendations. Our approach is based on an integer linear programming model maximizing the expected utility while satisfying a minimum exposure constraint. The model has fewer variables than previous work and thus can be deployed to larger datasets and allows the organization to define a minimum level of exposure for groups of items. We conduct an extensive empirical evaluation indicating that our new framework can increase the exposure of items from disadvantaged groups at a small cost of recommendation accuracy.
EnsMulHateCyb: Multilingual hate speech and cyberbully detection in online social media

Nowadays, users across the globe interact with one another for information exchange, communication, and association on various online social media. However, some individuals exploit these venues for malicious practices like hate speech and cyberbully. In this paper, we present an improved multilingual hate speech and cyberbully detection model using bagging-stacking based hybrid ensemble deep learning techniques. The proposed model utilizes Bi-directional Long Short-Term Memory (BiLSTM), Bi-directional Gated Recurrent Unit (Bi-GRU), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) techniques to enhance the overall performance. We first preprocess the multilingual data streams followed by adoption of Global vectors for word Representation (GloVe) embeddings to convert words to a vector representation in parallel enabling the data streams for binary classification task. In order to construct an architecture for the detection of hate speech and cyberbully, we introduce a heterogeneous fusion of multiple effective models in a unique approach such that CNN-LSTM utilizes a stacking approach with stochastic gradient descent to achieve optimal weights, whereas all the base learners used bagging ensemble approach with cross-validation to reach optimal weights. The final output layer of the proposed ensemble deep learning architecture is achieved using a super learner approach on base learners. To show the efficacy of the proposed model, we conduct the simulation on a total of nine real-world social media datasets in different languages and compared the results with other contemporary hate speech and cyberbully detection methods. The collected findings show that the proposed model outperforms other models on considered datasets and shows an improvement of at least 4.44% in F1 scores.
Enabling secure image transmission in unmanned aerial vehicle using digital image watermarking with H-Grey optimization

Drone technology, also known as Unmanned Aerial Vehicles (UAVs), has advanced rapidly in the last decade owing to the huge number of users. This approach has an immense opportunity in areas like healthcare, agriculture, and forestry. However, the transmission of digital images via drone technology is associated with vulnerabilities and risks, due to a lack of efficient security solutions. Digital Image Watermarking (DIW) has emerged as a viable solution for digital image transmission in UAV applications. However, achieving robustness, security, and imperceptibility in a watermarking system at the same time is a difficult task. To address the aforementioned concerns, this paper proposes a secure digital image watermarking scheme in the hybrid DWT-SVD domain. The scaling factor is chosen adaptively from the hybrid Harmony-Grey Wolf (H-Grey) optimization algorithm to maintain a balance between imperceptibility and robustness. In addition, a novel symmetric cryptographic-based four-level encryption approach PSMD (Partitioning, Substituting, Merging, Division) is proposed to address UAV image security concerns by using Fibonacci and prime number series to generate two random keys. The proposed encryption scheme provides high security at low computational cost. The experimental results show that the visual quality and robustness are both high (Avg. PSNR = 41.27 dB and Avg. SSIM = 0.99, NC = 0.99, and BER = 0.0008 calculated for 100 images). The subjective and objective experimental analysis indicates that the proposed encryption scheme is highly secure and the computational cost is also low. The average embedding and extraction time is 0.25 s and 0.09 s It is resistant to various image processing attacks. A comparison with some of the most recent popular schemes confirms the scheme's effectiveness. The presented DIW can be used for copyright protection, UAV image transmission applications, military applications, and other purposes.
Galaxy: A Scalable BFT and Privacy-Preserving Pub/Sub IoT Data Sharing Framework Based on Blockchain

The emergence of the Internet of Things (IoT) technology in recent years has led to a considerable amount of data to be shared across different organizations. The publish and subscribe (Pub/Sub) paradigm, with its asynchronous, one-to-many and decoupling characteristics, is considered to be a promising communication model in IoT. However, designing a Pub/Sub framework for IoT data sharing confronts two challenges: Byzantine faults and privacy concerns. Byzantine nodes that are subjectively malicious or hacked by attackers may discard or forge data in the broker network composed of untrusted IoT organizations. Unauthorized brokers or clients may try to obtain the content of publications or subscriptions, thus violating the IoT data privacy. Existing works have limitations in terms of relatively low scalability and high overhead in tackling these two challenges. In this paper, we propose Galaxy, a blockchain based Pub/Sub IoT data sharing framework. To achieve Byzantine fault-tolerant (BFT) Pub/Sub, Galaxy adopts sharding to improve scalability and achieve efficient BFT Pub/Sub workflow within each shard with a novel leader rotation scheme. In attaining privacy-preserving Pub/Sub, a secret key sharing and encrypted Pub/Sub scheme is designed in Galaxy to achieve low overhead without breaking the decoupling of the system. We implemented a prototype of Galaxy and deployed it on Alibaba Cloud for experimental evaluation. The experiment results show the feasibility and efficiency of Galaxy.
Multi-Label Feature Selection via Positive or Negative Correlation

Feature selection, a meaningful preprocessing technique in machine learning, plays a key role in multi-label learning to select more discriminative features. Recently, multi-label feature selection algorithms based on sparse regression model have received extensive attention. However, in the existing sparse regression model, feature-label correlations are obtained from a single viewpoint via a loss function. With the aim of learning richer information, in this article, the sparse regression model is integrated with a new feature-label correlation, which allows different viewpoints to be considered simultaneously. For this reason, a new correlation, called positive or negative correlation, is proposed. Furthermore, in many existing multi-label feature selection algorithms, each feature is associated with all of the labels, while in many scenarios, each feature is associated with only some labels. Aiming at the above two shortcomings, a new multi-label feature selection algorithm is proposed in four stages. First, a pseudo-label space is built and the global label correlation is embedded into this space. Second, the positive or negative correlation between features and labels is calculated. Third, a new objective function is established. The weight matrix and feature ranking results are obtained through the optimization process. Final, using the weight matrix as prior knowledge, the proposed algorithm exploits label-specific and label-group-specific features techniques to further improve performance. Extensive experiments on over than 20 multi-label datasets show its superior performance.
Revisiting Nakamoto Consensus in Asynchronous Networks: A Comprehensive Analysis of Bitcoin Safety and Chain Quality

The Bitcoin blockchain safety relies on strong network synchrony. Therefore, violating the blockchain safety requires strong adversaries that control a mining pool with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx$</tex-math> </inline-formula> 51% hash rate. In this paper, we show that the network synchrony does not hold in the real world Bitcoin network which can be exploited to feasibly violate the blockchain safety and chain quality. Towards that, first we construct the Bitcoin ideal functionality to formally specify its ideal execution model in a synchronous network. We then develop a large-scale data collection system through which we connect with more than 103K IP addresses of the Bitcoin nodes and identify 871 mining nodes. We contrast the ideal functionality against the real world measurements to expose the network anomalies that can be exploited to optimize the existing attacks. Particularly, we observe a non-uniform block propagation pattern among the mining nodes showing that the Bitcoin network is asynchronous in practice. To realize the threat of an asynchronous network, we present the attack that allows an adversary to orchestrate concurrent mining on multiple branches of the blockchain to violate common prefix and chain quality properties. We also propose the attack countermeasures by tweaking Bitcoin Core to model the Bitcoin ideal functionality. Our measurements, theoretical modeling, proposed attack, and countermeasures open new directions in the security evaluation of Bitcoin and similar blockchain systems.
SIFDriveNet: Speed and Image Fusion for Driving Behavior Classification Network

Driving behavior classification is an important direction in the field of social transportation systems and advanced driving assistance system (ADAS), which has attracted more and more attention in recent years. An accurate driving behavior classification algorithm plays a great role in traffic safety, energy saving, and other fields. In this article, we propose a novel vehicle speed and image fusion for driving behavior classification network (SIFDriveNet), which classifies driver behaviors into normal driving, aggressive driving, and drowsy driving. Our method has the following key advantages. First, in the research of driving behavior classification, we are the first to introduce a 2-D image with rich roadside information and convert speeds into a 2-D spectrogram expressing the time–frequency characteristics of speeds through short-time Fourier transform (STFT) while unifying the data space of image information and speed information. Second, we propose a tensor fusion method based on weight decomposition to fully fuse the vectors of the two modalities. This method maps the tensor outer product results to the low-dimensional space through weight decomposition and has a low computational cost while maintaining the fusion effect of the tensor outer product. In addition, we evaluated our model on the public UAH-DriveSet and compared it with the most advanced model. Experimental results show that our model has a better performance, and F1-score is 97.9% on all roads. Especially on the secondary road, our F1-score is 99.4%. Also, our model has strong generalization, and we have reached 99.3% F1 in distracted driving multimodal dataset. In addition, the inference speed reaches 411 FPS, enabling real-time needs. The code is available on https://github.com/alu222/SIFDriveNet.
Modeling Long- and Short-Term Service Recommendations with a Deep Multi-Interest Network for Edge Computing

Edge computing platforms enable application developers and content providers to provide context-aware services (such as service recommendations) using real-time wireless access network information. How to recommend the most suitable candidate from these numerous available services is an urgent task. Click-through rate (CTR) prediction is a core task of traditional service recommendation. However, many existing service recommender systems do not exploit user mobility for prediction, particularly in an edge computing environment. In this paper, we propose a model named long and short-term user preferences modeling with a multi-interest network based on user behavior. It uses a logarithmic network to capture multiple interests in different fields, enriching the representations of user short-term preferences. In terms of long-term preferences, users' comprehensive preferences are extracted in different periods and are fused using a nonlocal network. Extensive experiments on three datasets demonstrate that our model relying on user mobility can substantially improve the accuracy of service recommendation in edge computing compared with the state-of-the-art models.
A Server Placement Algorithm for Reducing Risk and Improving Power Utilization in Data Centers

As the power demand in data centers is increasing, the power capacity of the power supply system has become an essential resource to be optimized. Although many data centers use power oversubscription to make full use of the power capacity, there are unavoidable power supply risks associated with it. Therefore, how to improve the data center power capacity utilization while ensuring power supply security has become an important issue. To solve this problem, we first define it and propose a risk evaluation metric called Weighted Power Supply Risk (WPSRisk). Then, a method, named Hybrid Genetic Algorithm with Ant Colony System (HGAACS), is proposed to improve power capacity utilization and reduce power supply risks by optimizing the server placement in the power supply system. HGAACS uses historical power data of each server to find a better placement solution by population iteration. HGAACS possesses not only the remarkable local search ability of Ant Colony System (ACS), but also enhances the global search capability by incorporating genetic operators from Genetic Algorithm (GA). To verify the performance of HGAACS, we experimentally compare it with five other placement algorithms. The experimental results show that HGAACS can perform better than other algorithms in both improving power utilization and reducing the risk of power supply system.