Multiobjective multitasking optimization (MTO) is an emerging research direction in the evolutionary computation community, which tries to solve multiple optimization problems concurrently by utilizing shared search knowledge among related tasks. However, most existing algorithms of MTO achieve the knowledge transfer without quantifying the differences among tasks and ignore the differences in the characteristics of transfer operators, which may degrade the convergence speed. To alleviate this issue, this article proposes a multiobjective multitasking evolutionary algorithm with decomposition-based adaptive knowledge transfer (MMTEA-DAKT). Specifically, an adaptive subproblems selection method is designed, which adopts a decomposition-based framework to decompose the MTO problem into a series of single-objective optimization subproblems, aiming to adjust the proportion of knowledge transfer among all different tasks based on the improvement rate of each subproblem. Besides, an adaptive knowledge transfer strategy is devised to select the most appropriate knowledge transfer operator, which aims to improve the efficiency of knowledge transfer. To verify the effectiveness of our proposed MMTEA-DAKT, we compare it with several advanced related algorithms on three standard multiobjective multitasking test suites and the practical application of neural architecture search. The experimental results show that MMTEA-DAKT has a significant competitive advantage in solving most of the problems compared to several state-of-the-art algorithms.

The escalation of antibiotic resistance underscores the need for innovative approaches to combat bacterial infections. Phage therapy has emerged as a promising solution, wherein host determination plays an important role. Phage lysins, characterized by their specificity in targeting and cleaving corresponding host bacteria, serve as key players in this paradigm. In this study, we present a novel approach by leveraging genes of phage-encoded lytic enzymes for host prediction, culminating in the development of LHPre. Initially, gene fragments of phage-encoded lytic enzymes and their respective hosts were collected from the database. Second, DNA sequences were encoded using the Frequency Chaos Game Representation (FCGR) method, and pseudo samples were generated employing the Variational Autoencoder (VAE) model to address class imbalance. Finally, a prediction model was constructed using the Vision Transformer(Vit) model. Five-fold cross-validation results demonstrated that LHPre surpassed other state-of-the-art phage host prediction methods, achieving accuracies of 85.04%, 90.01%, and 93.39% at the species, genus, and family levels, respectively.

Deep Neuro-Fuzzy Systems (DNFS) have emerged as a hybrid machine learning approach that has found applications in a wide range of fields, including healthcare, transportation, and finance. However, we empirically demonstrate that DNFS is vulnerable to adversarial examples generated by various attack algorithms, raising numerous concerns about its reliability in security-critical scenarios. Existing defense mechanisms designed for DNNs often rely on specific knowledge of attacks and their parameters. Consequently, considering the diversity and uncertainty of potential attacking methods, designing defense strategies to enhance the robustness of DNN-based and DNFS-based models against multiple attacks still remains a challenging task. In this work, we propose a comprehensive defense mechanism, named Perturbation Destruction and Information Recovery (PDIR), for image classification tasks, that achieves practical robustness against multiple attacks. PDIR employs a combination of perturbation destruction through randomization and multiplication and information recovery using a pixel-to-pixel network. Experimental results demonstrate that PDIR outperforms state-of-the-art defenses such as JPEG, TVM, ADP, GAL, DVERGE, and TRS against both white-box and black-box attacks.

Material sensing holds significant potential in areas such as environmental awareness and security monitoring. While technologies like RFID, WIFI, and UWB offer potential solutions for portable, non-contact material identification, the need to place targets in fixed positions for identification has limited the flexibility of material sensing. In this article, we first innovatively apply the Range-Angle heatmap (RAheatmap) to effectively represent the distance, placement angle, and inherent material attributes to pave the way for precise material identification. Then propose an innovative system called Material-ID to utilize Commercial-Off-The-Shelf (COTS) millimeter wave (mmWave) radar for material sensing. Additionally, we endow the system with cross-domain adaptability to make it tailored to identify material reflection attributes and minimize the effects of variables such as distance and placement angle. The experiments prove the effectiveness of the proposed system.

The Machine-Learning-as-a-Service (MLaaS) framework allows one to grab low-hanging fruit of machine learning techniques and data science, without either much expertise for this sophisticated sphere or provision of specific infrastructures. However, the requirement of revealing all training data to the service provider raises new concerns in terms of privacy leakage, storage consumption, efficiency, bandwidth, etc. In this paper, we propose a lightweight privacy-preserving MLaaS framework by combining Compressive Sensing (CS) and Generative Networks. It’s constructed on the favorable facts observed in recent works that general inference tasks could be fulfilled with generative networks and classifier trained on compressed measurements, since the generator could model the data distribution and capture discriminative information which are useful for classification. To improve the performance of the MLaaS framework, the supervised generative models of the server are trained and optimized with prior knowledge provided by the client. In order to prevent the service provider from recovering the original data as well as identifying the queried results, a noise-addition mechanism is designed and adopted into the compressed data domain. Empirical results confirmed its performance superiority in accuracy and resource consumption against the state-of-the-art privacy preserving MLaaS frameworks.

A wireless federated learning system is investigated by allowing a server and multiple workers to exchange uncoded information via orthogonal wireless channels. Since the workers frequently upload local gradients to the server via band-limited channels, the uplink transmission from the workers to the server becomes a communication bottleneck. Therefore, a one-shot distributed principle component analysis (PCA) is leveraged to reduce the dimension of uploaded gradients to relieve the communication bottleneck. A PCA-based wireless federated learning (PCA-WFL) algorithm and its accelerated version (i.e., PCA-AWFL) are proposed based on the low-dimensional gradients and the Nesterov’s momentum. For the non-convex empirical risk, a finite-time analysis is performed to quantify the impacts of system hyper-parameters on the convergence of the PCA-WFL and PCA-AWFL algorithms. The PCA-AWFL algorithm is theoretically certified to converge faster than the PCA-WFL algorithm. Besides, the convergence rates of PCA-WFL and PCA-AWFL algorithms quantitatively reveal the linear speedup with respect to the number of workers over the vanilla gradient descent algorithm. Numerical results are used to demonstrate the improved convergence rates of the proposed PCA-WFL and PCA-AWFL algorithms over the benchmarks.

Identifying the substrates of ubiquitin protein ligase (E3) and deubiquitinases (DUB) contributes to the discovery of potential therapeutic targets for diseases. However, experimental identification of E3/DUB-substrate interactions is costly and time-consuming. Current computational methods for predicting E3/DUB-substrate interactions rely heavily on specific domain knowledge and involve complex and diverse biological data processing. To address this challenge, we proposed a deep learning prediction model, named Deep-USIpred, which predicts E3/DUB-substrate interactions using protein sequences. The proposed Deep-USIpred model encodes protein sequences with a pretrained model and utilizes 1DCNN-BNN deep learning algorithm to make a robust prediction model. We evaluated the performance of the proposed model on real datasets, and our experimental results show that it can achieve excellent prediction performance on the tasks of ESI and DSI. Our proposed method provides a promising alternative for the prediction of E3/DUB-substrate interactions, which has the potential to accelerate drug discovery for various diseases. The source code and dataset are available at https://github.com/PGTSING/Deep-USIpred.

Ubiquitylation is a critical post-translational modification (PTM) process that performs a critical role in a wide range of biological functions and is closely related to hallmarks of cancer, such as DNA damage response and oxidative stress. Over the past several years, deep learning have been widely employed in protein ubiquitylation site prediction tools. However, existing deep learning tools have a common feature that they treat protein sequences as input without considering spatial information of protein. This work exploits the three-dimensional structural protein to develop a novel graph-driven ubiquitylation site predictive model (GraphUbiquSite) combing capsule module to improve predictive accuracy. According to the experimental results on Protein Lysine Modification Database (PLMD), the proposed model can archive better performance in comparison with the state-of-the-art methods.

Recent studies show that introducing phonetic information into multi-task learning could significantly improve the performance of speaker embedding extraction. However, benefits of such architectures usually depend largely on the availibility of a well-matched dataset, and domain or language mismatch would result in obvious dropdown in performance. Meanwhile, the utilization of these massive mismatched data and application of these auxiliary tasks may bring many rich features that could be exploited. In this paper, we propose a phoneme-aware adaptation network with discrepancy minimization and dynamically-classified vector for text-independent speaker verification to address these abovementioned challenges. More specifically, our method first utilize the maximum mean discrepancy (MMD) as part of the total loss function to solve the mismatch between training data of the speaker subnet and the phoneme subnet. And then we use a dynamically-classified vector-guided softmax loss (DV-Softmax), which could adaptively emphasize different high-quality features and dynamically change their weights, to guide the discriminative speaker embedding. Experimental results on VoxCeleb1 data set confirmed its superiority against the other state-of-the-art phoneme adaptation methods, providing approximately 15% relative improvements in equal error rate (EER).

MOTIVATION: Identifying the target genes of transcription factors (TFs) is of great significance for biomedical researches. However, using biological experiments to identify TF-target gene interactions is still time consuming, expensive and limited to small scale. Existing computational methods for predicting underlying genes for TF to target is mainly proposed for their binding sites rather than the direct interaction. To bridge this gap, we in this work proposed a deep learning prediction model, named HGETGI, to identify the new TF-target gene interaction. Specifically, the proposed HGETGI model learns the patterns of the known interaction between TF and target gene complemented with their involvement in different human disease mechanisms. It performs prediction based on random walk for meta-path sampling and node embedding in a skip-gram manner. RESULTS: We evaluated the prediction performance of the proposed method on a real dataset and the experimental results show that it can achieve the average area under the curve of 0.8519 ± 0.0731 in fivefold cross validation. Besides, we conducted case studies on the prediction of two important kinds of TF, NFKB1 and TP53. As a result, 33 and 32 in the top-40 ranking lists of NFKB1 and TP53 were successfully confirmed by looking up another public database (hTftarget). It is envisioned that the proposed HGETGI method is feasible and effective for predicting TF-target gene interactions on a large scale. AVAILABILITY AND IMPLEMENTATION: The source code and dataset are available at https://github.com/PGTSING/HGETGI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Stroke has become a leading cause of death and long-term disability in the world with no effective treatment. Deep learning-based approaches have the potential to outperform existing stroke risk prediction models, but they rely on large well-labeled data. Due to the strict privacy protection policy in health-care systems, stroke data is usually distributed among different hospitals in small pieces. In addition, the positive and negative instances of such data are extremely imbalanced. Transfer learning can solve small data issue by exploiting the knowledge of a correlated domain, especially when multiple source of data are available. In this work, we propose a novel Hybrid Deep Transfer Learning-based Stroke Risk Prediction (HDTL-SRP) scheme to exploit the knowledge structure from multiple correlated sources (i.e., external stroke data, chronic diseases data, such as hypertension and diabetes). The proposed framework has been extensively tested in synthetic and real-world scenarios, and it outperforms the state-of-the-art stroke risk prediction models. It also shows the potential of real-world deployment among multiple hospitals aided with 5 G/B5G infrastructures.

Visual classification algorithms based-on Deep Neural Networks (DNN) have been widely adopted in autonomous vehicle design. However, DNN suffers from adversarial attacks including pixel attacks and patch attacks, and its adoption may introduce new vulnerability into such security-critical scenarios. Existing defense techniques only focus on defending against one category, either pixel attacks or patch attacks, but does not translate to the other. Hence, the design of a practical comprehensive real-time defense algorithm for DNN-based classifiers presents a challenging task in this adversarial context. This paper attempts to address the abovementioned problem by combining Compressive Sensing with Generative neural networks (CSG) to construct an efficient defense framework, in conjunction with the proposal of a classifier-aware adversarial training way. Extensive experiments have been conducted using the LISA road sign dataset to evaluate the performance of CSG. The results show its superiority in comprehensively defending adversarial examples generated using attacks including CW-L2, FGSM and Sticker, compared with other state-of-the-art defense techniques.

Secure Device-to-Device (D2D) communication is becoming increasingly important with the ever-growing number of Internet-of-Things (IoT) devices in our daily life. To achieve secure D2D communication, the key agreement between different IoT devices without any prior knowledge is becoming desirable. Although various approaches have been proposed in the literature, they suffer from a number of limitations, such as low key generation rate and short pairing distance. In this paper, we present InaudibleKey, an inaudible acoustic signal based key generation protocol for mobile devices. Based on acoustic channel reciprocity, InaudibleKey exploits the acoustic channel frequency response of two legitimate devices as a common secret to generating keys. InaudibleKey employs several novel technologies to significantly improve its performance. We conduct extensive experiments to evaluate the proposed system in different real environments. Compared to state-of-the-art works, InaudibleKey improves key generation rate by 3 times, extends pairing distance by 3.2 times, and reduces information reconciliation counts by 2.5 times. Security analysis demonstrates that InaudibleKey is resilient to a number of malicious attacks. We also implement InaudibleKey on modern smartphones and resource-limited IoT devices. Results show that it is energy-efficient and can run on both powerful and resource-limited IoT devices without incurring excessive resource consumption.

Secure Device-to-Device (D2D) communication is becoming increasingly important with the ever-growing number of Internet-of-Things (IoT) devices in our daily life. To achieve secure D2D communication, the key agreement between different IoT devices without any prior knowledge is becoming desirable. Although various approaches have been proposed in the literature, they suffer from a number of limitations, such as low key generation rate and short pairing distance. In this paper, we present an inaudible acoustic signal based key generation protocol for mobile devices. Based on acoustic channel reciprocity, our system exploits channel frequency response of two legitimate devices as a common secret to generate keys. Extensive experiments are conducted to evaluate the proposed system in different real environments. Evaluation results show that the proposed system can generate the same secret key for two mobile devices with high probability.

The radio frequency identification (RFID) technologies, which have been widely adopted in different Internet of Things (IoT) applications, are the fundamental building block for achieving smart factories, smart logistics, smart stores, etc. Besides knowing the ID of tags, the relative location information of different tags is of great importance since it contains the spatial relationship among different tags, which is essential for many object localization applications beyond the capacity of absolute localization approaches. In this article, we proposeTagSort, an RFID-based sorting system that exploits the physical layer information, i.e., the phase of RFID wireless signals to achieve the relative localization of different tags. Several novel filtering and peak detection algorithms are proposed to achieve accurate and robust detection of the order of tags. Extensive evaluation shows promising results (over 95% accuracy) and makeTagSorta promising system for future RFID sorting systems, thus enabling a variety of IoT applications and services.

This paper proposes a novel multiobjective particle swarm optimization algorithm with dynamic resource allocation, showing promising performance especially for tackling some complicated multiobjective optimization problems. With the decomposition approach, each particle is assigned to optimize one subproblem with a novel velocity update strategy to speed up the convergence. Moreover, a dynamic resource allocation strategy is designed based on the relative improvement of subproblems, which can reasonably allocate computational resource to the particles that are able to search superior solutions. By this way, the proposed algorithm not only has strong exploratory capability, but also can converge quickly to the true Pareto-optimal front. The experimental results fully demonstrate the superiority of our proposed algorithm over four state-of-the-art multiobjective optimization algorithms, when tackling thirty-five test problems.

The design of unified, efficient, and lightweight cryptographic platform for resource-constrained on-board devices such as sensors, microcontrollers, and actuators in the context of Internet of Vehicles remains an open and challenging problem, for both academic and industry. Elliptic curve cryptography (ECC) is considered as a promising encryption algorithm for the next generation communications, as it could provide the same strong security level using relatively smaller key size when compared to the currently used Rivest–Shamir–Adleman algorithm. However, traditional ECCs have the disadvantage of using a fixed curve, making it very easy to be intensively analyzed while being hard to construct a united platform for on-board devices with processors of different instruction lengths. To mitigate the above problem, this paper suggests a dynamic scalable elliptic curve cryptosystem. To synchronize the curve in use, a curve list of different security levels is generated and preserved on both parties. Since both parties randomly choose the curve and the prime number, a extra security level could be provided, so that the security level can still remain the same even using smaller key sizes, while the computation efficiency will be enhanced and the power consumption will be reduced, which is especially suitable for the application in on-board embedded devices. Detailed experimental results illustrate that the presented scheme improves the efficiency by 30% in average when compared with traditional ECC implementations on a similar security level. Therefore, the proposed scalable ECC scheme as a unified cryptographic platform is more economic for these on-board devices in vehicles.

This paper proposes a joint selective encryption and data hiding scheme based on compressed sensing (CS), with a focus to its application in secure imaging. Specifically, working with a semantic-secure stream cipher, we suggest to selectively encrypt the sign bits of the CS measurements during its quantization stage and insert the authentication information using a nonseparable histogram-shifting based data hiding scheme. The rationale behind the sign encryption is that CS measurements, when measured by random subspace projection, is random in nature and thus, from both theoretical and experimental points of view, the mean squared errors associated with authorized users and attackers are significant. Due to the indistinguishability of the output ciphertext and the nonlinearity of the CS decoder, it is robust, when comparing with the existing selective encryption system of multimedia data, against known error concealment attacks. When applied in imaging, we demonstrate that it could effectively degrade the visual quality level while saving the computation load by at least 90%. Moreover, we further show that a state-of-the-art data hiding system can be seamlessly incorporated into the sign encryption, thus allowing soft data authentication without heavy computation. The proposed scheme is expected to strengthen the security of applications in the field where both energy and privacy are the concerns, such as sensitive information protection for multimedia data in wireless sensor networks.