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Medical imaging shows the internal structures hidden in the skin and bone to diagnose the disease. Diabetes mellitus (DM) is a metabolic disorder that causes high blood sugar levels due to the inadequate secretion of insulin or the body does not respond properly to the secreted insulin. This paper proposes a non-invasive method to detect DM at an early stage based on the physiognomy extracted from tongue images. The tongue extends to identify the disease of a human body. However, unpredictable response of the human body parts such as the stomach, pancreas, liver and intestines revert in the tongue. The changes in the tongue ensure the dereliction of the internal organs of the human being. The changes are difference in the color and surface of the tongue. Processing of tongue image is done by fractional order Darwinian particle swarm optimization (FODPSO) algorithm. The system framework involves obtaining the image, alluring of the image, identifying the texture and color feature and finally classified as normal or diabetic. In this paper, the authors propose to diagnose DM at an early stage from tongue digital image. The tongue image is acquired and processed with FODPSO to extract edge and texture features. Tongue reflects and diagnoses diabetes in a person. [ABSTRACT FROM AUTHOR]

Network function virtualization (NFV) has been proposed to enable flexible management and deployment of the network service in cloud. In NFV architecture, a network service needs to invoke several service functions (SFs) in a particular order following the service chain function. The placement of SFs has significant impact on the performance of network services. However, stochastic nature of the network service arrivals and departures as well as meeting the end-to-end Quality of Service(QoS) makes the SFs placement problem even more challenging. In this paper, we firstly provide a system architecture for the SFs placement of cloud service with end-to-end QoS deadline. We then formulate the end-to-end service placement as a Markov decision process (MDP) which aims to minimize the placement cost and the end-to-end delay. In our MDP, the end-to-end delay of active services in the network is considered to be the state of the system, and the placement (nonplacement or placement) of SF is considered as the action. Also, we discuss the rationality of our analytical model by analyzing the Markov stochastic property of the end-to-end service placement. To obtain the optimal placement policy, we then propose an algorithm (Algorithm 1) for dynamic SFs placement based on our model and use successive approximations, i.e. |$\epsilon $| -iteration algorithm (Algorithm 2) to obtain action distribution. Finally, we evaluate the proposed MDP by comparing our optimal method with DDQP, DRL-QOR, MinPath and MinDelay for QoS optimization, including acceptance probability, average delay, resource utilization, load-balancing and reliability. [ABSTRACT FROM AUTHOR]

Density peaks clustering (DPC) is a relatively new density clustering algorithm. It is based on the idea that cluster centers always have relatively high local densities and are relatively far from the points with higher densities. With the aforementioned idea, a decision graph can be drawn, and cluster centers will be chosen easily with the aid of the decision graph. However, the algorithm has its own weaknesses. Because the algorithm calculates local density and allocates points based on the distances between certain points, the algorithm has difficulty in classifying points into proper groups with varying densities or nested structures. This paper proposes an improved density peaks clustering algorithm called Dratio-DPC to overcome this weakness. First, Dratio-DPC adjusts the original local density with a coefficient calculated with the density ratio. Second, Dratio-DPC takes density similarity into consideration to calculate the distances between one point and other points with higher local densities. We design and perform experiments on different benchmark datasets and compare the clustering results of Dratio-DPC, traditional clustering algorithms and three improved DPC algorithms. Comparison results show that Dratio-DPC is effective and applicable to a wider range of scenarios. [ABSTRACT FROM AUTHOR]

Cyberbullying is a significant concern in this digital age due to its harmful effects on individuals and society. Sadly, social media platforms have only exacerbated the problem, making it imperative to find effective ways to identify and prevent offensive content. While previous research has extensively focused on English and explored machine learning techniques to tackle this issue. To address this gap, this paper introduces a new hybrid deep learning model called Gray Wolf Algorithm-convolutional neural network (GWA-CNN), explicitly designed to detect cyberbullying in the Kurdish language on Twitter. The proposed model combines the CNN framework with an optimised GWA version to improve CNN's parameters and reduce training time. We evaluated GWA-CNN thoroughly utilizing the first-ever manually annotated Kurdish dataset of 30k tweets that have been meticulously curated and divided into three categories, namely sexism, racism and neutral expressions, and compared its performance to those of state-of-the-art algorithms such as Naïve Bayes, K-Nearest Neighbors, Recurrent Neural Networks, Gated Recurrent Units and attention-based transformer. The experimental results demonstrate that GWA-CNN exhibited superior performance in all scenarios, outperforming other approaches in detecting cyberbullying on Twitter. [ABSTRACT FROM AUTHOR]

Deep learning has been widely adopted in the field of network traffic classification due to its unique advantages in handling encrypted network traffic. However, most existing deep learning models can only classify known encrypted traffic that has been sampled and labeled. In this paper, we propose CM-UTC, a cost-sensitive matrix-based method for classifying unknown encrypted traffic. CM-UTC explores the probability distribution of the DNN output layer to filter out the unknown classes and further designs a cost-sensitive matrix to address the class imbalance problem. Additionally, we propose the utilization of the Harris Hawk optimization algorithm to modify the model parameters and improve its performance. The experiments are validated on two different datasets, and the results demonstrate that CM-UTC not only outperforms existing methods in terms of overall performance but also exhibits superior capability in correctly identifying samples from the minority class. [ABSTRACT FROM AUTHOR]

Energy consumption in 5G base stations remains consistently high, even during periods of low traffic loads, thereby resulting in unnecessary inefficiencies. To address this problem, this paper presents a novel approach by proposing a convolutional neural network (CNN)-long short-term memory (LSTM) traffic prediction model with a dual attention mechanism, coupled with the particle swarm optimization k-means algorithm for intelligent switch timing. The proposed CNN-LSTM model leverages a dual channel attention mechanism to bolster key feature information for long-term traffic data predictions. Specifically, a temporal attention mechanism is added to the LSTM to enhance the importance of temporal information. Moreover, the particle swarm optimization K-Means algorithm is proposed in order to cluster the traffic prediction results, output the corresponding time points of the lower traffic value and to obtain the optimal switch-off periods of the base station. Extensive experiments across multiple base stations over an extended period of time have validated our approach. The results show that this method offers accurate traffic prediction with minimal average errors in traffic prediction and the on/off timings of the base stations are in line with the "tide effect" of traffic, thereby achieving the goal of energy savings. [ABSTRACT FROM AUTHOR]

In the field of multimodal sentiment analysis, it is an important research task to fully extract modal features and perform efficient fusion. In response to the problems of insufficient semantic information and poor cross-modal fusion effect of traditional sentiment classification models, this paper proposes a composite hierarchical feature fusion method combined with prior knowledge. Firstly, the ALBERT (A Lite BERT) model and the improved ResNet model are constructed for feature extraction of text and image, respectively, and high-dimensional feature vectors are obtained. Secondly, to solve the problem of insufficient semantic information expression in cross-scene, a prior knowledge enhancement model is proposed to enrich the data characteristics of each modality. Finally, to solve the problem of poor cross-modal fusion effect, a composite hierarchical fusion model is proposed, which combines the temporal convolutional network and the attention mechanism to fuse the sequence features of each modality information and realizes the information interaction between different modalities. Experiments on MVSA-Single and MVSA-Multi datasets show that the proposed model is superior to a series of comparison models and has good adaptability in new scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a novel video hashing with tensor robust Principal Component Analysis (PCA) and Histogram of Optical Flow (HOF) for copy detection. In the proposed hashing, a video is divided into some video groups. For each video group, a low-rank secondary frame is constructed from the low-rank component decomposed by applying tensor robust PCA to the video group. Since the low-rank component can well indicate spatial-temporal intrinsic structure of the video group and it is slightly disturbed by digital operations, feature extraction from the low-rank secondary frames is discriminative and stable. Next, spatial features and temporal features are extracted from low-rank secondary frames by Charlier moments and HOF, respectively. Since the Charlier moments are robust to geometric transform and they can efficiently distinguish video frames with different contents, the use of Charlier moments can make robust and discriminative spatial features. As the HOF can measure the distribution of motion information between frames, the temporal features formed by HOFs can provide good discrimination. Hash is ultimately determined by quantizing the spatial and temporal features and concatenating the quantized results. Numerous experiments on open video datasets indicate that the proposed hashing is superior to some hashing baseline schemes in terms of classification and copy detection. [ABSTRACT FROM AUTHOR]

Log blockchain can be used to ensure the integrity of log data. However, current methods are facing the problems of throughput mismatch and rough audit granularity. Packaging multiple logs to generate integrity proofs improves throughput but reduces audit granularity, and the auditor can only locate the tampering log packet instead of specific records. This paper proposes CFAuditChain, an audit blockchain based on a cuckoo filter, where the proof of existence (PoE) of the single log is calculated while calculating the integrity proof of packet logs. The PoE is saved in the cuckoo filter and stored using blockchain for immutability. Therefore, the auditor can verify the credibility of each log based on the filter when the integrity proof of a log packet does not match. The theoretical analysis and experimental results show that CFAuditChain provides the granularity of audit logs down to the records level at an acceptable cost. [ABSTRACT FROM AUTHOR]

Blind image watermarking is regarded as a vital technology to provide copyright of digital images. Due to the rapid growth of deep neural networks, deep learning-based watermarking methods have been widely studied. However, most existing methods which adopt simple embedding and extraction structures cannot fully utilize the image features. In this paper, we propose a novel Single-Encoder-Dual-Decoder (SEDD) watermarking architecture to achieve high imperceptibility and strong robustness. Precisely, the single encoder utilizes normalizing flow to realize watermark embedding, which can effectively fuse the watermark and cover image. For watermark extraction, we introduce a parallel dual-decoder to improve the imperceptibility and extracting ability. Extensive experiments demonstrate that better watermark robustness and imperceptibility are obtained by SEDD architecture. Our method achieves a bit error rate less than 0.1% under most attacks such as JPEG compression, Gaussian blur and crop. Besides, the proposed method also obtains strong robustness under combined attacks and social platform processing. [ABSTRACT FROM AUTHOR]

The problem of detecting communities in real-world networks has been extensively studied in the past, but most of the existing approaches work on single-domain networks , i.e. they consider only one type of relationship between nodes. Single-domain networks may contain noisy edges and they may lack some important information. Thus, some authors have proposed to consider the multiple relationships that connect the nodes of a network, thus obtaining multi-domain networks. However, most community detection approaches are limited to multi-layer networks , i.e. networks generated from the superposition of several single-domain networks (called layers) that are regarded as independent of each other. In addition to being computationally expensive, multi-layer approaches might yield inaccurate results because they ignore potential dependencies between layers. This paper proposes a multi-domain discrete-time quantum walks (MDQW) model for multi-domain networks. First, the walking space of network nodes in multi-domain network is constructed. Second, the quantum permutation circuit of the coin state is designed based on the coded particle state. Then, using different coin states, the shift operator performs several quantum walks on the particles. Finally, the corresponding update rule is selected to move the node according to the measurement result of the quantum state. With continuous update iteration, the shift operator automatically optimizes the discovered community structure. We experimentally compared our MDQW method with four state-of-the-art competitors on five real datasets. We used the normalized mutual information (NMI) to compare clustering quality, and we report an increase in NMI of up to 3.51 of our MDQW method in comparison with the second-best performing competitor. The MDQW method is much faster than its competitors, allowing us to conclude that MDQW is a useful tool in the analysis of large real-life multi-domain networks. Finally, we illustrate the usefulness of our approach on two real-world case studies. [ABSTRACT FROM AUTHOR]

Detection of computer-generated (CG) images can reveal the authenticity and originality of digital images. However, recent cutting-edge image generation methods make it very difficult to distinguish CG images from natural photographs. In this paper, a novel method based on color patterns and enhanced texture learning is proposed to tackle this problem. We designed and implemented the backbone network with a separation-fusion learning strategy by constructing a multi-branch neural network. The luminance and chrominance patterns in dual-color spaces (RGB and YCbCr) are leveraged to achieve a robust representation of image differences. A channel-spatial attention module and a global texture enhancement module are also integrated into a backbone network to enhance the learning of inherent traces. Experiments on several commonly used benchmark datasets and a newly constructed dataset with more realistic and diverse images demonstrate that the proposed algorithm outperforms state-of-the-art competitors by a large margin. [ABSTRACT FROM AUTHOR]

Link residual closeness is a newly proposed measure for network vulnerability. In this model, vertices are perfectly reliable and the links fail independently of each other. It measures the vulnerability even when the removal of links does not disconnect the graph. In this paper, we characterize those graphs that maximize the link residual closeness over the connected graphs with fixed order and one additional parameter such as connectivity, edge connectivity, bipartiteness, independence number, matching number, chromatic number, number of cut vertices and number of cut edges. [ABSTRACT FROM AUTHOR]

The present era is at the peak of technological advancement in image and video processing techniques, with user-friendly accessible tools/techniques. This immersive technology development makes video forensics enormously challenging. Specifically, the passive approaches to object-based forgeries in videos are crucial for legal and judicial matters. Hence, to ensure the integrity of the videos, a scientific, statistical and passive investigation of videos is required to maintain the spatial and temporal information content. This paper aims to develop a passive approach for digging out the forgery traces by applying the motion residue windowing technique for object removal forgery in surveillance videos. The novel max averaging windowing techniques improve visual imprints of the object removal forgery in the videos from the existing methods in the literature. A deep learning approach is the next step for achieving forgery detection in surveillance videos. The proposed lightweight depth-separable layer-optimized CNN has fast execution speed, optimized in terms of parameters without compromising the desired accuracy. This network is trained at a frame level with 98.60% testing accuracy, followed by a pipeline architecture of the proposed model for detection of forgery at video level with 99.01% accuracy. The suggested model works better than current models regarding post-processing operations, compression rates, forged video detection accuracy, precision, recall and F1 score. [ABSTRACT FROM AUTHOR]

Recently, the field of Natural Language Processing (NLP) has made significant progress with the evolution of Contextualised Neural Language Models (CNLMs) and the emergence of large LMs. Traditional and static language models exhibit limitations in tasks demanding contextual comprehension due to their reliance on fixed representations. CNLMs such as BERT and Semantic Folding aim to produce feature-rich representations by considering a broader linguistic context. In this paper, Deep Learning-based Aspect Category Detection approaches are introduced to perform text classification. The study extensively assesses classification model performance, emphasising enhanced representativeness and optimised feature extraction resolution using CNLMs and their hybridised variants. The effectiveness of the proposed approaches is evaluated on benchmark datasets of 4500 reviews from the laptop and restaurant domains. The results show that the proposed approaches using hybridised CNLMs outperform state-of-the-art methods with an f-score of 0.85 for the laptop and f-scores higher than 0.90 for the restaurant dataset. This study represents a pioneering work as one of the initial research efforts aiming to jointly evaluate the representation performance of CNLMs with different architectures to determine their classification capabilities. The findings indicate that the proposed approaches can enable the development of more effective classification models in various NLP tasks. [ABSTRACT FROM AUTHOR]

Multi-dimensional packet classification is one of the most important functions to support various services in next generation routers. Both the memory-efficient data structure to support larger rule tables and the hardware architecture to achieve a higher throughput are desired. In this paper, we propose a parallel and pipelined architecture called Set-Pruning Segment Trees with Buckets (SPSTwB) for multi-dimensional packet classification. SPSTwB significantly reduces rule duplication based on a novel partitioning scheme and an efficient bucket merging scheme. The key feature of our proposed architecture is that memory consumption is reduced significantly regardless of the characteristics of various rule tables. In addition, the logic complexity of each pipeline stage is simplified by not storing rule IDs and priorities and thus it can run at a high clock rate. The proposed scheme needs less than 20 bytes per rule for various 100 K rule tables generated by ClassBench. In addition, the proposed scheme supports fast incremental rule update. The proposed pipelined architecture can achieve a throughput of 134 Gbps from the implementation on Xilinx Virtex-7 FPGA device with dual-ported Block RAM. [ABSTRACT FROM AUTHOR]

This research presents a new problem maximum scatter colored traveling salesman problem (MSCTSP), the objective of MSCTSP is to find Hamiltonian cycles with the minimal edge as max as possible, it is used to simulate the real-world applications of network and transport. Since MSCTSP has been proved to be a NP-hard problem, population-based algorithms can be used for solving it. However, the performances are not satisfactory. Thus, it is necessary to develop novel algorithms to obtain high quality feasible solution. Based on the above reason, the paper proposes a novel hybrid ITÖ (HITÖ) algorithm, which integrates the two new strategies: crossover operator and mutation strategy, into the standard ITÖ. In the iteration course of HITÖ, the dual-chromosome coding is used to code a feasible solution of MSCTSP, and the stochastic drift and volatility operators are used to explore and exploit new unknown region. During the process, drift operator is performed by crossover operator, volatility operator is carried out by mutation strategy, and they are both affected by activity intensity of particles which functionally depends on the radius and temperature. Experiments display HITÖ shows an improvement over comparative algorithms on solution quality. [ABSTRACT FROM AUTHOR]

A connected graph |$G$| is called strongly Menger edge connected if |$G$| has min{deg |$_{G}(x)$|⁠ , deg |$_{G}(y)$| } edge-disjoint paths between any two distinct vertices |$x$| and |$y$| in |$G$|⁠. In this paper, we consider two types of the strongly Menger edge connectivity of the line graphs of generalized |$n$| -dimensional hypercubes with faulty edges, namely the |$m$| -edge-fault-tolerant and |$m$| -conditional edge-fault-tolerant strongly Menger edge connectivity. We show that the line graphs of all generalized |$n$| -dimensional hypercubes are |$(2n-4)$| -edge-fault-tolerant strongly Menger edge connected for |$n\geq 3$| and |$(4n-10)$| -conditional edge-fault-tolerant strongly Menger edge connected for |$n\geq 4$|⁠. The two bounds for the maximum numbers of faulty edges are best possible. [ABSTRACT FROM AUTHOR]

Listing dense subgraphs is a fundamental task with a variety of network analytics applications. A lot of research has been done focusing on |$k$| -cliques, i.e. complete subgraphs on |$k$| nodes. However, requiring complete connectivity between the nodes of a subgraph may be too restrictive in many real applications. Hence, in this paper, we consider a natural relaxation of cliques, called |$k$| -diamonds and defined as cliques of size |$k$| with one missing edge. We first provide a sequential algorithm that, in |$O(nm^{(k-1)/2})$| time, counts and lists all the |$k$| -diamonds in large graphs, for any constant |$k \geq 4$|⁠. A parallel extension of the sequential algorithm is then proposed and analyzed in a MapReduce-style model, achieving the same local and total space usage of the state-of-the-art algorithms for |$k$| -cliques. The running time is optimal on dense graphs and |$O(\sqrt{m})$| larger than |$k$| -clique counting if the graph is sparse. Our algorithms compute induced diamonds by analyzing the structure of directed stars formed by the graph nodes and their neighbors. [ABSTRACT FROM AUTHOR]

State machines are widely used in industry and academia to capture behavioural models of control. They are included in popular notations, such as UML and its variants, and used (sometimes informally) to describe computational artefacts. In this paper, we present laws for state machines that we prove sound with respect to a process algebraic semantics for refinement, and complete, in that they are sufficient to reduce an arbitrary model to a normal form that isolates basic (action and control) elements. We consider two variants of UML-like state machines, both enriched with facilities to deal with time budgets, timeouts and deadlines over triggers and actions. In the first variant, machines are self-contained components, declaring all the variables, events and operations that they require or define. In contrast, in the second variant, machines are open, like in UML for instance. Laws for open state machines do not depend on a specific context of variables, events and operations, and normalization uses a novel operator for open-machine (de)composition. Our laws can be used in behaviour-preservation transformation techniques. Their applications are automated by a model-transformation engine. [ABSTRACT FROM AUTHOR]

According to Menger's theorem, connectivity and edge connectivity are closely related to node-disjoint paths and edge-disjoint paths, respectively. Node- and edge-disjoint paths can keep the effective transmission of information and confidentiality. Therefore, node-disjoint paths and edge-disjoint paths are two important parameters to measure the reliability of a network. For a faulty node (resp. edge) set |$S\subset V$| (resp. |$S\subset E$|⁠), a connected graph |$G=(V,E)$| is |$S$| -strongly Menger-node-connected (resp. Menger-edge-connected) if any two distinct nodes |$x$| and |$y$| in |$G-S$| are connected by |$\min \{\deg _{G-S}(x),\deg _{G-S}(y)\}$| internally node-disjoint (resp. edge-disjoint) paths in |$G-S$|⁠ , where |$\deg _{G-S}(x)$| and |$\deg _{G-S}(y)$| are the degrees of |$x$| and |$y$| in |$G-S$|⁠ , respectively. And most of the previous studies are based on networks that are triangle-free. In this paper, we consider the strongly Menger (edge) connectedness of a class of |$r$| -dimensional recursive networks RNCG |$G_{r}$| with triangles. Moreover, we show that |$G_{r}$| is |$(rl-l-1)$| -strongly Menger-node-connected. And then we show that |$G_{r}$| is |$[k+(r-1)l-2]$| -strongly Menger-edge-connected of order 1 and |$[2k+2(r-1)l-6]$| -strongly Menger-edge-connected of order 2. Since the class of |$r$| -dimensional recursive networks RNCG |$G_{r}$| includes not only data center networks DCell and generalized DCell but also interconnection network dragonfly, etc. all the results are appropriate for these networks. [ABSTRACT FROM AUTHOR]

A confidential reporting system (CRS) allows reporters to report concerns or problems in confidence without the fear of blame or reprisals. Nevertheless, privacy has been the primary concern of reporters. In this paper, we propose a privacy-preserving confidential reporting system with designated reporters (PPCRS-DR) to protect the privacy of reporters and the confidentiality of reports. Our PPCRS-DR provides the following interesting features: (1) for an event, an auditor can designate a reporter to report; (2) an auditor can neither see the report nor know the reporter's identity from an encrypted report if the reporter is not the designated one; (3) when an auditor is unavailable, he/she can temporarily designate a delegatee to collect and review reports on behalf of him/her. We formalize both the definition and security model of our PPCRS-DR, and propose a concrete construction. Furthermore, the security of the proposed PPCRS-DR is formally proven. The implementation shows that it is efficient. The novelty is to implement flexible decryption delegation of CRSs and protect reporters' privacy. [ABSTRACT FROM AUTHOR]

Two vertices |$u$| and |$v$| in a graph |$G=(V,E)$| are in the same orbit if there exists an automorphism |$\phi $| of |$G$| such that |$\phi (u)=v$|⁠. The orbit number of a graph |$G$|⁠ , denoted by |$Orb(G)$|⁠ , is the smallest number of orbits, which form a partition of |$V(G)$|⁠ , in |$G$|⁠. All vertex-transitive graphs |$G$| are with |$Orb(G)=1$|⁠. Since the |$n$| -dimensional hypercube, denoted by |$Q_{n}$|⁠ , is vertex-transitive, it follows that |$Orb(Q_{n})=1$| for |$n\geq 1$|⁠. Pai, Chang, and Yang proved that the |$n$| -dimensional folded crossed cube, denoted by |$FCQ_{n}$|⁠ , is vertex-transitive if and only if |$n\in \{1,2,4\}$|⁠ , namely |$Orb(FCQ_{1})=Orb(FCQ_{2})=Orb(FCQ_{4})=1$|⁠. In this paper, we prove that |$Orb(FCQ_{n})=2^{\lceil \frac{n}{2}\rceil -2}$| if |$n\geq 6$| is even and |$Orb(FCQ_{n}) = 2^{\lceil \frac{n}{2}\rceil -1}$| if |$n\geq 3$| is odd. [ABSTRACT FROM AUTHOR]

Mashups are web applications that expedite software development by reusing existing resources through integrating multiple application programming interfaces (APIs). Recommending the appropriate APIs plays a critical role in assisting developers in building such web applications easily and efficiently. The proliferation of publicly available APIs on the Internet has inspired the community to adopt various models to accomplish the recommendation task. Until present, considerable efforts have been made to recommend the optimal set of APIs, delivering fruitful results and achieving varying recommendation performance. This paper presents a timely review on the topic of API recommendations for mashup creation. Specifically, we investigate and compare not only traditional data mining approaches and recommendation techniques but also more recent approaches based on network representation learning and deep learning techniques. By analyzing the merits and pitfalls of existing approaches, we pinpoint a few promising directions to address the remaining challenges in the current research. This survey provides a timely comprehensive review of the API recommendation research and could be a useful reference for relevant researchers and practitioners. [ABSTRACT FROM AUTHOR]

Deep neural networks are vulnerable to backdoor attacks. Previous backdoor attacks have mainly focused on images. Unlike images composed of regular pixels, 3D point clouds are composed of irregular three-dimensional XYZ coordinates, which are widely used in areas such as autonomous driving and 3D measurement. As many deep neural networks have been developed for processing 3D point clouds, these networks also face the risk of backdoor attacks. Nevertheless, backdoor attacks on 3D point clouds have rarely been investigated. This paper proposes a stealthy backdoor attack on point clouds in the physical world, aiming to generate trainable non-rigid deformations as backdoor patterns. Instead of directly adding backdoor patterns onto the point clouds, we deform the 3D space of the point clouds to a new space, ensuring that all point clouds have the same backdoor deformation. We use point cloud alignment to overcome the inconsistency of backdoor deformation caused by shifting and scaling in the physical world. We also propose a physical transformation layer to combat the physical transformations. Additionally, we propose mask contrast learning to eliminate pseudo backdoor patterns to make the network's backdoor property stealthier. Extensive experiments indicate that the proposed method can achieve better attack success rates and stealthiness. [ABSTRACT FROM AUTHOR]

Violence detection is a critical task aimed at identifying violent behavior in video by extracting frames and applying classification models. However, the complexity of video data and the suddenness of violent events present significant hurdles in accurately pinpointing instances of violence, making the extraction of frames that indicate violence a challenging endeavor. Furthermore, designing and applying high-performance models for violence detection remains an open problem. Traditional models embed extracted spatial features from sampled frames directly into a temporal sequence, which ignores the spatio-temporal characteristics of video and limits the ability to express continuous changes between adjacent frames. To address the existing challenges, this paper proposes a novel framework called ACTION-VST. First, a keyframe extraction algorithm is developed to select frames that are most likely to represent violent scenes in videos. To transform visual sequences into spatio-temporal feature maps, a multi-path excitation module is proposed to activate spatio-temporal, channel and motion features. Next, an advanced Video Swin Transformer-based network is employed for both global and local spatio-temporal modeling, which enables comprehensive feature extraction and representation of violence. The proposed method was validated on two large-scale datasets, RLVS and RWF-2000, achieving accuracies of over 98 and 93%, respectively, surpassing the state of the art. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the first generic framework of policy-based remote user authentication from multiple biometrics. The proposed framework allows an authorized user to remotely authenticate herself to an authentication server using her multiple biometrics, which enhances both the security and usability of user authentications. The authentication server approves a user's authentication request if and only if the user's multiple biometrics satisfies an authentication policy. In particular, the authentication policy can be dynamically updated to satisfy different security and usability requirements in practice. We implement an instantiation of the proposed framework and report its performance under various authentication policies. [ABSTRACT FROM AUTHOR]

Recent studies show that neural networks are vulnerable to backdoor attacks, in which compromised networks behave normally for clean inputs but make mistakes when a pre-defined trigger appears. Although prior studies have designed various invisible triggers to avoid causing visual anomalies, they cannot evade some trigger detectors. In this paper, we consider the stealthiness of backdoor attacks from input space and feature representation space. We propose a novel backdoor attack named sparse backdoor attack, and investigate the minimum required trigger to induce the well-trained networks to make incorrect results. A U-net-based generator is employed to create triggers for each clean image. Considering the stealthiness of the trigger, we restrict the elements of the trigger between −1 and 1. In the aspect of the feature representation domain, we adopt an entanglement cost function to minimize the gap between feature representations of benign and malicious inputs. The inseparability of benign and malicious feature representations contributes to the stealthiness of our attack against various model diagnosis-based defences. We validate the effectiveness and generalization of our method by conducting extensive experiments on multiple datasets and networks. [ABSTRACT FROM AUTHOR]

The SM4 block cipher is a Chinese national standard and an ISO international standard. Since white-box cryptography has many real-life applications nowadays, a few white-box implementations of SM4 has been proposed, among which a type of constructions is dominated, which uses a linear or affine diagonal block encoding to protect the original three 32-bit branches entering a round function and uses its inverse as the input encoding to the S-box layer. In this paper, we analyse the security of this type of constructions against Lepoint et al.'s collision-based attack method. Our experiment under a small fraction of (encodings, round key) combinations shows that the rank of the concerned linear system is much less than the number of the involved unknowns, meaning these white-box SM4 implementations should resist Lepoint et al.'s method, but we leave it as an open problem whether there are such encodings that the rank of the corresponding linear system is slightly less than the number of the involved unknowns, in which scenario Lepoint et al.'s method may be used to recover a round key for the case with linear encodings and to remove most white-box operations until mainly some Boolean masks for the case with affine encodings. [ABSTRACT FROM AUTHOR]

As a structural topological index, the number of subtrees has great significance for the analysis and design of hybrid locally reliable networks. In this paper, with generating function and introducing a novel two-forest dual transformation technique, we solve the subtree enumerating problems of two representatives of the self-similar networks, such as the hierarchical lattice and |$(u,v)$| -flower networks. Moreover, by means of the circle weight transfer technique, two linear time algorithms of computing the subtree generation functions of these two families of networks are also proposed. The subtree density of two special cases for these self-similar networks is briefly discussed as an application. [ABSTRACT FROM AUTHOR]

Meta-learning is a pivotal and potentially influential machine learning approach to solve challenging problems in reinforcement learning. However, the costly hyper-parameter tuning for training stability of meta-learning is a known shortcoming and currently a hotspot of research. This paper addresses this shortcoming by introducing an online and easily trainable hyper-parameter optimization approach, called Meta Parameters Learning via Meta-Learning (MPML), to combine online hyper-parameter adjustment scheme into meta-learning algorithm, which reduces the need to tune hyper-parameters. Specifically, a basic learning rate for each training task is put forward. Besides, the proposed algorithm dynamically adapts multiple basic learning rate and a shared meta-learning rate through conducting gradient descent alongside the initial optimization steps. In addition, the sensitivity with respect to hyper-parameter choices in the proposed approach are also discussed compared with model-agnostic meta-learning method. The experimental results on reinforcement learning problems demonstrate MPML algorithm is easy to implement and delivers more highly competitive performance than existing meta-learning methods on a diverse set of challenging control tasks. [ABSTRACT FROM AUTHOR]

Chinese grammatical error correction (CGEC) is a significant challenge in Chinese natural language processing. Deep-learning-based models tend to have tens of millions or even hundreds of millions of parameters since they model the target task as a sequence-to-sequence problem. This may require a vast quantity of annotated corpora for training and parameter tuning. However, there are currently few open-source annotated corpora for the CGEC task; the existing researches mainly concentrate on using data augmentation technology to alleviate the data-hungry problem. In this paper, rather than expanding training data, we propose a competitive CGEC model from a new insight for reducing model parameters. The model contains three main components: a sequence learning module, a grammatical generalization module and a parameter sharing module. Experimental results on two Chinese benchmarks demonstrate that the proposed model could achieve competitive performance over several baselines. Even if the parameter number of our model is reduced by 1/3, it could reach a comparable |$F_{0.5}$| value of 30.75%. Furthermore, we utilize English datasets to evaluate the generalization and scalability of the proposed model. This could provide a new feasible research direction for CGEC research. [ABSTRACT FROM AUTHOR]

While tensor accelerated compilers have proven effective in deploying deep neural networks (DNN) on general-purpose hardware, optimizing for FPGA remains challenging due to the complex DNN architectures and the heterogeneous, semi-open compute units. This paper introduces the Automatic Kernel Generation for DNN on CPU-FPGA (AKGF) framework for efficient deployment of DNN on heterogeneous CPU-FPGA platforms. AKGF generates an intermediate representation (IR) of the DNN using TVM's Halide IR, annotates the operators of model layers in the IR to compute them on the corresponding hardware cores, and further optimizes the operator code for CPU and FPGA using ARM's function library and the polyhedral model to enhance model inference speed and power consumption. The experimental tests conducted on a CPU-FPGA board validate the effectiveness of AKGF, demonstrating significant acceleration ratios (up to 6.7x) compared to state-of-the-art accelerators while achieving a 2x power optimization. AKGF effectively leverages the computational capabilities of both CPU and FPGA for high-performance deployment of DNN on CPU-FPGA platforms. [ABSTRACT FROM AUTHOR]

Knowledge graph (KG) is introduced as side information into recommender systems, which can alleviate the sparsity and cold start problems in collaborative filtering. Existing studies mainly focus on modeling users' historical behavior data and KG-based propagation. However, they have the limitation of ignoring noise information during recommendation. We consider that noise exists in two parts (i.e. KG and user-item interaction data). In this paper, we propose Knowledge-aware Dual-Channel Graph Neural Networks (KDGNN) to improve the recommendation performance by reducing the noise in the recommendation process. Specifically, (1) for the noise in KG, we design a personalized gating mechanism, namely dual-channel balancing mechanism, to block the propagation of redundant information in KG. (2) For the noise in user-item interaction data, we integrate personalized and knowledge-aware signals to capture user preferences fully and use personalized knowledge-aware attention to denoise user-item interaction data. Compared with existing KG-based methods, we aim to propose a knowledge-aware recommendation method from a new perspective of denoising. We perform performance analysis on three real-world datasets, and experiment results demonstrate that KDGNN achieves strongly competitive performance compared with several compelling state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

This paper intends to introduce a novel groundwater prediction model by inducing the novel hydro indices that are not yet popular in earlier techniques. As per the proposed work, statistical features like mean, median, skewness and kurtosis are estimated. Moreover, the vegetation index includes simple ratio, normalized difference vegetation index, Kauth–Thomas Tasseled cap transformation and infrared index transformation. Furthermore, a novel hydro index is formulated by combining the statistical model function with the vegetation index. Subsequently, the detection process is carried out by ensemble technique, which includes the classifiers like random forest (RF), neural network (NN), support vector machine (SVM) and deep belief network (DBN). The final predicted result is attained from DBN. The performance of the adopted model is computed to the existing models with respect to certain measures. At learning rate 50, the maximum accuracy of the proposed model is 45.65, 34.78, 58.70, 72.83, 18.48 and 23.91% better than the existing models like SVM, RF, convolutional neural network, K-nearest neighbors, NN and artificial neural network, respectively. [ABSTRACT FROM AUTHOR]

The assessment of image quality provides the confidentiality, safety, quality and transparency of the obtained images. At the same time, there are many existing quality metric approaches that belong to the group of modification alteration procedures (pixel-based metric). These techniques have not been well-matched with perceptual image quality. The Perceptual Human Visual System (HVS) aspect drives our approach; The human visual system (HVS) is the best judge of image quality. This paper presents a new reduced reference image quality metric in the spatial domain that reduces the complexity of the image quality assessment. From the reference and distortion images, we extract four intrinsic features, namely, contrast, entropy, histogram and standard deviation. Then, we build the formal concept analysis matrix for reference and distortion images. Finally, we compare the obtained matrixes to evaluate the image quality. The performance of the proposed technique is assessed using LIVE, TID2013 and CSIQ datasets, and the obtained results are compared in terms of PSNR, SSIM and NCC metrics. Also, a comparison with more recent and relevant approaches is performed to highlight the superior performance of our proposed approach, the experimental results indicated that the proposed approach provides efficient performance among the compression, Gaussian blur, Contrast and add noise distortion types. [ABSTRACT FROM AUTHOR]

This paper defines a novel graph constructed on a residuated lattice using a specific definition of the fuzzy graph (called |$L$| -graph). These types of graphs also have applications in libraries, pharmacies and machine facilities. Among these applications, this research presents the |$L$| -graph automaton related to the |$L$| -graph constructed by a minimum zero forcing set. The |$L$| -graph automaton is used to find efficient searching for diseases that have the most similar symptoms. It has been demonstrated that if two |$L$| -graphs are isomorphic, then the related |$L$| -graph automata are isomorphic but not necessarily vice versa. New notions, |$L^{\prime\prime}$| -graph automata, pseudo-isomorphism and self-sufficiency are proved. Besides, by taking advantage of the properties of the residuated lattice, it has been proven that the two |$L$| -graph automata are equivalent if and only if the |$L^{\prime}$| -graph automata contracted of |$L$| -graph automata are equivalent. Ultimately, in light of the above, some related theorems are proved and several examples are provided to illustrate these new notions. [ABSTRACT FROM AUTHOR]

With the rapid development of blockchains, blockchain systems are moving on from a stand-alone manner to cross-chain interactions, and achieving interoperability is emerging as one of the essential features of blockchains. Unfortunately, existing mechanisms such as XCLAIM mostly focus on exchanging assets between two blockchains and it is slow and expensive to process each cross-chain trade among more than two blockchains as multiple transactions are required. In this paper, we present Interopera, a decentralized and efficient cross-chain trading protocol among two or more blockchains. Interopera atomically processes each cross-chain trade faster and more cheaply with fewer transactions by a two-phase lock/unlock process. Interopera also achieves efficient cross-chain communication by our presented Partitioned-FlyClient and Tx-FlyClient. Partitioned-FlyClient is based on FlyClient but more efficient with smaller proof size, reducing the storage and bandwidth overheads. Tx-FlyClient maintains efficiency even when cross-chain trades become frequent, instead of other mechanisms only being effective under low cross-chain trades volumes. We also develop a proof-of-concept implementation and the results demonstrate high efficiency of our protocol. [ABSTRACT FROM AUTHOR]

A large event sequence can generate episode rules that are patterns which help to identify the possible dependencies existing among event types. Frequent episodes occurring in a simple sequence of events are commonly used for mining the episodes from a sequential database. Mining serial positioning episode rules (MSPER) using a fixed-gap episode occurrence suffers from unsatisfied scalability with complex sequences to test whether an episode occurs in a sequence. Large number of redundant nodes was generated in the MSPER-trie-based data structure. In this paper, forward and backward search algorithm (FBSA) is proposed here to detect minimal occurrences of frequent peak episodes. An extensive correlation of parameter settings and the generating procedure of fixed-gap episodes are carried out. To generate a fixed-gap episode and estimate the variance that decides the parameter selection in event sequences, Spearman's correlation coefficient is used for verifying the sequence of occurrences of the episodes. MFSPER with FBSA is developed to eliminate the frequent sequence scans and redundant event sets. The MFSPER–FBSA stores the minimal occurrences of frequent peak episodes from the event sequences. The experimental evaluation on benchmark datasets shows that the proposed technique outperforms the existing methods with respect to memory, execution time, recall and precision. [ABSTRACT FROM AUTHOR]

When generating primes |$p$| and |$q$| for an RSA key, the algorithm specifies that if |$p-1$| and |$q-1$| must be relatively prime to the public exponent |$e$|⁠. If this is not done, then the decryption exponent is not well defined. However, what if a software bug allows the generation of public parameters |$N$| and |$e$| of an RSA key with this property and then it is subsequently used for encryption? Though this may seem like a purely academic question, a software bug in a preview release of the Windows 10 operating system makes this question of more than purely theoretical. Without a well defined decryption exponent, plaintexts encrypted to such keys will be undecryptable thus potentially losing user data, a serious software defect. Though the decryption exponent is no longer well defined, it is in fact possible to recover the a small number of potential plaintexts, if the prime factors |$p$| and |$q$| of the public modulus |$N$| are known. This paper presents an analysis of what steps fail in the RSA algorithm and derives a plaintext recovery algorithm. The runtime of this algorithm is |$O(e)$| making it practical to use, and it has been implemented in python. [ABSTRACT FROM AUTHOR]

Privacy and security in the medical field are major aspects to consider in the current era. This is due to the huge necessity for data among providers, payers and patients, respectively. Recently, more researchers are making their contributions in this field under different aspects. But, there need more enhancements concerning security. In this circumstance, this paper intends to propose a new IoT-dependent health care privacy preservation model with the impact of the machine learning algorithm. Here, the information or data from IoT devices is subjected to the proposed sanitization process via generating the optimal key. In this work, the utility of the machine learning model is the greatest gateway to generating optimal keys as it is already trained with the neural network. Moreover, identifying the optimal key is the greatest crisis, which is done by a new Improved Dragonfly Algorithm algorithm. Thereby, the sanitization process works, and finally, the sanitized data are uploaded to IoT. The data restoration is the inverse process of sanitization, which gives the original data. Finally, the performance of the proposed work is validated over state-of-the-art models in terms of sanitization and restoration analysis. [ABSTRACT FROM AUTHOR]

With the rapid increase in the practical applications of secure computation protocols, increasingly more research is focused on the efficiency of the symmetric-key primitives underlying them. Whereas traditional block ciphers have evolved to be efficient with respect to certain performance metrics, secure computation protocols call for a different efficiency metric: arithmetic complexity. Arithmetic complexity is viewed through the number and layout of nonlinear operations in the circuit implemented by the protocol. Symmetric-key algorithms that are optimized for this metric are said to be algebraic ciphers. It has been shown that recently proposed algebraic ciphers are greatly efficient in ZK and MPC protocols. However, there has not been many algebraic ciphers proposed targeting Fully Homomorphic Encryption (FHE). In this paper, we evaluate the behavior of Vision when implemented as a circuit in an FHE protocol. To this end, we present a state-of-the-art comparison of AES and Vision implemented using HElib. Counterintuitively, Vision does not deliver a better performance than AES in this setting. Then, by attempting to improve a bottleneck of the FHE implementation evaluating Vision we present a new cipher: Seljuk. Despite the improvement with respect to Vision , Seljuk does not deliver the expected performance. [ABSTRACT FROM AUTHOR]

Horizontal and vertical scalability have been widely studied in the context of computational resources. However, with the exponential growth in the number of connected objects, functional scalability (in terms of the size of software systems) is rapidly becoming a central challenge for building efficient service-oriented Internet of Things (IoT) systems that generate huge volumes of data continuously. As systems scale up, a centralized approach for moving data between services becomes infeasible because it leads to a single performance bottleneck. A distributed approach avoids such a bottleneck, but it incurs additional network traffic as data streams pass through multiple mediators. Decentralized data exchange is the only solution for realizing totally efficient IoT systems, since it avoids a single performance bottleneck and dramatically minimizes network traffic. In this paper, we present a functionally scalable approach that separates data and control for the realization of decentralized data flows in service-oriented IoT systems. Our approach is evaluated empirically, and the results show that it scales well with the size of IoT systems by substantially reducing both the number of data flows and network traffic in comparison with distributed data flows. [ABSTRACT FROM AUTHOR]

The Pilsung cipher is part of the North Korean Red Star operating system, which was leaked to the West in 2014. Initial analysis by Kryptos Logic reported a possibility of a class of weak keys due to the use of pseudo-random diffusion. Following this lead, we analyzed the cipher and identified a small class of such weak keys. We developed techniques for searching for a key that belongs to the class. After spending thousands of CPU hours, we found a supposedly weak key for a slightly weaker version of Pilsung, but the key did not behave as we expected. On further investigation we found out a crucial misunderstanding in a critical part of the cipher and that no such class of weak keys exists in Pilsung. Thus, this paper makes two main contributions to the art of cryptanalysis. First, it identifies and shows how to investigate a potential weakness in randomizing diffusion, which although does not exist in Pilsung, may affect future designs. Second, it highlights the need for early verification of results in order to identify errors before expending significant resources. [ABSTRACT FROM AUTHOR]

Neural-aided cryptanalysis is a challenging topic, in which the neural distinguisher (⁠|$\mathcal{ND}$|⁠) is a core module. In this paper, we propose a new |$\mathcal{ND}$| considering multiple ciphertext pairs simultaneously. Besides, multiple ciphertext pairs are constructed from different keys. The motivation is that the distinguishing accuracy can be improved by exploiting features derived from multiple ciphertext pairs. To verify this motivation, we have applied this new |$\mathcal{ND}$| to five different ciphers. Experiments show that taking multiple ciphertext pairs as input indeed brings accuracy improvement. Then, we prove that our new |$\mathcal{ND}$| applies to two different neural-aided key recovery attacks. Moreover, the accuracy improvement is helpful for reducing the data complexity of the neural-aided statistic attack. The code is available at https://github.com/AI-Lab-Y/ND_mc. [ABSTRACT FROM AUTHOR]

The fast correlation attack (FCA) is one of the most important cryptanalytic techniques against LFSR-based stream ciphers. In CRYPTO 2018, Todo et al. found a new property for the FCA and proposed a novel algorithm which was successfully applied to the Grain family of stream ciphers. Nevertheless, these techniques cannot be directly applied to Grain-like small state stream ciphers with keyed update, such as Plantlet, Fruit-v2 and Fruit80. In this paper, we study the security of Grain-like small state stream ciphers by the FCA. We first observe that the number of required parity-check equations can be reduced when there are multiple different parity-check equations. With exploiting the Skellam distribution, we introduce a sufficient condition to identify the correct LFSR initial state and derive a new relationship between the number and bias of the required parity-check equations. Then, a modified algorithm is presented based on this new relationship, which can recover the LFSR initial state no matter what the round key bits are. Under the condition that the LFSR initial state is known, an algorithm is given against the degraded system and to recover the NFSR state at some time instant, along with the round key bits. As cases study, we apply our cryptanalytic techniques to Plantlet, Fruit-v2 and Fruit-80. As a result, for Plantlet, our attack takes |$ 2^{73.75} $| time complexity and |$ 2^{73.06} $| keystream bits to recover the full 80-bit key. Regarding Fruit-v2, |$ 2^{55.34} $| time complexity and |$ 2^{55.62} $| keystream bits are needed to determine the secret key. As for Fruit-80, |$2^{64.47}$| time complexity and |$2^{62.82}$| keystream bits are required to recover the secret key. More flexible attacks can be obtained with lower data complexity at the cost of increasing the attack time. Especially, for Fruit-v2, a key recovery attack can be launched with data complexity of |$2^{42.38}$| and time complexity of |$2^{73.31}$|⁠. Moreover, we have implemented our attack methods on a toy version of Fruit-v2. The attack matches the expected complexities predicted by our theoretical analysis quite well, which proves the validity of our cryptanalytic techniques. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic has been a globally dangerous crisis that causes an increasingly high death rate. Applying machine learning to the computed-tomography (CT)-based COVID-19 diagnosis is essential and attracts the attention of the research community. This paper introduces an approach for simultaneously identifying COVID-19 disease and segmenting its manifestations on lung images. The proposed method is an asymmetric U-Net-like model improved with skip connections. The experiment was conducted on a light-weighted feature extractor called CRNet with a feature enhancement technique called atrous spatial pyramid pooling. Classifying between COVID-19 and non-COVID-19 cases recorded the highest mean scores of 97.1, 94.4, and 97.0% for accuracy, dice similarity coefficient (DSC) and F1 score, respectively. Alternatively, the respective highest mean scores of the classification between COVID-19 and community-acquired pneumonia were 99.89, 99.79, and 99.97%. The lesion segmentation performance was with the highest mean of 99.6 and 84.7% for, respectively, accuracy and DSC. [ABSTRACT FROM AUTHOR]

With the development of heterogeneous systems, the demand for high-level programming methods that ease heterogeneous programming and produce portable applications has become more urgent. This paper proposes DACL, the data associated computing language. DACL introduces data partition patterns to achieve architecture-independent parallelism expression. Meanwhile, DACL provides simplified language extensions, as well as programming features such as serialization of the computing process, parameterization of data attributes and modularity, thus reducing the difficulty of heterogeneous programming and improving programming productivity. The operational semantics show that DACL enables different levels of parallelism degree calculation and retains data access patterns, reserving optimization potential. To support cross-platform execution, the currently implemented source-to-source compilers employ OpenMP and OpenCL as the backend. We reconstructed multiple benchmarks selected from the Parboil and Rodinia benchmark suits with DACL and conducted a comparison test on CPU, GPU and MIC platforms. The code size of each rebuilt benchmark is roughly equivalent to that of the serial code, which is only 13%–64% of the benchmark OpenCL code. With the support of the compilation system, the reconstructed code can execute on different processors without modification, yielding a competitive or better performance to that of the manually written benchmark code. [ABSTRACT FROM AUTHOR]

By introducing the BHT algorithm into the slide attack on 1K-AES and the related-key attack on PRINCE, we present the corresponding quantum attacks in this paper. In the proposed quantum attacks, we generalize the BHT algorithm to the situation where the number of marked items is unknown ahead of time. Moreover, we give an implementation scheme of classifier oracle based on Quantum Phase Estimation algorithm in presented quantum attacks. The complexity analysis shows that the query complexity, time complexity and memory complexity of the presented quantum attacks are all |$\mathcal{O}(2^{n/3})$| when the success probability is about |$63\%$|⁠ , where |$n$| is the block size. Compared with the corresponding classical attacks, the proposed quantum attacks can achieve subquadratic speed-up under the same success probability no matter on query complexity, time complexity or memory complexity. Furthermore, the query complexity of the proposed quantum slide attack on 1K-AES is less than Grover search on 1K-AES by a factor of |$2^{n/6}.$| When compared with the Grover search on PRINCE, the query complexity of the presented quantum attack on PRINCE is reduced from |$\mathcal{O}(2^{n})$| to |$\mathcal{O}(2^{n/2}).$| When compared with the combination of Grover and Simon's algorithms on PRINCE, the query complexity of our quantum attack on PRINCE is reduced from |$\mathcal{O}(n\cdot 2^{n/2})$| to |$\mathcal{O}(2^{n/2}).$| Besides, the proposed quantum slide attack on 1K-AES indicates that the quantum slide attack could also be applied on Substitution-Permutation Network construction, apart from the iterated Even-Mansour cipher and Feistel constructions. [ABSTRACT FROM AUTHOR]