Concatenation Operation Research Articles

A Novel High-Efficiency Password Authentication and Key Agreement Protocol for Mobile Client-Server

With the development of wireless technology, people increasingly rely on mobile devices. Since most mobile devices transmit sensitive information via insecure public channels, it is important to design multiauthentication key agreement protocols for security protection. Traditional scholars tend to use traditional public-key cryptosystems (PKCs) in their protocols to improve security. High-cost operations (e.g., elliptic curve point multiplication and bilinear pairing) were widely used in their scheme but were not suitable for mobile devices because of limited computing resources. In this study, we designed a novel high-efficiency multiauthentication and key agreement protocol and demonstrate its security in the random oracle model. Compared with other protocols, our proposed scheme only uses string concatenation operations, one-way hash functions, and XOR operations. In addition, our protocol requires much fewer computing resources to achieve the same level of security.

A novel algorithm for small object detection based on YOLOv4.

Small object detection is one of the difficulties in the development of computer vision, especially in the case of complex image backgrounds, and the accuracy of small object detection still needs to be improved. In this article, we present a small object detection network based on YOLOv4, which solves some obstacles that hinder the performance of traditional methods in small object detection tasks in complex road environments, such as few effective features, the influence of image noise, and occlusion by large objects, and improves the detection of small objects in complex background situations such as drone aerial survey images. The improved network architecture reduces the computation and GPU memory consumption of the network by including the cross-stage partial network (CSPNet) structure into the spatial pyramid pool (SPP) structure in the YOLOv4 network and convolutional layers after concatenation operation. Secondly, the accuracy of the model on the small object detection task is improved by adding a more suitable small object detection head and removing one used for large object detection. Then, a new branch is added to extract feature information at a shallow location in the backbone part, and the feature information extracted from this branch is fused in the neck part to enrich the small object location information extracted by the model; when fusing feature information from different levels in the backbone, the fusion weight of useful information is increased by adding a weighting mechanism to improve detection performance at each scale. Finally, a coordinated attention (CA) module is embedded at a suitable location in the neck part, which enables the model to focus on spatial location relationships and inter-channel relationships and enhances feature representation capability. The proposed model has been tested to detect 10 different target objects in aerial images from drones and five different road traffic signal signs in images taken from vehicles in a complex road environment. The detection speed of the model meets the criteria of real-time detection, the model has better performance in terms of accuracy compared to the existing state-of-the-art detection models, and the model has only 44M parameters. On the drone aerial photography dataset, the average accuracy of YOLOv4 and YOLOv5L is 42.79% and 42.10%, respectively, while our model achieves an average accuracy (mAP) of 52.76%; on the urban road traffic light dataset, the proposed model achieves an average accuracy of 96.98%, which is also better than YOLOv4 (95.32%), YOLOv5L (94.79%) and other advanced models. The current work provides an efficient method for small object detection in complex road environments, which can be extended to scenarios involving small object detection, such as drone cruising and autonomous driving.

A few-shot semantic segmentation method based on adaptively mining correlation network

AbstractThe goal of few-shot semantic segmentation is to learn a segmentation model that can segment novel classes in queries when only a few annotated support examples are available. Due to large intra-class variations, the building of accurate semantic correlation remains a challenging job. Current methods typically use 4D kernels to learn the semantic correlation of feature maps. However, they still face the challenge of reducing the consumption of computation and memory while keeping the availability of correlations mined by their methods. In this paper, we propose the adaptively mining correlation network (AMCNet) to alleviate the aforementioned issues. The key points of AMCNet are the proposed adaptive separable 4D kernel and the learnable pyramid correlation module, which form the basic block for correlation encoder and provide a learnable concatenation operation over pyramid correlation tensors, respectively. Experiments on the PASCAL VOC 2012 dataset show that our AMCNet surpasses the state-of-the-art method by $0.7\%$ and $2.2\%$ on 1-shot and 5-shot segmentation scenarios, respectively.

MFCNet: A multi-modal fusion and calibration networks for 3D pancreas tumor segmentation on PET-CT images

In clinical diagnosis, positron emission tomography and computed tomography (PET-CT) images containing complementary information are fused. Tumor segmentation based on multi-modal PET-CT images is an important part of clinical diagnosis and treatment. However, the existing current PET-CT tumor segmentation methods mainly focus on positron emission tomography (PET) and computed tomography (CT) feature fusion, which weakens the specificity of the modality. In addition, the information interaction between different modal images is usually completed by simple addition or concatenation operations, but this has the disadvantage of introducing irrelevant information during the multi-modal semantic feature fusion, so effective features cannot be highlighted. To overcome this problem, this paper propose a novel Multi-modal Fusion and Calibration Networks (MFCNet) for tumor segmentation based on three-dimensional PET-CT images. First, a Multi-modal Fusion Down-sampling Block (MFDB) with a residual structure is developed. The proposed MFDB can fuse complementary features of multi-modal images while retaining the unique features of different modal images. Second, a Multi-modal Mutual Calibration Block (MMCB) based on the inception structure is designed. The MMCB can guide the network to focus on a tumor region by combining different branch decoding features using the attention mechanism and extracting multi-scale pathological features using a convolution kernel of different sizes. The proposed MFCNet is verified on both the public dataset (Head and Neck cancer) and the in-house dataset (pancreas cancer). The experimental results indicate that on the public and in-house datasets, the average Dice values of the proposed multi-modal segmentation network are 74.14% and 76.20%, while the average Hausdorff distances are 6.41 and 6.84, respectively. In addition, the experimental results show that the proposed MFCNet outperforms the state-of-the-art methods on the two datasets.

Monocular Depth Estimation Network Based on Swin Transformer

Estimating depth from a single image is challenging because a single 2D image may correspond to many different 3D scenes with the same depth. While most deep learning based depth prediction methods extract depth features using small convolutional kernels with small receptive fields, which results in deformed depth edges and inaccurate depth values of distant objects in the depth estimation results. Aiming at this problem, we propose a depth estimation network based on Swin Transformer and the encoder-decoder structure. We construct the encoder using the Swin Transformer network, which can encode long-range spatial dependency and extract features on various scales and across different channels. The decoder of the proposed network is in charge of fusing the features from the encoder by the operations of interpolation, concatenation, and convolution. Experiments on KITTI and NYUv2 datasets show that our proposed network can get more accurate depth edges and depth values than the state-of-the-art methods.

Towards a Provably Secure Authentication Protocol for Fog-Driven IoT-Based Systems

The emergence of fog-based Internet of Things (IoT) systems have played a significant role in enhancing the applicability of the IoT paradigm. In such systems, fog-nodes are proficient enough to retain, process and transmit the data coming from IoT devices. Nevertheless, as an extension of cloud computing, inheriting the security and privacy concerns of cloud computing is also inevitable in fog-based IoT systems. To deal with such challenges, a diverse range of security solutions are reported in the literature. However, most of them have several limitations (i.e., vulnerability to known security attacks and high computation overhead) that curtail their practical implementation applicability. Keeping these limitations in mind, this paper propose a privacy-preserving hash-based authenticated key agreement protocol using XOR and concatenation operations for fog-driven IoT systems. Using healthcare as a case study, the security of the novel protocol is evaluated by using informal and formal security analysis. In order to obtain the experimental results, the key cryptographic operations used at the user, fog node and cloud server-side are implemented on a mobile device, Arduino and cloud server, respectively. Findings from the performance evaluation results show that the proposed protocol has the least computation cost compared to several related competing protocols.

Reduced volume of diabetic pancreatic islets in rodents detected by synchrotron X-ray phase-contrast microtomography and deep learning network

The pancreatic islet is a highly structured micro-organ that produces insulin in response to rising blood glucose. Here we develop a label-free and automatic imaging approach to visualize the islets in situ in diabetic rodents by the synchrotron radiation X-ray phase-contrast microtomography (SRμCT) at the ID17 station of the European Synchrotron Radiation Facility. The large-size images (3.2mm×15.97mm) were acquired in the pancreas in STZ-treated mice and diabetic GK rats. Each pancreas was dissected by 3000 reconstructed images. The image datasets were further analysed by a self-developed deep learning method, AA-Net. All islets in the pancreas were segmented and visualized by the three-dimension (3D) reconstruction. After quantifying the volumes of the islets, we found that the number of larger islets (=>1500 μm3) was reduced by 2-fold (wt 1004±94 vs GK 419±122, P<0.001) in chronically developed diabetic GK rat, while in STZ-treated diabetic mouse the large islets were decreased by half (189±33 vs 90±29, P<0.001) compared to the untreated mice. Our study provides a label-free tool for detecting and quantifying pancreatic islets in situ. It implies the possibility of monitoring the state of pancreatic islets in vivo diabetes without labelling.

Lightweight Pairwise Key Distribution Scheme for IoTs

Embedding a pairwise key distribution approach in IoT systems is challenging as IoT devices have limited resources, such as memory, processing power, and battery life. This paper presents a secure and lightweight approach that is applied to IoT devices that are divided into Voronoi clusters. This proposed algorithm comprises XOR and concatenation operations for interactive authentication between the server and the IoT devices. Predominantly, the authentication is carried out by the server. It is observed that the algorithm is resilient against man-in-the-middle attacks, forward secrecy, Denial of Service (DoS) attacks, and offers mutual authentication. It is also observed that the given scheme has low communication and computing overheads compared to some existing methods.

Input-Output-Improved Reservoir Computing Based on Duffing Resonator Processing Dynamic Temperature Compensation for MEMS Resonant Accelerometer.

An MEMS resonant accelerometer is a temperature-sensitive device because temperature change affects the intrinsic resonant frequency of the inner silicon beam. Most classic temperature compensation methods, such as algorithm modeling and structure design, have large errors under rapid temperature changing due to the hysteresis of the temperature response of the accelerometer. To address this issue, we propose a novel reservoir computing (RC) structure based on a nonlinear silicon resonator, which is specifically improved for predicting dynamic information that is referred to as the input-output-improved reservoir computing (IOI-RC) algorithm. It combines the polynomial fitting with the RC on the input data mapping ensuring that the system always resides in the rich nonlinear state. Meanwhile, the output layer is also optimized by vector concatenation operation for higher memory capacity. Therefore, the new system has better performance in dynamic temperature compensation. In addition, the method is real-time, with easy hardware implementation that can be integrated with MEMS sensors. The experiment's result showed a 93% improvement in IOI-RC compared to raw data in a temperature range of -20-60 °C. The study confirmed the feasibility of RC in realizing dynamic temperature compensation precisely, which provides a potential real-time online temperature compensation method and a sensor system with edge computing.

Lightweight and efficient privacy‐preserving mutual authentication scheme to secure Internet of Things‐based smart healthcare

AbstractIn recent years, Internet of Things (IoT) technology has been adopted in numerous application areas, such as healthcare, agriculture, industrial automation, and many more. The use of IoT and other technologies like cloud computing and machine learning has made the modern healthcare system to be smart, automated, and efficient. However, the continuous proliferation of cyber‐attacks on IoT devices has increased IoT challenges like data security, privacy protection, authentication, and so forth. In smart healthcare systems, due to the lack of authentication protocols, attackers can undermine the availability, confidentiality, and integrity of both smart healthcare devices and data, which can be life‐threatening in some situations. In this article, a privacy‐preserving mutual authentication scheme for IoT‐enabled healthcare systems is proposed to achieve lightweight and effective authentication of network devices. To support the processing capabilities of the IoT devices, this proposed authentication scheme is designed using lightweight cryptographic primitives, namely XOR, concatenation, and hash operation. The proposed scheme can establish a secure session between an authorized device and a gateway, and prevent unauthorized devices from getting access to healthcare systems. The security analysis and performance analysis assess the proposed authentication technique's effectiveness over existing well‐known schemes.

PCDA-HNMP: Predicting circRNA-disease association using heterogeneous network and meta-path.

Increasing amounts of experimental studies have shown that circular RNAs (circRNAs) play important regulatory roles in human diseases through interactions with related microRNAs (miRNAs). CircRNAs have become new potential disease biomarkers and therapeutic targets. Predicting circRNA-disease association (CDA) is of great significance for exploring the pathogenesis of complex diseases, which can improve the diagnosis level of diseases and promote the targeted therapy of diseases. However, determination of CDAs through traditional clinical trials is usually time-consuming and expensive. Computational methods are now alternative ways to predict CDAs. In this study, a new computational method, named PCDA-HNMP, was designed. For obtaining informative features of circRNAs and diseases, a heterogeneous network was first constructed, which defined circRNAs, mRNAs, miRNAs and diseases as nodes and associations between them as edges. Then, a deep analysis was conducted on the heterogeneous network by extracting meta-paths connecting to circRNAs (diseases), thereby mining hidden associations between various circRNAs (diseases). These associations constituted the meta-path-induced networks for circRNAs and diseases. The features of circRNAs and diseases were derived from the aforementioned networks via mashup. On the other hand, miRNA-disease associations (mDAs) were employed to improve the model's performance. miRNA features were yielded from the meta-path-induced networks on miRNAs and circRNAs, which were constructed from the meta-paths connecting miRNAs and circRNAs in the heterogeneous network. A concatenation operation was adopted to build the features of CDAs and mDAs. Such representations of CDAs and mDAs were fed into XGBoost to set up the model. The five-fold cross-validation yielded an area under the curve (AUC) of 0.9846, which was better than those of some existing state-of-the-art methods. The employment of mDAs can really enhance the model's performance and the importance analysis on meta-path-induced networks shown that networks produced by the meta-paths containing validated CDAs provided the most important contributions.

Efficient Audiovisual Fusion for Active Speaker Detection

Active speaker detection (ASD) refers to detecting the speaking person among visible human instances in a video. Existing methods widely employed a similar audiovisual fusion approach, the concatenation. Although such a fusion approach is often argued to help enhance performance, it must be noted that neither feature modalities play an equal role. It forces the backend network to focus on learning intramodal rather than intermodal features. Another concern is that since the concatenation doubles the fused feature dimension that feeds from the audio and video module, it creates a higher computational overhead for the backend network. To address these problems, this work hypothesizes that instead of leveraging deterministic fusion operation, employing an efficient fusion technique may assist the network in learning efficiently and improve detection accuracy. This work proposes an efficient audiovisual fusion (AVF) with fewer feature dimensions that captures the correlations between facial regions and sound signals, focusing more on the discriminative facial features and associating them with the corresponding audio features. Furthermore, previous ASD works focus only on improving ASD performance by creating a large computational overhead using complex techniques such as adding sophisticated postprocessing, applying smoothing techniques on the classifier to refine the network outputs at multiple stages, or assembling the multiple network outputs. This work proposed a simple yet effective end-to-end ASD using the newly proposed feature fusion approach, the AVF. The proposed framework attained a mAP of 84.384% on the validation set of the most challenging audiovisual speaker detection benchmark, the AVA-ActiveSpeaker. With this, this work outperformed previous works that did not apply the postprocessing tasks and attained competitive detection accuracy compared to other works that employed different postprocessing tasks. The proposed model also learns better on the unsynchronized raw AVA-ActiveSpeaker dataset. The ablation experiments under different image scale settings and noisy signals show the AFV’s effectiveness and robustness than the concatenation operation.

Few-Shot Speaker Identification Using Lightweight Prototypical Network With Feature Grouping and Interaction

Existing methods for few-shot speaker identification (FSSI) obtain high accuracy, but their computational complexities and model sizes need to be reduced for lightweight applications. In this work, we propose a FSSI method using a lightweight prototypical network with the final goal to implement the FSSI on intelligent terminals with limited resources, such as smart watches and smart speakers. In the proposed prototypical network, an embedding module is designed to perform feature grouping for reducing the memory requirement and computational complexity, and feature interaction for enhancing the representational ability of the learned speaker embedding. In the proposed embedding module, audio feature of each speech sample is split into several low-dimensional feature subsets that are transformed by a recurrent convolutional block in parallel. Then, the operations of averaging, addition, concatenation, element-wise summation and statistics pooling are sequentially executed to learn a speaker embedding for each speech sample. The recurrent convolutional block consists of a block of bidirectional long short-term memory, and a block of de-redundancy convolution in which feature grouping and interaction are conducted too. Our method is compared to baseline methods on three datasets that are selected from three public speech corpora (VoxCeleb1, VoxCeleb2, and LibriSpeech). The results show that our method obtains higher accuracy under several conditions, and has advantages over all baseline methods in computational complexity and model size.

The effective skyline quantify-utility patterns mining algorithm with pruning strategies

Frequent itemset mining and high-utility itemset mining have been widely applied to the extraction of useful information from databases. However, with the proliferation of the Internet of Things, smart devices are generating vast amounts of data daily, and studies focusing on individual dimensions are increasingly unable to support decision-making. Hence, the concept of a skyline query considering frequency and utility (which returns a set of points that are not dominated by other points) was introduced. However, in most cases, firms are concerned about not only the frequency of purchases but also quantities. The skyline quantity-utility pattern (SQUP) considers both the quantity and utility of items. This paper proposes two algorithms, FSKYQUP-Miner and FSKYQUP, to efficiently mine SQUPs. The algorithms are based on the utility-quantity list structure and include an effective pruning strategy which calculates the minimum utility of SQUPs after one scan of the database and prunes undesired items in advance, which greatly reduces the number of concatenation operations. Furthermore, this paper proposes an array structure superior to utilmax for storing the maximum utility of quantities, which further improves the efficiency of pruning. Extensive comparison experiments on different datasets show that the proposed algorithms find all SQUPs accurately and efficiently.

Timescale standard to discriminate between hyperbolic and exponential discounting and construction of a nonadditive discounting model

Under the presupposition that human time perception is distorted in intertemporal choice, this study constructs a time scale in the framework of axiomatic measurement. First, the conditions (homogeneity of degree one or two) to identify the form of a time scale are proposed so that one can determine whether the hyperbolic or exponential is a more suitable function for modeling people’s discounting. Homogeneity of degree one implies that subjective time delay is measured by a power scale and its discount function becomes a power-exponential type, whereas homogeneity of degree two implies that subjective time delay is measured by a log scale and its discount function becomes a hyperbolic type. Second, a method is shown for constructing a model that can reflect subadditive and superadditive discounting. A non-commutative and non-associative concatenation operation is provided to generate a time string consisting of subdivided durations that incorporates the effect of repeated delay with a subdivided duration. The discounting model is yielded by substituting these strings in an exponential model equipped with a generalized time scale, and the determination of subadditivity or superadditivity depends on whether the discount function value of a product by the non-commutative and non-associative operation is, respectively, smaller or greater than that of a product by an operation of the usual extensive structure.

Lightweight, secure, efficient, and dynamic scheme for mutual authentication of devices in Internet‐of‐Things‐Fog environment

AbstractSecurity is a major challenge in the design and implementation of Internet‐of‐Things (IoT)‐Fog networks and it can be resolved by an efficient authentication mechanism. As the devices have limited resources like battery, processor and memory, any mechanism designed for IoT‐Fog has to be resource aware. In this paper, we proposed a lightweight, secure, dynamic, and efficient scheme for mutual authentication of devices in IoT‐Fog networks. In the proposed scheme, the devices obtain a primary security value from the cloud server and this acquired primary security value is used to authenticate them mutually. We used XOR operation, concatenation operation, one‐way hash function and symmetric key cryptography to accommodate resource constraint nature of devices. The proposed scheme has been analyzed for security formally using BAN logic and automated using Automated Validation of Internet Security Protocols and Applications &amp; ProVerif tools and it is proved that the proposed scheme is secure from the known attacks. The comparative analysis of the proposed scheme with the existing schemes proved that it is better than existing schemes and its experimental analysis showed that it consumes 20%–30% less energy compared to existing schemes. Our proposed scheme is better with reference to security and efficient in respect of computation, communication, and storage overheads.

A Segmentation Process to Isolate Various Cytological Components from Follicular Histology based on Texture Heterogeneity

Abstract: This research looks at an H&amp;E-stained Follicular Lymphoma tissue sample in order to isolate different cytological components based on textural heterogeneity. The goal of this study is to determine the morphological behavior of various cytological components such as nuclei, cytoplasm, extracellular material, and red blood cells so that oncologists can make better decisions and provide appropriate treatment during the prognosis of histopathological images that are sometimes incorrectly concluded. To isolate these cytological components, a convolution process is applied to follicular histology and 8 different oriented masks produced from the gabor filter, yielding 8 separate representations for each of the 8 various orientation masks of H&amp;E-stained FL histology. The information included in the 8 representations resulting from the application of the gaussian filter is summarized in twelve images that correspond to each of the filter's orientations. Second, the operation of concatenation is carried out over these 8 separate representations into a single 3-dimensional covariance image. The projected approach outperforms grey level-based texture analysis in FL histology, according to the results.

Deep multi-scale Gaussian residual networks for contextual-aware translation initiation site recognition

The dysregulation of the translation initiation causes some cancers and metabolic disorders. However, the experimental verification of translation initiation sites (TIS) is expensive and small-scale, and the co-occurrence interaction relationship from genomic sequences is essential for knowledge discovery of TIS. In this work, a deep Gaussian residual neural computational model (GNet) is proposed to learn dynamic embeddings for parameter learning of discriminative features via context-aware modeling, and accurately identify TIS via co-occurrence embedding. GNet includes multi-scale Gaussian gated convolutional networks and bidirectional gated recurrent units. Particularly, a Gaussian gated linear unit is devised to extract local co-occurrence embedding vectors of genomic sequences, and the unit can reduce vanishing gradient problems and enable the recognition model to obtain powerful learning capabilities. Moreover, a stochastic linear skip gated connection is designed to boost the information exchange and extract complex contextual features between low and high layers, and vanishing gradients can be largely alleviated during training. Then, the gated recurrent unit is used to extract global long-term dependency features via identity connections. Consequently, to obtain global embedding information of sequences, a concatenation operation is used to fuse local and long discriminative features. Experiments demonstrate that GNet is an efficient and effective TIS recognition model and achieves remarkable results over state-of-the-art methods.

Context-Based Contrastive Learning for Scene Text Recognition

Pursuing accurate and robust recognizers has been a long-lasting goal for scene text recognition (STR) researchers. Recently, attention-based methods have demonstrated their effectiveness and achieved impressive results on public benchmarks. The attention mechanism enables models to recognize scene text with severe visual distortions by leveraging contextual information. However, recent studies revealed that the implicit over-reliance of context leads to catastrophic out-of-vocabulary performance. On the contrary to the superior accuracy of the seen text, models are prone to misrecognize unseen text even with good image quality. We propose a novel framework, Context-based contrastive learning (ConCLR), to alleviate this issue. Our proposed method first generates characters with different contexts via simple image concatenation operations and then optimizes contrastive loss on their embeddings. By pulling together clusters of identical characters within various contexts and pushing apart clusters of different characters in embedding space, ConCLR suppresses the side-effect of overfitting to specific contexts and learns a more robust representation. Experiments show that ConCLR significantly improves out-of-vocabulary generalization and achieves state-of-the-art performance on public benchmarks together with attention-based recognizers.

GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

Most convolutional neural network (CNN) models have various difficulties in identifying crop diseases owing to morphological and physiological changes in crop tissues, and cells. Furthermore, a single crop disease can show different symptoms. Usually, the differences in symptoms between early crop disease and late crop disease stages include the area of disease and color of disease. This also poses additional difficulties for CNN models. Here, we propose a lightweight CNN model called GrapeNet for the identification of different symptom stages for specific grape diseases. The main components of GrapeNet are residual blocks, residual feature fusion blocks (RFFBs), and convolution block attention modules. The residual blocks are used to deepen the network depth and extract rich features. To alleviate the CNN performance degradation associated with a large number of hidden layers, we designed an RFFB module based on the residual block. It fuses the average pooled feature map before the residual block input and the high-dimensional feature maps after the residual block output by a concatenation operation, thereby achieving feature fusion at different depths. In addition, the convolutional block attention module (CBAM) is introduced after each RFFB module to extract valid disease information. The obtained results show that the identification accuracy was determined as 82.99%, 84.01%, 82.74%, 84.77%, 80.96%, 82.74%, 80.96%, 83.76%, and 86.29% for GoogLeNet, Vgg16, ResNet34, DenseNet121, MobileNetV2, MobileNetV3_large, ShuffleNetV2_×1.0, EfficientNetV2_s, and GrapeNet. The GrapeNet model achieved the best classification performance when compared with other classical models. The total number of parameters of the GrapeNet model only included 2.15 million. Compared with DenseNet121, which has the highest accuracy among classical network models, the number of parameters of GrapeNet was reduced by 4.81 million, thereby reducing the training time of GrapeNet by about two times compared with that of DenseNet121. Moreover, the visualization results of Grad-cam indicate that the introduction of CBAM can emphasize disease information and suppress irrelevant information. The overall results suggest that the GrapeNet model is useful for the automatic identification of grape leaf diseases.