Software refactoring is an important process in software development. During software refactoring, code smell is a popular research topic that refers to design or implementation flaws in the software. Large class is one of the most concerning code smells in software refactoring. Detecting and refactoring such problem has a profound impact on software quality. In past years, software metrics and clustering techniques have commonly been used for the large class detection. However, deep-learning-based approaches have also received considerable attention in recent studies. In this study, we apply graph neural networks (GNNs), an important division of deep learning, to address the problem of large class detection. First, to support the extensive data requirements of the deep learning task, we apply a semiautomatic approach to generate a substantial number of data samples. Next, we design a new type of directed heterogeneous graph (DHG) as an input graph using the methods similarity matrix and software metrics. We construct an input graph for each class sample and make the graph classification with GNNs to identify the smelly classes. In our experiments, we apply three typical GNN model architectures for large class detection and compare the results with those of previous studies. The results show that the proposed approach can achieve more accurate and stable detection performance.

Minutiae pattern extraction plays a crucial role in fingerprint registration and identification for electronic applications. However, the extraction accuracy is seriously compromised by the presence of contaminated ridge lines and complex background scenarios. General image processing-based methods, which rely on many prior hypotheses, fail to effectively handle minutiae extraction in complex scenarios. Previous works have shown that CNN-based methods can perform well in object detection tasks. However, the deep neural networks (DNNs)-based methods are restricted by the limitation of public labeled datasets due to legitimate privacy concerns. To address these challenges comprehensively, this paper presents a fully automated minutiae extraction method leveraging DNNs. Firstly, we create a fingerprint minutiae dataset using a semi-automated minutiae annotation algorithm. Subsequently, we propose a minutiae extraction model based on Residual Networks (Resnet) that enables end-to-end prediction of minutiae. Moreover, we introduce a novel non-maximal suppression (NMS) procedure, guided by the Generalized Intersection over Union (GIoU) metric, during the inference phase to effectively handle outliers. Experimental evaluations conducted on the NIST SD4 and FVC 2004 databases demonstrate the superiority of the proposed method over existing state-of-the-art minutiae extraction approaches.

Latin squares are a classical and well-studied topic of discrete mathematics, and recently Takeuti and Adachi (IACR ePrint, 2023) proposed (2, n)-threshold secret sharing based on mutually orthogonal Latin squares (MOLS). Hence efficient constructions of as large sets of MOLS as possible are also important from practical viewpoints. In this letter, we determine the maximum number of MOLS among a known class of Latin squares defined by weighted sums. We also mention some known property of Latin squares interpreted via the relation to secret sharing and a connection of Takeuti-Adachi’s scheme to Shamir’s secret sharing scheme.

Melanin, which is responsible for the appearance of spots and freckles, is an important indicator in evaluating skin condition. To assess the efficacy of cosmetics, skin condition scoring is performed by analyzing the distribution and amount of melanin from microscopic images of the stratum corneum cells. However, the current practice of diagnosing skin condition using stratum corneum cells images relies heavily on visual evaluation by experts. The goal of this study is to develop a quantitative evaluation system for skin condition based on melanin within unstained stratum corneum cells images. The proposed system utilizes principal component regression to perform five-level scoring, which is then compared with visual evaluation scores to assess the system’s usefulness. Additionally, we evaluated the impact of indicators related to melanin obtained from images on the scores, and verified which indicators are effective for evaluation. In conclusion, we confirmed that scoring is possible with an accuracy of more than 60% on a combination of several indicators, which is comparable to the accuracy of visual assessment.

This paper proposes a scatterer information estimation method using numerical data for the response waveform of a backward transient scattering field for both E- and H-polarizations when a two-dimensional (2-D) coated metal cylinder is selected as a scatterer. It is assumed that a line source and an observation point are placed at different locations. The four types of scatterer information covered in this paper are the relative permittivity of a surrounding medium, the relative permittivity of a coating medium layer and its thickness, and the radius of a coated metal cylinder. Specifically, a time-domain saddle-point technique (TD-SPT) is used to derive scatterer information estimation formulae from the amplitude intensity ratios (AIRs) of adjacent backward transient scattering field components. The estimates are obtained by substituting the numerical data of the response waveforms of the backward transient scattering field components into the estimation formulae and performing iterative calculations. Furthermore, a minimum thickness of a coating medium layer for which the estimation method is valid is derived, and two kinds of applicable conditions for the estimation method are proposed. The effectiveness of the scatterer information estimation method is verified by comparing the estimates with the set values. The noise tolerance and convergence characteristics of the estimation method and the method of controlling the estimation accuracy are also discussed.

In face super-resolution reconstruction, the interference caused by the texture and color of the hair region on the details and contours of the face region can negatively affect the reconstruction results. This paper proposes a semantic-based, dual-branch face super-resolution algorithm to address the issue of varying reconstruction complexities and mutual interference among different pixel semantics in face images. The algorithm clusters pixel semantic data to create a hierarchical representation, distinguishing between facial pixel regions and hair pixel regions. Subsequently, independent image enhancement is applied to these distinct pixel regions to mitigate their interference, resulting in a vivid, super-resolution face image.

Mutually unbiased bases (MUBs) are widely used in quantum information processing and play an important role in quantum cryptography, quantum state tomography and communications. It’s difficult to construct MUBs and remains unknown whether complete MUBs exist for any non prime power. Therefore, researchers have proposed the solution to construct approximately mutually unbiased bases (AMUBs) by weakening the inner product conditions. This paper constructs q AMUBs of ℂq, (q + 1) AMUBs of ℂq-1 and q AMUBs of ℂq-1 by using character sums over Galois rings and finite fields, where q is a power of a prime. The first construction of q AMUBs of ℂq is new which illustrates K AMUBs of ℂK can be achieved. The second and third constructions in this paper include the partial results about AMUBs constructed by W. Wang et al. in [9].

To improve the control performance of the permanent magnet synchronous motor speed control system, the fractional order calculus theory is combined with the sliding mode control to design the fractional order integral sliding mode sliding mode surface (FOISM) to improve the robustness of the system. Secondly, considering the existence of chattering phenomenon in sliding mode control, a new second-order sliding mode reaching law (NSOSMRL) is designed to improve the control accuracy of the system. Finally, the effectiveness of the proposed strategy is demonstrated by simulation.

The method of extracting impedance parameters of surface mounted (SMD) electronic components by test is suitable for components with unknown model or material information, but requires consideration of errors caused by non-coaxial and measurement fixtures. In this paper, a fixture for impedance measurement is designed according to the characteristics of passive devices, and the fixture de-embedding method is used to eliminate errors and improve the test accuracy. The method of obtaining S parameters of fixture based on full wave simulation proposed in this paper can provide a thought for obtaining S parameters in de-embedding. Taking a certain patch capacitor as an example, the S parameters for de-embedding were obtained using methods based on full wave simulation, 2×Thru, and ADS simulation, and de-embedding tests were conducted. The results indicate that obtaining the S parameter of the testing fixture based on full wave simulation and conducting de-embedding testing compared to ADS simulation can accurately extract the impedance parameters of SMD electronic components, which provides a reference for the study of electromagnetic interference (EMI) coupling mechanism.

Secret sharing is a cryptographic scheme to encode a secret to multiple shares being distributed to participants, so that only qualified sets of participants can restore the original secret from their shares. When we encode a secret by a secret sharing scheme and distribute shares, sometimes not all participants are accessible, and it is desirable to distribute shares to those participants before a secret information is determined. Secret sharing schemes for classical secrets have been known to be able to distribute some shares before a given secret. Lie et al. found a ((2, 3))-threshold secret sharing for quantum secrets can distribute some shares before a given secret. However, it is unknown whether distributing some shares before a given secret is possible with other access structures of secret sharing for quantum secrets. We propose a quantum secret sharing scheme for quantum secrets that can distribute some shares before a given secret with other access structures.

Permutation polynomials have been studied for a long time and have important applications in cryptography, coding theory and combinatorial designs. In this paper, by means of the multivariate method and the resultant, we propose four new classes of permutation quadrinomials over 𝔽q3, where q is a prime power. We also show that they are not quasi-multiplicative equivalent to known ones. Moreover, we compare their differential uniformity with that of some known classes of permutation trinomials for some small q.

Data resource sharing can guarantee the reliable and safe operation of distribution power grid. However, it faces the challenges of low security and high delay in the sharing process. Consortium blockchain can ensure the security and efficiency of data resource sharing, but it still faces problems such as arbitrary master node selection and high consensus delay. In this paper, we propose an improved practical Byzantine fault tolerance (PBFT) consensus algorithm based on intelligent consensus node selection to realize high-security and real-time data resource sharing for distribution power grid. Firstly, a blockchain-based data resource sharing model is constructed to realize secure data resource storage by combining the consortium blockchain and interplanetary file system (IPFS). Then, the improved PBFT consensus algorithm is proposed to optimize the consensus node selection based on the upper confidence bound of node performance. It prevents Byzantine nodes from participating in the consensus process, reduces the consensus delay, and improves the security of data resource sharing. The simulation results verify the effectiveness of the proposed algorithm.

In this study, we use a vision-based driving monitoring sensor to track drivers’ visual scanning behavior, a key factor for preventing traffic accidents. Our system evaluates driver’s behaviors by referencing the safety knowledge of professional driving instructors, and provides real-time voice-guided safety advice to encourage safer driving. Our system’s evaluation of safe driving behaviors matched the instructor’s evaluation with accuracy over 80%.

Wearing a mask correctly is an effective method to prevent respiratory infectious diseases. Correct mask use is a reliable approach for preventing contagious respiratory infections. However, when dealing with mask-wearing in some complex settings, the detection accuracy still needs to be enhanced. The technique for mask-wearing detection based on YOLOv7-Tiny is enhanced in this research. Distribution Shifting Convolutions (DSConv) based on YOLOv7-tiny are used instead of the 3×3 convolution in the original model to simplify computation and increase detection precision. To decrease the loss of coordinate regression and enhance the detection performance, we adopt the loss function Intersection over Union with Minimum Points Distance (MPDIoU) instead of Complete Intersection over Union (CIoU) in the original model. The model is introduced with the GSConv and VoVGSCSP modules, recognizing the model’s mobility. The P6 detection layer has been designed to increase detection precision for tiny targets in challenging environments and decrease missed and false positive detection rates. The robustness of the model is increased further by creating and marking a mask-wearing data set in a multi environment that uses Mixup and Mosaic technologies for data augmentation. The efficiency of the model is validated in this research using comparison and ablation experiments on the mask dataset. The results demonstrate that when compared to YOLOv7-tiny, the precision of the enhanced detection algorithm is improved by 5.4%, Recall by 1.8%, mAP@.5 by 3%, mAP@.5:.95 by 1.7%, while the FLOPs is decreased by 8.5G. Therefore, the improved detection algorithm realizes more real-time and accurate mask-wearing detection tasks.

The study proposes a personalised session-based recommender system that embeds items by using Word2Vec and sequentially updates the session and user embeddings with the hierarchicalization and aggregation of item embeddings. To process a recommendation request, the system constructs a real-time user embedding that considers users’ general preferences and sequential behaviour to handle short-term changes in user preferences with a low computational cost. The system performance was experimentally evaluated in terms of the accuracy, diversity, and novelty of the ranking of recommended items and the training and prediction times of the system for three different datasets. The results of these evaluations were then compared with those of the five baseline systems. According to the evaluation experiment, the proposed system achieved a relatively high recommendation accuracy compared with baseline systems and the diversity and novelty scores of the proposed system did not fall below 90% for any dataset. Furthermore, the training times of the Word2Vec-based systems, including the proposed system, were shorter than those of FPMC and GRU4Rec. The evaluation results suggest that the proposed recommender system succeeds in keeping the computational cost for training low while maintaining high-level recommendation accuracy, diversity, and novelty.

Information-centric networking (ICN) provides low-latency content delivery with in-network caching, but delivery latency depends on cache distance from consumers. To reduce delivery latency, a scheme to cluster domains and retain the main popular content in each cluster with a cache distribution range has been proposed, which enables consumers to retrieve content from neighboring clusters/caches. However, when the distribution of content popularity changes, all content caches may not be distributed adequately in a cluster, so consumers cannot retrieve them from nearby caches. We therefore propose a dynamic clustering scheme to adjust the cache distribution range in accordance with the change in content popularity and evaluate the effectiveness of the proposed scheme through simulation.

Cloud computing has become the mainstream computing paradigm nowadays. More and more data owners (DO) choose to outsource their data to a cloud service provider (CSP), who is responsible for data management and query processing on behalf of DO, so as to cut down operational costs for the DO.  However, in real-world applications, CSP may be untrusted, hence it is necessary to authenticate the query result returned from the CSP.  In this paper, we consider the problem of approximate string query result authentication in the context of database outsourcing. Based on Merkle Hash Tree (MHT) and Trie, we propose an authenticated tree structure named MTrie for authenticating approximate string query results. We design efficient algorithms for query processing and query result authentication. To verify effectiveness of our method, we have conducted extensive experiments on real datasets and the results show that our proposed method can effectively authenticate approximate string query results.

A method for bandwidth enhancement of radar cross section (RCS) reduction by metasurfaces was studied. Scattering cancellation is one of common methods for reducing RCS of target scatterers. It occurs when the wave scattered by the target scatterer and the wave scattered by the canceling scatterer are the same amplitude and opposite phase. Since bandwidth of scattering cancellation is usually narrow, we proposed the bandwidth enhancement method using metasurfaces, which can control the frequency dependence of the scattering phase. We designed and fabricated a metasurface composed of a patch array on a grounded dielectric substrate. Numerical and experimental evaluations confirmed that the metasurface enhances the bandwidth of 10dB RCS reduction by 52% bandwidth ratio of the metasurface from 34% bandwidth ratio of metallic cancelling scatterers.

We have realized a design automation platform of hardware accelerator for pairing operation over multiple elliptic curve parameters. Pairing operation is one of the fundamental operations to realize functional encryption. However, known as a computational complexity-heavy algorithm. Also because there have been not yet identified standard parameters, we need to choose curve parameters based on the required security level and affordable hardware resources. To explore this design optimization for each curve parameter is essential. In this research, we have realized an automated design platform for pairing hardware for such purposes. Optimization results show almost equivalent to those prior-art designs by hand.

Stabilizer-based quantum secret sharing has two methods to reconstruct a quantum secret: The erasure correcting procedure and the unitary procedure. It is known that the unitary procedure has a smaller circuit width. On the other hand, it is unknown which method has smaller depth and fewer circuit gates. In this letter, it is shown that the unitary procedure has smaller depth and fewer circuit gates than the erasure correcting procedure which follows a standard framework performing measurements and unitary operators according to the measurements outcomes, when the circuits are designed for quantum secret sharing using the [[5, 1, 3]] binary stabilizer code. The evaluation can be reversed if one discovers a better circuit for the erasure correcting procedure which does not follow the standard framework.