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Ranking the significance of scientific publications is a long-standing challenge. The network-based analysis is a natural and common approach for evaluating the scientific credit of papers. Although the number of citations has been widely used as a metric to rank papers, recently some iterative processes such as the well-known PageRank algorithm have been applied to the citation networks to address this problem. In this paper, we introduce nonlinearity to the PageRank algorithm when aggregating resources from different nodes to further enhance the effect of important papers. The validation of our method is performed on the data of American Physical Society (APS) journals. The results indicate that the nonlinearity improves the performance of the PageRank algorithm in terms of ranking effectiveness, as well as robustness against malicious manipulations. Although the nonlinearity analysis is based on the PageRank algorithm, it can be easily extended to other iterative ranking algorithms and similar improvements are expected. [ABSTRACT FROM AUTHOR]

The purpose of the digital image self-recovery is to restore high quality images as much as possible when the image is tampered. Existing algorithms can only achieve high recovered image quality with tiny tampering rate. To obtain high recovered image quality with large tampering rate, this paper proposes a digital image self-recovery algorithm based on improved JSCC (Joint Source-Channel Coding) optimizer. The algorithm performs quadtree decomposition of original grayscale image corresponding to different decomposition factors γ and performs bit-plane layering according to the block class obtained by quadtree decomposition. Size of each bit-plane is the number of pixels of each block class. Then, the original image is compressed by SPIHT, and the compressed bit-stream of SPIHT is segmented into bit-plane according to the size in order. The bit-planes are protected by different RS (Reed-Solomon) coders to get optimal decomposition result of corresponding γ. Finally, JSCC optimization is designed to get an optimal quality of recovered image. Experimental results show that, using our algorithm, for 2-LSB embedding, when the tampering rate is less the minimum TTR (Tolerable Tempering Rate), the PSNR is improved by 4.93dB. When the tampering rate is larger the minimum TTR, the PSNR is improved by 2dB. When 3-LSB watermark are embedded, the PSNR of recovered image is improved by 2.58dB on average. It shows that our improved optimizer effectively improves the quality of the recovered image at high tampering rates, compared with the similar algorithms. [ABSTRACT FROM AUTHOR]

Introduction: In the last decade, several journal's editors decided to publish alternative bibliometric indices parallel to the impact factor (IF): Scimago Journal Rank (SJR), Source Normalized Impact per Paper (SNIP), Eigenfactor Score (ES) and CiteScore™ (CiteScore); however, there is scarce information about the correlations among them. In this study, we aimed to evaluate the associations between this bibliometrics in the Radiology, Nuclear Medicine & Medical Imaging category of the Web of Knowledge. We hypothesized the IF did not show the best correlation with other metrics. Methods: Retrospective study. We used bibliometrics recorded from the 2017 publicly available versions of the Journal Citation Reports (JCR), SJR (www.scimagojr.com), SNIP (www.journalindicators.com), and CiteScore (www.scopus.com); we also included the Total Cites. We measured the correlations using the Spearman correlation coefficients (RS) for all combinations of the bivariate pair, performed pairwise comparisons of the RS values, and calculated the coefficients of determination. We also tested the statistical significance of the difference between r coefficients between groups. All analyses were conducted with the JMP Pro software. Results: The stronger bivariate correlations were represented by the ES↔Total Cites RS = 0.968, p < 0.001, R2 = 0.937; and the CiteScore↔SJR RS = 0.911, p < 0.001, R2 = 0.829. From 105 possible combinations of pairwise comparisons, 38 depicted a p value > 0.050 which would suggest interchangeability among bivariate correlations. Conclusions: Our findings support our hypothesis that the IF does not show the best correlation between other metrics. Radiologists, interventional radiologist, or nuclear medicine doctors should have a clear understanding of the associations among the journal's bibliometrics for their decision-making during the manuscript submission phase. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various papers within the issue on topics including an approach to decision-making in smart electricity markets, battery usage optimisation in electric vehicles and Beam search algorithms for multilabel learning.

An introduction to the journal is presented in which the editor discusses several papers selected at the 20th Annual European Symposium on Algorithms (ESA) held in Ljubljana, Slovenia on September 10?12, 2012 on topics including algorithmic, use of hash functions, and induced graph matching.

Body Area Networks (BAN) which consist of RF-enabled wearable and implantable sensory nodes are poised to be a promising interdisciplinary technology with novel uses in pervasive healthcare, personal entertainment and consumer electronics. The papers which appear in this special issue have been carefully selected from the best IEEE PIMRC 2009 conference papers addressing some of the most challenging issues in BAN such as propagation modeling, physical, and medium access control layers. Papers presented here propose models, algorithms and protocols exhibiting advance research in this promising field. [ABSTRACT FROM AUTHOR]

YOLOv5 is one of the most popular object detection algorithms, which is divided into multiple series according to the control of network depth and width. To realize the deployment of mobile devices or embedded devices, the paper proposes a lightweight aerial image object detection algorithm (LAI-YOLOv5s) based on the improvement of YOLOv5s with a relatively small amount of calculation and parameter and relatively fast reasoning speed. Firstly, to better detect small objects, the paper replaces the minimum detection head with the maximum detection head and proposes a new feature fusion method, DFM-CPFN(Deep Feature Map Cross Path Fusion Network), to enrich the semantic information of deep features. Secondly, the paper designs a new module based on VoVNet to improve the feature extraction ability of the backbone network. Finally, based on the idea of ShuffleNetV2, the paper makes the network more lightweight without affecting detection accuracy. Based on the VisDrone2019 dataset, the detection accuracy of LAI-YOLOv5s on the mAP@0.5 index is 8.3% higher than that of the original algorithm. Compared with other series of YOLOv5 and YOLOv3 algorithms, LAI-YOLOv5s has the advantages of low computational cost and high detection accuracy. [ABSTRACT FROM AUTHOR]

A general and adaptable method is proposed to reliably extract quantitative information from smartphone images of microfluidic sensors. By analyzing and processing the color information of selected standard substances, the influence of light conditions, device differences, and human factors could be significantly reduced. Machine learning and multivariate fitting methods were proved to be effective for chroma correction, and a key element was the training of sample size and the fitting form, respectively. A custom APP was developed and validated using a high-sensitivity chromium ion quantification paper chip. The average chroma deviations under different conditions were reduced by more than 75% in RGB color space, and the concentration test error was reduced by more than half compared with the commonly used method. The proposed approach could be a beneficial supplement to existing and potential colorimetry-based detection methods. [ABSTRACT FROM AUTHOR]

A sensorless control method of permanent magnet synchronous motor (PMSM) based on adaptive super-twisting algorithm sliding mode observer (STASMO) is proposed in this paper. The traditional sliding mode observer (SMO) algorithm has the problem of inherent high-frequency chattering due to the use of switching function. The phase delay will be caused by using low-pass filter to deal with the problem of high-frequency buffeting. In this paper, the chattering of the system is effectively suppressed by combining the super-twisting algorithm and the SMO algorithm. The chattering suppression ability of the algorithm is further improved by adding an adaptive coefficient associated with speed in front of the higher-order integral term of the observer. At the same time, the speed and rotor position information are extracted by normalized phase-locked loop (PLL), which avoids the use of low-pass filter and phase compensation module. In order to overcome the influence of the change of motor parameters on the control system, an online estimation method of motor parameters is proposed. The values of stator resistance and stator inductance are estimated online in real time, and the estimated values are fed back to the SMO, which improves the system performance and estimation accuracy. Through simulations and experiments, it is proved that the proposed algorithm can effectively suppress high-frequency chattering, effectively improve the estimation performance of PMSM sensorless control system, and obtain more accurate speed and rotor position information. [ABSTRACT FROM AUTHOR]

Cryptographic technology can effectively defend against malicious attackers to attack sensitive and private information. The core of cryptographic technology is cryptographic algorithm, and the cryptographic algorithm identification is the premise of in-depth analysis of cryptography. In the cryptanalysis of unknown cryptographic algorithm, the primary task is to identify the cryptographic algorithm used in the encryption and then carry out targeted analysis. With the rapid growth of Internet data, the increasing complexity of communication environment, and the increasing number of cryptographic algorithms, the single-layer identification scheme of cryptographic algorithm faces great challenges in terms of identification ability and stability. To solve these problems, on the basis of existing identification schemes, this paper proposes a new cluster division scheme CMSSBAM-cluster, and then proposes a multi-layer composite identification scheme of cryptographic algorithm using a composite structure. The scheme adopts the method of cluster division and single division to identify various cryptographic algorithms. Based on the idea of ensemble, the scheme uses the hybrid random forest and logistic regression (HRFLR) model for training, and conducts research on a data set consisting of 1700 ciphertext files encrypted by 17 cryptographic algorithms. In addition, two ensemble learning models, hybrid gradient boosting decision tree and logistic regression (HGBDTLR) model and hybrid k-neighbors and random forest (HKNNRF) model are used as controls to conduct controlled experiments in this paper. The experimental results show that multi-layer composite identification scheme of cryptographic algorithm based on HRFLR model has an accuracy rate close to 100% in the cluster division stage, and the identification results are higher than those of the other two models in both the cluster division and single division stages. In the last layer of cluster division, the identification accuracy of ECB and CBC encryption modes in block cryptosystem is significantly higher than that of the other two classification models by 35.2% and 36.1%. In single division, the identification accuracy is higher than HGBDTLR with a maximum of 9.8%, and higher than HKNNRF with a maximum of 7.5%. At the same time, the scheme proposed in this paper has significantly improved the identification effect compared with the single division identification accuracy of 17 cryptosystem directly and the 17 classification accuracy of 5.9% compared with random classification, which indicates that multi-layer composite identification scheme of cryptographic algorithm based on HRFLR model has significant advantages in the accuracy of identifying multiple cryptographic algorithms. [ABSTRACT FROM AUTHOR]

In order to improve the complexity of the chaotic system and ensure the relevant security indicators of the cryptographic algorithm, a new chaotic image encryption algorithm based on dynamic DNA coding and RNA computing is proposed. In this paper, we first construct a four-dimensional hyperchaotic system with more complex dynamics and then use the plaintext-related keystream generated by the hyperchaotic system to dynamically DNA encode the plaintext image, then perform RNA coding conversion and amino acid substitution box generation, and finally use an improved replacement sequence generator to generate pseudo-random sequences for replacement operations to generate the final ciphertext image. Theoretical analysis and simulation results show that the proposed algorithm has excellent performance in security indicators such as key space, the number of pixels change rate, the number average changing intensity, entropy, clipping attack, noise attack, and chosen plaintext attack. Therefore, the algorithm has higher security. [ABSTRACT FROM AUTHOR]

This paper proposes a bidirectional feature fusion pyramid (B-FPN) Single Shot Multiple Frame Detector (SSD) algorithm. First, a bidirectional feature pyramid (B-FPN) structure is constructed, which realizes the bidirectional fusion of the feature layers and improves the accuracy of detection. Second, we introduce coordinate attention (CA) to focus on the important channel features while preserving their location information, thereby increasing the focus on the important information. Finally, optimizing the loss function speeds up the convergence of the model and further improves the detection accuracy of the network. The experimental results show that on the VOC2007 dataset, the mAP of the algorithm in this paper is 76.48%, which is 3.52% higher than that of the SSD algorithm. On the COCO 2017 dataset, the mAP of the proposed algorithm is 3.85% higher than that of the SSD algorithm. Compared with other mainstream target detection algorithms, the algorithm in this paper has certain advantages in detection accuracy, and can also achieve satisfactory results in detection speed. Finally, the accuracy of foreign object recognition in the special environment of iron ore transportation is 98.26%. [ABSTRACT FROM AUTHOR]

Recommendation system is an important module of many online systems. As one of the mainstream methods in the current recommendation system, the recommendation algorithm based on graph neural network can not only learn the cooperation signal between nodes, but also detect the nonlinear high-order information in node interaction. However, like the traditional collaborative filtering algorithm, the collaborative filtering method based on graph neural network only depends on the interaction information of existing users and projects, and the recommendation effect is poor in the scenario of sparse data. Although incorporating item attributes as auxiliary information into the recommendation algorithm can alleviate this problem to a certain extent, directly using the item attributes that users browse to construct user embedded vectors cannot contain users' implicit preferences. Therefore, this paper constructs a user–user network based on the interaction information between users and projects, analyzes it by using community detection, divides user groups with common interests, mines the potential attributes of users, and designs a collaborative filtering recommendation algorithm based on community detection and graph neural network. The research on two large real data sets shows that our method is superior to the standard recommendation method and the latest collaborative filtering recommendation method based on graph neural network. [ABSTRACT FROM AUTHOR]

Background: The wide use of the term Cognitive Reserve (CR) is in need of a clear and shared definition of its concept and of the development of new tools, quick and easy to use and updated for the people of today. This study describes the online short CRIq (s-CRIq), the new shorter version of the CRIq, following an item analysis revision, and compares the data distribution of different samples. Methods: The s-CRIq was administered online to 435 people while another 440 filled out the s-CRIq in self-administration. A further 588 participants had been administered the original paper-and-pencil long CRIq and 344 the online long CRIq. Results: The major difference in the databases of s-CRIq versus the long versions is an increased score in education and in leisure activity. However, the density distributions of the total score of CRI in the 4 databases share 64% of their areas, and at least two of them share 84%. Conclusion: The s-CRIq proved to be a simple and easy-to-administer tool. Similarly, to the original version, the s-CRIq is freely available on the web, and it is our hope that it will be of fruitful use for researchers and clinicians alike. [ABSTRACT FROM AUTHOR]

The task offloading of mobile edge computing (MEC) is to find proper edge or cloud resources for the execution tasks to efficiently utilize resources and meet different user's requirements. However, it is difficult for task offloading when the number of tasks and resources providers increases and to optimize multiple objectives while satisfying users' requirements. In this paper, a new multi-objective strategy based on the biogeography-based optimization (BBO) algorithm is proposed for MEC offloading to satisfied users' multiple requirements (the execution time, energy consumption and cost). In this strategy, a time-energy consumption model and a cost model are constructed for task offloading firstly. Based on these models, the BBO algorithm is introduced into task offloading for MEC to solve the problem of multi-objective optimization. Compared with the traditional strategies, the offloading strategy based on BBO decreases the average task completion time by an average of 25.03%, and compared with the technique for order preference by similarity to an ideal solution (TOPSIS) strategy, the BBO offloading strategy proposed in this paper reduces energy consumption 75% and cost by 36.9%. The proposed strategy can well solve the problem of multi-objective optimization in the task offloading for MEC. [ABSTRACT FROM AUTHOR]

The real-time path planning of unmanned aerial vehicles (UAVs) in dynamic environments with moving threats is a difficult problem. To solve this problem, this paper proposes a time-based rapidly exploring random tree (time-based RRT*) algorithm, called the hierarchical rapidly exploring random tree algorithm based on potential function lazy planning and low-cost optimization (HPO-RRT*). The HPO-RRT* algorithm can guarantee path homotopy optimality and high planning efficiency. This algorithm uses a hierarchical architecture comprising a UAV perception system, path planner, and path optimizer. After the UAV perception system predicts moving threats and updates world information, the path planner obtains the heuristic path. First, the path planner uses the bias sampling method based on the artificial potential field function proposed in this paper to guide sampling to improve the efficiency and quality of sampling. Then, the tree is efficiently extended by the improved time-based lazy collision checking RRT* algorithm to obtain the heuristic path. Finally, a low-cost path optimizer quickly optimizes the heuristic path directly to optimize the path while avoiding additional calculations. Simulation results show that the proposed algorithm outperforms the three existing advanced algorithms in terms of addressing the real-time path-planning problem of UAVs in a dynamic environment. [ABSTRACT FROM AUTHOR]

A series of recent papers raises worries about the manipulative potential of algorithmic transparency (to wit, making visible the factors that influence an algorithm’s output). But while the concern is apt and relevant, it is based on a fraught understanding of manipulation. Therefore, this paper draws attention to the ‘indifference view’ of manipulation, which explains better than the ‘vulnerability view’ why algorithmic transparency has manipulative potential. The paper also raises pertinent research questions for future studies of manipulation in the context of algorithmic transparency. [ABSTRACT FROM AUTHOR]

Fog computing is considered a formidable next-generation complement to cloud computing. Nowadays, in light of the dramatic rise in the number of IoT devices, several problems have been raised in cloud architectures. By introducing fog computing as a mediate layer between the user devices and the cloud, one can extend cloud computing's processing and storage capability. Offloading can be utilized as a mechanism that transfers computations, data, and energy consumption from the resource-limited user devices to resource-rich fog/cloud layers to achieve an optimal experience in the quality of applications and improve the system performance. This paper provides a systematic and comprehensive study to evaluate fog offloading mechanisms' current and recent works. Each selected paper's pros and cons are explored and analyzed to state and address the present potentialities and issues of offloading mechanisms in a fog environment efficiently. We classify offloading mechanisms in a fog system into four groups, including computation-based, energy-based, storage-based, and hybrid approaches. Furthermore, this paper explores offloading metrics, applied algorithms, and evaluation methods related to the chosen offloading mechanisms in fog systems. Additionally, the open challenges and future trends derived from the reviewed studies are discussed. [ABSTRACT FROM AUTHOR]

Collecting and analyzing data from all devices to improve the efficiency of business processes is an important task of Industrial Internet of Things (IIoT). In the age of data explosion, extensive text data generated by the IIoT have given birth to a variety of text representation methods. The task of text representation is to convert the natural language to a form that computer can understand with retaining the original semantics. However, these methods are difficult to effectively extract the semantic features among words and distinguish polysemy in natural language. Combining the advantages of convolutional neural network (CNN) and variational autoencoder (VAE), this paper proposes an intelligent CNN-VAE text representation algorithm as an advanced learning method for social big data within next-generation IIoT, which help users identify the information collected by sensors and perform further processing. This method employs the convolution layer to capture the local features of the context and uses the variational technique to reconstruct feature space to make it conform to the normal distribution. In addition, the improved word2vec model based on topical word embedding (TWE) is utilized to add topical information to word vectors to distinguish polysemy. This paper takes the social big data as an example to illustrate the way of the proposed algorithm applied in the next-generation IIoT and utilizes Cnews dataset to verify the performance of proposed method with four evaluating metrics (i.e., recall, accuracy, precision, and F1-score). Experimental results indicate that the proposed method outperforms word2vec-avg and CNN-AE in K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) classifiers and distinguishes polysemy effectively. [ABSTRACT FROM AUTHOR]

S-box is the only non-linear device in the cryptographic algorithm, and its quality determines the lower limit strength of the cryptographic algorithm. However, because the image data is highly correlated, the traditional encryption methods and their S-boxes, such as AES and DES are not suitable for using in image encryption. Based on this, this paper proposes an S-box generation algorithm based on a 4D hyperchaotic system and improved particle swarm optimization. Firstly, this paper improves on the Lorenz chaotic system and proposes a 4D hyperchaotic system with a higher Lyapunov exponent and more complex dynamics. Secondly, the idea of simulated annealing algorithm is introduced into the particle swarm optimization algorithm, which further improves the efficiency of the particle swarm optimization algorithm and improves the problem that the particle swarm optimization algorithm is easy to fall into the local optimal solution. Then an improved particle swarm optimization algorithm is used to optimize the nonlinearity of the S-box to improve the performance of the S-box. Finally, use the generated S-box to design an image encryption algorithm and prove the security of the S-box. The experimental results show that the S-box designed in this paper has excellent performance in the five indicators of Nonlinearity, SAC, BIC-NL, LP, and DP. At the same time, the encryption result can resist common attacks, so it has strong multimedia security. [ABSTRACT FROM AUTHOR]

The recognition of abnormal behavior in surveillance video is the focus of current research, which has high research value and broad application possibilities. Its main applications are in the fields of intelligent surveillance, intelligent security, and smart cities, and it is of great significance to study the recognition of abnormal behaviors. Because of the complexity of human movement and the variability of the external environment, the recognition and detection of abnormal behaviors have some challenges. The recognition and detection of abnormal human behaviors in surveillance video still needs further research and development. This paper uses the multi-branch convolutional neural network to extract the spatial features of video frames for the first time, and as an encoder to pass the condensed features to the Gated Recurrent Unit (GRU), which extracts Temporal features from multiple video frames. And then the Gated Recurrent Unit output the result as the decoder. We did a series of comparative experiments on UCF-Crime dataset. And finally, we achieved an accuracy of 86.78% in the test set. The experimental results show that our multi-branch convolutional fusion neural network is better than previous surveillance video abnormal behavior recognition algorithms. At the same time, in order to verify the generalization performance and efficiency of the algorithm, we also conducted an experimental validation on the UCF-101 dataset in this paper, and the results show that the algorithm in this paper can also show a high accuracy rate on the UCF-101 dataset, and the speed of the algorithm is almost close to that of the C3D method with improved accuracy rate, making it possible to develop simple recognition applications based on the algorithm studied in this paper subsequently. [ABSTRACT FROM AUTHOR]

In order to reduce the impact of disguised missing value on data analysis, this paper proposes a new algorithm - The Detection Algorithm for Disguised Missing Value Based on Filter-Kmeans. The algorithm identifies mainly disguised missing value by the clustering effect, and is applied to the data set with certain probability of data points. Firstly, the suitable data object points are selected using the silhouette coefficient method and Bernoulli's law of large numbers. And then the weighted average distance is used to control the cluster traversal. Finally, the filtering operation is performed during the process of cluster traversal. According to the experimental results, the algorithm achieves better improvement in the precision ratio, recall ratio and F1-measure on the open dataset. [ABSTRACT FROM AUTHOR]

The article announces the selection of the study "Decomposition Algorithms for Stochastic Programming on a Computational Grid," written by Jeff Linderoth and Stephen Wright by the editorial board of the periodical "Computational Optimization and Applications," for the Best Paper Award 2004. The paper describes research carried out by the authors at the Argonne National Laboratory which was supported by the National Science Foundation (NSF). The research involved the development of middleware software, the discovery of new algorithms that could exploit the power of grid platforms while not being affected too seriously by its less felicitous features and the implementation of these algorithms using the resulting codes to solve touchstone problems in optimization.

To make the networks fitting for such ever-growing diverse needs, many new future networking technologies have been proposed, covering both the access network and the backbone network. Information Centric Network (ICN) evolves current Internet infrastructure from host-centric paradigm to the data or service name centric paradigm. In the paper "Energy-Efficient Sensory Data Gathering in IoT Networks with Mobile Edge Computing", Ren et al. study the energy-efficient sensory data gathering in mobile IoT networks to reduce energy consumption of resource-limited IoT devices, and prolong the network lifetime. [Extracted from the article]

Automated techniques and tools for the construction and analysis of systems are inevitable to manage the complexity of the current systems. Such techniques and tools are the subject of interest of the International Conference on Tools and Algorithms for the Construction and Analysis of Systems—TACAS. This special issue of Software Tools for Technology Transfer presents extended versions of five selected papers from the 25th edition of TACAS that took place in 2019. All of the papers included into this special issue aim at various aspects of automated design and formal verification and hence contribute to development of more reliable computer systems. [ABSTRACT FROM AUTHOR]

Fabric defect detection is an important part of the textile industry, aiming at the problems of many types of fabric defects, small size defects and unbalanced samples, an improved YOLOv5 fabric defect detection algorithm, FD-YOLOv5, was proposed. First, the coordinate attention module is embedded in the YOLOv5 backbone network structure to replace the bottleneck structure in the original network model. While reducing the amount of parameters and calculation, it enhances the ability of the network to extract features and improves the model's ability to detect small target defects. Secondly, a smoother Mish activation function is used in the original model convolution structure for model training, which improves the nonlinear expression ability of the model; the SIoU loss function considering the direction of the anchor box is used to improve the convergence speed and detection accuracy of the model. Finally, combining the focal loss and GHM loss functions as the target confidence loss function to solve the problem of sample imbalance in the fabric defect dataset. The experimental results based on the public fabric defect dataset of Aliyun TianChi shows that the mAP@.5 and mAP@.5:.95 of the improved algorithm are 65.1% and 30.4%, respectively, which are 8.3% and 3.2% higher than the original model, respectively, and the parameter amount, calculation amount and weight of the model are reduced by 8.4%, 11.2% and 14.3%, respectively, compared with the original model. Even compared with the state-of-the-art YOLOv7 model, the mAP@.5 value of the proposed model is improved by 6.5%. Although the FPS value is lower than YOLOv7 model, it also achieves a detection speed of 79 frames per second, which can meet the real-time demand. The experimental results demonstrate the effectiveness of the method in this paper, which can provide a reference for the automatic detection method of fabric defects. [ABSTRACT FROM AUTHOR]

The classical moving target segmentation (MTS) algorithm in a video can segment the moving targets out by calculating frame by frame, but the algorithm encounters a real-time problem as the data increases. Recently, the benefits of quantum computing in video processing have been demonstrated, but it is still scarce for MTS. In this paper, a quantum moving target segmentation algorithm for grayscale video based on background difference method is proposed, which can simultaneously model the background of all frames and perform background difference to segment the moving targets. In addition, a feasible quantum subtractor is designed to perform the background difference operation. Then, several quantum units, including quantum cyclic shift transformation, quantum background modeling, quantum background difference, and quantum binarization, are designed in detail to establish the complete quantum circuit. For a video containing 2 m frames (every frame is a 2 n × 2 n image with q grayscale levels), the complexity of our algorithm is O (n + q) . This is an exponential speedup over the classical algorithm and also outperforms the existing quantum algorithms. Finally, the experiment on IBM Q demonstrates the feasibility of our algorithm in this noisy intermediate-scale quantum (NISQ) era. [ABSTRACT FROM AUTHOR]

The growing uses of algorithm-based decision-making in human resources management have drawn considerable attention from different stakeholders. While prior literature mainly focused on stakeholders directly related to HR decisions (e.g., employees), this paper pertained to a third-party observer perspective and investigated how consumers would respond to companies' adoption of algorithm-based HR decision-making. Through five experimental studies, we showed that the adoption of algorithm-based (vs. human-based) HR decision-making could induce consumers' unfavorable ethicality inferences of the company (study 1); because implementing a calculative and data-driven approach (i.e. algorithm-based) to make employee-related decisions violates the deontological principles of respectful employee treatment (study 2). However, this effect was attenuated when consumers had high (vs. low) power distance beliefs (study 3); the algorithm served as assistance (vs. replacement) for human decisions (study 4); or the adoption was framed as employee-oriented (vs. company-oriented) motivated (study 5). Our findings suggested that consumers are aversive to algorithm-based HR decision-making because it is deontologically problematic regardless of its decision quality (i.e. accuracy). This paper contributes to the extant understanding of stakeholders' responses to algorithm-based HR decision-making and consumers' attitudes toward algorithm users. [ABSTRACT FROM AUTHOR]

The problem of finding a maximum size matching in a graph (known as the maximum matching problem) is one of the most classical problems in computer science. Despite a significant body of work dedicated to the study of this problem in the data stream model, the state-of-the-art single-pass semi-streaming algorithm for it is still a simple greedy algorithm that computes a maximal matching, and this way obtains 1 / 2 -approximation. Some previous works described two/three-pass algorithms that improve over this approximation ratio by using their second and third passes to improve the above mentioned maximal matching. One contribution of this paper continues this line of work by presenting new three-pass semi-streaming algorithms that work along these lines and obtain improved approximation ratios of 0.6111 and 0.5694 for triangle-free and general graphs, respectively. Unfortunately, a recent work Konrad and Naidu (Approximation, randomization, and combinatorial optimization. Algorithms and techniques, APPROX/RANDOM 2021, August 16–18, 2021. LIPIcs, vol 207, pp 19:1–19:18, 2021. https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2021.19) shows that the strategy of constructing a maximal matching in the first pass and then improving it in further passes has limitations. Additionally, this technique is unlikely to get us closer to single-pass semi-streaming algorithms obtaining a better than 1 / 2 -approximation. Therefore, it is interesting to come up with algorithms that do something else with their first pass (we term such algorithms non-maximal-matching-first algorithms). No such algorithms were previously known, and the main contribution of this paper is describing such algorithms that obtain approximation ratios of 0.5384 and 0.5555 in two and three passes, respectively, for general graphs. The main significance of our results is not in the numerical improvements, but in demonstrating the potential of non-maximal-matching-first algorithms. [ABSTRACT FROM AUTHOR]

The introduction of a multi-population structure in differential evolution (DE) algorithm has been proven to be an effective way to achieve algorithm adaptation and multi-strategy integration. However, in existing studies, the mutation strategy selection of each subpopulation during execution is fixed, resulting in poor self-adaptation of subpopulations. To solve this problem, a dynamic multi-strategy integrated differential evolution algorithm based on reinforcement learning (RLDMDE) is proposed in this paper. By employing reinforcement learning, each subpopulation can adaptively select the mutation strategy according to the current environmental state (population diversity). Based on the population state, this paper proposes an individual dynamic migration strategy to "reward" or "punish" the population to avoid wasting individual computing resources. Furthermore, this paper applies two methods of good point set and random opposition-based learning (ROBL) in the population initialization stage to improve the quality of the initial solutions. Finally, to evaluate the performance of the RLDMDE algorithm, this paper selects two benchmark function sets, CEC2013 and CEC2017, and six engineering design problems for testing. The results demonstrate that the RLDMDE algorithm has good performance and strong competitiveness in solving optimization problems. [ABSTRACT FROM AUTHOR]

To address the issues of poor detection accuracy and the large number of target detection model parameters in existing AGV monocular vision location detection algorithms, this paper presents an AGV vision location method based on Gaussian saliency heuristic. The proposed method introduces a fast and accurate AGV visual detection network called GAGV-net. In the GAGV-net network, a Gaussian saliency feature extraction module is designed to enhance the network's feature extraction capability, thereby reducing the required output for model fitting. To improve the accuracy of target detection, a joint multi-scale classification and detection task header are designed at the stage of target frame regression to classification. This header utilizes target features of different scales, thereby enhancing the accuracy of target detection. Experimental results demonstrate a 12% improvement in detection accuracy and a 27.38 FPS increase in detection speed compared to existing detection methods. Moreover, the proposed detection network significantly reduces the model's size, enhances the network model's deployability on AGVs, and greatly improves detection accuracy. [ABSTRACT FROM AUTHOR]

Compared with aeromagnetic total-field data, aeromagnetic vertical gradient field data contain less low-frequency information. In this paper, a continuation conversion filtering algorithm is proposed to filter out part of the low-frequency information of the aeromagnetic total-field data so that these data can be better compared with the total-field data obtained from aeromagnetic gradient data conversion. We discuss the feasibility of single aeromagnetic vertical gradient measurement in areas where it is inconvenient to erect base stations. We design a simple model and a complex model with a background field and random noise to analyze the conversion effect. The model analysis shows that the effect of applying the algorithm depends heavily on the selection of upward continuation height. The magnetization intensity of the background field also affects the selection of continuation height. When the magnetization intensity of the background field is weak, the continuation height chosen is the same as the buried depth of the background field. If the magnetization intensity of the background field is strong, then the higher the continuation height, the better the effect will be. The conclusion of the model analysis is applied to the analysis of the measured aeromagnetic data. In addition, we can conclude that the effect on total-field data of conversion by the continuation conversion filtering algorithm is better than that of conversion from the aeromagnetic vertical gradient data. [ABSTRACT FROM AUTHOR]

Deep learning shows great potential in low-light image enhancement, which can improve image brightness and contrast while keeping image natural. However, due to the lack of prior knowledge extracted manually or excessive amplification of noise, these methods result in poor quality of enhanced images. To solve these challenging problems, this paper proposes a dual-branch grayscale image enhancement network based on dense residual and attention mechanism. Low-light grayscale image is used as input. Firstly, features that fuse deep and shallow information are extracted through dense residual convolution branch network; secondly, texture features are extracted through U-Net branch network combined with attention mechanism; then the extracted features are integrated, and finally luminance is adjusted by a brightness adjustment module to output an enhanced grayscale image. In addition, a joint loss function is designed to measure the network training loss from brightness, texture, contrast and noise. A large number of quantitative and qualitative experiments on LOL and VE-LOL datasets show that the proposed method improves Peak Signal to Noise Ratio Index and Structural Similarity Index by 19.65 - 59.76% and 5.61 - 85.53%, respectively, compared with EnlightenGAN, KinD++, RUAS, LLFlow, etc. The proposed method is superior to the most famous methods, thanks to the deep feature extraction and fusion capability of dense residual convolutional network and texture extraction capability of U-Net network combined with attention mechanism. [ABSTRACT FROM AUTHOR]

This paper is about the opposite of judgement and calculation. This opposition has been a traditional anchor of critiques concerned with the rise of AI decision making over human judgement. Contrary to these approaches, it is argued that human judgement is not and cannot be replaced by calculation, but that it is human judgement that contextualises computational structures and gives them meaning and purpose. The article focuses on the epistemic structure of algorithms and artificial neural networks to find that they always depend on human judgement to be related to real life objects or purposes. By introducing the philosophical concept of judgement, it becomes clear that the property of judgement to provide meaning and purposiveness is based on the temporality of human life and the ambiguity of language, which quantitative processes lack. A juxtaposition shows that calculations and clustering can be used and referred to in more or less prejudiced and reflecting as well as opaque and transparent ways, but thereby always depend on human judgement. The paper clearly asserts that the transparency of AI is necessary for their autonomous use. This transparency requires the explicitness of the judgements that constitute these computational structures, thereby creating an awareness of the conditionality of such epistemic entities. [ABSTRACT FROM AUTHOR]

Global competition forces the process industries to continuously optimize plant operation. One of the latest trends in efficiency and plant availability improvement is to set up fault diagnosis and maintenance systems for online industrial use. This paper presents a methodology for developing industrial fault detection and diagnosis (FDD) systems. Since model- or data-based diagnosis of all components cannot be achieved online on a large-scale basis, the focus must be narrowed down to the most likely faulty components responsible for abnormal process behavior. One of the key elements here is fault analysis. The paper describes and briefly discusses also other development phases, process decomposition and the selection of FDD methods. The paper ends with an FDD case study of a large-scale industrial board machine including a description of the fault analysis and FDD algorithms for the resulting focus areas. Finally, the testing and validation results are presented and discussed. [ABSTRACT FROM AUTHOR]

The frequency at which new research documents are being published causes challenges for researchers who increasingly need access to relevant documents in order to conduct their research. Searching across a variety of databases and browsing millions of documents to find semantically relevant material is a time-consuming task. Recently, there has been a focus on recommendation algorithms that suggest relevant documents based on the current interests of the researchers. In this paper, we describe the implementation of seven commonly used algorithms and three aggregation algorithms. We evaluate the recommendation algorithms in a large-scale biomedical knowledge base with the goal of identifying relative weaknesses and strengths of each algorithm. We analyze the recommendations from each algorithm based on assessments of output as evaluated by 14 biomedical researchers. The results of our research provide unique insights into the performance of recommendation algorithms against the needs of modern-day biomedical researchers. [ABSTRACT FROM AUTHOR]

Image stitching is a fundamental pillar of computer vision, and its effectiveness hinges significantly on the quality of the feature descriptors. However, the existing feature descriptors face several challenges, including inadequate robustness to noise or rotational transformations and limited adaptability during hardware deployment. To address these limitations, this paper proposes a set of feature descriptors for image stitching named Lightweight Multi-Feature Descriptors (LMFD). Based on the extensive extraction of gradients, means, and global information surrounding the feature points, feature descriptors are generated through various combinations to enhance the image stitching process. This endows the algorithm with formidable rotational invariance and noise resistance, thereby improving its accuracy and reliability. Furthermore, the feature descriptors take the form of binary matrices consisting of 0s and 1s, not only facilitating more efficient hardware deployment but also enhancing computational efficiency. The utilization of binary matrices significantly reduces the computational complexity of the algorithm while preserving its efficacy. To validate the effectiveness of LMFD, rigorous experimentation was conducted on the Hpatches and 2D-HeLa datasets. The results demonstrate that LMFD outperforms state-of-the-art image matching algorithms in terms of accuracy. This empirical evidence solidifies the superiority of LMFD and substantiates its potential for practical applications in various domains. [ABSTRACT FROM AUTHOR]

The optimal operation of reservoir groups is a strongly constrained, multi-stage, and high-dimensional optimization problem. In response to this issue, this article couples the standard Pelican optimization algorithm with adaptive ε constraint methods, and further improves the optimization performance of the algorithm by initializing the population with a good point set, reverse differential evolution, and optimal individual t-distribution perturbation strategy. Based on this, an improved Pelican algorithm coupled with adaptive ε constraint method is proposed (ε-IPOA). The performance of the algorithm was tested through 24 constraint testing functions to find the optimal ability and solve constraint optimization problems. The results showed that the algorithm has strong optimization ability and stable performance. In this paper, we select Sanmenxia and Xiaolangdi reservoirs as the research objects, establish the maximum peak-cutting model of terrace reservoirs, apply the ε-IPOA algorithm to solve the model, and compare it with the ε-POA (Pelican algorithm coupled with adaptive ε constraint method) and ε-DE (Differential Evolution Algorithm) algorithms, the results indicate that ε. The peak flow rate of the Huayuankou control point solved by the IPOA algorithm is 12,319 m3/s, which is much lower than the safe overflow flow rate of 22,000 m3/s at the Huayuankou control point, with a peak shaving rate of 44%, and other algorithms do not find effective solutions meeting the constraint conditions. This paper provides a new idea for solving the problem of flood control optimal operation of cascade reservoirs. [ABSTRACT FROM AUTHOR]

Channel pruning has recently become a widely used model compression method. However, most existing channel pruning methods only prune to decrease the model size, such as the number of parameters or FLOPs, and hence the decrease in model size does not effectively lead to an improvement in inference performance. To address this problem, this paper proposes a latency-optimized channel pruning method for CNN inference acceleration on GPU platforms by latency stair-step discrimination, two-stage benefit assessment and latency-sharing channel pruning. Compared with recent state-of-the-art model compression methods, it can achieve significant improvements in inference performance with comparable compression rates and model accuracy. The contributions of this paper include the following: first, a three-point latency stair-step discrimination method is proposed for determining the candidate prunable coordinates with the best latency performance adapted to the current hardware. Then, a two-stage benefit assessment method based on interlayer dependencies is proposed for determining the optimal channel pruning rate of each layer in the network. Finally, a latency-sharing channel pruning framework is proposed to accelerate the model pruning adaptation process. The method proposed in this paper can significantly reduce the model inference latency on multiple types of GPU platforms. To verify the effectiveness, we use three general-purpose GPU platforms and two embedded GPU platforms to evaluate the algorithm performance. The experimental results show that for recent state-of-the-art CNNs, the proposed method can achieve a 22.0–6.6% latency reduction and a 1.3 –3.0 inference performance improvement as well as a 1.2–4.3 pruning adaptation speedup with high model accuracy. [ABSTRACT FROM AUTHOR]

Machine learning is frequently being leveraged to tackle problems in the health sector including utilization for clinical decision-support. Its use has historically been focused on single modal data. Attempts to improve prediction and mimic the multimodal nature of clinical expert decision-making has been met in the biomedical field of machine learning by fusing disparate data. This review was conducted to summarize the current studies in this field and identify topics ripe for future research. We conducted this review in accordance with the PRISMA extension for Scoping Reviews to characterize multi-modal data fusion in health. Search strings were established and used in databases: PubMed, Google Scholar, and IEEEXplore from 2011 to 2021. A final set of 128 articles were included in the analysis. The most common health areas utilizing multi-modal methods were neurology and oncology. Early fusion was the most common data merging strategy. Notably, there was an improvement in predictive performance when using data fusion. Lacking from the papers were clear clinical deployment strategies, FDA-approval, and analysis of how using multimodal approaches from diverse sub-populations may improve biases and healthcare disparities. These findings provide a summary on multimodal data fusion as applied to health diagnosis/prognosis problems. Few papers compared the outputs of a multimodal approach with a unimodal prediction. However, those that did achieved an average increase of 6.4% in predictive accuracy. Multi-modal machine learning, while more robust in its estimations over unimodal methods, has drawbacks in its scalability and the time-consuming nature of information concatenation. [ABSTRACT FROM AUTHOR]

Recently, we are witnessing an enormous burst of data due to the ever-increasing number of Internet of Things (IoT) devices. The traditional cloud computing paradigm has failed to scale; to be specific, its latency and bandwidth utilization are remarkably increased and consequently, Quality of Service (QoS) is decreased. On the other hand, the data management scope in fog computing require much more considerations in terms of performance and scalability. This is because of deploying IoT applications over fog nodes considering their resource-limited and heterogeneity. However, to the best of our knowledge, there is not any literature review that systematically categorizes these issues. In this paper, we have presented a classification of data replica placement approaches considering four main categories: framework-based, graph-based, heuristic-based, and meta-heuristic-based algorithms. To sum up, the primary contribution of this study is as follows: studying articles on data replica placement in fog computing, as well as presenting their strengths and weaknesses, providing a comprehensive systematic review of current approaches and categorizing them comprehensively, discussing research challenges, and future works to improve computing and evaluation mechanisms in the fog computing environment. This paper generally provides a classification, briefly explains the reviewed techniques, and then compares these methods in the end. [ABSTRACT FROM AUTHOR]

We would like to thank the authors for contributing their manuscripts, the referees for thoroughly reviewing these articles and the WADS 2021 program committee for helping with the selection of these articles. This special issue is dedicated to selected papers from those presented at the 17th Algorithms and Data Structures Symposium (WADS 2021), which was held from August 9-11 fully online. [Extracted from the article]

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The article discusses various reports published within the issue, including "A Framework for Modeling Positive Class Expansion With Single Snapshot," by Yang Yu and Zhi-Hua Zhou, "Multi-Sorting Algorithm for Finding Pairs of Similar Short Substrings From Large-Scale String Data," by Takeaki Uno, and "Large-Scale K-Means Clustering With User-Centric Privacy-Preservation," by Jun Sakuma and Shigenobu Kobayashi.

This paper proposes a new chaotic-based encryption algorithm to encrypt images securely and effectively. A combination of Arnold cat, logistic mapping and image blocking has been used to design this algorithm. The proposed algorithm is a special algorithm that not only does it use image blocking once to relocate the pixels in four areas of the image separately but also uses an Arnold cat mapping to relocate the pixels across the whole image once again. This results in a higher number of pixel relocations which makes it impossible to decode without the key. The other interesting and new point is the particular use of chaotic logistic mapping to develop three image keys and to combine them with the input image, and to develop four other image keys to encrypt the image. Relocation of the input image blocks and the images generated by the logistic mapping are conducted through sorting the numbers generated by the mapping and how they have been relocated. Then, Arnold cat is used to relocate the pixels across the whole image (which is a combination of the input image and three images generated by logistic mapping). Eventually, the encryption operation is conducted on the obtained image using the encryption key (the image generated through the combination of the four keys). Numerous statistical tests and security analyses indicate the excellent security of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Background subtraction is a prerequisite for a wide range of applications, including video surveillance systems. A significant number of algorithms are often developed and published in different publication mediums in the area, such as workshops, symposiums, conferences, and journals. An important task in presenting a new background subtraction algorithms is to clearly show that its performance outperforms the performance of the state-of-the-art algorithms. In this paper, we present recommendations on how to evaluate the performance of background subtraction algorithms for surveillance systems. We identified, through a systematic mapping, the key steps and components of this evaluation process – procedures, methods, and tools – most used by the authors in each of these steps. Considering this statistical analysis, we perform a theoretical analysis of the most used key components to identify their pros and cons. Then, we define a set of recommendations that aim to standardize and clarify the performance evaluation process of a new background subtraction algorithm. [ABSTRACT FROM AUTHOR]

This special section is devoted to a selection of papers that appeared originally in the Proceedings of TACAS 2001, the 7th International Conference on Tools and Algorithms for the Construction and Analysis of Systems [1] which took place in Genova, Italy in April 2001 as a constituent event of the European joint conferences on theory and Practice of Software. All papers present approaches, tools and algorithms that aim at extending the scope of formal techniques (coverage of systems specifications, data structures, size, and their trade-offs) for validation, verification, and testing of software systems. They are by no means a complete account of the numerous ways in which real software and software systems may become subject to rigorous investigation, but they provide an interesting sampling of novel approaches towards scalability of formal methods-based validation. [ABSTRACT FROM AUTHOR]