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There are a series of challenges in microgrid transactions, and blockchain technology holds the promise of addressing these challenges. However, with the increasing number of users in microgrid transactions, existing blockchain systems may struggle to meet the growing demands for transactions. Therefore, this paper proposes an efficient and secure blockchain consensus algorithm designed to meet the demands of large-scale microgrid electricity transactions. The algorithm begins by utilizing a Spectral clustering algorithm to partition the blockchain network into different lower-level consensus set based on the transaction characteristics of nodes. Subsequently, a dual-layer consensus process is employed to enhance the efficiency of consensus. Additionally, we have designed a secure consensus set leader election strategy to promptly identify leaders with excellent performance. Finally, we have introduced an authentication method that combines zero-knowledge proofs and key sharing to further mitigate the risk of malicious nodes participating in the consensus. Theoretical analysis indicates that our proposed consensus algorithm, incorporating multiple layers of security measures, effectively withstands blockchain attacks such as denial of service. Simulation experiment results demonstrate that our algorithm outperforms similar blockchain algorithms significantly in terms of communication overhead, consensus latency, and throughput. [ABSTRACT FROM AUTHOR]

Federated learning (FL) represents a promising distributed machine learning paradigm for resolving data isolation due to data privacy concerns. Nevertheless, most vanilla FL algorithms, which depend on a server, encounter the problem of reliability and a high communication burden in real cases. Decentralized federated learning (DFL) that does not follow the star topology faces the challenges of weight divergence and inferior communication efficiency. In this paper, a novel DFL framework called federated incremental subgradient-proximal (FedISP) is proposed that utilizes the incremental method to perform model updates to alleviate weight divergence. In our setup, multiple clients are distributed in a ring topology and communicate in a cyclic manner, which significantly mitigates the communication load. A convergence guarantee is given under the convex condition to demonstrate the impact of the learning rate on our algorithms, which further improves the performance of FedISP. Extensive experiments on benchmark datasets validate the effectiveness of the proposed approach in both independent and identically distributed (IID) and non-IID settings while illustrating the advantages of the FedISP algorithm in achieving model consensus and saving communication costs. [ABSTRACT FROM AUTHOR]

In emergency management, the transportation scheduling of emergency supplies and relief personnel can be regarded as the multi-objective shortest path problem with mixed time window (MOSPPMTW), which has high requirements for timeliness and effectiveness, but the current solution algorithms cannot simultaneously take into account the solution accuracy and computational speed, which is very unfavorable for emergency path decision-making. In this paper, we establish MOSPPMTW matching emergency rescue scenarios, which simultaneously enables the supplies and rescuers to arrive at the emergency scene as soon as possible in the shortest time and at the smallest cost. To solve the complete Pareto optimal surface, we present a ripple spreading algorithm (RSA), which determines the complete Pareto frontier by performing a ripple relay race to obtain the set of Pareto optimal path solutions. The proposed RSA algorithm does not require an initial solution and iterative iterations and only needs to be run once to obtain the solution set. Furthermore, we prove the optimality and time complexity of RSA and conduct multiple sets of example simulation experiments. Compared with other algorithms, RSA performs better in terms of computational speed and solution quality. The advantage is especially more obvious in the computation of large-scale problems. It is applicable to various emergency disaster relief scenarios and can meet the requirements of fast response and timeliness. [ABSTRACT FROM AUTHOR]

Recently an algorithm to build SL(2, ℤ) duals, including mirror duals, of 3d N = 4 quiver theories and their 4d N = 1 uplift has been introduced. In this work we use this new tool to study the so-called bad theories. Our approach allows us to determine exactly indices/partition functions for generic values of fugacities/real mass and FI parameters revealing their surprising feature: the 4d index/3d partition function of a bad theory behaves as a sum of distributions rather than an ordinary function of the deformation parameters. We focus on the bad SQCD, with U(Nc) gauge group in 3d and USp(2Nc) in 4d, while in an upcoming paper we will consider linear quivers which, in the 3d case, have both unitary and special unitary bad nodes. [ABSTRACT FROM AUTHOR]

The idea of predicting links in multiplex networks has gained increasing interest in recent years. In this paper, we propose a comprehensive framework which benefits from the structural information of auxiliary layers to predict links on a target layer of multiplex networks. Specifically, we assume that the likelihood of the existence of a link between two nodes is determined by the contributions from both the nodes' neighbors on the target layer and their counterparts' neighbors on a manually network generated by auxiliary layers. The final likelihood matrix is acquired by an iterative algorithm. In addition, we show advantages of our methods for predicting links on sparse and dense networks as well as on networks with assortative and disassortative structural layers. The effectiveness of the proposed methods are evaluated through extensive experiments on real-world multiplex networks. [ABSTRACT FROM AUTHOR]

In this paper, the properties of 10 different feature selection algorithms for generalized additive models (GAMs) are compared on one simulated and two real-world datasets under concurvity. Concurvity can be interpreted as a redundancy in the feature set of a GAM. Like multicollinearity in linear models, concurvity causes unstable parameter estimates in GAMs and makes the marginal effect of features harder interpret. Feature selection algorithms for GAMs can be separated into four clusters: stepwise, boosting, regularization and concurvity controlled methods. Our numerical results show that algorithms with no constraints on concurvity tend to select a large feature set, without significant improvements in predictive performance compared to a more parsimonious feature set. A large feature set is accompanied by harmful concurvity in the proposed models. To tackle the concurvity phenomenon, recent feature selection algorithms such as the mRMR and the HSIC-Lasso incorporated some constraints on concurvity in their objective function. However, these algorithms interpret concurvity as pairwise non-linear relationship between features, so they do not account for the case when a feature can be accurately estimated as a multivariate function of several other features. This is confirmed by our numerical results. Our own solution to the problem, a hybrid genetic–harmony search algorithm (HA) introduces constrains on multivariate concurvity directly. Due to this constraint, the HA proposes a small and not redundant feature set with predictive performance similar to that of models with far more features. [ABSTRACT FROM AUTHOR]

During the manufacturing process of printed circuit boards (PCBs), quality defects can occur, which can affect the performance and reliability of PCBs. Existing deep learning-based PCB defect detection methods are difficult to simultaneously achieve the goals of high detection accuracy, fast detection speed, and small number of parameters. Therefore, this paper proposes a PCB defect detection algorithm based on CDI-YOLO. Firstly, the coordinate attention mechanism (CA) is introduced to improve the backbone and neck network of YOLOv7-tiny, enhance the feature extraction capability of the model, and thus improve the accuracy of model detection. Secondly, DSConv is used to replace part of the common convolution in YOLOv7-tiny to achieve lower computing costs and faster detection speed. Finally, Inner-CIoU is used as the bounding box regression loss function of CDI-YOLO to speed up the bounding box regression process. The experimental results show that the method achieves 98.3% mAP on the PCB defect dataset, the detection speed is 128 frames per second (FPS), the parameters is 5.8 M, and the giga floating-point operations per second (GFLOPs) is 12.6 G. Compared with the existing methods, the comprehensive performance of this method has advantages. [ABSTRACT FROM AUTHOR]

Online platforms and social networking have increased in the contemporary years. They are now a major news source worldwide, leading to the online proliferation of Fake News (FNs). These FNs are alarming because they fundamentally reshape public opinion, which may cause customers to leave these online platforms, threatening the reputations of several organizations and industries. This rapid dissemination of FNs makes it imperative for automated systems to detect them, encouraging many researchers to propose various systems to classify news articles and detect FNs automatically. In this paper, a Fake News Detection (FND) methodology is presented based on an effective IBAVO-AO algorithm, which stands for hybridization of African Vultures Optimization (AVO) and Aquila Optimization (AO) algorithms, with an extreme gradient boosting Tree (Xgb-Tree) classifier. The suggested methodology involves three main phases: Initially, the unstructured FNs dataset is analyzed, and the essential features are extracted by tokenizing, encoding, and padding the input news words into a sequence of integers utilizing the GLOVE approach. Then, the extracted features are filtered using the effective Relief algorithm to select only the appropriate ones. Finally, the recovered features are used to classify the news items using the suggested IBAVO-AO algorithm based on the Xgb-Tree classifier. Hence, the suggested methodology is distinguished from prior models in that it performs automatic data pre-processing, optimization, and classification tasks. The proposed methodology is carried out on the ISOT-FNs dataset, containing more than 44 thousand multiple news articles divided into truthful and fake. We validated the proposed methodology's reliability by examining numerous evaluation metrics involving accuracy, fitness values, the number of selected features, Kappa, Precision, Recall, F1-score, Specificity, Sensitivity, ROC_AUC, and MCC. Then, the proposed methodology is compared against the most common meta-heuristic optimization algorithms utilizing the ISOT-FNs. The experimental results reveal that the suggested methodology achieved optimal classification accuracy and F1-score and successfully categorized more than 92.5% of news articles compared to its peers. This study will assist researchers in expanding their understanding of meta-heuristic optimization algorithms applications for FND. [ABSTRACT FROM AUTHOR]

In this paper, we consider the graph signals are sparse in the graph Fourier domain and propose an iterative threshold compressed sensing reconstruction (ITCSR) algorithm to reconstruct sparse graph signals in the graph Fourier domain. The proposed ITCSR algorithm derives from the well-known compressed sensing by considering a threshold for sparsity-promoting reconstruction of the underlying graph signals. The proposed ITCSR algorithm enhances the performance of sparse graph signal reconstruction by introducing a threshold function to determine a suitable threshold. Furthermore, we demonstrate that the suitable parameters for the threshold can be automatically determined by leveraging the sparrow search algorithm. Moreover, we analytically prove the convergence property of the proposed ITCSR algorithm. In the experimental, numerical tests with synthetic as well as 3D point cloud data demonstrate the merits of the proposed ITCSR algorithm relative to the baseline algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we engage with and expand on the keynote talk about the "Right to Audit" given by Prof. Christian Sandvig at the International Conference on Computational Social Science 2021 through a critical reflection on power asymmetries in the algorithm auditing field. We elaborate on the challenges and asymmetries mentioned by Sandvig — such as those related to legal issues and the disparity between early-career and senior researchers. We also contribute a discussion of the asymmetries that were not covered by Sandvig but that we find critically important: those related to other disparities between researchers, incentive structures related to the access to data from companies, targets of auditing and users and their rights. We also discuss the implications these asymmetries have for algorithm auditing research such as the Western-centrism and the lack of the diversity of perspectives. While we focus on the field of algorithm auditing specifically, we suggest some of the discussed asymmetries affect Computational Social Science more generally and need to be reflected on and addressed. [ABSTRACT FROM AUTHOR]

The paper proposes a nonlinear controller called dual super-twisting sliding mode command (DSTSMC) for controlling and regulating the rotor side converter (RSC) of multi-rotor wind power systems that use doubly-fed induction generators. It was proposed that this controller be developed as an alternative to the direct power control (DPC), which makes use of a pulse width modulation (PWM) strategy to regulate the RSC's functioning. Overcoming the power/current quality issue with the proposed technique (DPC-DSTSMC-PWM) is characterized by great robustness and excellent performance. The designed strategy was contrasted with the standard method of control and other methods already in use. So, the unique proposed control strategy's robustness, performance, efficiency, and efficacy in enhancing system characteristics were tested and validated in Matlab/Simulink. In both tests, the proposed method resulted in significant improvements, reducing active power ripples by 83.33%, 57.14%, and 48.57% in the proposed tests. When compared with the traditional regulation method, the reduction rates of reactive power ripples are 64.06%, 52.47%, and 68.7% in the tests. However, in contrast to the conventional method, the proposed tests showed a decrease of between 72.46%, 50%, and 76.22% in the value of total harmonic distortion (THD) of the provided currents. These ratios show how effective the proposed plan is in ameliorating and enhancing aspects of the energy system. [ABSTRACT FROM AUTHOR]

In this paper, a Cluster-based Synthetic minority oversampling technique (SMOTE) Both-sampling (CSBBoost) ensemble algorithm is proposed for classifying imbalanced data. In this algorithm, a combination of over-sampling, under-sampling, and different ensemble algorithms, including Extreme Gradient Boosting (XGBoost), random forest, and bagging, is employed in order to achieve a balanced dataset and address the issues including redundancy of data after over-sampling, information loss in under-sampling, and random sample selection for sampling and sample generation. The performance of the proposed algorithm is evaluated and compared to different state-of-the-art competing algorithms based on 20 benchmark imbalanced datasets in terms of the harmonic mean of precision and recall (F1) and area under the receiver operating characteristics curve (AUC) measures. Based on the results, the proposed CSBBoost algorithm performs significantly better than the competing algorithms. In addition, a real-world dataset is used to demonstrate the applicability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Identifying all frequent high utility occupancy itemsets (FHUOIs) in a quantitative transaction dataset is a new trend in data mining. By combining both factors of frequency and utility occupancy, these patterns are more suitable for several applications in the real world. These patterns not only reflect the interests of most users but also contribute a high proportion of the utility in supporting transactions. Nonetheless, the set of all discovered FHUOIs may be very large, especially for large and dense datasets or for low values of predefined minimum thresholds. For this reason, it is often quite challenging for users to analyze and use the obtained patterns. To address this issue, this paper proposes two novel algorithms named MaxCloFHUOIM and CloFHUOIM to extract compact representations of FHUOIs. The former is designed to simultaneously mine two concise representations of FHUOIs that consist of all closed FHUOIs and all maximal FHUOIs, whereas the latter only discovers the closed FHUOIs, which provide a lossless summary of all FHUOIs. The proposed algorithms rely on a novel weak upper bound on utility occupancy, to reduce the search space by quickly pruning itemsets with low utility occupancy. Especially, the algorithms integrate two new efficient strategies to prune non-closed FHUOI candidate branches early in the prefix search tree. Results from an in-depth experimental evaluation conducted on several benchmark real-life and synthetic quantitative datasets demonstrate that MaxCloFHUOIM and CloFHUOIM have excellent performance in terms of runtime, memory usage, and scalability. In particular, the proposed algorithms are up to two orders of magnitude faster than a baseline algorithm. [ABSTRACT FROM AUTHOR]

Aaronson defined Forrelation (2010) as a measure of correlation between a Boolean function f and the Walsh–Hadamard transform of another function g . In a recent work, we have studied different cryptographically important spectra of Boolean functions through the lens of Forrelation. In this paper, we explore a similar kind of correlation in terms of nega-Hadamard transform. We call it nega-Forrelation and obtain a more efficient sampling strategy for nega-Hadamard transform compared to the existing results. Moreover, we present an efficient sampling strategy for nega-crosscorrelation (and consequently nega-autocorrelation) spectra too, by tweaking the nega-Forrelation technique. Finally, we connect the hidden shift finding algorithm for bent functions (Rötteler, 2010) with the Forrelation algorithm and extend it for the negabent functions. [ABSTRACT FROM AUTHOR]

Whenever a new supervised machine learning (ML) algorithm or solution is developed, it is imperative to evaluate the predictive performance it attains for diverse datasets. This is done in order to stress test the strengths and weaknesses of the novel algorithms and provide evidence for situations in which they are most useful. A common practice is to gather some datasets from public benchmark repositories for such an evaluation. But little or no specific criteria are used in the selection of these datasets, which is often ad-hoc. In this paper, the importance of gathering a diverse benchmark of datasets in order to properly evaluate ML models and really understand their capabilities is investigated. Leveraging from meta-learning studies evaluating the diversity of public repositories of datasets, this paper introduces an optimization method to choose varied classification and regression datasets from a pool of candidate datasets. The method is based on maximum coverage, circular packing, and the meta-heuristic Lichtenberg Algorithm for ensuring that diverse datasets able to challenge the ML algorithms more broadly are chosen. The selections were compared experimentally with a random selection of datasets and with clustering by k-medoids and proved to be more effective regarding the diversity of the chosen benchmarks and the ability to challenge the ML algorithms at different levels. [ABSTRACT FROM AUTHOR]

Triangle enumeration is a foundation brick for solving harder graph problems related to social networks, the Internet and transportation, to name a few applications. This problem is well studied in the theory literature, but remains an open problem with big data. In this paper, we defend the idea of solving triangle enumeration with SQL queries evaluating the steps of a new adaptive algorithm with linear speedup. Such SQL approach provides scalability beyond RAM limits, automatic parallel processing and more importantly: linear speedup as more machines are added. We present theory results and experimental validation showing our solution works well with large graphs analyzed on a parallel cluster with many machines, producing a balanced workload even with highly skewed degree vertices. We consider two types of distributed systems: (1) a parallel DBMS that evaluates SQL queries, and (2) a parallel HPC cluster calling the MPI library (called via Python). Extensive benchmark experiments with large graphs show our SQL solution offers many advantages over MPI and competing graph analytic systems. [ABSTRACT FROM AUTHOR]

Anomaly detection within the context of healthcare billing requires a method or algorithm which is flexible to the practicalities and requirements of manual case review, the volumes and associated effort of which can determine whether anomalous output is ultimately actioned or not. In this paper, we apply a modified version of a previously introduced anomaly detection algorithm to address this very issue by enacting refined targeting capability based on the identification of sub-extreme anomalies. By balancing the anomaly identification process with appropriate threshold setting tailored to each sample health provider discipline, it is shown that final candidate volumes are controlled with greater accuracy and sensitivity. A comparison with standard local outlier factor (LOF) scores is included for benchmark purposes. [ABSTRACT FROM AUTHOR]

With the birth of the IoT era, it is evident that the existing number of devices is going to rise exponentially. Any two devices will communicate with each other using the same frequency band with limited availability. Therefore, it is of vital importance that this frequency band used for communication be used efficiently to accommodate the maximum number of devices with the available radio resources. Cognitive radio (CR) technology serves this exact purpose. The stated one is an intelligent radio that is made to automatically identify the optimal wireless channel in the available wireless spectrum at a given instant. An important functionality of CR is spectrum sensing. Energy detection is a very popular algorithm used for spectrum sensing in CR technology for efficient allocation of radio resources to the devices intended to communicate with each other. Energy detection detects the presence of a primary user (PU) signal by continuously monitoring a selected frequency bandwidth. The conventional energy detection technique is known to perform poorly in lower SNR ranges. This paper works towards the improvement of the energy detection algorithm with the help of machine learning (ML). The ML model uses the general properties of the signal as training data and classifies between a PU signal and noise at very low SNR ranges (− 25 to − 10 dB). In this research, a K-nearest neighbours (KNN) model is selected for its versatility and simplicity. Upon testing the model with an out-of-sample dataset, the KNN model produced a detection accuracy of 94.5%. [ABSTRACT FROM AUTHOR]

For state estimation of multi-source asynchronous measurement systems with measurement missing phenomena, this paper proposes a distributed sequential inverse covariance intersection (DSICI) fusion algorithm based on conditional Kalman filtering method. It is mainly divided into synchronized state space module, local filtering module and fusion estimation module. The missing measurements occurring in the system are modelled and described by a set of random variables obeying a Bernoulli distribution. The synchronized state space module uses a state iteration method to synchronize the asynchronous measurement system at the moment of measurement update and it ensures the integrity of the measurement information. The local filtering module uses a conditional Kalman filtering algorithm for filter estimation. The reliability of the local filtering results is guaranteed because the local estimator designs a method to interact information with the domain sensors. The fusion estimation module designs a DSICI fusion algorithm with higher accuracy and satisfying consistency, which fuses the filtering results provided by each sensor when the relevant information between multiple sensors is unknown. Simulation examples demonstrate the excellent performance of the proposed algorithm, with a 33% improvement in accuracy over existing algorithms and an iteration time of less than 3 ms. [ABSTRACT FROM AUTHOR]

In large-scale unmanned aerial vehicle (UAV) swarm confrontation scenarios, the design of decision-making and coordination strategies becomes extremely difficult. Multi-Agent Reinforcement Learning (MARL), as a novel decision-making approach to address this issue, faces challenges such as poor scalability and the curse of dimensionality. To overcome these challenges, the paper proposes a Hierarchical Attention Actor-Critic (HAAC) algorithm. The HAAC algorithm includes a centralized critic network based on a Hierarchical Two-stage Attention Network (H2ANet), along with a hierarchical actor policy network that combines rules and reinforcement learning approaches. H2ANet is specifically designed to model the relationships between UAVs and extract crucial information from neighboring UAVs, enabling the generation of advanced cooperative and competitive strategies. The HAAC algorithm effectively reduces the dimensionality of both action and state spaces. Experimental results conducted demonstrate that the HAAC algorithm outperforms existing methods and is able to extend its learned policies to large-scale scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a numerical algorithm for environmental/economic load dispatch (EELD) with emissions constraints, which takes into account the emissions trading system's effect on electricity generation cost and is implemented using a Python computer program. The developed program is applied to a power system of six (6) fossil-fueled electricity generating units with NOx, SO2 and CO2 constraints and proved to be significantly beneficial not only for the environment but also for the power company and the consumers. The proposed algorithm uses multi-objective optimization and incorporates both fuel and emissions allowances costs. The schedule of generating units is calculated and the testing of all possible weighting factor combinations with resolution of 0.01 is carried out showing that the proposed algorithm is fast, cost effective and environmentally friendly. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms, widely applied across various domains due to their simplicity and strong optimization capabilities, play a crucial role in problem-solving. While the Aquila Optimizer is recognized for its effectiveness, it often exhibits slow convergence rates and susceptibility to local optima in certain scenarios. To address these concerns, this paper introduces an enhanced version, termed Tent-enhanced Aquila Optimizer (TEAO). TEAO incorporates the Tent chaotic map to initialize the Aquila population, promoting a more uniform distribution within the solution space. To balance exploration and exploitation, novel formulas are proposed, accelerating convergence while ensuring precision. The effectiveness of the TEAO algorithm is validated through a comprehensive comparison with 14 state-of-the-art algorithms using 23 classical benchmark test functions. Additionally, to assess the practical feasibility of the approach, TEAO is applied to six constrained engineering problems and benchmarked against the performance of the same 14 algorithms. All experimental results consistently demonstrate that TEAO outperforms other advanced algorithms in terms of solution quality and stability, establishing it as a more competitive choice for optimization tasks. [ABSTRACT FROM AUTHOR]

The paper proposes a SOC (State of Charge) estimation method for lead–carbon batteries based on the GA-MIUKF algorithm. The GA-MIUKF algorithm combines GA (Genetic Algorithm) for global search and optimization with the MI-UKF (Multi-innovation Unscented Kalman Filter) algorithm for estimating the SOC of lead–carbon batteries. By establishing an equivalent circuit model for the battery, the GA is employed to globally search and optimize the battery model parameters and the noise variance parameters in the MI-UKF algorithm. Comparative analyses with the UKF (Unscented Kalman Filter) algorithms and MI-UKF algorithms reveal that the SOC estimation method based on the GA-MIUKF algorithm yields more accurate results for lead–carbon battery SOC estimation, with an average estimation error of 2.0%. This highlights the efficacy of the proposed approach in enhancing SOC estimation precision. [ABSTRACT FROM AUTHOR]

Sparsity finds applications in diverse areas such as statistics, machine learning, and signal processing. Computations over sparse structures are less complex compared to their dense counterparts and need less storage. This paper proposes a heuristic method for retrieving sparse approximate solutions of optimization problems via minimizing the ℓ p quasi-norm, where 0 < p < 1 . An iterative two-block algorithm for minimizing the ℓ p quasi-norm subject to convex constraints is proposed. The proposed algorithm requires solving for the roots of a scalar degree polynomial as opposed to applying a soft thresholding operator in the case of ℓ 1 norm minimization. The algorithm's merit relies on its ability to solve the ℓ p quasi-norm minimization subject to any convex constraints set. For the specific case of constraints defined by differentiable functions with Lipschitz continuous gradient, a second, faster algorithm is proposed. Using a proximal gradient step, we mitigate the convex projection step and hence enhance the algorithm's speed while proving its convergence. We present various applications where the proposed algorithm excels, namely, sparse signal reconstruction, system identification, and matrix completion. The results demonstrate the significant gains obtained by the proposed algorithm compared to other ℓ p quasi-norm based methods presented in previous literature. [ABSTRACT FROM AUTHOR]

The complex domain adaptive filtering algorithms has shown excellent performance in the field of signal processing. Among them, the well-known complex least mean square (CLMS) algorithm has been widely used in practical projects. However, the CLMS algorithm only considers the case where the output signal is disturbed by noise, ignores the input signal may also be interfered by noise. Moreover, the signal is likely to be disturbed by impulsive noise in practice. Based on the above problems, the complex total least mean M-estimation (CTLMM) adaptive algorithm based on the M-estimation function is put forward in this paper. The algorithm can well handle the case where both input and output signals are subject to complex domain noise interference, and it also shows robustness with impulsive noise. This paper also analyzes the local stability of the CTLMM algorithm and discuss the theoretical steady-state error of the algorithm. Finally, simulation results verify the advantages of CTLMM algorithm and the rightness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

In this paper, a novel Adaptive Parameter Strategy Differential Evolution (APSDE) algorithm is proposed to overcome the parameters dependence and avoid local optima. The Parameter Update Mechanism (PUM), which has three different strategies, is used to reduce the dependence on parameters of DE. The Adaptive Proportion Adjustment Mechanism (APAM) is used to balance the proportion of PUM strategies in different development terms of exploitation and exploration, and the Random Restart Mechanism (RRM) is used to improve population diversity when exploitation is in stagnation. The proposed algorithm is verified in the CEC2018 test functions and the results show that APSDE has good abilities of exploitation, exploration, convergence, and stability. Secondly, Midcourse Guidance Maneuver Decision-making (MGMD) in Beyond Visual Range (BVR) air combat is studied and transformed into a single objective variational optimization problem, a MGMD system based on APSDE is established. Finally, the simulation of MGMD is carried out. The APSDE ranks first in the typical MGMD scenario experiment. In the adaptive Midcourse guidance confrontation, the winning rate of APSDE is 54%, and the statistical results show that the APSDE has an excellent MGMD ability. [ABSTRACT FROM AUTHOR]

Abstractive summarization (AS) aims to generate more flexible and informative descriptions than extractive summarization. Nevertheless, it often distorts or fabricates facts in the original article. To address this problem, some existing approaches attempt to evaluate or verify factual consistency, or design models to reduce factual errors. However, most of the efforts either have limited effects or result in lower rouge scores while reducing factual errors. In other words, it is challenging to promote factual consistency while maintaining the informativeness of generated summaries. Inspired by the knowledge graph embedding technique, in this paper, we propose a novel cross-modal knowledge guided model (CKGM) for AS, which embeds a multimodal knowledge graph (MKG) combining image entity-relationship information and textual factual information (FI) into BERT to accomplish cross-modal information interaction and knowledge expansion. The pre-training method obtains rich contextual semantic information, while the knowledge graph supplements the textual information. In addition, an entity memory embedding algorithm is further proposed to improve information fusion efficiency and model training speed. We elaborately conducted ablation experiments and evaluated our model on the Visual Genome, FewRel, MSCOCO, and CNN/DailyMail datasets. Experimental results demonstrate that our model can significantly improve the FI consistency and informativeness of generated summaries. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is one of the most efficient evolution algorithms (ES) for dealing with nonlinear, complicated and difficult global optimization problems. The main contribution of this paper can be summarized in three directions: Firstly, a novel crossover rate (CR) generation scheme based on the zscore value of fitness, named fcr, is introduced. For a minimization problem, the proposed CR generation strategy always assigns a smaller CR value to individual with smaller fitness value. Therefore, the parameters of individuals with better fitness are inherited by their offspring with high probability. In the second direction, the control parameters are adjusted by unused bimodal settings in which each parameter setting is selected according to the evolution status of individual. The third direction of our work is introducing the L1 norm distance as the weights for updating the mean value of crossover rate and scale factor. Theoretically, compared with L2 norm, L1-norm is more efficient to suppress outliers in the difference vector. These modifications are first integrated with the mutation strategy of JADE, then a modified version, named JADEfcr, is proposed. In addition, to improve the optimization ability further, another variant LJADEfcr by using a linear population reduction mechanism is considered. So as to confirm and examine the performance of JADEfcr and LJADEfcr, numerical experiments are conducted on 29 optimization problems defined by CEC2017 benchmark. For JADEfcr, its experimental results are made a comparison with twelve state-of-the-art algorithms. The comparative study demonstrates that in terms of robustness, stability and solution quality, JADEfcr are better and highly competitive with these well-known algorithms. For LJADEfcr, its results are compared with JADEfcr and other nine powerful algorithms including four recent algorithms and five top algorithms on CEC2017 competition. Experimental results indicate that LJADEfcr is superior and statistically competitive with these excellent algorithms in terms of robustness, stability and the quality of the obtained solutions. [ABSTRACT FROM AUTHOR]

The theory of Formal Concept Analysis (FCA) finds diverse applications in fields like knowledge extraction, cognitive concept learning and data mining. The construction of a concept lattice significantly influences the effectiveness of formal concept analysis; hence, the development of high-performance algorithms for concept construction is crucial. In this paper, we introduce a novel algorithm called "Bit-Close" for formal concept construction. Bit-Close leverages bit representation and operations, fundamental to computer science, to enhance the In-Close algorithm. Furthermore, we explore the parallel method of Bit-Close. Our experimental results, obtained from multiple public and random datasets, demonstrate that Bit-Close outperforms In-Close by approximately 20% and is significantly better than other competing algorithms. [ABSTRACT FROM AUTHOR]

Surrogate-assisted evolutionary algorithms (SAEAs) are popular for solving expensive optimization problems. However, most existing SAEAs are designed for solving single-objective or multiobjective optimization problems with two or three objectives. Few works had been reported to deal with expensive many-objective optimization problems with more than three objectives because of two difficulties. One is the curse of dimensionality caused by many-objective problems, and the other is the fewer computational resources available in a limited time for expensive optimization problems. Since an effective selection method can better solve the many-objective optimization problems, high-efficiency search and accurate model can save computational resources for expensive optimization problems, this paper proposes a diverse/converged individual competition algorithm, which owns a novel diverse/converged individual competition selection mechanism, a hybrid search mechanism, and a segmentation approach. The diverse/converged individual competition selection mechanism maintains a good balance between the convergence and diversity of the selected solutions for solving many-objective optimization problems. The hybrid search mechanism performs a memetic search and genetic search at different stages of the evolution process to further generate superior solutions. The segmentation approach uses two different populations with small numbers to build two surrogate models which will predict different areas, and it can improve the accuracy of the prediction. The proposed algorithm is compared with several state-of-art algorithms on widely used benchmark functions. The experimental results show that the proposed algorithm performs significantly better than the compared algorithms. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the existing line segment detectors will detect overdense meaningless textures, this paper proposes a fusing contour features optimization method for line segment detector, called CF-Lines. We define a new line segment attribute, called "line segment associate contour(LAC)" attribute, which includes the contour features, the length and the angle of line segment. After using existing algorithms to detect contours and line segments, CF-Lines calculates the LAC of all line segments. When the LAC is greater than the threshold and passes the quantitative verification, the line segment is removed as overdense meaningless texture. Using the YorkUrban and Wireframe datasets, the CF-Lines is tested and compared with original detectors. Experimental results show that CF-Lines performs better than the original detectors in average precision, F-score, average length of line segments and average number of line segments. [ABSTRACT FROM AUTHOR]

Threshold public-key encryption (threshold PKE) has various useful applications. A lot of threshold PKE schemes are proposed based on RSA, Diffie–Hellman and lattice, but to the best of our knowledge, code-based threshold PKEs have not been proposed. In this paper, we provide three IND-CCA secure code-based threshold PKE schemes. The first scheme is the concrete instantiation of Dodis–Katz conversion (Dodis and Katz, TCC'05) that converts an IND-CCA secure PKE into an IND-CCA secure threshold PKE using parallel encryption and a signature scheme. This approach provides non-interactive threshold decryption, but ciphertexts are large (about 16 kilobytes for 128-bit security) due to long code-based signatures even in the state-of-the-art one. The second scheme is a new parallel encryption-based construction without signature schemes. Unlike the Dodis–Katz conversion, our parallel encryption converts an OW-CPA secure PKE into an OW-CPA secure threshold PKE. To enhance security, we use Cong et al.'s conversion (Cong et al., ASIACRYPT'21). Thanks to eliminating signatures, its ciphertext is 512 bytes, which is only 3% of the first scheme. The decryption process needs an MPC for computing hash functions, but decryption of OW-CPA secure PKE can be done locally. The third scheme is an MPC-based threshold PKE scheme from code-based assumption. We take the same approach Cong et al. took to construct efficient lattice-based threshold PKEs. We build an MPC for the decryption algorithm of OW-CPA secure Classic McEliece PKE. This scheme has the shortest ciphertext among the three schemes at just 192 bytes. Compared to the regular CCA secure Classic McEliece PKE, the additional ciphertext length is only 100 bytes. The cons are heavy distributed computation in the decryption process. [ABSTRACT FROM AUTHOR]

In order to realize the efficient development of unconventional oil and gas, the measurement accuracy of wellbore spacing in the drilling of parallel horizontal wells is more and more required. Although the Rotating Magnet Ranging System or Magnetic Guidance Tool is used to achieve a good ranging effect in the drilling of dual horizontal wells, the position measurement of the magnetic sub leads to a large ranging error. A new ranging algorithm for the Two Sensor Packages-Rotating Magnet Ranging System is presented in this paper. The algorithm takes the magnetic signal generated by the rotation of the magnetic sub at a fixed position, the tilt measurement data of the two wells, the length of the magnetic sub, and the distance between the two fluxgate sensors as input parameters to avoid measuring the position of the magnetic sub and to reduce the influence of the degree of non-parallelism and the length of the magnetic sub. The simulation and experiment demonstrate that the inclination and azimuth angles of the two wells have a significant impact on the magnetic ranging results when the ranging well sections are not parallel and that the distance between the bottom of the drill bit and the center of the magnetic sub cannot be ignored. Moreover, the accuracy of the relative distance calculated by this new algorithm can reach 97%, and the error of direction calculation is less than 3°. Applying this algorithm in the field can successfully aid in controlling the spacing of cluster horizontal wells more accurately. [ABSTRACT FROM AUTHOR]

Combined with one-time pad encryption scheme, quantum key distribution guarantees the unconditional security of communication in theory. However, error correction and privacy amplification in the post-processing phase of quantum key distribution result in high time delay, which limits the final secret key generation rate and the practicability of quantum key distribution systems. To alleviate this limitation, this paper proposes an efficient post-processing algorithm based on polar codes for quantum key distribution. In this algorithm, by analyzing the channel capacity of the main channel and the wiretap channel respectively under the Wyner's wiretap channel model, we design a codeword structure of polar codes, so that the error correction and privacy amplification could be completed synchronously in a single step. Through combining error correction and privacy amplification into one single step, this efficient post-processing algorithm reduces complexity of the system and lower the post-processing delay. Besides, the reliable and secure communicaiton conditions for this algorithm has been given in this paper. Simulation results show that this post-processing algorithm satisfies the reliable and secure communication conditions well. [ABSTRACT FROM AUTHOR]

Current approaches for the prospective regulatory assessment of chemicals do not account sufficiently for elevated mixture risks. The Mixture Assessment Factor (MAF, better labeled a Mixture Allocation Factor) has been suggested for mixtures of industrial chemicals in the EU's Chemicals Strategy for Sustainability, as a pragmatic tool to account for potential mixture risks already during the risk and safety assessment of individual chemicals. The MAF is to be applied in scenarios in which specific mixture risk assessments are not possible, due to a lack of data and/or the complexity of the relevant exposure scenarios. Several approaches and algorithms for calculating a MAF have been suggested in the literature. The MAFexact, which is a member of the larger MAFceiling class, is defined as the maximum fraction of the risk quotient of each chemical that is still acceptable to occur in a mixture, without the sum of risk quotients exceeding 1. This paper provides a comparative overview of the different MAF types discussed in the literature. It argues that the MAFexact is the most promising approach in the context of chemical registration and authorization under regulatory frameworks such as REACH because this approach ensures a protection level that is similar to the protection level used in the current safety assessment of individual chemicals under REACH. Other MAF approaches either disproportionally impact low-risk substances, without leading to any appreciable risk reduction, or hamper risk communication because they lead to fluctuating residual risks after the MAF application. The paper also presents a case study comparing the different MAF approaches and finally discusses the MAF concept in the wider context of chemical regulation. [ABSTRACT FROM AUTHOR]

In this research paper, performance of an efficient audio contents integrity verification algorithm using Discrete Cosine Transform (DCT) has been analyzed and studied in presence the different noise and attacks. This approach of audio contents verification is designed based on a self-mark idea, the embedded mark is generated from the same audio file after transforming the sensitive 2-D audio file into two equal size partitions. Integrity and confidentiality verification of classified recorded audio scenario also, has been tested and evaluated in this research paper using the encryption technique to encrypt the marked audio files. Different computer experiments simulation have been executed to evaluate and analysis the performance of the stand-alone self-mark algorithm and with the encryption stage merging. These experiments tested the performance of these two security approaches with presence different attacks. Reverse processes are executed to verify the contents integrity of the audio files. According to the quality of the extracted audio file from the verification process compared to the original audio file, this audio contents integrity algorithm is applicable and robust. Quality of the extracted audio file is evaluated using the different metrics tools. The different attacks are employed to measure the robustness and reliability of this algorithm. The statistical analysis of the results has been presented, it is proved to have high detection sensitivity to observe and discover any audio manipulations even they were inaudible. [ABSTRACT FROM AUTHOR]

With the purpose of providing higher data rate and ultra-reliable and low-latency communications for the users, employing the small cells in the upcoming Fifth-Generation-New Radio (5G-NR) cellular networks and beyond is leading to the serious challenges in mobility management due to dynamicity of the user equipments (UEs). Among different issues related to the mobility of the UEs, the handover management is one of the key procedures to let the UEs experience a high quality of service (QoS)/quality of experience (QoE). So far, many protocols and algorithms have been proposed to enhance the mobility management in 5G-NR from various aspects, but still a thorough survey has not been incorporated to summarize the recent advances and future directions of the mentioned issue. Thus, the handover management and the corresponding challenges stand in the center of concentrations in this paper, with a glance on 4G to the advancements of 5G-NR. The challenges include QoS/QoE, throughput, delay, traffic load, the attacks during authentication process and resource allocation. To overcome the mentioned challenges, the handover procedure is evaluated by some key performance indicators such as handover ratio, handover failure, radio link failure and such like which depend on the received signal quality. The lack of new advancements and novel aspects of handover plus the separation of security and handover issues in previous works are perceived as research gaps and motivations for the current paper. In this regard, this paper aims to specify and analyze the technical issues, to provide an overview on the proposed methods and recent advances and to bring the future directions into the limelight. We categorize the concurrent standards and methods about the handover, and then, we survey the proposed algorithms including theoretical, algorithm-based and pattern-capturing approaches following the authentication process along with the vulnerabilities and the algorithms to counteract the attacks during handover. Also, we study various issues corresponding to network performance during the handover procedure. Finally, we discuss the open problems and future research directions. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) target localization by using hybrid time difference of arrival (TDOA) and angle of arrival (AOA) measurements from multiple sensors has been an active research area for several decades due to its extensive applications in various fields. For this nonlinear estimation problem, the pseudolinear system of equations constructed by using the measurements generally acts as the basis of numerous localization algorithms. In this paper, we aim to improve the performance of 3D TDOA–AOA localization by introducing the constrained total least squares (CTLS) framework wherein the inherent characteristics of the pseudolinear equations can be properly taken into consideration. On the basis of the total least squares model, the CTLS model for 3D TDOA–AOA localization is established by imposing the inherent characteristics of the pseudolinear equations as additional constraints. Then, the multi-constraint optimization problem in CTLS model is solved by using an iterative algorithm based on successive projections. Extensive numerical simulations are accomplished for evaluating the performance of the proposed CTLS algorithm. The results show that the proposed algorithm gives moderate accuracy enhancement with acceptable computational cost, and more importantly, it is more robust to large measurement noise than the compared algorithms. [ABSTRACT FROM AUTHOR]

This paper considers the identification problem of the linear continuous time-delay systems. By using the multi-frequency responses, a stochastic gradient gradient-based iterative (SG-GI) algorithm is derived. The proposed algorithm can estimate the unknown parameters and the unknown time delays simultaneously. To improve the parameter estimation accuracy of the SG-GI algorithm, a multi-innovation stochastic gradient gradient-based iterative (MISG-GI) algorithm is derived by using the multi-innovation identification theory. In addition, a forgetting factor is introduced to increase the parameter estimation accuracy. The resulting algorithm is called the multi-innovation forgetting gradient gradient-based iterative (MIFG-GI) algorithm. The effectiveness of the proposed strategies is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

In the new era, the key measure to accelerate the construction of smart city, so as to promote the modernization of urban governance system and governance capacity, is to establish a good urban innovation ecosystem, and guide its continuous evolution to the direction of the highest efficiency and the best performance. Focusing on solving the practical problem of "how the urban innovation ecosystem evolves", this paper develops a NK algorithm using BP neural network and DEMATEL method. First, through literature research, constructing the urban innovation ecosystem including five subsystems of innovation talents, innovation subjects, innovation resources, innovation environment and innovation network. Then, taking Beijing as an example, the weights and the number of epistatic relationships of each subsystem in its innovation ecosystem are calculated by BP neural network and DEMATEL method, and the NK model is modified; on this basis, the fitness values corresponding to different states of the system are calculated using MATLAB software, and the optimal evolution path of Beijing innovation ecosystem is determined through the comparison of 100,000 simulation results. The results show that the optimal evolution path of Beijing's innovation ecosystem is to create a favorable environment and culture for innovation first; then increase the input of innovation resources; and then promote the development of innovation network assets; on this basis, cultivate, attract and retain innovative talents; and finally strengthen the construction of innovation subjects. [ABSTRACT FROM AUTHOR]

Improving classification performance is an essential goal for various practical applications. Feature selection has become an important data preprocessing step in machine learning systems. However, many effective methods based on heuristic search strategies have the problem of high running costs. This paper proposes an efficient multiobjective feature selection method based on artificial immune algorithm optimization. It introduces a clone selection algorithm to explore the search space of optimal feature subsets. According to the target requirements of feature selection, combined with biological research results, this method introduces genome shuffling technology and a conditional lethal mutation mechanism to improve the search performance of the algorithm. Experimental comparisons are conducted on 21 benchmark datasets with 17 advanced feature selection methods in terms of classification accuracy, the number of feature subsets, and computational cost. The results show that the algorithm achieves the smallest number of selected features (only 3.26% compared to the lowest) and better average classification accuracy with a much lower average computational cost than others (only 3.62% compared to the lowest). [ABSTRACT FROM AUTHOR]

In this paper we complete the exploration of connected components of the space of BPS Wilson loops in three-dimensional N = 4 Chern-Simons-matter theory on S3. The algorithm is to start with a supersymmetric Wilson loop, choose a preserved supercharge, and look for BPS deformations built out of the matter fields in the proper representations. Using this, we discover many new moduli spaces of nonconformal BPS Wilson loops preserving a single or two supercharges, which are subsets of the symmetries of the 1/4 and 3/8 BPS operators. Along with the those previously found in [1-2] and [3], the total moduli spaces are closed under this formalism. [ABSTRACT FROM AUTHOR]

By the asymptotic oracle property, non-convex penalties represented by minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) have attracted much attentions in high-dimensional data analysis, and have been widely used in signal processing, image restoration, matrix estimation, etc. However, in view of their non-convex and non-smooth characteristics, they are computationally challenging. Almost all existing algorithms converge locally, and the proper selection of initial values is crucial. Therefore, in actual operation, they often combine a warm-starting technique to meet the rigid requirement that the initial value must be sufficiently close to the optimal solution of the corresponding problem. In this paper, based on the DC (difference of convex functions) property of MCP and SCAD penalties, we aim to design a global two-stage algorithm for the high-dimensional least squares linear regression problems. A key idea for making the proposed algorithm to be efficient is to use the primal dual active set with continuation (PDASC) method to solve the corresponding sub-problems. Theoretically, we not only prove the global convergence of the proposed algorithm, but also verify that the generated iterative sequence converges to a d-stationary point. In terms of computational performance, the abundant research of simulation and real data show that the algorithm in this paper is superior to the latest SSN method and the classic coordinate descent (CD) algorithm for solving non-convex penalized high-dimensional linear regression problems. [ABSTRACT FROM AUTHOR]

The physical cloud servers consists of n number of machines for serving the requirements posted via Virtual Machines by the users. The allocation of requested Virtual Machines to appropriate Physical Servers is one among the challenging task exists in Cloud Computing. During the deployment phase several objectives needs to be considered which includes the total power consumption, the resource wastage, network propagation delay and so on. Allocating VM's to PM's with such fashion will lead the service providers financially. In this paper a swarm-based algorithm is modified for solving VM placement in terms of multi-objective perspective. Minimizing total power consumption and minimizing the resource wastage are the objectives considered in this paper. The results show the significance of the proposed model in terms of objective space and with the performance indicators such as ONGV and Spacing ( S p ). [ABSTRACT FROM AUTHOR]

Linear motion commands of multi-axis computer numerical control (CNC) machine tools need to be smoothed at the transition corners, because the velocity discontinuities at corners can result in fluctuations on machine tool motions and lead to poor surface quality. However, few studies have reported on the transition between non-line contour. The paper proposes a novel two-step local corner smoothing method for non-line contour transition, including path smoothing and feedrate scheduling. In path smoothing, a non-uniform rational B-spline (NURBS) curve is adopted to smooth the adjacent non-line contour of screw rotor section while constraining the cornering error with the set tolerance. The maximum deviation between the original trajectory and smoothed toolpath can be analytically calculated. In feedrate scheduling, a jerk-limited S-curve feedrate scheduling scheme is developed based on machine tool dynamics. Finally, in order to verify the correctness of the proposed method, a series of numerical simulations and actual machining and measurement experiments are conducted, and the simulations and experimental results have showed the good performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Needle-in-a-Haystack problems exist across a wide range of applications including rare disease prediction, ecological resource management, fraud detection, and material property optimization. A Needle-in-a-Haystack problem arises when there is an extreme imbalance of optimum conditions relative to the size of the dataset. However, current state-of-the-art optimization algorithms are not designed with the capabilities to find solutions to these challenging multidimensional Needle-in-a-Haystack problems, resulting in slow convergence or pigeonholing into a local minimum. In this paper, we present a Zooming Memory-Based Initialization algorithm, entitled ZoMBI, that builds on conventional Bayesian optimization principles to quickly and efficiently optimize Needle-in-a-Haystack problems in both less time and fewer experiments. The ZoMBI algorithm demonstrates compute time speed-ups of 400× compared to traditional Bayesian optimization as well as efficiently discovering optima in under 100 experiments that are up to 3× more highly optimized than those discovered by similar methods. [ABSTRACT FROM AUTHOR]

In the network economy domain, urban competitiveness refers to the comparison between cities in terms of competition and development. It is the ability to gain competitive advantage under different factors. The evaluation of urban competitiveness will help cities to learn from each other, and provides reference for the government to enhance urban competitiveness. Unlike various studies in the literature exploiting only the non-linear characteristics of urban competitiveness, this paper selects BP (Back Propagation) network as the main framework for evaluation. A Genetic Algorithm BP (GABP) network based on genetic optimization is utilized. The weights are optimized besides the crossover mutation of GA algorithm. To compensate the slow prediction in the stand-alone mode, this work proposes a MapReduce (MP) based method; MR-GABP via cloud computing. The model ensures effective urban competitiveness evaluation with improved convergence speed and threshold generation speed. The systematic experiments conducted verify effectiveness of the method while the results obtained reveal that performance of the method is better than the other methods in terms of accuracy and recall yielded as 95.1% and 92.6% respectively. [ABSTRACT FROM AUTHOR]

360-degree videos have become increasingly popular with the application of virtual reality (VR) technology. To encode such kind of videos with ultra-high resolution, an efficient and real-time video encoder becomes a key requirement. The Versatile Video Coding (VVC) standard has good coding performance. However, it has pretty high computational complexity which increasing the application cost of 360-degree videos. Among them, the decision of the quadtree with nested multi-type tree (QTMT) partitioning structure is one of the time-consuming procedures. In this paper, based on the characteristics of 360-degree video with Equirectangular projection (ERP) format, the empirical variogram combined with Mahalanobis distance is introduced to measure the difference between the horizontal and vertical directions of the CU, and a fast partition algorithm is proposed. The experimental results show that the algorithm saves 32.13% of the coding time with only an increase of 0.66% in BDBR. [ABSTRACT FROM AUTHOR]

We present a keyphrase extraction algorithm named TopicLPRank in this paper, which is an improved TopicRank algorithm. Different from the TopicRank which only uses the relative distance information of the text, we think that the length and absolute position of the text candidate keyphrases also have a certain influence on the results of the model for extraction keyphrases. Therefore, the proposed TopicLPRank incorporates these two factors on the basis of the TopicRank. The experimental results show that adding the location information and length information of candidate keyphrases can, respectively, increase the F-Score of the model by around 2.7 % points and 1.7 % points, which is equivalent to an increase of 19.6 and 12.3 % compared with the TopicRank. At the same time, the fusion of the length and location information of the candidate keyphrase can increase the F-Score by around 3.5 percentage points, which is equivalent to an increase of 25.21 % compared with the TopicRank in the dataset NUS. [ABSTRACT FROM AUTHOR]

Research in computer analysis of medical images bears many promises to improve patients' health. However, a number of systematic challenges are slowing down the progress of the field, from limitations of the data, such as biases, to research incentives, such as optimizing for publication. In this paper we review roadblocks to developing and assessing methods. Building our analysis on evidence from the literature and data challenges, we show that at every step, potential biases can creep in. On a positive note, we also discuss on-going efforts to counteract these problems. Finally we provide recommendations on how to further address these problems in the future. [ABSTRACT FROM AUTHOR]