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There is increasing focus on applying deep learning methods to electrocardiograms (ECGs), with recent studies showing that neural networks (NNs) can predict future heart failure or atrial fibrillation from the ECG alone. However, large numbers of ECGs are needed to train NNs, and many ECGs are currently only in paper format, which are not suitable for NN training. We developed a fully-automated online ECG digitisation tool to convert scanned paper ECGs into digital signals. Using automated horizontal and vertical anchor point detection, the algorithm automatically segments the ECG image into separate images for the 12 leads and a dynamical morphological algorithm is then applied to extract the signal of interest. We then validated the performance of the algorithm on 515 digital ECGs, of which 45 were printed, scanned and redigitised. The automated digitisation tool achieved 99.0% correlation between the digitised signals and the ground truth ECG (n = 515 standard 3-by-4 ECGs) after excluding ECGs with overlap of lead signals. Without exclusion, the performance of average correlation was from 90 to 97% across the leads on all 3-by-4 ECGs. There was a 97% correlation for 12-by-1 and 3-by-1 ECG formats after excluding ECGs with overlap of lead signals. Without exclusion, the average correlation of some leads in 12-by-1 ECGs was 60–70% and the average correlation of 3-by-1 ECGs achieved 80–90%. ECGs that were printed, scanned, and redigitised, our tool achieved 96% correlation with the original signals. We have developed and validated a fully-automated, user-friendly, online ECG digitisation tool. Unlike other available tools, this does not require any manual segmentation of ECG signals. Our tool can facilitate the rapid and automated digitisation of large repositories of paper ECGs to allow them to be used for deep learning projects. [ABSTRACT FROM AUTHOR]

This special issue contains six revised and extended versions of tool papers that appeared in the proceedings of TACAS 2021, the 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems. The issue is dedicated to the realization of algorithms in tools and the studies of the application of these tools for analysing hard- and software systems. [ABSTRACT FROM AUTHOR]

This work describes the log-MAP (BCJR) algorithm implementation of a close to capacity dirty paper coding CODEC. The CODEC consists of eight deep pipeline processors. It decodes blocks of 975 bits in 26.9 ms using less than 9.7% of low-cost FPGA (and no DSP blocks). Two pipelines, for alpha and beta, calculate the values of gamma (of the BCJR) to reduce the storage requirements. The final log-likelihood ratio (LLR) is calculated together with alpha, reusing intermediate results. The number of bits used by the different signals of the processor is easily configurable. It was set to six bits to the channel measure signals and eight bits to log of probability signals like alpha, beta, and others. The CODEC clock was 100 MHz. The achieved bit rate is 36.2 Kbps per CODEC, but multiple CODECs can be fit into a single chip. The CODEC is 3.49 dB from the channel capacity. [ABSTRACT FROM AUTHOR]

The latent block model is used to simultaneously rank the rows and columns of a matrix to reveal a block structure. The algorithms used for estimation are often time consuming. However, recent work shows that the log-likelihood ratios are equivalent under the complete and observed (with unknown labels) models and the groups posterior distribution to converge as the size of the data increases to a Dirac mass located at the actual groups configuration. Based on these observations, the algorithm Largest Gaps is proposed in this paper to perform clustering using only the marginals of the matrix, when the number of blocks is very small with respect to the size of the whole matrix in the case of binary data. In addition, a model selection method is incorporated with a proof of its consistency. Thus, this paper shows that studying simplistic configurations (few blocks compared to the size of the matrix or very contrasting blocks) with complex algorithms is useless since the marginals already give very good parameter and classification estimates. [ABSTRACT FROM AUTHOR]

Recent research has focused on applying blockchain technology to solve security-related problems in Internet of Things (IoT) networks. However, the inherent scalability issues of blockchain technology become apparent in the presence of a vast number of IoT devices and the substantial data generated by these networks. Therefore, in this paper, we use a lightweight consensus algorithm to cater to these problems. We propose a scalable blockchain-based framework for managing IoT data, catering to a large number of devices. This framework utilizes the Delegated Proof of Stake (DPoS) consensus algorithm to ensure enhanced performance and efficiency in resource-constrained IoT networks. DPoS being a lightweight consensus algorithm leverages a selected number of elected delegates to validate and confirm transactions, thus mitigating the performance and efficiency degradation in the blockchain-based IoT networks. In this paper, we implemented an Interplanetary File System (IPFS) for distributed storage, and Docker to evaluate the network performance in terms of throughput, latency, and resource utilization. We divided our analysis into four parts: Latency, throughput, resource utilization, and file upload time and speed in distributed storage evaluation. Our empirical findings demonstrate that our framework exhibits low latency, measuring less than 0.976 ms. The proposed technique outperforms Proof of Stake (PoS), representing a state-of-the-art consensus technique. We also demonstrate that the proposed approach is useful in IoT applications where low latency or resource efficiency is required. [ABSTRACT FROM AUTHOR]

In order to address the challenges of identifying, detecting, and tracking moving objects in video surveillance, this paper emphasizes image-based dynamic entity detection. It delves into the complexities of numerous moving objects, dense targets, and intricate backgrounds. Leveraging the You Only Look Once (YOLOv3) algorithm framework, this paper proposes improvements in image segmentation and data filtering to address these challenges. These enhancements form a novel multi-object detection algorithm based on an improved YOLOv3 framework, specifically designed for video applications. Experimental validation demonstrates the feasibility of this algorithm, with success rates exceeding 60% for videos such as "jogging", "subway", "video 1", and "video 2". Notably, the detection success rates for "jogging" and "video 1" consistently surpass 80%, indicating outstanding detection performance. Although the accuracy slightly decreases for "Bolt" and "Walking2", success rates still hover around 70%. Comparative analysis with other algorithms reveals that this method's tracking accuracy surpasses that of particle filters, Discriminative Scale Space Tracker (DSST), and Scale Adaptive Multiple Features (SAMF) algorithms, with an accuracy of 0.822. This indicates superior overall performance in target tracking. Therefore, the improved YOLOv3-based multi-object detection and tracking algorithm demonstrates robust filtering and detection capabilities in noise-resistant experiments, making it highly suitable for various detection tasks in practical applications. It can address inherent limitations such as missed detections, false positives, and imprecise localization. These improvements significantly enhance the efficiency and accuracy of target detection, providing valuable insights for researchers in the field of object detection, tracking, and recognition in video surveillance. [ABSTRACT FROM AUTHOR]

In industry, the task of defect classification and defect localization is an important part of defect detection system. However, existing studies only focus on one task and it is difficult to ensure the accuracy of both tasks. This paper proposes a defect detection system based on improved Yolo_v4, which greatly improves the detection ability of minor defects. For K_Means algorithm clustering prianchors question with strong subjectivity, the paper proposes the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to determine the number of Anchors. To solve the problem of low detection rate of small targets caused by insufficient reuse rate of low-level features in CSPDarknet53 feature extraction network, this paper proposes an ECA-DenseNet-BC-121 feature extraction network to improve it. And the Dual Channel Feature Enhancement (DCFE) module is proposed to improve the local information loss and gradient propagation obstruction caused by quad chain convolution in PANet networks to improve the robustness of the model. The experimental results on the fabric surface defect detection datasets show that the mAP of the improved Yolo_v4 is 98.97%, which is 7.67% higher than SSD, 3.75% higher than Faster_RCNN, 10.82% higher than Yolo_v4 tiny, and 5.35% higher than Yolo_v4, and the detection speed reaches 39.4 fps. It can meet the real-time monitoring needs of industrial sites. [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]

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]

This paper aims to propose a table tennis target detection (TD) method based on deep learning (DL) and multi-scale feature fusion (MFF) to improve the detection accuracy of the ball in table tennis competition, optimize the training process of athletes, and improve the technical level. In this paper, DL technology is used to improve the accuracy of table tennis TD through MFF guidance. Initially, based on the FAST Region-based Convolutional Neural Network (FAST R-CNN), the TD is carried out in the table tennis match. Then, through the method of MFF guidance, different levels of feature information are fused, which improves the accuracy of TD. Through the experimental verification on the test set, it is found that the mean Average Precision (mAP) value of the target detection algorithm (TDA) proposed here reaches 87.3%, which is obviously superior to other TDAs and has higher robustness. The DL TDA combined with the proposed MFF can be applied to various detection fields and can help the application of TD in real life. [ABSTRACT FROM AUTHOR]

With the rapid development of AI and big data mining technologies, computerized medical decision-making has become increasingly prominent. The aim of high-utility pattern mining (HUPM) is to discover meaningful patterns in medical databases that contribute to maximizing the utility from the perspective of diagnosis. However, HUPM pays less attention to the interpretability and explainability of these patterns in medical decision-making scenarios. This paper proposes a novel algorithm called the Improved fuzzy high-utility pattern mining (IF-HUPM) to address this problem. First, the paper applies a fuzzy preprocessing method to divide the fuzzy intervals of a medical quantitative data set, which enhances the fuzziness and interpretability of the data. Next, in the process of IF-HUPM, both fuzzy tree and list structures are employed to calculate fuzzy high-utility values. By combining the characteristics of the one-stage and two-stage algorithms of HUPM, an adaptive-phase Fuzzy HUPM hybrid frame is proposed. The experimental results demonstrate that the proposed IF-HUPM algorithm enhances both accuracy and efficiency and the mining process requires less time and space on average. [ABSTRACT FROM AUTHOR]

The aim of this paper is to propose efficient algorithms for computing the energetic lower bounds of the cumulative sheduling problem. So we have to schedule in a minimal makespan a set of non preemptive tasks on a resource with a given capacity. A task has a release date, a processing time and a tail and requires a given amount of the resource capacity during its processing. The energetic lower bound is the largest value of the makespan such that the energetic reasoning check cannot detect unfeasability. We report some algorithms to practically compute this lower bound with efficient complexity. These algorithms rely on iterative augmentations of C max until the cumulative constraint or the energetic constraint is satisfied. Our Computational results show their practical efficiencies. [ABSTRACT FROM AUTHOR]

It is becoming increasingly common for authoritarian regimes to leverage digital technologies to surveil, repress and manipulate their citizens. Experts typically refer to this practice as digital authoritarianism (DA). Existing definitions of DA consistently presuppose a politically repressive agent intentionally exploiting digital technologies to pursue authoritarian ends. I refer to this as the intention-based definition. This paper argues that this definition is untenable as a general description of DA. I begin by illustrating the current predominance of the intention-based definition (Section 2). Section 3 then presents four counterexamples to this definition: benign surveillance; digital sovereignty; attention-harvesting algorithms; and tech-induced loneliness. In each case, we witness authoritarianism being promoted by digital technologies without any evidence of this being intentionally caused by politically repressive agents. Based on these observations, I contend that the intention-based definition is underinclusive and is therefore unsustainable. Section 4 outlines an improved definition of DA – what I call the promotion-based definition. Since this more expansive definition does not posit intentional, politically repressive agency as a precondition of DA, it can accommodate the counterexamples discussed in Section 3. Moreover, it enables us to catch a broader spectrum of cases of DA, such as tech-induced loneliness, which those adhering to the intention-based definition are prone to overlook. After outlining further practical benefits of the promotion-based definition, I argue that we still need to distinguish between intentional and unintentional forms of DA since they call for distinct types of remedial action. [ABSTRACT FROM AUTHOR]

In the world of virtual and connected networks, the study of network's computing capabilities through graph embedding has grown in prominence. In order to implement algorithms created for the guest graph in the host graph, embedding involves simulating one architecture, called the guest, into another, called the host. In this paper, we have obtained the optimal wirelength of embedding (K 9 - C 9) n into Mesh M (3 n , 3 n) , generalized book graph GB [ 9 , 3 n - 1 , 3 n - 1 ] and extended firecracker tree EF 9 n . [ABSTRACT FROM AUTHOR]

In wireless sensor networks, location information is crucial to effectively use the event information recorded by the sensors. However, localizing mobile sensor nodes in resource-constrained networks presents several challenges, including determining the optimal number of anchor nodes, handling mobility, designing a path loss model, considering network topology, and addressing scalability and the number of localized nodes. To overcome these challenges, this paper proposes a coordinate-based auto-localization algorithm (CALA) with a single anchor node for mobile sensor nodes. The proposed algorithm uses an analytical model to determine the location of the target node by considering a parallel coordinate system and retrieving the original location of the target node by moving it to two different locations. The algorithm uses received signal strength indicator (RSSI) values for distance calculation while considering Rayleigh fading in the path loss model. The proposed algorithm's performance is evaluated using various parameter settings, including mobility, node density, fading, path loss exponent, and different random seed values. The study finds that fading and path loss significantly influence the localization process, leading to an accuracy range of 10 to 30% when measuring distances using RSSI. The proposed method shows a 30% improvement in localization accuracy when the number of nodes increases from 5 to 20, achieving an average localization accuracy of 90% in a network with 20 sensor nodes. Furthermore, the study offers an in-depth investigation of the effect of various random generating situations on localization accuracy. Overall, the proposed algorithm offers a promising solution to the challenges of localizing mobile sensor nodes in resource-constrained networks. [ABSTRACT FROM AUTHOR]

As a significant branch of production scheduling problem, the Flexibility Job Shop Scheduling Problem (FJSP) is a typical NP-hard problem. Most conventional flexible workshop scheduling primarily focuses on performance aspects involving production efficiency such as time and quality. In recent years, due to increased energy costs and environmental pollution, 'low-carbon scheduling' has garnered attention as a new scheduling paradigm among scholars and engineers. This paper investigates a low-carbon flexible Job shop scheduling problem, proposing a Grey Wolf Optimization algorithm (SC-GWO), aiming to minimize the sum of carbon emission costs and makespan costs. This algorithm employs the Grey Wolf Algorithm (GWO) as the fundamental optimization method, adaptively choosing between global and local searches based on the dispersion degree of individuals. Firstly, integrating the Sine Cosine Algorithm (SCA), the sinusoidal cosine search mechanism is applied to GWO to enhance its local search capability. Secondly, a new leader selection mechanism is introduced to prevent leaders from falling into local optima, thus improving the algorithm's global exploration capability. Utilizing a nonlinear convergence factor strategy controls the global exploration and local exploitation capabilities in different algorithm stages, enhancing optimization accuracy and accelerating convergence, achieving a dynamic balance between the two. Finally, validation of the SC-GWO algorithm's ability to solve low-carbon scheduling problems in flexible job shop scheduling instances is conducted. Experimental results demonstrate the superior performance of SC-GWO in solving low-carbon flexible workshop scheduling instances. Comparative experiments against four other advanced algorithms on 22 classic benchmark test functions confirm SC-GWO's better convergence. Through standard test functions like Bandimarte instances applied to solve FJSP, experimental results showcase the excellent optimization performance of SC-GWO. Compared to HGWO and GWO, the makespan time is reduced by 22.25% and 39.27%, respectively. The proposed SC-GWO algorithm demonstrates favorable solving effects on flexible job shop scheduling instances, meeting actual production scheduling needs. [ABSTRACT FROM AUTHOR]

This paper proposes the Love Evolution Algorithm (LEA), a novel evolutionary algorithm inspired by the stimulus–value–role theory. The optimization process of the LEA includes three phases: stimulus, value, and role. Both partners evolve through these phases and benefit from them regardless of the outcome of the relationship. This inspiration is abstracted into mathematical models for global optimization. The efficiency of the LEA is validated through numerical experiments with CEC2017 benchmark functions, outperforming seven metaheuristic algorithms as evidenced by the Wilcoxon signed-rank test and the Friedman test. Further tests using the CEC2022 benchmark functions confirm the competitiveness of the LEA compared to seven state-of-the-art metaheuristics. Lastly, the study extends to real-world problems, demonstrating the performance of the LEA across eight diverse engineering problems. Source codes of the LEA are publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/159101-love-evolution-algorithm. [ABSTRACT FROM AUTHOR]

One of the most important strategies used to mitigate the adverse impacts of traffic growth on mobile networks is caching. By caching at the edge, the backhaul traffic load is reduced, and the quality of service for the user is increased. Developing an effective caching algorithm requires accurate prediction of the future popularity of the content, which is a challenging issue. In recent years, deep learning models have achieved high predictive accuracy due to advancements in data availability and increased computing power. In this paper, we present a caching algorithm called the user preference-aware content caching algorithm (UPACA). This algorithm is specifically designed for an edge content delivery platform where users can access content services provided by a remote content provider. UPACA operates in two steps. In the first step, the proposed collaborative filtering-based popularity prediction algorithm (CFPA) is used to predict future content popularities. CFPA utilizes a gated residual variational autoencoder collaborative filtering model to predict users' future preferences and calculate the future popularity of content. This algorithm considers the popularity of the content as well as the number and timing of content requests. Experimental results demonstrate that UPACA outperforms previous methods in terms of cache hit rates and user utilities. [ABSTRACT FROM AUTHOR]

Optimization of scheduling problems that involve single or multiple machines with a multimodal objective function and linear or non-linear constraints are generally NP-Hard. The Artificial Bee Colony (ABC) algorithm is a metaheuristic proposed about eighteen years ago and has gained popularity in the swarm intelligence-based evolutionary computation approaches. Various scheduling requirements in diverse domains viz. Electric Vehicle charging, Operating Room, Nurse, Automatic Guided Vehicle (AGV), production, manufacturing, hydrothermal, Printed Circuit Board (PCB), and call centre scheduling are studied. The NP-Hard nature of these scheduling problems render exact methods incompetent for large problem instances thus requiring meta-heuristics such as ABC to produce feasible solutions in a reasonable computation time. The adaptation of ABC in these domains and the outcomes and performance of ABC are highlighted. Based on the case studies, it is found that the requirements of scheduling problems that would benefit from ABC are those that have a planning horizon requiring a near-optimal solution in a reasonably fast time when run on minimal computing capacity. Future works that would merit the application and enhancement of existing ABC techniques are proposed. The case studies presented in this paper motivate R&D Engineers and young researchers to apply ABC techniques of optimization and scheduling in different research topics for achieving energy and cost efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we present an underdetermined estimation algorithm for two-dimensional (2-D) direction of arrival (DOA) based on three-parallel co-prime arrays. By exploiting the correlation among the received signals in both the time and spatial domains, a conjugate augmented spatial–temporal virtual array is constructed, which significantly increases the degrees of freedom (DOF) and expands the array aperture, effectively filling the holes within the physical array. By vectorizing the covariance matrix obtained from the spatial–temporal virtual array, we can transform the 2-D DOA estimation problem into a one-dimensional (1-D) sparse recovery problem. Thereafter, the excellent gridless methods: sparse and parametric approach (SPA) and the matrix pencil and pairing (MAPP), are adopted to efficiently resolve the 1-D sparse recovery problem, enabling the automatic pairing of the 2-D angles. And simulation results are provided to validate the enhanced accuracy of the proposed algorithm in comparison with other existing methods. [ABSTRACT FROM AUTHOR]

This paper introduces a novel algorithm for optimizing the coefficients of the digital filters used in incremental delta-sigma analog-to-digital converters (IDSC). This algorithm is modified from constrained linear least squares (LS) to improve the signal-to-noise-and-distortion ratio (SNDR) of IDSC and minimize the oversampling rate (OSR) of the modulator, which enhances system speed and reduces power consumption. In the case of the same SNDR, the first-order IDSC with the proposed filter can reduce the OSR of the modulator by 50%, at least compared to the IDSC with the conventional filter. The second-order IDSC with the proposed filter can reach a higher SNDR of 106 dB with an OSR of 64. Considering the nonlinearity of the integrator, the SNDR of IDSC with the proposed filter is also 10 dB greater than that of the conventional filters and 3dB greater than that of the IDSC using the near-optimal algorithm filter. The experimental results indicate that the proposed filter possesses an excellent figure of merit of 0.028 pJ / conv . [ABSTRACT FROM AUTHOR]

The controlled autoregressive autoregressive moving average (CARARMA) models are of popularity to describe the evolution characteristics of dynamical systems. To overcome the identification obstacle resulting from colored noises, this paper studies the identification of the CARARMA models by forming an intermediate correlated noise model. In order to realize the real-time prediction function of the models, the on-line identification scheme is developed by constructing the dynamical objective functions based on the real-time sampled observations. Firstly, a rolling optimization cost function is built based on the observation at a single sampling instant to catch the modal information at a single time point and a generalized extended stochastic gradient (GESG) algorithm is proposed through the stochastic gradient optimization. Secondly, a rolling window cost function is built in accordance with the dynamical batch observations within data window by extending the proposed GESG algorithm and the multi-innovation generalized extended stochastic gradient algorithm is derived. Thirdly, from the perspective of theoretical analysis, the convergence proof of the proposed algorithm is provided based on the stochastic martingale convergence theory. Finally, the simulation analysis and comparison studies are provided to show the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Optimal dispatch in power systems is a complex mathematical model of nonlinear programming with many physical constraints, which is difficult to solve by conventional methods. Thus, intelligent algorithms are now viable options for resolving the nonlinear scheduling issues of microgrids. In this paper, we propose a double-layer optimization strategy based on the multi-point improved gray wolf algorithm (MPIGWO). The inner layer optimizes load profiles with time-of-use tariffs. The outer one achieves a fast search for the optimal solution and prevents getting stuck in a local optimum, which improves the gray wolf algorithm significantly. First, the Bernoulli map is used to randomly generate the initial population. Second, the efficiency of optimization can be improved by modifying the attenuation factor based on the Sin function and then updating the exact weight factor of the position to reasonably select the best position. Finally, an improved dimensional learning-based hunting (IDLH) search strategy is employed to determine the optimal solution. The numerical case study shows that the proposed double-layer optimization strategy can implement dynamic scheduling for distributed power sources while lowering the costs of economic operation and environmental protection significantly. [ABSTRACT FROM AUTHOR]

Simon's problem is one of the most important problems demonstrating the power of quantum algorithms, as it greatly inspired the proposal of Shor's algorithm. The generalized Simon's problem is a natural extension of Simon's problem and also a special hidden subgroup problem: Given a function f : { 0 , 1 } n → { 0 , 1 } m , it is promised that there exists a hidden subgroup S ≤ Z 2 n of rank k such that for any x , y ∈ { 0 , 1 } n , f (x) = f (y) iff x ⊕ y ∈ S . The goal of generalized Simon's problem is to find the hidden subgroup S. In this paper, we present two key contributions. Firstly, we characterize the structure of the generalized Simon's problem in distributed scenario and introduce a corresponding distributed quantum algorithm. Secondly, we refine the algorithm to ensure exactness due to the application of quantum amplitude amplification technique. Our algorithm offers exponential speedup compared to the distributed classical algorithm. When contrasted with the quantum algorithm for the generalized Simon's problem, our algorithm's oracle requires fewer qubits, thus making it easier to be physically implemented. Particularly, the exact distributed quantum algorithm we develop for the generalized Simon's problem outperforms the best previously proposed distributed quantum algorithm for Simon's problem in terms of generalizability and exactness. [ABSTRACT FROM AUTHOR]

Due to the large computational requirements of object detection algorithms, high-resolution remote sensing vehicle detection always involves numerous small objects, high level of background complexity, and challenges in balancing model accuracy and parameter count. The attention mechanism and efficient architecture lightweight-YOLO (AMEA-YOLO) is proposed in this paper. A lightweight network as the backbone network of AMEA-YOLO is designed, and it could maintain model accuracy and ensure good lightweight. FasterNet is employed to accelerate model training speed. The enhanced deep second-order channel attention module (EnhancedSOCA) is utilized to improve the image high-resolution. In addition, a lightweight module is devised to further reduce the model's weight. The implementation of the HardSwish activation function improves model accuracy. The experimental results indicate that the AMEA-YOLO algorithm could ensure model lightweight and accurate performance. [ABSTRACT FROM AUTHOR]

Power system operators are faced with the problem of unit commitment belonging to mixed integer programming, which becomes very complicated, as units become large-scale and highly constrained. Because unit commitment problem is a binary problem with commitment and de-commitment, a discrete/binary optimization algorithm with superior performance is required. This paper proposes a novel hybrid binary bat algorithm for unit commitment problem, which consists of two process. To begin with, the proposed binary bat algorithm is applied to determining the commitment schedule of unit commitment problem. Specifically, an improved crossover operator based on exponential-logic-modulo map is proposed to enhance the convergence and maintain the diversity of populations. To prevent the algorithm from falling into a local optimum, a local mutation strategy performs local perturbation. Chaotic map is responsible for updating some parameters to increase the performance of the proposed algorithm. Furthermore, Lambda-iteration method is adopted to solve economic load dispatch in continuous space. Constraint handling is performed using the heuristic constraint produce. The effectiveness of the proposed algorithm is verified by benchmark functions and test systems. Additionally, the simulation results are compared with other well-established heuristic and binary approaches. [ABSTRACT FROM AUTHOR]

Nowadays, establishing security in data transmission is essential, and it is achieved by cryptography. Encryption of still or video images in specific applications such as Internet of Things, medical and satellite imaging, in applications requiring high-speed encryption, or even in applications where a personal computer is unavailable or cannot be used needs special hardware. In this paper, an image encryption algorithm based on chaos theory named Parallel Chaotic Checksum-based Image Encryption or PCCIE algorithm is proposed that has been able to provide a fast, efficient and secure algorithm with a hardware perspective on the design and parallel system structure. Using high-level synthesis, PCCIE is implemented on a Field Programmable Gate Array (FPGA). The proposed algorithm using small and independent local image buffers solved FPGA internal memory limitation for encryption of large images. Ultimately, a Hexa-core crypto-processor with single-precision floating-point and fixed-point precision has been designed, capable of encrypting 256 × 256 and Full HD images in 2.13 and 59.52 milliseconds, respectively, at 469 and 16 frames per second. [ABSTRACT FROM AUTHOR]

The sparse triangular solver (SpTRSV) is mostly used for scientific and engineering applications. The structured-grid triangular solver of regular dependencies (STRSV) is a special kind of SpTRSV. Some general SpTRSVs that disregards the regularity of the matrix are unsuitable for solving this problem. This paper proposes an efficient parallel algorithm for STRSV on the SW26010 (a kind of China independently designed many-core processors), namely swStructTRSV. The algorithm makes full use of the fine-grained and low latency communication characteristics of the SW26010 to reduce the waiting time for synchronization, maximizes the regularity of access to improve memory access bandwidth, and achieves overlap between memory access and computation simultaneously. Moreover, the idea of the algorithm can be extended to incomplete LU factorization (ILU factorization) because of consistent dependencies. The experimental results on a core group(8 * 8 network composed of 64 cores) of SW26010 show that swStructTRSV can achieve an average speedup of over 30 in the sequential version. swStructTRSV on SW26010 achieves solving speedups of 2.2 and 6.3 over the fast STRSV (fSpTRSV) previously implemented on SW26010 and MKL on Intel Xeon Gold 6132, respectively. swStructTRSV significantly outperforms cuSparse on NVIDIA TITAN RTX in terms of overall execution time. [ABSTRACT FROM AUTHOR]

A neural network model needs to be manually designed for ancient mural dynasty recognition, and this paper proposes an ancient mural dynasty recognition algorithm that is based on a neural architecture search (NAS). First, the structural edge information of mural images is extracted for use by the neural network model in recognizing mural missions. Second, an NAS algorithm that is based on contrast selection (CS) simplifies the architecture search to an incremental CS and then searches for the optimal network architecture on the mural dataset. Finally, the identified optimal network architecture is used for training and testing to complete the mural dynasty recognition task. The results show that the top accuracy of the proposed method on the mural dataset is 88.10%, the recall rate is 87.52%, and the precision rate is 87.69%. Each evaluation index used by the neural network model is superior to that of classical network models such as AlexNet and ResNet-50. Compared with NAS methods such as ASNG and MIGO, the accuracy of mural dynasty recognition is higher by an average of 4.27% when using the proposed method. The proposed method is verified on CIFAR-10, CIFAR-100, ImageNet16-120 and other datasets and achieves a good recognition accuracy in the NAS-bench-201 search space, which averages 93.26%, 70.73% and 45.34%, respectively, on the abovementioned datasets. [ABSTRACT FROM AUTHOR]

The Manta Ray Foraging Optimization Algorithm (MRFO) is a metaheuristic algorithm for solving real-world problems. However, MRFO suffers from slow convergence precision and is easily trapped in a local optimal. Hence, to overcome these deficiencies, this paper proposes an Improved MRFO algorithm (IMRFO) that employs Tent chaotic mapping, the bidirectional search strategy, and the Levy flight strategy. Among these strategies, Tent chaotic mapping distributes the manta ray more uniformly and improves the quality of the initial solution, while the bidirectional search strategy expands the search area. The Levy flight strategy strengthens the algorithm's ability to escape from local optimal. To verify IMRFO's performance, the algorithm is compared with 10 other algorithms on 23 benchmark functions, the CEC2017 and CEC2022 benchmark suites, and five engineering problems, with statistical analysis illustrating the superiority and significance of the difference between IMRFO and other algorithms. The results indicate that the IMRFO outperforms the competitor optimization algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates the finite-time and fixed-time bipartite consensus problem of multiple Euler–Lagrange systems (MELSs) under directed interaction topologies, considering external disturbances. Two novel distributed hierarchical control algorithms are developed to address the challenging problems. Specifically, a new distributed estimator is designed to estimate the states of agents in finite/fixed-time. Moreover, based on the obtained estimations, the finite/fixed-time controller is presented to accomplish the bipartite consensus of the MELSs in the local control layer. By utilizing Lyapunov stability and the nearest neighbor-interactions theory, sufficient conditions for achieving the designed performance of the MELSs are derived. Simulation examples are provided to illustrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The performance of a narrowband active noise control (NANC) system can be significantly degraded due to the frequency mismatch (FM). In this paper, the statistical performance of a typical FxLMS-based NANC system in the presence of FM is analyzed in detail. Difference equations governing the system dynamics and closed-form steady-state mean-square error expressions are derived and discussed. The stochastic analysis results reveal that the FM introduces small troublesome sinusoids into the NANC system. The controller has to track these sinusoids to minimize the residual noise, which leads to a serious performance deterioration. As a by-product, the optimal step sizes that minimize the effect of a relatively small FM are also derived. The findings significantly enrich our understanding of the stochastic behavior of the FxLMS algorithm in the presence of FM and also provide some useful information for NANC system design. Extensive simulations are conducted to confirm the validity of the analytical findings. [ABSTRACT FROM AUTHOR]

Effective resource allocation is a crucial aspect in improving the performance of visible light communication (VLC)/hybrid WiFi networks. This study presents a dynamic resource allocation algorithm for multiuser scenarios. A fuzzy logic technique is employed to select the network resources with higher scores for communication. In this sense, to ensure fairness in the hybrid VLC-WiFi network, an enhanced proportional fairness (PF) algorithm is utilized. The algorithm considers the access delay and the distance to the access point as users move, striking a reasonable balance between fair allocation of user resources and maximizing system resource utilization. Therefore, to enhance the user experience in real-time scheduling, a compensation factor is introduced to compensate users with higher latency, increasing their priority. A thorough analysis is provided for the throughput, latency, utility value, and fairness models of the VLC-WiFi heterogeneous network system. The algorithm proposed in this paper is simulated and compared with four traditional algorithms, namely the Round-Robin algorithm, maximum carrier to interference ratio (Max C/I) algorithm, modified largest weighted delay first algorithm, and PF algorithm, under different metrics. Simulation results show that the proposed method is effective, and the improved algorithm yields better fairness and higher throughput compared to the conventional algorithm. As the number of frames increases, the fairness index of the proposed algorithm increases the fastest and gradually converges to 1. The throughput of this method is significantly greater than that of previous algorithms, reaching a stable rate of roughly 1.3 Mbps. The suggested algorithm is approximately 150% higher than the throughput achieved by conventional PF algorithms. The algorithm improves user satisfaction in different regions, and the utility value remains around 0.975. The packet loss rate is approximately 75% lower than traditional typical algorithms, and the average latency is approximately 60% lower. Simulation results show that the proposed algorithm has good application prospects in heterogeneous networks. [ABSTRACT FROM AUTHOR]

Arithmetic optimization algorithm (AOA) is a meta-heuristic optimization method based on mathematical operators proposed in recent years. Although it has good performance, it can also lead to insufficient local search ability and falling into local optima when solving complex optimization problems. In order to make up for the above shortcomings, the optimization performance of AOA is further improved. This paper proposes a hybrid algorithm based on AOA and particle swarm optimization (PSO) called HAOAPSO. Firstly, a compound opposition-based learning (COBL) strategy is introduced to broaden the scope of finding optimal solutions to help the algorithm better jump out of local optima. Secondly, PSO is combined with AOA that integrates COBL to improve the algorithm's local search ability, so as to improve the overall search efficiency of the algorithm. In addition, experiments are performed on 23 classical benchmark functions with different characteristics and five engineering design optimization problems, and the experimental results of HAOAPSO are compared with those of other well-known optimization algorithms to comprehensively evaluate the performance of the proposed algorithm. The simulation results show that HAOAPSO can provide better solutions in most cases when solving global optimization problems such as engineering, with better convergence speed and accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a massively parallel approach of the multilevel fast multipole algorithm (PMLFMA) on homegrown many-core SW26010 cluster of China, noted as (SW-PMLFMA), for 3-D electromagnetic scattering problems. In this approach, the multilevel fast multipole algorithm (MLFMA) octree is first partitioned among management processing elements (MPEs) of SW26010 processors following the ternary partitioning scheme using the message passing interface (MPI). Then, the computationally intensive parts of the PMLFMA on each MPI process, matrix filling, aggregation and disaggregation are accelerated by using all the 64 computing processing elements (CPEs) in the same core group of the MPE via the Athread parallel programming model. Different parallelization strategies are designed for many-core accelerators to ensure a high computational throughput. In coincidence with the special characteristic of local Lagrange interpolation, the compressed sparse row (CSR) and the compressed sparse column (CSC) sparse matrix storage format is used for storing interpolation and anterpolation matrices, respectively, together with a specially designed cache mechanism of hybrid dynamic and static buffers using the scratchpad memory (SPM) to improve data access efficiency. Numerical results are included to demonstrate the efficiency and versatility of the proposed method. The proposed parallel scheme is shown to have excellent speedup. [ABSTRACT FROM AUTHOR]

This paper considers Bayesian analysis of data from seemingly unrelated regression models whose errors have a distribution that is scale mixtures of normal distributions. A non-iterative Bayesian sampling algorithm is developed to obtain the posterior samples, which eliminates the convergence problems in iterative Markov Chain Monte Carlo (MCMC) approach. The performances of the proposed algorithm are illustrated through simulation studies, and the results show that it appears to outperform the MCMC approach and is time-efficient compared to the existing methods. In the case of outliers, the model selection criteria results indicate that the heavy-tailed SUR models is more robust than the normal SUR models. Also, a real data example is analyzed using the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a feasible expectation-conditional-maximization (ECM) algorithm is developed for finding the maximum likelihood estimates of parameters of the skew-normal based stochastic frontier model. The closed-form formulas for updating parameters in CM-steps are derived. The proposed methodology is illustrated with simulations and a real data example, where we find the new ECM algorithm outperforms the numerical approach adopted in the previous study. [ABSTRACT FROM AUTHOR]

The location planning of relief distribution centres (DCs) is crucial in humanitarian logistics as it directly influences the disaster response and service to the affected victims. In light of the critical role of facility location in humanitarian logistics planning, the study proposes a two-stage relief distribution location problem. The first stage of the model determines the minimum number of relief DCs, and the second stage find the optimal location of these DCs to minimize the total cost. To address a more realistic situation, restrictions are imposed on the coverage area and capacity of each DCs. In addition, for optimally solving this complex NP-hard problem, a novel two-phase algorithm with exploration and exploitation phase is developed in the paper. The first phase of the algorithm i.e., exploration phase identifies a near-optimal solution while the second phase i.e. exploitation phase enhances the solution quality through a close circular proximity investigation. Furthermore, the comparative analysis of the proposed algorithm with other well-known algorithms such as genetic algorithm, pattern search, fmincon, multistart and hybrid heuristics is also reported and computationally tested from small to large data sets. The results reveal that the proposed two-phase algorithm is more efficient and effective when compared to the conventional metaheuristic methods. [ABSTRACT FROM AUTHOR]

The Mini-Mental State Examination (MMSE) is a widely employed screening tool for the severity of cognitive impairment. Among the MMSE items, the pentagon copying test (PCT) requires participants to accurately replicate a sample of two interlocking pentagons. While the PCT is traditionally scored on a binary scale, there have been limited developments of granular scoring scale to assess task performance. In this paper, we present a novel three-stage algorithm, called Quantification of Interlocking Pentagons (QIP) which quantifies PCT performance by computing the areas of individual pentagons and their intersection areas, and a balance ratio between the areas of the two individual pentagons. The three stages of the QIP algorithm include: (1) detection of line segments, (2) unraveling of the interlocking pentagons, and (3) quantification of areas. A set of 497 PCTs from 84 participants including their baseline and follow-up PCTs from the Rush Memory and Aging Project was selected blinded about their cognitive and clinical status. Analysis of the quantified data revealed a significant inverse relationship between age and balance ratio (beta = − 0.49, p = 0.0033), indicating that older age was associated with a smaller balance ratio. In addition, balance ratio was associated with perceptual speed (r = 0.71, p = 0.0135), vascular risk factors (beta = − 3.96, p = 0.0269), and medical conditions (beta = − 2.78, p = 0.0389). The QIP algorithm can serve as a useful tool for enhancing the scoring of performance in the PCT. [ABSTRACT FROM AUTHOR]

High precision is a very important index in target tracking. In order to improve the prediction accuracy of target tracking, an optimized Kalman filter approach based on improved Gray Wolf algorithm (IGWO-OKF) is proposed in this paper. Since the convergence speed of traditional Gray Wolf algorithm is slow, meanwhile, the number of gray wolves and the choice of the maximum number of iterations has a great influence on the algorithm, a nonlinear control parameter combination adjustment strategy is proposed. An improved Grey Wolf Optimization algorithm (IGWO) is formed by determining the best combination of adjustment parameters through the fastest iteration speed of the algorithm. The improved Grey Wolf Optimization algorithm (IGWO) is formed, and the process noise covariance matrix and observation noise covariance matrix in Kalman filter are optimized by IGWO. The proposed approach is applied into. The experiment results show that the proposed IGWO-OKF approach has low error, high accuracy and good prediction effect. [ABSTRACT FROM AUTHOR]

This paper presents a fusion algorithm based on the enhanced RRT* TEB algorithm. The enhanced RRT* algorithm is utilized for generating an optimal global path. Firstly, proposing an adaptive sampling function and extending node bias to accelerate global path generation and mitigate local optimality. Secondly, eliminating path redundancy to minimize path length. Thirdly, imposing constraints on the turning angle of the path to enhance path smoothness. Conducting kinematic modeling of the mobile robot and optimizing the TEB algorithm to align the trajectory with the mobile robot's kinematics. The integration of these two algorithms culminates in the development of a fusion algorithm. Simulation and experimental results demonstrate that, in contrast to the traditional RRT* algorithm, the enhanced RRT* algorithm achieves a 5.8% reduction in path length and a 62.5% decrease in the number of turning points. Utilizing the fusion algorithm for path planning, the mobile robot generates a superior, seamlessly smooth global path, adept at circumventing obstacles. Furthermore, the local trajectory meticulously conforms to the kinematic constraints of the mobile robot. [ABSTRACT FROM AUTHOR]

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]

Blind source separation (BSS), as a digital signal processing approach, focuses on estimating the underlying source signals from their linear mixtures without any prior information about the source signals and mixing matrix. Conventional methods for the BSS, however, are incapable of separating the complex-valued source signals. By leveraging the negative conjugate gradient to minimize the least mean square error reconstruction (LMSER) principle in complex domain, this paper proposes a collection of least-squares algorithms for complex-valued BSS (CBSS), including least-mean square (LMS)-type algorithms and recursive least-squares (RLS)-type algorithms. We demonstrate the availability of the proposed algorithms in both circular and non-circular source signals separation. Especially, the RLS algorithm for the CBSS without prewhitening is superior in cross-talking criterion to the others, as verified by computer simulations on artificial source signals. [ABSTRACT FROM AUTHOR]

High-accuracy positioning plays an important role in future 5G-Advanced cellular networks. However, it is quite challenging to utilize 5G signals to estimate positioning parameters accurately under multipath environments. To establish a high-precision real-time location system (RTLS), which is suitable for indoor scenarios, advanced parameter estimation techniques with low computational complexity should be developed. This paper proposes a modified subspace-based time-of-arrival (TOA) estimation algorithm for 5G signals with ultra-high accuracy and reduced complexity under multipath environments. A novel smoothing scheme termed as grouped subcarrier smoothing (GSS) is proposed to reduce the dimension of the covariance matrix involved in typical subspace-based methods. Then, the computational complexity of the corresponding eigenspace calculation is reduced. The TOA estimation performance is enhanced by constructing multiple snapshots of the estimated channel frequency response vector in the frequency domain. Finally, the super-resolution TOA estimate can be obtained using only one orthogonal frequency division multiplexing (OFDM) symbol. Simulation results show that the proposed method achieves centimeter-level TOA estimation accuracy under multipath channels when the signal-to-noise ratio (SNR) is high. [ABSTRACT FROM AUTHOR]

In this paper, a stochastic model is presented for the nonparametric variable step-size normalized least-mean-square (NP-VSS-NLMS) algorithm. This algorithm has demonstrated potential in practical applications and hence a deeper understanding of its behavior becomes crucial. In this context, model expressions are obtained for characterizing the algorithm behavior in the transient phase as well as in the steady state, considering a system identification problem and Gaussian input data. Such expressions reveal interesting algorithm characteristics that are useful for establishing design guidelines and for the advancement of more refined algorithms. Simulation results for various operating scenarios ratified both the model's accuracy and the algorithm's superior performance relative to other recent and relevant algorithms from the literature. [ABSTRACT FROM AUTHOR]

The utilization of cloud computing environments is highly popular for carrying out workflow executions due to its ability to provide clients with immediate access to computing resources. Among the various workflow scheduling problems in the cloud, deadline-constrained workflow scheduling has garnered increasing attention in recent years. This paper introduces a hybrid scheduling algorithm known as Partial Critical Path–Ant Colony Optimization (PCP–ACO), which aims to minimize the execution cost of a workflow while ensuring that it meets the user-defined deadline in cloud environments. PCP–ACO is a list scheduling algorithm that combines the PCP heuristic algorithm with the meta-heuristic ACO to achieve faster convergence. The list scheduling algorithm consists of two phases: task ordering and resource selection. In the case of PCP–ACO, the first step involves calculating a topological sort of the workflow tasks to assign priority to each task. Subsequently, the ACO meta-heuristic is employed to allocate the appropriate resource to each task of the workflow, based on their respective sub-deadlines that are computed using the PCP heuristic. In order to evaluate the effectiveness of the proposed algorithm, several experiments were conducted using five real scientific workflows. The results demonstrate that PCP–ACO outperforms the IC-PCP, L-ACO, and HP-GA algorithms in terms of average execution cost, achieving reductions of 19%, 17.3%, and 21.5%, respectively. [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]

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]

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]