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Although it was claimed that the MKR-α method can improve the overshoot and nonlinear stability characteristics of the KR-α method, it seems that it still has a high frequency overshoot in steady-state responses and a weak instability. Three examples are applied to numerically illustrate the two adverse properties. A loading-correction term is introduced into the displacement difference equation to remove the adverse overshoot in high frequency steady-state responses. Besides, it is analytically verified that the MKR-α method has an adverse weak instability. Although the problem of high frequency overshoot in steady-state responses can be overcome, there is no way to eliminate the adverse weak instability for both the KR-α method and MKR-α method. Thus, the applications of the two families of integration methods are very limited. It is demonstrated that a high frequency numerical damping is incapable of mitigating the overshoot caused by a weak instability. [ABSTRACT FROM AUTHOR]

This paper examines the work practices involved in making data legible to machines and machine output legible to humans. The study is based on ethnographic research of a team of art experts at DNArt – a data classification system that features a growing database of art images, a classification scheme, a similarity matching algorithm, and a website that together serve as a consumer judgment device in an emerging online market for art. I analyze interactions from meeting observations, interviews, documentation, and online interaction data to show how non-technical art experts explain and repair sociotechnical breakdowns – when their expectations for similarity between art images and artists differ from the similarity relations produced by the algorithm. By repairing breakdowns, the art experts construct the algorithm anew, as a legitimate revealer of similarity in art. In doing so, the team's repair work is folded back into the black box of the algorithm, rendering it invisible and unacknowledged, sometimes even by the experts themselves. [ABSTRACT FROM AUTHOR]

The significant proliferation of research papers in both conferences and journals has made it difficult for researchers to easily access relevant scholarly papers for academic learning. This has been a substantial problem for many researchers. Conferences, in comparison with journals, have an aspect of social learning and networking, which leads to personal familiarisation through various interactions among researchers. In this paper, we improve the social awareness of conference participants by proposing a novel folksonomy-based paper recommendation algorithm, called socially aware recommendation of scholarly papers (SARSP). SARSP recommends papers issued by active participants (APs), to other Group Profile Participants at the same conference based on preference similarity of their research interests. In addition, SARSP computes the social ties between an AP and other conference participants to effectively generate social recommendations of scholarly papers. We evaluate our proposed algorithm using a real-world data-set. Our experimental results confirm that SARSP has significant improvement over other existing methods. [ABSTRACT FROM AUTHOR]

In-clinic follow-up to assess completion of medical abortion is no longer a requirement according to World Health Organization guidance, provided adequate counselling is given. However, timely recognition of ongoing pregnancy, complications or incomplete abortion, which require treatment, is important. As part of a larger trial, this study aimed to establish whether women having a medical abortion could self-assess whether their abortion was complete using an automated, interactive questionnaire on their mobile phones. All 469 participants received standard abortion care and all returnees filled in a self-assessment on paper at clinic follow-up 2–3 weeks later. The 234 women allocated to receive the phone messages were also asked to do a mobile phone assessment at home ten days post-misoprostol. Completion of the mobile assessment was tracked by computer and all completed assessments, paper and mobile, were compared to providers’ assessments at clinic follow-up. Of the 226 women able to access the mobile phone assessment, 176 (78%) completed it; 161 of them (93%) reported it was easy to do so. Neither mobile nor paper self-assessments predicted all cases needing additional treatment at follow-up. Prediction of complete procedures was good; 71% of mobile assessments and 91% of paper assessments were accurate. We conclude that an interactive questionnaire assessing completion of medical abortion on mobile phones is feasible in the South African setting; however, it should be done later than day 10 and combined with an appropriate pregnancy test to accurately detect incomplete procedures. [ABSTRACT FROM AUTHOR]

Fractional-order derivative is attracting more and more attention of researchers in image processing because of its better property in restoring more texture than the total variation. To improve the performance of fractional-order variation model in image restoration, a truncated fractional-order variation model was proposed in Chan and Liang [Truncated fractional-order variation model for image restoration, J. Oper. Res. Soc. China]. In this paper, we propose a dual approach to solve this truncated fractional-order variation model on noise removal. The proposed algorithm is based on the dual approach proposed by Chambolle [An algorithm for total variation minimisation and applications, J. Math Imaging Vis. 20 (2004), pp. 89–97]. Conversely, the Chambolle's dual approach can be treated as a special case of the proposed algorithm with fractional order α = 1. The work of this paper modifies the result in Zhang et al. [Adaptive fractional-order multi-scale method for image denoising, J. Math. Imaging Vis. 43(1) (2012), pp. 39–49. Springer Netherlands 0924–9907, Computer Science, pp. 1–11, 2011], where the convergence is not analysed. Based on the truncation, the convergence of the proposed dual method can be analysed and the convergence criteria can be provided. In addition, the accuracy of the reconstruction is improved after the truncation is taken. [ABSTRACT FROM AUTHOR]

The two basic equations in the mentioned article are wrong. [ABSTRACT FROM AUTHOR]

The use of emotion to drive robotic interaction continues to grow across a range of use cases, from social robotics to increased survivability. Nevertheless, these efforts remain isolated from each other and are not easily compared between papers and projects. To this end an extensive survey of 1427 IEEE and ACM publications was conducted, covering robotics and emotion. The survey first resulted in broad categorizations of key trends covering emotional input and output. This was followed by an extended analysis on 232 papers that focused on the internal processing of emotion, where emotion was handled through some kind of algorithm and not just as an input or output. From this analysis, three broad categories were developed: emotional intelligence, emotional model, and implementation. Emotional intelligence captured the manner in which emotion was handled and included the subcategories: algorithm, mapping, and history. The emotional model category captured the emotion categories and number of emotions used, while the implementation category tracked the role, purpose, and platform. This paper concludes with a summary of key features discovered through the process, future opportunities, and a discussion of the intrinsic challenges emerging from the interaction of emotion and robotics. [ABSTRACT FROM AUTHOR]

There are great interests of designing research metrics and indices to measure the research impacts in research institutes. Unfortunately, most of those indices ignore critical design issues, e.g. the disparity between domains, the impact of journals or conferences in which papers are published, normalising the range of the index values to certain intervals, and the scalability of using the index to rank different research entities. In this paper, a new normalised fuzzy index, (NFindex), is proposed as a fuzzy-based research impact metric. The proposed index is a scalable index whose values are normalised to the percentage levels. NFindex achieves both inter-discipline normalisation and intra-discipline consistency. The capability of NFindex to achieve the inter-discipline normalisation enables fair comparison between different research domains regardless their nature in terms of influence and contribution to other research areas, e.g. natural science. Therefore, NFindex gives a universal normalised single-number metric that can be used by research institutes to solve the problem of inter-discipline scholar ranking. Moreover, it can help universal ranking of universities and research institutes according to their research capabilities and impacts. The obtained results, on diverse research areas, prove the potential of NFindex in terms of both intra-discipline consistency and inter-discipline normalisation. [ABSTRACT FROM AUTHOR]

Three-colour four-way traffic light completed 100 years in 2020. Even though the traffic light in the form of Semaphore arms has been in use in London since 1868, electric traffic lights came into existence in 1912 and the standard three-colour four-way light in 1920. Research is continuously being carried out to develop better algorithms to improve safety, reduce travel delays, and optimize road capacity. Hence a review of the evolution of traffic lights is warranted. This paper presents an all-round review using a six-prong approach. Timeline of the evolution of the literature in the last 100 years, the evolution of hardware, algorithms, traffic control schemes, standards and the pedestrian lights and count down timer are the six areas in which the review is carried out. A timeline of the different keywords related to the various algorithms in use is presented. This article delves into the thinking and meticulous approach of early researchers and practitioners of the field while dwelling on the past. They laid the rock-solid foundation of today's research. Also, future research areas like connected vehicles and automated vehicles are pointed out, and a summary of the findings is presented at the end. [ABSTRACT FROM AUTHOR]

With series on conventional television and pay TV platforms now a key element of media consumption, they have gained increasing academic attention in the last decade, both as a main object of study and in combination with other social phenomena. However, the boundaries of this line of research, which draws together researchers from different fields, have become increasingly blurred. This paper undertook a bibliometric investigation to understand how this line of research has come about, what its characteristics are, the main streams within it, and the extent to which the rise in publications reflects a mature and consolidated field of research in its own right. This analysis focused on the development of scientific production on television series indexed by database Scopus between 2010 and 2019 (n = 1,679 documents). More specifically, this study analyzed authorship, journals, national output and international collaboration, co-citation of keywords to ascertain the main intellectual trends in the area and the co-occurrence of references to find out if there is a theoretical body of works that serve as a foundation for this research. The results show symptoms of immaturity, such as a lack of continuity in authority, little concurrence between specialized journals and the most cited authors and works, vagueness in both the keyword clusters and the papers that are often cited together. In addition, a large number of the most cited works come from fields outside Communication that consider television series an accessory aspect of their main theme, such as their impact on the influx of tourists as a result due to the consumption of cultural works. On the other hand, this points to a strong, versatile line of research capable of hosting research on new and old media and related to various topics. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate that the relation between two jobs defined by min{ai, bj} ≤ min{bi, aj}, used in Johnson’s theorem, is not transitive. However, both the theorem and Johnson’s algorithm are correct. [ABSTRACT FROM AUTHOR]

To solve the control problem of time-varying state-scale nonlinear systems whose initial state is not affected by settling time, fixed-time convergence algorithms are proposed for first-order systems and higher-order systems in this paper. First, a scalar model is used to illustrate how the time-varying feedback parameter can guarantee that the system achieves asymptotic stability while achieving finite-time convergence, and it is proved that the settling time obtained in this paper is only related to the prescribed boundary. This allows us to design the settling time with an appropriate parameter based on the prescribed boundary. To exhibit the effectiveness and extensibility of the proposed algorithm for first-order scalar systems, the results are subsequently extended to general higher-order systems based on the backstepping method. By introducing numerical simulation results, this paper verifies that the proposed algorithm will make the system achieve asymptotic stability and its output can converge to a given boundary, regardless of the system's initial states. [ABSTRACT FROM AUTHOR]

This paper aims to build a machine learning and a neural network model to detect the probability of money laundering in banks. The paper's data came from a simulation of actual transactions flagged for money laundering in Middle Eastern banks. The main findings highlight that criminal networks mainly use the integration stage to integrate money into the financial system. Fraudsters prefer to launder funds in the early hours, morning followed by the business day's afternoon time intervals. Additionally, the Naïve Bayes and Random Forest classifiers were identified as the two best-performing models to predict bank money laundering transactions. [ABSTRACT FROM AUTHOR]

In practical manufacturing systems, the job processing time usually varies with the performance change of manufacturing resources, among which the learning and deteriorating effects are typical characteristics. Due to the interests from both academic exploration and industrial innovation, the research on scheduling problems with these effects is abundant and diverse. However, some studied problems need to be strengthened in combination with realistic production scenarios. This paper provides a concise guide to scheduling problems with these effects, giving a comprehensive review and critical hints for future research. A novel classification scheme is designed based on four levels of different domains, i.e. effects, processing ways, processing time functions, and manufacturing environments. Based on this scheme, the scheduling problems are first distinguished into three categories: learning effects, deteriorating effects, and combined effects. In each category, models are then refined along three lines: general processing way, batch scheduling, and group scheduling. Combined with the attributes of actual processing time functions and manufacturing environments, the evolvement of related scheduling models and a critical analysis on the proposed algorithms are well analysed. Afterwards, the research gaps are revealed and the research directions are indicated from the perspectives of practical applications, time functions, and designed algorithms. [ABSTRACT FROM AUTHOR]

This paper focuses on a two-stage machining and welding scheduling problem based on an investigation at a structural metal manufacturing plant, aiming to minimise the total makespan. Several parts processed at Stage one according to classical job-shop scheduling are grouped into a single construction component at the second welding stage. Fabrication of the construction component cannot begin until all comprising parts have been completed at Stage one. This paper establishes a novel mathematic model to minimise the total makespan by mainly considering the dominance relationship between the construction component and the corresponding parts. In order to solve this two-stage problem, we propose an improved harmony search algorithm. A local search method is applied to the best vector at each iteration, so that a more optimal vector can be subsequently realised. The average value, minimum value, relative percentage deviation and standard deviation are discussed in the experimental section, and the proposed local best harmony search algorithm outperforms the genetic algorithm, immune algorithm and harmony search algorithm without local search. Moreover, six optimal solutions are given as Gantt charts, which vividly illustrate that the mathematical model established in this paper can facilitate the development of a better scheduling scheme. [ABSTRACT FROM AUTHOR]

The papers are grouped into three categories: AI methods for manufacturing systems, AI developments specifically in semiconductor manufacturing, and AI in additive manufacturing and maintenance. They combine a deep neural network model and Markov decision processes (MDP) to rapidly generate near optimal dynamic control policies for problems that are too large to be only solved by MDP, thus showing the potential of machine learning in controlling unreliable manufacturing systems. [Extracted from the article]

In the Industry 4.0, edge industrial services such as smart robotic services are widely used in smart factory. The workflow of these services mainly consists of task decomposition and resource allocation. The long scheduling time, high communication delay and load imbalance among edge nodes are the challenging problems. Traditional cloud manufacturing platforms are difficult to meet the new requirements. It is hard for the existing scheduling methods to maintain a balance between algorithm complexity and performance. Training scheduling data by deep learning has become a feasible method to achieve fast prediction of the scheduling results. In this paper, a cloud edge-based two-level hybrid scheduling learning model is put forward at first. Then an improved bat scheduling algorithm with interference factors and variable step size (VSSBA) is proposed. And then, according to the historical scheduling data, the improved long and short-term memory networks (LSTM) model is put forward for fast prediction of the cloud-edge collaborative scheduling results. Experiments show that our proposed learning model can improve the performance of the cloud manufacturing platform in real-life applications efficiently. Finally, future research issues and challenges are identified. [ABSTRACT FROM AUTHOR]

This paper proposes a scheduling strategy for irregular curved blocks to address the complex spatiotemporal coupling scheduling problem related to the entered time, the entered sequence, the setting positions and the rotated angles for the curved blocks in a shipbuilding yard. The strategy presents a makespan-based curved blocks - classification and selection rule to fulfil the programming time for the entry of the curved blocks into the workplace and realises the suppression on the delay. Useless stepping search of curved blocks in occupied workplace is avoided by combining the lowest centre-of-gravity rule with the calculation method of the remained workplace proposed in this paper. A modified genetic ant colony algorithm was proposed, which apply the ease to premature characteristics of GA and the excellent local optimisation ability of ACO, to let and promote the algorithm falls into local optimum. Then the large-scale and full-range mutation will be implemented to make the algorithm jump out of the original local optimisation to search more local optimal solutions so that the global optimal solution can be achieved. Finally, a software system for algorithm verification was developed which conducts the comparative analysis of the algorithms and verifies the validity of the algorithm proposed. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Natural Fibers is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents an efficient method to improve the far-field radiation pattern of concentric circular antenna array (CCAA) design using two stochastic optimization algorithms known as social spider algorithm (SSA) and modified social spider algorithm (MSSA). Low side lobe level (SLL) plays a crucial role in reducing the interference with the other frequency components along the entire side lobes of the far-field radiation pattern. SSA and MSAA are the state-of-the-art evolutionary optimization techniques which are applied here to determine the optimal current amplitude and the inter-element distance between two consecutive antennae of the 3-ring CCAA. In this paper, the optimal results achieved by using SSA, MSSA for (4, 6, 8) elements and (8, 10, 12) elements 3-ring CCAAs, with and without centre elements are reported. The results achieved by employing SSA and MSSA show a considerable improvement in SLL reduction as compared to the uniform and the other array patterns reported in the state-of-the-art literature. [ABSTRACT FROM AUTHOR]

In feature based image matching, distinctive features in images are detected and represented by feature descriptors. Matching is then carried out by assessing the similarity of the descriptors of potentially conjugate points. In this paper, we first shortly discuss the general framework. Then, we review feature detection as well as the determination of affine shape and orientation of local features, before analyzing feature description in more detail. In the feature description review, the general framework of local feature description is presented first. Then, the review discusses the evolution from hand-crafted feature descriptors, e.g. SIFT (Scale Invariant Feature Transform), to machine learning and deep learning based descriptors. The machine learning models, the training loss and the respective training data of learning-based algorithms are looked at in more detail; subsequently the various advantages and challenges of the different approaches are discussed. Finally, we present and assess some current research directions before concluding the paper. [ABSTRACT FROM AUTHOR]

This paper introduces a solution method for the multimission selective maintenance problem (SMP) that combines column-generation (CG) and genetic algorithms (GAs). The multimission SMP is an optimisation problem that arises when a system performs a sequence of missions separated by breaks of finite duration. During these finite breaks, only a subset of possible maintenance actions can be performed due to resource limitations. The problem is in deciding what actions to perform during each break duration such that the system meets or exceeds a minimum target reliability for all missions. The resulting optimisation problems are usually modelled as mixed integer nonlinear mathematical programmes, which are hard to solve. They are usually solved using metaheuristics. We propose a solution method based on CG framework in which the subproblems are solved using a GA. By integrating the GA within the classical CG framework, high-quality solutions can be obtained very quickly. The proposed solution method is capable of solving systems composed of both parallel and k-out-of-n:G subsystems. This hybrid CG algorithm is shown to obtain near optimal solutions and outperform other metaheuristic solution methods; it is also shown to be capable of solving large-scale systems composed of many subsystems and hundreds of components in a reasonable amount of time. [ABSTRACT FROM AUTHOR]

The paper aims at creating a bridge between media and migration studies and critical algorithm studies. By adopting a media ecological approach and a mutual shaping of technology and society perspective, in this paper, we explore the factors that lead, especially in Italy, to discriminant and stigmatizing image search results, related to specific groups of immigrants living in the country. We performed a content analysis of Google-Images search results with regard to the largest immigrant communities hosted in France, Germany, Italy, and the United Kingdom. Results show that the depiction of Romanian, Albanian, Moroccan, and Algerian immigrant communities on Google.it is flattened on a univocal stigmatized representation that shows them as criminals, which is not the case in other countries. Most of these stigmatizing images derive from local online newspapers, which questions the interplay between newsmaking choices and routines, and algorithms logics. [ABSTRACT FROM AUTHOR]

The multi-objective optimisation of high-speed rail profile with small radius curve is studied in the paper. A multi-objective mathematical model for rail profile optimisation of high-speed railway is established. The CN60 rail profile is parameterised into a series of generalised functions of design variables. In order to guarantee the smoothness of the rail profile and meet the maximum grinding depth of rail in China, the constraints are employed. The wheel-rail vertical clearance and equivalent conicity of wheelset are taken as objective functions, and a rail wear prediction programme is compiled. Contact line method is employed to complete the detection algorithm of wheel-rail contact points. Finally, NSGA-II Algorithm is adopted to solve the Pareto-optimal front of the optimisation model. A set of solutions are retrieved from the Pareto optimal front solution as the optimised profile. The optimised rail profile and the original rail profile are matched with the LMA wheel profile (a certain worn type of wheel profiles for EMU in China) respectively. It is testified that the rail profile could effectively reduce the rail wear and improve curving performance. The new method proposed in this paper can provide some reference for the optimisation design of high-speed rail profile with small radius curve. [ABSTRACT FROM AUTHOR]

Human activities are being increasingly regulated by means of technologies. Smart borders regulating human movement are no exception. I argue that the process of digitization – including through AI, Big Data and algorithmic processing – falls short of respecting (fundamental) rights to the extent to which it ignores what I term to be the problem of indeterminacy. While adopting a data feminist approach in this paper, assuming that data is the 'new oil', that is power, I begin theorizing indeterminacy from the imminent risks of datafication as a new instrument of oppression perpetuating injustice and widening inequality gaps. I conclude that technologies regulating human activities must stand ethical scrutiny, especially if they can and do result in (human) rights violations. Unlike the oil being extracted from the ground, data is de facto extracted from people endowed with agency, autonomy, rights and contexts – all which ought to be respected and protected. [ABSTRACT FROM AUTHOR]

In this paper, I ask how parents understand and make sense of their children's relationships with social media algorithms. Drawing upon 30 think-aloud interviews with parents raising children aged 0 to 18 in England, in this paper, I pay attention to parents' understandings of and consequent approaches to platform algorithms in relation to their children's lives. I locate this work within user-centric research on people's understandings of algorithms, and research about parents' perspectives on data and datafication in relation to sharenting. Through my data, I draw out four modes – misunderstandings, parked understandings, transactional understandings and pro-active understandings. I suggest that parents' often flawed understandings of their children's myriad interfaces with algorithms deserve scrutiny not through a lens of blame or individualised parental (ir) responsibility but within cross-cutting contexts of parenting cultures and families' diverse contextual resources and restraints. I conclude by highlighting attention to parents' approaches to algorithms in children's lives as critical to parents' data and algorithm literacies. Prior State of Knowledge: Parents in diverse contexts try to understand and support their children's digital lives, and also often share content about their children on a variety of platforms. Prior research has shed significant light on the datafication of childhood. Novel Contributions: This study investigates parents' diverse understandings of algorithms underlying social media platforms and the ways in which they approach algorithms in their children's lives. Practical Implications: Parents' knowledge about algorithms and datafication is uneven. Policymakers need to better support adult media literacies, including data and algorithm literacies. Schools' communication to families and carers could also become key vehicles to raise awareness about datafication. [ABSTRACT FROM AUTHOR]

This paper presents a novel application of the metaheuristic Slime Mould Algorithm (SMA) to the optimal tuning of interval type-2 fuzzy controllers. Inserting the information feedback model F1 in SMA leads to a new version of the metaheuristic algorithm, further referred to as SMAF1. The paper discusses implementation details specific to interval type-2 fuzzy controllers for the position control of processes modelled by nonlinear servo systems with an integral component and dead zone plus saturation nonlinearity. The linear PI controllers are tuned on the basis of the Extended Symmetrical Optimum method using only one tuning parameter and next fuzzified to result in interval type-2 fuzzy controllers. The optimisation requires the minimisation of a discrete-time objective function expressed as the sum of time multiplied by squared control errors, and the vector variable is the parameter vector of the Mamdani PI fuzzy controller. Experimental results conclusively illustrate the superiority of SMAF1 and SMA in comparison with other metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of estimating a stochastic signal from nonlinear uncertain observations with time-correlated additive noise described by a first-order Markov process. Random deception attacks are assumed to be launched by an adversary, and both this phenomenon and the uncertainty in the observations are modelled by two sets of Bernoulli random variables. Under the assumption that the evolution model generating the signal to be estimated is unknown and only the mean and covariance functions of the processes involved in the observation equation are available, recursive algorithms based on linear approximations of the real observations are proposed for the least-squares filtering and fixed-point smoothing problems. Finally, the feasibility and effectiveness of the developed estimation algorithms are verified by a numerical simulation example, where the impact of uncertain observation and deception attack probabilities on estimation accuracy is evaluated. [ABSTRACT FROM AUTHOR]

Image restoration is used to develop the quality of image that is triggered by various noises and blurring. During this causes, certain areas in the images are vanished. The existing works does not provide sufficient restoration process with high accuracy. Therefore, a new image restoration system based on Optimized Deep Generative Adversarial Network (DGAN) with Reinforced Black Widow algorithm (BWOA) is proposed in this paper to increase the restoration accuracy and reducing the noises. At first, the input image is converted as gray scale image and the multi-scale edge information is removed as damaged area of an image by constructing a smooth function. Here, the extracted multi-scale edge information is given to the DGAN model. After that, the images are trained to create the best fake images through continuous play among generator and discriminator. Then, the detected images are restored in the original image with high accuracy. The hyper parameters of the DGAN are optimized by using the BWOA. The major objective of this paper is ‘to increase the restoration accuracy and the quality of the image by decreasing the noises occurred in the input image.’ The simulation process is performed on the MATLAB platform. The proposed DGAN-BWOA-IR attains higher restoration accuracy of 9.3%, higher PSNR value 74.589(db), SSIM 9.023% the proposed system is likened with the existing approaches, such as plug-and-play image restoration including deep denoiser prior (DCNN-IR), Learning enriched features for rapid image restoration with enhancement (LEF-IR), Exploiting deep generative prior for versatile image restoration with manipulation (GAN-IR), respectively. [ABSTRACT FROM AUTHOR]

Research into cultural tastes has commonly sought to analyze and understand preferences in terms of notions of familiarity. Such approaches are inadequate, however, when it comes to examining our engagement with unfamiliar cultural content. This paper responds to this gap by examining how people respond to algorithmic recommendations of culture through a case study of unfamiliar Australian art music. It firstly identifies three different "techniques' by which audiences engage with and value music: functional, emotional, and intellectual. The analysis then examines how these techniques, together with measures of familiarity and the acoustic "materiality" of the music itself, combine to predict the affective ratings given to music recommendations. The findings show that audiences display a surprising capacity to engage with the unfamiliar. The paper argues for the need to develop more nuanced understandings of the relationship between familiarity and preferences which are capable of accommodating a taste for the unfamiliar. [ABSTRACT FROM AUTHOR]

As the world's transport infrastructure ages, the importance of bridge condition monitoring is becoming increasingly acknowledged. Large-scale deployment of existing inspection and monitoring techniques is infeasible due to cost and logistical challenges. The concept of using sensors located within vehicles for low cost 'drive-by' monitoring has become the focus of much attention in recent years. This paper presents a new data-driven approach for drive-by bridge monitoring. Machine learning techniques are leveraged to allow the influence of vehicle speed to be considered and the Operating Deflection Shape Ratio (ODSR) is presented as an alternative damage-sensitive feature to the commonly used frequency spectrum. Extensive laboratory experiments demonstrate that the method is capable of detecting midspan cracking and seized bearings. A statistical classification approach is adopted to classify damage indicators as either 'damaged' or 'healthy'. Classification accuracy is seen to vary between 65-96% and is similar whether using the frequency spectrum or ODSR. Based on the results of the laboratory testing, it is expected that this approach could be implemented on a large scale to act as an early warning tool for infrastructure owners to identify bridges presenting signs of distress or deterioration. [ABSTRACT FROM AUTHOR]

In this paper we present and provide a proof for a set of non-recursive formulae arising in the computation of the largest output-nulling and the smallest input-containing subspaces which have been used in a variety of contexts in the framework of the geometric approach. These expressions have been used in the literature both in the strictly proper and in the non-strictly proper case, but, to the best of our knowledge, a proof is still missing. These formulae are established here in the general possibly non-strictly proper case. Some ancillary side results of independent interest are also proposed. [ABSTRACT FROM AUTHOR]

This paper, based on an ethnography of the new gaming culture of Animal Crossing New Horizons in China, contributes to the scholarly investigation of digital class formation. New Horizons is a Japanese console game that became popular among Chinese urban professionals during the pandemic. Following Bourdieu's framework, we analyze the dispositions and practices of New Horizons players, using the concept of "digital distinction" to define how gamers acquire and display cultural tastes and symbolic practices through competitive and relational engagements mediated by digital devices. The paper argues that contemporary classes are ephemeral dispositions mediated by digital fields materialized through media practices. This argument challenges the "capital-centric" approach to digital divides to better reflect the dialectical value production and transferences in the process of digital class formation. Digital capitalism produces an increasing diversity of digital fields that activate people's particular dispositions through media algorithms, content design, institutional constraints, and relational spaces. [ABSTRACT FROM AUTHOR]

The paper considers the design of sparse arrays in the interference active environment to minimise the system complexity and achieve reduced hardware cost. For this, we propose a sparse array design methodology to attain maximum signal-to-interference plus noise ratio (MaxSINR) in the presence of interfering signals. We formulate the optimisation problem as a real-valued quadratically constrained quadratic program (QCQP) with the non-convex $\textstyle\ell_p$ ℓ p norm to promote sparsity, which is iteratively controlled in the proposed approach. We employ the semidefinite relaxation (SDR) technique and the principle of the majorization-minimisation (MM) algorithm to solve the non-convex QCQP problem. The efficacy of the proposed algorithm is demonstrated through the simulation results. The sparse array obtained through the proposed method performs well when compared with the reweighted $\textstyle\ell_1$ ℓ 1 algorithm. [ABSTRACT FROM AUTHOR]

This research introduces innovative approaches to enhance intrusion detection systems (IDSs) by addressing critical challenges in existing methods. Various machine-learning techniques, including nature-inspired metaheuristics, Bayesian algorithms, and swarm intelligence, have been proposed in the past for attribute selection and IDS performance improvement. However, these methods have often fallen short in terms of detection accuracy, detection rate, precision, and F-score. To tackle these issues, the paper presents a novel hybrid feature selection approach combining the Bat metaheuristic algorithm with the Residue Number System (RNS). Initially, the Bat algorithm is utilized to partition training data and eliminate irrelevant attributes. Recognizing the Bat algorithm's slower training and testing times, RNS is incorporated to enhance processing speed. Additionally, principal component analysis (PCA) is employed for feature extraction. In a second phase, RNS is excluded for feature selection, allowing the Bat algorithm to perform this task while PCA handles feature extraction. Subsequently, classification is conducted using naive bayes, and k-Nearest Neighbors. Experimental results demonstrate the remarkable effectiveness of combining RNS with the Bat algorithm, achieving outstanding detection rates, accuracy, and F-scores. Notably, the fusion approach doubles processing speed. The findings are further validated through benchmarking against existing intrusion detection methods, establishing their competitiveness. [ABSTRACT FROM AUTHOR]

In this paper, we propose a sparse least squares (SLS) optimization model for solving multilinear equations, in which the sparsity constraint on the solutions can effectively reduce storage and computation costs. By employing variational properties of the sparsity set, along with differentiation properties of the objective function in the SLS model, the first-order optimality conditions are analysed in terms of the stationary points. Based on the equivalent characterization of the stationary points, we propose the Newton Hard-Threshold Pursuit (NHTP) algorithm and establish its locally quadratic convergence under some regularity conditions. Numerical experiments conducted on simulated datasets including cases of Completely Positive(CP)-tensors and symmetric strong M-tensors illustrate the efficiency of our proposed NHTP method. [ABSTRACT FROM AUTHOR]

In recent years, deepfakes have become part and parcel of contemporary algorithmic culture. It is regularly claimed that they have the potential to introduce novel modes of societal disruption, violence, and harm. Yet, over-emphasising the power of deepfakes risks occluding frictions, struggles, and logics that already persist in the digital landscape. Arguing for a conceptualisation of deepfakes as an assemblage of differential tensions in society, we explore how they represent both a rupture and a continuation of the variegated politics of the image in the social world. The paper analyses the tensions of deepfakes through three distinct case studies: bodies, politics, and ideas of objectivity. Ultimately, we argue that the tensions and ethicopolitical implications of deepfakes are not reducible to a problem that can be solved through a logic of algorithmic detection and verification. [ABSTRACT FROM AUTHOR]

This paper studies the problem of event-triggered impulsive control for discrete-time systems. A novel periodic event-triggering scheme with two tunable parameters is presented to determine the moments of updating impulsive control signals which are called event times. Sufficient conditions are established to guarantee asymptotic stability of the resulting impulsive systems. It is worth mentioning that the event times are different from the impulse times, that is, the control signals are updated at each event time but the actuator performs the impulsive control tasks at a later time due to time delays. The effectiveness of our theoretical result with the proposed scheme is illustrated by three examples. [ABSTRACT FROM AUTHOR]

The scene-rendering mechanism based on binocular vision is one of the key techniques for the VR globe to achieve immersion-type visualization of global 3D scenes. However, this special rendering mechanism also requires that the 3D scene is continuously drawn twice within one frame, which significantly affects the rendering efficiency of VR globes. Therefore, we propose a binocular parallel rendering method. This method first improves the current rendering process of VR globes by assigning the rendering tasks for the left and right camera of VR to be processed on different CPU cores, thereby achieving parallel rendering of binocular scenes. Second, due to the problem of inconsistent resolution of binocular scenes caused by different viewpoints for the left and right cameras, we propose a resolution synchronize algorithm. this algorithm conducts real-time synchronization on the resolution of scene in the rendering process and thus avoids the problem of erroneous binocular stereo matching. Finally, we validate the effectiveness of the method in this paper through experiments. The results of experiments indicate that while the method in this paper can ensure the consistency of binocular scene resolution, it can decrease the frame time of VR globes by approximately 27% on average. [ABSTRACT FROM AUTHOR]

In CNC toolpath generation process, the operation of linking toolpaths from different sub-machining regions is common and inevitable. Apparently, the jumping toolpath between machining regions is invalid. They do not contribute to the machining process but only waste valuable manufacturing time; therefore, these toolpaths should be as short as possible. Many methods have been used to link toolpaths, such as the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO) or even the greedy algorithm. However, GA and PSO require multiple iterations to find the global optimum, while greedy algorithm selects the current shortest connection each time without considering the global optimum. To reduce the total length of non-productive toolpaths and save computing time, in this paper, a new method is proposed by modeling the toolpath linking problem purely as a traveling salesman problem (TSP). The initial toolpaths in different subregions are generated in ordinary ways. Each toolpath of a subregion has two endpoints, which can be simplified as a line segment. In this way, the toolpath linking problem can be considered as a segment TSP: finding the shortest tour through all the segments. In this paper, the efficient TSP solver using Lin-Kernighan–Helsgaun (LKH) algorithm is employed and modified for the segment TSP application. The distance function between 'cities' is redefined to adapt the segments TSP. Finally, the feasibility of the proposed method is verified with several examples. The comparison with the result of traditional greedy algorithm proves the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present mechanisms and algorithms implemented for improving Bitcoin cryptocurrency efficiency and security and to examine the block propagation times from a selected period before and after SegWit was introduced. In this paper, Segregated Witness Implementation issues were verified based both on the simulation and real data from the Bitcoin network. Based on the block propagation times calculated in the simulator, as well as bitcoin network real data, the efficiency and safety of Bitcoin have been analysed and validated. [ABSTRACT FROM AUTHOR]

This paper addresses the semi-online scheduling problem of minimising the total weighted completion time on a single machine, where a combination of information on jobs release dates and processing times is considered. In this study, jobs can only arrive at known future times and a lower bound on jobs processing times is known in advance. A new semi-online algorithm is presented and is shown to be the best possible for the considered problem. In order to make this statement, a new lower bound on the competitive ratio of any semi-online algorithm for the problem is developed and, using competitive analysis, the proposed semi-online algorithm is shown to have a competitive ratio that matches the lower bound. [ABSTRACT FROM AUTHOR]

Inspired by real-life applications, mainly in hand-intensive manufacturing, the incorporation of learning effects into scheduling problems has garnered attention in recent years. This paper deals with the flowshop scheduling problem with a learning effect, when minimising the makespan. Four approaches to model the learning effect, well-known in the literature, are considered. Mathematical models are providing for each case. A solver allows us to find the optimal solution in small problem instances, while a Simulated Annealing algorithm is proposed to deal with large problem instances. In the latter, the initial solution is obtained using the well-known Nawaz-Enscore-Ham algorithm, and two local search operators are evaluated. Computational experiments are carried out using benchmark datasets from the literature. The Simulated Annealing algorithm shows a better result for learning approaches with fast learning effects as compared to slow learning effects. Finally, for industrial decision makers, some insights about how the learning effect model might affect the makespan minimisation flowshop scheduling problem are presented. [ABSTRACT FROM AUTHOR]

This paper focuses on the precast supply chain scheduling problem with time-dependent transportation time to minimise the total weighted tardiness (PSCSP_TDT |TWT). In the problem, an order sequence and several job sequences are to be determined simultaneously. At first, through in-depth analysis of problem structure and real data from a precast manufacturer, we approximate the problem into a three-stage order scheduling problem by combining the seven production stages into one differentiation stage, and then explore some useful properties of the schedules for the approximate problem. Subsequently, to solve the small instances for the PSCSP_TDT |TWT, we propose an approximate model-based hybrid dynamic programming and heuristic (AMHDPH) and obtain a lower bound as a by-product of the algorithm. For dealing with medium-or large instances, with considering the complexity of the problem, we propose four approximate model-based hybrid iterated greedy (AMHIG) algorithms by integration of constructive heuristics, structural properties of solutions, an iterated greedy, and a correction heuristic. Comprehensive computational results show that the AMHDPH generates tight lower bounds for small instances and solves the most of small instances to optimality within 60 seconds. Whereas the best AMHIG generates feasible solutions with an average optimality gap below 5 percent for around 70 percent instances. [ABSTRACT FROM AUTHOR]

The path planned for an automated guided vehicle in, for example, a production facility is often the lowest-cost path in a (weighted) geometric graph. The weights in the graph may represent a distance or travel time. Sometimes turning costs are taken into account; turns (and decelerations before and accelerations after turning) take time, so it is desirable to minimise turns in the path. Several well-known algorithms can be used to find the lowest-cost path in a geometric graph. In this paper, we focus on the A ∗ algorithm, which uses an (internal) search heuristic to find the lowest-cost path. In the current literature, generally, either turning costs are not taken into account in the heuristic or the heuristic can only be used for specific graph structures. We propose an improved heuristic for the A ∗ algorithm that can be used to find the lowest-cost path in a geometric graph with turning costs. Our heuristic is proven to be monotone and admissible. Moreover, our heuristic provides a higher lower bound estimate for the actual costs compared to other heuristics found in the literature, causing the lowest-cost path to be found faster (i.e. with less iterations). We validate this through an extensive comparative study. [ABSTRACT FROM AUTHOR]

"Incremental Learning (IL)" is the niche area of "Machine Learning." It is of utmost essential to keep learning incremental for ever-increasing data from all domains for effectual decisions, predications and solving problems. This can be achieved effectually by applying "Incremental Clustering" methods on real-time data sources. IL can be achieved by "Incremental Clustering" easily as well as effectively. To achieve worldwide data analysis related to the data and to achieve broader perspectives, it is essential to deploy "Incremental Clustering" algorithms on distributed platforms, which will enable them to accept data from varied sources; analyze it and produce distributed worldwide solutions. This paper hence focuses on understanding the current status of "Distributed Incremental Clustering Algorithms (DICA)," its scope, limitations and other details so as to formulate better than the best algorithm in future. To enhance the analysis further Word-Clouds of impactful papers were explored and added in this paper, along with the details about platforms used to implement DICA by various upcoming researchers, readers and authors. [ABSTRACT FROM AUTHOR]

In many seasonal industries, customer demands are constantly changing over time, and accordingly the facility layout should be re-optimized in a timely manner to adapt to changing material handling patterns among manufacturing departments. This paper investigates the aperiodic facility layout problem (AFLP) that involves arranging facilities layout and re-layout aperiodically in a dynamic manufacturing environment during a given planning horizon. The AFLP is decomposed into a master problem and a combination set of static facility layout problems (FLPs, the slave problems) without loss of optimality, and all problems are formulated as mixed-integer linear programming (MILP) models that can be solved by MIP solvers for small-sized problems. An exact backward dynamic programming (BDP) algorithm with a computational complexity of O(n2) is developed for the master problem, and an improved linear programming based problem evolution algorithm (PEA-LP) is developed for the traditional static FLP. Computational experiments are conducted on two new problems and twelve well-known benchmark problems from the literature, and the experimental results show that the proposed solution approach is promising for solving the AFLP with practical sizes of problem instances. In addition, the improved PEA-LP found new best solutions for five benchmark problems. [ABSTRACT FROM AUTHOR]

Service composition is a core issue of cloud manufacturing (CMfg) to integrate distributed manufacturing services for customised manufacturing tasks. Existing studies focus on the quality of service (QoS) in composition by assuming that each service is independent with each other. However, the correlation between services determines whether a composition is feasible in practice and is a primary factor of its QoS. This paper considers two typical correlations, composability-oriented correlation and quality-oriented correlation. The composability-oriented correlation is modelled as a group of constraints to decide whether a solution is feasible. The influence of the quality-oriented correlation between two services on the overall QoS of a composition is quantified by a discount percentage based on their correlation degrees. A mathematical model of correlation-aware service composition is then established. To solve this problem, a many-objective memetic algorithm termed HypE-C (Hypervolume Estimation Algorithm for Multiobjective Optimisation involving Correlation) is designed. Three correlation-based local search strategies are established in the frame of HypE (Hypervolume Estimation Algorithm for Multiobjective Optimisation) to achieve better trade-off among multiple conflicting QoS criteria. Experiments demonstrate the effectiveness of the proposed algorithm HypE-C compared with five many-objective algorithms on eliminating infeasible search space and providing high QoS service composition solutions. [ABSTRACT FROM AUTHOR]

The development of unmanned aerial vehicle (UAV) oblique photogrammetric technology provides a good foundation for the rapid construction of large-scale and high-definition real-scene 3D models. However, due to the limitations of the modeling process, irrelevant feature data cannot be eliminated in the modeling stage. The built models contain irrelevant features and model distortions caused by errors. At present, most existing clipping algorithms cannot effectively clip real-scene 3D models that are organized as a whole or with levels of detail (LODs). Therefore, this paper proposes a novel algorithm for clipping real-scene 3D models from any perspective based on clipping boundary lines that fit the surfaces of the models. The results of the clipping experiments for 3D models constructed with oblique UAV images show that this algorithm can effectively clip any part of the 3D models, that the clipping results of each level model closely fit the corresponding clipping boundary lines, and that the accuracy of the clipping results is very high. Additionally, the time complexity of the algorithm is O(n2). In conclusion, the algorithm proposed in this paper provides correct and effective clipping results for real-scene 3D models with LODs that are constructed with photogrammetric or 3D laser scanning data. [ABSTRACT FROM AUTHOR]