Search

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]

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]

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]

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]

To optimize anthropogenic material streams, the production process, as well as the quality of the products, must be known. With knowledge of these requirements, it is possible to use extra applied algorithms—in this case evolutionary algorithms as part of artificial intelligence—for the optimization of these secondary material streams. The benefit of this application is the fast and precise calculation of the local and global optima of the optimizing problem. This calculation method uses the benefits of the biological reproduction by applications of mutation, selection, and recombination to find one of the best results in a huge amount of possible and potential results. For the use of secondary materials in the paper production it could be proven that in spite of high quotes of secondary materials in different paper classes, there are some paper classes in which the amount of secondary material could be raised without losing any quality. [ABSTRACT FROM AUTHOR]

Summarizes the construction of paper folding. Method for approximating rational subdivisions or arbitrary angles and line segments; Angle-folding algorithm; Approximating angles, regular polygons and star polygons; Dissection of angles into equal parts.

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]

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]

In this paper, we present an algorithm that solves a paper reel layout problem where the available space is divided into equal-size cells. The problem is to find a layout with the minimum transportation cost subject to adjacency and other constraints. A genetic algorithm is used in a two-stage iterative approach to solve the problem. Computational results seem to indicate the efficiency and effectiveness of the proposed solution method. [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]

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 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]

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]

Algorithm performance evaluation is so entrenched in the machine learning community that one could call it an addiction. Like most addictions, it is harmful and very difficult to give up. It is harmful because it has serious limitations. Yet, we have great faith in practicing it in a ritualistic manner: we follow a fixed set of rules telling us the measure, the data sets and the statistical test to use. When we read a paper, even as reviewers, we are not sufficiently critical of results that follow these rules. Here, we will debate what are the limitations and how to best address them. This article may not cure the addiction but hopefully it will be a good first step along that road. [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]

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.)

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]

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]

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]

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 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 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]

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]

The imaging spectrometer MOS on IRS-P3 was launched in March 1996 as the first example of a new generation of ocean colour sensors. It consists of three different spectrometers in the visible/near-infrared spectral region with 18 channels. The IRS-P3 mission is focused on the remote sensing of case 2 water, particularly the derivation of different water constituents in coastal waters. Due to the more complex spectral behaviour of case 2 water, a new methodological approach was developed which works directly with satellite measured top-of-atmosphere radiance and accounts for the correlation of the different water constituents as well as for the spectral shape. This paper gives an overview of the mission, the scientific goals and the development and improvement of the retrieval algorithms. The potential of the algorithm is demonstrated and examples of selected European coasts are shown. Derived maps of water constituents are presented. [ABSTRACT FROM AUTHOR]

This paper considers the problem of scheduling and sequencing jobs on machines in a two-stage production process. The problem is motivated bra real-world application concerning a major paper products plant which produces business forms. Here, the printing and the collating operations constitute the two production stages. We develop a model for this problem and propose a solution approach based on the decoupling of the problem into two single stage problems. Each single stage problem, which is the main focus of this paper, is further decomposed into an allocation subproblem and a sequencing subproblem of jobs on machines. Both exact and heuristic algorithms are developed for these subproblems. An overall scheme is proposed for linking together the information and solutions provided by these decomposed system components. Different versions of the algorithm are tested on industrial data. and recommendations are made for implementation. [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]

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]

"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]

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]

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 two basic equations in the mentioned article are wrong. [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]

In the distribution system, voltage sags and swell have adverse effects on sensitive and critical loads. Dynamic Voltage Restorer (DVR) play a vital role to mitigate voltage sags and swell through its organized control when connected in series with the system. Various conventional control schemes have been reported for the control of DVR. The performance of DVR with these schemes has been successful in the mitigation of voltage sags/swell, but drawbacks, such as undershoot, and overshoot during sag dynamics are observed in the regulated load terminal voltage. These drawbacks may cause malfunctioning of the equipment connected and hence their elimination is indispensable. With this inspiration, a novel Adaline filter-based adaptive control scheme using learning-rate parameter-based Least-Mean-Square algorithm has been proposed. This scheme has the capability to overcome the drawbacks with the conventional control schemes thus improving the performance of DVR. In this paper, the performance of DVR using proposed algorithm is compared with that using synchronous reference frame (SRF)-based conventional control scheme. The performance is studied and validated through simulation results under different voltage sag and swell conditions in MATLAB software using Sim Power System toolboxes. [ABSTRACT FROM AUTHOR]

This paper proposes an unconventional architecture and algorithm for implementing reservoir computing on FPGA. An architecture-oriented algorithm with improved throughput and architecture designed to reduce memory and hardware resource requirements are presented. The proposed architecture exhibits good performance in terms of benchmarks for reservoir computing. A prediction accelerator for reservoir computing that operates on 55.45 mW at 450 K fps with <3000 LEs is realized by implementing the architecture on FPGA. The proposed approach presents a novel FPGA implementation of reservoir computing focussing on both algorithms and architecture that may serve as a basis for applications of AI at network edge. [ABSTRACT FROM AUTHOR]

This paper introduces a robust procedure for controlling the false discovery rate utilizing empirical likelihood. Traditional approaches assume a normal or parametric distribution as the null distribution. However, it may be challenging to constrain the null distribution within specific parametric models. We focus on the cases where the null distribution may not precisely follow a normal distribution. Multiple testing procedures based on exact normality can lead to misleading outcomes. To address this issue, we adopt the empirical likelihood to estimate the null distribution. Additionally, we introduce the concept of a pilot distribution to establish constraints on the null distribution, which aids in estimating the empirical null distribution. We present a fast algorithm and provide theoretical justification for its efficiency. Furthermore, simulation studies demonstrate that our method outperforms existing approaches in controlling the false discovery rate. We also include examples involving gene expression data and compare the performance of different methods. [ABSTRACT FROM AUTHOR]

Our study in this paper is focused on the split equality fixed-point problem with firmly quasi-non-expansive operators in infinite-dimensional Hilbert spaces. A self-adaptive simultaneous scheme is introduced, and its weak convergence is established under mild and standard assumptions. The new proposed scheme generalizes and extends some related works in the literature, and its simple structure makes it easy for implementation and numerical testing. Primary experiments presented in this paper, in finite- and infinite-dimensional spaces, emphasize their practical advantages over existing results. [ABSTRACT FROM AUTHOR]

Cloud manufacturing is a consumer-centric requirement-driven manufacturing paradigm that integrates distributed resources for providing services to consumers in an on-demand manner. Scheduling of multiple tasks is an important technical means for satisfying consumer requirements in cloud manufacturing. However, high individualised requirements and the associated complex task structures complicate the task scheduling in cloud manufacturing. This paper establishes a more comprehensive model for scheduling multiple distinct tasks with complicated manufacturing processes. The hierarchical relationships (a mixture of dependency and independency) of subtasks within tasks are considered. The objectives involve three kinds of time and cost factors, namely processing time, setup time, transfer time and the respective cost. In addition, service quality is also considered into the optimisation objective. Two multi-objective-meta-heuristic algorithms, i.e. ACO-based multi-objective algorithm (MACO) and NSGA-II-based multi-objective algorithm (MGA), are designed to solve the scheduling problem. A detailed analysis of the performance of the two algorithms is performed by applying them to several different scheduling instances. Experimental results indicate that in most cases the MACO algorithm can obtain a more diverse set of Pareto solutions hence offering more alternatives to meet widely different users' needs. [ABSTRACT FROM AUTHOR]

Neighbourhood search algorithms are often the most effective known approaches for solving partitioning problems. In this paper, we consider the capacitated examination timetabling problem as a partitioning problem and present an examination timetabling methodology that is based upon the large neighbourhood search algorithm that was originally developed by Ahuja and Orlin. It is based on searching a very large neighbourhood of solutions using graph theoretical algorithms implemented on a so-called improvement graph. In this paper, we present a tabu-based large neighbourhood search, in which the improvement moves are kept in a tabu list for a certain number of iterations. We have drawn upon Ahuja-Orlin's methodology incorporated with tabu lists and have developed an effective examination timetabling solution scheme which we evaluated on capacitated problem benchmark data sets from the literature. The capacitated problem includes the consideration of room capacities and, as such, represents an issue that is of particular importance in real-world situations. We compare our approach against other methodologies that have appeared in the literature over recent years. Our computational experiments indicate that the approach we describe produces the best known results on a number of these benchmark problems. [ABSTRACT FROM AUTHOR]

Copyright of INFOR 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.)

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]

This case study of North Xiazhu Village in China examines how algorithmic practices and the construction of physical and symbolical places mutually shape and constitute one another in the context of a new phenomenon: livestreaming villages. Such villages have emerged as a result of the widespread popularity of social media featuring algorithmic recommendations. Algorithmic practices—defined as users' individual or collective strategies and actions in response to the algorithmic mechanisms that distribute traffic—have become key variables in the place-making of livestreaming villages. By analyzing how algorithmic practices are formed, implemented, shared, organized, and interwoven with place-making, this paper seeks to examine how the traditional village of North Xiazhu has transformed into a livestreaming village. Meanwhile, this paper seeks to broaden our understanding of the social opportunities facilitated by new technologies, recognizing the constitutive role of algorithmic practices in the making of places and communities. [ABSTRACT FROM AUTHOR]

Inspired by classical feature descriptors in motion matching, this paper proposes a multimodal failure matching point collection method, which is defined as FMP. FMP is, in fact, a collection of unstable features with a low matching degree in the conventional matching task. Based on FMP, a novel model for the saliency detection of motion object is developed. Models are evaluated on the DAVIS and SegTrackv2 datasets and compared with recently advanced object detection algorithms. The comparison results demonstrate the availability and effectiveness of FMP in the detection of motion object saliency. [ABSTRACT FROM AUTHOR]