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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.

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]

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]

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 this paper, we introduce a modified Tseng's extragradient method with a new step-length rule to solve pseudo-monotone variational inequalities in real Hilbert spaces. Under suitable conditions, the sequence generated by this algorithm strongly converges to the common elements of the solution set of pseudo-monotone variational inequality problems and the fixed point set of k-demicontractive mappings. Finally, we give some numerical experiments to illustrate the effectiveness of the proposed algorithm. The main results of this paper generalize and improve some known results in the literature. [ABSTRACT FROM AUTHOR]

This paper describes a 2-stage tactic for achieving an odd-order variable-bandwidth (OO-VBW) filter with absolutely guaranteed stability. The design methodology aims to minimize the p-norm amplitude-response error while maintaining the OO-VBW-filter's stability. In order to tune OO-VBW filter's amplitude response, we utilize a kind of functions of the bandwidth (BW)-tuning parameter to express the filter's coefficients. Because those functions have changeable function values, the designed OO-VBW-filter's amplitude response possesses variability. Another important concern in this paper is the stability issue. When the filter coefficient values expressed as function values are changed, the OO-VBW filter that has feedback structures may become unstable. This necessitates that the OO-VBW-filter's stability must always be preserved during real-time tuning. In order to stabilize such an OO-VBW filter that has feedback structures, a coefficient-conversion (CC) algorithm is adopted, and it is incorporated into the 2-stage methodology for obtaining the OO-VBW filter. To illustrate both the stability and high approximation accuracy, the design of an OO-VBW bandpass filter is simulated. The simulation details verify that both stability and high accuracy can be successfully achieved. [ABSTRACT FROM AUTHOR]

In this paper, the radial basis function neural network based on the orthogonal least squares (OLS-RBFNN) method is proposed for the first time for the calibration of the six-port receiver, which realises six-port accurate reception in the true sense. This algorithm overcomes the problems of an ageing circuit, narrow working frequency band and nonlinearity of the six-port receiver hardware. The RBFNN is a three-layer forward network, including an input layer, a hidden layer and an output layer. The transformation function of the hidden layer is the radial basis function. A six-port receiver test platform with an operating frequency band of 2.0–8.0 GHz is built to verify the performance of the six-port receiver based on OLS-RBFNN. The test signal is a 16-QAM signal with different bandwidths and different frequency bands. The test indicator uses error vector magnitude (EVM)， which represents the error between the received signal and the ideal signal sent. At last, the EVM of all tested data is less than 1.07%, and the best EVM is 0.57%. Therefore, the calibration accuracy of the OLS-RBFNN proposed in this paper is very high. [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]

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]

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]

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 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]

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]

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]

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]

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]

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 presents an adaptive memetic algorithm based on a new neighbourhood structure (AMA) for solving the assembly job shop scheduling problem, with the aim of minimising the maximum completion time (makespan). To utilise the knowledge of problem, a theoretical analysis is conducted to explore the criteria for feasible and effective movement of operations under assembly constraints, and a multi-operation precise joint movement neighbourhood structure is proposed accordingly. In the AMA, to ensure the feasibility of solutions during the evolution process, a feasible encoding mechanism based on the constraint degree of operations is designed, a greedy active decoding method as well as feasible crossover operation based on independent operation chains are designed specifically for this encoding method. To avoid premature convergence of the population, a population update operator with diversity adaptive control is proposed. Finally, by comparing the results with five state-of-the-art algorithms, the superiority of AMA in terms of solution quality and stability is verified, particularly with the update of known optimal solutions for 11 instances. [ABSTRACT FROM AUTHOR]

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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 stability of Wireless Sensor Networks (WSNs) is a crucial requirement in real-time applications such as military, defense, and other surveillance systems. Clustering in WSNs is one of the most predominant techniques, offering benefits such as minimizing communication time, optimizing energy utilization, lengthening the network lifespan, and ensuring network stability. Moreover, achieving the balance between energy consumption and maintaining network stability is significantly influenced by the cluster size. This research paper addresses the challenges associated with clustered-based routing and cluster formation paradigm, introducing a novel algorithm employing the principle of Artificial Neural Networks (ANN). In particular, the proposed algorithm integrates a hybrid strategy of Self Organizing Map (SOM) and K-mean clustering to form energy-efficient clusters, even ensuring the energy distribution evenly among them. The validity of the proposed algorithm has been confirmed through experimental analysis conducted using MATLAB. The simulation results demonstrate that the hybrid combination of SOM and K-means clustering is highly efficient in minimizing energy consumption and maintaining network stability. The findings also reveal an average of 80% stable network lifetime and achieve a packet reception ratio (PRR) of 98% which is much higher than two other protocols i.e. LEACH and MODLEACH protocols. [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]

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]

The aim of this paper is to introduce a forward-backward-forward algorithm with inertial extrapolation to solve the problem of finding a common solution to the variational inequality problem (CSVIP) in a Hadamard manifold. Using a self-adaptive step size, we obtain convergence results under some standard conditions. Numerical examples are given to illustrate the theoretical analysis. Our result is a generalization and extension of previously announced results in this direction in the literature. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a generalized inexact scalarized proximal point algorithm to find Pareto-Clarke critical points and Pareto efficient solutions of quasiconvex multivalued functions defined on Hadamard manifolds considering vectorial and scalar errors to find a critical point of the regularized proximal function in each iteration. Under some assumptions on the problem, we obtain the global convergence of the sequence to a Pareto-Clarke critical point and assuming an extra condition on the proximal parameters we establish convergence to a Pareto efficient solution, approximately linear/superlinear rate of convergence and finite termination of the algorithm. In the convex case, we prove the convergence to a Pareto efficient solution point (more than a weak Pareto efficient solution point). The results of the paper are new even in the Euclidean space. [ABSTRACT FROM AUTHOR]

In this paper, an $ H_{\infty } $ H ∞ negative imaginary static output feedback (SOF) controller is designed for a low frequency (LF) networked control system (NCS) with time-delays. A difference operator is introduced to deal with the time-delays in a feedback configuration via integral quadratic constraints (IQCs). Both $ H_{\infty } $ H ∞ performance and negative imaginariness are analysed via LF IQCs and a generalised Kalman–Yakubovich–Popov (GKYP) lemma. A two-stage algorithm is presented to obtain the $ H_{\infty } $ H ∞ negative imaginary SOF controller for the LF NCS with time-delays. Simulation results are given to show the effectiveness and superiority of the $ H_{\infty } $ H ∞ negative imaginary SOF controller. [ABSTRACT FROM AUTHOR]

In this paper we propose a new parameter estimator that ensures global exponential convergence of linear regression models requiring only the necessary assumption of identifiability of the regression equation, which we show is equivalent to interval excitation of the regressor vector. An extension to – separable and monotonic – nonlinear parameterisations is also given. The estimators are shown to be robust to additive measurement noise and – not necessarily slow-parameter variations. Moreover, a version of the estimator that is robust with respect to sinusoidal disturbances with unknown internal model is given. Simulation results that illustrate the performance of the estimator compared with other algorithms are given. [ABSTRACT FROM AUTHOR]

Infrared small target detection (ISTD) is a significant technique for search and rescue applications. The lack of intrinsic target features and complex backgrounds make small target detection challenging. Furthermore, existing deep learning-based methods overlook the imbalance among categories when designing supervision method. For robust detection, we propose a Pyramid Attention U-shaped network (PAU2-Net) in this paper. Specifically, we designed a pyramid attention encoder to establish long-range channel dependencies and enhance the interaction between local and global information. It extracts small target features and enhances high-level understanding of the scene at the same time, which helps reduce false alarm rates. In our proposed adaptive multiscale supervision method, we generate a group of multiscale labels by pooling to guide the deep outputs of the network. The labels maintain the target position information at different scales and help preserving the target information as the network deepens. Comparison experiments on SIRST-V2 and NUDT-SIRST datasets show that our method can achieve superior performance in terms of detection rate and precision compared with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

This article addresses the pervasive issue of digital piracy in the movie industry. Despite substantial efforts to combat piracy, its impact remains significant, resulting in substantial financial losses for companies. Digital rights management (DRM) has been introduced as a countermeasure to counter this challenge. While the security of video applications, including commercial and military videos, has gained prominence in recent research, this paper focuses on enhancing the encryption algorithm and presenting encryption results. Specifically, it explores the AES 128 encryption algorithm's vulnerabilities in the MPEG-DASH media file and proposes a randomized algorithm for video file encryption. To bolster security, the randomized approach leverages three robust algorithms: AES 256, Two-Fish, and Serpent. The randomized algorithm demonstrated significantly higher resistance to brute-force attacks than AES 128 CBC, as exemplified by a test scenario with a key length of 32 and 100 media chunks. By utilizing this combination of unbroken algorithms, the paper offers an efficient solution for offline-protected media storage, ultimately strengthening the security and privacy of the MPEG-DASH DRM system and mitigating the risk of media file piracy. The randomized algorithm consistently outperformed AES 128 CBC in terms of execution time across multiple media files, with the randomized algorithm completing the encryption process in 34,610 milliseconds for Media File 1, compared to AES 128 CBC, which required 59,910 milliseconds, showcasing a notable enhancement in speed. [ABSTRACT FROM AUTHOR]

This paper studies a two-agent scheduling problem on a single batch machine. Each job j has a processing time pj and a size sj. Jobs should be delivered to the agent as soon as possible. The objective is to minimize the service span of the first agent subject to an upper bound on the makespan of the other agent. We propose two approximation algorithms when jobs have identical processing times and in the general case. Then we conduct theoretical analyses to provide the provable guarantees on the performances of the algorithms. Finally, we conduct simulation analyses based on randomly generated instances to evaluate the average performances of the algorithm in the general case. [ABSTRACT FROM AUTHOR]

In STS, there has long existed an unease about the analysis of powerful actors and dominant technoscientific narratives. A core concern for the field has been how particular objects, phenomena, and people are excluded from technoscientific realities. However, a key problem in dealing with exclusion in STS is that our methods call us to 'follow the actors,' which often leads to reifying our interlocutors' matters of concern. This paper proposes an analytical strategy that turns our analytical attention to the actors' work rendering things absent—a strategy of analyzing ontological overflows. The aim of this analytical move is to shift focus from construction to de-construction and to highlight the importance of processes of exclusion. By exploring the actors' making of the absence of Zika—and by extension, the construction of the absence of various technoscientific phenomena—an analytical strategy is outlined that allows us to attend to the overflows of technoscience. Four types of overflows are analyzed: conglomeration, exclusion, scarcity, and indeterminacy, each illustrating how the making of absences shapes technoscientific objects. For instance, the decision of what counts as a thing, the handling of absent data, and the translation of computational uncertainties into absence of prediction. This analytical strategy highlights where there exist spaces for power and choice—where choices can be made, by whom, and by what means. By analyzing the making of absence, we can explore how objects, phenomena, and people are marginalized or rendered absent in technoscientific processes. [ABSTRACT FROM AUTHOR]

Numerical models play a primary role in Cultural Heritage preservation. Nevertheless, the design of a realistic model remains challenging due not only to the complex behavior of masonry but also to the asynchronous building phases, the damage induced by natural and anthropic aggression, and the associated repairs. This paper discusses the impact of the information provided by an in-depth analysis of the construction history on the updating process of a Finite Element building model. The case study is the church of Sant'Agata del Mugello (Italy); for this building, a previous historical–archaeological study identified and recorded the asynchronous construction phases, the repair techniques, and the damage induced by three historical earthquakes (1542, 1611, and 1919) – moreover, a dense ambient vibration survey allowed to identify the modal parameters. The information from previous works is summarized in five Finite Element models with increasing complexity. A vibration-based model updating methodology based on a Particle Swarm Algorithm is developed. This work shows that the best minimization of the difference between the numerical and experimental modal parameters is obtained with the numerical model considering the identified construction techniques, repair phases, and connection relations between the bell tower and the nave. [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]

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]

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]

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]

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]

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 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]

"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]

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