Multiple object tracking is an important but challenging computer vision problem. The complex motion of objects makes tracking difficult during long periods of object occlusion, and as a result occlusions frequently cause fragmented tracks with gaps. Previous works use linear interpolation to fill in such gaps, a technique which is only able to model simple motion. As a result, tracked bounding box

Based on the classical MapReduce concept, we propose an extended MapReduce scheduling model. In the extended MapReduce scheduling problem, we assumed that each job contains an open-map task (the map task can be divided into multiple unparallel operations) and series-reduce tasks (each reduce task consists of only one operation). Different from the classical MapReduce scheduling problem, we also assume

In this paper, we address a hybrid flow-shop scheduling problem with the objective of minimizing the makespan and the cost of delay. The concerned problem considers the diversity of the customers’ requirements, which influences the procedures of the productions and increases the complexity of the problem. The features of the problem are inspired by the real-world situations, and the problem is formulated

The integration of the new-generation information technology and the automobile manufacturing industry has significantly improved the production efficiency of the automobile manufacturing industry, but it will also increase the technology application cost of the automobile manufacturing industry. The boundary value of the income change of the automobile manufacturing industry can be obtained by examining

For traditional broadcasting formats, implementation of accessible audio strategies for hard of hearing people have used a binary, intelligibility-based approach. In this approach, sounds are categorized either as speech, contributing to comprehension of content, or non-speech, which can mask the speech and reduce intelligibility. Audio accessibility solutions have therefore focused on speech enhancement

In this work, the results of usability and user experience (UX) evaluation of a serious video game for the cognitive stimulation and motor rehabilitation of the upper limb of the elderly are presented. The serious game includes features that allow (1) performing cooperative therapy exercises between two patients, (2) remote session configuration therapy, and (3) monitoring/analyzing the sessions’ results

An isogeometric approach for solving the Laplace–Beltrami equation on a two-dimensional manifold embedded in three-dimensional space using a Galerkin method based on Catmull–Clark subdivision surfaces is presented and assessed. The scalar-valued Laplace–Beltrami equation requires only \(C^0\) continuity and is adopted to elucidate key features and properties of the isogeometric method using Catmull–Clark

In this work, we propose a parameter estimation framework for fracture propagation problems. The fracture problem is described by a phase-field method. Parameter estimation is realized with a Bayesian approach. Here, the focus is on uncertainties arising in the solid material parameters and the critical energy release rate. A reference value (obtained on a sufficiently refined mesh) as the replacement

GPGPU architectures have become established as the dominant parallelization and performance platform achieving exceptional popularization and empowering domains such as regular algebra, machine learning, image detection and self-driving cars. However, irregular applications struggle to fully realize GPGPU performance as a result of control flow divergence and memory divergence due to irregular memory

Convolutional neural network (CNN) inference is commonly performed with 8-bit integer values. However, higher precision floating-point inference is required. Existing processors support 16- or 32 bit FP but do not typically support custom precision FP. We propose hardware optimized bit-sliced floating-point operators (HOBFLOPS), a method of generating efficient custom-precision emulated bitsliced software

Ising machines use physics to naturally guide a dynamical system towards an optimal state which can be read out as a heuristical solution to a combinatorial optimization problem. Such designs that use nature as a computing mechanism can lead to higher performance and/or lower operation costs. Quantum annealers are a prominent example of such efforts. However, existing Ising machines are generally bulky

The stable set problem is a fundamental combinatorial optimisation problem, that is known to be very difficult in both theory and practice. Some of the solution algorithms in the literature are based on 0-1 linear programming formulations. We examine an entire family of such formulations, based on so-called clique and nodal inequalities. As well as proving some theoretical results, we conduct extensive

Manual short-term scheduling in underground mines is a time-consuming and error-prone activity. In this work, we present a Constraint Programming approach capable of automating the short-term scheduling process in a cut-and-fill mine. The approach extends previous work by accounting for fleet travel times, and thus captures an important aspect of the real-world machine scheduling problem. We introduce

In this paper, we address a discrete facility location problem where a retailer aims at locating new facilities with possibly different characteristics. Customers visit the facilities based on their preferences which are represented as probabilities. These probabilities are determined in a novel way by using a fuzzy clustering algorithm. It is assumed that the sum of the probabilities with which customers

Firms often use a facility to produce both Make-to-Stock (MTS) and Make-to-Order (MTO) items. This study provides insights into conditions favoring the adoption of such a mixed MTS-MTO production strategy. Past studies ignore the influence of a one-to-many relation between semi-finished goods and products, and a guaranteed lead-time for MTO products, with uncertain product demand. The problem is modeled

In the large-scale online services built upon data centers from heterogeneous clouds, user requests come from all over the world and will create stochastic demands for various resources in these data centers. It is an impending need to optimally schedule resource while considering the heterogeneity of different data centers. The corresponding general scheduling problem is a highly complex non-linear

In the existing works, system of addition-min fuzzy relation inequalities (FRIs) was introduced to model a peer-to-peer (P2P) file sharing system. For decreasing the network congestion, a minimax programming has been studied. However, in such programming, the different weights of the terminals (or peers) were not considered. In order to distinguish the different importance degrees of the terminals

This paper studies a supply chain with a core manufacturer and a financially constrained retailer in the context of an imperfect capital market. We examine the capital-constrained decision objectives of profit maximization with and without bankruptcy costs, and extend to the multi-attribute utility (MAU) maximization, including attributes of expected sales revenue, bankruptcy cost, and service level

To leverage information sharing in online social networks, online retailers (e-tailers) have launched social networking functions on their platforms. The objective of e-tailers’ social networks is to empower consumers to connect and share product information. However, the e-tailers have not used these social networks to provide product recommendations to customers. Our goal is to aid e-retailers in

It is generally accepted that populations are useful for the global exploration of multi-modal optimisation problems. Indeed, several theoretical results are available showing such advantages over single-trajectory search heuristics. In this paper we provide evidence that evolving populations via crossover and mutation may also benefit the optimisation time for hillclimbing unimodal functions. In particular

Given two strings S and T, each of length at most n, the longest common substring (LCS) problem is to find a longest substring common to S and T. This is a classical problem in computer science with an \(\mathcal {O}(n)\)-time solution. In the fully dynamic setting, edit operations are allowed in either of the two strings, and the problem is to find an LCS after each edit. We present the first solution

An area of biometrics that has recently attracted much attention is gender and age classification. Its applications can be found not only in the fields of security and surveillance, but also in the context of marketing and demographic information gathering. In addition, extracting this information from a biometric sample can help to decrease the time to identify the exact individual. In this paper

Understanding temporal dependencies of videos is fundamental for vision problems, but deep learning–based models are still insufficient in this field. In this article, we propose a novel deep multiplicative neural network (DMNN) for learning hierarchical long-term representations from video. The DMNN is built upon the multiplicative block that remembers the pairwise transformations between consecutive

With the advancement in ICT, web search engines have become a preferred source to find health-related information published over the Internet. Google alone receives more than one billion health-related queries on a daily basis. However, in order to provide the results most relevant to the user, WSEs maintain the users’ profiles. These profiles may contain private and sensitive information such as the

The interest in face recognition studies has grown rapidly in the last decade. One of the most important problems in face recognition is the identification of ethnics of people. In this study, a new deep learning convolutional neural network is designed to create a new model that can recognize the ethnics of people through their facial features. The new dataset for ethnics of people consists of 3141

In order to identify the modal parameters of time invariant three-dimensional engineering structures with damping and small nonlinearity, a novel isometric feature mapping (Isomap)-based three-dimensional operational modal analysis (OMA) method is proposed to extract nonlinear features in this paper. Using this Isomap-based OMA method, a low-dimensional embedding matrix is multiplied by a transformation

Knowledge graph is a kind of semantic network for information retrieval. How to construct a knowledge graph that can serve the power system based on the behavior data of dispatchers is a hot research topic in the area of electric power artificial intelligence. In this paper, we propose a method to construct the dispatch knowledge graph for the power grid. By leveraging on dispatch data from the power

Scheduling is a fundamental factor in managing the network resource of the Internet of things. For IoT systems such as production lines, to operate effectively, it is necessary to find an intelligent management scheme, i.e., schedule, for network resources. In this study, we focus on multiskill resource-constrained project scheduling problem (MS-RCPSP), a combinational optimization problem that has

In recent years, millions of source codes are generated in different languages on a daily basis all over the world. A deep neural network-based intelligent support model for source code completion would be a great advantage in software engineering and programming education fields. Vast numbers of syntax, logical, and other critical errors that cannot be detected by normal compilers continue to exist

One of the major challenges in the formal verification of embedded system software is the complexity and substantially large size of the implementation. The problem becomes crucial when the embedded system is a complex medical device that is executing convoluted algorithms. In refinement-based verification, both specification and implementation are expressed as transition systems. Each behavior of

Digitalization and technological innovations provide many benefits for citizens worldwide, increasing the quality of life in several ways, including by enhancing access to information. However, people with disabilities can face challenges when retrieving online information as they often use assistive technologies that might not be fully supported by web pages. Municipalities’ websites are an important

The growing sophistication of cyber attacks, vulnerabilities in high computing systems and increasing dependency on cryptography to protect our digital data make it more important to keep secret keys safe and secure. Few major issues on secret keys like incorrect use of keys, inappropriate storage of keys, inadequate protection of keys, insecure movement of keys, lack of audit logging, insider threats

With the advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors, new opportunities are emerging for applying deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge. This can facilitate the advancement of the medical Internet of Things (IoT) systems and Point of Care (PoC) devices. In this paper, we provide a tutorial describing

A relevant application domain for agent-based software is given by mobile and wearable applications. In this context, the impressive progress of technologies in the last decade makes it possible to explore the use of agent-oriented programming languages and frameworks based on cognitive architectures, such as the Belief–Desire–Intention (BDI) one. Accordingly, in this paper we provide a comprehensive

Due to the rapid increase in technological innovation and corresponding increase in patent applications, automatic patent classification systems are very helpful for both individual inventors and patent attorneys in classifying patents. However, previous studies have neglected the question of what content patents include and how to represent patent content effectively in a structured form to predict

Through specific efforts to eliminate poverty over several decades, a large number of poverty-stricken families in China have been lifted out of poverty although, in some rural areas, there still exist a large number of very poor people. In 2013, to realize the aim of eliminating poverty in the whole of China by 2020, a specific, targeted poverty alleviation policy was proposed. A suitable evaluation

We present a pair arithmetic for the four basic operations and square root. It can be regarded as a simplified, more-efficient double-double arithmetic. The central assumption on the underlying arithmetic is the first standard model for error analysis for operations on a discrete set of real numbers. Neither do we require a floating-point grid nor a rounding to nearest property. Based on that, we define

Multicomplex and multidual numbers are two generalizations of complex numbers with multiple imaginary axes, useful for numerical computation of derivatives with machine precision. The similarities between multicomplex and multidual algebras allowed us to create a unified library to use either one for sensitivity analysis. This library can be used to compute arbitrary order derivates of functions of

Stochastic differential equations (SDEs) are widely used to model systems affected by random processes. In general, the analysis of an SDE model requires numerical solutions to be generated many times over multiple parameter combinations. However, this process often requires considerable computational resources to be practicable. Due to the embarrassingly parallel nature of the task, devices such as

A general-purpose C++ software program called CGPOPS is described for solving multiple-phase optimal control problems using adaptive direct orthogonal collocation methods. The software employs a Legendre-Gauss-Radau direct orthogonal collocation method to transcribe the continuous optimal control problem into a large sparse nonlinear programming problem (NLP). A class of hp mesh refinement methods

During the past 15 years, schema mappings have been extensively used in formalizing and studying such critical data interoperability tasks as data exchange and data integration. Much of the work has focused on GLAV mappings, i.e., schema mappings specified by source-to-target tuple-generating dependencies (s-t tgds), and on schema mappings specified by second-order tgds (SO tgds), which constitute

The final presidential debate in the U.S. was one of the most widely followed programs of 2016. The main broadcasters aired it live on television and streamed it through social media with closed captioning so that all Americans could access this highly awaited political event. After the debate night, most networks uploaded the full program or fragments of it to their websites in order to make it available

Quality accidents (QAs) of high frequencies in various fields have caused large economic and reputational losses to manufacturers, and identification of the root causes of vicious QAs is a top priority and a major challenge for manufacturers. Especially in the era of big data, the large number of data could be collected from the product life cycle easily, these high-dimensional big data always bear

In the decision-making process, various approximate weighting methods such as equal weights, rank-order centroid weights, rank-sum weights, and rank reciprocal weights, have been proposed to deal with multi-attribute decision-making (MADM) problems in situations where only rank order information can be captured. In this paper, we seek to contribute fresh insights through the development of an integrated

In recent years, pellet has received increasing attention among other biofuels due to its low storage costs and high combustion efficiency. The traceability of pellet quality along the entire supply chain is a critical issue, since fraudulent behaviours, such as the replacement with lower quality pellet, may both cause an economic damage and harm consumers’ health. Traditionally, pellet quality is

Simultaneous distribution-free surveillance (SDFS) f location and scale parameters of a process based on a single plotting statistic has emerged as a prevalent research issue in the past seven years. Most of the SDFS schemes are, however, appropriate for monitoring two-sided shifts in location-scale. In monitoring service quality or time to an event, among others, decreasing process median or variability

Since security return cannot be accurately estimated using past data, in this paper it is assumed to take values in a given ellipsoidal uncertainty set. This paper aims to discuss a robust multi-objective portfolio selection problem based on the minimax regret criterion under an ellipsoidal uncertainty sets, in which the two objective functions are the portfolio return to be maximized and the mean

Today, due to the increasing environmental hazards and governmental regulations, as well as the limitation of sources of production, researchers have paid special attention to the design of closed-loop green supply chain networks. The closed-loop supply chain networks (CLSCN) include the returns processes and the producers aim to capturing additional value considering further integration of all supply

In RGBD saliency detection research field, RGB and depth cues are generally given the same status by RGBD saliency models. However, they ignore that both modalities are significantly different in inherent attribution so that effective features cannot be drawn from depth maps. In order to address this issue, this paper proposes a novel RGBD saliency model including two key components: the contrast-guided

Face landmarking, defined as the detection of fiducial points on faces, has received a lot of attention over the last two decades within the computer vision community. While research literature documents major advances using state-of-art deep convolutional neural networks, earlier cascaded regression tree-based approaches remain a relevant alternative for low-cost, low-power embedded systems. Yet,

Evacuation behavior is an important factor which must be considered in the design of public structures. With the continuous complexity of structure, more and more factors should be considered in evacuation. The traditional design based on experience may have some limitations in practice. Based on the deep neural network model, the evacuation design simulation for subway station buildings is implemented

In recent years, Autonomous Driving Systems (ADS) become more and more popular and reliable. Road markings are important for drivers and advanced driver assistance systems by better understanding the road environment. While the detection of road markings may suffer a lot from various illumination, weather conditions and angles of view, most traditional road marking detection methods use fixed threshold

(m,k)-firm real-time tasks require meeting the deadline of at least m jobs out of any k consecutive jobs. When compared to hard real-time tasks, (m,k)$-firm tasks open up the possibility of tighter resource-dimensioning in implementations. Firmness analysis verifies the satisfaction of (m,k)-firmness conditions. Scheduling policies under which a set of periodic tasks runs on a resource influence the

Static random access memory– (SRAM) based field programmable gate arrays (FPGAs) are currently facing challenges of limited capacity and high leakage power. To solve this problem, non-volatile memory (NVM) is proposed as the alternative to build non-volatile FPGAs (NVFPGAs). Even though the feasibility of NVFPGA has been confirmed, the utilization of multiple level cells (MLCs) has not been fully exploited

Over the past decade, data recorded (due to digitization) in healthcare sectors have continued to increase, intriguing the thought about big data in healthcare. There already exists plenty of information, ready for analysis. Researchers are always putting their best effort to find valuable insight from the healthcare big data for quality medical services. This article provides a systematic review study

Tomato is an important vegetable crop cultivated worldwide coming next only to potato. However, the crop can be damaged due to various diseases. It is important for the farmer to know the type of disease for timely treatment of the crop. It has been observed that leaves are clear indicator of specific diseases. A number of Machine Learning (ML) algorithms and Convolution Neural Network (CNN) models