Authorship attribution is the process of identifying the author of a text. Approaches to tackling it have been conventionally divided into classification-based ones, which work well for small numbers of candidate authors, and similarity-based methods, which are applicable for larger numbers of authors or for authors beyond the training set; these existing similarity-based methods have only embodied

The social brain hypothesis proposes that enlarged brains have evolved in response to the increasing cognitive demands that complex social life in larger groups places on primates and other mammals. However, this reasoning can be challenged by evidence that brain size has decreased in the evolutionary transitions from solitary to social larger groups in the case of Neolithic humans and some eusocial

This paper proposes a novel system for managing visual attention in social robots. This system is based on a client/server approach that allows integration with a cognitive architecture controlling the robot. The core of this architecture is a distributed knowledge graph, in which the perceptual needs are expressed by the presence of arcs to stimuli that need to be perceived. The attention server sends

We formulate and prove logical characterizations of fuzzy simulations for fuzzy labeled transition systems under the Gödel semantics. Apart from the usual setting, we consider also the setting that allows graded modal operators, which are used to talk about the number of successors with a certain property of the current state. Our characterizations are formulated w.r.t. the existential fragment of

In a Bayesian network (BN), the Markov boundary (MB) presents the local causal structure around a target. Due to the interpretability and robustness, it has been widely applied to feature selection and BN structure learning. However, existing MB discovery algorithms might fail to identify some true positives, leading to poor performance in real-world applications. To tackle this issue, we introduce

With the introduction of the regularization term in the formulation of the well-known twin support vector machine (TWSVM) for classification, twin bounded support vector machine (TBSVM) method was proposed recently as an improved version by implementing the structural risk minimization principle. However, TBSVM employs hinge loss function and it is sensitive to noise and unstable to re-sampling. Since

The vehicle fuel consumption gauge is a vehicle’s basic device that usually records the instan- taneous as well as average fuel consumption of the vehicle, which brings a lot of convenience during the driving process. The individual real-time fuel consumption data are meaningless, but its continuous real-time fuel consumption data contains some information that may reveal the user’s privacy, which

Handwritten signature verification is a widely used biometric for person identity authentication in document forensics. Despite the tremendous efforts in past research, offline signature verification still remains a challenge, particularly in discriminating between genuine signatures and skilled forgeries, because the difference of appearance between genuine and skilled forgery may be smaller than

With huge cheap micro-sensing devices deployed, wireless sensor network (WSN) gathers information from the region and delivers it to the base station (BS) for further decision. The hotspot problem occurs when cluster head (CH) nearer to BS may die prematurely due to uneven energy depletion resulting in partitioning the network. To overcome the issue of hotspot or energy hole, unequal clustering is

An improved text classification method based on domain ontology is proposed in this paper to organize the mass information that records node enterprises’ innovation activities under the supply chain environment. This method can classify the documents of node enterprises under the supply chain without a training set. It achieves a precision of 80% for documents’ classification, which outperforms the

Software usability is usually used in reference to the hierarchical software usability model by researchers and is an important aspect of user experience and software quality. Thus, evaluation of software usability is an essential parameter for managing and regulating a software. However, it has been difficult to establish a precise evaluation method for this problem. A large number of usability factors

In this paper, we propose an efficient method to estimate the Weingarten map for point cloud data sampled from manifold embedded in Euclidean space. A statistical model is established to analyze the asymptotic property of the estimator. In particular, we show the convergence rate as the sample size tends to infinity. We verify the convergence rate through simulated data and apply the estimated Weingarten

Food Recommender Systems (FRS) have the potential to support informed and satisfying food choices. However, to realize their full potential, FRS must engage with the complexity of the choices people make around food. For example, while taste and ingredients are important, contextual and practical factors also play a critical role in food choice. Much of the previous literature on FRS has focused on

Robotic soccer simulation is a challenging area, where the development of new techniques is paramount to remain competitive. Robotic skill evolution has accelerated with recent developments in deep learning algorithms, leading to improvements in behavior number and complexity. Shooting a ball towards a defined target is one of the most basic yet indispensable skills in soccer. However, fast and accurate

Traffic sign detection has made great progress with the rise of deep learning in recent years. As a result of the complex and changeable traffic environment, detecting small traffic signs in a real-world scene is still a challenging problem. In this paper, a novel group multi-scale attention pyramid network is proposed to address the problem. Specifically, to aggregate the feature at different scales

Occlusion awareness is one of the most challenging problems in several fields such as multimedia, remote sensing, computer vision, and computer graphics. Realistic interaction applications are suffering from dealing with occlusion and collision problems in a dynamic environment. Creating dense 3D reconstruction methods is the best solution to solve this issue. However, these methods have poor performance

The information security domain focuses on security needs at all levels in a computing environment in either the Internet of Things, Cloud Computing, Cloud of Things, or any other implementation. Data, devices, services, or applications and communication are required to be protected and provided by information security shields at all levels and in all working states. Remote authentication is required

Cloud computing has emerged as a cutting-edge technology which is widely used by both private and public institutions, since it eliminates the capital expense of buying, maintaining, and setting up both hardware and software. Clients pay for the services they use, under the so-called Service Level Agreements (SLAs), which are the contracts that establish the terms and costs of the services. In this

Recently semi-supervised non-negative matrix factorization (NMF) has received a lot of attentions in computer vision, information retrieval and pattern recognition, because that partial label information can produce considerable improvement in learning accuracy of the algorithms. However, the existing semi-supervised NMF algorithms cannot make full use of label information, that is, they cannot guarantee

Nevertheless, the accuracy of a multilevel image thresholding technique using 1D or 2D Tsallis entropy is limited. To overcome this, we propose a maximum 3D Tsallis entropy-based multilevel thresholding method. The idea of 3D Tsallis entropy is introduced. Opposed to the 1D/2D Tsallis entropy, the 3D Tsallis entropy-based approach is more robust, it performs well even in the case of the low signal-to-noise-ratio

Computed tomography (CT) scanning is a fast and painless procedure that can capture clear imaging information beneath the abdomen and is widely used to help diagnose and monitor disease progress. The pelvic lymph node is a key indicator of colorectal cancer metastasis. In the traditional process, an experienced radiologist must read all the CT scanning images slice by slice to track the lymph nodes

Data-driven healthcare uses predictive analytics to enhance decision-making and personalized healthcare. Developing prognostic models is one of the applications of predictive analytics in medical environments. Various studies have used machine learning techniques for this purpose. However, there is no specific standard for choosing prediction models for different medical purposes. In this paper, the

In their recent paper in this journal, Delgrande, Renne and Sack study qualitative probability and discuss its role in Artificial Intelligence. Building on related work by de Finetti, Scott, Segerberg and others, the authors provide general axioms for qualitative probability. In this paper we investigate Koopman's conditional qualitative probability from the computational viewpoint. An important part

Pandemic novel Coronavirus (Covid‐19) is an infectious disease that primarily spreads by droplets of nose discharge when sneezing and saliva from the mouth when coughing, that had first been reported in Wuhan, China in December 2019. Covid‐19 became a global pandemic, which led to a harmful impact on the world. Many predictive models of Covid‐19 are being proposed by academic researchers around the

In AI research, synthesizing a plan of action has typically used descriptive models of the actions that abstractly specify what might happen as a result of an action, and are tailored for efficiently computing state transitions. However, executing the planned actions has needed operational models, in which rich computational control structures and closed-loop online decision-making are used to specify

ABSTRACT Active aging technologies are increasingly designed to support an active lifestyle. However, the way in which they are designed can raise different barriers to acceptance of and use by older adults. Their designers can adopt a negative stereotype of aging. Thorough understanding of user requirements is central to this problem. This paper investigates user requirements for technologies that

ABSTRACT We introduce “4E,” a new design approach of reconfigurable displays that can change their form factors by capitalizing on four quality properties inspired by applied material: extensibility, extendability, expandability, and extractability. This approach is applicable to both fixed and portable displays. We define and exemplify each property, highlighting the key differences in how reconfigurable

ABSTRACT New advancements in technology have made machines teachers, or technology-powered robots or AI that assist in the overall learning experience, a possibility. Though adoption rates are currently low, colleges and universities will likely incorporate some aspects of machine teachers (e.g., AI, robots) in their curriculums in the foreseeable future. However, little is known about how to create

ABSTRACT Crowd rating derives a collective value based on massive homogeneous contributions from individual users, where using gamification design is a promising approach to encouraging users’ motivation and participation. Drawing upon self-determination theory and motivational affordances theory, this study investigates how different gamification objects, as building blocks for feedback-giving mechanics

Since its inception, Healthcare 4.0 has empowered the integration of advanced technologies to create and improve the quality of healthcare services. The delivery of healthcare services has come a long way from physical appointments with doctors to remote health monitoring and disease prediction, surgery assistive systems. This advancement has only been possible because of the integration of cutting-edge

The usage of data has grown exponentially well across prominent sectors in both public and private organizations. Security policies for the data are inevitable as data plays a major role even in prominent sectors like healthcare, defense. Accessing the data from the cloud is one of the easiest ways which is why most of the cloud service vendors pay more attention to secure the data on the cloud. Cyber

Strengthening technological innovation and improving independent intellectual property rights is one of the most important tasks for the development of our country's integrated circuit enterprises. In addition to the main body of technological innovation-the enterprise's own efforts, it is also necessary to create an environment for enterprise technological innovation as a base for enterprise service

Feature selection (FS) has become an indispensable data preprocessing task because of the huge amount of high dimensional data being generated by current technologies. These high dimensional data contains irrelevant, redundant, and noisy features that deteriorate classification accuracy. FS reduces dimensionality by removing the unwanted features thus improves classification accuracy. FS can be considered

Object detection in videos is a fundamental technology for applications such as monitoring. Since video frames are treated as independent input images, static detectors ignore the temporal information of objects when detecting objects in videos, generating redundant calculations in the detection process. In this paper, based on the spatiotemporal continuity of video objects, we propose an attention-guided

An emerging optimisation problem from the real-world applications, named the multi-point dynamic aggregation (MPDA) problem, has become one of the active research topics of the multi-robot system. This paper focuses on a multi-objective MPDA problem which is to design an execution plan of the robots to minimise the number of robots and the maximal completion time of all the tasks. The strongly-coupled

Clustering by fast search and find of density peaks (DPC) is a popular clustering method based on density and distance. In DPC, each non-center point’s cluster label is led by its nearest point with higher density, which may cause some misclassifications of non-center points and interfere with the choice of correct cluster centers in the decision graph. To avoid these defects, we propose a novel clustering

Spiking Neural Networks (SNNs) have been attached great importance due to their biological plausibility and high energy-efficiency on neuromorphic chips. As these chips are usually resource-constrained, the compression of SNNs is thus crucial along the road of practical use of SNNs. Most existing methods directly apply pruning approaches in artificial neural networks (ANNs) to SNNs, which ignore the

Heuristic optimisation algorithms are in high demand due to the overwhelming amount of complex optimisation problems that need to be solved. The complexity of these problems is well beyond the boundaries of applicability of exact optimisation algorithms and therefore require modern heuristics to find feasible solutions quickly. These heuristics and their effects are almost always evaluated and explained

In this paper, two novel algorithms for features selection are proposed. The first one is a filter method while the second is wrapper method. Both the proposed algorithm use the crowding distance used in the multiobjective optimization as a metric in order to sort the features. The less crowded features have great effects on the target attribute (class). The experimental results have shown the effectiveness

Drug repositioning, which recommends approved drugs to potential targets by predicting drug-target interactions (DTIs), can save the cost and shorten the period of drug development. In this work, we propose a novel knowledge graph based deep learning method, named KG-DTI, for DTIs predictions. Specifically, a knowledge graph of 29,607 positive drug-target pairs is constructed by DistMult embedding

The K-means algorithm is a popular clustering method, which is sensitive to the initialization of samples and selecting the number of clusters. Its performance on high-dimensional datasets is considerably influenced. Principal component analysis (PCA) is a linear dimensionless reduction method that is closely related to the K-means algorithm. Dimension reduction leads to the selection of initial centers

Real social network feature many small groups with information exchange among groups. Groups often overlap with each other, and individuals can be in multiple groups. By simulating the information transmission process in different networks with structural changes, we can obtain a deeper understanding of the attribute characteristics of complex networks and the law of information transmission. Therefore

The Internet of Things (IoT) appliances often expose sensitive data, either directly or indirectly. They may, for instance, tell whether you are at home right now or what your long or short-term habits are. Therefore, it is crucial to protect such devices against adversaries and has in place an early warning system which indicates compromised devices in a quick and efficient manner. In this paper,

Software systems have an increasing value in our lives, as our society relies on them for the numerous services they provide. However, as our need for larger and more complex software systems grows, the risks involved in their operation also grows, with possible consequences in terms of significant material and social losses. The rational management of software defects and possible failures is a fundamental

Classification methods play a significant role in the fault diagnosis field. However, they cannot effectively recognize new types of fault data and improve their classification performance timely through learning the testing data, for they lack continual learning ability. Inspired by the continual learning mechanism of the biological immune system, we propose a continual learning classification method

A short introduction to survival analysis and censored data is included in this paper. A thorough literature review in the field of cure models has been done. An overview on the most important and recent approaches on parametric, semiparametric and nonparametric mixture cure models is also included. The main nonparametric and semiparametric approaches were applied to a real time dataset of COVID-19

Local dimming is a feature on light-emitting diode (LED) TVs that dims the backlight behind parts of the screen that are displaying black and brights the backlight behind parts of the screen that are displaying white. To improve contrast ratios (CRs) and reduce power consumption for liquid crystal displays (LCDs), local dimming technology has been widely studied. In this paper, we propose a deep CNN-based

The development of estate price prediction systems is one of the issues that researchers are paying the most attention to. A good estate price prediction system can shorten the time it takes buyers to consider estates and invigorate the estate market. Generally speaking, an estate price prediction system considers the temporal and spatial features of the estate. In addition, the estate price prediction

Collecting large-scale data with clean labels for supervised training is practically challenging. It is easier to collect a dataset with noisy labels, but such noise may degrade the performance of deep neural networks (DNNs). This paper targets at this challenge by wisely leveraging both relatively clean data and relatively noisy data. In this work, we propose meta-joint optimization (MJO), a novel

This research introduces a fusion architecture that integrates balanced scorecards (BSCs) and network data envelopment analysis (NDEA) to conduct a performance evaluation task from multiple perspectives. The architecture is able to capture the dynamics of production processes and sub-processes, uncover some of the components behind successful business practices, and shed light on needed actions for

Most of the recent deep reinforcement learning advances take an RL-centric perspective and focus on refinements of the training objective. We diverge from this view and show we can recover the performance of these developments not by changing the objective, but by regularising the value-function estimator. Constraining the Lipschitz constant of a single layer using spectral normalisation is sufficient

We provide some basic and sensible definitions of different types of digital twins and recommendations on when and how to use them. Following up on our recent publication of the Learning Causal Digital Twin, this article reports on a stochastic formulation and solution of the problem. Structural Vector Autoregressive Model (SVAR) for Causal estimation is recast as a state-space model. Kalman filter

We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations. For conditional image synthesis, we further improve sample quality with classifier guidance: a simple, compute-efficient method for trading off diversity for sample

In the mean field regime, neural networks are appropriately scaled so that as the width tends to infinity, the learning dynamics tends to a nonlinear and nontrivial dynamical limit, known as the mean field limit. This lends a way to study large-width neural networks via analyzing the mean field limit. Recent works have successfully applied such analysis to two-layer networks and provided global convergence

In this paper, two novel algorithms for features selection are proposed. The first one is a filter method while the second is wrapper method. Both the proposed algorithm use the crowding distance used in the multiobjective optimization as a metric in order to sort the features. The less crowded features have great effects on the target attribute (class). The experimental results have shown the effectiveness

Over the past decade, wind energy has gained more attention in the world. However, owing to its indirectness and volatility properties, wind power penetration has increased the difficulty and complexity in dispatching and planning of electric power systems. Therefore, it is needed to make the high-precision wind power prediction in order to balance the electrical power. For this purpose, in this study

In this paper we derive an explicit formula for calculating the marginal likelihood of a given factorization of a categorical dataset. Since the marginal likelihood is proportional to the posterior probability of the factorization, these likelihoods can be used to order all possible factorizations and select the "best" way to factor the overall distribution from which the dataset is drawn. The best

When an agent encounters a continual stream of new tasks in the lifelong learning setting, it leverages the knowledge it gained from the earlier tasks to help learn the new tasks better. In such a scenario, identifying an efficient knowledge representation becomes a challenging problem. Most research works propose to either store a subset of examples from the past tasks in a replay buffer, dedicate