The purpose of this paper is to build an agent-based model which is adapted to study the information cascades in financial markets. Thus, we design and implement a model with asynchronous continuous-time management, populated by heterogeneous traders, connected by an interaction network with directed and weighted edges, which allows them to observe and learn about the actions of their peers. The proposed

A new pandemic attack happened over the world in the last month of the year 2019 which disrupt the lifestyle of everyone around the globe. All the related research communities are trying to identify the behaviour of pandemic so that they can know when it ends but every time it makes them surprise by giving new values of different parameters. In this paper, support vector regression (SVR) and deep neural

The ubiquitous cloud computing services provide a new paradigm to the work-from-home environment adopted by the enterprise in the unprecedented crisis of the COVID-19 outbreak. However, the change in work culture would also increase the chances of the cybersecurity attack, MAC spoofing attack, and DDoS/DoS attack due to the divergent incoming traffic from the untrusted network for accessing the enterprise’s

The COVID-19 pandemic resulted in a significant increase in the workload for the emergency systems and healthcare providers all around the world. The emergency systems are dealing with large number of patients in various stages of deteriorating conditions which require significant medical expertise for accurate and rapid diagnosis and treatment. This issue will become more prominent in places with

The Coronavirus disease (COVID-19) is an infectious disease caused by the newly discovered Severe Acute Respiratory Syndrome Coronavirus two (SARS-CoV-2). Most of the people do not have the acquired immunity to fight this virus. There is no specific treatment or medicine to cure the disease. The effects of this disease appear to vary from individual to individual, right from mild cough, fever to respiratory

Data stream classification is widely popular in the field of network monitoring, sensor network and electronic commerce, etc. However, in the real-world applications, recurring concept drifting and label missing in data streams seriously aggravate the difficulty on the classification solutions. And this challenge has received little attention from the research community. Motivated by this, we propose

Card-based cryptography provides simple and practicable protocols for performing secure multi-party computation with just a deck of cards. For the sake of simplicity, this is often done using cards with only two symbols, e.g., \(\clubsuit \) and \(\heartsuit \) . Within this paper, we also target the setting where all cards carry distinct symbols, catering for use-cases with commonly available standard

Due to its impact, COVID-19 has been stressing the academy to search for curing, mitigating, or controlling it. It is believed that under-reporting is a relevant factor in determining the actual mortality rate and, if not considered, can cause significant misinformation. Therefore, this work aims to estimate the under-reporting of cases and deaths of COVID-19 in Brazilian states using data from the

Traditional classifiers often fail to produce desired classification accuracy because of inadequate training samples present in microRNA (miRNA) gene expression cancer datasets. In this context, we propose a novel semi-supervised ensemble learning (SSEL) strategy combining the (advantages of) semi-supervised learning and ensemble learning which is able to produce better results than the individual

One of the most challenging issues dealing with dynamic social networks, with evolving community structures, is to understand and model the way in which these structures evolve over time. Studies on tracking and predicting community evolution generally split these networks into time-series of static networks, each including interactions aggregated over discrete time intervals (hours, days, and years)

Heart disease prediction is a critical task regarding human health. It is based on deriving an Machine Learning model from medical parameters to predict risk levels. In this work, we propose and test novel ensemble methods for heart disease prediction. Randomness analysis of distance sequences is utilized to derive a classifier, which is served as a base estimator of a bagging scheme. Method is successfully

COVID-19 (also known as SARS-COV-2) pandemic has spread in the entire world. It is a contagious disease that easily spreads from one person in direct contact to another, classified by experts in five categories: asymptomatic, mild, moderate, severe, and critical. Already more than 66 million people got infected worldwide with more than 22 million active patients as of 5 December 2020 and the rate is

In card-based cryptography, designing AND protocols in committed format is a major research topic. The state-of-the-art AND protocol proposed by Koch, Walzer, and Härtel in ASIACRYPT 2015 uses only four cards, which is the minimum permissible number. The minimality of their protocol relies on somewhat complicated shuffles having non-uniform probabilities of possible outcomes. Restricting the allowed

Card-based cryptography, introduced by den Boer aims to realize multiparty computation (MPC) by using physical cards. We propose several efficient card-based protocols for the millionaires’ problem by introducing a new operation called Private Permutation (PP) instead of the shuffle used in most of existing card-based cryptography. Shuffle is a useful randomization technique by exploiting the property

The manuscript presents a bragging-based ensemble forecasting model for predicting the number of incidences of a disease based on past occurrences. The objectives of this research work are to enhance accuracy, reduce overfitting, and handle overdrift; the proposed model has shown promising results in terms of error metrics. The collated dataset of the diseases is collected from the official government

It is known that secure computation can be done by using a deck of physical cards. This area is called card-based cryptography. Shinagawa et al. (in: Provable security—9th international conference, ProvSec 2015, Kanazawa, Japan, 2015) proposed regular n -sided polygon cards that enable to compute functions over $${\mathbb {Z}}/n{\mathbb {Z}}$$ Z / n Z . In particular, they designed efficient protocols

From a mixed perspective of the fields of computational creativity and design research, we present a formal framework called Computational Creative Design Systems Framework (CCDSF) for describing, analyzing, and modeling Computational Creative Design Systems (CCDSs), either as autonomous agents or as collaborative systems integrating different agents, including both pure computational and human–computer

Computers have enhanced productivity and cost-effectiveness in all of the creative industries, and their value as tools is rarely doubted. But can machines serve as more than mere tools, and assume the role and responsibilities of a co-creative partner, or even become goal-setting, autonomous creators in their own right? These are the questions that define the discipline of computational creativity

This paper proposes new card-based cryptographic protocols to calculate logic functions with the minimum number of cards using private operations under the semi-honest model. Though various card-based cryptographic protocols were shown, the minimum number of cards used in the protocol has not been achieved yet for many problems. Operations executed by a player where the other players cannot see are

Many of the stories at the core of narrative entertainment involve a number of plot lines that combine to give them interest. The present paper sets out to solve the problem of how several different plot lines, each one of them complete in its own sense, can be combined into a single linear sequence that works reasonably well as a plot. Starting from a brief review of how existing storytelling systems

Inductive logic programming (ILP) is a framework of learning logic programs from examples and background knowledge. In some real-world applications, we are particularly interested in learning aspects of system dynamics that are characterized by state transitions for which logic programs are shown to be expressive. In this work, we study this particular form of ILP—learning logic programs from state/interpretation

Numberlink is a logic puzzle with an objective to connect all pairs of cells with the same number by non-crossing paths in a rectangular grid. In this paper, we propose a physical protocol of zero-knowledge proof for Numberlink using a deck of cards, which allows a prover to convince a verifier that he/she knows a solution without revealing it. In particular, the protocol shows how to physically count

Evaluating the extent to which computer-produced stories are structured like human-invented narratives can be an important component of the quality of a story plot. In this paper, we report on an empirical experiment in which human subjects have invented short plots in a constrained scenario. The stories were annotated according to features commonly found in existing automatic story generators. The

The field of bisociative literature-based discovery aims at mining scientific literature to reveal yet uncovered connections between different fields of specialization. This paper outlines several outlier-based literature mining approaches to bridging term detection and the lessons learned from selected biomedical literature-based discovery applications. The paper addresses also new prospects in bisociative

The emoji connection between visual representation and semantic knowledge, together with its large conceptual coverage have the potential to be exploited in computational approaches to the visual representation of concepts. An example of a system that explores this potential is Emojinating—a system that uses a process of visual blending of existing emoji to represent concepts. In this paper, we use

We propose a new way of using the betweenness centrality measure with co-author networks from an academic literature database to evaluate young researchers. It is difficult to discover and evaluate promising young researchers with indexes based on the number of cited papers, such as the h-index to which published papers introduce a lag and whose impact only becomes apparent after they have been cited

This work proposes a methodology for conducting field work in computational creativity, referring to field work as the effort of actively making a system or its artifacts widely accessible outside the academic world. Field work aims to study how creative computer agents and/or their products influence society, and how the dynamics that arise from the interaction between people and those inventive machines

Deep Learning provides useful insights by analyzing information especially in the field of finance with advanced computing technology. Although, RNN–LSTM network with the advantage of sequential learning has achieved great success in the past for time series prediction. Conversely, developing and selecting the best computational optimized RNN–LSTM network for intra-day stock market forecasting is a

Stories are made to be told, yet the computational generation of stories has principally focused on stories as textual artifacts, rather than on the telling, or indeed the performance, of stories. The performative aspect of stories, in which a teller brings a tale to life, requires more than the written word. We humans use our bodies to enact a story, through the apt use of motion, space, timing and

A knowledge-based methodology is proposed for sentiment analysis on social networks. The work was focused on semantic processing taking into account the content handling the public user’s opinions as excerpts of knowledge. Our approach implements knowledge graphs, similarity measures, graph theory algorithms, and a disambiguation process. The results obtained were compared with data retrieved from

Understanding the role of network for epidemic spread is a major concern in the study of complex network and disease spread. Localization of Principal EigenVector (PEV) of the network’s adjacency matrix has been explored in recent past by many researchers. Most of the previous works suggest the localization of PEV through maximization of inverse participation ratio (IPR). The network with maximum IPR

Synesthesia is a neurological phenomenon in which stimulation of one sensory or cognitive pathway leads to automatic, involuntary experiences in a second sensory or cognitive pathway. Many synesthetes used their capabilities as inspiration for their works. Drawing on this phenomenon, this paper presents a model able to create chord progressions using colors as the synesthetic input. In particular,

Semantic textual similarity methods are becoming increasingly crucial in text mining research areas such as text retrieval and summarization. Existing methods of text similarity have often been computed by their shallow or syntactic representation rather than considering their semantic content and meanings. This paper focuses mainly on computing the similarity between sentences without a supervised

The article “Special Issue on Molecular Robotics”, written by Satoshi Murata was originally published electronically on the publisher's internet portal (currently SpringerLink) on 18th May 2020 with open access.

We have great pleasure in presenting this special issue of New Generation Computing for Molecular Robotics. This special issue includes research papers on the latest results of molecular robotics, as well as tutorial papers that introduce fundamental knowledge of molecular robotics for non-specialized readers. One of the research papers from the forefront of DNA computing technology is on the development

Chemical reactions in small-volume reactors such as biological cells are highly variable due to the stochastic collision events of the small number of molecules. To analyze and design these stochastic biomolecular reactions, computational tools have been developed based on rigorous mathematical foundations. This paper reviews fundamental theory and computational tools for the modeling, analysis, and

Distributed computation consists of local computations and their interactions. We consider a molecular robot system as a distributed system composed of mobile computing entities with very weak capabilities, i.e., computing entities are anonymous (indistinguishable), oblivious (memory-less), and uniform (following a common local computation rule). The key property of such a distributed system is self-organization

In this tutorial, recent development of pattern generation algorithms based on DNA computing will be overviewed. Natural pattern generation, especially in biological organisms, are often driven by spatio-temporal chemical reactions. Various reaction–diffusion systems have been proposed to generate artificial patterns out of DNA sequences. To program DNA reaction–diffusion systems, in addition to the

This paper investigates the control of multi-agent systems inspired by chemotaxis of microorganisms. Chemotaxis is a biological phenomenon wherein organisms in an environment are attracted to food but move away from toxins. The problem addressed here is a formation control problem, i.e., a design problem of distributed controllers wherein the relative positions of agents become the desired positions

Owing to the absolute significance of the business process management, companies have become thoroughly oriented toward a good modeling of the business processes. Although the business process modeling is a substantial part in workflow automation, business process designers often misunderstand domain concepts or relationships due to their lack of precise business knowledge. Such a semantic equivocation

Language designers usually need to implement parsers and printers. Despite being two closely related programs, in practice they are often designed separately, and then need to be revised and kept consistent as the language evolves. It will be more convenient if the parser and printer can be unified and developed in a single program, with their consistency guaranteed automatically. Furthermore, in certain

Practical design of the XOR gate is an important milestone in the field of DNA computing. In this study, we aim to develop an enzyme-free XOR gate driven by a toehold-mediated strand displacement mechanism possessing the true detection property. The advantages of our design are as follows: dual-rail logic is not required, the explicit use of the NOT gate is avoided, the circuit structure is simple

This tutorial paper focuses on the design, simulation, and optimization of polymerase–exonuclease–nickase (PEN) toolbox systems. The PEN toolbox is a molecular programming framework that relies on the interaction between DNA molecules and enzymes to produce highly non-linear dynamic systems. The flip side of that expressivity is that the design of systems relies heavily on trial-and-error, thus calling

In this paper, we propose Knowledge Graph (KG), an articulated underlying semantic structure, as a semantic bridge between humans, systems, and scientific knowledge. To illustrate our proposal, we focus on KG-based intelligent survey systems. In state-of-the-art systems, information is hard-coded or implicit, making it hard for researchers to reuse, customise, link, or transmit structured knowledge

This special issue entitled “Deep Learning and Symbol Emergence” presents discussion of technology beyond deep learning toward human intelligence. In recent years, deep learning has achieved phenomenal success in various fields such as image recognition, speech recognition, game-playing, and natural language processing. Nevertheless, discussion persists among researchers on the limitations of deep

The editorial board would like to introduce a new area editor, Shinnosuke Seki, who is establishing a new area for the journal: “Theory of Natural Computing”. He welcomes theoretical studies on internationally accepted models of computation, including cellular automata, chemical reaction networks, Lindenmayer (L-) systems, Nubot models, Oritatami systems, P systems (membrane computing), reaction systems

Although several RDF knowledge bases are available through the Linked Open Data (LOD) initiative, the ontology schema of such linked datasets is not very rich. In particular, they lack object properties. The problem of finding new object properties between any two given classes has not been investigated in detail in the context of Linked Data. In the first part of this paper, we present DARO ( D etecting

To provide robots with the flexibility they need to cope with various environments, motion generation techniques using deep learning have been proposed. Generalization in deep learning is expected to enable flexible processing in unknown situations and flexible motion generation. Motion generation models have been proposed to realize specific robot tasks, and their operation successes in unknown situations

This paper describes a framework for the development of an integrative cognitive system based on probabilistic generative models (PGMs) called Neuro-SERKET. Neuro-SERKET is an extension of SERKET, which can compose elemental PGMs developed in a distributed manner and provide a scheme that allows the composed PGMs to learn throughout the system in an unsupervised way. In addition to the head-to-tail

Recently, attention mechanisms have significantly boosted the performance of natural language processing using deep learning. An attention mechanism can select the information to be used, such as by conducting a dictionary lookup; this information is then used, for example, to select the next utterance word in a sentence. In neuroscience, the basis of the function of sequentially selecting words is

The technologies for collecting and analyzing data are developing significantly, enabling us to gain new knowledge from various types of data. Recently, the considerably increasing expectation for cross-domain data exchange and cooperation has attracted attention from data markets. However, as we lack data-utilization knowledge, an accurate evaluation of data is difficult; a non-price indicator for

Language is a complex evolving system and it is not a trivial task to model the dynamics of processes occurring during its evolution. Therefore, modeling language evolution has attracted the interest of several researchers giving rise to a lot of models in the literature of the last millennium. This work reviews the literature devoted to computationally represent the evolution of human language through

* Scope of This Issue The purpose of this special issue is to bring together researchers in the Semantic Technology research community and other areas of semantic-related technologies to present their innovative research results or novel applications of semantic technologies. In this special issue, we solicit papers on various aspects of semantic technology from various fields such as ontology, semantic

High-utility itemset mining (HUIM) is an emerging data mining topic. It aims to find the high-utility itemsets by considering both the internal (i.e., quantity) and external (i.e., profit) utilities of items. High-average-utility itemset mining (HAUIM) is an extension of the HUIM, which provides a more fair measurement named average-utility, by taking into account the length of itemsets in addition

In recent years, there has been an increasing interest in numerical semantic labeling, in which the meaning of an unknown numerical column is assigned by the label of the most relevant columns in predefined knowledge bases. Previous methods used the p value of a statistical hypothesis test to estimate the relevance and thus strongly depend on the distribution and data domain. In other words, they are

We investigated the interconnection on knowledge of biological molecules, biological phenomena, and diseases to efficiently collect information regarding the functions of chemical compounds and gene products, roles, applications, and involvements in diseases using knowledge graphs (KGs) developed from Resource Description Framework (RDF) data and ontologies. NikkajiRDF linked open data provide information

The internet and the development of the semantic web have created the opportunity to provide structured legal data on the web. However, most legal information is in text. It is difficult to automatically determine the right natural language answer about the law to a given natural language question. One approach is to develop systems of legal ontologies and rules. Our example ontology represents semantic

A method of representing algebraic age word problems for automated solving has been proposed. Schemas pertinent to the domain have been represented as logical predicates and functions and used as an intermediate representation. Though data-driven methods have dominated the paradigm for solving word problems recently, we demonstrate that representing these word problems formally has advantages. We illustrate

We propose algorithms to perform operations concurrently on treaps in a shared memory multi-core environment. Concurrent treaps hold the advantage of using nodes’ priority for maintaining the height of the treaps. To achieve synchronization, concurrent treaps make use of fine-grained locking mechanism along with logical ordering and physical ordering of nodes’ keys. We initially study the throughput

One of the major tasks involved in the development of a knowledge-based problem solver is domain knowledge representation and reasoning. In this paper, we address this task for a knowledge-based kinematics word problem solver that solves problems from the domain of kinematics automatically, where solving each problem involves identifying the value of a specific unknown quantity referred to, within