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The development of the computer and what is now known as artificial intelligence (AI) has evolved over more than two centuries in a long series of steps. The date of the invention of the first computer is estimated at 1822, when Charles Babbage (1791-1871) developed his first design of a working computer on paper, based mainly on a Jacquard loom. He worked on his project together with Augusta Ada King, Countess Lovelace (née Byron) (Ada Lovelace) (1815-1852), whom he called the "Sorceress of Numbers." This work will present the profile and achievements of Charles Babbage, Augusta Ada King, Countess Lovelace, and Alan Mathison Turing (1912 - 1954), who is considered the father of computer science and artificial intelligence, and then provide an outline of the tumultuous events affecting AI up to the present. [ABSTRACT FROM AUTHOR]

The eight papers included in this special issue represent a selection of extended contributions presented at the 17th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2022 held in Salamanca, Spain, September 6th-8th, 2022, and organized by the BISITE group at University of Salamanca. SOCO 2022 international conference represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines which investigate, simulate, and analyse very complex issues and phenomena. This special issue is aimed at practitioners, researchers, and postgraduate students who are engaged in developing and applying advanced intelligent systems principles to solve real-world problems in the mentioned fields. [ABSTRACT FROM AUTHOR]

Achieving a widely accepted definition of human intelligence has been challenging, a situation mirrored by the diverse definitions of artificial intelligence in computer science. By critically examining published definitions, highlighting both consistencies and inconsistencies, this paper proposes a refined nomenclature that harmonizes conceptualizations across the two disciplines. Abstract and operational definitions for human and artificial intelligence are proposed that emphasize maximal capacity for completing novel goals successfully through respective perceptual-cognitive and computational processes. Additionally, support for considering intelligence, both human and artificial, as consistent with a multidimensional model of capabilities is provided. The implications of current practices in artificial intelligence training and testing are also described, as they can be expected to lead to artificial achievement or expertise rather than artificial intelligence. Paralleling psychometrics, 'AI metrics' is suggested as a needed computer science discipline that acknowledges the importance of test reliability and validity, as well as standardized measurement procedures in artificial system evaluations. Drawing parallels with human general intelligence, artificial general intelligence (AGI) is described as a reflection of the shared variance in artificial system performances. We conclude that current evidence more greatly supports the observation of artificial achievement and expertise over artificial intelligence. However, interdisciplinary collaborations, based on common understandings of the nature of intelligence, as well as sound measurement practices, could facilitate scientific innovations that help bridge the gap between artificial and human-like intelligence. • Proposes unified definitions for human and artificial intelligence. • Distinguishes between artificial achievement/expertise and artificial intelligence. • Advocates for AI metrics to ensure good quality AI system evaluations. • Describes artificial general intelligence (AGI) mirroring human general intelligence. • Evidence currently favours presence of artificial achievement over intelligence. [ABSTRACT FROM AUTHOR]

The seven papers included in this special issue represent a selection of extended contributions presented at the 16th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2021, held in Bilbao, Spain, September 22nd-24th, 2021, and organized by the BISITE group and the University of Deusto. The SOCO 2021 international conference represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines that investigate, simulate, and analyse very complex issues and phenomena. This special issue is aimed at practitioners, researchers, and postgraduate students who are engaged in developing and applying advanced intelligent systems principles to solve real-world problems in the mentioned fields. [ABSTRACT FROM AUTHOR]

• Model personalisation requires more than anatomical personalisation. • Model uncertainty and sensitivity are important considerations for clinical interpretation. • Model verification and validation are critical for trust underpinning clinical decision support. • The cardiovascular digital twin will support diagnosis and prognosis by responding continuously to increasing volumes of information collected as the individual goes about their daily life. The aim of this position paper is to provide a brief overview of the current status of cardiovascular modelling and of the processes required and some of the challenges to be addressed to see wider exploitation in both personal health management and clinical practice. In most branches of engineering the concept of the digital twin, informed by extensive and continuous monitoring and coupled with robust data assimilation and simulation techniques, is gaining traction: the Gartner Group listed it as one of the top ten digital trends in 2018. The cardiovascular modelling community is starting to develop a much more systematic approach to the combination of physics, mathematics, control theory, artificial intelligence, machine learning, computer science and advanced engineering methodology, as well as working more closely with the clinical community to better understand and exploit physiological measurements, and indeed to develop jointly better measurement protocols informed by model-based understanding. Developments in physiological modelling, model personalisation, model outcome uncertainty, and the role of models in clinical decision support are addressed and 'where-next' steps and challenges discussed. [ABSTRACT FROM AUTHOR]

Modern businesses benefit significantly from advances in computer vision technology, one of the important sectors of artificially intelligent and computer science research. Advanced computer vision issues like image processing, object recognition, and biometric authentication can benefit from using deep learning methods. As smart devices and facilities advance rapidly, current networks such as 4 G and the forthcoming 5 G networks may not adapt to the rapidly increasing demand. Classification of images, object classification, and facial recognition software are some of the most difficult computer vision problems that can be solved using deep learning methods. As a new paradigm for 6Core network design and analysis, artificial intelligence (AI) has recently been used. Therefore, in this paper, the 6 G wireless network is used along with Deep Learning to solve the above challenges by introducing a new methodology named Optimizing Computer Vision with AI-enabled technology (OCV-AI). This research uses deep learning – efficiency algorithms (DL-EA) for computer vision to address the issues mentioned and improve the system's outcome. Therefore, deep learning 6 G proposed frameworks (Dl-6 G) are suggested in this paper to recognize pattern recognition and intelligent management systems and provide driven methodology planned to be provisioned automatically. For Advanced analytics wise, 6 G networks can summarize the significant areas for future research and potential solutions, including image enhancement, machine vision, and access control. [ABSTRACT FROM AUTHOR]

Depth information has been discovered to improve the performance of computer vision tasks, such as semantic segmentation and object recognition. However, careful acquisition of depth data needs highly developed depth sensors which are expensive. As a classic computer vision task, depth estimation from a single image has obtained promising results based on supervised learning methods. In this paper, we investigate the extension of color images with corresponding deep-regressed depth images in boosting the performance of semantic segmentation. Furthermore, the usage of combining color channels with the estimated depth or the ground truth depth channel is compared. Specifically, there are two stages in our work. Firstly, we adopt the framework of convolutional neural networks (CNN) for the depth estimation by combing the global depth network and the depth gradient network. After refining based on these two networks, the depth image map can be estimated in a deep-regressed manner. Secondly, after augmenting the color images with the predicted depth images, fully convolutional networks (FCN) are further used to implement the pixel-level semantic labeling. In the experiments, we employ two popular RGBD datasets, i.e., SUNRGBD and NYUDv2, for 37 and 40-class semantic segmentation, respectively. By comparing with the ground truth depth images, experimental results demonstrate that the networks trained on the estimated depth images can achieve comparable performance on facilitating the accuracy of semantic segmentation task. [ABSTRACT FROM AUTHOR]

Portfolio optimization (PO) has been catching more and more attention in the artificial intelligence and the machine learning communities. In this paper, we propose a novel Trend Representation based Log-density Regularization (TRLR) system for portfolio optimization. Its novelty falls into two aspects. First, it introduces a log-density regularization to the increasing factor of portfolio, which is seldom addressed by previous PO systems. It reflects a relationship between the portfolio and the price relative at an equilibrium point. Second, TRLR exploits a novel trend representation by taking the time variable as regressor in a weighted ridge regression, hence TRLR captures price trend patterns effectively. Extensive experiments conducted on 5 benchmark datasets from real-world financial markets demonstrate that TRLR achieves significantly better performance than other state-of-the-art strategies and runs fast, which shows its effectiveness and efficiency for large-scale applications. [ABSTRACT FROM AUTHOR]

This survey paper attempts to study the existence, importance and impact of the notion of context in modern humanistic computing. Given its inherent diversity, the term is nowadays widely acknowledged among computer science tasks and has become a major topic of interest in several of its sub-fields, ranging from contextual semantics to social networks, social media and recently emerged innovative applications, such as travel routing. We start by presenting a brief review of contextual semantics as nowadays they are considered suitable for most common content analysis problems. Focus is also given within the next survey sections on the impact of context within the social networks and media field that came into sight over the last years. A short, closing discussion on the identified challenges and potential future research directions concludes this survey. [ABSTRACT FROM AUTHOR]

In recent years, Earth system sciences are urgently calling for innovation on improving accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in many subdomains amid the exponentially accumulated datasets and the promising artificial intelligence (AI) revolution in computer science. This paper presents work led by the NASA Earth Science Data Systems Working Groups and ESIP machine learning cluster to give a comprehensive overview of AI in Earth sciences. It holistically introduces the current status, technology, use cases, challenges, and opportunities, and provides all the levels of AI practitioners in geosciences with an overall big picture and to "blow away the fog to get a clearer vision" about the future development of Earth AI. The paper covers all the majorspheres in the Earth system and investigates representative AI research in each domain. Widely used AI algorithms and computing cyberinfrastructure are briefly introduced. The mandatory steps in a typical workflow of specializing AI to solve Earth scientific problems are decomposed and analyzed. Eventually, it concludes with the grand challenges and reveals the opportunities to give some guidance and pre-warnings on allocating resources wisely to achieve the ambitious Earth AI goals in the future. • A bird's eye view of the AI application in all spectrum of geosciences is provided. • The mandatory modular steps of typical Earth AI workflows are summarized. • Twelve grand challenges in Earth AI and potential opportunities are introduced. [ABSTRACT FROM AUTHOR]

[Display omitted] • AI and automation can help fungi-based bioprocesses to increase production yields. • Machine learning can discover non-linear interactions during fungi cultivation. • Soft sensors enable the understanding of the complex bioprocess dynamics. • Robots minimize direct labor costs while improving the quality of fungal products. • Further improvements are needed for automating fungi-based industrial bioprocesses. By utilizing their powerful metabolic versatility, filamentous fungi can be utilized in bioprocesses aimed at achieving circular economy. With the current digital transformation within the biomanufacturing sector, the interest of automating fungi-based systems has intensified. The purpose of this paper was therefore to review the potentials connected to the use of automation and artificial intelligence in fungi-based systems. Automation is characterized by the substitution of manual tasks with mechanized tools. Artificial intelligence is, on the other hand, a domain within computer science that aims at designing tools and machines with the capacity to execute functions that would usually require human aptitude. Process flexibility, enhanced data reliability and increased productivity are some of the benefits of integrating automation and artificial intelligence in fungi-based bioprocesses. One of the existing gaps that requires further investigation is the use of such data-based technologies in the production of food from fungi. [ABSTRACT FROM AUTHOR]

Boolean functions play essential roles in certain symmetric cryptographic schemes and error-correcting codes. Apart from the cryptographic criteria satisfied by Boolean functions, constructions of functions with few Walsh spectral value are also of particular interests in cryptographic applications. Due to the problem that the five-value spectrum functions with duals being not basic plateaued functions remains to be answered, in this paper we intend to investigate the construction on non-basic plateaued duals of five-value spectrum functions. The issue of constructing non-basic totally disjoint spectra plateaued functions is dealt with from two aspects. We firstly address the characterization of non-basic plateaued functions corresponding to Walsh supports with unequal cardinality. Then the necessary and sufficient conditions of the spectral design of a partially non-basic five-value spectrum function are proposed. Meanwhile, we also provide the sufficient condition for constructing a valid five-value spectrum function whose duals are both defined on non-affine subspaces. Finally, the design of totally disjoint spectra functions on non-affine subspaces with equal cardinality is also studied. [ABSTRACT FROM AUTHOR]

The SNOW-V stream cipher is a candidate cipher for 5G mobile communication system, and the SNOW-Vi stream cipher is a faster variant of SNOW-V. In this paper, we analyze the resistance of the reduced-round SNOW-V and SNOW-Vi against the chosen IV differential distinguishing attacks in the initialization phase. Firstly, we use the divide-and-conquer strategy to give a Mixed-integer Linear Programming (MILP) model to describe the probabilities within the Differential Distribution Table (DDT) of large S-boxes, which can more accurately search the best differential characteristics of SNOW-V and SNOW-Vi than using the probability lower bound of the DDT in the original MILP model. Secondly, we point out that the registers R 1 , R 2 , R 3 of SNOW-Vi filled with 0 were not considered in the original MILP model, resulting in the MILP model may return impossible differential characteristics. Then, we give the splicing search technology of C program and MILP model to solve this problem. Thirdly, based on these two studies, we present a more efficient and accurate MILP model for searching the best differential characteristics of the reduced-round SNOW-V and SNOW-Vi. The improved MILP model reduces the complexity of the distinguishing attacks. Finally, we give two multiple differential attack techniques on SNOW-V/SNOW-Vi structure, and apply them to the reduced-round SNOW-V and SNOW-Vi, respectively. While improving the results of single differential attacks, we also reduce the complexity of differential attack on the 4-round SNOW-V from 2 97 to 2 59. 985 , which can give the effective attack under the data limitation 2 64 for the single key. In addition, we give the first differential attack on 5-round SNOW-Vi, and the time complexity and data complexity is 2 96. 84. The success rate is 0.97725. Although this has exceeded the data complexity limitation of the single key attack, it is meaningful for us to analyze the security of SNOW-Vi. [ABSTRACT FROM AUTHOR]

Fog computing as an extension of cloud computing has attracted maximum attention over the recent years due to its suitability and applicability. The Fog computing is identified to handle the issues of inadequate mobility support and location sensitiveness and maximized latency. In the Internet of Things (IoT), a collection of IoT devices needs to be connected to the Fog nodes for supporting the center of the cloud service to process and store partial data part in prior. This possibility of connecting IoT devices with Fog nodes has the maximum possibility of enhancing the service quality and minimize the pressure involved in data processing in real time. However, Fog nodes used in data processing are considered from a diversified number of challenging issues introduced by the privacy violation of IoT devices, modification attacks and false data injection attack. In this paper, a Verifiable Quaternion Fully Homomorphic Encryption (VQFHE) scheme is proposed for mitigating False Data Injection Attacks by means of privacy preservation and fault tolerance in Fog computing scenario. This proposed VQFHE scheme derives the benefits of Quaternions for ensures that data injection always happens from the legitimate devices, inspite to the honest or curious behavior of the cloud control center (CCC) and Fog nodes. The simulation results conducted using iFogSim simulator confirmed its predominance over the benchmarked schemes with respect to communication overhead and computation overhead. [ABSTRACT FROM AUTHOR]

This paper introduces an improved version of the secure hash algorithms, SHA-1, and SHA-2. The proposed work produces a strengthened secure hash design using the fusion between the SHA-1 and SHA-2 hash standards. This design helps to protect the SHA-1 and SHA-2 against collision and length extension attacks. The proposed design is incorporated in the round steps of the hash functions using the function manipulators of the SHA-1 and SHA-2. Moreover, the proposed design employs a padding method that is different from the one used in the SHA-1 and SHA-2 construction. The proposed design is verified by the official test vectors that were confirmed by the National Institute of Standard and Technology. Moreover, the avalanche effect, hamming distance, and bit-hit properties were studied using different test cases. The proposed design gives over 52 percent of the avalanche effect and over 84 hamming distance. The proposed design was tested against collision attack and produced unique hash values for real collided examples and shows resistance against length extension attack. [ABSTRACT FROM AUTHOR]

Nowadays, advanced persistent threats (APTs) compromise critical large-scale networks more frequently, however, detecting these APTs is difficult. Since security sensors often miss some key steps of APTs, a lot of missed alerts exist. Moreover, large-scale networks often generate thousands of repeated alerts every day. To address this issue, a novel method for APT reconstruction in large-scale networks is proposed for attack forensics and traceability in this paper. Additionally, the proposed method consumes low transmission cost, and it doesn't require raw data from terminal devices, which makes it more appropriate for large-scale networks with numerous terminal devices. Specifically, an alert reduction and correlation method is first designed to reduce the alert volume. Then, these alerts are transformed into an alert graph, and the key missed alerts are obtained by searching history alerts and logs using Monte Carlo Tree Search (MCTS), which fast assembles multi-stage attacks with high confidence. The evaluation results on the modified CSE-CIC-IDS-2018 dataset indicate that the proposed method is robust against false negatives. Compared with other research, the proposed method has more advantages in efficiency and accuracy, and it can detect APTs within a longer time horizon. [ABSTRACT FROM AUTHOR]

Fully homomorphic encryption enables to perform arbitrary computations over encrypted data opening a new avenue for the secure outsourced computation in cloud computing. Data sorting is a classical computational problem in computer science. Previously, all the secure sorting algorithms based on fully homomorphic encryption made use of the bit-level encryption, which requires that each bit of the input data is encrypted separately. However, the bit-level encryption incurs expensive computational overheads and a large ciphertext expansion. In this paper, we propose a secure word-level sorting scheme based on fully homomorphic encryption. We introduce a set of novel techniques to design an efficient word-level sorting algorithm. These include a new technique to compare two encrypted integers, a constant extraction technique to postprocess the output ciphertext of the comparison algorithm, an equality test method to check whether two ciphertexts encrypt the same integer and the single instruction multiple data technique to reduce the ciphertext sizes and improve the computational performance. The experimental results show that our word-level sorting algorithm outperforms the state-of-the-art bit-level sorting algorithm by more than 10 times. [ABSTRACT FROM AUTHOR]

The paper examines how the EU General Data Protection Regulation (GDPR) is applied to the development of AI products and services, drawing attention to the differences between academic and commercial research. The GDPR aims to encourage innovation by providing several exemptions from its strict rules for scientific research. Still, the GDPR defines scientific research in a broad manner, which includes academic and commercial research. However, corporations conducting commercial research might not have in place a similar level of ethical and institutional safeguards as academic researchers. Furthermore, corporate secrecy and opaque algorithms in AI research might pose barriers to oversight. The aim of this paper is to stress the limits of the GDPR research exemption and to find the proper balance between privacy and innovation. The paper argues that commercial AI research should not benefit from the GDPR research exemption unless there is a public interest and has similar safeguards to academic research, such as review by research ethics committees. Since the GDPR provides this broad exemption, it is crucial to clarify the limits and requirements of scientific research, before the application of AI drastically transforms this field. [ABSTRACT FROM AUTHOR]

Noise is common in any real-world data set and may adversely affect classifiers built under the effect of such type of disturbance. Some of these classifiers are widely recognized for their good performance when dealing with imperfect data. However, the noise robustness of the classifiers is an important issue in noisy environments and it must be carefully studied. Both performance and robustness are two independent concepts that are usually considered separately, but the conclusions reached with one of these metrics do not necessarily imply the same conclusions with the other. Therefore, involving both concepts seems to be crucial in order determine the expected behavior of the classifiers against noise. Existing measures fail to properly integrate these two concepts, and they are also not well suited to compare different techniques over the same data. This paper proposes a new measure to establish the expected behavior of a classifier with noisy data trying to minimize the problems of considering performance and robustness individually: the Equalized Loss of Accuracy (ELA). The advantages of ELA against other robustness metrics are studied and all of them are also compared. Both the analysis of the distinct measures and the empirical results show that ELA is able to overcome the aforementioned problems that the rest of the robustness metrics may produce, having a better behavior when comparing different classifiers over the same data set. [ABSTRACT FROM AUTHOR]

In this paper we introduce a simple and efficient extension of the Extreme Learning Machine (ELM) network (Huang et al., 2006 [19] ), which is very robust to label noise, a type of outlier occurring in classification tasks. Such outliers usually result from mistakes during labeling of the data points (e.g. misjudgment of a specialist) or from typing errors during creation of data files (e.g. by striking an incorrect key on a keyboard). The proposed variant of the ELM, henceforth named Robust ELM (RELM), is designed using M -estimators to compute the output weights instead of the standard ordinary least squares (OLS) method. We evaluate the performance of the RELM using batch and recursive learning rules, and also introduce a model selection strategy based on Particle Swarm Optimization (PSO) to find an optimal architecture for datasets contaminated with non-Gaussian noise and outliers. By means of comprehensive computer simulations using synthetic and real-world datasets, we show that the proposed Robust ELM classifiers consistently outperforms the original version. [ABSTRACT FROM AUTHOR]

In classification, noise may deteriorate the system performance and increase the complexity of the models built. In order to mitigate its consequences, several approaches have been proposed in the literature. Among them, noise filtering, which removes noisy examples from the training data, is one of the most used techniques. This paper proposes a new noise filtering method that combines several filtering strategies in order to increase the accuracy of the classification algorithms used after the filtering process. The filtering is based on the fusion of the predictions of several classifiers used to detect the presence of noise. We translate the idea behind multiple classifier systems, where the information gathered from different models is combined, to noise filtering. In this way, we consider the combination of classifiers instead of using only one to detect noise. Additionally, the proposed method follows an iterative noise filtering scheme that allows us to avoid the usage of detected noisy examples in each new iteration of the filtering process. Finally, we introduce a noisy score to control the filtering sensitivity, in such a way that the amount of noisy examples removed in each iteration can be adapted to the necessities of the practitioner. The first two strategies (use of multiple classifiers and iterative filtering) are used to improve the filtering accuracy, whereas the last one (the noisy score) controls the level of conservation of the filter removing potentially noisy examples. The validity of the proposed method is studied in an exhaustive experimental study. We compare the new filtering method against several state-of-the-art methods to deal with datasets with class noise and study their efficacy in three classifiers with different sensitivity to noise. [ABSTRACT FROM AUTHOR]

The growing availability of large collections of digitized artworks has disclosed new opportunities to develop intelligent systems for the automatic analysis of fine arts. Among other benefits, these tools can foster a deeper understanding of fine arts, ultimately supporting the spread of culture. However, most of the systems proposed in the literature are only based on visual features of digitized artwork images, which are sometimes only integrated with some metadata and textual comments. A Knowledge Graph (KG) that integrates a rich body of information about artworks, artists, painting schools, etc., in a unified structured framework, can provide a valuable resource for more powerful information retrieval and knowledge discovery tools in the artistic domain. To this end, in this paper we present A r t G r a p h : 1 1 A r t G r a p h and associated code are publicly available on https://doi.org/10.5281/zenodo.6337958.. an artistic KG based on WikiArt and DBpedia. The graph already provides knowledge discovery capabilities without having to train a learning system. In addition, we propose a novel KG-enabled fine art classification method based on A r t G r a p h , which is used to perform artwork attribute prediction tasks. The method extracts embeddings from A r t G r a p h and injects them as "contextual" knowledge into a Deep Learning model. Compared to the state-of-the-art, the proposed model provides encouraging results, suggesting that the exploitation of KGs in combination with Deep Learning can pave the way for bridging the gap between the Humanities and Computer Science communities. • Automatic art analysis can promote a deeper understanding of fine arts. • Knowledge Graphs and Deep Learning can provide tools to support this analysis. • We introduce A r t G r a p h , an artistic Knowledge Graph. • We present a classification method that jointly exploits visual and graph embeddings. • The approach can help bridge the gap between the Humanities and Computer Science. [ABSTRACT FROM AUTHOR]

Boolean Satisfiability problem (SAT) plays a prominent role in many domains of computer science and artificial intelligence due to its significant importance in both theory and applications. Algorithms for solving SAT problems can be categorized into two main classes: complete algorithms and incomplete algorithms (typically stochastic local search (SLS) algorithms). SLS algorithms are among the most effective for solving uniform random SAT problems, while hybrid algorithms achieved great breakthroughs for solving hard random SAT (HRS) problem recently. However, there is a lack of algorithms that can effectively solve both uniform random SAT and HRS problems. In this paper, a new SLS algorithm named SelectNTS is proposed aiming at solving both uniform random SAT and HRS problem effectively. SelectNTS is essentially an improved probability selection based local search algorithm, the core of which includes new clause and variable selection heuristics: a new clause weighting scheme and a biased random walk strategy are utilized to select a clause, while a new probability selection strategy with the variation of configuration checking strategy is used to select a variable. Extensive experimental results show that SelectNTS outperforms the state-of-the-art random SAT algorithms and hybrid algorithms in solving both uniform random SAT and HRS problems effectively. [ABSTRACT FROM AUTHOR]

In this paper we describe a tool that can be effectively used to evaluate student learning outcomes using concept maps and Markov chain analysis. The main purpose of this tool is to advance the use of artificial intelligence techniques by using concept maps and Markov chains in evaluating a student’s understanding of a particular topic of study using concept maps. The method used in the tool makes use of XML parsing to perform the required evaluation. For the purpose of experimenting this tool we have taken into consideration concept maps developed by students enrolled in two different courses in Computer Science. The result of this experimentation is also discussed. [ABSTRACT FROM AUTHOR]

Being aware of people’s unuttered knowledge needs is the prerequisite of providing “active” and “in time” knowledge assistance. However, such an awareness of knowledge needs has been achieved at a high cost since most existing methods rely on the manually defined rules or a large amount of user data to work. In this paper, we formulate the problematic situations in task processing as knowledge application context (KAC), and propose to elicit KACs semi-automatically from domain Q&A archives. Assuming that the KACs frequently occurring and semantically matching with the user’s task context are more likely to imply the knowledge needs of the user, we design a mechanism of knowledge need awareness (KNA) to predict users’ knowledge needs in complex tasks. Experimental results show that the proposed method has significantly outperformed the information retrieval approaches used as baselines. The study provides a new method for reusing the contextualized knowledge in Q&A and thereby opens up a new way to build efficient active knowledge systems. [ABSTRACT FROM AUTHOR]

Clustering is an essential tool in data mining research and applications. It is the subject of active research in many fields of study, such as computer science, data science, statistics, pattern recognition, artificial intelligence, and machine learning. Several clustering techniques have been proposed and implemented, and most of them successfully find excellent quality or optimal clustering results in the domains mentioned earlier. However, there has been a gradual shift in the choice of clustering methods among domain experts and practitioners alike, which is precipitated by the fact that most traditional clustering algorithms still depend on the number of clusters provided a priori. These conventional clustering algorithms cannot effectively handle real-world data clustering analysis problems where the number of clusters in data objects cannot be easily identified. Also, they cannot effectively manage problems where the optimal number of clusters for a high-dimensional dataset cannot be easily determined. Therefore, there is a need for improved, flexible, and efficient clustering techniques. Recently, a variety of efficient clustering algorithms have been proposed in the literature, and these algorithms produced good results when evaluated on real-world clustering problems. This study presents an up-to-date systematic and comprehensive review of traditional and state-of-the-art clustering techniques for different domains. This survey considers clustering from a more practical perspective. It shows the outstanding role of clustering in various disciplines, such as education, marketing, medicine, biology, and bioinformatics. It also discusses the application of clustering to different fields attracting intensive efforts among the scientific community, such as big data, artificial intelligence, and robotics. This survey paper will be beneficial for both practitioners and researchers. It will serve as a good reference point for researchers and practitioners to design improved and efficient state-of-the-art clustering algorithms. • Provide an up-to-date comprehensive review of the different clustering techniques. • Highlight novel and most recent practical applications areas of clustering. • Provide a convenient research path for new researchers. • Help experts develop new algorithms for emerging challenges in the research area. [ABSTRACT FROM AUTHOR]

The shortest path problem, one of the most classical graph problems, has been addressed in many different ways suitable for various settings in the fields of computer science and artificial intelligence. In this paper, we revisit a distributed control solution, namely the continuous-time adaptive Bellman–Ford algorithm, to the shortest path problem. While previous work only concerned its global asymptotic stability, we not only prove its global asymptotic stability by formulating a Lyapunov function, but characterize the initial conditions under which the algorithm will converge exponentially, and show that the algorithm is globally ultimately bounded under persistent bounded perturbations based on the proposed Lyapunov function. [ABSTRACT FROM AUTHOR]

Kinematic analysis is often performed in a lab using optical cameras combined with reflective markers. With the advent of artificial intelligence techniques such as deep neural networks, it is now possible to perform such analyses without markers, making outdoor applications feasible. In this paper I summarise 2D markerless approaches for estimating joint angles, highlighting their strengths and limitations. In computer science, so-called "pose estimation" algorithms have existed for many years. These methods involve training a neural network to detect features (e.g. anatomical landmarks) using a process called supervised learning, which requires "training" images to be manually annotated. Manual labelling has several limitations, including labeller subjectivity, the requirement for anatomical knowledge, and issues related to training data quality and quantity. Neural networks typically require thousands of training examples before they can make accurate predictions, so training datasets are usually labelled by multiple people, each of whom has their own biases, which ultimately affects neural network performance. A recent approach, called transfer learning, involves modifying a model trained to perform a certain task so that it retains some learned features and is then re-trained to perform a new task. This can drastically reduce the required number of training images. Although development is ongoing, existing markerless systems may already be accurate enough for some applications, e.g. coaching or rehabilitation. Accuracy may be further improved by leveraging novel approaches and incorporating realistic physiological constraints, ultimately resulting in low-cost markerless systems that could be deployed both in and outside of the lab. [ABSTRACT FROM AUTHOR]

With the arrival of Artificial Intelligence (AI), the nature of the firm is changing and economic theory can provide guidance to businesses as well as to politics when formulating adequate strategies for this unknown terrain. By interpreting AI as a new type of agent within the firm, the theory of the firm can serve as a lingua franca to connect computer sciences and social sciences when dealing with the interdisciplinary phenomenon of AI. To achieve this, this paper adopts the perspective of the economic theory of the firm to systematically explore the changes that AI brings to the institution of the firm. In total, five interrelated propositions are discussed that are rooted in the traditional theory but trace the nature of the Artificially Intelligent Firm: AI intensifies the effects of economic rationality on the firm (1). AI introduces a new type of information asymmetry (2). AI can perforate the boundaries of the firm (3). AI can create triangular agency relationships (4) and AI has the potential to remove traditional limits of integration (5). Image 1 • AI is a factor of production that can perforate the boundaries of the firm. • AI creates a new type of information asymmetry. • AI can lead to triangular agency relationships. • AI has the potential to remove traditional limits of integration. [ABSTRACT FROM AUTHOR]

Due to the rapid progress of artificial intelligence, computer science, sensor technology, and other related disciplines, multiprocessor control systems can effectively improve the computing power of controllers and meet robot control performance. However, the amount of information storage and data is very large, and it is difficult to achieve centralized storage of resources. Therefore, it is necessary to use the continuous advancement of video digitization technology and storage technology to effectively solve the problems of multimedia storage, long-distance transmission, and sharing of courseware. Based on this, this paper studies the robotic multiprocessor control system of digital media based on BS structure, which can effectively reduce the bandwidth pressure and conflicts of the interconnected network and memory, and overcome the bottleneck caused by the performance of the multiprocessor system. The actual operation proves that the robot can improve the computing power of the controller and meet the needs of the robot's control performance. [ABSTRACT FROM AUTHOR]