Brain-machine interface （BMI） and cyborg intelligence

Brain-machine interfaces （BMIs） aim at building a direct communication pathway between the brain and an external device, and represent an area of re- search where significant progress has been made during the past decade. Based on BMIs, mind infor- mation can be read out by neural signals to control machine actuators, and machine-coded sensory in- formation can be delivered to specific areas in the brain. BMI techniques provide the opportunity to integrate machine intelligence with biological intel- ligence at multiple levels to develop a more powerful intelligent system, thereby creating a new field called ＇cyborg intelligence＇.     

Design and evaluation of hospital-based business intelligence system (HBIS): A foundation for design science research methodology

This paper describes the development of a hospital-based business intelligent system (HBIS) based on a novel developmental methodology, called the design science research methodology (DSRM), and implemented in a regional general hospital in Taiwan. A design science research methodology is adopted to cover six activities: problem identification and motivation, definition of solution objectives, design and development, demonstration, evaluation, and communication. Based on the DSRM developmental method, HBIS was successfully developed and deployed in the hospital case, and a survey of users shows positive results. In addition, the support and involvement of top management in HBIS development is found to be a critical success factor, and system implementation allowed the hospital to significantly improve performance of managerial indicators for the three abovementioned dimensions. This study contributes a novel developmental methodology from the Information Systems (IS) field as a reference model for future HBIS development, along with the integration of indicators from three major managerial dimensions - NHI, hospital accreditation, and healthcare quality.     

Applications of an interaction,process, integration and intelligence (IPII) design approach for ergonomics solutions

This paper first reviews current ergonomics design approaches in delivering digital solutions to achieve a unified experience from interaction and business process design perspectives. Then, it analyses the opportunities that new technologies may bring in for enhancing current ergonomics design approaches from integration and intelligence design perspectives. To address the challenges in today’s ergonomics practices in delivering digital solutions, an interaction, process, integration and intelligence (IPII) design approach is proposed. A case study is presented that implemented the IPII approach. The quantitative data gathered from the case study demonstrates that the IPII approach has achieved significant advantages in reaching the goal of a unified experience and operational benefits for delivering digital solutions. The IPII approach also demonstrates improvements compared to today’s ergonomics design approaches, such as user-centred design, for digital solutions. Finally, the paper highlights the contributions of the IPII approach for future ergonomics practices in delivering digital solutions.

Practitioner Summary: In addition to the interaction design for the UI of digital solutions, as is the case in current typical ergonomics practice, the IPII adds three additional design components: process, integration and intelligence design. The case study demonstrates the advantages of the IPII, providing an enhanced approach for designing digital solutions.

Abbreviations: IPII: interaction, process, integration and intelligence; IEA: International Ergonomics; Association; HFE: human factors/ ergonomics; HCD: human-centred design; UX: user experience; UI: user interface; ISO: International Organization for Standardization; UCD: user-centred design; ERP: enterprise resource planning; E2E experience: end-to-end experience; UXD: user experience design; AI: artificial intelligence; ML: machine learning; HCI: human-computer interaction; IaaS: infrastructure as a service; PaaS: platform as a service; SaaS: software as a service; CRM: customer relation management; SCM: supply chain management; HCM: human capability management; BI: business intelligence; BOMA: Bill of Materials Application; POC: proof of concept; TCM: transition change management; SMEs: subject matter experts; PMO: program management office; UAT: user acceptance test; iBPMS: intelligent business process management suite     

Intelligence in the internet age: The emergence and evolution of Open Source Intelligence (OSINT)

This paper introduces the concept of Open Source Intelligence (OSINT) as an important component for understanding human problem solving in the 21st century. OSINT is in many ways the result of changing human–information relationships resulting from the emergence and growing dominance of the Internet and the World Wide Web in everyday life. This paper suggests that the Internet/Web changes the dynamic relationship between what Cattell and Horn have identified as the two general factors of human intelligence: crystallized intelligence and fluid intelligence. The Internet/Web open up new possibilities for accessing information and transcending over-determined cultural intelligence in problem solving. This offers fluid intelligence, which often trails off in adulthood, a new vitality across the lifespan. But the diminishment of crystallized intelligence, and especially cultural intelligence, also presents a number of important problems in maintenance of cohesive, social cooperatives. The development of OSINT (using tools and ethos created by the Open Source movement of the last few decades) offers both a framework for reaching beyond the boundaries of traditional cultural intelligence and ways to create cooperative, open, problem solving communities. The Internet/Web will continue to create confusion and fear as we move deeper into this new age, but also presents extraordinary possibilities for augmenting human intellect if we can understand it and learn to harness its potential.     

Graphical user interface (GUI) testing: Systematic mapping and repository

ContextGUI testing is system testing of a software that has a graphical-user interface (GUI) front-end. Because system testing entails that the entire software system, including the user interface, be tested as a whole, during GUI testing, test cases—modeled as sequences of user input events—are developed and executed on the software by exercising the GUI’s widgets (e.g., text boxes and clickable buttons). More than 230 articles have appeared in the area of GUI testing since 1991.ObjectiveIn this paper, we study this existing body of knowledge using a systematic mapping (SM).MethodThe SM is conducted using the guidelines proposed by Petersen et al. We pose three sets of research questions. We define selection and exclusion criteria. From the initial pool of 230 articles, published in years 1991–2011, our final pool consisted of 136 articles. We systematically develop a classification scheme and map the selected articles to this scheme.ResultsWe present two types of results. First, we report the demographics and bibliometrics trends in this domain, including: top-cited articles, active researchers, top venues, and active countries in this research area. Moreover, we derive the trends, for instance, in terms of types of articles, sources of information to derive test cases, types of evaluations used in articles, etc. Our second major result is a publicly-accessible repository that contains all our mapping data. We plan to update this repository on a regular basis, making it a “live” resource for all researchers.ConclusionOur SM provides an overview of existing GUI testing approaches and helps spot areas in the field that require more attention from the research community. For example, much work is needed to connect academic model-based techniques with commercially available tools. To this end, studies are needed to compare the state-of-the-art in GUI testing in academic techniques and industrial tools.     

A computational intelligence technique for the effective diagnosis of diabetic patients using principal component analysis (PCA) and modified fuzzy SLIQ decision tree approach

Knowledge inference systems are built to identify hidden and logical patterns in huge data. Decision trees play a vital role in knowledge discovery but crisp decision tree algorithms have a problem with sharp decision boundaries which may not be implicated to all knowledge inference systems. A fuzzy decision tree algorithm overcomes this drawback. Fuzzy decision trees are implemented through fuzzification of the decision boundaries without disturbing the attribute values. Data reduction also plays a crucial role in many classification problems. In this research article, it presents an approach using principal component analysis and modified Gini index based fuzzy SLIQ decision tree algorithm. The PCA is used for dimensionality reduction, and modified Gini index fuzzy SLIQ decision tree algorithm to construct decision rules. Finally, through PID data set, the method is validated in the simulation experiment in MATLAB.     

Datenbank-DIALOG: how to communicate with your database in German (and enjoy it)

The paper discusses the requirements for practical natural language interfaces (NLIs), claiming that acceptance is not only based on the linguistic capabilities of a system but on other features as well, particularly portability and ease of handling. It is shown how these features have been realised in Datenbank-DIALOG^*, a German language interface to relational databases. After a brief look at the linguistic capabilities of Datenbank-DIALOG we demonstrate our approach towards a portable and easy-to-handle system. We also show the dependability of the solutions on the design of the core system. The aspects mentioned have rarely been treated in the technical literature, furthermore it is their combination and interaction that makes Datanbank-DIALOG a suitable device for casual users of databases.     

多Agent系统分布式问题求解的代数模型方法(Ⅱ): 群体智能和社会动力学

该文是组合论文中第二篇，讨论多Agent系统分布式问题求解的代数模型中的特性层和动力学层，即没粒度Agent群体的宏观群体智能的形式优化代数模型以及宏观社会智能与Agent个体间微观社会行为之间的社会动力学模型，提出了基于这种新的代数模型方法的超分布超并行社会智能问题求解算法，关于分布式多任务自组织规划和资源自组织分配的仿真实验以及与其它方法的比较分析，表明了该文提出了代数模型和问题求解方法的特点和有效性。     

Driver sitting comfort and discomfort (part II): Relationships with and prediction from interface pressure

Pressure at the driver–seat interface has been used as an objective method to assess seat design, yet existing evidence regarding its efficacy is mixed. The current study examined associations between three subjective ratings (overall, comfort, and discomfort) and 36 measures describing driver–seat interface pressure, and identified pressure level, contact area, and ratio (local to global) variables that could be effectively used to improve subjective responses. Each of 27 participants was involved in six separate driving sessions which included combinations of two seats (from vehicles ranked high and low on overall comfort), two vehicle classes (sedan and SUV), and two driving venues (lab-based and field). Several pressure variables were identified as more effective for assessing sitting comfort and discomfort across a range of individual statures. Based on the results, specific approaches are recommended to improve the sitting experience: (1) lower pressure ratios at the buttocks and higher pressure ratios at the upper and lower back; and (2) balanced pressure between the bilateral buttocks, and between the lower and upper body. Finally, separate analyses supported that human–seat interface pressure was more strongly related with overall and comfort ratings than with discomfort ratings.

Relevance to industry

Several interface pressure variables were identified that showed associations with subjective responses during sitting. Use of these measures is suggested to improve the quality of car seats.     

Ensemble data mining modeling in corrosion of concrete sewer: A comparative study of network-based (MLPNN & RBFNN) and tree-based (RF,CHAID, & CART) models

This research aims to evaluate ensemble learning (bagging, boosting, and modified bagging) potential in predicting microbially induced concrete corrosion in sewer systems from the data mining (DM) perspective. Particular focus is laid on ensemble techniques for network-based DM methods, including multi-layer perceptron neural network (MLPNN) and radial basis function neural network (RBFNN) as well as tree-based DM methods, such as chi-square automatic interaction detector (CHAID), classification and regression tree (CART), and random forests (RF). Hence, an interdisciplinary approach is presented by combining findings from material sciences and hydrochemistry as well as data mining analyses to predict concrete corrosion. The effective factors on concrete corrosion such as time, gas temperature, gas-phase H₂S concentration, relative humidity, pH, and exposure phase are considered as the models’ inputs. All 433 datasets are randomly selected to construct an individual model and twenty component models of boosting, bagging, and modified bagging based on training, validating, and testing for each DM base learners. Considering some model performance indices, (e.g., Root mean square error, RMSE; mean absolute percentage error, MAPE; correlation coefficient, r) the best ensemble predictive models are selected. The results obtained indicate that the prediction ability of the random forests DM model is superior to the other ensemble learners, followed by the ensemble Bag-CHAID method. On average, the ensemble tree-based models acted better than the ensemble network-based models; nevertheless, it was also found that taking the advantages of ensemble learning would enhance the general performance of individual DM models by more than 10%.     

Repository and Mutation based Particle Swarm Optimization (RMPSO): A new PSO variant applied to reconstruction of Gene Regulatory Network

Particle Swarm Optimization (PSO) is a meta-heuristic approach based on swarm intelligence, which is inspired by the social behaviour of bird flocking or fish schooling. The main disadvantage of the basic PSO is that it suffers from premature convergence. To prevent the process of search from premature convergence as well as to improve the exploration and exploitation capability as a whole, here, in this paper, a modified variant, named Repository and Mutation based PSO (RMPSO) is proposed. In RMPSO variant, apart from applying five-staged successive mutation strategies for improving the swarm best as referred in Enhanced Leader PSO (ELPSO), two extra repositories have been introduced and maintained to store personal best and global best solutions having same fitness values. In each step, the personal and global best solutions are chosen randomly from their respective repositories which enhance exploration capability further, retaining the exploitation capability. The computational experiment on benchmark problem instances shows that in most of the cases, RMPSO performs better than other algorithms in terms of the statistical metrics taken into account. Moreover, the performance of the proposed algorithm remains consistent in most of the cases when the dimension of the problem is scaled up. RMPSO is further applied to a practical scenario: the reconstruction of Gene Regulatory Networks (GRN) based on Recurrent Neural Network (RNN) model. The experimental results ensure that the RMPSO performs better than the state-of-the-art methods in the synthetic gene data set (gold standard) as well as real gene data set.     

Cooperative group optimization with ants (CGO-AS): Leverage optimization with mixed individual and social learning

We present CGO-AS, a generalized ant system (AS) implemented in the framework of cooperative group optimization (CGO), to show the leveraged optimization with a mixed individual and social learning. Ant colony is a simple yet efficient natural system for understanding the effects of primary intelligence on optimization. However, existing AS algorithms are mostly focusing on their capability of using social heuristic cues while ignoring their individual learning. CGO can integrate the advantages of a cooperative group and a low-level algorithm portfolio design, and the agents of CGO can explore both individual and social search. In CGO-AS, each ant (agent) is added with an individual memory, and is implemented with a novel search strategy to use individual and social cues in a controlled proportion. The presented CGO-AS is therefore especially useful in exposing the power of the mixed individual and social learning for improving optimization. The optimization performance is tested with instances of the traveling salesman problem (TSP). The results prove that a cooperative ant group using both individual and social learning obtains a better performance than the systems solely using either individual or social learning. The best performance is achieved under the condition when agents use individual memory as their primary information source, and simultaneously use social memory as their searching guidance. In comparison with existing AS systems, CGO-AS retains a faster learning speed toward those higher-quality solutions, especially in the later learning cycles. The leverage in optimization by CGO-AS is highly possible due to its inherent feature of adaptively maintaining the population diversity in the individual memory of agents, and of accelerating the learning process with accumulated knowledge in the social memory.     

DOLCE ergo SUMO: On foundational and domain models in the SmartWeb Integrated Ontology (SWIntO)

Increased availability of mobile computing, such as personal digital assistants (PDAs), creates the potential for constant and intelligent access to up-to-date, integrated and detailed information from the Web, regardless of one's actual geographical position. Intelligent question-answering requires the representation of knowledge from various domains, such as the navigational and discourse context of the user, potential user questions, the information provided by Web services and so on, for example in the form of ontologies. Within the context of the SmartWeb project, we have developed a number of domain-specific ontologies that are relevant for mobile and intelligent user interfaces to open-domain question-answering and information services on the Web. To integrate the various domain-specific ontologies, we have developed a foundational ontology, the SmartSUMO ontology, on the basis of the DOLCE and SUMO ontologies. This allows us to combine all the developed ontologies into a single SmartWeb Integrated Ontology (SWIntO) having a common modeling basis with conceptual clarity and the provision of ontology design patterns for modeling consistency. In this paper, we present SWIntO, describe the design choices we made in its construction, illustrate the use of the ontology through a number of applications, and discuss some of the lessons learned from our experiences.     

多Agent系统分布式问题求解的代数模型方法(Ⅰ): 社会行为、社会局势和社会动力学

该组论文提出一种新的代数模型方法，用于多Agent系统超分布超并行社会智能问题求解，该方法通过社会动力学和社会智能，统一地处理各种复杂的并行的社会行为，用于求解用常规方法难以处理的许多社会交互问题，本文是组合论文中第一篇，提出多Agent系统分布式问题求解的代数模型结构，讨论多Agent系统中典型社会行为模式及其性质，建立形式化描述，同时也论述了代数模型中的社会局势和社会动力学。     

Autonomous Vehicles and Avoiding the Trolley (Dilemma): Vehicle Perception,Classification, and the Challenges of Framing Decision Ethics

Abstract

This article aims to introduce a degree of technological and ethical realism to the framing of autonomous vehicle perception and decisionality. The objective is to move the socioethical dialog surrounding autonomous vehicle decisionality from the dominance of “trolley framings” to more pressing ethical issues. The article argues that more realistic ethical framings of autonomous vehicle technologies should focus on the matters of HMI, machine perception, classification, and data privacy, which are some distance from the decisionality framing premise of the MIT Moral Machine experiment. To support this claim the article appeals to state-of-the-art technologies and emerging technologies concerning autonomous vehicle perception and decisionality, as a means to inform and frame ethical contexts. This is further supported by considering a context specific ethical framing for each time phase we anticipate regarding emerging autonomous vehicle technology.     

Motivation profiles of online Poker players and the role of interface preferences: A laddering study among amateur and (semi-) professionals

Online Poker has become an increasingly popular form of gambling. In this study, the qualitative method of laddering interviews based on means-end chain theory was used to offer new insights in online Poker players’ psychological motives, and the way in which Poker website characteristics shape gambling preferences. A total of 18 Belgian young adults, experienced in Poker playing, were recruited via snowball sampling, of which 6 professionals (relying on online Poker as the sole source of income), 6 semi-professionals (playing for money, but not relying on it as a sole source of income) and 6 amateurs (not relying on Poker money for income). We focused on 2 Poker websites, PokerStars and Facebook Zynga Poker. Results revealed that an increase in the dependency on Poker profits shifted motives from learning towards monetary incentives. Yet, playing for real money could not be considered as a purely extrinsic motivation as it greatly determined the game play dynamics and experiences, and this both in the (semi-) professionals and amateur players. Finally, our study indicates that responsible gaming features should reconcile monetary worth with values of control, trust, entertainment and game play action.