Local weather prediction system for a heating plant using cognitive approaches

Present-day requirements emphasize the need of saving energy. It relates mainly to industrial companies, where the minimization of energy consumption is one of their most important tasks they face. In our paper, we deal with the design of the so-called weather prediction system (WPS) for the needs of a heating plant. The primary task of such a WPS is timely predicting expected heat consumption to prepare the technology characterized by long delays in advance. Heat prediction depends primarily on weather so the crucial part of WPS is the weather, especially temperature, prediction. However, a prediction system needs a variety of further data, too. Therefore, WPS must be regarded as a complex system, including data collection, its processing, own prediction and eventual decision support. This paper gives the overview about existing data processing systems and prediction methods and then it describes a concrete design of a WPS with distributed data measuring points (stations), which are processed using a structure of neural networks based on multilayer perceptrons (MLP) with a combination of fuzzy logic. Based on real experiments we show that also such simple means as MLPs are able to solve complex problems. The paper contains a basic methodology for designing similar WPS, too.     

Automated analysis of timed security: a case study on web privacy

This paper presents a case study on an automated analysis of real-time security models. The case study on a web system (originally proposed by Felten and Schneider) is presented that shows a timing attack on the privacy of browser users. Three different approaches are followed: LH-Timed Automata (analyzed using the model checker HyTech), finite-state automata (analyzed using the model checker NuSMV), and process algebras (analyzed using the model checker CWB-NC ). A comparative analysis of these three approaches is given.     

Different dynamic causal relationship approaches for cognitive maps

We study in this work the problem of adaptation on cognitive maps (CMs). We review different approaches of adaptation for CM, based on the idea that the causal relationships of the CM change during their phase of execution (runtime). Particularly, we study three dynamic causal relationships: the first one where the relationships between the concepts are defined as fuzzy rules, and the concepts and the relationship are fuzzy variables; the second one where mathematical models that describe the real system are used to define the causal relationships; and finally, in the last one the causal relationships are defined by generic logic rules based on the state of the concepts of the map. Each one can be used to model different types of systems, because each one exploits specific characteristics of the modeled system. These approaches are tested in different problems, giving very good results, and demonstrating that the utilization of CM as dynamic models is reliable and good.     

User study of a life-supporting humanoid directed in a multimodal language

This article describes a user study of a life-supporting humanoid directed in a multimodal language, and discusses the results. Twenty inexperienced users commanded the humanoid in a computer-simulated remote home environment in the multimodal language by pressing keypad buttons and speaking to the robot. The results show that they comprehended the language well and were able to give commands successfully. They often chose a press-button action in place of verbal phrases to specify a direction, speed, length, angle, and/or temperature value, and preferred multimodal commands to spoken commands. However, they did not think that it was very easy to give commands in the language. This article discusses the results and points out both strong and weak points of the language and our robot.     

多模态网络融合在轻度认知障碍分类中的应用

针对早期轻度认知障碍（MCI）根据医学诊断认知量表评估极有可能无法判断的问题,提出了一种多模态网络融合的MCI辅助诊断分类方法。基于图论的复杂网络分析方法在神经影像领域的应用已得到广泛认可,但采用不同模态的成像技术研究脑部疾病对大脑网络拓扑结构属性的影响会产生不同结果。首先,使用弥散张量成像（DTI）与静息态功能磁共振成像（rs-fMRI）数据构建大脑结构和功能连接的融合网络。然后,融合网络的拓扑属性被施以单因素方差分析（ANOVA）,选择具有显著差异的属性作为分类特征。最后,利用支持向量机（SVM）留一法交叉验证对健康组和MCI组分类,估算准确率。实验结果表明,所提方法的分类结果准确率达到94.44%,相较单一模态数据法的分类结果有明显提高。所提方法诊断出的MCI患者在扣带回、颞上回以及额叶和顶叶部分区域等许多脑区表现出显著异常,与已有研究结果基本一致。     

Quantum-inspired multimodal fusion for video sentiment analysis

We tackle the crucial challenge of fusing different modalities of features for multimodal sentiment analysis. Mainly based on neural networks, existing approaches largely model multimodal interactions in an implicit and hard-to-understand manner. We address this limitation with inspirations from quantum theory, which contains principled methods for modeling complicated interactions and correlations. In our quantum-inspired framework, the word interaction within a single modality and the interaction across modalities are formulated with superposition and entanglement respectively at different stages. The complex-valued neural network implementation of the framework achieves comparable results to state-of-the-art systems on two benchmarking video sentiment analysis datasets. In the meantime, we produce the unimodal and bimodal sentiment directly from the model to interpret the entangled decision.     

Automated modeling for structural analysis

The numerical computations required for structural analysis are well defined and easy to automate. If structural analysis is to be automated fully, however, the modeling process, as well as the numerical computations, must be automated. This paper outlines a procedure for the automation of the entire modeling and analysis sequence.Structural analysis is considered as one of several planning, design, and analysis applications to be included in a computerintegrated design system. An essential ingredient of such a system is a central data base that supports all applications. A data base using a hierarchicalcomponent-connection concept is first described. A procedure is then outlined by which a modeler program can extract analysis models from this data base. Whereas the central data base describes the structure in terms of its components and connections, the analysis model is in terms of nodes, elements, substructures, slaving constraints, and so on. The features required in a structural analysis program to analyze the resulting model are also described. This program follows well-established principles, but is substantially different in its arhitecture from conventional analysis programs.A simple frame building is used as an example. However, the procedure is believed to be applicable to structures of a variety of types.     

Cognitive ergonomics for data analysis. Experimental study of cognitive limitations in a data-based judgement task

ABSTRACT

Today’s ever-increasing amount of data places new demands on cognitive ergonomics and requires new design ideas to ensure successful human–data interaction. Our aim was to identify the cognitive factors that must be considered when designing systems to improve decision-making based on large amounts of data. We constructed a task that simulates the typical cognitive demands people encounter in data analysis situations. We demonstrate some essential cognitive limitations using a behavioural experiment with 20 participants. The studied task presented the participants with critical and noncritical attributes that contained information on two groups of people. They had to select the response option (group) with the higher level of critical attributes. The results showed that accuracy of judgement decreased as the amount of information increased, and that judgement was affected by irrelevant information. Our results thus demonstrate critical cognitive limitations when people utilise data and suggest a cognitive bias in data-based decision-making. Therefore, when designing for cognition, we should consider the human cognitive limitations that are manifested in a data analysis context. Furthermore, we need general cognitive ergonomic guidelines for design that support the utilisation of data and improve data-based decision-making.     

MORE – a multimodal observation and analysis system for social interaction research

The MORE system is designed for observation and machine-aided analysis of social interaction in real life situations, such as classroom teaching scenarios and business meetings. The system utilizes a multichannel approach to collect data whereby multiple streams of data in a number of different modalities are obtained from each situation. Typically the system collects a 360-degree video and audio feed from multiple microphones set up in the space. The system includes an advanced server backend component that is capable of performing video processing, feature extraction and archiving operations on behalf of the user. The feature extraction services form a key part of the system and rely on advanced signal analysis techniques, such as speech processing, motion activity detection and facial expression recognition in order to speed up the analysis of large data sets. The provided web interface weaves the multiple streams of information together, utilizes the extracted features as metadata on the audio and video data and lets the user dive into analyzing the recorded events. The objective of the system is to facilitate easy navigation of multimodal data and enable the analysis of the recorded situations for the purposes of, for example, behavioral studies, teacher training and business development. A further unique feature of the system is its low setup overhead and high portability as the lightest MORE setup only requires a laptop computer and the selected set of sensors on site.     

A probabilistic risk analysis for multimodal entry control

Entry control is an important security measure that prevents undesired persons from entering secure areas. The advanced risk analysis presented in this paper makes it possible to distinguish between acceptable and unacceptable entries, based on several entry sensors, such as fingerprint readers, and intelligent methods that learn behavior from previous entries. We have extended the intelligent layer in two ways: first, by adding a meta-learning layer that combines the output of specific intelligent modules, and second, by constructing a Bayesian network to integrate the predictions of the learning and meta-learning modules. The obtained results represent an important improvement in detecting security attacks.     

Automated sentence analysis for language instruction

Conclusion Our principle intention has been to show how a general-purpose pattern-matching procedure can serve as the basis for a simple automated system for the grammatical analysis of sentences produced by students. From a pedagogical point of view, the design of instructional modules in terms of simple phrase-structure grammars appears to be feasible and general, at least for elementary topics in grammar and usage. A virtue of the system is that it requires the student to perform a task which is very much like that for which he is being trained, i.e., the creation of grammatical and at least minimally effective sentences. It also provides rapid feedback. An efficient large-scale system for processing student interaction with modules which are restricted to fairly simple grammars would be adequate for a considerable body of instructional material. We are pleased to acknowledge the assistance of Ms. Gay Brookes who designed a number of lessons and participated in the early use of the system, and of Mr. David Barach who designed and programmed the PDP-8 grammatical analyzer. The project was initially supported by a grant from the Board of Higher Education University Program through the auspices of the Office of the University Dean for Community College Affairs.     

The analysis of embodied communicative feedback in multimodal corpora: a prerequisite for behavior simulation

Communicative feedback refers to unobtrusive (usually short) vocal or bodily expressions whereby a recipient of information can inform a contributor of information about whether he/she is able and willing to communicate, perceive the information, and understand the information. This paper provides a theory for embodied communicative feedback, describing the different dimensions and features involved. It also provides a corpus analysis part, describing a first data coding and analysis method geared to find the features postulated by the theory. The corpus analysis part describes different methods and statistical procedures and discusses their applicability and the possible insights gained with these methods.     

Automated text-based analysis for decision-making research

We present results from a study on constructing and evaluating a support tool for the extraction of patterns in distributed decision -making processes, based on design criteria elicited from a study on the work process involved in studying such decision-making. Specifically, we devised and evaluated an analysis tool for C² researchers who study simulated decision-making scenarios for command teams. The analysis tool used text clustering as an underlying pattern extraction technique and was evaluated together with C² researchers in a workshop to establish whether the design criteria were valid and the approach taken with the analysis tool was sound. Design criteria elicited from an earlier study with researchers (open-endedness and transparency) were highly consistent with the results from the workshop. Specifically, evaluation results indicate that successful deployment of advanced analysis tools requires that tools can treat multiple data sources and offer rich opportunities for manipulation and interaction (open-endedness) and careful design of visual presentations and explanations of the techniques used (transparency). Finally, the results point to the high relevance and promise of using text clustering as a support for analysis of C² data.     

Sentiment analysis of multimodal twitter data

Multimedia Tools and Applications - Text-driven sentiment analysis has been widely studied in the past decade, on both random and benchmark textual Twitter datasets. Few pertinent studies have also...     

Automatic analysis of multimodal group actions in meetings

This paper investigates the recognition of group actions in meetings. A framework is employed in which group actions result from the interactions of the individual participants. The group actions are modeled using different HMM-based approaches, where the observations are provided by a set of audiovisual features monitoring the actions of individuals. Experiments demonstrate the importance of taking interactions into account in modeling the group actions. It is also shown that the visual modality contains useful information, even for predominantly audio-based events, motivating a multimodal approach to meeting analysis.     

带时间窗的整车多式联运模型研究与解析*

限时性的快速货物运输已成为货运发展的趋势之一,多式联运则是较佳的运输组织形式。针对我国汽车消费市场飞速发展的大环境,以实现汽车整车时效性物流费用最小化为目的,建立了时间和容量约束下的多式联运方式0-1整数规划模型,根据模型的特点,设计了自然数编码的遗传算法,最后结合实例检验了模型和算法的有效性。     

Ensemble-based regression analysis of multimodal medical data for osteopenia diagnosis

Areal bone mineral density (aBMD) is used in clinical practice to diagnose osteoporosis. In previous studies, aBMD was estimated from diagnostic computed tomography (dCT) images, but a battery of medical tests was also taken that can be used to improve the regression performance. However, it is difficult to exploit the multimodal data as the additional features have poor informativeness and may lead to overfitting. An ensemble-based framework is proposed to improve the regression accuracy and robustness on multimodal medical data with a high relative dimensionality. Instead of case-wise bootstrap aggregating, a filtering-based metalearner scheme was employed to build feature-wise ensembles. The proposed approach was evaluated on clinical data and was found to be superior to bagging and other ensemble methods. The feature-wise ensembling approach can also be used to automatically determine if any multimodal features are related to bone mineral density. Several blood measurements were identified to be linked with bone mineral density, and a literature search supported the automatic identification results.     

Cognitive simulation as a tool for cognitive task analysis

Cognitive simulations are runnable computer programs that represent models of human cognitive activities. We show how one cognitive simulation built as a model of some of the cognitive processes involved in dynamic fault management can be used in conjunction with small-scale empirical data on human performance to uncover the cognitive demands of a task, to identify where intention errors are likely to occur, and to point to improvements in the person-machine system. The simulation, called Cognitive Environment Simulation or CES, has been exercised on several nuclear power plant accident scenarios. Here we report one case to illustrate how a cognitive simulation tool such as CES can be used to clarify the cognitive demands of a problem-solving situation as part of a cognitive task analysis.