Discrete noiseless duel with a skewsymmetric payoff function on the unit square for models of socioeconomic competitive processes with a finite number of pure strategies

A discrete noiseless duel is defined on the unit square; each player in the duel has a finite number of pure strategies uniformly distributed on the unit segment. The theorem on the existence of individual solutions of the discrete noiseless duel in pure strategies is proved. The construction of a program procedure for solving the discrete noiseless duel is presented.     

On Achieving Stable Mutual Deterrence

Abstract

In this paper it is established that there are strong, stabilizing mechanisms in an “arms race” based on mutual deterrence, despite ambiguous capabilities of ballistic missiles and despite time delays or errors inherent in intelligence estimates. It is shown analytically that the introduction of multiple-warhead technology (MIRV) does not lead to an ever-spiraling arms race. However, it can dramatically increase the number of boosters required by each party. The analysis is also applied to strategic confrontations involving more than two major powers, and it is shown that the stable solutions in these cases can require vastly increased force levels. Perturbation of two-party mutual deterrence by a relatively small third party is also analyzed.

It is shown that an ABM defense of only retaliatory missile forces may well be less escalatory and less expensive than the alternative of increasing the land-based missile forces. The paper also examines “ambiguous” ABM, i.e., defenses deployed to optionally defend strategic forces or urban-industrial centers. It is shown that complete reliance on ambiguous defenses is almost always undesirable, even if they are costless, in the context of mutual deterrence.

Finally, bomber forces and ICBM forces used as deterrents are compared in terms of relative costs and vulnerabilities for cases with and without ABM defense of missile forces. In all cases wherein existent missile forces are inadequate, it is shown that the addition of unambiguous ABM defense of missiles or the addition of a bomber force may be attractive and comparable alternatives to missile force increases. Considering third-party proliferation (which might be a fact in the 1980's), it is shown that reliance on a land-based missile force may be prohibitively expensive.

In an illustrative two-party case, plausible for the 1980's, it is shown that if only five percent of a bomber force could be expected to deliver its weapons, then bomber forces are preferred over undefended missile forces unless a bomber costs more than 26 missiles. In the case of three countries attempting to maintain a multilateral deterrence policy, this 26-to-l ratio becomes greater than 1,600-to-l.     

A Stylometric Analysis of Yaşar Kemal’s İ<Emphasis Type="Italic">nce Memed</Emphasis> Tetralogy

We analyze four ?nce Memed novels of Ya?ar Kemal using six style markers: “most frequent words,” “syllable counts,” “word type – or part of speech – information,” “sentence length in terms of words,” “word length in text,” and “word length in vocabulary.” For analysis we divide each novel into five thousand word text blocks and count the frequencies of each style marker in these blocks. The style markers showing the best separation are “most frequent words” and “sentence lengths.” We use stepwise discriminant analysis to determine the best discriminators of each style marker. We then use these markers in cross validation based discriminant analysis. Further investigation based on multiple analysis of variance (MANOVA) reveals how the attributes of each style marker group distinguish among the volumes.     

User demographics prediction based on mobile data

Demographics prediction is an important component of user profile modeling. The accurate prediction of users’ demographics can help promote many applications, ranging from web search, personalization to behavior targeting. In this paper, we focus on how to predict users’ demographics, including “gender”, “job type”, “marital status”, “age” and “number of family members”, based on mobile data, such as users’ usage logs, physical activities and environmental contexts. The core idea is to build a supervised learning framework, where each user is represented as a feature vector and users’ demographics are considered as prediction targets. The most important component is to construct features from raw data and then supervised learning models can be applied. We propose a feature construction framework, CFC (contextual feature construction), where each feature is defined as the conditional probability of one user activity under the given contexts. Consequently, besides employing standard supervised learning models, we propose a regularized multi-task learning framework to model different kinds of demographics predictions collectively. We also propose a cost-sensitive classification framework for regression tasks, in order to benefit from the existing dimension reduction methods. Finally, due to the limited training instances, we employ ensemble to avoid overfitting. The experimental results show that the framework achieves classification accuracies on “gender”, “job” and “marital status” as high as 96%, 83% and 86%, respectively, and achieves Root Mean Square Error (RMSE) on “age” and “number of family members” as low as 0.69 and 0.66 respectively, under the leave-one-out evaluation.     

Local matching of surfaces using a boundary-centered radial decomposition

A new surface representation, the “distance profile” is introduced. A distance profile is a decomposition of a surface into “distance contours,” each of which is the locus of all points a certain distance r from a point called the “center point.” The distance contour is in turn represented as a plot of the contour's distance from its centroid as a function of arc length along the contour. The distance profile representation is invariant with respect to rotation about the center point and thus is ideal for “rigid matching” (where the correspondence function between two objects is a rotation and/or translation). For global matching, the center point can be the centroid of the surface. For local matching, we compute multiple distance profiles, each centered around a “critical point” on the surface (a minimum or maximum of curvature or a sharp peak in the surface). This comprises the “boundary-centered radial decomposition.” An implementation of a local surface matcher based on theboundary-centered radial decomposition is described.     

An Efficient Two-Phase Model for Computing Influential Nodes in Social Networks Using Social Actions

在社会网络的影响的测量在数据采矿社区收到了很多注意。影响最大化指发现尽量利用信息或产品采纳的有影响的用户的过程。在真实设置,在一个社会网络的一个用户的影响能被行动的集合建模(例如,份额,重新鸣叫,注释) 在其出版物以后由网络的另外的用户表现了。就我们的知识而言,在文学的所有建议模型同等地对待这些行动。然而,它是明显的一工具少些比一样的出版的份额影响的一份出版物相似。这建议每个行动有它影响的自己的水平(或重要性) 。在这份报纸,我们建议一个模型(叫的社会基于行动的影响最大化模型, SAIM ) 为在社会网络的影响最大化。在 SAIM,行动没在测量一个个人的影响力量同等地被考虑,并且它由二主要的步组成。在第一步,我们在社会网络计算每个个人的影响力量。这影响力量用 PageRank 从用户行动被计算。在这步的结束,我们得到每个节点被它的影响力量在标记的一个加权的社会网络。在 SAIM 的第二步,我们计算一个新概念说出 influence-BFS 树的使用的有影响的节点的一个最佳的集合。在大规模真实世界、合成的社会网络上进行的实验在计算揭示我们的模型 SAIM 的好表演,在可接受的时间规模,允许信息的最大的传播的有影响的节点的一个最小的集合。     

MULTIPLE SLIDING SURFACE CONTROL FOR SYSTEMS IN NONLINEAR BLOCK CONTROLLABLE FORM

ABSTRACT

It is well-known that the multiple sliding surface control was developed to eliminate the problem of “explosion of complexity” inherent in the celebrated backstepping method. In this paper, we extend the multiple sliding surface control method further for systems in nonlinear block controllable form. As an application of the proposed control scheme, the robust attitude control law was synthesized for highly maneuverable missiles. Contrary to the classical SISO methods, all nonlinearities of missile attitude dynamics as well as coupling effects between roll, yaw and pitch channels due to roll rate are fully accommodated in the designed nonlinear control law.     

Recent advances in the multi-stream HMM/ANN hybrid approach to noise robust ASR

In this article we review several successful extensions to the standard hidden-Markov-model/artificial neural network (HMM/ANN) hybrid, which have recently made important contributions to the field of noise robust automatic speech recognition. The first extension to the standard hybrid was the “multi-band hybrid”, in which a separate ANN is trained on each frequency sub-band, followed by some form of weighted combination of ANN state posterior probability outputs prior to decoding. However, due to the inaccurate assumption of sub-band independence, this system usually gives degraded performance, except in the case of narrow-band noise. All of the systems which we review overcome this independence assumption and give improved performance in noise, while also improving or not significantly degrading performance with clean speech. The “all-combinations multi-band” hybrid trains a separate ANN for each sub-band combination. This, however, typically requires a large number of ANNs. The “all-combinations multi-stream” hybrid trains an ANN expert for every combination of just a small number of complementary data streams. Multiple ANN posteriors combination using maximum a-posteriori (MAP) weighting gives rise to the further successful strategy of hypothesis level combination by MAP selection. An alternative strategy for exploiting the classification capacity of ANNs is the “tandem hybrid” approach in which one or more ANN classifiers are trained with multi-condition data to generate discriminative and noise robust features for input to a standard ASR system. The “multi-stream tandem hybrid” trains an ANN for a number of complementary feature streams, permitting multi-stream data fusion. The “narrow-band tandem hybrid” trains an ANN for a number of particularly narrow frequency sub-bands. This gives improved robustness to noises not seen during training. Of the systems presented, all of the multi-stream systems provide generic models for multi-modal data fusion. Test results for each system are presented and discussed.     

A pulsed plasma thruster fault detection and isolation strategy for formation flying of satellites

The main objective of this paper is to develop a dynamic neural network-based fault detection and isolation (FDI) scheme for pulsed plasma thrusters (PPTs) that are employed in the attitude control subsystem (ACS) of satellites tasked to perform formation flying (FF) missions. A hierarchical methodology is proposed that consists of three fault detection and isolation (FDI) approaches, namely (i) a “low-level” FDI scheme, (ii) a “high-level” FDI scheme, and (iii) an “integrated” FDI scheme. Based on the data from the electrical circuit of the PPTs, the proposed “low-level” FDI scheme can detect and isolate faults in the PPT actuators with a good level of accuracy, however the precision level is poor and below expectations with the misclassification rates as expressed by False Healthy and False Faulty parameters being too high. The proposed “high-level” FDI scheme utilizes data from the relative attitudes of the FF mission. This scheme has good detection capabilities, however its isolation capabilities are not adequate. Finally, the proposed “integrated” FDI scheme takes advantage of the strengths of each of the above two schemes while reducing their individual weaknesses. The results demonstrate a high level of accuracy (99.79%) and precision (99.94%) with a misclassification rates that are quite negligible (less than 1%). Furthermore, the proposed “integrated” FDI scheme provides additional and interesting information related to the effects of faults in the thrust production levels that would not have been available from simply using the low or the high level schemes alone.     

Dynamic shading of a building envelope based on rotating polarized film system controlled by one-dimensional cellular automata in regular tessellations (triangular,square and hexagonal)

The original prototype of the cellular automaton (CA) shading system (CASS) for building facades was based on rectangular array of cells and used liquid crystal technology. This paper introduces polarized film shading system (PFSS) – an alternative approach based on opto-mechanical modules whose opacity is a function of the rotation of polarized film elements. PFSS in regular tessellations: triangular, square and hexagonal are discussed. Simulations for each type of tessellation are presented and visualized. Visual attractiveness of emergent CA patterns manifested by “particles” and “solitons” is discussed.     

The effects of trust and constructive controversy on student achievement and attitude in online cooperative learning environments

This study investigated the effects of trust and constructive controversy on student achievement and attitude in online cooperative learning environments. Students in one university course were randomly assigned to one of the two treatment groups after they took part in a common initial workshop on general cooperative learning skills. The “trust” and the “constructive controversy” groups received subsequent associated skills training. The overall results indicated that after each group received the treatment during online cooperative group activities, the “trust” groups had significantly higher achievement than the “constructive controversy” groups. In addition, the “trust” groups had significantly more positive attitudes toward online cooperative learning than the “constructive controversy” groups. Specifically, using “trust” building strategies was significantly more effective than using “constructive controversy” strategies for improving the ‘openness and sharing’ and ‘acceptance and support’ components of student attitudes in online cooperative learning environments.     

A persuasive-based latent class segmentation analysis of luxury brand websites

Based on the development of the use of websites by brands, the purpose of this paper is to identify and describe different groups of luxury brands bearing in mind the persuasiveness of their websites (informativeness, usability, credibility, inspiration, involvement and reciprocity). The data for this study were collected from 197 luxury websites through content analysis methodology. Then, latent class cluster analysis was employed to identify the segments obtained in this study. The results confirm the existence of three segments of luxury brands according to website persuasiveness: “exclusive websites”, “transparent and accessible websites” and “old-fashioned websites”. This study helps luxury brand managers to evaluate the degree of persuasiveness of each group, determines how attractive the websites in each group are and suggests the measures necessary to improve their websites.     

Supervised fuzzy logic modeling for building earthquake hazard assessment

Building hazard assessment prior to earthquake occurrence exposes interesting problems especially in earthquake prone areas. Such an assessment provides an early warning system for building owners as well as the local and central administrators about the possible hazards that may occur in the next scenario earthquake event, and hence pre- and post-earthquake preparedness can be arranged according to a systematic program. For such an achievement, it is necessary to have efficient models for the prediction of hazard scale of each building within the study area. Although there are subjective intensity index methods for such evaluations, the objective of this paper is to propose a useful tool through fuzzy logic (FL) to classify the buildings that would be vulnerable to earthquake hazard. The FL is a soft computing intelligent reasoning methodology, which is rapid, simple and easily applicable with logical and rational association between the building-hazard categories and the most effective factors. In this paper, among the most important factors are the story number (building height), story height ratio, cantilever extension ratio, moment of inertia (stiffness), number of frames, column and shear wall area percentages. Their relationships with the five hazard categories are presented through a supervised hazard center classification method. These five categories are “none”, “slight”, “moderate”, “extensive”, and “complete” hazard classes. A new supervised FL classification methodology is proposed similar to the classical fuzzy c-means procedure for the allocation of hazard categories to individual buildings. The application of the methodology is presented for Zeytinburnu quarter of Istanbul City, Turkey. It is observed that out of 747 inventoried buildings 7.6%, 50.0%, 14.6%, 20.1%, and 7.7% are subject to expected earthquake with “none”, “slight”, “moderate”, “extensive”, and “complete” hazard classes, respectively.     

An Empirical Study of Home IoT Services in South Korea: The Moderating Effect of the Usage Experience

This article examines the responses of users to home Internet of Things (IoT) services in South Korea, which is taking progressive steps in the field of IoT. It is important to investigate the user’s response because home IoT users are the core users of the IoT business. To this end, the research model includes two trust constructs — “trust in the service provider” and “institutional trust”; two risk constructs — “perceived security risk” and “perceived privacy risk”; and “perceived benefit” construct. This study has two main objectives: (1) to establish the functional relationship among the five constructs listed above; (2) to examine the moderating role of home IoT usage experience in these relationships. The study first reviews the literature on home IoT services and describes the Korean situation. Data were collected from residents living in a smart apartment complex. They were made aware of not only the benefits of home IoT but also the security and privacy risks before they moved into their new homes. The research model was empirically analyzed with structural equation modeling (SEM) using Amos 22.0. The results show that (1) “trust in the service provider” negatively influences “perceived security risk” and “perceived privacy risk” while “institutional trust” does not have a significant influence on them, (2) “perceived security risk” and “perceived privacy risk” negatively influence “perceived benefit,” and (3) “trust in service provider” does not directly influence “perceived benefit” while “institutional trust” has a positive and direct influence on it. In addition, there is a significant moderating effect of home IoT usage experience on some paths. Finally, the study’s findings and limitations are discussed, and potential avenues for future research are suggested.     

Using probabilistic reasoning over time to self-recognize

Using the probabilistic methods outlined in this paper, a robot can learn to recognize its own motor-controlled body parts, or their mirror reflections, without prior knowledge of their appearance. For each item in its visual field, the robot calculates the likelihoods of each of three dynamic Bayesian models, corresponding to the categories of “self”, “animate other”, or “inanimate”. Each model fully incorporates the object’s entire motion history and the robot’s whole motor history in constant update time, via the forward algorithm. The parameters for each model are learned in an unsupervised fashion as the robot experiments with its arm over a period of four minutes. The robot demonstrated robust recognition of its mirror image, while classifying the nearby experimenter as “animate other”, across 20 experiments. Adversarial experiments, in which a subject mirrored the robot’s motion showed that as long as the robot had seen the subject move for as little as 5 s before mirroring, the evidence was “remembered” across a full minute of mimicry.     

Inductive hierarchical nonnegative graph embedding for “verb–object” image classification

Most existing image classification algorithms mainly focus on dealing with images with only “object” concepts. However, in real-world cases, a great variety of images contain “verb–object” concepts, rather than only “object” ones. The hierarchical structure embedded in these “verb–object” concepts can help to enhance classification. However, traditional feature representation methods cannot utilize it. To tackle this problem, we present in this paper a novel approach, called inductive hierarchical nonnegative graph embedding. By assuming that those “verb–object” concept images which share the same “object” part but different “verb” part have a specific hierarchical structure, we integrate this hierarchical structure into the nonnegative graph embedding technique, together with the definition of inductive matrix, to (1) conduct effective feature extraction from hierarchical structure, (2) easily transfer each new testing sample into its low-dimensional nonnegative representation, and (3) perform image classification of “verb–object” concept images. Extensive experiments compared with the state-of-the-art algorithms on nonnegative data factorization demonstrate the classification power of proposed approach on “verb–object” concept images classification.     

基于HMM的足球视频语义结构分析

提出了一种足球视频的语义结构,即足球视频由多个语义事件构成,每个语义事件由数个语义镜头组成。为了分析这种语义结构,建立了“精彩事件”和“一般事件”两种语义事件的多个隐马尔科夫模型(HMMs),并提出了场地比率、人脸比率、边缘、运动强度四种特征作为HMMs的观测值输入。利用HMM的三种算法训练HMMs,分析出精彩事件,并为每个镜头标注语义。     

Belief,awareness, and limited reasoning

Several new logics for belief and knowledge are introduced and studied, all of which have the property that agents are not logically omniscient. In particular, in these logics, the set of beliefs of an agent does not necessarily contain all valid formulas. Thus, these logics are more suitable than traditional logics for modelling beliefs of humans (or machines) with limited reasoning capabilities. Our first logic is essentially an extension of Levesque's logic of implicit and explicit belief, where we extend to allow multiple agents and higher-level belief (i.e., beliefs about beliefs). Our second logic deals explicitly with “awareness,” where, roughly speaking, it is necessary to be aware of a concept before one can have beliefs about it. Our third logic gives a model of “local reasoning,” where an agent is viewed as a “society of minds,” each with its own cluster of beliefs, which may contradict each other.     

A comprehensive four-stage framework for evaluation of Information Communication Technologies for Development interventions

ABSTRACT

In spite of almost three decades of efforts in the domain of Information Communication Technologies for Development (ICTD), the validity and effectiveness of the relationship between ICT and D (Development) are not clear, perhaps due to a lack of a comprehensive framework for evaluating ICTD interventions. Comprehensive evaluation of ICTD interventions is, however, difficult as its outcomes are often intangible in nature. The aim of this paper is to develop a comprehensive evaluation framework for ICTD interventions with a specific focus on “D” aspects of these interventions. This work does not focus only on “impact” evaluation of ICTD interventions but also considers other key aspects of project life cycle in the evaluation process. To develop this framework, we draw inspirations from the domains of “program evaluation” and “IS evaluation” and further refine it with learnings from previous ICTD studies. The result of this exercise is a four-stage framework with each stage containing details of assessment points and key considerations that need to be covered in the context of an ICTD intervention. The framework is then validated using an existing case study drawn from the ICTD literature. The applicability and benefits of the framework are then discussed in the light of this case study.