Anonymous Geo-Forwarding in MANETs through Location Cloaking Abstract-In this paper, we address the problem of destination anonymity for applications in mobile ad hoc networks where geographic information is ready for use in both ad hoc routing and Internet services. Geographic forwarding becomes a lightweight routing protocol in favor of the scenarios. Traditionally, the anonymity of an entity of interest can be achieved by hiding it among a group of other entities with similar characteristics, i.e., an anonymity set. In mobile ad hoc networks, generating and maintaining an anonymity set for any ad hoc node is challenging because of the node mobility and, consequently, the dynamic network topology. We propose protocols that use the destination position to generate a geographic area called an anonymity zone (AZ). A packet for a destination is delivered to all the nodes in the AZ, which make up the anonymity set. The size of the anonymity set may decrease, because nodes are mobile, yet the corresponding anonymity set management is simple. We design techniques to further improve node anonymity and reduce communication overhead. We use analysis and extensive simulation to study the node anonymity and routing performance and to determine the parameters that most impact the anonymity level that can be achieved by our protocol. M OBILE ad hoc networks (MANETs) are envisioned as an effective solution for extending infrastructurebased wireless network communications and/or self constructing when fixed infrastructures are not available. For example, wireless mesh networks and WiMax both include a MANET as an optional extension to the multihop wireless infrastructure. First, responder networks and vehicular networks also take much of their form as MANETs [1] , and MANET has been used in tactical applications. Communication privacy in MANETs is of increasing concern for a large variety of application domains, and therefore, techniques for achieving high privacy assurance are required. An important privacy requirement for MANETs is represented by the anonymity of the communicating parties. In general wireless networks, many scenarios have shown that anonymity is critical. For example, the relationship of the identities of WLAN or cellular users and their locations needs to be hidden from third parties [2] , [3] , locations of sources in sensor networks should not be traced by malicious nodes [4] , and active paths and network topology need to be protected in MANETs; otherwise, nodes could be traced [5] .. In this paper, we investigate an application scenario under which Location-Based Services (LBSs) [6] , [7] are accessed from a MANET segment. Anonymity is particularly crucial when an ad hoc node receives sensitive data from well-known servers. This receiver may not wish its identity to be revealed to the network or any third party while communicating with those servers. We refer to this requirement as destination anonymity. In this work, we use client/destination and server/source interchangeably.. In this application scenario, as geographic information is required for LBSs, it is then natural to use geographicposition-assisted routing protocols to deliver data from the server to the client [8] , [9] , [10] . Such a routing protocol has the advantage of being scalable and lightweight. In addition, geographic-position-assisted routing has the potential of supporting identity anonymity in that a node's position, not its ID, is required in routing. For example, in Greedy Perimeter Stateless Routing (GPSR) [10] , node positions are used; thus, the real identity of a node, e.g., a destination, is hidden. The local and stateless routing operation also helps preserve privacy. However, in many applications [13] , [12] , position information is sensitive data. Presenting the position during the communication gives the adversary the opportunity to trace down a destination according to its position and then identify the destination in a face-to-face manner. Through such a probing attack, adversaries can easily link nodes' locations to their identities.. The goal of our paper is to explore the advantages of geographic-assisted routing while, at the same time, dealing with the probing attack mentioned above, i.e., to address the privacy problem when using the aforementioned sensitive position data. We will focus on achieving destination anonymity in this paper. When the servers are well known, source anonymity is less important. On the other hand, if source anonymity is required to achieve unlinkability between a client and the server, a packet can be transmitted without carrying source identity.. We propose an anonymous geographic routing algorithm that uses fuzzy destination positions. The notion of fuzzy position is used in privacy-preserving LBSs [13] , [14] . Under such an approach, a mobile user intentionally provides inaccurate positions to services in order to protect its real positions. Here, we use a fuzzy position in geographic forwarding (geo-forwarding) to prevent adversaries from obtaining the real position of a node and, therefore, to prevent a destination ID from being discovered based on its position. A client generates a pseudo destination (PD), which is chosen in such a way that when data packets are forwarded to the location, the real client has a high probability of receiving them. Such a position is sent to the application server, toward which the server sends packets. The successful delivery in such a routing algorithm relies on the broadcast nature of wireless communication, where a transmission can always be received by all the nodes within the transmission range of the sender. Therefore, if the real destination is located in a geographic area that is not far away from the PD or from the forwarding path, it will receive the packets. Such a geographic area, which we refer to as an anonymity zone (AZ), is the key concept of our design. The destination anonymity is determined by the number of nodes that are located in the AZ, and the protocol is thus called the zone-based anonymous positioning routing (ZAP) protocol.. The main design challenges of ZAP are how large anonymity sets are maintained and how data packets, given only pseudo location information, are delivered. The major contributions of this paper are two approaches tackling the problems, namely, a destination-based AZ and a routebased AZ. The former uses a geographic area around the fuzzy destination as the AZ. Based on the concept, a basic ZAP protocol, namely, ZAP with PD (PD-ZAP), and dynamically expansible zone, namely, geocasting-based anonymous protocol (G-ZAP), are proposed. The latter approach turns the entire routing path with extra hops into an AZ, i.e., ZAP with Route Redundancy (RR-ZAP).. ZAP is a best effort protocol. It provides destination privacy, measured by the size of the anonymity set, with probability. While a guaranteed anonymity level is not a design goal, G-ZAP and RR-ZAP provide effective methods for increasing the anonymity set size. We present analysis showing how the anonymity set is affected by nodal density, mobility, and communication durations. In reality, a node can estimate local nodal density and mobility through analyzing neighborhood transmissions. Compared to the approaches that achieve a predefined anonymity size, e.g., multicasting to a fixed number of nodes, the idea proposed here greatly simplifies the operation and significantly reduces the overhead, with a tolerance probabilistic anonymity level. In addition, the three variants reveal different advantages by the nature of the design, e.g., an efficient "geocast" strategy with G-ZAP, reduced "hot spots," and strategy of maximizing entropy in RR-ZAP. We develop mathematical models for analyzing different parameters in order to obtain guidelines for AZ management. Both simulation and analytical work are presented in the paper. This work is based on our early paper [34] , with substantial new and revised content on scheme design, analysis, and evaluations. The remainder of this paper is organized as follows: Section 2 introduces system and attack models that are relevant to our protocols. The section also describes the service model of the ZAP protocol. Section 3 presents the details of the ZAP protocol, introducing the three variants in sequel. Section 4 discusses protocol anonymity and the attack. Section 5 presents analysis for several key design metrics. Section 6 reports evaluation results from both the numerical analysis and the simulation. Section 7 discusses related works. Finally, Section 8 concludes this paper. In this paper, we have proposed ZAP, an anonymous georouting protocol that adopts fuzzy positions to create AZ for destination anonymity. Nodes residing in the AZ form the anonymity set, which protects the real destination. Because nodes are mobile, the anonymity set in our protocols is dynamic, unlike the case of wired networks. We have introduced a set of strategies that effectively increase the anonymity set: the PD-ZAP, G-ZAP, and RR-ZAP protocols. We use both analysis and simulation to study various performance aspects such as node anonymity and packet delivery percentage. We have found that RR-ZAP, which uses redundant routes to improve anonymity, can achieve a high packet delivery ratio and assure the highest anonymity. If the anonymity requirement is not high, PD-ZAP can be used, because it achieves efficient node anonymity and still achieves a good routing performance in many cases. G-ZAP has to trade the performance for anonymity. The main problem arises from contentions in the AZ. Thus, it is suitable for low-density networks, for which the other approaches are less effective. Jun Liu received the BE degree in computer science from Beijing (Northern) Jiaotong University in 1998, the MS degree in computer science from Loyola University Chicago in 2001, and the PhD degree in computer science from the University of Alabama in 2007. He is currently a senior analyst in Manhattan Associates. His research interests include protocol design, performance evaluation, security and privacy, routing, and power-aware solutions for wireless mobile networks and sensor networks.. Xiaoyan Hong received the BS and ME degrees in computer science from Zhejiang University, Hangzhou, China, and the PhD degree in computer science from the University of California, Los Angeles, in 2003. She is currently an assistant professor in the Department of Computer Science, University of Alabama. Her research interests include network protocol design, performance evaluation and implementation for multihop, mobile and wireless networks, and wireless sensor networks. Her current research is focused on mobility, privacy, security, routing, and monitoring issues. . For more information on this or any other computing topic, please visit our Digital Library at www.computer.org/publications/dlib. In this section, we present an analysis of various design issues that relate to the different ZAP approaches. We build mathematical models for destination anonymity evaluation. We then calculate the packet delivery ratio in PD-ZAP, assessing the impact from the distance parameter, i.e., the distance between the real and the PDs. Finally, we address the problem of how the best destination anonymity in RR-ZAP can be achieved based on the redundant route generation through random matching. When applicable, the analysis assumes a uniform distribution of node density and a random mobility following the model used by many early work. Anonymous communication protocols studied for wired Internet have followed MIX [27] techniques, Onion routing [28] , broadcast [29] , or multicast [30] approaches. However, they are not always applicable to MANETs. For example, protocols based on MIX [27] techniques and Onion routing [28] require a priori underlying security associations among entities via a fixed infrastructure, which is very difficult in dynamic and self-organizing MANETs. Approaches using broadcast [29] or multicast [30] have high bandwidth demand. The obstacles against achieving communicationend privacy, especially destination anonymity, also come from the fact that in on-demand routing protocols such as AODV [31] and DSR [32] , global flooding is required at the route discovery stage. The destination identity is carried in the request; therefore, it is revealed to the entire network. All nodes in the network may thus become aware about communications being established.. To date, several approaches have been reported addressing anonymity in MANETs, including protocols using single routing path [11] , [5] , [33] , [37] . In particular, under the AO2P protocol [11] , the destination position is the only position information disclosed in the network for routing. As in traditional positioning algorithms, in AO2P, the route is discovered by delivering a routing request from the source toward the position of the destination. However, AO2P does not rely on the local position information exchange. The other relevant approach is the untraceable on-demand routing protocol called ANODR [5] . This protocol uses an onion structure for routing discovery. To reduce the cost and latency of the encryption/decryption, a symmetric key based on Boomerang Onions is used. Once a route is discovered, pseudorandom numbers are used as temporary IDs for each link along the route. Each node only knows the pseudo numbers for its previous hop and next hop. Communication privacy is achieved, because real IDs are not revealed.. In the MASK protocol [33] , a neighborhood authentication protocol that allows neighboring nodes to authenticate each other without revealing their identities is designed for communication anonymity. However, the approach needs to reveal the destination ID for on-demand route discovery. Therefore, only a conditional anonymity can be achieved for the destination; that is, a tracer knows which node is the destination, yet the tracer does not know where the destination is. Source anonymity for ad hoc routing has been investigated by Yang et al. [37] . Under their approach, each intermediate node substitutes the source identity in a route request with its own identity. Layered encryption is used in route reply packets to set up onions for data communications. The protocol does not hide each other's ID when sending/receiving routing messages nor the destination's identity in the flooding. Thus, within a neighborhood, no protection is ensured for identity anonymity.. Non-single-path approaches are introduced in [38] and [39] . In [38] , a packet coding technique is used to combine multicast and onion routing to address the anonymity threats coming from both the global and local adversaries. The source and destination anonymity is protected through forming multicast forests, and unlinkability is ensured by onion-based packet encryption. The need for global routing information helps build a multicast forest but limits its use to high-bandwidth networks. Multipath routing is used in [39] , where data traffic is split to and forwarded through multiple paths randomly over time. To evaluate the traffic privacy, an entropy metric is designed to characterize the limited knowledge that a single intermediate node can gather. The scheme employs source routing where the source will build a path poll for traffic splitting. Thus, it is designed for static mesh network with a fixed infrastructure.. Geographic zone has been used for protecting user privacy. For example, Mix Zone [35] is designed to prevent application servers from tracing their users' movement. Within a mix zone, a mobile user obtains LBSs without revealing its accurate position. In [40] , an anonymity proxy enables spatial and temporal anonymity by perturbing the location data in terms of transmitting time and position information, enabling location privacy in using LBSs. While the above two work are middleware solutions, the Motion Mix approach [36] explores mobility for protection. It uses the geographic area generated due to the attacker's inability to pinpoint a transmission. Any nodes moving in and out of the motion mix becomes a protection against nearby eavesdroppers. Geographic zone in ZAP is used differently from these approaches because of the different attacker model and design goal.

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Weighted Coxeter graphs and generalized geometric representations of Coxeter Groups Abstract. We introduce the notion of weighted Coxeter graph and associate to it a certain generalization of the standard geometric representation of a Coxeter group. We prove sufficient conditions for faithfulness and non-faithfulness of such a representation. In the case when the weighted Coxeter graph is balanced we discuss how the generalized geometric representation is related to the numbers game played on the Coxeter graph. In this paper we define a notion of the legal weight function and a certain generalization of the standard geometric representation of a Coxeter group which is associated to such a function. We give two sufficient conditions for faithfulness and non-faithfulness of this representation in terms of certain properties of the weight function. This research can be continued in various directions. We list here several problems which remain open and seem to be interesting. Problem 1. Is the converse of Theorem 4.1 true? I. e. is it true that the representation R Γ,f is faithful if and only if there is no cycle such that the product of weights along this cycle is an element of finite order greater than one? Problem 2. What can be said about the numbers game associated to the weighted Coxeter graph Γ f when the representation R Γ,f is faithful but the weighted graph Γ f is not balanced? Problem 3. Let the representation R Γ,f be non-faithful. Describe the quotient of the Coxeter group W that we get in this case. Can we define the generalized numbers game in this case? Is this game related to this quotient in any way? Can any quotient be obtained by finding a suitable legal weight function?. The list of problems can be continued.

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A comparative study of the sense of presence and anxiety in an invisible marker versus a marker augmented reality system for the treatment of phobia towards small animals Phobia towards small animals has been treated using exposure in vivo and virtual reality. Recently, augmented reality (AR) This paper presents an augmented reality (AR) invisible marker-tracking system for the treatment of a phobia towards small animals. AR refers to the introduction of virtual content into the real world, that is, the user is seeing an image composed of a real image and virtual elements superimposed over it. A phobia towards small animals has traditionally been treated using exposure in vivo and also using virtual reality (VR) (e.g. Carlin, Hoffman & Weghorst, 1997) . Recently, the used of AR was presented as an alternative technology for this type of treatment Botella et al., 2005) . In that work, visible markers were used for tracking. In this paper, we present a second version of the visible marker-tracking AR system (VMARS) in which markers that are invisible to the naked eye (invisible markers) are used for tracking. The invisible marker-tracking AR system (IMARS) uses visible markers that are drawn with a special ink. In visible-marker-tracking systems, users relate the marker with the appearance of virtual elements. In our case, visible markers warn the patients of the appearance of animals and this could produce an initial anxiety without the appearance of the virtual element that really should produce this anxiety. We consider the non-appearance of the markers to be important, especially in some steps of the protocol followed during treatment. One common step of protocols for treating patients with phobia towards small animals is that patients have to search for the feared animal in the same way they would do so in their house. In our case, to simulate this search, we use three boxes, under one of which a marker is randomly placed. The animals will appear over the marker. At first, when the patient sees part of the marker, the system still does not show the animals, but the patient knows they will appear and s/he can decide not to finish raising the box, and the animals will not appear. If the marker were not visible, the patient would not know that the animals were going to appear, and when they did appear, they would produce the desired surprise. To avoid the one-to-one correspondence between the appearance of the marker and the appearance of the animals, it could be possible to pepper the environment with a large number of markers and just randomly make one active at any given time. But, in both cases, the markers appear artificially in the environment.. In our work, the primary hypothesis is that the IMARS will induce a similar or higher sense of presence and will provoke a similar or higher level of anxiety than the VMARS with non-phobic participants. Our first objective was to develop a new IMARS. Our second objective was to examine the sense of presence and the anxiety level using IMARS and VMARS. This paper is organized as follows. Section 2 focuses on background about the use of VR for the treatment of a phobia towards small animals, the use of AR for psychological treatments, and tracking and registration methods used in AR. Section 3 presents the software and hardware requirements as well as the technical features of IMARS. Section 4 describes the participants and the procedure of the study. Section 5 presents the results of the study for the sense of presence and the level of anxiety. Finally, in section 6, we present our conclusions, the limitations of this study, and future work. We have presented the first IMARS for the treatment of a phobia towards small animals. In this work, we have carried out a study with twenty-four non-phobic participants, and we have compared the visible marker-tracking and the invisible marker-tracking systems. The results indicate that IMARS induces a similar or higher sense of presence, and it also provokes a similar or higher anxiety level in important steps for therapy. Moreover, 83.33% of the total participants reported that they did not have the same sensations/surprise using the two systems, and they scored the advantage of using IMARS at 5.19±2.25 (on a scale from 1 to 10). However, if only the group with higher fear levels is considered, 100% of the participants reported that they did not have the same sensations/surprise with the two systems, scoring the advantage of using IMARS at 6.38±1.60 (on a scale from 1 to 10). Since VMARS has already been successfully used for treatment with real patients Botella et al., 2005) , and since IMARS induces a similar or higher sense of presence and also provokes similar or higher anxiety, we believe that IMARS could also be a useful tool for treatment. However, future studies should corroborate whether or not IMARS is a suitable tool for treatment and whether there are any differences in treatment using IMARS and VMARS. The main drawback of this study is the number of participants in each group, especially in the subgroup of participants who had higher fear levels, which had only eight participants. A new study with a larger sample would be required in order to assure high practical significance. Another drawback of our study is the impossibility of establishing correlations between presence and anxiety. This is due to the fact that these two measures were asked for at different moments. This could be solved by asking for presence scores and anxiety levels at the same time.. In visible-marker-tracking systems, users relate the marker with the appearance of virtual elements. Instead of using these visible-marker-tracking systems, a more natural, less obtrusive, and more surprising systems without markers could be developed (e.g. using Studierstube Tracker rather than ARToolKit). Moreover, markerless techniques such as those described in section 2.3 could also be used for developing a similar system to IMARS. The two systems, IMARS and VMARS, have the problems associated with marker tracking: fast movements or changing light conditions cause tracking to fail, the entire marker must be in view for tracking to work, the problem of occluding the marker, etc. In this work, we have not evaluated these interface issues. Taking these interface issues into account, the two systems could be improved by adding cues to the interface in order to alert the user when the marker tracking is failing or about to fail (Xu et al., 2008) .. With regard to other future works, first, presence is a subjective condition and the use of selfreports could give rise to some errors if the user does not give the correct score. As mentioned in subsection 3.1, it is possible to use physiological measures to measure presence (Meehan et al., 2002) , for example, using galvanic skin response (GSR). Several studies have investigated the effect of GSR for the treatment of phobias and shock (e.g. for fear of flying (Wiederhold et al., 2002) ). It is also possible to use methods in which the measure depends on the data collected during the experience (Slater & Steed, 2000) . One possible future study would be to incorporate these measures and contrast the results with the ones obtained with questionnaire responses.. Finally, IMARS and VMARS have the same functionality and performance. As an invisible marker-tracking system, IMARS has two advantages: the user does not see any intruding element in the scene; and the fusion of the virtual objects with the real scene is totally natural. These advantages can be useful not only for psychological applications, but also for any application in which the nonappearance of the marker is important such as applications for art exhibitions or publicity.

O O O O O O O O O O O O O O O O O B I O O O O O B I I I B I I I O O O O O O O O O O O B I O O O O O B I O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


A Low-Complexity Down-Mixing Structure on Quadraphonic Headsets for Surround Audio This work presents a four-channel headset achieving a 5.1-channel-like hearing experience using a low-complexity head-related transfer function (HRTF) model and a simplified reverberator. The proposed down-mixing architecture enhances the sound localization capability of a headset using the HRTF and by simulating multiple sound reflections in a room using Moorer's reverberator. Since the HRTF has large memory and computation requirements, the common-acoustical-pole and zero (CAPZ) model can be used to reshape the lower-order HRTF model. From a power consumption viewpoint, the CAPZ model reduces computation complexity by approximately 40%. The subjective listening tests in this study shows that the proposed four-channel headset performs much better than stereo headphones. On the other hand, the four-channel headset that can be implemented by off-the-shelf components preserves the privacy with low cost. With the rapid development of information technology and internet access in recent years, digital video and audio applications have become commonplace in human life. Traditional mono or stereo audio playback is often un-able to satisfy the increasing demands of human perception for multimedia. Placing multiple channels (such as 5.1 channels) at different positions can create different three-dimensional (3D) spatial effects.. The market is currently capable of providing high quality multichannel audio systems, but loudspeaker systems are relatively expensive. However, space limitations often mean it is not suitable to place a multichannel system in a crowded space, as this may interfere with neighboring people. A conventional earphone set provides privacy, but not surround sound. Therefore, it is desirable to produce a headset that provides privacy and the ability to experience the rich multichannel audio of DVD or Blu-ray multimedia at much lower cost than a complete loudspeaker system.. People can easily experience the sound source changes produced by sophisticated multichannel systems in theaters. The rich spatial information in these signals creates a virtual sound field for the listener. However, the same signals in a headphone, without any spatial cues may blur the sound location and yield unnatural perception. This phenomenon is called the "in head" effect. To overcome this problem, several studies have attempted to enhance the acoustic field to construct approximate out-of-head sound field [1] - [3] . The human auditory system (HAS) uses the physical distance of two ears and the difference of sound arriving time to localize sound sources. In this transmission path, the sound characteristic may be affected by obstructions and time difference to ears. Head-related Transfer Function (HRTF) is often used to synthesize spatial sound. The HRTF represents all the information of the sound transmission from a given source position to the ear drum. Those spatial cues of the Inter-aural Intensity Differences (IIDs) and Inter-aural Time Differences (ITDs) thoroughly describe the filtering effect of the head, torso, and pinna [4] , [5] . Since the major part of HRTF that is included in the loudspeaker path will not present in the earphone path, adding HRTF in the headphone system can certainly enhance HAS perception.. The "virtual loudspeaker" is an interesting issue in headset systems. A prolific sound field typically places stereo or 5.1-channel loudspeakers in a space. The virtual loudspeaker maps the loudspeaker position in a real environment to headset devices. One approach uses the HRTF to filter the signals from the loudspeaker location in a degree according to the propagation of sound from the speaker to the listener's two ears [4] , [6] . In general, the HRTF can be realized by a Finite Impulse Response (FIR) model with long coefficients to accurately present the spatial characteristic. To reduce the number of coefficients, an Infinite Impulse Response (IIR) filter has been used to modeling the HRTF [7] , [8] . The IIR just needs fewer order of pole/zero that can efficiently generate the response of FIR. However, a different set of poles and zeros is still needed corresponding to each different sound direction. Thus, the commonacoustical-pole and zero (CAPZ) model has been proposed for modeling head-related transfer function [9] . This approach is based on the acoustic resonance of the pinna, and reconstructs HRTF coefficients using a set of common-pole and variable zeros for every sound direction. This approach greatly reduces storage requirement and computation capability.. The HRTF is evaluated from an anechoic chamber without considering the information of sound reflection from walls. In real world, it is necessary to consider multiple reflections of sound waves to simulate a complete acoustic environment [10] , [11] . When the panning technique of Copyright c 2013 The Institute of Electronics, Information and Communication Engineers virtual loudspeaker creates a room system in headphones, it requests spatial information of the sound travel path to simulate the room effect. Wang et al. simulated a stereo sound field enhancement system [12] . Their system can achieve approximately normal stereo sound propagation in a space. However, it still cannot simulate a home cinema, such as a 5.1 multichannel environment. Hence, the current study proposes a down-mixing 5.1-channel structure to a fourchannel structure using CAPZ technique to reduce implementation cost and a simplified reverberator to simulate the sound reflection of actual sound field.. The remainder of the paper is organized as follows. Section 2 describes the proposed multichannel down-mixing structure with HRTF and reverberator. Section 3 presents the implementation results. Finally, Sect. 4 draws conclusions. This study proposes a four-channel headset capable of simulating multichannel surround audio while maintaining privacy. The proposed system combines low-complexity HRTF and artificial reverberation. The four-channel system uses the CAPZ model to reduce computation complexity and adopts an efficient artificial reverberator to simulate the reverberation of a real environment. Subjective listening tests show that the proposed four-channel headset performs much better than stereo headphones. Conversely, the fourchannel headset preserves privacy at a low cost. Hence, our method that provides a multi-channel effect embedded in headset with lower power consumption and possesses great commercial potential.

O O O O O O O O B I O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Hypergeometric transforms in certain classes of analytic functions In the present investigation, we obtain certain sufficient conditions for special analytic functions to be in the class of normalized analytic functions satisfying the conditionwhere β is a given real number.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Optimal parameters study for sample entropy-based atrial fibrillation organization analysis Sample Entropy (SampEn) is a nonlinear regularity index that requires the a priori selection of three parameters: the length of the sequences to be compared, m, the patterns similarity tolerance, r, and the number of samples under analysis, N . Appropriate values for m, r and N have been recommended and widely used in the literature for the application of SampEn to some physiological time series, such as heart rate, hormonal data, etc. However, no guidelines exist for the selection of that values in other cases. Therefore, an optimal parameters study should be required for the application of SampEn to not previously analyzed biomedical signals. In the present work, a thorough analysis on the optimal values for m, r and N is presented within the context of atrial fibrillation (AF) organization estimation, computed from surface electrocardiogram recordings. Recently, the evaluation of AF organization through SampEn, has revealed clinically useful information that could be used for a better treatment of this arrhythmia. The present study analyzed optimal SampEn parameter values within two different scenarios of AF organization estimation, such as the prediction of paroxysmal AF termination and the electrical cardioversion outcome in persistent AF. As a result, interesting recommendations about the selection of m, r and N , together with the relationship between N and the sampling rate (f s ) were obtained. More precisely, (i) the proportion between N and f s should be higher than one second and f s ≥ 256 Hz, (ii) overlapping between adjacent N -length windows does not improve AF organization estimation with respect to the analysis of non-overlapping windows, and (iii) values of m and r maximizing successful classification for the analyzed AF databases should be considered within a range wider than the proposed in the literature for heart rate analysis, i. e. m = 1 and m = 2 and r between 0.1 and 0.25 times the standard deviation of the data. The application of nonlinear regularity metrics to physiological signals is a valuable tool because "hidden information" related to underlying mechanisms can be obtained [1, 2] . To this respect, the employment of sample entropy (SampEn) to estimate non-invasively atrial fibrillation (AF) organization has revealed clinically useful information, which could be used for a better treatment of the arrhythmia [3] [4] [5] . The organization estimation of AF is a relevant aspect to improve its comprehension, since its mechanisms are still unexplained [6] despite of affecting up to 1% of the general population [7, 8] .. Given a time series with N data points, the a priori selection of two unknown parameters, m and r, is required to compute SampEn [2] . The parameter m determines the length of the sequences to be compared and r is the tolerance for accepting similar patterns between two segments. Although these parameters are critical in determining the outcome of SampEn, no guidelines exist for optimizing their values. Typically recommended m and r values are m = 1 and m = 2 and r between 0.1 and 0.25 times the standard deviation (SD) of the data [9] . This recommendation is largely based on the application of approximate entropy (ApEn) to relatively slow dynamic signals such as heart rate [1, 9, 10] and hormone secretion data [11] . Given that SampEn is a modified version of ApEn to solve its shortcomings, such as bias, relative inconsistency and dependence on the sample length [2] , these values are also applicable to SampEn.. However, a recent work has demonstrated that the typically recommended values for ApEn are not always appropriate for fast dynamic signals [12] . As a consequence, since only few values, within the range suggested in the literature [9] , were tested in the previous works where SampEn was applied to AF organization estimation [3] [4] [5] , the main goal of the present study is to carry out an in depth analysis on SampEn parameters able to achieve optimized classification of AF events which are directly dependent on AF organization. Thus, several combinations of m and r, over a range wider than the one typically recommended, together with the number of analyzed samples, N , and the overlapping effect between adjacent N -length windows have been analyzed. Additionally, since in AF organization estimation, SampEn is applied to a time series that depends on the original ECG sampling rate (f s ), the relationship between N and f s is also addressed.. Due to the lack of a method able to generate AF signals with a priori controlled organization, the use of simulated signals is not possible. As a consequence, real AF signals with organizationdependent events were selected. Two different scenarios, such as the prediction of paroxysmal AF termination and the electrical cardioversion (ECV) outcome in persistent AF, in which organization plays an important role, as corroborated by invasive recordings [13] , were analyzed. The motivation of this study was based on the fact that no generalized guidelines exist for the selection of m, r and N when SampEn has to be applied to biomedical data. In this respect, only some values of m and r have been recommended and widely used for heart rate regularity analysis [1, 9, 10] and hormone secretion data [11] . On the other hand, Lewis et al [30] analyzed the optimal parameters when SampEn was applied to estimate the regularity of QT time series during rest and exercise.. Additionally, similar studies were developed to characterize surface electromyographic signals [31] and the variability of respiratory patterns [32] . Anyway, a survey on the optimal parameters selection is recommended for the application of SampEn to not previously analyzed biomedical signals.. The results presented in Tables 1, 2 , 3 and 4 showed that, for a given N , when f s was double and higher than 128 Hz, SampEn was approximately reduced by a half. To elucidate a possible explanation of this fact, it is obvious that the higher the f s , the lower the temporal distance between consecutive samples acquired from the original signal. As a consequence, the probability of finding equal patterns (i.e. with a distance lower than r) will increase, because the signal variation from sample to sample will be reduced. In addition, for f s of 256 Hz and above, the differences between each subset, both for paroxysmal and persistent AF, were maintained independently of N . On the other hand, for a specific f s higher than 128 Hz, when N increased, SampEn also increased up to a reduced variation point. However, this behavior was not observed for f s = 128 Hz and below. In this case, SampEn showed random variations with N and a reduced diagnostic accuracy was also noticed.. Overall, a first recommendation is that the MAW sampling rate should be equal or higher than 256 Hz for an appropriate AF organization evaluation with SampEn.. On the other hand, considering the reduced variation point of N (SampEn variation < ±5%). where a constant accuracy was reached for both AF types (92% for paroxysmal and 79.37% for persistent), a clear relationship between N and f s can be elucidated for all of these situations. As the time period analyzed with SampEn, T , is the proportion between N and f s , for all these cases we have that T = Regarding overlapping between adjacent N -length windows, SampEn only presented slight variations with all the tested factors. Thereby, other finding that merits consideration for the application of SampEn is that no accuracy improvement was provided by overlapping. As a consequence, it can be avoided to reduce computational cost.. With regard to optimal values for m and r, developed experiments showed that a high diagnostic accuracy (92% for paroxysmal and 79.37% for persistent AF) can be reached through several combinations. Therefore, not only one, but several combinations of m and r can classify AF events with very good accuracy. However, the region outlined by these combinations was notably independent on the values of N and f s and on the type of analyzed AF episodes, since slight differences in m and r were only noticed for all the cases. In addition, it is noteworthy that this region was considerably larger than the one typically recommended in the literature [9] , because it was approximately delim-ited by 1 ≤ m ≤ 5 and 0.1 ≤ r ≤ 0.6. Nevertheless, these results prove that previous works used adequate values for the SampEn parameters in the estimation of AF organization [3] [4] [5] . However, a better diagnostic precision could be reached for each prediction (96% for paroxysmal and 82.54% for persistent AF) with a combination of m and r placed within the wider indicated region. An interesting observation is that optimal values reporting the highest diagnostic accuracy were dissimilar depending on the combination of N and f s and on each type of AF. As a consequence, it can be suggested that the optimal combination of m and r has to be searched, within the proposed range, before the first application of SampEn to a non-previously analyzed database. This should only be made with very few recordings of that new database. In this way, the highest predictive ability will be reached.. Nevertheless, as global conclusion of the developed tests, it could be suggested that the selection of m between 2 and 3 and r between 0.3 and 0.4 would probably yield optimal classification results in most of the situations related to AF.. As indicated in Section 1, a previous work has recently shown that the selection of a low r, between 0.1 and 0.2 times the SD, can lead to an incorrect regularity estimation with ApEn on fast dynamic signals [12] . To circumvent this limitation, Lu et al [12] proposed to choose the r that maximizes ApEn for each analyzed signal. In addition, the authors provided the equations needed to estimate automatically the recommended r for m = 2, m = 3 and m = 4. With the objective of analyzing if a better classification could be reached for both predictions, SampEn was computed making use of the r value provided by the proposed equations and m = 2, 3 and 4. This experiment was developed for the two combinations of N and f s used in previous tests. In both combinations, accuracy was 88% for spontaneous AF termination prediction and 76.19% for ECV result, independently of the used m value. In this case, cross-validation provided a correct classification of 86% and 76.19% of grouped cases, respectively. This observation leads to the conclusion that selecting the r that maximizes SampEn is inappropriate for AF organization evaluation, because other values of r will provide better classification outcomes.. A possible limitation of this study could be the use of paroxysmal and persistent AF recordings sampled at a fixed rate of 128 Hz and 1024 Hz, respectively, since upsampling involves interpolation and could provoke an overestimation of the analyzed time series regularity with SampEn. Initially, it would be preferable to digitize the recordings directly at the different sampling rates analyzed in the study. Other alternative could be to use the highest studied sampling rate and only apply downsampling operations to both databases. In any case, the effect of interpolation was tested by comparing SampEn between downsampled and interpolated ECV signals for each sampling rate.. Results yielded a relative difference in SampEn between both sets lower than 0.085% in the worst case. As a consequence, it can be considered that the interpolation operation does not alter the result A final reflexion that has to be addressed is the kind of optimization achieved through the proposed methodology. In fact, the study presented herein optimizes SampEn parameters to maximize classification performance in different scenarios and databases of AF. However, the specific events which are classified are directly associated to measurable differences in AF organization, as discussed in [3-5, 21, 25] . Hence, it can be considered that the achieved results also maximize the estimation of AF organization via SampEn. In other words, the consequence of a method able to estimate robustly AF organization will be the ability to distinguish successfully between terminating and non-terminating paroxysmal AF patients and between successful and unsuccessful AF ECV patients.. To summarize, the present study has demonstrated that the selection of m, r and N plays a critical role in determining the outcome of SampEn when applied to physiological time series. Overall, this work has studied optimal parameters to apply SampEn as an AF organization estimator. The authors consider that both the methodology and the outcomes provided by this study could serve as a startup

O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


The spatio-temporal modeling for criminal incidents Law enforcement agencies monitor criminal incidents. With additional geographic and demographic data, law enforcement analysts look for spatio-temporal patterns in these incidents in order to predict future criminal activity. When done correctly these predictions can inform actions that can improve security and reduce the impact of crime. Effective prediction requires the development of models that can find and incorporate the important associative and causative variables available in the data. This paper describes a new approach that uses spatio-temporal generalized additive models (ST-GAMs) to discover underlying factors related to crimes and predict future incidents. In addition, the paper shows extensions of the ST-GAM approach to produce local spatio-temporal generalized additive models (LST-GAMs). These local models can better predict criminal incidents conditioned on regions. Both models can fully utilize a variety of data types, such as spatial, temporal, geographic, and demographic data, to make predictions. We describe how to estimate the parameters for ST-GAM using iteratively re-weighted least squares and maximum likelihood and show that the resulting estimates provide for model interpretability. This paper also discusses methods to generate regions for LST-GAM. Lastly the paper discusses the evaluation of LST-GAM and ST-GAM with actual criminal incident data from Charlottesville, Virginia. The evaluation results show that both models from this new approach outperform previous spatial models in predicting future criminal incidents. Law enforcement agencies have the need to model the spatio-temporal patterns of crimes. With a model of criminal incidents, they can study the causality of crimes and predict the locations and time of future criminal activity. If the model can predict future crimes accurately, law enforcement can deploy resources to improve security and reduce threats. Typical actions taken by law enforcement include walking and driving patrols, surveillance systems, and neighborhood watch programs.. Many types of data are available to assist building such models. Law enforcement agencies in the United States usually monitor criminal incidents as they occur. For example, they have locations and times of criminal incidents, as well as victim and perpetrator information. In addition to criminal incident data, most agencies can also acquire spatial information from geographic information systems (GIS) and demographic and economic data from the census.. Several techniques and models have been developed to meet the need for predictive policing with available data. One of the most popular methods is the spatial hot spot model. In the hot spot model [1] , current criminal incident data are collected and clustered over space. The locations of such clusters are so-called hot spots. The model assumes the current crime clusters to persist over the forecast horizon. Future criminal incidents are predicted to occur in these same areas. Methods to generate hot spots include spatial histograms, clustering, mixture models, scan statistics, and density estimation. The hot spot model only utilizes criminal incident data, such as types of crimes, locations and time of criminal incidents. It only shows the current patterns of crimes without the insight into the relationship between crimes and environment over time. As the local environment changes, the hot spot model cannot indicate the changes of crime patterns.. To address this problem, more sophisticated statistical models using both criminal data and environmental data have been built by researchers. Liu and Brown [2] applied a point pattern density model to criminal incidents. The spatial density of criminal incidents was assumed to be conditioned on features associated with locations. These features included geographic features, such as the distances to the nearest interstate highways, demographic features and consumer expenditure features. Xue and Brown [3] and Smith and Brown [4] developed a spatial choice model. They assumed criminals made choices to pick places that could be modeled by random utility maximization. This utility maximization is over all alternatives, where the utility is defined by the gain from crimes and the risk of being caught. Brown, Dalton, and Hoyle [5] then discussed a method that uses generalized linear models (GLM) to compute the risk over a territory. They first partitioned the space into grids. Each grid was associated with a response indicating whether incidents happened and features about the grid. Then, a spatial GLM was built with all grids. They applied the spatial GLM to predict terrorist events. Results showed the spatial GLM had better prediction performance than the density models. Rodrigues and Diggle [6] combined point process models and generalized additive models (GAM) to build a semiparametric point source model. In their model, features affected the risk nonlinearly. They applied the model to study the effect of installed security cameras on crimes.. None of the above models directly incorporate the temporal information of criminal incidents. For instance, Liu and Brown use Bayesian to model building that can include a variety of time series but no specific approach it recommended or tested. Other models usually estimate different parameter sets based upon coarse divisions of time. For example, these models use criminal incidents that happened within the most recent year to generate hot spots for this year. Another intuitive method discussed by Ivaha, Al-Madfai, Higgs, and Ware [7] first models the temporal behaviors of crimes with time series models and then models the spatial behaviors given the predicted number of incidents at a certain time. However, this approach does not model interactions between space and time. Recent research has developed spatio-temporal approaches that apply Generalized Additive Models (GAM) to combine spatial, temporal, and other (e.g., demographic) features for prediction. This approach with GAM has performed well in predicting continuous response variables such as matter concentrations [8] . However, to date no similar approach has been attempted for criminal incident prediction.. In this paper, we describe a new approach to criminal incident prediction using spatio-temporal generalized additive model (ST-GAM) and local spatio-temporal generalized additive model (LST-GAM). The paper is organized as follows. Section 2 defines the problem of criminal incident modeling formally, describes ST-GAM and LST-GAM in detail, and discusses how to estimate parameters as well as evaluate spatial prediction performance. Section 3 applies both ST-GAM and LST-GAM to model the breaking and entering incidents in Charlottesville, VA and evaluates the prediction performance. Finally, section 4 gives conclusion and suggestions on future work. In this paper, we describe the spatio-temporal generalized additive model (ST-GAM) and show its extension into the local spatio-temporal generalized additive model (LST-GAM) Figure 9 Estimations of LST-GAM in region S 1 . Figure 10 Estimations of LST-GAM in region S 2 .. to predict criminal incidents. Both ST-GAM and LST-GAM can fully utilize many different types of data, such as spatial and temporal data, geographic data, demographic data, etc. ST-GAM can be easily estimated by available algorithms and has good interpretability. LST-GAM models the different criminal patterns based on different regions, and thus is more flexible than ST-GAM. We also showed that ST-GAM is a special case of LST-GAM. Methods to estimate LST-GAM are described in Section 2.4.. To evaluate this new approach we applied both models to predict breaking and entering incidents in Charlottesville, Virginia. Based on our assessments with the real criminal incident data, both models can predict future incidents accurately. Results showed that our two models outperformed the previous spatial GLM and the hot spot model. Compared with ST-GAM, LST-GAM had better performance in prediction. Law enforcement agencies can use ST-GAM and LST-GAM to model criminal incidents, predict future incidents and prevent crimes. In addition, those two models can be applied to other areas with the need to study the spatio-temporal patterns and predict future incidents. For example, we can use ST-GAM and LST-GAM to predict terrorist events and car accidents.. Several methods can be investigated to improve both models. To select features for modeling, more sophisticated methods like penalized regression methods [10] can be incorporated into ST-GAM to choose features automatically. For LST-GAM, better methods can be developed to generate optimal regions. For example, regions can be generated to minimize the loss function discussed in section 2.1. Our previous study [20] showed narratives provided important information about crimes. We can use text mining models to extract text features from narratives and add these features in ST-GAM and LST-GAM.

O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


A Hormone-Based Controller for Evaluation-Minimal Evolution in Decentrally Controlled Systems Abstract One of the main challenges in automatic controller synthesis is to develop methods that can successfully be applied for complex tasks. The difficulty is increased even more in the case of settings with multiple interacting agents. We apply the artificial homeostatic hormone system (AHHS) approach, which is inspired by the signaling network of unicellular organisms, to control a system of several independently acting agents decentrally. The approach is designed for evaluation-minimal, artificial evolution in order to be applicable to complex modular robotics scenarios. The performance of AHHS controllers is compared with neuroevolution of augmenting topologies (NEAT) in the coupled inverted pendulums benchmark. AHHS controllers are found to be better for multimodular settings. We analyze the evolved controllers with regard to the usage of sensory inputs and the emerging oscillations, and we give a nonlinear dynamics interpretation. The generalization of evolved controllers to initial conditions far from the original conditions is investigated and found to be good. Similarly, the performance of controllers scales well even with module numbers different from the original domain the controller was evolved for. Two reference implementations of a similar controller approach are reported and shown to have shortcomings. We discuss the related work and conclude by summarizing the main contributions of our work. The survival of natural creatures and the success of artificial creatures is importantly affected by their cognitive abilities. An embodied, mobile, autonomous agent has to process sensory inputs and has to control its actuators appropriately in order to survive or to fulfill its assigned task. The number of possible actions that could potentially be performed by such an agent is generally high. Also, there are many constraints on the feasibility of actions, either due to limitations of the agentsʼ bodies or induced by the environment.. The organs in natural agents, which select appropriate actions based on perceptions, emerged through processes of natural selection. The variety of naturally evolved control systems is vast and goes much beyond the frequently stated example of the central nervous system of vertebrates. This is most evident in the case of unicellular organisms that often show nontrivial behavior without possessing a single nerve cell, for example, Paramecium [2, 6] .. Generally, nature has evolved several principles of communication that partially act in parallel within organisms. In unicellular organisms (microorganisms, microbes), receptors can alter the production of chemical cell signals, which diffuse within the cell and integrate. These signals are not solely a way to transport information through space, because they are sometimes part of complex biochemical cascades. Some of these signals also allow the organism to switch between different modes of operation (e.g., motion principles or physiological states), as, for example, in Paramecium [6] . As cells are internally structured by compartmentalization, these processes can be interpreted as biochemical computation in space and time [4] [5] [6] 48] . Generally, this sort of communication can be interpreted as broadcast communication, because there is no direct link between sender and receiver, nor is there a dedicated addressed message. However, as intracellular chemical signals usually affect specific organelles or actuators and as specific compositions of chemical signals are often correlated with specific cell states, an emitter-channel-receiver model can still be applied to this sort of communication.. Within cells of multicellular organisms (metazoa), several pathways of communication exist, which differ concerning the processes they exploit. Between neighboring cells, transmembrane proteins act as important mechanisms to transport chemical molecules against the concentration gradient, which makes this sort of communication significantly different from basal diffusion. Among the most prominent examples are ion pumps.. Generally, cell-to-cell signaling can be divided into three categories [1] : communication between cells based on direct contact and strong dependence on local morphology ( juxtacrine signaling ), communication over short distances with medium dependence on local morphology ( paracrine signaling ), and communication over large distances and/or scales with weak dependence on local morphology (endocrine signaling ). Gap junctions and notch signaling, belonging to the first category, bind communication pathways strongly to the local morphology around the senderʼs location. Paracrine hormones and neurotransmitters, still having a strong linkage between morphology (location of the communicating cells) and communication functionality, belong to the second category. In the third category, endocrine hormones allow communication over greater distances and show only a weak coupling between morphology and communication functionality.. Returning to the main focus of this article, we interpret juxtacrine signaling and paracrine signaling as belonging to one group of communication mechanisms, in that they display a clear morphological (topology-mediated) coupling between sender and receiver. Also, far-reaching neural communication can be classified as belonging to this group, in that neural communication follows a dedicated pathway within the nervous system. We argue that the concept of artificial neural networks (ANNs) is mainly inspired by this group of directed communication (unicast), as is also evidently indicated by the directed edges between neurons.. In addition, we interpret intracellular signal processing and endocrine communication as belonging to a second group of communication mechanisms, in that these communication principles exhibit a rather loose morphological coupling (except for receptor-mediated linkages) between sender and receiver. These processes are built on the diffusion of chemical signals. The design principles of the artificial homeostatic hormone system (AHHS) clearly reflect this group of natural broadcast communications (undirected communications).. It is to be noted that presenting these two groups as distinct paradigms is a simplification in that they are two extremes in a continuous transition between paradigms of biological communication. For example, neuroendocrine cells and transmembrane proteins are features that add aspects of one group to the mechanisms of the other group. Also, other means of communication or communication clues such as mechanical forces, temperatures, or external light inputs might be important in intercellular and intracellular communication.. Reaching out for new sources of inspiration in the context of engineering controllers of modular technical devices is worthwhile, although the high capabilities (especially) of the human brain are unquestioned. However, the artificial synthesis of controllers of that complexity is out of reach.. Actually, the synthesis of any controller that sufficiently completes the information processes for an agent living in a dynamic, unpredictable environment is challenging, and the non-neuronal information processing of unicellular organisms definitely generates nontrivial behaviors [4] . In this article we have reported an application and intensive analysis of the AHHS controller approach, which is inspired by signaling networks in unicellular organisms. We have reported a comparison with NEAT on the coupled-inverted-pendulums benchmark that revealed a significantly better performance of the AHHS controller for multi-module settings with a limited number of evaluations. It can be considered a main result of this work that the AHHS approach can be interpreted as an ANN with a rather exceptional network function based on weight functions depending on the outputs of connected neurons. This concept might also be a promising approach for ANNs.. The lookup-table implementation of the AHHS approach introduces an interesting difference between the genotype and the phenotype of the controllers. The genotype is designed for high evolvability, while the phenotype is optimized for low computational complexity.. An advantage of the AHHS over, for example, the ANN, NEAT, or HyperNEAT, is that a full but simple representation of an AHHS controller is possible as shown in Figure 9 . Note that the networks evolved by the NEAT approach are much more complex and contain between 25 and 40 nodes.. A nonlinear dynamics analysis of the evolved controllers is possible, especially in view of the onedimensionality due to our using only one hormone in this work. This analysis showed that the controllers are mostly based on periodic attractors that emerge during evolution.. The investigation of two reference implementations showed that evolving X-to-hormone mappings based on summations of functions is advantageous over direct encoding of such mappings. Hence, our approach of evolving the mappings indirectly via groups of tunable subrules seems to be particularly suitable for methods of artificial evolution.. Generally the AHHS approach does not require predefined topologies. In this work the network topology was not predefined, because the controllers were initialized with fully connected networks. Unnecessary connections had to be turned off explicitly by evolution, as is seen in Figure 9 (zero lines for sensors 0 through 6). The probability of fully connected networks is reduced either by decreasing the number of rules (M ) or by increasing the number of hormones (N ). The relation between M and N also changes the representable shapes of the X-to-hormone mappings (cf. Figure 4a) . Initialization with almost no connections would also be possible if the addition of rules (i.e., growing genome lengths) were allowed.. The application of the AHHS approach to other domains has been shown previously. The evolution of controllers for single robots and for modular robotics has been shown in [23, 24, 66] . The analysis of single robot controllers was reported in [54] . The implementation of a simple AHHS controller on hardware was reported in [56, 65] . Still, further investigations of additional domains would be desirable, as well as intensive comparisons with other methods such as HyperNEAT, GasNets, and GRN-based methods.. The analysis has shown that the proposed approach is superior to state-of-the-art approaches in the considered domain with local control, in that evolved controllers generalize to other initializations and scale with the number of modules. This opens up many possibilities. For example, controllers can be evolved within simpler scenarios in order to save resources. Furthermore, changes in the number of coupled modules could also be managed dynamically by the same controller without external adaptations. The good scaling and generalization properties could probably even be improved by re-evolving the controllers in new or more complex domains. In addition, this method could be combined with environmental incremental evolution as reported by Nakamura et al. [40] . Our future work will mainly be the application of the proposed approach to the multimodular robotics domain. In the following, we report several results of applying the AHHS approach to the coupled inverted pendulums benchmark. We compare the evolution of AHHS controllers with that in NEAT [62] , we investigate how the performance of the AHHS can be improved, we investigate several internal processes of AHHS controllers, and we analyze the scaling behavior of AHHS controllers to a higher number of carts.. The initial settings as shown in Table 3 (cart and pendulum positions, etc.) are maintained except for the explicit investigation of varied initial settings. In the case of AHHS we use a mutation rate of 0.4 per hormone and per rule, and values are changed by at most ±60% of the maximal value (randomly uniform). We use a mere linearly proportional selection with elitism of three (the three best individuals are guaranteed to stay in the population).. All of this research is done with a focus on modular robotics applications [24, 67, 51] . The main application is the offline evolution of controllers for modular robots based on simulations. In these simulations, the application of a physics engine (simulation of friction, inertia, etc.) is important because the evolved behaviors often rely, for example, on friction [75] . A drawback is the very high computational cost, which considerably reduces the number of feasible evaluations. Therefore, we have to limit both the population size and the number of generations. The experiments in this article are based on the coupled-inverted-pendulums benchmark, which has low computational costs, and we would have the resources to increase the evaluation number. However, we want to investigate the performance of the AHHS approach with few evaluations in order to transfer the results to the application of offline evolution of controllers for modular robotics. Hence, the population size is limited to 100 genomes and the number of generations is 200 (see Table 3 ), which results in 2 × 10 4 evaluations. The proposed AHHS controller approach has methodological similarities to many other approaches; however, the unrestricted evolution of hormone-to-hormone reactions as well as sensor-to-hormone and hormone-to-actuator mappings seems to be a unique feature. In the following we compare our approach with neural network approaches, gene regulatory networks, and reaction-diffusion approaches.. 8.1 Artificial Neural Networks, NEAT, HyperNEAT, and GasNet It seems to be justified to call ANNs (in particular, continuous-time recurrent neural networks) the standard approach in the automatic synthesis of controllers, especially in the field of evolutionary robotics [14, 27, 31, 43, 44, 69] . However, the standard ANN approach fails for complex tasks, which failure is, as mentioned in the introduction, documented by the absence of complex tasks in the literature [42] . The difference of the AHHS approach from the ANN is mainly the concept of hormone diffusion and, as mentioned above, the X-to-hormone mappings, which can be interpreted as network functions with weights depending on the outputs of connected neurons.. In contrast to the standard ANN approach, NEAT [63] and HyperNEAT [64] have verifiably higher evolvability. NEAT differs in the sophisticated methods of evolutionary computation, while the underlying ANN is that of the standard approach. Figure 18 . Box-and-whisker plots of the fitnesses of the best evolved controllers for the performance comparison between the approach based on Fourier series and the best AHHS approach (AHHS-noise approach for multiple modules, and standard approach for one module).. AHHSs can be compared with ANNs in the following way: Assume (and for one example it is empirically shown in this article) that it is a good way to solve the problem of controller synthesis by means of function fitting for sensor-to-hormone, hormone-to-hormone, and hormone-to-actuator mappings as reported in this work. Obviously, ANNs are general enough to solve it, in principle, in the same way. However, it is improbable that separated function-fitting clusters to implement these mappings will emerge within the network, even if NEAT is used.. HyperNEAT differs from NEAT by its generation of connectivity patterns that are comparable to the spatiality introduced by the compartmentalization using AHHS. Otherwise there seem not to be many similarities between these two approaches.. Many more similarities are found in comparing the AHHS with GasNet [31] , because in addition to the standard ANN they both introduce diffusing hormones and hence represent hybrid approaches. The related artificial homeostatic systems [41, 70, 71] emphasize a homeostatic aspect similar to the internal dynamics of the AHHS.

O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O