Query: A 7.6 mW, 214-fs RMS jitter 10-GHz phase-locked loop for 40-Gb/s serial link transmitter based on two-stage ring oscillator in 65-nm CMOS

Relevant passages:
Jitter and Phase Noise in Ring Oscillators <=SEP=> A companion analysis of clock jitter and phase noise of single-ended and differential ring oscillators is presented. The impulse sensitivity functions are used to derive expressions for the jitter and phase noise of ring oscillators. The effect of the number of stages, power dissipation, frequency of oscillation, and shortchannel effects on the jitter and phase noise of ring oscillators is analyzed. Jitter and phase noise due to substrate and supply noise is discussed, and the effect of symmetry on the upconversion of 1/f noise is demonstrated. Several new design insights are given for low jitter/phase-noise design. Good agreement between theory and measurements is observed.

Strong Injection Locking in Low- $Q$ LC Oscillators: Modeling and Application in a Forwarded-Clock I/O Receiver <=SEP=> A general model for injection-locked LC oscillators (LC-ILOs) is presented that is valid for any tank quality factor and injection strength. Important properties of an ILO such as lock-range, phase shift, bandwidth and response to input jitter are described. An LC-ILO together with a half-rate data sampler is implemented as a forwarded-clock I/O receiver in 45-nm CMOS. A strongly-injected low-Q LC oscillator enables clock deskew across 1UI and rejects high-frequency clock jitter. The complete 27 Gb/s ILO-based data receiver has an overall power efficiency of 1.6 mW/Gb/s.

A study of injection locking and pulling in oscillators <=SEP=> Injection locking characteristics of oscillators are derived and a graphical analysis is presented that describes injection pulling in time and frequency domains. An identity obtained from phase and envelope equations is used to express the requisite oscillator nonlinearity and interpret phase noise reduction. The behavior of phase-locked oscillators under injection pulling is also formulated.

A study of phase noise in CMOS oscillators <=SEP=> This paper presents a study of phase noise in two inductorless CMOS oscillators. First-order analysis of a linear oscillatory system leads to a noise shaping function and a new definition of Q. A linear model of CMOS ring oscillators is used to calculate their phase noise, and three phase noise phenomena, namely, additive noise, high-frequency multiplicative noise, and low-frequency multiplicative noise, are identified and formulated. Based on the same concepts, a CMOS relaxation oscillator is also analyzed. Issues and techniques related to simulation of noise in the time domain are described, and two prototypes fabricated in a 0.5m CMOS technology are used to investigate the accuracy of the theoretical predictions. Compared with the measured results, the calculated phase noise values of a 2-GHz ring oscillator and a 900-MHz relaxation oscillator at 5 MHz offset have an error of approximately 4 dB.

A 27Gb/s Forwarded-Clock I/O Receiver Using an Injection-Locked LC-DCO in 45nm CMOS <=SEP=> This paper describes a method for both filtering and deskewing a link clock using a differential injection-locked LC-DCO and demonstrates a forwarded-clock data receiver using this technique operating at 27 Gb/s.

Acoustic Modeling for Google Home <=SEP=> This paper describes the technical and system building advances made to the Google Home multichannel speech recognition system, which was launched in November 2016. Technical advances include an adaptive dereverberation frontend, the use of neural network models that do multichannel processing jointly with acoustic modeling, and Grid-LSTMs to model frequency variations. On the system level, improvements include adapting the model using Google Home specific data. We present results on a variety of multichannel sets. The combination of technical and system advances result in a reduction of WER of 8-28% relative compared to the current production system.

Path Reducing Watershed for the GPU <=SEP=> The watershed transform is a popular image segmentation procedure from mathematical morphology used in many applications of computer vision. This paper proposes a novel parallel watershed procedure designed for GPU implementation. Our algorithm constructs paths of steepest descent and reduces these paths into direct pointers to catchment basin minima in logarithmic time, also crucially incorporating successful resolution of plateaux. Three implementation variants and their parameters are analysed through experiments on 2D and 3D images; a comparison against the state-of-the-art shows a runtime improvement of around 30%. For 3D images of 128 megavoxels execution times of approximately 1.5–2 seconds are achieved.

The discrete wavelet transform: wedding the a trous and Mallat algorithms <=SEP=> 

The Nature, Importance, and Difficulty of Machine Ethics <=SEP=> The question of whether machine ethics exists or might exist in the future is difficult to answer if we can't agree on what counts as machine ethics. Some might argue that machine ethics obviously exists because humans are machines and humans have ethics. Others could argue that machine ethics obviously doesn't exist because ethics is simply emotional expression and machines can't have emotions. A wide range of positions on machine ethics are possible, and a discussion of the issue could rapidly propel us into deep and unsettled philosophical issues. Perhaps, understandably, few in the scientific arena pursue the issue of machine ethics. As we expand computers' decision-making roles in practical matters, such as computers driving cars, ethical considerations are inevitable. Computer scientists and engineers must examine the possibilities for machine ethics because, knowingly or not, they've already engaged in some form of it. Before we can discuss possible implementations of machine ethics, however, we need to be clear about what we're asserting or denying

Learning-based approach for online lane change intention prediction <=SEP=> Predicting driver behavior is a key component for Advanced Driver Assistance Systems (ADAS). In this paper, a novel approach based on Support Vector Machine and Bayesian filtering is proposed for online lane change intention prediction. The approach uses the multiclass probabilistic outputs of the Support Vector Machine as an input to the Bayesian filter, and the output of the Bayesian filter is used for the final prediction of lane changes. A lane tracker integrated in a passenger vehicle is used for real-world data collection for the purpose of training and testing. Data from different drivers on different highways were used to evaluate the robustness of the approach. The results demonstrate that the proposed approach is able to predict driver intention to change lanes on average 1.3 seconds in advance, with a maximum prediction horizon of 3.29 seconds.

On-Line Economic Optimization of Energy Systems Using Weather Forecast Information <=SEP=> We establish an on-line optimization framework to exploit weather forecast information in the operation of energy systems. We argue that anticipating the weather conditions can lead to more proactive and cost-effective operations. The framework is based on the solution of a stochastic dynamic real-time optimization (D-RTO) problem incorporating forecasts generated from a state-of-the-art weather prediction model. The necessary uncertainty information is extracted from the weather model using an ensemble approach. The accuracy of the forecast trends and uncertainty bounds are validated using real meteorological data. We present a numerical simulation study in a building system to demonstrate the developments.

On the Role of Seed Lexicons in Learning Bilingual Word Embeddings <=SEP=> A shared bilingual word embedding space (SBWES) is an indispensable resource in a variety of cross-language NLP and IR tasks. A common approach to the SBWES induction is to learn a mapping function between monolingual semantic spaces, where the mapping critically relies on a seed word lexicon used in the learning process. In this work, we analyze the importance and properties of seed lexicons for the SBWES induction across different dimensions (i.e., lexicon source, lexicon size, translation method, translation pair reliability). On the basis of our analysis, we propose a simple but effective hybrid bilingual word embedding (BWE) model. This model (HYBWE) learns the mapping between two monolingual embedding spaces using only highly reliable symmetric translation pairs from a seed document-level embedding space. We perform bilingual lexicon learning (BLL) with 3 language pairs and show that by carefully selecting reliable translation pairs our new HYBWE model outperforms benchmarking BWE learning models, all of which use more expensive bilingual signals. Effectively, we demonstrate that a SBWES may be induced by leveraging only a very weak bilingual signal (document alignments) along with monolingual data.

Benefits of an active spine supported bounding locomotion with a small compliant quadruped robot <=SEP=> We studied the effect of the control of an active spine versus a fixed spine, on a quadruped robot running in bound gait. Active spine supported actuation led to faster locomotion, with less foot sliding on the ground, and a higher stability to go straight forward. However, we did no observe an improvement of cost of transport of the spine-actuated, faster robot system compared to the rigid spine.

Testing predictions from personality neuroscience. Brain structure and the big five. <=SEP=> We used a new theory of the biological basis of the Big Five personality traits to generate hypotheses about the association of each trait with the volume of different brain regions. Controlling for age, sex, and whole-brain volume, results from structural magnetic resonance imaging of 116 healthy adults supported our hypotheses for four of the five traits: Extraversion, Neuroticism, Agreeableness, and Conscientiousness. Extraversion covaried with volume of medial orbitofrontal cortex, a brain region involved in processing reward information. Neuroticism covaried with volume of brain regions associated with threat, punishment, and negative affect. Agreeableness covaried with volume in regions that process information about the intentions and mental states of other individuals. Conscientiousness covaried with volume in lateral prefrontal cortex, a region involved in planning and the voluntary control of behavior. These findings support our biologically based, explanatory model of the Big Five and demonstrate the potential of personality neuroscience (i.e., the systematic study of individual differences in personality using neuroscience methods) as a discipline.

Dynamic Motions by a Quadruped Musculoskeletal Robot with Angle-Dependent Moment Arms <=SEP=> Current robotics have failed to replicate the great dexterity of the animal’s motions. We believe that it is because most of the works have focused on the controller part rather than on its body structure. Indeed, the musculoskeletal system is essential to produce the animal’s motions and several researchers have attempted to exploit it [1][2][3]. However, most of them have used simple mono-articular muscles and torque control which cannot replicate the nonlinearity of the living tissues, muscles and nerves [4]. In contrast, bipedal robots with monoand bi-articular muscles for performing adaptive motions [5][6][7] have been produced. To further investigate the role of the musculoskeletal system for animal motions, we desgned a quadruped robot with monoand bi-articular muscles. We used artificial pneumatic muscles as actuators due to their high mass/power ratio and their high compliance. Although these actuators replicate several characteristics of biological muscles in terms of damping and elasticity, the length-tension relation differs from them [8][9]. In the result, the angle-torque relation of the robot with pneumatic muscles differed substantially from the one of animals. Thus, we tried to bring the angle-torque relation as close as the one of living tissues by devising the angle-moment arm relation.

Beyond traditional DTN routing: social networks for opportunistic communication <=SEP=> This article examines the evolution of routing protocols for intermittently connected ad hoc networks and discusses the trend toward socialbased routing protocols. A survey of current routing solutions is presented, where routing protocols for opportunistic networks are classified based on the network graph employed. The need to capture performance trade-offs from a multi-objective perspective is highlighted.

iHadoop: Asynchronous Iterations for MapReduce <=SEP=> MapReduce is a distributed programming framework designed to ease the development of scalable data-intensive applications for large clusters of commodity machines. Most machine learning and data mining applications involve iterative computations over large datasets, such as the Web hyperlink structures and social network graphs. Yet, the MapReduce model does not efficiently support this important class of applications. The architecture of MapReduce, most critically its dataflow techniques and task scheduling, is completely unaware of the nature of iterative applications, tasks are scheduled according to a policy that optimizes the execution for a single iteration which wastes bandwidth, I/O, and CPU cycles when compared with an optimal execution for a consecutive set of iterations. This work presents iHadoop, a modified MapReduce model, and an associated implementation, optimized for iterative computations. The iHadoop model schedules iterations asynchronously. It connects the output of one iteration to the next, allowing both to process their data concurrently. iHadoop's task scheduler exploits inter-iteration data locality by scheduling tasks that exhibit a producer/consumer relation on the same physical machine allowing a fast local data transfer. For those iterative applications that require satisfying certain criteria before termination, iHadoop runs the check concurrently during the execution of the subsequent iteration to further reduce the application's latency. This paper also describes our implementation of the iHadoop model, and evaluates its performance against Hadoop, the widely used open source implementation of MapReduce. Experiments using different data analysis applications over real-world and synthetic datasets show that iHadoop performs better than Hadoop for iterative algorithms, reducing execution time of iterative applications by 25% on average. Furthermore, integrating iHadoop with HaLoop, a variant Hadoop implementation that caches invariant data between iterations, reduces execution time by 38% on average.

Modeling and animating eye blinks <=SEP=> Facial animation often falls short in conveying the nuances present in the facial dynamics of humans. In this article, we investigate the subtleties of the spatial and temporal aspects of eye blinks. Conventional methods for eye blink animation generally employ temporally and spatially symmetric sequences; however, naturally occurring blinks in humans show a pronounced asymmetry on both dimensions. We present an analysis of naturally occurring blinks that was performed by tracking data from high-speed video using active appearance models. Based on this analysis, we generate a set of key-frame parameters that closely match naturally occurring blinks. We compare the perceived naturalness of blinks that are animated based on real data to those created using textbook animation curves. The eye blinks are animated on two characters, a photorealistic model and a cartoon model, to determine the influence of character style. We find that the animated blinks generated from the human data model with fully closing eyelids are consistently perceived as more natural than those created using the various types of blink dynamics proposed in animation textbooks.

Emotion Detection From Text Documents <=SEP=> Emotion Detection is one of the most emerging issues in human computer interaction. A sufficient amount of work has been done by researchers to detect emotions from facial and audio information whereas recognizing emotions from textual data is still a fresh and hot research area. This paper presented a knowledge based survey on emotion detection based on textual data and the methods used for this purpose. At the next step paper also proposed a new architecture for recognizing emotions from text document. Proposed architecture is composed of two main parts, emotion ontology and emotion detector algorithm. Proposed emotion detector system takes a text document and the emotion ontology as inputs and produces one of the six emotion classes (i.e. love, joy, anger, sadness, fear and surprise) as the output.

Honesty on the Streets : A Natural Field Experiment on Newspaper Purchasing <=SEP=> A publisher uses an honor system for selling a newspaper in the street. The customers are supposed to pay, but they can also pay less than the price or not pay at all. We conduct an experiment to study honesty in this market. The results show that appealing to honesty increases payments, whereas reminding the customers of the legal norm has no effect. Furthermore, appealing to honesty does not affect the behavior of the dishonest. These findings suggest that some people have internalized an honesty norm, whereas others have not, and that the willingness to pay to obey the norm differs among individuals. In a follow-up survey study we find that honesty is associated with family characteristics, self-esteem, social connectedness, trust in the legal system, and compliance with tax regulations.

Conformal Surface Parameterization for Texture Mapping <=SEP=> In this paper , we give an explicit methodfor mappingany simply connectedsurfaceonto the spherein a mannerwhich preservesangles. This techniquerelieson certainconformalmappingsfrom differentialgeometry. Our methodprovidesa new way to automaticallyassigntexturecoordinates to complex undulatingsurfaces. We demonstratea finite elementmethod thatcanbeusedto applyour mappingtechniqueto a triangulatedgeometric descriptionof a surface.

Semi-supervised deep learning by metric embedding <=SEP=> Deep networks are successfully used as classification models yielding state-ofthe-art results when trained on a large number of labeled samples. These models, however, are usually much less suited for semi-supervised problems because of their tendency to overfit easily when trained on small amounts of data. In this work we will explore a new training objective that is targeting a semi-supervised regime with only a small subset of labeled data. This criterion is based on a deep metric embedding over distance relations within the set of labeled samples, together with constraints over the embeddings of the unlabeled set. The final learned representations are discriminative in euclidean space, and hence can be used with subsequent nearest-neighbor classification using the labeled samples.

A hybrid edge-cloud architecture for reducing on-demand gaming latency <=SEP=> The cloud was originally designed to provide general-purpose computing using commodity hardware and its focus was on increasing resource consolidation as a means to lower cost. Hence, it was not particularly adapted to the requirements of multimedia applications that are highly latency sensitive and require specialized hardware, such as graphical processing units. Existing cloud infrastructure is dimensioned to serve general-purpose workloads and to meet end-user requirements by providing high throughput. In this paper, we investigate the effectiveness of using this general-purpose infrastructure for serving latency-sensitive multimedia applications. In particular, we examine on-demand gaming, also known as cloud gaming, which has the potential to change the video game industry. We demonstrate through a large-scale measurement study that the existing cloud infrastructure is unable to meet the strict latency requirements necessary for acceptable on-demand game play. Furthermore, we investigate the effectiveness of incorporating edge servers, which are servers located near end-users (e.g., CDN servers), to improve end-user coverage. Specifically, we examine an edge-server-only infrastructure and a hybrid infrastructure that consists of using edge servers in addition to the cloud. We find that a hybrid infrastructure significantly improves the number of end-users served. However, the number of satisfied end-users in a hybrid deployment largely depends on the various deployment parameters. Therefore, we evaluate various strategies that determine two such parameters, namely, the location of on-demand gaming servers and the games that are placed on these servers. We find that, through both a careful selection of on-demand gaming servers and the games to place on these servers, we significantly increase the number of end-users served over the basic random selection and placement strategies.

Explainable , Normative , and Justified Agency <=SEP=> In this paper, we pose a new challenge for AI researchers – to develop intelligent systems that support justified agency. We illustrate this ability with examples and relate it to two more basic topics that are receiving increased attention – agents that explain their decisions and ones that follow societal norms. In each case, we describe the target abilities, consider design alternatives, note some open questions, and review prior research. After this, we return to justified agency, offering a hypothesis about its relation to explanatory and normative behavior. We conclude by proposing testbeds and experiments to evaluate this empirical claim and encouraging other researchers to contribute to this crucial area. 1 Background and Motivation Autonomous artifacts, from self-driving cars to drones to household robots, are becoming more widely adopted, and this trend seems likely to accelerate in the future. The increasing reliance on these devices has raised concerns about our ability to understand their behavior and our capacity to ensure their safety. Before intelligent agents can gain widespred acceptance in society, they must be able to communicate their decision making to humans in ways that convince us they actually share our aims. This challenge involves two distinct but complementary issues. The first is the need for agents to explain the reasons they carried out particular courses of action in terms that we can understand. Langley et al. (2017) have referred to this as explainable agency. The second is the need for assurance that, when agents pursue explicit goals, they will also follow the many implicit rules of society. We will refer to this ability as normative agency. Both of these functions are necessary underpinnings of trustable autonomous systems, and two capabilities are closely intertwined. Consider a scenario in which a person drives a friend with a ruptured appendix to the hospital. The driver exceeds the speed limit, weaves in and out of traffic, runs through red lights, and even drives on a sidewalk, although he is still careful to avoid hitting other cars or losing control on turns. Afterwards, the driver explains that he took such drastic actions because he thought the passenger’s life was in danger, Copyright c © 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. so reaching the hospital in short order had higher priority than being polite to others along the way or obeying traffic laws. Humans can defend their behavior in this manner even when they violate serious societal norms, and we want intelligent agents of the future to exhibit the same ability to justify their decisions and actions on demand. We discuss this challenge in the sections that follow, arguing that such justified agency is an important topic that deserves substantial attention from AI researchers. We first examine the two related topics of explainable agency and normative agency. In each case, we describe the desired abilities, offer illustrative examples, and touch on relevant research. We also consider the space of agent designs, including some open issues that require additional work. After this, we turn to justified agency, arguing that it combines the first two abilities and proposing a hypothesis about what else is required to support it. We close by considering some testbeds and experiments that would let the research community evaluate progress in this critical arena. 2 Explainable Agency People can usually explain their reasons for making decisions and taking actions. When someone purchases a microwave oven, rearranges furniture in a room, or plans a vacation, he can state the choices considered, why he selected one alternative over the others, and even how his decision might have differed in other circumstances. Retrospective reports are seldom perfect, but they often offer important windows into the decision process. As intelligent agents become both more autonomous and more prevalent, it is essential that they support similar abilities. We will say that: • An intelligent system exhibits explainable agency if it can provide, on request, the reasons for its activities. Consider some examples from the realm of autonomous vehicles. If we ask a self-driving car why it followed a given route, it should be able to state that the path had few lights and stop signs while still being reasonably short. More importantly, when we ask the vehicle why it swerved into another car, it should explain that it was the only way to avoid hitting an unexpected jaywalker. Like humans, such agents should have reasons for their actions and they should be able to communicate them to others when asked. Tackling this problem requires that we make design decisions about representations and processes needed to support explainable agency, some of which we will borrow from Langley et al.’s (2017) analysis. One issue concerns what will count as legitimate explanations. Should plausible post hoc rationalizations be acceptable if an agent’s decisionmaking procedures are not interpretable or should we require genuine insights into why the agent took its actions? We argue that only the latter should be viewed as reasonable accounts, which implies that the agents should make decisions in ways that are transparent and easily communicated. There are many well-established methods in the AI arsenal that meet this criterion, but opaque policies induced from large training sets will not suffice. Another design choice involves whether to explain activities in terms of individual actions or higher-level structures. Research in machine learning has emphasized reactive control (e.g., Erwig et al., 2018), which supports the first alternative, whereas AI planning systems make choices about entire sequences of actions. We predict that humans will find plan-oriented explanations more familiar and easier to understand, and thus offer a natural approach to adopt. Even when a plan goes awry during execution, an agent can still give the reasons it decided to change course and how its new plan responded to the surprise. Some frameworks, like hierarchical task networks, also specify plans at multiple levels of abstraction, which would let a system offer more or less detailed explanations, down to individual actions if desired. Any intelligent agent must use information to make decisions about which activities to pursue, and a third issue concerns how to encode this content. One response, widely adopted in AI planning research, relies on symbolic goals, often stated as logical expressions. Another option, popular in game-playing systems, instead uses numeric evaluation functions that comprise sets of weighted features. Each approach has advantages for explainable agency: goals provide explicit end points for chains of actions, while functions show how such plans handle trade offs. Although these typically appear in isolation, they can also be combined. For instance, Langley et al. (2016) describe an agent architecture that associates functions with goals, using their weighted sum to guide planning. Such hybrid frameworks offer one promising approach to building explainable agents. Within this design space, we still need research on a number of open issues about explainable agency: These include extending intelligent systems to: • Generate explanatory content. When deciding on courses of action, an agent must consider different alternatives, evaluate them, and select one of the options to pursue. This should take place during generation of plans and during their execution, producing traces that can be used in later explanations of the agent’s activities. • Store generated content. As it makes these decisions, an agent must cache information about the choices that it considered and the reasons that it favored one over the others, in an episodic memory or similar repository. This requires not just retaining the content about decisions, but also indexing it in ways that support later access. • Retrieve stored content. After it has completed an activity, an agent must be able to retrieve decision traces that are relevant to different types of questions. This requires transforming the queries into cues and using them to access content in episodic memory about alternatives considered, their evaluations, and the final choices made. • Communicate retrieved content. Once it has retrieved episodic traces in response to a question, an agent must identify those aspects most relevant to the query, translate them into an understandable form, and share the answer. This should include no more detail than needed to convey the reasons for making the decision under examination. Research on analogical planning (e.g., Jones and Langley, 2005; Veloso et al., 1995) has addressed issues of storage, indexing, and retrieval, but not for the purpose of self report. Leake (1992) presented a computational theory of explanation, but it focused on accounts of other agents’ behaviors. The AI literature also includes other research relevant to this topic. Early explanation systems recorded inference chains and recounted them when asked to justify their conclusions (Swartout, Paris, and Moore, 1991), with some systems supporting higher-level accounts with meta-level rules (Clancey, 1983), but these did not deal with physical activities. More relevant work comes from Johnson (1994) and van Lent et al. (2004), who developed agents for military mssions that recorded their decisions, offered reasons on request, and anwered counterfactual queries. However, they dealt with knowledge-guided execution rather than agentgenerated plans and, despite linking actions to objectives, did not state why some activities were preferable to others. In more recent work, Briggs and Scheutz (2015) have reported an interactive robot that gives reasons why it cannot carry out some task, drawing on five explanation types, including lack of knowledge and physical ability. Th

Simulation in software engineering training <=SEP=> Simulation is frequently used for training in many application areas like aviation and economics, but not in software engineering. We present the SESAM project which focuses on software engineering education using simulation. In the SESAM project a simulator was developed. Using this simulator, a student can take the role of a software project manager. The simulated software project can be finished within a couple of hours because it is simulated in “quick-motion” mode.
In this paper, the background and goals of the SESAM project are presented. A new simulation model, the so called QA model, is introduced. The model behavior is demonstrated by investigating and comparing different strategies for software development. The results of experiments based on the QA model are reported. Finally, conclusions are drawn from the experiments and future work is outlined.

Return-Oriented Flush-Reload Side Channels on ARM and Their Implications for Android Devices <=SEP=> Cache side-channel attacks have been extensively studied on x86 architectures, but much less so on ARM processors. The technical challenges to conduct side-channel attacks on ARM, presumably, stem from the poorly documented ARM cache implementations, such as cache coherence protocols and cache flush operations, and also the lack of understanding of how different cache implementations will affect side-channel attacks. This paper presents a systematic exploration of vectors for flush-reload attacks on ARM processors. flush-reload attacks are among the most well-known cache side-channel attacks on x86. It has been shown in previous work that they are capable of exfiltrating sensitive information with high fidelity. We demonstrate in this work a novel construction of flush-reload side channels on last-level caches of ARM processors, which, particularly, exploits return-oriented programming techniques to reload instructions. We also demonstrate several attacks on Android OS (e.g., detecting hardware events and tracing software execution paths) to highlight the implications of such attacks for Android devices.

Learning Ontologies for the Semantic Web <=SEP=> The Semantic Web relies heavily on the formal ontologies that structure underlying data for the purpose of comprehensive and transportable machine understanding. Therefore, the success of the Semantic Web depends strongly on the proliferation of ontologies, which requires fast and easy engineering of ontologies and avoidance of a knowledge acquisition bottleneck. Ontology Learning greatly facilitates the construction of ontologies by the ontology engineer. The vision of ontology learning that we propose here includes a number of complementary disciplines that feed on di erent types of unstructured, semi-structured and fully structured data in order to support a semi-automatic, cooperative ontology engineering process. Our ontology learning framework proceeds through ontology import, extraction, pruning, re nement, and evaluation giving the ontology engineer a wealth of coordinated tools for ontology modeling. Besides of the general framework and architecture, we show in this paper some exemplary techniques in the ontology learning cycle that we have implemented in our ontology learning environment, TextTo-Onto, such as ontology learning from free text, from dictionaries, or from legacy ontologies, and refer to some others that need to complement the complete architecture, such as reverse engineering of ontologies from database schemata or learning from XML documents. 1. ONTOLOGIES FOR THE SEMANTIC WEB Conceptual structures that de ne an underlying ontology are germane to the idea of machine processable data on the Semantic Web. Ontologies are (meta)data schemas, providing a controlled vocabulary of concepts, each with an explicitly de ned and machine processable semantics. By de ning shared and common domain theories, ontologies help both people and machines to communicate concisely, supporting the exchange of semantics and not only syntax. Hence, the Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission by the authors. Semantic Web Workshop 2001 Hongkong, China Copyright by the authors. cheap and fast construction of domain-speci c ontologies is crucial for the success and the proliferation of the Semantic Web. Though ontology engineering tools have become mature over the last decade (cf. [9]), the manual acquisition of ontologies still remains a tedious, cumbersome task resulting easily in a knowledge acquisition bottleneck. Having developed our ontology engineering workbench, OntoEdit, we had to face exactly this issue, in particular we were given questions like Can you develop an ontology fast? (time) Is it diÆcult to build an ontology? (diÆculty) How do you know that you've got the ontology right? (con dence) In fact, these problems on time, diÆculty and con dence that we ended up with were similar to what knowledge engineers had dealt with over the last two decades when they elaborated on methodologies for knowledge acquisition or workbenches for de ning knowledge bases. A method that proved extremely bene cial for the knowledge acquisition task was the integration of knowledge acquisition with machine learning techniques [33]. The drawback of these approaches, e.g. the work described in [21], however, was their rather strong focus on structured knowledge or data bases, from which they induced their rules. In contrast, in the Web environment that we encounter when building Web ontologies, the structured knowledge or data base is rather the exception than the norm. Hence, intelligent means for an ontology engineer takes on a di erent meaning than the | very seminal | integration architectures for more conventional knowledge acquisition [7]. Our notion of Ontology Learning aims at the integration of a multitude of disciplines in order to facilitate the construction of ontologies, in particular machine learning. Because the fully automatic acquisition of knowledge by machines remains in the distant future, we consider the process of ontology learning as semi-automatic with human intervention, adopting the paradigm of balanced cooperative modeling [20] for the construction of ontologies for the Semantic Web. This objective in mind, we have built an architecture that combines knowledge acquisition with machine learning, feeding on the resources that we nowadays nd on the syntactic Web, viz. free text, semi-structured text, schema de nitions (DTDs), etc. Thereby, modules in our framework serve different steps in the engineering cycle, which here consists of the following ve steps (cf. Figure 1): First, existing ontologies are imported and reused by merging existing structures or de ning mapping rules between existing structures and the ontology to be established. For instance, [26] describe how ontological structures contained in Cyc are used in order to facilitate the construction of a domain-speci c ontology. Second, in the ontology extraction phase major parts of the target ontology are modeled with learning support feeding from web documents. Third, this rough outline of the target ontology needs to be pruned in order to better adjust the ontology to its prime purpose. Fourth, ontology re nement pro ts from the given domain ontology, but completes the ontology at a ne granularity (also in contrast to extraction). Fifth, the prime target application serves as a measure for validating the resulting ontology [31]. Finally, one may revolve again in this cycle, e.g. for including new domains into the constructed ontology or for maintaining and updating its scope. 2. THE ONTOLOGY LEARNING KILLER APPLICATION Though ontologies and their underlying data in the Semantic Web are envisioned to be reusable for a wide range of possibly unforeseen applications, a particular target application remains the touchstone for a given ontology. In our case, we have been dealing with ontology-based knowledge portals that structure Web content and that allow for structured provisioning and accessing of data [29, 30]. Knowledge portals are information intermediaries for knowledge accessing and sharing on the Web. The development of a knowledge portal consists of the tasks of structuring the knowledge, establishing means for providing new knowledge and accessing the knowledge contained in the portal. A considerable part of development and maintenance of the portal lies in integrating legacy information as well as in constructing and maintaining the ontology in vast, often unknown, terrain. For instance, a knowledge portal may focus on the electronics sector, integrating comparative shopping in conjunction with manuals, reports and opinions about current electronic products. The creation of the background ontology for this knowledge portal involves tremendous e orts for engineering the conceptual structures that underly existing warehouse databases, product catalogues, user manuals, test reports and newsgroup discussions. Correspondingly, ontology structures must be constructed from database schemata, a given product thesaurus (like BMEcat), XML documents and document type de nitions (DTDs), and free texts. Still worse, signi cant parts of these (meta-)data change extremely fast and, hence, require a regular update of the corresponding ontology parts. Thus, very di erent types of (meta-)data might be useful input for the construction of the ontology. However, in practice one needs comprehensive support in order to deal Just like Tim Berners-Lee did not forsee online auctions being a common business model of the Web of 2000. Comprehensive support with ontology learning need not necessarily imply the top-notch learning algorithms, but may rely more heavily on appropriate tool support and methodology. with this wealth. Hence, there comes the need for a range of di erent techniques: Structured data and meta data require reverse engineering approaches, free text may contribute to ontology learning directly or through information extraction approaches. Semi-structured data may nally require and pro t from both. In the following we elaborate on our ontology learning framework. Thereby we approach di erent techniques for di erent types of data, showing parts of our architecture, its current status, and parts that may complement our current Text-To-Onto environment. A general overview of ontology learning techniques as well as corresponding references may be found in Section 9. 3. AN ARCHITECTURE FOR ONTOLOGY LEARNING Given the task of constructing and maintaining an ontology for a Semantic Web application, e.g. for an ontologybased knowledge portal that we have been dealing with (cf. [29]), we have produced a wish list of what kind of support we would fancy. 3.1 Ontology Engineering WorkbenchOntoEdit As core to our approach we have built a graphical user interface to support the ontology engineering process manually performed by the ontology engineer. Here, we o er sophisticated graphical means for manual modeling and re ning the nal ontology. Di erent views are o ered to the user targeting the epistemological level rather than a particular representation language. However, the ontological structures built there may be exported to standard Semantic Web representation languages, such as OIL and DAML-ONT, as well as our own F-Logic based extensions of RDF(S). In addition, executable representations for constraint checking and application debugging can be generated and then accessed via SilRi, our F-Logic inference engine, that is directly connected with OntoEdit. The sophisticated ontology engineering tools we knew, e.g. the Prot eg e modeling environment for knowledge-based systems [9], would o er capabilities roughly comparable to OntoEdit. However, given the task of constructing a knowledge portal, we f

Combination features and models for human detection <=SEP=> This paper presents effective combination models with certain combination features for human detection. In the past several years, many existing features/models have achieved impressive progress, but their performances are still limited by the biases rooted in their self-structures, that is, a particular kind of feature/model may work well for some types of human bodies, but not for all the types. To tackle this difficult problem, we combine certain complementary features/models together with effective organization/fusion methods. Specifically, the HOG features, color features and bar-shape features are combined together with a cell-based histogram structure to form the so-called HOG-III features. Moreover, the detections from different models are fused together with the new proposed weighted-NMS algorithm, which enhances the probable “true” activations as well as suppresses the overlapped detections. The experiments on PASCAL VOC datasets demonstrate that, both the HOG-III features and the weighted-NMS fusion algorithm are effective (obvious improvement for detection performance) and efficient (relatively less computation cost): When applied to human detection task with the Grammar model and Poselet model, they can boost the detection performance significantly; Also, when extended to detection of the whole VOC 20 object categories with the deformable part-based model and deep CNN-based model, they still show competitive improvements.

Movie recommendation system using clustering and pattern recognition network <=SEP=> As the world is going global, people have a lot to choose from when it comes to movies. There are different genres, cultures and languages to choose from in the world of movies. This arises the issue of recommending movies to users in automated systems. So far, a decent number of works has been done in this field. But there is always room for renovation. This paper proposed a machine learning approach to recommend movies to users using K-means clustering algorithm to separate similar users and creating a neural network for each cluster.

Multimedia, hypermedia, and hypertext: Motivation considered and reconsidered <=SEP=> Computer-based instruction (CBI) is becoming increasingly popular in the classroom, particularly because the latest technological advancements allow for visually rich and interactive environments. While the inherent nature of CBIs is often thought to engage learners, research examining the role of motivation in learning with these environments has resulted in mixed findings. These findings are further complicated by unique design characteristics of distinct CBIs. This literature review synthesizes research that has examined the role of theoretically-grounded constructs of motivation in the context of three popular CBIs, multimedia, hypermedia, and hypertext. Specifically, this literature review considered empirical studies that examined the effect of these CBIs on motivation, in addition to the effect of motivation on learning outcomes and the learning process within the context of these environments. The literature review concludes with a theoretical consideration of previous research and a discussion of a framework for future directions. 2010 Published by Elsevier Ltd.