The Impact of Power Allocation on Cooperative Non-orthogonal Multiple Access Networks With SWIPT

In this paper, a cooperative non-orthogonal multiple access (NOMA) network is considered, where a source communicates with two users through an energy harvesting relay. The impact of two types of NOMA power allocation policies, namely NOMA with fixed power allocation (F-NOMA) and cognitive radio inspired NOMA (CR-NOMA), on the considered cooperative simultaneous wireless information and power transfer (SWIPT) system is investigated. In particular, closed-form expressions for the outage probability and their high SNR approximations are derived to characterize the performance of SWIPT-F-NOMA and SWIPT-CR-NOMA. These developed analytical results demonstrate that the two power allocation policies realize different tradeoffs between the reception reliability, user fairness and system complexity. Compared with the conventional SWIPT relaying networks with orthogonal multiple access (OMA), the proposed NOMA schemes can effectively reduce the outage probability, although all of them realize the same diversity gain.


Boosting automatic event extraction from the literature using domain adaptation and coreference resolution

MOTIVATION
In recent years, several biomedical event extraction (EE) systems have been developed. However, the nature of the annotated training corpora, as well as the training process itself, can limit the performance levels of the trained EE systems. In particular, most event-annotated corpora do not deal adequately with coreference. This impacts on the trained systems' ability to recognize biomedical entities, thus affecting their performance in extracting events accurately. Additionally, the fact that most EE systems are trained on a single annotated corpus further restricts their coverage.

RESULTS
We have enhanced our existing EE system, EventMine, in two ways. First, we developed a new coreference resolution (CR) system and integrated it with EventMine. The standalone performance of our CR system in resolving anaphoric references to proteins is considerably higher than the best ranked system in the COREF subtask of the BioNLP'11 Shared Task. Secondly, the improved EventMine incorporates domain adaptation (DA) methods, which extend EE coverage by allowing several different annotated corpora to be used during training. Combined with a novel set of methods to increase the generality and efficiency of EventMine, the integration of both CR and DA have resulted in significant improvements in EE, ranging between 0.5% and 3.4% F-Score. The enhanced EventMine outperforms the highest ranked systems from the BioNLP'09 shared task, and from the GENIA and Infectious Diseases subtasks of the BioNLP'11 shared task.

AVAILABILITY
The improved version of EventMine, incorporating the CR system and DA methods, is available at: http://www.nactem.ac.uk/EventMine/.


Dynamic Resource Allocation in Ad-Hoc Mobile Cloud Computing

Mobile Cloud Computing (MCC) has draw extensive research attentions due to the increasingly demand on the energy consumption and execution time constraints on Mobile Devices (MDs). MCC has been well investigated in the scenarios when workload is offloaded to a remote cloud or a cloudlet. However, in those scenarios, infrastructure is required to provide connections between MDs and the cloud#x002F;cloudlet. To facilitate MCC for environment when infrastructure is not available, Ad-hoc MCC which allows MDs to share and process workload coordinately is discussed [1]. In this study, the detailed resource allocation problem in Ad-hoc MCC, including how to assign tasks to MDs, how tasks are executed on each MD, and how to arrange task and task result return transmissions so that interference among different MDs can be avoided are carefully studied. Since the problem is NP-hard, a heuristic algorithm is then introduced. As shown in the evaluation section, when no infrastructure is available, compared with the case when an application is executed solely on a single MD, the proposed algorithm can reduce an application's response time significantly.


Bayesian face recognition

We propose a new technique for direct visual matching of images for the purposes of face recognition and image retrieval, using a probabilistic measure of similarity, based primarily on a Bayesian (MAP) analysis of image differences. The performance advantage of this probabilistic matching technique over standard Euclidean nearest-neighbor eigenface matching was demonstrated using results from DARPA’s 1996 “FERET” face recognition competition, in which this Bayesian matching algorithm was found to be the top performer. In addition, we derive a simple method of replacing costly computation of nonlinear (on-line) Bayesian similarity measures by inexpensive linear (off-line) subspace projections and simple Euclidean norms, thus resulting in a significant computational speed-up for implementation with very large databases. Appears in: Pattern Recognition, Vol. 33, No. 11, pps. 1771-1782, November, 2000. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c Mitsubishi Electric Research Laboratories, Inc., 2002 201 Broadway, Cambridge, Massachusetts 02139 Appears in: Pattern Recognition, Vol. 33, No. 11, pps. 1771-1782, November, 2000.


Generative adversarial networks and adversarial methods in biomedical image analysis

Generative adversarial networks (GANs) and other adversarial methods are based on a game-theoretical perspective on joint optimization of two neural networks as players in a game. Adversarial techniques have been extensively used to synthesize and analyze biomedical images. We provide an introduction to GANs and adversarial methods, with an overview of biomedical image analysis tasks that have benefited from such methods. We conclude with a discussion of strengths and limitations of adversarial methods in biomedical image analysis, and propose potential future research directions.


Co-Clustering Structural Temporal Data with Applications to Semiconductor Manufacturing

Recent years have witnessed data explosion in semiconductor manufacturing due to advances in instrumentation and storage techniques. The large amount of data associated with process variables monitored over time form a rich reservoir of information, which can be used for a variety of purposes, such as anomaly detection, quality control, and fault diagnostics. In particular, following the same recipe for a certain Integrated Circuit device, multiple tools and chambers can be deployed for the production of this device, during which multiple time series can be collected, such as temperature, impedance, gas flow, electric bias, etc. These time series naturally fit into a two-dimensional array (matrix), i.e., each element in this array corresponds to a time series for one process variable from one chamber. To leverage the rich structural information in such temporal data, in this article, we propose a novel framework named C-Struts to simultaneously cluster on the two dimensions of this array. In this framework, we interpret the structural information as a set of constraints on the cluster membership, introduce an auxiliary probability distribution accordingly, and design an iterative algorithm to assign each time series to a certain cluster on each dimension. Furthermore, we establish the equivalence between C-Struts and a generic optimization problem, which is able to accommodate various distance functions. Extensive experiments on synthetic, benchmark, as well as manufacturing datasets demonstrate the effectiveness of the proposed method.


A Narrative Literature Review and E-Commerce Website Research

In this study, a narrative literature review regarding culture and e-commerce website design has been introduced. Cultural aspect and e-commerce website design will play a significant role for successful global e-commerce sites in the future. Future success of businesses will rely on e-commerce. To compete in the global e-commerce marketplace, local businesses need to focus on designing culturally friendly e-commerce websites. To the best of my knowledge, there has been insignificant research conducted on correlations between culture and e-commerce website design. The research shows that there are correlations between e-commerce, culture, and website design. The result of the study indicates that cultural aspects influence e-commerce website design. This study aims to deliver a reference source for information systems and information technology researchers interested in culture and e-commerce website design, and will show lessfocused research areas in addition to future directions.


C R ] 4 J ul 2 01 7 A general framework for Bitcoin analytics

Modern cryptocurrencies exploit decentralised ledgers — the so-called blockchains — to record a public and unalterable history of transactions. These ledgers represent a rich, and increasingly growing, source of information, in part of difficult interpretation and undisclosed meaning. Many analytics, mostly based on ad-hoc engineered solutions, are being developed to discover relevant knowledge from these data. We introduce a framework for the development of custom analytics on Bitcoin — the most preeminent cryptocurrency — which also allows to integrate data within the blockchain with data retrieved form external sources. We illustrate the flexibility and effectiveness of our analytics framework by means of paradigmatic use cases.


ON STAGNATION OF THE DIFFERENTIAL EVOLUTION ALGORITHM

This article discusses the stagnation of an evolutionary optimization algorithm called Differential Evolution. Stagnation problem refers to a situation in which the optimum seeking process stagnates before finding a globally optimal solution. Typically, stagnation occurs virtually without any obvious reason. The stagnation differs from the premature convergence so that the population remains diverse and unconverged after stagnation, but the optimization process does not progress anymore. The reasons for this problem have remained unknown so far. This article uncovers this problem describing the basic nature of stagnation phenomena, a mechanism behind it and some reasons for stagnation. Advices for reducing the risk of stagnation are concluded on basis of the new findings.


Agent-Agnostic Human-in-the-Loop Reinforcement Learning

Providing Reinforcement Learning agents with expert advice can dramatically improve various aspects of learning. Prior work has developed teaching protocols that enable agents to learn efficiently in complex environments; many of these methods tailor the teacher’s guidance to agents with a particular representation or underlying learning scheme, offering effective but specialized teaching procedures. In this work, we explore protocol programs, an agent-agnostic schema for Human-in-the-Loop Reinforcement Learning. Our goal is to incorporate the beneficial properties of a human teacher into Reinforcement Learning without making strong assumptions about the inner workings of the agent. We show how to represent existing approaches such as action pruning, reward shaping, and training in simulation as special cases of our schema and conduct preliminary experiments on simple domains.


Analyzing the effects of disk-pointer corruption

The long-term availability of data stored in a file system depends on how well it safeguards on-disk pointers used to access the data. Ideally, a system would correct all pointer errors. In this paper, we examine how well corruption-handling techniques work in reality. We develop a new technique called type-aware pointer corruption to systematically explore how a file system reacts to corrupt pointers. This approach reduces the exploration space for corruption experiments and works without source code. We use type-aware pointer corruption to examine Windows NTFS and Linux ext3. We find that they rely on type and sanity checks to detect corruption, and NTFS recovers using replication in some instances. However, NTFS and ext3 do not recover from most corruptions, including many scenarios for which they possess sufficient redundant information, leading to further corruption, crashes, and unmountable file systems. We use our study to identify important lessons for handling corrupt pointers.


The trade-off between taxi time and fuel consumption in airport ground movement

Environmental issues play an important role across many sectors. This is particularly the case in the air transportation industry. One area which has remained relatively unexplored in this context is the ground movement problem for aircraft on the airport’s surface. Aircraft have to be routed from a gate to a runway and vice versa and a key area of study is whether fuel burn and environmental impact improvements will best result from purely minimising the taxi times or whether it is also important to avoid multiple acceleration phases. This paper presents a newly developed multiobjective approach for analysing the trade-off between taxi time and fuel consumption during taxiing. The approach consists of a combination of a graph-based routing algorithm and a population adaptive immune algorithm to discover different speed profiles of aircraft. Analysis with data from a European hub airport has highlighted the impressive performance of the new approach. Furthermore, it is shown that the S. Ravizza ( ) · J.A.D. Atkin School of Computer Science, University of Nottingham, Jubilee Campus, Nottingham NG8 1BB, UK e-mail: smr@cs.nott.ac.uk J.A.D. Atkin e-mail: jaa@cs.nott.ac.uk J. Chen · P. Stewart School of Engineering, University of Lincoln, Brayford Pool, Lincoln LN6 7TS, UK J. Chen e-mail: juchen@lincoln.ac.uk P. Stewart e-mail: pstewart@lincoln.ac.uk E.K. Burke Department of Computing and Mathematics, University of Stirling, Cottrell Building, Stirling FK9 4LA, UK e-mail: e.k.burke@stir.ac.uk 26 S. Ravizza et al. trade-off between taxi time and fuel consumption is very sensitive to the fuel-related objective function which is used.


Competitive intelligence process and tools for intelligence analysis

Purpose – The purpose of this survey research is twofold. First, to study and report the process that is commonly used to create and maintain a competitive intelligence (CI) program in organizations. And second, to provide an analysis of several emergent text mining, web mining and visualization-based CI tools, which are specific to collection and analysis of intelligence. Design/methodology/approach – A range of recently published research literature on CI processes, applications, tools and technologies to collect and analyze competitive information within organizations is reviewed to explore their current state, issues and challenges learned from their practice. Findings – The paper provides executive decision makers and strategic managers a better understanding of what methods are available and appropriate to the decisions they must make and the steps involved in CI undertaking. Originality/value – The findings of this research provide the managers of CI programs a context for understanding which tools and techniques are better suited to their specific types of problems; and help them develop and evaluate a usable set of tools and best practices to apply to their industry.


A clustering algorithm based on graph connectivity


A finger on the pulse: temporal rhythms and information seeking in medical work

Most cooperative work takes place in information-rich environments. However, studies of "information work" tend to focus on the decontextualized access and retrieval problems faced by individual information seekers. Our work is directed towards understanding how information management is seamlessly integrated into the course of everyday activities. Drawing on an ethnographic study of medical work, we explore the relationship between information and temporal coordination and discuss the role of temporal patterns or "rhythms" in providing individuals with the means to coordinate information and work.


Comprehensive and Efficient Protection of Kernel Control Data

Protecting kernel control data (e.g., function pointers and return addresses) has been a serious issue plaguing rootkit defenders. In particular, rootkit authors only need to compromise one piece of control data to launch their attacks, while defenders need to protect thousands of such values widely scattered across kernel memory space. Worse, some of this data (e.g., return addresses) is volatile and can be dynamically generated at run time. Existing solutions, however, offer either incomplete protection or excessive performance overhead. To overcome these limitations, we present indexed hooks, a scheme that greatly facilitates kernel control-flow enforcement by thoroughly transforming and restricting kernel control data to take only legal jump targets (allowed by the kernel's control-flow graph). By doing so, we can severely limit the attackers' possibility of exploiting them as an infection vector to launch rootkit attacks. To validate our approach, we have developed a compiler-based prototype that implements this technique in the FreeBSD 8.0 kernel, transforming 49 025 control transfer instructions (~7.25% of the code base) to use indexed hooks instead of direct pointers. Our evaluation results indicate that our approach is generic, effective, and can be implemented on commodity hardware with a low performance overhead (<;5% based on benchmarks).


Fine-grained accelerators for sparse machine learning workloads

Text analytics applications using machine learning techniques have grown in importance with ever increasing amount of data being generated from web-scale applications, social media and digital repositories. Apart from being large in size, these generated data are often unstructured and are heavily sparse in nature. The performance of these applications on current systems is hampered by hard to predict branches and low compute-per-byte ratio. This paper proposes a set of fine-grained accelerators that improve the performance and energy-envelope of these applications by an order of magnitude.


Filter technologies for wireless base stations

Phenomenal growth in the telecommunication industry in recent years has brought significant advances in filter technology as new communication systems emerged, demanding more stringent filter characteristics. In particular, the growth of the wireless communication industry has spurred tremendous activity in the area of microwave filter miniaturization and has been responsible for many advances made in this field. The filters that are currently being used in wireless base stations can be divided into two main categories: coaxial cavity resonator filters and dielectric resonator (DR) filters. While coaxial cavity filters have limited quality factor (Q) values, they offer the lowest cost design and are still being widely employed, particularly in wide bandwidth applications. With increased demands for high performance wireless systems, dielectric resonator filters are emerging as the baseline design for wireless base stations. Over the next five years, dielectric resonator filters are expected to have a significant share of the overall wireless base station filter market. High-temperature superconductor (HTS) filters are also expected to have a share of this market, particularly for systems, which have very stringent requirements for out-of-band interference. In this article, we begin by reviewing the main filter requirements, highlighting the technologies that are being currently employed. Emerging filter technologies that have the potential to replace the existing technologies are then described.


Exploring the physical layer frontiers of cellular uplink

Communication systems in practice are subject to many technical/technological constraints and restrictions. Multiple input, multiple output (MIMO) processing in current wireless communications, as an example, mostly employs codebook-based pre-coding to save computational complexity at the transmitters and receivers. In such cases, closed form expressions for capacity or bit-error probability are often unattainable; effects of realistic signal processing algorithms on the performance of practical communication systems rather have to be studied in simulation environments. The Vienna LTE-A Uplink Simulator is a 3GPP LTE-A standard compliant MATLAB-based link level simulator that is publicly available under an academic use license, facilitating reproducible evaluations of signal processing algorithms and transceiver designs in wireless communications. This paper reviews research results that have been obtained by means of the Vienna LTE-A Uplink Simulator, highlights the effects of single-carrier frequency-division multiplexing (as the distinguishing feature to LTE-A downlink), extends known link adaptation concepts to uplink transmission, shows the implications of the uplink pilot pattern for gathering channel state information at the receiver and completes with possible future research directions.


A Semantic Similarity Approach to Paraphrase Detection

This paper presents a novel approach to the problem of paraphrase identification. Although paraphrases often make use of synonymous or near synonymous terms, many previous approaches have either ignored or made limited use of information about similarities between word meanings. We present an algorithm for paraphrase identification which makes extensive use of word similarity information derived from WordNet (Fellbaum, 1998). The approach is evaluated using the Microsoft Research Paraphrase Corpus (Dolan et al., 2004), a standard resource for this task, and found to outperform previously published methods.


A keyword extraction method from twitter messages represented as graphs

Twitter is a microblog service that generates a huge amount of textual content daily. All this content needs to be explored by means of text mining, natural language processing, information retrieval, and other techniques. In this context, automatic keyword extraction is a task of great usefulness. A fundamental step in text mining techniques consists of building a model for text representation. The model known as vector space model, VSM, is the most well-known and used among these techniques. However, some difficulties and limitations of VSM, such as scalability and sparsity, motivate the proposal of alternative approaches. This paper proposes a keyword extraction method for tweet collections that represents texts as graphs and applies centrality measures for finding the relevant vertices (keywords). To assess the performance of the proposed approach, three different sets of experiments are performed. The first experiment applies TKG to a text from the Time magazine and compares its performance with that of the literature. The second set of experiments takes tweets from three different TV shows, applies TKG and compares it with TFIDF and KEA, having human classifications as benchmarks. Finally, these three algorithms are applied to tweets sets of increasing size and their computational running time is measured and compared. Altogether, these experiments provide a general overview of how TKG can be used in practice, its performance when compared with other standard approaches, and how it scales to larger data instances. The results show that TKG is a novel and robust proposal to extract keywords from texts, particularly from short messages, such


Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition

Face recognition has witnessed great progress in recent years, mainly attributed to the high-capacity model designed and the abundant labeled data collected. However, it becomes more and more prohibitive to scale up the current million-level identity annotations. In this work, we show that unlabeled face data can be as effective as the labeled ones. Here, we consider a setting closely mimicking the real-world scenario, where the unlabeled data are collected from unconstrained environments and their identities are exclusive from the labeled ones. Our main insight is that although the class information is not available, we can still faithfully approximate these semantic relationships by constructing a relational graph in a bottom-up manner. We propose Consensus-Driven Propagation (CDP) to tackle this challenging problem with two modules, the “committee” and the “mediator”, which select positive face pairs robustly by carefully aggregating multi-view information. Extensive experiments validate the effectiveness of both modules to discard outliers and mine hard positives. With CDP, we achieve a compelling accuracy of 78.18% on MegaFace identification challenge by using only 9% of the labels, comparing to 61.78% when no unlabeled data are used and 78.52% when all labels are employed.


Sleep Staging by Modeling Sleep Stage Transitions using Deep CRF

Sleep plays a vital role in human health, both mental and physical. Sleep disorders like sleep apnea are increasing in prevalence, with the rapid increase in factors like obesity. Sleep apnea is most commonly treated with Continuous Positive Air Pressure (CPAP) therapy, which maintains the appropriate pressure to ensure continuous airflow. It is widely accepted that in addition to preventing air passage collapse, increase in deep and REM sleep stages would be good metrics for how well the CPAP therapy is working in improving sleep health. Presently, however, there is no mechanism to easily detect a patient’s sleep stages from CPAP flow data alone. We propose, for the first time, an automated sleep staging model based only on the flow signal. Recently deep neural networks have shown high accuracy on sleep staging by eliminating handcrafted features. However, these methods focus exclusively on extracting informative features from the input signal, without paying much attention to the dynamics of sleep stages in the output sequence. We propose an end-to-end framework that uses a combination of deep convolution and recurrent neural networks to extract high-level features from raw flow signal and then uses a structured output layer based on a conditional random field to model the temporal transition structure of the sleep stages. We improve upon the previous methods by 10% using our model, that can be augmented to the previous sleep staging deep learning methods. We also show that our method can be used to accurately track sleep metrics like sleep efficiency calculated from sleep stages that can be deployed for monitoring the response of CPAP therapy on sleep apnea patients. Apart from the technical contributions, we expect this study to motivate new research questions in sleep science, especially towards the understanding of sleep architecture trajectory among patients under CPAP therapy.


Optimisation of quasi-resonant induction cookers

In this paper, the optimisation possibilities of a quasi-resonant induction cooker are investigated. Simulations show that main losses of the induction cooker occur in the heating coil and the RC-IGBT (power switch). The performance of the coil can be improved mainly by minimising the coil resistance. The IGBT-optimisation is based on the reduction of tail current in soft switching mode. The tail current is reduced by using lifetime engineering and reduction of the IGBT-thickness. The losses of the optimised IGBT are about 30% lower. As a result, the cooling system of the IGBT can be smaller and cheaper. Together with some optimisations of the circuit the overall losses of the system could be improved by almost 10% or 1 percentage point.


Penile length, digit length, and anogenital distance according to birth weight in newborn male infants

PURPOSE
Anogential distance (AGD) and the 2:4 digit length ratio appear to provide a reliable guide to fetal androgen exposure. We intended to investigate the current status of penile size and the relationship between penile length and AGD or digit length according to birth weight in Korean newborn infants.

MATERIALS AND METHODS
Between May 2013 and February 2014, among a total of 78 newborn male infants, 55 infants were prospectively included in this study. Newborn male infants with a gestational age of 38 to 42 weeks and birth weight>2.5 kg were assigned to the NW group (n=24) and those with a gestational age<38 weeks and birth weight<2.5 kg were assigned to the LW group (n=31). Penile size and other variables were compared between the two groups.

RESULTS
Stretched penile length of the NW group was 3.3 ± 0.2 cm, which did not differ significantly from that reported in 1987. All parameters including height, weight, penile length, testicular size, AGD, and digit length were significantly lower in the LW group than in the NW group. However, there were no significant differences in AGD ratio or 2:4 digit length ratio between the two groups.

CONCLUSIONS
The penile length of newborn infants has not changed over the last quarter century in Korea. With normal penile appearance, the AGD ratio and 2:4 digit length ratio are consistent irrespective of birth weight, whereas AGD, digit length, and penile length are significantly smaller in newborns with low birth weight.


Ultra-wide bandwidth time-hopping spread-spectrum impulse radio for wireless multiple-access communications

Attractive features of time-hopping spread-spectrum multiple-access systems employing impulse signal technology are outlined, and emerging design issues are described. Performance of such communications systems in terms of achievable transmission rate and multiple-access capability are estimated for both analog and digital data modulation formats under ideal multiple-access channel conditions.


MODELING AND CONTROL OF BALL AND BEAM SYSTEM USING MODEL BASED AND NON-MODEL BASED CONTROL APPROACHES

The ball and beam system is a laboratory equipment with high nonlinearity in its dynamics. The aims of this research are to model the ball and beam system considering nonlinear factors and coupling effect and to design controllers to control the ball position. The LQR is designed considering two Degrees-of-Freedom and


Cloud Computing - The Business Perspective

If cloud computing (CC) is to achieve its potential, there needs to be a clear understanding of the various issues involved, both from the perspectives of the providers and the consumers of the technology. There is an equally urgent need for understanding the business-related issues surrounding CC. We interviewed several industry executives who are either involved as developers or are evaluating CC as an enterprise user. We identify the strengths, weaknesses, opportunities and threats for the industry. We also identify the various issues that will affect the different stakeholders of CC. We issue a set of recommendations for the practitioners who will provide and manage this technology. For IS researchers, we outline the different areas of research that need attention so that we are in a position to advise the industry in the years to come. Finally, we outline some of the key issues facing governmental agencies who will be involved in the regulation of cloud computing.


Cloud computing is getting popular and IT giants such as Google, Amazon, Microsoft, IBM have started their cloud computing infrastructure. However, current cloud implementations are often isolated from other cloud implementations. This paper gives an overview survey of current cloud computing architectures, discusses issues that current cloud computing implementations have and proposes a Service-Oriented Cloud Computing Architecture (SOCCA) so that clouds can interoperate with each other. Furthermore, the SOCCA also proposes high level designs to better support multi-tenancy feature of cloud computing.


The Case for Energy-Proportional Computing

Energy-proportional designs would enable large energy savings in servers, potentially doubling their efficiency in real-life use. Achieving energy proportionality will require significant improvements in the energy usage profile of every system component, particularly the memory and disk subsystems.


The Chinese Wall Security Policy

Everyone who has seen the movie Wall Street wi~l have seen a commercial security policy in action. The recent work of Clark and Wilson and the WIPCIS initiative (the Workshop on Integrity Policy for Computer Information Systems) has drawn attention to the existence of a wide range of commercial security policies which are both significantly different from each other and quite alien to current “military” thin king as implemented in products for the security mari<et place. This paper presents a basic mathematical theory which implements one such policy, the Chinese Wall, and shows that it cannot be correctly represented by a 13e11-LaPadula model. The Chinese Wall policy combines commercial discretion with legally enforceable mandatory con trols. It is required in the operation of many financial services organizations and is, therefore, perhaps as significant t to the financial world as Bel!-LaPadula ’s policies are to the military. MIRQK?LK7T,K2N Until recently, military security policy thinking has dominated the direction of computer security research both in the US and the UK. Clark and Wilson’s seminal paper [1] has, however, drawn attention to the fact that commercial security needs are just as important as those of the de fence community and, through the WIPCIS initiative [2], that the problems of the commercial community are at least as diverse and relevant to the computer scientist. There are many well defined commercial security policies covering all aspects of Clark and Wilson’s model [3]. One of these, the Chinese Wall security policy is perhaps as significant to some parts of the commercial world as Bell and LaPadula’s policies [4, 5] are to the military. It can be most easily visualized as the code of practice that must be followed by a market analyst working for a financial institution providing corporate business services. Such an analyst must uphold the confidentiality of information provided to him by his firm’s clients; this means he cannot advise corporations where he has insider knowledge of the plans, status or standing of a competitor. However, the analyst is free to advise corporations which are not in competition with each other, and also to draw on general market information. Many other instances of Chinese Walls are found in the financial world. Unlike Bell and LaPadula, access to data is not constrained by attributes of the data in question but by what data the subject already holds access rights to. Essentially, datasets are grouped into “conf I ict of interest classes” and by mandatory ruling all subject= are allowed access to at most one dataset belonging to each such conflict of inte rest class; the actual choice of dataset is totally unrestrained provided that this mandatory rule is satisfied. We assert that such policies cannot be correctly model led by Bell -LaPadula. It should be noted that in the United Kingdom the Chinese Wall requirements of the UK Stock Exchange [6] have the authority of law [7] and thus represent a mandatory security policy whether implemented by manual or automated means. Furthermore, correct implementation of this policy is important to English Financial Institutions since it provides a legitimate defence against certain penal classes of offence under their Iak[.


Investigating learner attitudes toward e-books as learning tools: based on the activity theory approach

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Sport motivation scale-6 (SMS-6): A revised six-factor sport motivation scale

Background and Purpose: The sport motivation scale (SMS; [Pelletier, L. G., Fortier, M. S., Vallerand, R. J., Tuson, K. M., Brière, N. M., & Blais, M. R. (1995). Toward a new measure of intrinsic motivation, extrinsic motivation, and amotivation in sports: The sport motivation scale (SMS). Journal of Sport and Exercise Psychology, 17, 35–53]) was developed to measure an athlete’s motivation toward sport participation. However, the SMS does not measure the most autonomous form of extrinsic motivation, integrated regulation, which is inconsistent with self-determination theory (SDT; [Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behaviour. New York: Plenum Press]) upon which the instrument is based. Moreover, several studies (e.g., [Martens, M. P., & Webber, S. N. (2002). Psychometric properties of the sport motivation scale: An evaluation with college varsity athletes from the U.S. Journal of Sport and Exercise Psychology, 24, 254–270]) have questioned the factorial validity of the SMS. Hence, the purpose of this study was to develop a revised version of the SMS, including integrated regulation. Method: In Stage 1, the factorial validity of the SMS was examined using confirmatory factor analysis (CFA) on data collected from 614 Australians (elite athletes and university students). In Stage 2, the scale was revised by including integrated regulation items and replacing problematic items through an iterative process using CFA for data collected from 557 Australian university students. Concurrent validity of the revised scale was examined by evaluating correlations with the dispositional flow scale-2 (DFS-2) [Jackson, ARTICLE IN PRESS www.elsevier.com/locate/psychsport 1469-0292/$ see front matter r 2007 Published by Elsevier Ltd. doi:10.1016/j.psychsport.2006.12.005 Corresponding author. Tel.: +61 7 33656765; fax: +61 7 33656877. E-mail addresses: cmallett@hms.uq.edu.au (C. Mallett), masato@hms.uq.edu.au (M. Kawabata), newc@psy.uq.edu.au (P. Newcombe), andres.otero@gmail.com (A. Otero-Forero). Author's personal copy S. A., & Eklund, R. C. (2004). The flow scales manual. Morgantown, WV: Fitness Information Technology]. Results: The revision led to development of a six-factor 24-item scale (SMS-6) that indicated a more parsimonious and improved fitting model consistent with SDT. Correlations between the SMS-6 and DFS-2 factors support the concurrent validity of the revised scale. Conclusion: From statistical and theoretical viewpoints, the revised SMS-6 was preferable to the original SMS, except for the discriminant validity issue of identification regulation. Further examination of the instrument is necessary by cross-validating the findings from this study. r 2007 Published by Elsevier Ltd.


Understanding semantic change of words over centuries

In this paper, we propose to model and analyze changes that occur to an entity in terms of changes in the words that co-occur with the entity over time. We propose to do an in-depth analysis of how this co-occurrence changes over time, how the change influences the state (semantic, role) of the entity, and how the change may correspond to events occurring in the same period of time. We propose to identify clusters of topics surrounding the entity over time using Topics-Over-Time (TOT) and k-means clustering. We conduct this analysis on Google Books Ngram dataset. We show how clustering words that co-occur with an entity of interest in 5-grams can shed some lights to the nature of change that occurs to the entity and identify the period for which the change occurs. We find that the period identified by our model precisely coincides with events in the same period that correspond to the change that occurs.


A Robust Digital Image Watermarking Scheme Using Hybrid DWT-DCT-SVD Technique

Protection of digital multimedia content has become an increasingly important issue for content owners and service providers. As watermarking is identified as a major technology to achieve copyright protection, the relevant literature includes several distinct approaches for embedding data into a multimedia element (primarily images, audio, and video). In this paper, we present a hybrid watermarking scheme based on Discrete Wavelet Transform – Discrete Cosine Transform – Singular Value Decomposition (DWT-DCT-SVD). Robustness is achieved by taking DCT of the DWT coefficients of the HL band of DWT. After applying DCT we map the DCT coefficients in a zig – zag order into four quadrants and apply the SVD to each quadrant. These four quadrants represent frequency bands from the lowest to the highest. The singular values in each quadrant are then modified by the singular values of the DWT-DCT transformed visual watermark. We show that embedding data in lowest frequencies is resistant to most of the attacks and some attacks are resistant to other frequency bands.


Filing, piling, and everything in between: The dynamics of E-mail inbox management

Managing the constant flow of incoming messages is a daily challenge faced by knowledge workers who use technologies such as e-mail and other digital communication tools. This study focuses on the most ubiquitous of these technologies, e-mail, and unobtrusively explores the ongoing inbox-management activities of thousands of users worldwide over a period of 8 months. The study describes the dynamics of these inboxes throughout the day and the week as users strive to handle incoming messages, read them, classify them, respond to them in a timely manner, and archive them for future reference, all while carrying out the daily tasks of knowledge workers. It then tests several hypotheses about the influence of specific inbox-management behaviors in mitigating the causes of e-mail overload, and proposes a continuous index that quantifies one of these inbox-management behaviors. This inbox clearing index (ICI) expands on the widely cited trichotomous classification of users into frequent filers, spring cleaners, and no filers, as suggested by Whittaker and Sidner (1996). We propose that the ICI allows shifting the focus, from classifying users to characterizing a diversity of user behaviors and measuring the relationships between these behaviors and desired outcomes.


A Symbolic Approach for Explaining Errors in Image Classification Tasks

Machine learning algorithms, despite their increasing success in handling object recognition tasks, still seldom perform without error. Often the process of understanding why the algorithm has failed is the task of the human who, using domain knowledge and contextual information, can discover systematic shortcomings in either the data or the algorithm. This paper presents an approach where the process of reasoning about errors emerging from a machine learning framework is automated using symbolic techniques. By utilizing spatial and geometrical reasoning between objects in a scene, the system is able to describe misclassified regions in relation to its context. The system is demonstrated in the remote sensing domain where objects and entities are detected in satellite images.


Rumors Detection in Sina Weibo Based on Text and User Characteristics

M414 such as Twitter and Sina Weibo have brought convenience to people in recent years, but there are a large number of internet rumors bring serious harm to us. Because of the fuzziness and concealment of rumors, the automatic identification of microblog rumors is very difficult, which brings great challenges to microblog's supervision. In this paper, for the text information of rumors, we construct a new emotional classification method, and use this method to analyze the emotional orientation of comments, and then calculate the proportion of negative emotional comments. Combined with user information at the same time, we analyze retweet-list and comment-list, and then judge whether there are authoritative users who participated in the spread of rumors. Moreover, we extract features from the information of user and calculate the user's reputation value. Finally, we use the training classifier to detect microblog rumors. Experimental results illustrate the efficacy and efficiency of the proposed new features in microblog rumors detection.


High-order entropy-compressed text indexes

We present a novel implementation of compressed suffix arrays exhibiting new tradeoffs between search time and space occupancy for a given text (or sequence) of n symbols over an alphabet σ, where each symbol is encoded by lg|σ| bits. We show that compressed suffix arrays use just nH<inf>h</inf> + σ bits, while retaining full text indexing functionalities, such as searching any pattern sequence of length m in O(m lg |σ| + polylog(n)) time. The term H<inf>h</inf> ≤ lg |σ| denotes the hth-order empirical entropy of the text, which means that our index is nearly optimal in space apart from lower-order terms, achieving asymptotically the empirical entropy of the text (with a multiplicative constant 1). If the text is highly compressible so that H<inf>n</inf> = o(1) and the alphabet size is small, we obtain a text index with o(m) search time that requires only o(n) bits. Further results and tradeoffs are reported in the paper.


MBMF: Model-Based Priors for Model-Free Reinforcement Learning

Reinforcement Learning is divided in two main paradigms: model-free and model-based. Each of these two paradigms has strengths and limitations, and has been successfully applied to real world domains that are appropriate to its corresponding strengths. In this paper, we present a new approach aimed at bridging the gap between these two paradigms. We aim to take the best of the two paradigms and combine them in an approach that is at the same time dataefficient and cost-savvy. We do so by learning a probabilistic dynamics model and leveraging it as a prior for the intertwined model-free optimization. As a result, our approach can exploit the generality and structure of the dynamics model, but is also capable of ignoring its inevitable inaccuracies, by directly incorporating the evidence provided by the direct observation of the cost. As a proof-of-concept, we demonstrate on simulated tasks that our approach outperforms purely model-based and model-free approaches, as well as the approach of simply switching from a model-based to a model-free setting.


Variable flux permanent magnet synchronous machine (VF-PMSM) design to meet electric vehicle traction requirements with reduced losses

Variable flux permanent magnet synchronous machines (VF-PMSMs) in which the magnetization state (MS) of low coercive force (low-Hc) permanent magnets can be actively controlled to reduce losses in applications that require wide-speed operation have been proposed recently. While prior focus has been on achieving MS manipulation without over-sizing the inverter and obtaining higher torque capability, this paper extends the design objectives to include the power requirements of an electric vehicle traction motor over its entire speed range. Finite element methods are used to study the effect of combinations of low-Hc and high-Hc permanent magnets arranged in either series or parallel on the performance of VF-PMSMs. It is shown that while both configurations help improve the torque density, only the series configuration can help improve the high speed power capability. Experimental results showing the variable MS property, torque-speed capability and loss reduction capability of a series magnet configuration VF-PMSM test machine are presented.


Sudoku Squares and Chromatic Polynomials

T he Sudoku puzzle has become a very popular puzzle that many newspapers carry as a daily feature. The puzzle consists of a 9×9 grid in which some of the entries of the grid have a number from 1 to 9. One is then required to complete the grid in such a way that every row, every column, and every one of the nine 3× 3 sub-grids contain the digits from 1 to 9 exactly once. The sub-grids are shown in Figure 1.


Simultaneous algebraic reconstruction technique (SART): a superior implementation of the art algorithm.

In this paper we have discussed what appears to be a superior implementation of the Algebraic Reconstruction Technique (ART). The method is based on 1) simultaneous application of the error correction terms as computed by ART for all rays in a given projection; 2) longitudinal weighting of the correction terms back-distributed along the rays; and 3) using bilinear elements for discrete approximation to the ray integrals of a continuous image. Since this implementation generates a good reconstruction in only one iteration, it also appears to have a computational advantage over the more traditional implementation of ART. Potential applications of this implementation include image reconstruction in conjunction with ray tracing for ultrasound and microwave tomography in which the curved nature of the rays leads to a non-uniform ray density across the image.


Finding Steiner Forests in Planar Graphs

Several routing problems such as VLSI river routing and single-layer routing can be formulated as a problem of finding a Steiner forest in a planar (grid) graph. Given an unweighted planar graph G together with nets of terminals, our problem is to find a Steiner forest, i.e., vertex-disjoint trees, each of which interconnects all the terminals of a net. This paper gives an efficient algorithm to solve the problem for the case all terminals lie on two face boundaries of G. Also obtained is an algorithm for finding a maximum number of internally vertexdisjoint paths connecting two specified vertices in a planar graph G. Both run in 0( nlog n) time if G has n vertices.


Causality in thought.

Causal knowledge plays a crucial role in human thought, but the nature of causal representation and inference remains a puzzle. Can human causal inference be captured by relations of probabilistic dependency, or does it draw on richer forms of representation? This article explores this question by reviewing research in reasoning, decision making, various forms of judgment, and attribution. We endorse causal Bayesian networks as the best normative framework and as a productive guide to theory building. However, it is incomplete as an account of causal thinking. On the basis of a range of experimental work, we identify three hallmarks of causal reasoning-the role of mechanism, narrative, and mental simulation-all of which go beyond mere probabilistic knowledge. We propose that the hallmarks are closely related. Mental simulations are representations over time of mechanisms. When multiple actors are involved, these simulations are aggregated into narratives.


Get Out of my Picture! Internet-based Inpainting

We present a method to replace a user specified target region of a photograph by using other photographs of the same scene downloaded from the Internet via viewpoint invariant image search. Each of the retrieved images is first geometrically then photometrically registered with the query photograph. Geometric registration is achieved using multiple homographies and photometric registration is performed using a global affine transformation on image intensities. Each retrieved image proposes a possible solution for the target region. In the final step we combine these proposals into a single visually consistent result, using a Markov random field optimisation to choose seams between proposals, followed by gradient domain fusion. We demonstrate removal of objects and people in challenging photographs of Oxford landmarks containing complex image structures.


Biodegradable polymeric nanoparticles based drug delivery systems.

Biodegradable nanoparticles have been used frequently as drug delivery vehicles due to its grand bioavailability, better encapsulation, control release and less toxic properties. Various nanoparticulate systems, general synthesis and encapsulation process, control release and improvement of therapeutic value of nanoencapsulated drugs are covered in this review. We have highlighted the impact of nanoencapsulation of various disease related drugs on biodegradable nanoparticles such as PLGA, PLA, chitosan, gelatin, polycaprolactone and poly-alkyl-cyanoacrylates.


DeepBE: Learning Deep Binary Encoding for Multi-label Classification

The track 2 and track 3 of ChaLearn 2016 can be considered as Multi-Label Classification problems. We present a framework of learning deep binary encoding (DeepBE) to deal with multi-label problems by transforming multi-labels to single labels. The transformation of DeepBE is in a hidden pattern, which can be well addressed by deep convolutions neural networks (CNNs). Furthermore, we adopt an ensemble strategy to enhance the learning robustness. This strategy is inspired by its effectiveness in fine-grained image recognition (FGIR) problem, while most of face related tasks such as track 2 and track 3 are also FGIR problems. By DeepBE, we got 5.45% and 10.84% mean square error for track 2 and track 3 respectively. Additionally, we proposed an algorithm adaption method to treat the multiple labels of track 2 directly and got 6.84% mean square error.


Deep neural architectures for large scale android malware analysis

Android is arguably the most widely used mobile operating system in the world. Due to its widespead use and huge user base, it has attracted a lot of attention from the unsavory crowd of malware writers. Traditionally, techniques to counter such malicious software involved manually analyzing code and figuring out whether it was malicious or benign. However, due to the immense pace at which newer malware families are surfacing, such an approach is no longer feasible. Machine learning offers a way to tackle this issue of speed by automating the classification task. While several efforts have been made to use traditional machine learning techniques to Android malware detection, no reasonable effort has been made to utilize the newer, deep learning models in this domain. In this paper, we apply several deep learning models including fully connected, convolutional and recurrent neural networks as well as autoencoders and deep belief networks to detect Android malware from a large scale dataset of more than 55 GBs of Android malware. Further, we apply Bayesian machine learning to this problem domain to see how it fares with the deep learning based models while also providing insights into the dataset. We show that we are able to achieve better results using these models as compared to the state-of-the-art approaches. Our best model gets an F1 score of 0.986 with an AUC of 0.983 as compared to the existing best F1 score of 0.875 and AUC of 0.953.


Temporal Information Extraction

Research on information extraction (IE) seeks to distill relational tuples from natural language text, such as the contents of the WWW. Most IE work has focussed on identifying static facts, encoding them as binary relations. This is unfortunate, because the vast majority of facts are fluents, only holding true during an interval of time. It is less helpful to extract PresidentOf(Bill-Clinton, USA) without the temporal scope 1/20/93 1/20/01. This paper presents TIE, a novel, information-extraction system, which distills facts from text while inducing as much temporal information as possible. In addition to recognizing temporal relations between times and events, TIE performs global inference, enforcing transitivity to bound the start and ending times for each event. We introduce the notion of temporal entropy as a way to evaluate the performance of temporal IE systems and present experiments showing that TIE outperforms three alternative approaches.


A comparison of Haar-like, LBP and HOG approaches to concrete and asphalt runway detection in high resolution imagery

In this article, the three most used object detection approaches, Linear Binary Pattern cascade, Haarlike cascade, and Histogram of Oriented Gradients with Support Vector Machine are applied to automatic runway detection in high resolution satellite imagery and their results are compared. They have been employed predominantly for human feature recognition and this paper tests theirs applicability to runways. The results show that they can be indeed employed for this purpose with LBP and Haar approaches performing better than HOG+SVM.


Life beyond Distributed Transactions: an Apostate's Opinion

Many decades of work have been invested in the area of distributed transactions including protocols such as 2PC, Paxos, and various approaches to quorum. These protocols provide the application programmer a façade of global serializability. Personally, I have invested a nontrivial portion of my career as a strong advocate for the implementation and use of platforms providing guarantees of global serializability. My experience over the last decade has led me to liken these platforms to the Maginot Line. In general, application developers simply do not implement large scalable applications assuming distributed transactions. When they attempt to use distributed transactions, the projects founder because the performance costs and fragility make them impractical. Natural selection kicks in... 1 The Maginot Line was a huge fortress that ran the length of the Franco-German border and was constructed at great expense between World War I and World War II. It successfully kept the German army from directly crossing the border between France and Germany. It was quickly bypassed by the Germans in 1940 who invaded through Belgium. This article is published under a Creative Commons License Agreement (http://creativecommons.org/licenses/by/2.5/). You may copy, distribute, display, and perform the work, make derivative works and make commercial use of the work, but you must attribute the work to the author and CIDR 2007. 3 Biennial Conference on Innovative DataSystems Research (CIDR) January 7-10, Asilomar, California USA. Instead, applications are built using different techniques which do not provide the same transactional guarantees but still meet the needs of their businesses. This paper explores and names some of the practical approaches used in the implementations of large-scale mission-critical applications in a world which rejects distributed transactions. We discuss the management of fine-grained pieces of application data which may be repartitioned over time as the application grows. We also discuss the design patterns used in sending messages between these repartitionable pieces of data. The reason for starting this discussion is to raise awareness of new patterns for two reasons. First, it is my belief that this awareness can ease the challenges of people hand-crafting very large scalable applications. Second, by observing the patterns, hopefully the industry can work towards the creation of platforms that make it easier to build these very large applications.


Lane detection using spline model

In this paper, a Catmull±Rom spline-based lane model which describes the perspective eect of parallel lines has been proposed for generic lane boundary. Since Catmull±Rom spline can form arbitrary shapes by dierent sets of control points, it can describe a wider range of lane structures compared with other lane models, i.e. straight and parabolic models. Moreover, the lane detection problem has been formulated here as the problem of determining the set of control points of lane model. The proposed algorithm ®rst detects the vanishing point (line) by using a Hough-like technique and then solves the lane detection problem by suggesting a maximum likelihood approach. Also, we have employed a multi-resolution strategy for rapidly achieving an accurate solution. This coarse-to-®ne matching oers us an acceptable solution at an aordable computational cost, and thus speeds up the process of lane detection. As a result, the proposed method is robust to noise, shadows, and illumination variations in the captured road images, and is also applicable to both the marked and the unmarked roads. Ó 2000 Elsevier Science B.V. All rights reserved.


The Ant System Applied to the Quadratic Assignment Problem

In recent years there has been growing interest in algorithms inspired by the observation of natural phenomena to define computational procedures which can solve complex problems. In this article we introduce a distributed heuristic algorithm which was inspired by the observation of the behavior of ant colonies and we propose its use for the Quadratic Assignment Problem. Finally the results obtained in solving some classical instances of the problem are compared with those obtained from other evolutionary heuristics to evaluate the quality of the system proposed. ^Dipartimento di Scienze dell'Informazione, Università di Bologna, Via Sacchi 3, 47023, Cesena, Italy, EU, maniezzo@csr.unibo.it & Dipartimento di Elettronica e Informazione, Politecnico di Milano, Via Ponzio 34/5, 20133, Milano, Italy, EU, colorni@elet.polimi.it #IRIDIA, Université Libre de Bruxelles, Av. Franklin Roosevelt 50, CP 194/6, 1050 Brussels, Belgium, EU, mdorigo@ulb.ac.be


Optimization, Learning and Natural Algorithms


Optimization by simulated annealing


1 Positive Feedback as a Search Strategy

A combination of distributed computation, positive feedback and constructive greedy heuristic is proposed as a new approach to stochastic optimization and problem solving. Positive feedback accounts for rapid discovery of very good solutions, distributed computation avoids premature convergence, and greedy heuristic helps the procedure to find acceptable solutions in the early stages of the search process. An application of the proposed methodology to the classical travelling salesman problem shows that the system can rapidly provide very good, if not optimal, solutions. We report on many simulation results and discuss the working of the algorithm. Some hints about how this approach can be applied to a variety of optimization problems are also given.


A Multi-Dialect, Multi-Genre Corpus of Informal Written Arabic

This paper presents a multi-dialect, multi-genre, human annotated corpus of dialectal Arabic with data obtained from both online newspaper commentary and Twitter. Most Arabic corpora are small and focus on Modern Standard Arabic (MSA). There has been recent interest, however, in the construction of dialectal Arabic corpora (Zaidan and Callison-Burch, 2011a; Al-Sabbagh and Girju, 2012). This work differs from previously constructed corpora in two ways. First, we include coverage of five dialects of Arabic: Egyptian, Gulf, Levantine, Maghrebi and Iraqi. This is the most complete coverage of any dialectal corpus known to the authors. In addition to data, we provide results for the Arabic dialect identification task that outperform those reported in Zaidan and Callison-Burch (2011a).


Building Rome on a Cloudless Day

This paper introduces an approach for dense 3D reconstruction from unregistered Internet-scale photo collections with about 3 million images within the span of a day on a single PC (“cloudless”). Our method advances image clustering, stereo, stereo fusion and structure from motion to achieve high computational performance. We leverage geometric and appearance constraints to obtain a highly parallel implementation on modern graphics processors and multi-core architectures. This leads to two orders of magnitude higher performance on an order of magnitude larger dataset than competing state-of-the-art approaches.


Device-free gesture tracking using acoustic signals: demo

In this demo, we present LLAP, a hand tracking system that uses ultrasound to localize the hand of the user to enable device-free gesture inputs. LLAP utilizes speakers and microphones on Commercial-Off-The-Shelf (COTS) mobile devices to play and record sound waves that are inaudible to humans. By measuring the phase of the sound signal reflected by the hands or fingers of the user, we can accurately measure the gesture movements. With a single pair of speaker/microphone, LLAP can track hand movement with accuracy of 3.5 mm. For devices with two microphones, LLAP enables drawing-in-the air capability with tracking accuracy of 4.6 mm. Moreover, the latency for LLAP is smaller than 15 ms for both the Android and the iOS platforms so that LLAP can be used for real-time applications.


Compressive coded aperture imaging

Nonlinear image reconstruction based upon sparse representations of images has recently received widespread attention with the emerging framework of compressed sensing (CS). This theory indicates that, when feasible, judicious selection of the type of distortion induced by measurement systems may dramatically improve our ability to perform image reconstruction. However, applying compressed sensing theory to practical imaging systems poses a key challenge: physical constraints typically make it infeasible to actually measure many of the random projections described in the literature, and therefore, innovative and sophisticated imaging systems must be carefully designed to effectively exploit CS theory. In video settings, the performance of an imaging system is characterized by both pixel resolution and field of view. In this work, we propose compressive imaging techniques for improving the performance of video imaging systems in the presence of constraints on the focal plane array size. In particular, we describe a novel yet practical approach that combines coded aperture imaging to enhance pixel resolution with superimposing subframes of a scene onto a single focal plane array to increase field of view. Specifically, the proposed method superimposes coded observations and uses wavelet-based sparsity recovery algorithms to reconstruct the original subframes. We demonstrate the effectiveness of this approach by reconstructing with high resolution the constituent images of a video sequence.


A multimodal feature learning approach for sentiment analysis of social network multimedia

In this paper we investigate the use of a multimodal feature learning approach, using neural network based models such as Skip-gram and Denoising Autoencoders, to address sentiment analysis of micro-blogging content, such as Twitter short messages, that are composed by a short text and, possibly, an image. The approach used in this work is motivated by the recent advances in: i) training language models based on neural networks that have proved to be extremely efficient when dealing with web-scale text corpora, and have shown very good performances when dealing with syntactic and semantic word similarities; ii) unsupervised learning, with neural networks, of robust visual features, that are recoverable from partial observations that may be due to occlusions or noisy and heavily modified images. We propose a novel architecture that incorporates these neural networks, testing it on several standard Twitter datasets, and showing that the approach is efficient and obtains good classification results.


Child murder by parents: a psychiatric review of filicide.


A lifespan database of adult facial stimuli.

Faces constitute a unique and widely used category of stimuli. In spite of their importance, there are few collections of faces for use in research, none of which adequately represent the different ages of faces across the lifespan. This lack of a range of ages has limited the majority of researchers to using predominantly young faces as stimuli even when their hypotheses concern both young and old participants. We describe a database of 575 individual faces ranging from ages 18 to 93. Our database was developed to be more representative of age groups across the lifespan, with a special emphasis on recruiting older adults. The resulting database has faces of 218 adults age 18-29, 76 adults age 30-49, 123 adults age 50-69, and 158 adults age 70 and older. These faces may be acquired for research purposes from http://agingmind.cns.uiuc.edu/facedb/. This will allow researchers interested in using facial stimuli access to a wider age range of adult faces than has previously been available.


Quantifying Trading Behavior in Financial Markets Using Google Trends

Crises in financial markets affect humans worldwide. Detailed market data on trading decisions reflect some of the complex human behavior that has led to these crises. We suggest that massive new data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market movements. By analyzing changes in Google query volumes for search terms related to finance, we find patterns that may be interpreted as "early warning signs" of stock market moves. Our results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior.


Low-power high-performance SAR ADC with redundancy and digital background calibration

As technology scales, the improved speed and energy efficiency make the successiveapproximation-register (SAR) architecture an attractive alternative for applications that require high-speed and high-accuracy analog-to-digital converters (ADCs). In SAR ADCs, the key linearity and speed limiting factors are capacitor mismatch and incomplete digital-to-analog converter (DAC)/reference voltage settling. In this thesis, a sub-radix-2 SAR ADC is presented with several new contributions. The main contributions include investigation of using digital error correction (redundancy) in SAR ADCs for dynamic error correction and speed improvement, development of two new calibration algorithms to digitally correct for manufacturing mismatches, design of new architecture to incorporate redundancy within the architecture itself while achieving 94% better energy efficiency compared to conventional switching algorithm, development of a new capacitor DAC structure to improve the SNR by four times with improved matching, joint design of the analog and digital circuits to create an asynchronous platform in order to reach the targeted performance, and analysis of key circuit blocks to enable the design to meet noise, power and timing requirements. The design is fabricated in standard 1P9M 65nm CMOS technology with 1.2V supply. The active die area is 0.083mm with full rail-to-rail input swing of 2.4VP−P . A 67.4dB SNDR, 78.1dB SFDR, +1.0/-0.9 LSB12 INL and +0.5/-0.7 LSB12 DNL are achieved at 50MS/s at Nyquist rate. The total power consumption, including the estimated calibration and reference power, is 2.1mW, corresponding to 21.9fJ/conv.step FoM. This ADC achieves the best FoM of any ADCs with greater than 10b ENOB and 10MS/s sampling rate. Thesis Supervisor: Duane S. Boning Title: Professor of Electrical Engineering and Computer Science Thesis Supervisor: Hae-Seung Lee Title: Professor of Electrical Engineering and Computer Science


Effects of time-to-trigger parameter on handover performance in SON-based LTE systems

In this paper, we evaluate handover (HO) performance when we apply various time-to-trigger (TTT) methods in self-organizing network (SON)-based long term evolution (LTE) systems. Although TTT can mitigate the wasteful ping-pong HO effect, it also can cause undesirable radio link failure (RLF) due to delayed HO. The optimal HO timings that produce the lowest ping-pong rate within allowable RLF rate vary depending on user equipment (UE) speeds and neighboring cell configurations. To achieve efficient HO timings, we propose and investigate two methods: “adaptive” and “grouping.” In the “adaptive method,” we select the adaptive TTT value for each UE speed based on RLF rate of 2%. The “grouping method” classifies UE speeds into three ranges and assigns the proper TTT value to each range. To apply the LTE specification more effectively, we suggest the criteria of grouping, and propose the proper TTT value for each range. We consider HO in two neighboring cell configurations: from a macro cell to either a macro cell or a pico cell. Simulation results show that the HO performance of the “adaptive method” is greatly improved compared to that of applying fixed TTT values. The results also show that the performance of the “grouping method”, using proposed criteria and proper TTT values, is comparable to that of the “adaptive method.”


A compact filtering power divider based on SIW triangular cavities

A compact substrate-integrated waveguide (SIW) filtering power divider with high isolation is presented in this paper based on SIW triangular cavities. An isolation resistance is exploited to achieve high isolation between two output ports. The design parameters can be realized by determining proper offset positions of the feeding ports and coupling windows, which can be fixed by the synthesized Qe and Mij. By using three triangular cavities as the basic elements, a filtering power divider is designed and fabricated on a single-layer substrate with the standard printed circuit boards (PCB) process. This filtering power divider operates at the center frequency of 11.4 GHz with the bandwidths of 0.75 GHz. There are good agreements in the simulated and measured results.


Rethinking the fear circuit: the central nucleus of the amygdala is required for the acquisition, consolidation, and expression of Pavlovian fear conditioning.

In the standard model of pavlovian fear learning, sensory input from neutral and aversive stimuli converge in the lateral nucleus of the amygdala (LA), in which alterations in synaptic transmission encode the association. During fear expression, the LA is thought to engage the central nucleus of the amygdala (CE), which serves as the principal output nucleus for the expression of conditioned fear responses. In the present study, we reexamined the roles of LA and CE. Specifically, we asked whether CE, like LA, might also be involved in fear learning and memory consolidation. Using functional inactivation methods, we first show that CE is involved not only in the expression but also the acquisition of fear conditioning. Next, we show that inhibition of protein synthesis in CE after training impairs fear memory consolidation. These findings indicate that CE is not only involved in fear expression but, like LA, is also involved in the learning and consolidation of pavlovian fear conditioning.


Deconstructing the Blockchain to Approach Physical Limits

The concept of a blockchain was invented by Satoshi Nakamoto to maintain a distributed ledger for an electronic payment system, Bitcoin. In addition to its security, important performance measures of a blockchain protocol are its transaction throughput, confirmation latency and confirmation reliability. Existing systems operate far away from these physical limits. In this work we introduce Prism , a new proof-of-work blockchain protocol, which can achieve 1) security against up to 50% adversarial hashing power; 2) optimal throughput up to the capacity C of the network; 3) confirmation latency for honest transactions proportional to the propagation delay D, with confirmation error probability exponentially small in the bandwidth-delay product CD; 4) eventual total ordering of all transactions. Our approach to the design of this protocol is based on deconstructing the blockchain into its basic functionalities and systematically scaling up these functionalities to approach their physical limits.


A comparative study of classification algorithms for forecasting rainfall

India is an agricultural country which largely depends on monsoon for irrigation purpose. A large amount of water is consumed for industrial production, crop yield and domestic use. Rainfall forecasting is thus very important and necessary for growth of the country. Weather factors including mean temperature, dew point temperature, humidity, pressure of sea and speed of wind and have been used to forecasts the rainfall. The dataset of 2245 samples of New Delhi from June to September (rainfall period) from 1996 to 2014 has been collected from a website named Weather Underground. The training dataset is used to train the classifier using Classification and Regression Tree algorithm, Naive Bayes approach, K nearest Neighbour and 5-10-1 Pattern Recognition Neural Network and its accuracy is tested on a test dataset. Pattern Recognition networks has given 82.1% accurate results, KNN with 80.7% correct forecasts ranks second, Classification and Regression Tree(CART) gives 80.3% while Naive Bayes provides 78.9% correctly classified samples.


Vertical ridge augmentation in the esthetic zone.

The reconstruction of deficient alveolar ridges using vertical and/or horizontal guided bone regeneration techniques allows for ideal implant placement, which is crucial for function and also for esthetically successful outcomes. Unlike in the past, when meeting a patient's functional demands was sufficient, many patients now have greater expectations from their implant restoration. Hence, it is no longer enough simply to restore the edentulous space with a functioning tooth or teeth. It has been suggested that patients now measure their final restoration using the contralateral natural tooth as the gold standard. Both subjective and objective levels of patient information on dental implants have increased significantly in the last decade. As a result of this demand, implant literature has inherited and developed specific esthetic parameters and patient-centered outcomes from studies in the restorative field. Unfortunately, studies reporting on guided bone regeneration in the esthetic zone entirely lack such parameters and outcomes. Currently, there is a strong need for a consensus on objective and well-defined parameters to assess the esthetics in bone regeneration and subsequently on implant dentistry.


A New Discrete Particle Swarm Optimization Algorithm

Particle Swarm Optimization (PSO) has been shown to perform very well on a wide range of optimization problems. One of the drawbacks to PSO is that the base algorithm assumes continuous variables. In this paper, we present a version of PSO that is able to optimize over discrete variables. This new PSO algorithm, which we call Integer and Categorical PSO (ICPSO), incorporates ideas from Estimation of Distribution Algorithms (EDAs) in that particles represent probability distributions rather than solution values, and the PSO update modifies the probability distributions. In this paper, we describe our new algorithm and compare its performance against other discrete PSO algorithms. In our experiments, we demonstrate that our algorithm outperforms comparable methods on both discrete benchmark functions and NK landscapes, a mathematical framework that generates tunable fitness landscapes for evaluating EAs.


Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery

Learning efficient image representations is at the core of the scene classification task of remote sensing imagery. The existing methods for solving the scene classification task, based on either feature coding approaches with low-level hand-engineered features or unsupervised feature learning, can only generate mid-level image features with limited representative ability, which essentially prevents them from achieving better performance. Recently, the deep convolutional neural networks (CNNs), which are hierarchical architectures trained on large-scale datasets, have shown astounding performance in object recognition and detection. However, it is still not clear how to use these deep convolutional neural networks for high-resolution remote sensing (HRRS) scene classification. In this paper, we investigate how to transfer features from these successfully pre-trained CNNs for HRRS scene classification. We propose two scenarios for generating image features via extracting CNN features from different layers. In the first scenario, the activation vectors extracted from fully-connected layers are regarded as the final image features; in the second scenario, we extract dense features from the last convolutional layer at multiple scales and then encode the dense features into global image features through commonly used feature coding approaches. Extensive experiments on two public scene classification datasets demonstrate that the image features obtained by the two proposed scenarios, even with a simple linear classifier, can result in remarkable performance and improve the state-of-the-art by a significant margin. The results reveal that the features Remote Sens. 2015, 7 14681 from pre-trained CNNs generalize well to HRRS datasets and are more expressive than the lowand mid-level features. Moreover, we tentatively combine features extracted from different CNN models for better performance.


Memory Leak Analysis by Contradiction

We present a novel leak detection algorithm. To prove the absence of a memory leak, the algorithm assumes its presence and runs a backward heap analysis to disprove this assumption. We have implemented this approach in a memory leak analysis tool and used it to analyze several routines that manipulate linked lists and trees. Because of the reverse nature of the algorithm, the analysis can locally reason about the absence of memory leaks. We have also used the tool as a scalable, but unsound leak detector for C programs. The tool has found several bugs in larger programs from the SPEC2000 suite.


Sign language recognition using Microsoft Kinect

In last decade lot of efforts had been made by research community to create sign language recognition system which provide a medium of communication for differently-abled people and their machine translations help others having trouble in understanding such sign languages. Computer vision and machine learning can be collectively applied to create such systems. In this paper, we present a sign language recognition system which makes use of depth images that were captured using a Microsoft Kinect® camera. Using computer vision algorithms, we develop a characteristic depth and motion profile for each sign language gesture. The feature matrix thus generated was trained using a multi-class SVM classifier and the final results were compared with existing techniques. The dataset used is of sign language gestures for the digits 0-9.


Injury Risk Quantification for Industrial Robots in Collaborative Operation with Humans

While the topic of human-safe robots has an appreciable academic history, only today are various solutions to this challenge becoming economically relevant. We are witnessing a maturing of technology and market expectations, paired with initiatives in the area of standardization. One of the remaining hurdles to routine deployment of robots for collaborative operation with humans is obtaining a well-founded understanding of low-level injuries of the type that such robots can potentially inflict. For the assessment of collaborative robot designs and systems, the low-level injury potential must be unambiguously quantified. A classification scheme for holding such information is presented and an outlook on its use is given.


Acceptance Model in the Domains of LIS and Education : A Review of Selected Literature

The Technology Acceptance Model (TAM) is a theoretical framework that is most extensively utilized in explaining an individual’s acceptance of an information technology or an information system. This study reviews numerous literature related to the TAM focusing on TAM applications in the domains of Library and Information Science (LIS) and Education. The different studies in these areas were evaluated to understand the modifications incorporated into this model. The study attempts provide insight on future trends in the technology acceptance model as well to help identify gaps in literature where future research could be conducted.


Risk Aversion and Expected-Utility Theory : A Calibration Theorem

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A review of empirical research on manufacturing flexibility

Manufacturing flexibility is widely recognized as a critical component to achieving a competitive advantage in the marketplace. A comprehensive look at the empirical research pertaining to manufacturing flexibility highlights the very fragmented nature of this body of work. We present a comprehensive contingency-based framework for examining the content related issues involving the relationships and variables included in past studies. We also examine several important Ž . research designrmethodology issues e.g., sampling, data collection and measurement and propose solutions to some identified problems. q 2000 Elsevier Science B.V. All rights reserved.


Risk Management : Coordinating Corporate Investment and Financing Policies

This paper develops a general framework for analyzing corporate risk management policies. We begin by observing that if external sources of finance are more costly to corporations than internally generated funds, there will typically be a benefit to hedging: hedging adds value to the extent that it helps ensure that a corporation has sufficient internal funds available to take advantage of attractive investment opportunities. We then argue that this simple observation has wide ranging implications for the design of risk management strategies. We delineate how these strategies should depend on such factors as shocks to investment and financing opportunities. We also discuss exchange rate hedging strategies for multinationals, as well as strategies involving "nonlinear" instruments like options. CORPORATIONS TAKE RISK MANAGEMENT very seriously—recent surveys find that risk management is ranked by financial executives as one of their most important objectives.^ Given its real-world prominence, one might guess that the topic of risk management would command a great deal of attention from researchers in finance, and that practitioners would therefore have a welldeveloped body of wisdom from which to draw in formulating hedging strategies. Such a guess would, however, be at best only partially correct. Finance theory does do a good job of instructing firms on the implementation of hedges. For example, if a refining company decides that it wants to use options to reduce its exposure to oil prices by a certain amount, a BlackScholes type model can help the company calculate the number of contracts needed. Indeed, there is an extensive literature that covers numerous practical aspects of what might be termed "hedging mechanics," from the computation of hedge ratios to the institutional peculiarities of individual contracts. Unfortunately, finance theory has had much less clear cut guidance to offer on the logically prior questions of hedging strategy: What sorts of risks * Froot is from Harvard and NBER, Scharfstein is from MIT and NBER, and Stein is from MIT and NBER. We thank Don Lessard, Tim Luehrman, Andre Perold, Raghuram Rajan, Julio Rotemberg, and Stew Myers for helpful discussions. We are also grateful to the IFSRC and the Center for Energy Policy Research at MIT, the Department of Research at Harvard Business School, the National Science Foundation, and Batterymarch Financial Management for generous financial support. ' See Rawls and Smithson (1990).


Financing Constraints and Corporate Investment

EMPIRICAL models of business investment rely generally on the assumption of a "representative firm" that responds to prices set in centralized securities markets. Indeed, if all firms have equal access to capital markets, firms' responses to changes in the cost of capital or tax-based investment incentives differ only because of differences in investment demand. A firm's financial structure is irrelevant to investment because external funds provide a perfect substitute for internal capital. In general, with perfect capital markets, a firm's investment decisions are independent of its financial condition. An alternative research agenda, however, has been based on the view that internal and external capital are not perfect substitutes. According to this view, investment may depend on financial factors, such as the availability of internal finance, access to new debt or equity finance, or the functioning of particular credit markets. For example, a firm's internal cash flow may affect investment spending because of a "financ-


Visual landmarks sharpen grid cell metric and confer context specificity to neurons of the medial entorhinal cortex

Neurons of the medial entorhinal cortex (MEC) provide spatial representations critical for navigation. In this network, the periodic firing fields of grid cells act as a metric element for position. The location of the grid firing fields depends on interactions between self-motion information, geometrical properties of the environment and nonmetric contextual cues. Here, we test whether visual information, including nonmetric contextual cues, also regulates the firing rate of MEC neurons. Removal of visual landmarks caused a profound impairment in grid cell periodicity. Moreover, the speed code of MEC neurons changed in darkness and the activity of border cells became less confined to environmental boundaries. Half of the MEC neurons changed their firing rate in darkness. Manipulations of nonmetric visual cues that left the boundaries of a 1D environment in place caused rate changes in grid cells. These findings reveal context specificity in the rate code of MEC neurons.


Synergistic Analysis of Evolving Graphs

Evolving graph processing involves repeating analyses, which are often iterative, over multiple snapshots of the graph corresponding to different points in time. Since the snapshots of an evolving graph share a great number of vertices and edges, traditional approaches that process these snapshots one at a time without exploiting this overlap contain much wasted effort on both data loading and computation, making them extremely inefficient. In this article, we identify major sources of inefficiencies and present two optimization techniques to address them. First, we propose a technique for amortizing the fetch cost by merging fetching of values for different snapshots of the same vertex. Second, we propose a technique for amortizing the processing cost by feeding values computed by earlier snapshots into later snapshots. We have implemented these optimizations in two distributed graph processing systems, namely, GraphLab and ASPIRE. Our experiments with multiple real evolving graphs and algorithms show that, on average fetch amortization speeds up execution of GraphLab and ASPIRE by 5.2× and 4.1× , respectively. Amortizing the processing cost yields additional average speedups of 2× and 7.9×, respectively.


Assessment of methodological quality of primary studies by systematic reviews: results of the metaquality cross sectional study.

OBJECTIVES
To describe how the methodological quality of primary studies is assessed in systematic reviews and whether the quality assessment is taken into account in the interpretation of results.

DATA SOURCES
Cochrane systematic reviews and systematic reviews in paper based journals.

STUDY SELECTION
965 systematic reviews (809 Cochrane reviews and 156 paper based reviews) published between 1995 and 2002.

DATA SYNTHESIS
The methodological quality of primary studies was assessed in 854 of the 965 systematic reviews (88.5%). This occurred more often in Cochrane reviews than in paper based reviews (93.9% v 60.3%, P < 0.0001). Overall, only 496 (51.4%) used the quality assessment in the analysis and interpretation of the results or in their discussion, with no significant differences between Cochrane reviews and paper based reviews (52% v 49%, P = 0.58). The tools and methods used for quality assessment varied widely.

CONCLUSIONS
Cochrane reviews fared better than systematic reviews published in paper based journals in terms of assessment of methodological quality of primary studies, although they both largely failed to take it into account in the interpretation of results. Methods for assessment of methodological quality by systematic reviews are still in their infancy and there is substantial room for improvement.


An Ontology-Based Approach to Model-Driven Software Product Lines

Software development in highly variable domains constrained by tight regulations and with many business concepts involved results in hard to deliver and maintain applications, due to the complexity of dealing with the large number of concepts provided by the different parties and system involved in the process. One way to tackle these problems is thru combining software product lines and model-driven software development supported by ontologies. Software product lines and model-driven approaches would promote reuse on the software artifacts and, if supported by an ontological layer, those artifacts would be domain-validated. We intend to create a new conceptual framework for software development with domain validated models in highly variable domains. To define such a framework we will propose a model that relates several dimensions and areas of software development thru time and abstraction levels. This model would guarantee to the software house traceability of components, domain validated artifacts, easy to maintain and reusable components, due to the relations and mappings we propose to establish in the conceptual framework, between the software artifacts and the ontology.


Pixel-Oriented Visualization of Change in Social Networks

We propose a new approach to visualize social networks. Most common network visualizations rely on graph drawing. While without doubt useful, graphs suffer from limitations like cluttering and important patterns may not be realized especially when networks change over time. Our approach adapts pixel-oriented visualization techniques to social networks as an addition to traditional graph visualizations. The visualization is exemplified using social networks based on corporate wikis.


Beyond Recommender Systems: Helping People Help Each Other

The Internet and World Wide Web have brought us into a world of endless possibiliti es: interactive Web sites to experience, music to li sten to, conversations to participate in, and every conceivable consumer item to order. But this world also is one of endless choice: how can we select from a huge universe of items of widely varying quality? Computational recommender systems have emerged to address this issue. They enable people to share their opinions and benefit from each other’s experience. We present a framework for understanding recommender systems and survey a number of distinct approaches in terms of this framework. We also suggest two main research challenges: (1) helping people form communities of interest while respecting personal privacy, and (2) developing algorithms that combine multiple types of information to compute recommendations.


Context-aware application scheduling in mobile systems: what will users do and not do next?

Usage patterns of mobile devices depend on a variety of factors such as time, location, and previous actions. Hence, context-awareness can be the key to make mobile systems to become personalized and situation dependent in managing their resources. We first reveal new findings from our own Android user experiment: (i) the launching probabilities of applications follow Zipf's law, and (ii) inter-running and running times of applications conform to log-normal distributions. We also find context-dependency in application usage patterns, for which we classify contexts in a personalized manner with unsupervised learning methods. Using the knowledge acquired, we develop a novel context-aware application scheduling framework, CAS that adaptively unloads and preloads background applications in a timely manner. Our trace-driven simulations with 96 user traces demonstrate the benefits of CAS over existing algorithms. We also verify the practicality of CAS by implementing it on the Android platform.


Multimodal feature extraction and fusion for semantic mining of soccer video: a survey

This paper presents a classified review of soccer video analysis works. The existing approaches in the aspects of highlight event detection, video summarization and retrieval based on video stream, ball and player tracking for provision of match statistics, technical and tactical analysis and application of different sources in soccer video analysis have been surveyed. In addition, some major existing commercial softwares developed for video analysis are introduced and compared. With regard to the existing challenge for automatic and realtime provision of video analysis, different computer vision approaches are discussed and compared. Audio, video and text feature extraction methods have been investigated and the future trends for improvement of the reviewed systems have been introduced in terms of response time optimization, increase of precision and eliminating the need of human intervention for video analysis.


Confusion Matrix

A confusion matrix (Kohavi and Provost, 1998) contains information about actual and predicted classifications done by a classification system. Performance of such systems is commonly evaluated using the data in the matrix. The following table shows the confusion matrix for a two class classifier. The entries in the confusion matrix have the following meaning in the context of our study: ● a is the number of correct predictions that an instance is negative, ● b is the number of incorrect predictions that an instance is positive, ● c is the number of incorrect of predictions that an instance negative, and ● d is the number of correct predictions that an instance is positive. Predicted Negative Positive Actual Negative a b Positive c d