What campus-based students think about the quality and benefits of e-learning

There is a trend in Irish universities to utilise the benefits of the e-learning as a mechanism to improve learning performance of campus-based students. Whilst traditional methods, such as face-to-face lectures, tutorials, and mentoring, remain dominant in the educational sector, universities are investing heavily in learning technologies, to facilitate improvements with respect to the quality of learning. The technology to support reuse and sharing of educational resources, or learning objects, is becoming more stable, with interoperability standards maturing. However, debate has raged about what constitutes effective use of learning technology. This research expands upon a study carried out in 2003 examining students’ perceptions of e-learning in a large undergraduate accounting class environment. As a result, improvements were made to the instructional design of the course, to enable students to engage interactively with content. The subsequent study, reported in this paper, adopted a broad range of techniques to understand students’ learning experience in depth. The findings of this research provide an insight into how these students really work and learn using technologies, if at all. It is hoped that our findings will improve the experience for both students and lecturers who engage in teaching and learning through this medium. 502 British Journal of Educational Technology Vol 36 No 3 2005 © British Educational Communications and Technology Agency, 2005. Introduction A recent trend in higher education is to create and provide online access to course materials. Over the past two decades academics and institutes of higher education have been diversifying their delivery of instruction through new Internet media such as learning management systems, asynchronous distance learning, and online classrooms amongst a myriad of other burgeoning educational technologies. This combination of traditional face-to-face lectures or tutorials, and web-based course content is better known as “blended learning,” purporting to blend the best aspects of real and virtual environments. Many Irish universities have invested in architectures and platforms to support their teaching staff in delivering material to students in a blended manner. Other institutions have adopted a lecturer-driven approach, whereby teaching staff are left to their own devices to supplement their lectures and tutorials with online material, hosted via their own web servers and typically open source, freeware software, or basic web pages. The reasons behind the drive to incorporate technology into the educational process are threefold. First, pressure to utilise information and communications technology (ICT) at university level comes from changes in the student demography. The rise of “full time part time students” is a phenomenon of recent years, where school leavers take part-time jobs whilst attending university, leaving less time for evening tutorials or weekend study. In addition, there is a drive for what is known as lifelong learning, whereby adults are increasingly returning to institutions of higher education to take supplementary courses whilst in full-time employment, or during short career breaks. In this regard, the Irish government has made a commitment to have 15% of adults in continuing higher education by 2006. The figure currently stands at less than 5% (Department of Education and Science, 2000). This movement to adult continuing education was also reflected in a statement made by Information Commission Society (Ireland): “The issue of integration IT to the teaching process is an important part of future improvements, which it will be crucial to pursue” (Boylan, 2000, p. 31). Second, changes in the market for delivery of education is also shifting. Private for-profit higher education institutions are offering a wide range of certificate and degree courses. The Irish Open University Initiative “Oscail” and its UK equivalent offer distance learning diploma and degree courses. Many of these courses are delivered through ICT, along with weekend face-to-face tutorials. This distance learning market has also seen the arrival of new entrants, such as the Atlantic University Alliance, a collaboration between National University of Ireland, Galway, University College Cork, and the University of Limerick. Similar undertakings are underway in other institutes of higher education, whereby expertise in delivering blended learning within the institution is being extended towards distance learning courses by using a significant amount of ICT to overcome geographic and time barriers. Third, another pressure on traditional institutions of higher education comes from innovations in new technologies. John Seely Brown and Paul Duiguid claim that these technologies “offer new ways to think of producing, distributing and consuming acaWhat students think about e-learning 503 © British Educational Communications and Technology Agency, 2005. demic material. As with so many other institutions, new technologies have caused universities to rethink not simply isolated features but their entire mission and how they go about it” (Seely Brown & Duiguid, 2000). This sentiment is also being echoed by government policy towards higher education where the Irish State is attempting to set certain parameters of performance covering such areas as equity of access, commitment to e-learning and to ICT, commitment to life-long learning, and outreach to other communities. In the subject domain of accountancy, the integration of technology into the classroom is also becoming a central issue for accounting educators for an additional reason. Accountancy professionals in industry require competency in accountancy-related information technology (IT) skills. Higher education institutions, in response, have tried to simulate authentic learning scenarios in the accountancy class and incorporate IT topics into their programmes. However, the efficacy of introducing technology in accounting education remains unclear. Positive learning effects associated with IT in accounting education have been noted (Abdolmohammadi et al , 1998; Fetters, McKenzie & Callaghan, 1986; Friedman, 1981). Equally, evidence has shown that technology has negatively impacted on learning (Dickens & Harper, 1986; Togo & McNamee, 1997). Indeed, some critics harshly criticise its introduction to educational contexts. Selwyn (2002, p. 172) claims in generic terms that “...in the final analysis educational technology remains a diverting pastime and ‘add-on’ to the curriculum for some teaching and learners, whilst for most in education its impact has been slight.” This paper hopes to look at some of the issues raised by individual students with regard to the use of technology in the teaching and learning process. Rather than focusing on macro issues, such as the political decisions behind the integration of technology, this research aims to examine individuals’ acceptance or rejection of using ICT. Exploring the perspective of the individual student when discussing the use of technology is paramount to beginning to understand the nature of ICT in higher educational settings. This is of importance both to policy makers and education technologists in the debate on the role of ICT in education at an organisational level. The module and the medium This research focused on one module of The Principles of Accounting being taught to approximately 600 first-year undergraduate students at the University of Limerick. The module under investigation was part of a wider course, leading to a degree in Business Studies or Law and Accounting. The majority of students were between 17 and 19 years of age, coming from distributed second-level schools in Ireland. A minority were Erasmus students from various European countries, or mature students over the age of 23. For all, except overseas students, this was their first experience in using an e-learning platform, although most had previously informal used the web to gather information, or prepare coursework in second-level education, prior to entering university. This module was delivered by using a blended learning approach, supplementing weekly lectures, tutorials, and laboratory sessions with online course content, interac504 British Journal of Educational Technology Vol 36 No 3 2005 © British Educational Communications and Technology Agency, 2005. tive quizzes, and Excel tasks. A range of different e-learning assessments complemented the traditional weekly meetings. Students were asked to submit a compulsory paperbased accounting case study, and additionally were offered two optional ICT assignments, a stream of online multiple-choice quizzes, and an Excel project. A module web site provided course details, additional readings, and supplementary links. Employing multiple teaching methods simultaneously is a form of blended learning (Saunders & Werner, 2003). Alternative instructional resources can stimulate positive effects on accounting students’ learning experiences, according to (Rebele et al , 1998). Furthermore, instructional innovations are desirable to develop accounting students’ IT competencies (Albrecht & Sack, 2000). Figure 1 illustrates the staged delivery of both the traditional and online elements of the module. The university does not provide institutional support for e-learning on a campus-wide scale, but two other modules in their first year also used a blended learning delivery approach, providing lecture notes online, or sample answers, in the subject area of economics and mathematics. Both of these other modules differ in marking scheme and in the extent to which online supports are provided such as discussion boards, or positive marking schemes for continuous online assessments. As such, blended learning is not yet a norm across modules for first-year students at this current time. Methods The research developed on experience gained in a previous study on a prior


A Facial Expression Recognition System Using Convolutional Networks

Facial expression recognition has been an active research area in the past ten years, with a growing application area like avatar animation and neuromarketing. The recognition of facial expressions is not an easy problem for machine learning methods, since different people can vary in the way that they show their expressions. And even an image of the same person in one expression can vary in brightness, background and position. Therefore, facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of standard methods, like Convolutional Network and specific image pre-processing steps. Convolutional networks, and the most machine learning methods, achieve better accuracy depending on a given feature set. Therefore, a study of some image pre-processing operations that extract only expression specific features of a face image is also presented. The experiments were carried out using a largely used public database for this problem. A study of the impact of each image pre-processing operation in the accuracy rate is presented. To the best of our knowledge, our method achieves the best result in the literature, 97.81% of accuracy, and takes less time to train than state-of-the-art methods.


Analysis of user keyword similarity in online social networks

How do two people become friends? What role does homophily play in bringing two people closer to help them forge friendship? Is the similarity between two friends different from the similarity between any two people? How does the similarity between a friend of a friend compare to similarity between direct friends? In this work, our goal is to answer these questions. We study the relationship between semantic similarity of user profile entries and the social network topology. A user profile in an on-line social network is characterized by its profile entries. The entries are termed as user keywords. We develop a model to relate keywords based on their semantic relationship and define similarity functions to quantify the similarity between a pair of users. First, we present a ‘forest model’ to categorize keywords across multiple categorization trees and define the notion of distance between keywords. Second, we use the keyword distance to define similarity functions between a pair of users. Third, we analyze a set of Facebook data according to the model to determine the effect of homophily in on-line social networks. Based on our evaluations, we conclude that direct friends are more similar than any other user pair. However, the more striking observation is that except for direct friends, similarities between users are approximately equal, irrespective of the topological distance between them.


"Industrie 4.0" and Smart Manufacturing - A Review of Research Issues and Application Examples


Effects of energy drinks on the cardiovascular system

Throughout the last decade, the use of energy drinks has been increasingly looked upon with caution as potentially dangerous due to their perceived strong concentration of caffeine aside from other substances such as taurine, guarana, and L-carnitine that are largely unknown to the general public. In addition, a large number of energy drink intoxications have been reported all over the world including cases of seizures and arrhythmias. In this paper, we focus on the effect of energy drinks on the cardiovascular system and whether the current ongoing call for the products' sales and regulation of their contents should continue.


Boosting item keyword search with spreading activation

Most keyword search engines return directly matching keyword phrases. However, publishers cannot anticipate all possible ways in which users would search for the items in their documents. In fact, many times, there may be no direct keyword match between a keyword search phrase and descriptions of relevant items that are perfect matches for the search. We present an automated, high precision-based information retrieval solution to boost item find-ability by bridging the semantic gap between item information and popular keyword search phrases. Our solution achieves an average of 80% F-measure for various boosted matches for keyword search phrases in various categories.


Advanced Cooling for Power Electronics

Power electronics devices such as MOSFET's, GTO's, IGBT's, IGCT's etc. are now widely used to efficiently deliver electrical power in home electronics, industrial drives, telecommunication, transport, electric grid and numerous other applications. This paper discusses cooling technologies that have evolved in step to remove increasing levels of heat dissipation and manage junction temperatures to achieve goals for efficiency, cost, and reliability. Cooling technologies rely on heat spreading and convection. In applications that use natural or forced air cooling, water heat pipes provide efficient heat spreading for size and weight reduction. Previous concepts are reviewed and an improved heat sink concept with staggered fin density is described for more isothermal cooling. Where gravity can drive liquid flow, thermosiphons provide efficient heat transport to remote fin volumes that can be oriented for natural and/or forced air cooling. Liquid cold plates (LCP's) offer the means to cool high heat loads and heat fluxes including double sided cooling for the highest density packaging. LCP's can be used both in single phase cooling systems with aqueous or oil based coolants and in two-phase cooling systems with dielectric fluids and refrigerants. Previous concepts are reviewed and new concepts including an air cooled heat sink, a thermosiphon heat sink, a vortex flow LCP and a shear flow direct contact cooling concept are described.


Evaluating a PID, pure pursuit, and weighted steering controller for an autonomous land vehicle

Wright Laboratory, at Tyndall AFB, Florida, has contracted the University ofFlorida to develop autonomous navigation systems for a variety ofrobotic vehicles, capable ofperforming tasks associated with the location and removal of bombs and mines. One ofthe tasks involves surveying closed target ranges for unexploded buried munitions. Accuracy in path following is critical to the task. There are hundreds of acres that currently require surveying. The sites are typically divided into regions, where each mission can take up to 4.5 hours. These sites are usually surveyed along parallel rows. By improving the accuracy ofpath following, the distance betweenthe rows can be increased to nearly the detection width ofthe ground penetrating sensors, resulting in increased acreage surveyed per mission. This paper evaluates a high-level PID and a pure pursuit steering controller. The controllers were combined into a weighted solution so that the desirable features of each controller is preserved. This strategy was demonstrated in simulation and implemented on a Navigation Test Vehicle (NTV). For a test path ofvarying curvature, the average lateral control error was 2 cm at a vehicle speed of 1.34 mIs.


A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification

Convolutional Neural Networks (CNNs) have recently achieved remarkably strong performance on sentence classification tasks (Kim, 2014; Kalchbrenner et al., 2014). However, these models require practitioners to specify the exact model architecture and accompanying hyperparameters, e.g., the choice of filter region size, regularization parameters, and so on. It is currently unknown how sensitive model performance is to changes in these configurations for the task of sentence classification. We thus conduct a sensitivity analysis of one-layer CNNs to explore the effect of architecture components on model performance; our aim is to distinguish between important and comparatively inconsequential design decisions for sentence classification. We focus on one-layer CNNs (to the exclusion of more complex models) due to their comparative simplicity and strong empirical performance (Kim, 2014). We derive practical advice from our extensive empirical results for those interested in getting the most out of CNNs for sentence classification. One important observation borne out by our experimental results is that researchers should report performance variances, as these can be substantial due to stochastic initialization and inference.


Effective Use of Word Order for Text Categorization with Convolutional Neural Networks

Convolutional neural network (CNN) is a neural network that can make use of the internal structure of data such as the 2D structure of image data. This paper studies CNN on text categorization to exploit the 1D structure (namely, word order) of text data for accurate prediction. Instead of using low-dimensional word vectors as input as is often done, we directly apply CNN to high-dimensional text data, which leads to directly learning embedding of small text regions for use in classification. In addition to a straightforward adaptation of CNN from image to text, a simple but new variation which employs bag-ofword conversion in the convolution layer is proposed. An extension to combine multiple convolution layers is also explored for higher accuracy. The experiments demonstrate the effectiveness of our approach in comparison with state-of-the-art methods.


Improving TCP Throughput over Two-Way Asymmetric Links: Analysis and Solutions

The sharing of a common buffer by TCP data segments and acknowledgments in a network or internet has been known to produce the effect of ack compression, often causing dramatic reductions in throughput. We study several schemes for improving the performance of two-way TCP traffic over asymmetric links where the bandwidths in the two directions may differ substantially, possibly by many orders of magnitude. These approaches reduce the effect of ack compression by carefully controlling the flow of data packets and acknowledgments. We first examine a scheme where acknowledgments are transmitted at a higher priority than data. By analysis and simulation, we show that prioritizing acks can lead to starvation of the low-bandwidth connection. Next, we introduce and analyze a connection-level backpressure mechanism designed to limit the maximum amount of data buffered in the outgoing IP queue of the source of the low-bandwidth connection. We show that this approach, while minimizing the queueing delay for acks, results in unfair bandwidth allocation on the slow link. Finally, our preferred solution separates the acks from data packets in the outgoing queue, and makes use of a connection-level bandwidth allocation mechanism to control their bandwidth shares. We show that this scheme overcomes the limitations of the previous approaches, provides isolation, and enables precise control of the connection throughputs. We present analytical models of the dynamic behavior of each of these approaches, derive closed-form expressions for the expected connection efficiencies in each case, and validate them with simulation results.


Multiagent Systems Engineering

This paper describes the Multiagent Systems Engineering (MaSE) methodology. MaSE is a general purpose, methodology for developing heterogeneous multiagent systems. MaSE uses a number of graphically based models to describe system goals, behaviors, agent types, and agent communication interfaces. MaSE also provides a way to specify architecture-independent detailed definition of the internal agent design. An example of applying the MaSE methodology is also presented.


RoXSum: Leveraging Data Aggregation and Batch Processing for XML Routing

Content-based routing is the primary form of communication within publish/subscribe systems. In those systems data transmission is performed by sophisticated overlay networks of content-based routers, which match data messages against registered subscriptions and forward them based on this matching. Despite their inherent complexities, such systems are expected to deliver information in a timely and scalable fashion. As a result, their successful deployment is a strenuous task. Relevant efforts have so far focused on the construction of the overlay network and the filtering of messages at each broker. However, the efficient transmission of messages has received less attention. In this work, we propose a solution that gracefully handles the transmission task, while providing performance benefits for the matching task as well. Along those lines, we design RoXSum, a message representation scheme that aggregates the routing information from multiple documents in a way that permits subscription matching directly on the aggregated content. Our performance study shows that RoXSum is a viable and effective technique, as it speeds up message routing for more than an order of magnitude.


Relation Extraction Using Multi-Encoder LSTM Network on a Distant Supervised Dataset

Relation extraction techniques are used to find potential relational facts in textual content. Relation Extraction systems require huge amount of training data to model semantics of sentences and identify relations. Distant supervision, often used to construct training data for relation extraction, produces noisy alignments that can hurt the performance of relation extraction systems. To this end, we propose a simple, yet effective, technique to automatically compute confidence levels of alignments. We compute the confidence values of automatically labeled content using co-occurrence statistics of relations and dependency patterns of aligned sentences. Thereafter, we propose a novel multi-encoder bidirectional Long Short Term Memory (LSTM) model to identify relations in a given sentence. We use different features (words, part-of-speech (POS) tags and dependency paths) in each encoder and concatenate the hidden states of all the encoders to predict the relations. Our experiments show that a multi-encoder network can handle different features together to predict relations more accurately (~9% improvement over a single encoder model). We also conduct visualization experiments to show that our model learns intermediate representations effectively to identify relations in sentences.


REFLECTIONS ON FREQUENCY EFFECTS IN LANGUAGE PROCESSING

This response addresses the following points raised in the commentaries: (a) complementary learning mechanisms, the distinction between explicit and implicit memory, and the neuroscience of “noticing”; (b) what must and what need not be noticed for learning; (c) when frequency fails to drive learning, which addresses factors such as failing to notice cues, perseveration, transfer from L1, developmental readiness, thinking too hard, pedagogical input, and practicing; (d) attention and form-focused instruction; (e) conscious and unconscious knowledge of frequency; (f) sequences of acquisition—from formula, through low-scope pattern, to construction; (g) the Fundamental Difference hypothesis; (h) the blind faith of categorical grammar; (i) Labovian variationist perspectives; (j) parsimony and theory testing; (k) universals and predispositions; and (l) wanna-contractions. It concludes by emphasizing that language acquisition is a process of dynamic emergence and that learners’ language is a product of their history of usage in communicative interaction.


OPAQUE: An Asymmetric PAKE Protocol Secure Against Pre-Computation Attacks

Password-Authenticated Key Exchange (PAKE) protocols allow two parties that only share a password to establish a shared key in a way that is immune to offline attacks. Asymmetric PAKE (aPAKE) strengthens this notion for the more common client-server setting where the server stores a mapping of the password and security is required even upon server compromise, that is, the only allowed attack in this case is an (inevitable) offline exhaustive dictionary attack against individual user passwords. Unfortunately, current aPAKE protocols (that dispense with the use of servers’ public keys) allow for pre-computation attacks that lead to the instantaneous compromise of user passwords upon server compromise, thus forgoing much of the intended aPAKE security. Indeed, these protocols use – in essential ways – deterministic password mappings or use random “salt” transmitted in the clear from servers to users, and thus are vulnerable to pre-computation attacks. We initiate the study of Strong aPAKE protocols that are secure as aPAKE’s but are also secure against pre-computation attacks. We formalize this notion in the Universally Composable (UC) settings and present two modular constructions using an Oblivious PRF as a main tool. The first builds a Strong aPAKE from any aPAKE (which in turn can be constructed from any PAKE [23]) while the second builds a Strong aPAKE from any authenticated key-exchange protocol secure against reverse impersonation (a.k.a. KCI). Using the latter transformation, we show a practical instantiation of a UC-secure Strong aPAKE in the Random Oracle model. The protocol (“OPAQUE”) consists of 2 messages (3 with mutual authentication), requires 3 and 4 exponentiations for server and client, respectively (2 to 4 of which can be fixed-base depending on optimizations), provides forward secrecy, is PKI-free, supports user-side hash iterations, has a built-in facility for password-based storage and retrieval of secrets and credentials, and accommodates a user-transparent server-side threshold implementation.


Death by complete decapitation of motorcyclist wearing full face helmet: case report.

We describe a case of complete decapitation following a motorcycle accident in which the victim was wearing a full face helmet. A young man lost control of his motorcycle and was thrown about 20 m, hitting his head against the barrier separating a tramline from the road. The resulting trauma caused his decapitation, the only fatal wound ascertained by the various forensic investigations. The authors present this rare case and compare it against the other two cases reported in the literature, providing some observations on the ways in which this injury can come about. The absence of abrasions or signs that the wound edges came into contact with a metal structure, the presence of signs of impact on the side of the helmet and the finding of a transversal fracture at the base of the skull point to the violent action of a side-to-side opposite force, due to the resistance provided by the lower edge of the protective helmet.


A Regularization Method with Inference of Trust and Distrust in Recommender Systems

In this study we investigate the recommendation problem with trust and distrust relationships to overcome the sparsity of users’ preferences, accounting for the fact that users trust the recommendations of their friends, and they do not accept the recommendations of their foes. In addition, not only users’ preferences are sparse, but also users’ social relationships. So, we first propose an inference step with multiple random walks to predict the implicit-missing trust relationships that users might have in recommender systems, while considering users’ explicit trust and distrust relationships during the inference. We introduce a regularization method and design an objective function with a social regularization term to weigh the influence of friends’ trust and foes’ distrust degrees on users’ preferences. We formulate the objective function of our regularization method as a minimization problem with respect to the users’ and items’ latent features and then we solve our recommendation problem via gradient descent. Our experiments confirm that our approach preserves relatively high recommendation accuracy in the presence of sparsity in both the users’ preferences and social relationships, significantly outperforming several state-of-the-art methods.


A New Algorithm of Fully-automatic Red-eye Removal

the redeye effect is typically formed in consumer photos taken with a built-in camera flash. The paper presents a new algorithm of the automatic removal of redeye from digital photos. The proposed algorithm can remove red-eye automatically without manual intervention. First, Detecting faces by using the AdaBoost algorithm. Second, the red-eyes are located by using operations of segmentation, morphology and geometric constraint. Finally, correcting the located red-eyes completely. The experimental results are satisfied with high correction rates, relatively low computational complexity and robustness.


Autonomous Underwater Vehicles : Trends and Transformations

Three examples of inter-agency cooperation utilizing current generation, individual Autonomous Underwater Vehicles (AUVs) are described consistent with recent recommendations of the U.S. Commission on Ocean Policy. The first steps in transforming individual AUVs into adaptive, networked systems are underway. To realize an affordable and deployable system, a network-class AUV must be designed with cost–size constraints not necessarily applied in developing solo AUVs. Vehicle types are suggested based on function and ocean operating regime: surface layer, interior and bottom layer. Implications for platform, navigation and control subsystems are explored and practical formulations for autonomy and intelligence are postulated for comparing performance and judging behavior. Laws and conventions governing intelligent maritime navigation are reviewed and an autonomous controller with conventional collision avoidance behavior is described. Network-class cost constraints can be achieved through economies of scale. Productivity and efficiency in AUV manufacturing will increase if constructive competition is maintained. Constructive strategies include interface and operating standards. Professional societies and industry trade groups have a leadership role to play in establishing public, open standards. cations are described at many conferences and in an expanding literature of journal publications. Griffiths (2003) provides recent developments in AUV design, construction and operation. A number of commercial manufacturers have emerged to supply the growing market. Clearly, individual AUVs are evolving into useful tools that extend current measurement methods. Three examples involving current generation, individual AUVs will serve to illustrate trends in inter-agency cooperation utilizing this technology. Following these examples, we examine factors that will transform current measurement methods. Network externalities associated with interagency cooperation will play a role in driving this transformation. Recently the Navy joined with the U. S. Environmental Protection Agency, the Narragansett Bay Estuary Program, and the Autonomous Undersea Systems Institute to demonstrate the effectiveness of using a Solar Powered Autonomous Underwater Vehicle (SAUV II) to measure dissolved oxgyen concentrations in Greenwich Bay, Rhode Island. Utilization of an AUV to rapidly move continuously-recording dissolved oxygen sensors over large spatial areas to map the frequency and extent of hypoxia demonstrates cost-effective water quality monitoring (Crimmins, 2005). Real-time monitoring of dissolved oxygen over large spatial scales is important for predicting the onset of hypoxia and mapping its extent (RI Dept Env Mgmt, 2004). The SAUV II (Figure 1) is a solar powered AUV designed for long endurance missions such as monitoring, surveillance, or station keeping where real time bi-directional communications to shore are critical. The SAUV II operates continuously for several months using solar energy to recharge its lithium ion batteries during daylight hours. The SAUV II was fitted with a fast-response galvanic oxygen micro-sensor (AMT Analysenmesstechnik GmbH). This sensor provides rapid in situ profiling of dissolved oxygen at depths of up to 100 m with a response time of a few hundred milliseconds. The SAUV II was also fitted with a NXIC conductivity-temperature-depth (CTD) sensor (Falmouth Scientific Inc.). CTD data are essential for detecting water column stratification, which affects the development and breakdown of hypoxia (Crimmins, 2005). T T R E N D S o move forward wisely with ocean science and technology, the United States needs a unified strategy that engages national and international collaboration. Part VII of the Report by the U.S. Commission on Ocean Policy, “Science-Based Decisions: Understanding of the Oceans,” not only affirms this assertion, but also identifies a framework for federal agencies to proactively develop and implement policies that embrace the spirit of inter-agency cooperation and cost-effective shared technology. The Commission states that a larger, coordinated investment in infrastructure and technology development is necessary to conduct and support ocean science (Commissioners, 2004; Crimmins, 2004a). Future ocean sampling and surveillance systems should be capable of global deployment, sustained presence, three dimensional adaptive aperture, real time control and robust performance. These requirements can be met affordably by a network of Autonomous Underwater Vehicles (AUVs). The first generation of AUVs has transitioned from dream to reality (Griffiths, 2003). Engineering progress is now regularly reported and appli-


Ensemble Classifiers for Network Intrusion Detection System

Two of the major challenges in designing anomaly intrusion detection are to maximize detection accuracy and to minimize false alarm rate. In addressing this issue, this paper proposes an ensemble of one-class classifiers where each adopts different learning paradigms. The techniques deployed in this ensemble model are; Linear Genetic Programming (LGP), Adaptive Neural Fuzzy Inference System (ANFIS) and Random Forest (RF). The strengths from the individual models were evaluated and ensemble rule was formulated. Prior to classification, a 2-tier feature selection process was performed to expedite the detection process. Empirical results show an improvement in detection accuracy for all classes of network traffic; Normal, Probe, DoS, U2R and R2L. Random Forest, which is an ensemble learning technique that generates many classification trees and aggregates the individual result was also able to address imbalance dataset problem that many of machine learning techniques fail to sufficiently address it.


Design and implementation of digital fractional order PID controller using optimal pole-zero approximation method for magnetic levitation system

The aim of this paper is to employ fractional order proportional integral derivative ( FO-PID ) controller and integer order PID controller to control the position of the levitated object in a magnetic levitation system ( MLS ), which is inherently nonlinear and unstable system. The proposal is to deploy discrete optimal pole-zero approximation method for realization of digital fractional order controller. An approach of phase shaping by slope cancellation of asymptotic phase plots for zeros and poles within given bandwidth is explored. The controller parameters are tuned using dynamic particle swarm optimization ( dPSO ) technique. Effectiveness of the proposed control scheme is verified by simulation and experimental results. The performance of realized digital FO-PID controller has been compared with that of the integer order PID controllers. It is observed that effort required in fractional order control is smaller as compared with its integer counterpart for obtaining the same system performance.


Buddha ' s Brain : Neuroplasticity and Meditation

In a recent visit to the United States, the Dalai Lama gave a speech at the Society for Neuroscience's annual meeting in Washington, D.C. Over the past several years, he has helped recruit Tibetan Buddhist monks for, and directly encouraged research on the brain and meditation in the Waisman Laboratory for Brain Imaging and Behavior at the University of Wisconsin-Madison. The findings from studies in this unusual sample as well as related research efforts, suggest that, over the course of meditating for tens of thousands of hours, the long-term practitioners had actually altered the structure and function of their brains. In this article we discuss neuroplasticity, which encompasses such alterations, and the findings from these studies. Further, we comment on the associated signal processing (SP) challenges, current status and how SP can contribute to advance these studies.


The most effective exercise for strengthening the supraspinatus muscle: evaluation by magnetic resonance imaging.

BACKGROUND
Electromyography has been used to determine the best exercise for strengthening the supraspinatus muscle, but conflicting results have been reported. Magnetic resonance imaging T2 relaxation time appears to be more accurate in determining muscle activation.

PURPOSE
To determine the best exercises for strengthening the supraspinatus muscle.

STUDY DESIGN
Criterion standard.

METHODS
Six male volunteers performed three exercises: the empty can, the full can, and horizontal abduction. Immediately before and after each exercise, magnetic resonance imaging examinations were performed and changes in relaxation time for the subscapularis, supraspinatus, infraspinatus, teres minor, and deltoid muscles were recorded.

RESULTS
The supraspinatus muscle had the greatest change among the studied muscles in relaxation time for the empty can (10.5 ms) and full can (10.5 ms) exercises. After the horizontal abduction exercise the change in relaxation time for the supraspinatus muscle (3.6 ms) was significantly smaller than that for the posterior deltoid muscle (11.5 ms) and not significantly different from that of the other muscles studied.

CONCLUSION
The empty can and full can exercises were most effective in activating the supraspinatus muscle.


Neural Network Model for Earthquake Prediction Using DMETER Data and Seismic Belt Information

The mechanism of the earthquake remains to be investigated, though some anomalies associated with earthquakes have been found by DEMETER satellite observations. The next step is to use the DEMETER data for earthquake prediction. It is a useful and feasible way to use the self-adaptive artificial neural network to construct relations between various symptom factors and earthquake occurrences. The back-propagation neural network is quite suitable to express the nonlinear relation between earthquake and various anomalies. In this paper a series of physical quantities measured by the DEMETER satellite including Electron density, Electron temperature, ions temperature and oxygen ion density, together with seismic belt information are used to form sample sets for a back-propagation neural network. The neural network model then can be used to conduct the prediction. In the end, validation tests are performed based on those important seismic events happened in 2008.


Collaborative computer-aided design - research and development status

In order to facilitate product design and realization processes, presently, research is actively carried out for developing methodologies and technologies of collaborative computer-aided design systems to support design teams geographically dispersed based on the quickly evolving information technologies. In this paper, the developed collaborative systems, methodologies and technologies, which are organized as a horizontal or a hierarchical manner, are reviewed. Meanwhile, a 3D streaming technology, which can effectively transmit visualization information across networks for Web applications, is highlighted and the algorithms behind it are disclosed.


Analyzing the performance differences between pattern matching and compressed pattern matching on texts

In this study the statistics of pattern matching on text data and the statistics of compressed pattern matching on compressed form of the same text data are compared. A new application has been developed to count the character matching numbers in compressed and uncompressed texts individually. Also a new text compression algorithm that allows compressed pattern matching by using classical pattern matching algorithms without any change is presented in this paper. In this paper while the presented compression algorithm based on digram and trigram substitution has been giving about 30-35% compression factor, the duration of compressed pattern matching on compressed text is calculated less than the duration of pattern matching on uncompressed text. Also it is confirmed that the number of character comparison on compressed texts while doing a compressed pattern matching is less than the number of character comparison on uncompressed texts. Thus the aim of the developed compression algorithm is to point out the difference in text processing between compressed and uncompressed text and to form opinions for another applications.


A restricted Boltzmann machine based two-lead electrocardiography classification

An restricted Boltzmann machine learning algorithm were proposed in the two-lead heart beat classification problem. ECG classification is a complex pattern recognition problem. The unsupervised learning algorithm of restricted Boltzmann machine is ideal in mining the massive unlabelled ECG wave beats collected in the heart healthcare monitoring applications. A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs. In this paper a deep belief network was constructed and the RBM based algorithm was used in the classification problem. Under the recommended twelve classes by the ANSI/AAMI EC57: 1998/(R)2008 standard as the waveform labels, the algorithm was evaluated on the two-lead ECG dataset of MIT-BIH and gets the performance with accuracy of 98.829%. The proposed algorithm performed well in the two-lead ECG classification problem, which could be generalized to multi-lead unsupervised ECG classification or detection problems.


Energy-Aware Genetic Algorithms for Task Scheduling in Cloud Computing

For the cloud computing, task scheduling problems are of paramount importance. It becomes more challenging when takes into account energy consumption, traditional make span criteria and users QoS as objectives. This paper considers independent tasks scheduling in cloud computing as a bi-objective minimization problem with make span and energy consumption as the scheduling criteria. We use Dynamic Voltage Scaling (DVS) to minimize energy consumption and propose two algorithms. These two algorithms use the methods of unify and double fitness to define the fitness function and select individuals. They adopt the genetic algorithm to parallel find the reasonable scheduling scheme. The simulation results demonstrate the two algorithms can efficiently find the right compromise between make span and energy consumption.


A* Parsing: Fast Exact Viterbi Parse Selection

A PCFGparsingcandramaticallyreducethetime requiredto find theexactViterbi parseby conservatively estimatingoutsideViterbi probabilities.Wediscussvariousestimatesandgive efficientalgorithmsfor computingthem.On Penntreebanksentences, ourmostdetailedestimate reducesthetotalnumberof edgesprocessedto lessthan3%of thatrequiredby exhausti ve parsing,andevenasimplerestimatewhichcanbepre-computedin underaminutestill reduces thework by a factorof 5. ThealgorithmextendstheclassicA graphsearchprocedureto a certainhypergraphassociatedwith parsing.Unlike best-firstandfinite-beammethodsfor achieving thiskind of speed-up, theA parseris guaranteedto returnthemostlikely parse,not justanapproximation.Thealgorithmis alsocorrectfor awide rangeof parsercontrolstrategies andmaintainsaworst-casecubictime bound. A Parsing: FastExact Viterbi ParseSelection


Statistical language learning

The $64,000 question in computational linguistics these days is: “What should I read to learn about statistical natural language processing?” I have been asked this question over and over, and each time I have given basically the same reply: there is no text that addresses this topic directly, and the best one can do is find a good probability-theory textbook and a good information-theory textbook, and supplement those texts with an assortment of conference papers and journal articles. Understanding the disappointment this answer provoked, I was delighted to hear that someone had finally written a bookdirectly addressing this topic. However, after reading Eugene Charniak’s Statistical Language Learning, I have very mixed feelings about the impact this bookmight have on the ever-growing field of statistical NLP. The book begins with a very brief description of the classic artificial intelligence approach toNLP (chapter 1), including morphology, syntax, semantics, and pragmatics. It presents a few definitions from probability theory and information theory (chapter 2), then proceeds to introduce hidden Markov models (chapters 3–4) and probabilistic context-free grammars (chapters 5–6). The book concludes with a few chapters discussing advanced topics in statistical language learning, such as grammar induction (chapter 7), syntactic disambiguation (chapter 8), word clustering (chapter 9), andword sense disambiguation (chapter 10). To its credit, the book serves as an interesting popular discussion of statistical modeling in NLP. It is well-written and entertaining, and very accessible to the reader with a limited mathematical background. It presents a good selection of statistical NLP topics to introduce the reader to the field. And the descriptions of the forward-backward algorithm for hidden Markov models and the inside-outside algorithm for probabilistic context-free grammars are intuitive and easy to follow. However, as a resource for someone interested in entering this area of research, this book falls far short of its author’s goals. These goals are clearly stated in the preface:


The PASCAL Recognising Textual Entailment Challenge

This paper describes the PASCAL Network of Excellence first Recognising Textual Entailment (RTE-1) Challenge benchmark. The RTE task is defined as recognizing, given two text fragments, whether the meaning of one text can be inferred (entailed) from the other. This application-independent task is suggested as capturing major inferences about the variability of semantic expression which are commonly needed across multiple applications. The Challenge has raised noticeable attention in the research community, attracting 17 submissions from diverse groups, suggesting the generic relevance of the task.


Adaptive duplicate detection using learnable string similarity measures

The problem of identifying approximately duplicate records in databases is an essential step for data cleaning and data integration processes. Most existing approaches have relied on generic or manually tuned distance metrics for estimating the similarity of potential duplicates. In this paper, we present a framework for improving duplicate detection using trainable measures of textual similarity. We propose to employ learnable text distance functions for each database field, and show that such measures are capable of adapting to the specific notion of similarity that is appropriate for the field's domain. We present two learnable text similarity measures suitable for this task: an extended variant of learnable string edit distance, and a novel vector-space based measure that employs a Support Vector Machine (SVM) for training. Experimental results on a range of datasets show that our framework can improve duplicate detection accuracy over traditional techniques.


Scaling Textual Inference to the Web

Most Web-based Q/A systems work by finding pages that contain an explicit answer to a question. These systems are helpless if the answer has to be inferred from multiple sentences, possibly on different pages. To solve this problem, we introduce the HOLMES system, which utilizes textual inference (TI) over tuples extracted from text. Whereas previous work on TI (e.g., the literature on textual entailment) has been applied to paragraph-sized texts, HOLMES utilizes knowledge-based model construction to scale TI to a corpus of 117 million Web pages. Given only a few minutes, HOLMES doubles recall for example queries in three disparate domains (geography, business, and nutrition). Importantly, HOLMES’s runtime is linear in the size of its input corpus due to a surprising property of many textual relations in the Web corpus—they are “approximately” functional in a well-defined sense.


Diversified Visual Attention Networks for Fine-Grained Object Classification

Fine-grained object classification attracts increasing attention in multimedia applications. However, it is a quite challenging problem due to the subtle interclass difference and large intraclass variation. Recently, visual attention models have been applied to automatically localize the discriminative regions of an image for better capturing critical difference, which have demonstrated promising performance. Unfortunately, without consideration of the diversity in attention process, most of existing attention models perform poorly in classifying fine-grained objects. In this paper, we propose a diversified visual attention network (DVAN) to address the problem of fine-grained object classification, which substantially relieves the dependency on strongly supervised information for learning to localize discriminative regions com-pared with attention-less models. More importantly, DVAN explicitly pursues the diversity of attention and is able to gather discriminative information to the maximal extent. Multiple attention canvases are generated to extract convolutional features for attention. An LSTM recurrent unit is employed to learn the attentiveness and discrimination of attention canvases. The proposed DVAN has the ability to attend the object from coarse to fine granularity, and a dynamic internal representation for classification is built up by incrementally combining the information from different locations and scales of the image. Extensive experiments conducted on CUB-2011, Stanford Dogs, and Stanford Cars datasets have demonstrated that the pro-posed DVAN achieves competitive performance compared to the state-of-the-art approaches, without using any prior knowledge, user interaction, or external resource in training and testing.


DB2 Design Advisor: Integrated Automatic Physical Database Design

The DB2 Design Advisor in IBM® DB2® Universal DatabaseTM (DB2 UDB) Version 8.2 for Linux®, UNIX® and Windows® is a tool that, for a given workload, automatically recommends physical design features that are any subset of indexes, materialized query tables (also called materialized views), shared-nothing database partitionings, and multidimensional clustering of tables. Our work is the very first industrial-strength tool that covers the design of as many as four different features, a significant advance to existing tools, which support no more than just indexes and materialized views. Building such a tool is challenging, because of not only the large search space introduced by the interactions among features, but also the extensibility needed by the tool to support additional features in the future. We adopt a novel “hybrid” approach in the Design Advisor that allows us to take important interdependencies into account as well as to encapsulate design features as separate components to lower the reengineering cost. The Design Advisor also features a built-in module that automatically reduces the given workload, and therefore provides great scalability for the tool. Our experimental results demonstrate that our tool can quickly provide good physical design recommendations that satisfy users’ requirements.


Differential Privacy as a Mutual Information Constraint

Differential privacy is a precise mathematical constraint meant to ensure privacy of individual pieces of information in a database even while queries are being answered about the aggregate. Intuitively, one must come to terms with what differential privacy does and does not guarantee. For example, the definition prevents a strong adversary who knows all but one entry in the database from further inferring about the last one. This strong adversary assumption can be overlooked, resulting in misinterpretation of the privacy guarantee of differential privacy. Herein we give an equivalent definition of privacy using mutual information that makes plain some of the subtleties of differential privacy. The mutual-information differential privacy is in fact sandwiched between ε-differential privacy and (ε,δ)-differential privacy in terms of its strength. In contrast to previous works using unconditional mutual information, differential privacy is fundamentally related to conditional mutual information, accompanied by a maximization over the database distribution. The conceptual advantage of using mutual information, aside from yielding a simpler and more intuitive definition of differential privacy, is that its properties are well understood. Several properties of differential privacy are easily verified for the mutual information alternative, such as composition theorems.


Large-signal-modulation of high-efficiency light-emitting diodes for optical communication

The dynamic behavior of high-efficiency light-emitting diodes (LEDs) is investigated theoretically and experimentally. A detailed theoretical description of the switch-on and switch-off transients of LEDs is derived. In the limit of small-signal modulation, the well-established exponential behavior is obtained. However, in the case of high injection, which is easily reached for thin active layer LEDs, the small-signal time constant is found to be up to a factor of two faster than the radiative recombination lifetime. Using such quantum-well LEDs, we have demonstrated optical data transfer with wide open eye diagrams at bit rates up to 2 Gbit/s. In addition, we have combined the use of thin active layers with the concept of surface-textured thin-film LEDs, which allow a significant improvement in the light extraction efficiency. With LEDs operating at 0.5 Gbit/s and 1 Gbit/s, we have achieved external quantum efficiencies of 36% and 29%, respectively.


Highly Efficient Doherty Amplifier Based on Class-E Topology for WCDMA Applications

This letter reports a high-efficiency gallium nitride (GaN) high-electron mobility transistor (HEMT) Doherty power amplifier (DPA) based on the class-E topology for wideband code-division multiple-access (WCDMA) applications. The class-E topology is employed as the carrier and peaking cells of the Doherty configuration. For validations, the proposed DPA is designed and implemented with 25-W GaN HEMTs at 2.14 GHz. For the proposed DPA, the power-added efficiency (PAE) and drain efficiency of 56.1% and 61.2% are achieved at 40 dBm (6-dB backoff power from Psat) for a continuous wave. For a 1-carrier WCDMA signal, the PAE of 44.8% is obtained with an adjacent channel leakage ratio (ACLR) of -31 dBc at 37 dBm, which is an 8.9% improvement over the conventional DPA with an ACLR of -36.4 dBc.


Botanical insecticides, deterrents, and repellents in modern agriculture and an increasingly regulated world.

Botanical insecticides have long been touted as attractive alternatives to synthetic chemical insecticides for pest management because botanicals reputedly pose little threat to the environment or to human health. The body of scientific literature documenting bioactivity of plant derivatives to arthropod pests continues to expand, yet only a handful of botanicals are currently used in agriculture in the industrialized world, and there are few prospects for commercial development of new botanical products. Pyrethrum and neem are well established commercially, pesticides based on plant essential oils have recently entered the marketplace, and the use of rotenone appears to be waning. A number of plant substances have been considered for use as insect antifeedants or repellents, but apart from some natural mosquito repellents, little commercial success has ensued for plant substances that modify arthropod behavior. Several factors appear to limit the success of botanicals, most notably regulatory barriers and the availability of competing products (newer synthetics, fermentation products, microbials) that are cost-effective and relatively safe compared with their predecessors. In the context of agricultural pest management, botanical insecticides are best suited for use in organic food production in industrialized countries but can play a much greater role in the production and postharvest protection of food in developing countries.


Event-based Sensing for Space Situational Awareness

A revolutionary type of imaging device, known as a silicon retina or event-based sensor, has recently been developed and is gaining in popularity in the field of artificial vision systems. These devices are inspired by a biological retina and operate in a significantly different way to traditional CCD-based imaging sensors. While a CCD produces frames of pixel intensities, an event-based sensor produces a continuous stream of events, each of which is generated when a pixel detects a change in log light intensity. These pixels operate asynchronously and independently, producing an event-based output with high temporal resolution. There are also no fixed exposure times, allowing these devices to offer a very high dynamic range independently for each pixel. Additionally, these devices offer high-speed, low power operation and a sparse spatiotemporal output. As a consequence, the data from these sensors must be interpreted in a significantly different way to traditional imaging sensors and this paper explores the advantages this technology provides for space imaging. The applicability and capabilities of event-based sensors for SSA applications are demonstrated through telescope field trials. Trial results have confirmed that the devices are capable of observing resident space objects from LEO through to GEO orbital regimes. Significantly, observations of RSOs were made during both day-time and nighttime (terminator) conditions without modification to the camera or optics. The event based sensor’s ability to image stars and satellites during day-time hours offers a dramatic capability increase for terrestrial optical sensors. This paper shows the field testing and validation of two different architectures of event-based imaging sensors. An eventbased sensor’s asynchronous output has an intrinsically low data-rate. In addition to low-bandwidth communications requirements, the low weight, low-power and high-speed make them ideally suitable to meeting the demanding challenges required by space-based SSA systems. Results from these experiments and the systems developed highlight the applicability of event-based sensors to ground and space-based SSA tasks.


Separable spectro-temporal Gabor filter bank features: Reducing the complexity of robust features for automatic speech recognition.

To test if simultaneous spectral and temporal processing is required to extract robust features for automatic speech recognition (ASR), the robust spectro-temporal two-dimensional-Gabor filter bank (GBFB) front-end from Schädler, Meyer, and Kollmeier [J. Acoust. Soc. Am. 131, 4134-4151 (2012)] was de-composed into a spectral one-dimensional-Gabor filter bank and a temporal one-dimensional-Gabor filter bank. A feature set that is extracted with these separate spectral and temporal modulation filter banks was introduced, the separate Gabor filter bank (SGBFB) features, and evaluated on the CHiME (Computational Hearing in Multisource Environments) keywords-in-noise recognition task. From the perspective of robust ASR, the results showed that spectral and temporal processing can be performed independently and are not required to interact with each other. Using SGBFB features permitted the signal-to-noise ratio (SNR) to be lowered by 1.2 dB while still performing as well as the GBFB-based reference system, which corresponds to a relative improvement of the word error rate by 12.8%. Additionally, the real time factor of the spectro-temporal processing could be reduced by more than an order of magnitude. Compared to human listeners, the SNR needed to be 13 dB higher when using Mel-frequency cepstral coefficient features, 11 dB higher when using GBFB features, and 9 dB higher when using SGBFB features to achieve the same recognition performance.


Achievements : Re ̄ ections on the Measurement of Women ' s Empowerment

This paper begins from the understanding that women's empowerment is about the process by which those who have been denied the ability to make strategic life choices acquire such an ability. Awide gap separates this processual understanding of empowerment from the more instrumentalist forms of advocacy which have required the measurement and quanti®cation of empowerment. The ability to exercise choice incorporates three inter-related dimensions: resources (de®ned broadly to include not only access, but also future claims, to both material and human and social resources); agency (including processes of decision making, as well as less measurable manifestations of agency such as negotiation, deception and manipulation); and achievements (well-being outcomes). A number of studies of women's empowerment are analysed to make some important methodological points about the measurement of empowerment. The paper argues that these three dimensions of choice are indivisible in determining the meaning of an indicator and hence its validity as a measure of empowerment. The notion of choice is further quali®ed by referring to the conditions of choice, its content and consequences. These quali®cations represent an attempt to incorporate the structural parameters of individual choice in the analysis of women's empowerment. CONCEPTUALIZING EMPOWERMENT


Low power LVDS transmitter with low common mode variation for 1GB/s-per pin operation

This paper presents the design of a low voltage differential signalling (LVDS) transmitter intended using a LCD panel interface and is capable of transmitting 1GB/s per pin data rate. The transmitter is fully LVDS standard compatible which is achieved by employing a common mode feedback circuit to meet all Ac and DC specifications over PVT variation, specifically due to the implementation of feedback circuit, the common mode variation is made very less quiescent current of 5.5mA. Further, the implementation incorporates the idea of sharing of bias blocks among different transmitters in full chip resulting in less active area. The transmitter is implemented in 3.3V, 0.35/spl mu/ CMOS technology. In this implementation the active area (excluding ESD) per transmitter is 0.039 mm square. The initial silicon data (output offset voltage of 1.20V and differential voltage of 320mV) at room temperature shows the consistency of the simulation results.


MAGIC—Memristor-Aided Logic

Memristors are passive components with a varying resistance that depends on the previous voltage applied across the device. While memristors are naturally used as memory, memristors can also be used for other applications, including logic circuits. In this brief, a memristor-only logic family, i.e., memristor-aided logic (MAGIC), is presented. In each MAGIC logic gate, memristors serve as an input with previously stored data, and an additional memristor serves as an output. The topology of a MAGIC nor gate is similar to the structure of a common memristor-based crossbar memory array. A MAGIC nor gate can therefore be placed within memory, providing opportunities for novel non-von Neumann computer architectures. Other MAGIC gates also exist (e.g., and, or, not, and nand) and are described in this brief.


Viewing Real-World Faces in 3D

We present a data-driven method for estimating the 3D shapes of faces viewed in single, unconstrained photos (aka "in-the-wild"). Our method was designed with an emphasis on robustness and efficiency - with the explicit goal of deployment in real-world applications which reconstruct and display faces in 3D. Our key observation is that for many practical applications, warping the shape of a reference face to match the appearance of a query, is enough to produce realistic impressions of the query's 3D shape. Doing so, however, requires matching visual features between the (possibly very different) query and reference images, while ensuring that a plausible face shape is produced. To this end, we describe an optimization process which seeks to maximize the similarity of appearances and depths, jointly, to those of a reference model. We describe our system for monocular face shape reconstruction and present both qualitative and quantitative experiments, comparing our method against alternative systems, and demonstrating its capabilities. Finally, as a testament to its suitability for real-world applications, we offer an open, on-line implementation of our system, providing unique means of instant 3D viewing of faces appearing in web photos.


Sensorized Garments and Textrode-Enabled Measurement Instrumentation for Ambulatory Assessment of the Autonomic Nervous System Response in the ATREC Project

Advances in textile materials, technology and miniaturization of electronics for measurement instrumentation has boosted the development of wearable measurement systems. In several projects sensorized garments and non-invasive instrumentation have been integrated to assess on emotional, cognitive responses as well as physical arousal and status of mental stress through the study of the autonomous nervous system. Assessing the mental state of workers under stressful conditions is critical to identify which workers are in the proper state of mind and which are not ready to undertake a mission, which might consequently risk their own life and the lives of others. The project Assessment in Real Time of the Stress in Combatants (ATREC) aims to enable real time assessment of mental stress of the Spanish Armed Forces during military activities using a wearable measurement system containing sensorized garments and textile-enabled non-invasive instrumentation. This work describes the multiparametric sensorized garments and measurement instrumentation implemented in the first phase of the project required to evaluate physiological indicators and recording candidates that can be useful for detection of mental stress. For such purpose different sensorized garments have been constructed: a textrode chest-strap system with six repositionable textrodes, a sensorized glove and an upper-arm strap. The implemented textile-enabled instrumentation contains one skin galvanometer, two temperature sensors for skin and environmental temperature and an impedance pneumographer containing a 1-channel ECG amplifier to record cardiogenic biopotentials. With such combinations of garments and non-invasive measurement devices, a multiparametric wearable measurement system has been implemented able to record the following physiological parameters: heart and respiration rate, skin galvanic response, environmental and peripheral temperature. To ensure the proper functioning of the implemented garments and devices the full series of 12 sets have been functionally tested recording cardiogenic biopotential, thoracic impedance, galvanic skin response and temperature values. The experimental results indicate that the implemented wearable measurement systems operate according to the specifications and are ready to be used for mental stress experiments, which will be executed in the coming phases of the project with dozens of healthy volunteers.


CPU and/or GPU: Revisiting the GPU Vs. CPU Myth

Parallel computing using accelerators has gained widespre ad research attention in the past few years. In particular, using GPUs for general purpose computing has brought forth several success stories with respect to time taken, cost, power, and other metrics. Howev er, accelerator based computing has significantly relegated the role of CPUs in computation. As CPUs evo lv and also offer matching computational resources, it is important to also include CPUs in the comput ation. We call this thehybrid computing model. Indeed, most computer systems of the present age offe r a d gree of heterogeneity and therefore such a model is quite natural. We reevaluate the claim of a recent paper by Lee et al.(ISCA 20 10). We argue that the right question arising out of Lee et al. (ISCA 2010) should be how to use a CPU+ GPU platform efficiently, instead of whether one should use a CPU or a GPU exclusively. To this end, we experiment with a set of 13 diverse workloads ranging from databases, image processing, spars e matrix kernels, and graphs. We experiment with two different hybrid platforms: one consisting of a 6-c ore Intel i7-980X CPU and an NVidia Tesla T10 GPU, and another consisting of an Intel E7400 dual core CP U with an NVidia GT520 GPU. On both these platforms, we show that hybrid solutions offer good ad vantage over CPU or GPU alone solutions. On both these platforms, we also show that our solutions are 9 0% resource efficient on average. Our work therefore suggests that hybrid computing can offer tremendous advantages at not only research-scale platforms but also the more realistic scale yst ms with significant performance gains and resource efficiency to the large scale user community.


Research on data augmentation for image classification based on convolution neural networks

The performance of deep convolution neural networks will be further enhanced with the expansion of the training data set. For the image classification tasks, it is necessary to expand the insufficient training image samples through various data augmentation methods. This paper explores the impact of various data augmentation methods on image classification tasks with deep convolution Neural network, in which Alexnet is employed as the pre-training network model and a subset of CIFAR10 and ImageNet (10 categories) are selected as the original data set. The data augmentation methods used in this paper include: GAN/WGAN, Flipping, Cropping, Shifting, PCA jittering, Color jittering, Noise, Rotation, and some combinations. Experimental results show that, under the same condition of multiple increasing, the performance evaluation on small-scale data sets is more obvious, the four individual methods (Cropping, Flipping, WGAN, Rotation) perform generally better than others, and some appropriate combination methods are slightly more effective than the individuals.


Ricci Flow for 3D Shape Analysis

Ricci flow is a powerful curvature flow method, which is invariant to rigid motion, scaling, isometric, and conformal deformations. We present the first application of surface Ricci flow in computer vision. Previous methods based on conformal geometry, which only handle 3D shapes with simple topology, are subsumed by the Ricci flow-based method, which handles surfaces with arbitrary topology. We present a general framework for the computation of Ricci flow, which can design any Riemannian metric by user-defined curvature. The solution to Ricci flow is unique and robust to noise. We provide implementation details for Ricci flow on discrete surfaces of either Euclidean or hyperbolic background geometry. Our Ricci flow-based method can convert all 3D problems into 2D domains and offers a general framework for 3D shape analysis. We demonstrate the applicability of this intrinsic shape representation through standard shape analysis problems, such as 3D shape matching and registration, and shape indexing. Surfaces with large nonrigid anisotropic deformations can be registered using Ricci flow with constraints of feature points and curves. We show how conformal equivalence can be used to index shapes in a 3D surface shape space with the use of Teichmuller space coordinates. Experimental results are shown on 3D face data sets with large expression deformations and on dynamic heart data.


DARPA's explainable artificial intelligence (XAI) program

The DARPA's Explainable Artificial Intelligence (XAI) program endeavors to create AI systems whose learned models and decisions can be understood and appropriately trusted by end users. This talk will summarize the XAI program and present highlights from these Phase 1 evaluations.


Automatic Identification of Retinal Arteries and Veins in Fundus Images using Local Binary Patterns

Artery and vein (AV) classification of retinal images is a key to necessary tasks, such as automated measurement of arteriolar-to-venular diameter ratio (AVR). This paper comprehensively reviews the state-of-the art in AV classification methods. To improve on previous methods, a new Local Binary Pattern-based method (LBP) is proposed. Beside its simplicity, LBP is robust against low contrast and low quality fundus images; and it helps the process by including additional AV texture and shape information. Experimental results compare the performance of the new method with the state-of-the art; and also methods with different feature extraction and classification schemas.


Improving code review effectiveness through reviewer recommendations

Effectively performing code review increases the quality of software and reduces occurrence of defects. However, this requires reviewers with experiences and deep understandings of system code. Manual selection of such reviewers can be a costly and time-consuming task. To reduce this cost, we propose a reviewer recommendation algorithm determining file path similarity called FPS algorithm. Using three OSS projects as case studies, FPS algorithm was accurate up to 77.97%, which significantly outperformed the previous approach.


Side-channel leakage aware instruction scheduling

Speed-optimized side-channel protected software implementations of block ciphers are important for the security of embedded IoT devices based on general-purpose microcontrollers. The recent work of Schwabe et al. published at SAC 2016 introduced a bit-sliced implementation of AES and a first-order Boolean-masked version of it, targeting ARM Cortex-M CPU cores. The authors claim to be secure against timing as well as first-order power and electromagnetic side-channel attacks. However, the author's security claims are not taking the actual leakage characteristics of the underlying CPU architecture into account, hence making the scheme potentially vulnerable to first-order attacks in practice. In this work we show indeed that such a masking scheme can be attacked very easily by first-order electromagnetic side-channel attacks. In order to fix the issue and provide practical first-order security, we provide a strategy to schedule program instructions in way that the specific leakage of the CPU does not impair the side-channel countermeasure.


Rationalizing Sentiment Analysis in Tensorflow

Sentiment analysis using deep learning models is a leading subject of interest in Natural Language Processing that is as powerful as it is opaque. Current state-ofthe-art models can produce accurate predictions, but they provide little insight as to why the model predicted this sentiment. Businesses relying on these models might be less likely to act on insight given the lack of evidence for predictions. These people would be more likely to trust such predictions if a brief explanation of the outcome is provided. Recent work by Lei et al [4]. has set forth a framework for a multi-aspect sentiment analysis concurrently providing text rationalization with each prediction. This framework sets forth a two-part approach, which summarizes a review and predicts a sentiment. In this paper, we explore the performance of this framework, seeking to recreate and improve upon it in TensorFlow.


Grid Connected PV Systems with Smart Grid functionality

..................................................................................................................................... ii Declaration ................................................................................................................................ iii Acknowledgements ................................................................................................................... iv Table of


Speech Enhancement Using a-Minimum Mean-Square Error Short-Time Spectral Amplitude Estimator

Absstroct-This paper focuses on the class of speech enhancement systems which capitalize on the major importance of the short-time spectral amplitude (STSA) of the speech signal in its perception. A system which utilizes a minimum mean-square error (MMSE) STSA estimator is proposed and then compared with other widely used systems which are based on Wiener filtering and the " spectral subtraction " algorithm. In this paper we derive the MMSE STSA estimator, based on modeling speech and noise spectral components as statistically independent Gaussian random variables. We analyze the performance of the proposed STSA estimator and compare it with a STSA estimator derived from the Wiener estimator. We also examine the MMSE STSA estimator under uncertainty of signal presence in the noisy observations. In constructing the enhanced signal, the MMSE STSA estimator is combined with the complex exponential of the noisy phase. It is shown here that the latter is the MMSE estimator of the complex exponential of the original phase, which does not affect the STSA estimation. The proposed approach results in a significant reduction of the noise, and provides enhanced speech with colorless residual noise. The complexity of the proposed algorithm is approximately that of other systems in the discussed class.


Elimination of the musical noise phenomenon with the Ephraim and Malah noise suppressor

This paper presents a study of the noise suppression technique proposed by Ephraim and Malah. This technique has been used recently for the restoration of degraded audio recordings because it is free of the frequently encountered ‘musical noise’ artifact. It is demonstrated how this artifact is actually eliminated without bringing distortion to the recorded signal even if the noise is only poorly stationary.


Speech enhancement using a minimum mean-square error log-spectral amplitude estimator


Enhancement and bandwidth compression of noisy speech

Over the past several years there has been considerable attention focused on the problem of enhancement and bandwidth compression of speech degraded by additive background noise. This interest is motivated by several factors including a broad set of important applications, the apparent lack of robustness in current speech-compression systems and the development of several potentially promising and practical solutions. One objective of this paper is to provide an overview of the variety of techniques that have been proposed for enhancement and bandwidth compression of speech degraded by additive background noise. A second objective is to suggest a unifying framework in terms of which the relationships between these systems is more visible and which hopefully provides a structure which will suggest fruitful directions for further research.


Narrative Schema Stability in News Text

We investigate the stability of narrative schemas (Chambers & Jurafsky, 2009) automatically induced from a news corpus, representing recurring narratives in a corpus. If such techniques produce meaningful results, we should expect that small changes to the corpus will result in only small changes to the induced schemas. We describe experiments involving successive ablation of a corpus and cross-validation at each stage of ablation, on schemas generated by three different techniques over a general news corpus and topically-specific subcorpora. We also develop a method for evaluating the similarity between sets of narrative schemas, and thus the stability of the schema induction algorithms. This stability analysis affirms the heterogeneous/homogeneous document category hypothesis first presented in Simonson & Davis (2016), whose technique is problematically limited. Additionally, increased ablation leads to increasing stability, so the smaller the remaining corpus, the more stable schema generation appears to be. We surmise that as a corpus grows larger, novel and more varied narratives continue to appear and stability declines, though at some point this decline levels off as new additions to the corpus consist essentially of “more of the same.”


Content Centric Networking in tactical and emergency MANETs

Reliable and secure content distribution in a disruptive environment is a critical challenge due to high mobile and lossy channels. Traditional IP networking and wireless protocols tend to perform poorly. In this paper, we propose Content Centric Networking (CCN) for emergency wireless ad hoc environments. CCN is a novel communication architecture capable to access and retrieve content by name. This new approach achieves scalability, security, and efficient network resource management in large scale disaster recovery and battlefield networks. Existing Internet CCN schemes cannot be directly applied to wireless mobile ad hoc networks due to different environments and specific limitations. Thus, we must extend the CCN architecture by introducing features and requirements especially designed for disruptive networks. We prove feasibility and performance gain of the new design via implementation and experimentation.


Tunable VHF Miniaturized Helical Filters

In this paper, a miniaturization technique and a tuning concept that enable the realization of highly miniaturized and frequency reconfigurable helical filters are reported for the first time. The proposed filter design is based on tunable helical resonators with an internal volume of only 8.6 cm 3 and variable capacitive loading. A second-order bandpass filter was built and measured as a proof-of-concept demonstrator. The helical filter is manufactured with a CNC process and tuning is achieved through piezoelectrically actuated tuners, which are bonded over the helix apex. A measured center frequency tuning between 257-365 MHz was realized with an insertion loss between 0.3-3.2 dB including connector losses. The measured filter bandwidth varied between 6%-7.7%. Tunable resonators were also manufactured so as to extract the unloaded quality factor. It was measured between 392-639 for a center frequency tuning of 2.5:1 (156.5-395 MHz).


Blind Speech Separation and Enhancement With GCC-NMF

We present a blind source separation algorithm named GCC-NMF that combines unsupervised dictionary learning via non-negative matrix factorization NMF with spatial localization via the generalized cross correlation GCC method. Dictionary learning is performed on the mixture signal, with separation subsequently achieved by grouping dictionary atoms, at each point in time, according to their spatial origins. The resulting source separation algorithm is simple yet flexible, requiring no prior knowledge or information. Separation quality is evaluated for three tasks using stereo recordings from the publicly available SiSEC signal separation evaluation campaign: 3 and 4 concurrent speakers in reverberant environments, speech mixed with real-world background noise, and noisy recordings of a moving speaker. Performance is quantified using perceptually motivated and SNR-based measures with the PEASS and BSS Eval toolkits, respectively. We evaluate the effects of model parameters on separation quality, and compare our approach with other unsupervised and semi-supervised speech separation and enhancement approaches. We show that GCC-NMF is a flexible source separation algorithm, outperforming task-specific approaches in each of the three settings, including both blind as well as several informed approaches that require prior knowledge or information.


Anomaly detection approach using hybrid algorithm of data mining technique

The excessive use of the communication networks, rising of Internet of Things leads to increases the vulnerability to the important and secret information. advance attacking techniques and number of attackers are increasing radically. Intrusion is one of the main threats to the internet. Hence security issues had been big problem, so that various techniques and approaches have been presented to address the limitations of intrusion detection system such as low accuracy, high false alarm rate, and time consuming. This paper proposes a hybrid machine learning technique for network intrusion detection based on combination of K-means clustering and Sequential Minimal Optimization (SMO) classification. It introduces hybrid approach that able to reduce the rate of false positive alarm, false negative alarm rate, to improve the detection rate and detect zero-day attackers. The NSL-KDD dataset has been used in the proposed technique.. The classification has been performed by using Sequential Minimal Optimization. After training and testing the proposed hybrid machine learning technique, the results have shown that the proposed technique (K-mean + SMO) has achieved a positive detection rate of (94.48%) and reduce the false alarm rate to (1.2%) and achieved accuracy of (97.3695%).


A Deep Network with Visual Text Composition Behavior

While natural languages are compositional, how state-of-the-art neural models achieve compositionality is still unclear. We propose a deep network, which not only achieves competitive accuracy for text classification, but also exhibits compositional behavior. That is, while creating hierarchical representations of a piece of text, such as a sentence, the lower layers of the network distribute their layer-specific attention weights to individual words. In contrast, the higher layers compose meaningful phrases and clauses, whose lengths increase as the networks get deeper until fully composing the sentence.


Balanced-to-Unbalanced Bandpass Filters and the Antenna Application

Novel balanced-to-unbalanced (balun) bandpass filters are presented and carefully examined. By properly converting a symmetric four-port balanced-to-balanced bandpass filter to a three-port device, with two ports forming a balanced output and one port remaining unbalanced, a novel three-port balun bandpass filter with both functions of balun and bandpass filter may be realized. Specifically, a balun bandpass filter is implemented with its center frequency at 0.99 GHz and a 3-dB fractional bandwidth (Bomega) of 10.1%. The implemented balun bandpass filter presents an excellent in-band balance performance with common-mode rejection ratio (Rcm) better than 41 dB (the corresponding amplitude and phase imbalances are within 0.003deg and 1deg) over the pass- band. In this study, a wideband balun bandpass filter is also implemented so as to improve the wideband performance of the quasi- Yagi antenna. Being integrated with the proposed wideband balun bandpass filter, the implemented quasi-Yagi antenna achieves a bandwidth of 48% for IS11 | < -10 dB, with front-to-back ratio better than 13 dB, cross-polarization smaller than -20 dB, and peak gain of 3-5 dBi within the wide operating bandwidth.


Active defence through deceptive IPS

Modern security mechanisms such as Unified Threat Management (UTM), Next-Generation Firewalls and Security Information and Event Management (SIEM) have become more sophisticated over recent years, promising advanced security features and immediate mitigation of the most advanced threats. While this appears promising, in practice even this cutting-edge technology often fails to protect modern organisations as they are being targeted by attacks that were previously unknown to the security industry. Most security mechanisms are based on a database of previously known attack artefacts (signatures) and they will fail on slightly modified or new attacks. The need for threat intelligence is in complete contrast with the way current security solutions are responding to the threats they identify, as they immediately block them without attempting to acquire any further information. In this report, we present and evaluate a security mechanism that operates as an intrusion prevention system which uses honeypots to deceive an attacker, prevent a security breach and which allows the potential acquisition of intelligence on each intrusion attempt. a aThis article is published online by Computer Weekly as part of the 2017 Royal Holloway information security thesis series http://www.computerweekly.com/ehandbook/Active-defence-through-deceptive-IPS. It is based on an MSc dissertation written as part of the MSc in Information Security at the ISG, Royal Holloway, University of London. The full thesis is published on the ISG’s website at https://www.royalholloway.ac.uk/isg/.


Gait Generation With Smooth Transition Using CPG-Based Locomotion Control for Hexapod Walking Robot

This paper presents a locomotion control method based on central pattern generator (CPG) for hexapod walking robot to achieve gait generation with smooth transition. By deriving an analytical limit cycle approximation of the Van der Pol oscillator, a simple diffusive coupling scheme is proposed to construct a ring-shape CPG network with phase-locked behavior. The stability of the proposed network is proved using synchronization analysis with guaranteed uniform ultimate boundedness of the synchronous errors. In contrast to conventional numerical methods in tuning parameters of the CPG network, our method provides an explicit result that could analytically determine the network parameters to yield the rhythmic waveforms with prescribed frequency, amplitude, and phase relationship among neurons. Employing the proposed network to govern the swing/stance phase according to the profile of the resulting CPG signals, a locomotion control strategy for the hexapod robot is further developed to manipulate the leg movements during the gait cycle. By simply adjusting the phase lags of the CPG network, the proposed control strategy is capable of generating stable walking gaits for hexapod robots and achieving smooth transition among the generated gaits. The simulation and experimental results have demonstrated the effectiveness of the proposed locomotion control method.


Microbial Fuel Cells for Wastewater Treatment

1.1 Screening The influent is strained to remove all large objects carried in the sewage stream. This is most commonly performed with an automated mechanically-raked bar screen in modern plants serving large populations, whilst in smaller or less modern plants a manually-cleaned screen may be used. The raking action of a mechanical bar screen is typically paced according to the accumulation on the bar screens and/or flow rate. The solids are collected and later disposed of in landfill or incinerated. Bar screens or mesh screens of varying sizes may be used to optimise solids removal, so as to trap and remove the floating matter, such as pieces of cloth, paper, wood, kitchen refuse, etc. These floating materials will choke pipes or adversely affect the working of the pumps if not removed. They should be placed before the grit chambers. However, if the quality of grit is not of much importance, as in the case of landfilling etc., screens may even be placed after the grit chambers. They may sometimes be accommodated in the body of the grit chambers themselves.


Detecting hot topics from Twitter: A multiview approach

Twitter is widely used all over the world, and a huge number of hot topics are generated by Twitter users in real time. These topics are able to reflect almost every aspect of people’s daily lives. Therefore, the detection of topics in Twitter can be used in many real applications, such as monitoring public opinion, hot product recommendation and incidence detection. However, the performance of traditional topic detection methods is still far from perfect largely owing to the tweets’ features, such as their limited length and arbitrary abbreviations. To address these problems, we propose a novel framework (MVTD) for Twitter topic detection using multiview clustering, which can integrate multirelations among tweets, such as semantic relations, social tag relations and temporal relations. We also propose some methods for measuring relations among tweets. In particular, to better measure the semantic similarity of tweets, we propose a new document similarity measure based on a suffix tree (STVSM). In addition, a new keyword extraction method based on a suffix tree is proposed. Experiments on real datasets show that the performance of MVTD is much better than that of a single view, and it is useful for detecting topics from Twitter.


Job insecurity and organizational citizenship behavior: exploring curvilinear and moderated relationships.

This article examined a curvilinear relationship between job insecurity and organizational citizenship behavior (OCB). Drawing from social exchange theory and research on personal control, we developed and tested an explanation for employees' reactions to job insecurity based on their conceptualization of their social exchange relationship with the organization at different levels of job insecurity. Using data from 244 Chinese employees and 102 supervisory ratings of OCB, we found support for a U-shaped relationship between job insecurity and OCB. Moreover, 2 factors--psychological capital and subordinate-supervisor guanxi--moderated the curvilinear relationship, such that the curvilinear relationship is more pronounced among those with lower psychological capital or less positive subordinate-supervisor guanxi.


A study on the road accidents using data investigation and visualization in Los Baños, Laguna, Philippines

Road safety is one of the most vital and crucial part of every countries daily economic growth. It gives so much impact in public health specially in the Philippines. Securing its safety will be a big help to a particular country's economic growth. The main objective of this paper is to analyze road accident's data to discover hidden pattern that can be used as precautionary measure to at least lessen the accident that occur yearly in Los Baños, Laguna, Philippines. Predicting algorithms such as Decision Tree, Naïve Bayes and Rule induction were used to identify factors affecting accident in Los Baños, Laguna. Using these three classifier the following are the results obtained by the researchers; for Decision Tree 92.84% accuracy occurred with 0.797 kappa while in Naïve Bayes 91.50% accuracy was generated with 0.741 kappa and 92.50% accuracy for Rule Induction and 0.783 kappa was produced. The researchers discovered that the place where an accident happened don't have significant correlation on the fatality of the victim. On the other hand, the researchers also found that the time and day play a vital role on the fatality or severity of victims of road accident particularly car collision.


Feature reduction and selection for EMG signal classification

0957-4174/$ see front matter 2012 Elsevier Ltd. A doi:10.1016/j.eswa.2012.01.102 ⇑ Corresponding author. Tel.: +66 74 55 8831; fax: E-mail address: angkoon.p@hotmail.com (A. Phiny Feature extraction is a significant method to extract the useful information which is hidden in surface electromyography (EMG) signal and to remove the unwanted part and interferences. To be successful in classification of the EMG signal, selection of a feature vector ought to be carefully considered. However, numerous studies of the EMG signal classification have used a feature set that have contained a number of redundant features. In this study, most complete and up-to-date thirty-seven time domain and frequency domain features have been proposed to be studied their properties. The results, which were verified by scatter plot of features, statistical analysis and classifier, indicated that most time domain features are superfluity and redundancy. They can be grouped according to mathematical property and information into four main types: energy and complexity, frequency, prediction model, and time-dependence. On the other hand, all frequency domain features are calculated based on statistical parameters of EMG power spectral density. Its performance in class separability viewpoint is not suitable for EMG recognition system. Recommendation of features to avoid the usage of redundant features for classifier in EMG signal classification applications is also proposed in this study. 2012 Elsevier Ltd. All rights reserved.


An Introduction to the Theory of Mechanism Design

1 The authors will donate all payments that they receive from the publisher for this book to Amnesty International.