A corpus-based investigation of junk emails

Almost everyone who has an email account receives from time to time unwanted emails. These emails can be jokes from friends or commercial product offers from unknown people. In this paper we focus on these unwanted messages which try to promote a product or service, or to offer some “hot” business opportunities. These messages are called junk emails. Several methods to filter junk emails were proposed, but none considers the linguistic characteristics of junk emails. In this paper, we investigate the linguistic features of a corpus of junk emails, and try to decide if they constitute a distinct genre. Our corpus of junk emails was build from the messages received by the authors over a period of time. Initially, the corpus consisted of 1563, but after eliminating the duplications automatically we kept only 673 files, totalising just over 373,000 tokens. In order to decide if the junk emails constitute a different genre, a comparison with a corpus of leaflets extracted from BNC and with the whole BNC corpus is carried out. Several characteristics at the lexical and grammatical levels were identified.

Vector bending sensors based on fibre Bragg gratings inscribed by infrared femtosecond laser

A novel, direction-sensitive bending sensor based on an asymmetric fiber Bragg grating (FBG) inscribed by an infrared femtosecond laser was demonstrated. The technique is based on tight transverse confinement of the femto-inscribed structures and can be directly applied in conventional, untreated singlemode fibers. The FBG structure was inscribed by an amplified, titanium sapphire laser system. The grating cross-section was elongated along the direction of the laser beam with the transverse dimensions of approximately 1 by 2 μm. It was suggested that the sensitivity of the device can be improved by inscribing smaller spatial features and by implementing more complex grating designs aimed at maximizing the effect of strain.

Using interior point algorithms for the solution of linear programs with special structural features

Linear Programming (LP) is a powerful decision making tool extensively used in various economic and engineering activities. In the early stages the success of LP was mainly due to the efficiency of the simplex method. After the appearance of Karmarkar's paper, the focus of most research was shifted to the field of interior point methods. The present work is concerned with investigating and efficiently implementing the latest techniques in this field taking sparsity into account. The performance of these implementations on different classes of LP problems is reported here. The preconditional conjugate gradient method is one of the most powerful tools for the solution of the least square problem, present in every iteration of all interior point methods. The effect of using different preconditioners on a range of problems with various condition numbers is presented. Decomposition algorithms has been one of the main fields of research in linear programming over the last few years. After reviewing the latest decomposition techniques, three promising methods were chosen the implemented. Sparsity is again a consideration and suggestions have been included to allow improvements when solving problems with these methods. Finally, experimental results on randomly generated data are reported and compared with an interior point method. The efficient implementation of the decomposition methods considered in this study requires the solution of quadratic subproblems. A review of recent work on algorithms for convex quadratic was performed. The most promising algorithms are discussed and implemented taking sparsity into account. The related performance of these algorithms on randomly generated separable and non-separable problems is also reported.

Soft [beta]-adrenoceptor agonists for topical use in psoriasis

Psoriasis is characterised by epidermal proliferation and inflammation resulting in the appearance of elevated erythematous plaques. The ratio of c~AMP/c~GMP is decreased in psoriatic skin and when the epidermal cell surface receptors are stimulated by β-adrenergic agonists, intracellular ATP is transformed into c-AMP, thus restoring the c~AMP/c~GMP levels. This thesis describes a series of β-adrenoceptor agonists for topical delivery based upon the soft-drug approach. Soft drugs are defined as biologically active, therapeutically useful chemical compounds (drugs) characterised by a predictable and controllable In vivo destruction (metabolism) to non-toxic moieties. after they achieve their therapeutic role, The N-substituent can accommodate a broad range of structures and here the alkoxycarbonylethyl group has been used to provide metabolic susceptability. The increased polarity of the dihydroxy acid, expected after metabolic conversion of the soft~drug, ethyl N-[2'-(3',4'-dihydroxyphenyl)-2'-hydroxyethyl]-3- aminopropionate, should eliminate agonist activity. Further. to prevent oxidation and enhance topical delivery, the catechol hydroxyl groups have been esterified to produce a pro-soft-drug which generates the soft-drug in enzymic systems. The chemical hydrolysis of the pro-soft-drug proceeded via the formation of the dlpivaloyloxy acid and it failed to generate the active dihydroxy ester soft-drug. In contrast, in the presence of porcine liver carboxyesterase, the hydrolysis of the pro-soft drug proceeded via the formation of the required active soft-drug. This compound, thus, has the appropnate kinetic features to enable it to be evaluated further as a drug for the treatment of psoriasis. The pH rate-profile for the hydrolysis of soft-drug indicated a maximum stability at pH ∼ 4.0. The individual rate constants for the degradation and the pKa were analysed by nonlinear regression. The pKa of 7.40 is in excellent agreement with that determined by direct titration (7.43) and indicates that satisfactory convergence was achieved. The soft-drug was poorly transported across a silicone membrane; it was also air-sensitive due to oxidation of the catechol group. The transport of the pro-soft-drug was more efficient and, over the donor pH range 3-8, increased with pH. At lower values, the largely protonated species was not transported. However, above pH 7. chemical degradation was rapid so that a donor pH of 5-6 was optimum. The β-adrenergic agonist activity of these compounds was tested in vitro by measuring chronotropic and inotropic responses in the guinea pig atria and relaxation of guinea pig trachea precontracted with acetylcholine (10-3 M). The soft~drug was a full agonist on the tracheal preparation but was less potent than isoprenaline. Responses of the soft~drug were competitively antagonised by propranolol (10-6 M). The soft~drug produced an increase in force and rate of the isolated atrial preparatIon. The propyl analogue was equally potent with ED50 of 6.52 x 10-7 M. In contrast, at equivalent doses, the dihydroxy acid showed no activity; only a marginal effect was observed on the tracheal preparation. For the pro~soft-drug, responses were of slow onset, in both preparations, with a slowly developing relaxatlon of the tracheal preparatlon at high concentrations (10-5 M). This is consistent with in vitro results where the dipivaloyl groups are hydrolysed more readily than the ethyl ester to gIve the active soft-drug. These results confirm the validity tif the pro-soft-drug approach to the deUvery of β-adrenoceptor agonists.

Brillouin based distributed fibre sensor incorporating a mode-locked Brillouin fibre ring laser

A pulsed Brillouin fibre ring laser has been developed and we describe its main features. The pump and the Brillouin laser are shown to form an excellent dual frequency source for distributed sensing. A first application for fire detection is demonstrated.

Novel approaches in nonlinear optical fibre-based signal processing

All-optical data processing is expected to play a major role in future optical communications. Nonlinear effects in optical fibres have many attractive features and a great, not yet fully explored potential in optical signal processing. Here, we overview our recent advances in developing novel techniques and approaches to all-optical processing based on optical fibre nonlinearities.

Content-based VLE designs improve learning efficiency in constructivist statistics education

Background: We introduced a series of computer-supported workshops in our undergraduate statistics courses, in the hope that it would help students to gain a deeper understanding of statistical concepts. This raised questions about the appropriate design of the Virtual Learning Environment (VLE) in which such an approach had to be implemented. Therefore, we investigated two competing software design models for VLEs. In the first system, all learning features were a function of the classical VLE. The second system was designed from the perspective that learning features should be a function of the course's core content (statistical analyses), which required us to develop a specific-purpose Statistical Learning Environment (SLE) based on Reproducible Computing and newly developed Peer Review (PR) technology. Objectives: The main research question is whether the second VLE design improved learning efficiency as compared to the standard type of VLE design that is commonly used in education. As a secondary objective we provide empirical evidence about the usefulness of PR as a constructivist learning activity which supports non-rote learning. Finally, this paper illustrates that it is possible to introduce a constructivist learning approach in large student populations, based on adequately designed educational technology, without subsuming educational content to technological convenience. Methods: Both VLE systems were tested within a two-year quasi-experiment based on a Reliable Nonequivalent Group Design. This approach allowed us to draw valid conclusions about the treatment effect of the changed VLE design, even though the systems were implemented in successive years. The methodological aspects about the experiment's internal validity are explained extensively. Results: The effect of the design change is shown to have substantially increased the efficiency of constructivist, computer-assisted learning activities for all cohorts of the student population under investigation. The findings demonstrate that a content-based design outperforms the traditional VLE-based design. © 2011 Wessa et al.

PSS as a service based strategy for western manufacturers

A product-service system (PSS) is a subtle blend of products and services that are offered as an integrated solution to customers. Much existing work on PSS has originated from Scandinavia and has been motivated by a sustainability agenda. Although valuable, this form has limited appeal to western manufacturers. However, by expanding the concepts of PSS to also embrace leading thinking on large scale complex service systems and informated products and services, it is possible to put forward the basis of a service business model that offers the means to differentiate from competitors who simply offer lower priced products. This paper aims to build this case. It reports the state-of-the-art of PSS, defines the concept, reports on its origin and features, discusses examples of applications, and finally proposes a research strategy for future work on this topic.

Developmental trajectories of part-based and configural object recognition in adolescence.

Three experiments assessed the development of children's part and configural (part-relational) processing in object recognition during adolescence. In total, 312 school children aged 7-16 years and 80 adults were tested in 3-alternative forced choice (3-AFC) tasks. They judged the correct appearance of upright and inverted presented familiar animals, artifacts, and newly learned multipart objects, which had been manipulated either in terms of individual parts or part relations. Manipulation of part relations was constrained to either metric (animals, artifacts, and multipart objects) or categorical (multipart objects only) changes. For animals and artifacts, even the youngest children were close to adult levels for the correct recognition of an individual part change. By contrast, it was not until 11-12 years of age that they achieved similar levels of performance with regard to altered metric part relations. For the newly learned multipart objects, performance was equivalent throughout the tested age range for upright presented stimuli in the case of categorical part-specific and part-relational changes. In the case of metric manipulations, the results confirmed the data pattern observed for animals and artifacts. Together, the results provide converging evidence, with studies of face recognition, for a surprisingly late consolidation of configural-metric relative to part-based object recognition.

Self-training from labeled features for sentiment analysis

Sentiment analysis concerns about automatically identifying sentiment or opinion expressed in a given piece of text. Most prior work either use prior lexical knowledge defined as sentiment polarity of words or view the task as a text classification problem and rely on labeled corpora to train a sentiment classifier. While lexicon-based approaches do not adapt well to different domains, corpus-based approaches require expensive manual annotation effort. In this paper, we propose a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon with preferences on expectations of sentiment labels of those lexicon words being expressed using generalized expectation criteria. Documents classified with high confidence are then used as pseudo-labeled examples for automatical domain-specific feature acquisition. The word-class distributions of such self-learned features are estimated from the pseudo-labeled examples and are used to train another classifier by constraining the model's predictions on unlabeled instances. Experiments on both the movie-review data and the multi-domain sentiment dataset show that our approach attains comparable or better performance than existing weakly-supervised sentiment classification methods despite using no labeled documents.

Ontology-based protein-protein interactions extraction from literature using the hidden vector state model

This paper proposes a novel framework of incorporating protein-protein interactions (PPI) ontology knowledge into PPI extraction from biomedical literature in order to address the emerging challenges of deep natural language understanding. It is built upon the existing work on relation extraction using the Hidden Vector State (HVS) model. The HVS model belongs to the category of statistical learning methods. It can be trained directly from un-annotated data in a constrained way whilst at the same time being able to capture the underlying named entity relationships. However, it is difficult to incorporate background knowledge or non-local information into the HVS model. This paper proposes to represent the HVS model as a conditionally trained undirected graphical model in which non-local features derived from PPI ontology through inference would be easily incorporated. The seamless fusion of ontology inference with statistical learning produces a new paradigm to information extraction.

Case based design of knitwear

In the developed world we are surrounded by man-made objects, but most people give little thought to the complex processes needed for their design. The design of hand knitting is complex because much of the domain knowledge is tacit. The objective of this thesis is to devise a methodology to help designers to work within design constraints, whilst facilitating creativity. A hybrid solution including computer aided design (CAD) and case based reasoning (CBR) is proposed. The CAD system creates designs using domain-specific rules and these designs are employed for initial seeding of the case base and the management of constraints. CBR reuses the designer's previous experience. The key aspects in the CBR system are measuring the similarity of cases and adapting past solutions to the current problem. Similarity is measured by asking the user to rank the importance of features; the ranks are then used to calculate weights for an algorithm which compares the specifications of designs. A novel adaptation operator called rule difference replay (RDR) is created. When the specifications to a new design is presented, the CAD program uses it to construct a design constituting an approximate solution. The most similar design from the case-base is then retrieved and RDR replays the changes previously made to the retrieved design on the new solution. A measure of solution similarity that can validate subjective success scores is created. Specification similarity can be used as a guide whether to invoke CBR, in a hybrid CAD-CBR system. If the newly resulted design is suffciently similar to a previous design, then CBR is invoked; otherwise CAD is used. The application of RDR to knitwear design has demonstrated the flexibility to overcome deficiencies in rules that try to automate creativity, and has the potential to be applied to other domains such as interior design.

Doing business in India:building research-based practice

In response to the increasing interest in the growth and developments in the Indian economy, and the dynamic nature of the rapidly changing Indian business environment, this textbook is designed to provide a comprehensive guide to doing business in the Indian context. Written by academic experts in their respective fields, this book is divided into three parts: the Indian business context, conducting business in India, and India and the world. Key information is presented on a wide range of topics, including: •Both the shortcomings and opportunities associated with the Indian business environment •The economic development model in India •Critical skills for negotiation and incentives for foreign investors, including case studies of Italian companies that have entered the Indian market in different ways •Business culture in India, including particular customs and etiquette In addition to the pedagogical features, each chapter contains a set of key issues, and there is also a list of useful websites covering a wide range of business needs. This book introduces students to business in India, and will be also be of use to investors, organisations and managers who are already doing business, or intend to start one, in India.

Human single-donor composite skin substitutes based on collagen and polycaprolactone copolymer

The development and characterization of an enhanced composite skin substitute based on collagen and poly(e-caprolactone) are reported. Considering the features of excellent biocompatibility, easy-manipulated property and exempt from cross-linking related toxicity observed in the 1:20 biocomposites, skin substitutes were developed by seeding human single-donor keratinocytes and fibroblasts alone on both sides of the 1:20 biocomposite to allow for separation of two cell types and preserving cell signals transmission via micro-pores with a porosity of 28.8 ± 16.1 µm. The bi-layered skin substitute exhibited both differentiated epidermis and fibrous dermis in vitro. Less Keratinocyte Growth Factor production was measured in the co-cultured skin model compared to fibroblast alone condition indicating a favorable microenvironment for epidermal homeostasis. Moreover, fast wound closure, epidermal differentiation, and abundant dermal collagen deposition were observed in composite skin in vivo. In summary, the beneficial characteristics of the new skin substitutes exploited the potential for pharmaceutical screening and clinical application.

Applying Information Foraging Theory to understand user interaction with content-based image retrieval

The paper proposes an ISE (Information goal, Search strategy, Evaluation threshold) user classification model based on Information Foraging Theory for understanding user interaction with content-based image retrieval (CBIR). The proposed model is verified by a multiple linear regression analysis based on 50 users' interaction features collected from a task-based user study of interactive CBIR systems. To our best knowledge, this is the first principled user classification model in CBIR verified by a formal and systematic qualitative analysis of extensive user interaction data. Copyright 2010 ACM.