title: A Literature Review on Sampling Techniques in Semiconductor Manufacturing

abstract: This paper reviews sampling techniques for inspection in semiconductor manufacturing. We discuss the strengths and weaknesses of techniques developed in the last last 20 years for excursion monitoring (when a process or machine falls out of specifications) and control. Sampling techniques are classified into three main groups: static, adaptive, and dynamic. For each group, a classification is performed per year, approach, and industrial deployment. A comparison between the groups indicates a complementarity strongly linked to the semiconductor environment. Benefits and drawbacks of each group are discussed, showing significant improvements from static to dynamic through adaptive sampling techniques. Dynamic sampling seems to be more appropriate for modern semiconductor plants.


title: Co-Optimization of Circuits, Layout and Lithography for Predictive Technology Scaling Beyond Gratings

abstract: The financial backbone of the semiconductor industry is based on doubling the functional density of integrated circuits every two years at fixed wafer costs and die yields. The increasing demands for 'computational' rather than 'physical' lithography to achieve the aggressive density targets, along with the complex device-engineering solutions needed to maintain the power density objectives, have caused a rapid escalation in systematic yield limiters that threaten scaling. Specifically, the traditional contract between design and manufacturing based solely on design rules is no longer sufficient to guarantee functional silicon and instead requires a convoluted set of restrictions that force complex modifications to the already costly design flows. In this paper, we claim that a far superior result can be achieved by moving the design-to-manufacturing interface from design rules to a higher level of abstraction based on a defined set of pre-characterized layout templates. We will demonstrate how this methodology can simplify optical proximity correction and lithography processes for sub-32 nm technology nodes, along with various digital block design examples for synthesized intellectual property (IP) cores. Furthermore, with a cost-per-good-die analysis we will show that this methodology will extend economical scaling to sub-32 nm technology nodes.


title: Using data mining to find fraud in HCFA health care claims.

abstract: Data mining can be/used to detect health care fraud and abuse through visualization of very large data sets to isolate new and unusual patterns of activity. Data mining has allowed better direction and use of health care fraud detection and investigative resources by recognizing and quantifying the underlying indicators of fraudulent claims, fraudulent providers, and fraudulent beneficiaries. A large amount of work must be performed prior to the actual data mining. These precursory tasks include: customer discussions, data extraction and cleaning, transformation of the database, and auditing (basic statistics and visualization of the information) of the data. This paper describes the tasks performed in support of a project for HCFA (Health Care Financing Administration).


title: Semisupervised Online Multikernel Similarity Learning for Image Retrieval

abstract: Metric learning plays a fundamental role in the fields of multimedia retrieval and pattern recognition. Recently, an online multikernel similarity (OMKS) learning method has been presented for content-based image retrieval (CBIR), which was shown to be promising for capturing the intrinsic nonlinear relations within multimodal features from large-scale data. However, the similarity function in this method is learned only from labeled images. In this paper, we present a new framework to exploit unlabeled images and develop a semisupervised OMKS algorithm. The proposed method is a multistage algorithm consisting of feature selection, selective ensemble learning, active sample selection, and triplet generation. The novel aspects of our work are the introduction of classification confidence to evaluate the labeling process and select the reliably labeled images to train the metric function, and a method for reliable triplet generation, where a new criterion for sample selection is used to improve the accuracy of label prediction for unlabeled images. Our proposed method offers advantages in challenging scenarios, in particular, for a small set of labeled images with high-dimensional features. Experimental results demonstrate the effectiveness of the proposed method as compared with several baseline methods.


title: A Mutually Beneficial Integration of Data Mining and Information Extraction

abstract: Text mining concerns applying data mining techniques to unstructured text. Information extraction(IE) is a form of shallow text understanding that locates specific pieces of data in natural language documents, transforming unstructured text into a structured database. This paper describes a system called DISCOTEX, that combines IE and data mining methodologies to perform text mining as well as improve the performance of the underlying extraction system. Rules mined from a database extracted from a corpus of texts are used to predict additional information to extract from future documents, thereby improving the recall of IE. Encouraging results are presented on applying these techniques to a corpus of computer job announcement postings from an Internet newsgroup.


title: Classroom observation data and instruction in primary mathematics education: improving design and rigour

abstract: The use of formal observation in primary mathematics classrooms is supported in the literature as a viable method of determining effective teaching strategies and appropriate tasks for inclusion in the early years of mathematics learning. The twofold aim of this study was to (a) investigate predictive relationships between primary mathematics classroom observational data and student achievement data, and (b) to examine the impact of providing periodic classroom observational data feedback to teachers using a Relational-Feedback-Intervention (RFI) Database Model. This observational research effort focused on an empirical examination of student engagement levels in time spent on specific learning activities observed in primary mathematics classrooms as predictors of student competency outcomes in mathematics. Data were collected from more than 2,000 primary classroom observations in 17 primary schools during 2009–2011 and from standardised end-of-year tests for mathematics achievement. Results revealed predictive relationships among several types of teaching and learning tasks with student achievement. Specifically, the use of mathematics concepts, technology and hands-on materials in primary mathematics classrooms was found to produce substantive predictors of increased student mathematics achievement. Additional findings supported the use of periodic classroom observation data reporting as a positive influence on teachers’ decisions in determining instructional tasks for inclusion in primary mathematics classrooms. Study results indicate classroom observational research involving a RFI Database Model is a productive tool for improving teaching and learning in primary mathematics classrooms.


title: Reining in the Outliers in Map-Reduce Clusters using Mantri

abstract: Experience froman operational Map-Reduce cluster reveals that outliers significantly prolong job completion. The causes for outliers include run-time contention for processor, memory and other resources, disk failures, varying bandwidth and congestion along network paths and, imbalance in task workload. We present Mantri, a system that monitors tasks and culls outliers using cause- and resource-aware techniques. Mantri's strategies include restarting outliers, network-aware placement of tasks and protecting outputs of valuable tasks. Using real-time progress reports, Mantri detects and acts on outliers early in their lifetime. Early action frees up resources that can be used by subsequent tasks and expedites the job overall. Acting based on the causes and the resource and opportunity cost of actions lets Mantri improve over prior work that only duplicates the laggards. Deployment in Bing's production clusters and trace-driven simulations show that Mantri improves job completion times by 32%.


title: Web robot detection techniques: overview and limitations

abstract: Most modern Web robots that crawl the Internet to support value-added services and technologies possess sophisticated data collection and analysis capabilities. Some of these robots, however, may be ill-behaved or malicious, and hence, may impose a significant strain on a Web server. It is thus necessary to detect Web robots in order to block undesirable ones from accessing the server. Such detection is also essential to ensure that the robot traffic is considered appropriately in the performance and capacity planning of Web servers. Despite a variety of Web robot detection techniques, there is no consensus regarding a single technique, or even a specific "type" of technique, that performs well in practice. Therefore, to aid in the development of a practically applicable robot detection technique, this survey presents a critical analysis and comparison of the prevalent detection approaches. We propose a framework to classify the existing detection techniques into four categories based on their underlying detection philosophy. We compare the different classes to gain insights into those characteristics that make up an effective robot detection scheme. Finally, we discuss why the contemporary techniques fail to offer a general solution to the robot detection problem and propose a set of key ingredients necessary for strong Web robot detection.


title: Recent Updates in the Treatment of Neurodegenerative Disorders Using Natural Compounds

abstract: Neurodegenerative diseases are characterized by protein aggregates and inflammation as well as oxidative stress in the central nervous system (CNS). Multiple biological processes are linked to neurodegenerative diseases such as depletion or insufficient synthesis of neurotransmitters, oxidative stress, abnormal ubiquitination. Furthermore, damaging of blood brain barrier (BBB) in the CNS also leads to various CNS-related diseases. Even though synthetic drugs are used for the management of Alzheimer's disease, Parkinson's disease, autism, and many other chronic illnesses, they are not without side effects. The attentions of researchers have been inclined towards the phytochemicals, many of which have minimal side effects. Phytochemicals are promising therapeutic agents because many phytochemicals have anti-inflammatory, antioxidative as well as anticholinesterase activities. Various drugs of either synthetic or natural origin applied in the treatment of brain disorders need to cross the BBB before they can be used. This paper covers various researches related to phytochemicals used in the management of neurodegenerative disorders.


title: Nonconscious Processes and Health

abstract: Objectives: Health behavior theories focus on the role of conscious, reflective factors (e.g., behavioral intentions, risk perceptions) in predicting and changing behavior. Dual-process models, on the other hand, propose that health actions are guided not only by a conscious, reflective, rule-based system but also by a nonconscious, impulsive, associative system. This article argues that research on health decisions, actions, and outcomes will be enriched by greater consideration of nonconscious processes. Methods: A narrative review is presented that delineates research on implicit cognition, implicit affect, and implicit motivation. In each case, we describe the key ideas, how they have been taken up in health psychology, and the possibilities for behavior change interventions, before outlining directions that might profitably be taken in future research. Results: Correlational research on implicit cognitive and affective processes (attentional bias and implicit attitudes) has recently been supplemented by intervention studies using implementation intentions and practice-based training that show promising effects. Studies of implicit motivation (health goal priming) have also observed encouraging findings. There is considerable scope for further investigations of implicit affect control, unconscious thought, and the automatization of striving for health goals. Conclusion: Research on nonconscious processes holds significant potential that can and should be developed by health psychologists. Consideration of impulsive as well as reflective processes will engender new targets for intervention and should ultimately enhance the effectiveness of behavior change efforts.


title: Orientation sensitivity to graspable objects: An fMRI adaptation study

abstract: Abstract It has been proposed that vision-for-perception and vision-for-action are subserved by distinct streams of visual processing, the ventral and dorsal stream, respectively [Milner, A. D., Goodale, M. A., 1995. The visual brain in action. Oxford University Press, Oxford]. Such a distinction has been supported by a recent functional magnetic resonance (fMR) adaptation study [Valyear, K. F., Culham, J. C., Sharif, N., Westwood, D., Goodale, M. A., 2006. A double dissociation between sensitivity to changes in object identity and object orientation in the ventral and dorsal visual streams: A human fMRI study. Neuropsychologia 44, 218–228], which demonstrated selectivity to object identity but not object orientation within the ventral stream, and selectivity to object orientation but not object identity within the dorsal stream. These results were interpreted as suggesting that changes to object identity (but not to orientation) would alter the representation of the stimulus in the perceptual/recognition system, whereas changes in object orientation (but not necessarily identity) would alter the coding of the stimulus within a visuomotor system concerned with behaviour such as grasping. If orientation sensitivity in the dorsal stream does reflect such a potential for action, then this sensitivity should be specific to graspable objects. Using an fMR adaptation paradigm, we presented participants with an image of either a graspable or non-graspable stimulus, followed by the same image in either the original orientation or its mirror image. One region within the dorsal stream, the lateral occipito-parietal junction (lOPJ), was shown to be sensitive to orientation changes for graspable stimuli; this region did not show orientation sensitivity for non-graspable stimuli. Thus, it appears that the sensitivity to orientation changes in this region is specific to graspable objects, presumably because such changes affect the affordances of graspable but not non-graspable objects.


title: Social isolation and delayed discovery of bodies in houses: the value of forensic pathology, anthropology, odontology and entomology in the medico-legal investigation.

abstract: The bodies of socially isolated people may remain undiscovered in their own houses for prolonged periods. Occasionally the body is in situ for sufficient time to become skeletonised, or partially so. Medico-legal investigation of these cases is complicated by degradation and contamination of evidence. Thus, a multidisciplinary forensic investigation is recommended. The potential contributions of forensic pathology, anthropology, odontology and entomology are outlined here with reference to two cases that occurred in Victoria, Australia, in 2003. Forensic pathologists are often unable to determine the cause of death in skeletonised bodies, however, they may find evidence to support either a natural or unnatural mode of death, and they may describe skeletal pathology or trauma, and identify skeletal features to support radiological identification of the deceased. Anthropologists can provide supplementary evidence of skeletal trauma. Additionally, they can assess age, sex, stature and racial affiliation from skeletal remains. Odontologists can identify individuals through comparison with ante-mortem dental records; however, potential difficulties exist in identifying the treating dentist of a socially isolated person. Odontologists may also examine the teeth and oro-facial skeleton for trauma. Entomologists may estimate minimum death time and/or season of death. Entomological examination of insect remains may also confirm that a body has lain in situ for a considerable period.


title: Patent Classifications as Indicators of Intellectual Organization

abstract: Using the 138,751 patents filed in 2006 under the Patent Cooperation Treaty, co-classification analysis is pursued on the basis of three- and four-digit codes in the International Patent Classification (IPC, 8th edition). The co-classifications among the patents enable us to analyze and visualize the relations among technologies at different levels of aggregation. The hypothesis that classifications might be considered as the organizers of patents into classes, and that therefore co-classification patterns--more than co-citation patterns--might be useful for mapping, is not corroborated. The classifications hang weakly together, even at the four-digit level at the country level, more specificity can be made visible. However, countries are not the appropriate units of analysis because patent portfolios are largely similar in many advanced countries in terms of the classes attributed. Instead of classes, one may wish to explore the mapping of title words as a better approach to visualize the intellectual organization of patents.


title: Outlier Detection for Temporal Data

abstract: Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier detection focused on time series-based outliers (in statistics). Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio-temporal data. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large number of applications generate temporal datasets. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc., are all temporal. This stresses the need for an organized and detailed study of outliers with respect to such temporal data. In the past decade, there has been a lot of research on various forms of temporal data including consecutive data snapshots, series of data snapshots and data streams. Besides the initial work on time series, researchers have focused on rich forms of data including multiple data streams, spatio-temporal data, network data, community distribution data, etc. Compared to general outlier detection, techniques for temporal outlier detection are very different. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. We start with the basics and then ramp up the reader to the main ideas in state-of-the-art outlier detection techniques. We motivate the importance of temporal outlier detection and brief the challenges beyond usual outlier detection. Then, we list down a taxonomy of proposed techniques for temporal outlier detection. Such techniques broadly include statistical techniques (like AR models, Markov models, histograms, neural networks), distance- and density-based approaches, grouping-based approaches (clustering, community detection), network-based approaches, and spatio-temporal outlier detection approaches. We summarize by presenting a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers.


title: Best Subset Selection via a Modern Optimization Lens

abstract: In the period 1991–2015, algorithmic advances in Mixed Integer Optimization (MIO) coupled with hardware improvements have resulted in an astonishing 450 billion factor speedup in solving MIO problems. We present a MIO approach for solving the classical best subset selection problem of choosing k out of p features in linear regression given n observations. We develop a discrete extension of modern first-order continuous optimization methods to find high quality feasible solutions that we use as warm starts to a MIO solver that finds provably optimal solutions. The resulting algorithm (a) provides a solution with a guarantee on its suboptimality even if we terminate the algorithm early, (b) can accommodate side constraints on the coefficients of the linear regression and (c) extends to finding best subset solutions for the least absolute deviation loss function. Using a wide variety of synthetic and real datasets, we demonstrate that our approach solves problems with n in the 1000s and p in the 100s in minutes to provable optimality, and finds near optimal solutions for n in the 100s and p in the 1000s in minutes. We also establish via numerical experiments that the MIO approach performs better than Lasso and other popularly used sparse learning procedures, in terms of achieving sparse solutions with good predictive power.


title: Social-Aware Data Dissemination via Device-to-Device Communications: Fusing Social and Mobile Networks with Incentive Constraints

abstract: Nowadays, pervasive mobile devices not only pose new challenges for existing wireless networks to accommodate the surging demands, but also offer new opportunities to support various services. For example, device-to-device (D2D) communications provide a promising paradigm for data dissemination with low resource cost and high energy efficiency. In this paper, we propose a three-phase approach for D2D data dissemination, which exploits social-awareness and opportunistic contacts with user mobility. The proposed approach includes one phase of seed selection and two subsequent phases of data forwarding. In Phase I, we build a social-physical graph model, which combines the social network and the mobile network with opportunistic transmissions. Then we partition the social-physical graph into communities using the Girvan-Newman algorithm based on edge-betweenness, and select seeds for the communities according to vertex-closeness. In Phase II, data forwarding only takes place among socially connected users. In Phase III, the base station intervenes to enable data forwarding among cooperative users. For Phases II and III, we propose the new mechanisms for message selection and cooperation pairing, which take into account both altruistic and selfish behaviors of users. The theoretical analysis for the message selection mechanism proves its truthfulness and approximation ratio in the worst case. Extensive simulation results further demonstrate the effectiveness of the proposed three-phase approach with various synthetic and real tracing datasets.


A Type-2 Fuzzy MCDM Method for Ranking Private Universities in İ stanbul


title: The Role of Population Genetics in Resistance Research and Management

abstract: Pesticide resistance is an evolutionary phenomenon (Dobzhansky 1951), which cannot be fully understood without genetic data. Genetic studies are a major tool in developing improved methods of detecting resistance (Chapter 2), for investigating the mechanisms of resistance (Chapters 3 and 4), and in choosing approaches to manage resistance (Chapters 6, 9, and 10).


Monte Carlo Simulations Applied to Uncertainty in Measurement


title: Association Rule Mining of Household Electrical Energy Usage

abstract: In a Smart Grid network, household energy consumption is monitored by the use of residential smart meters. However, these do not provide data for individual appliance consumption. In this paper we consider publicly available disaggregated electrical energy data obtained from six homes. Using the Apriori algorithm we determine association rules between appliances so as to understand which appliances are used in conjunction with others. This information can then be used to determine better ways to control the on/off, activity of these devices. Our objective was to determine if such associations can in fact be determined. We conclude that this is indeed the case as demonstrated by our results. In general, houses with a large number of appliances generated more association rules while the accuracy of classifying rules reduced with an increase in the number of appliances.


title: An edge-based color-aided method for license plate detection

abstract: In this paper, the problem of license plate detection is considered. Low quality images due to severe illumination conditions, vehicle motion, viewpoint and distance changes, complex background, etc. are some of popular problems which have to be considered. In order to alleviate these problems, two different image enhancement methods (using intensity variance and edge density) are proposed. The aim is to increase contrast of plate-like regions to avoid missing plate location especially in poor quality images. Furthermore, a novel match filter is designed to detect candidate regions as plate. This filter models the vertical edge density of plate region regarding its neighborhood. As the filtering procedure is simple, this approach can be used for real-time applications. In the proposed method, we also use colored texture in the plate as a cue for plate detection. This feature is preserved under viewpoint change. In order to characterize the color information in plate, the MNS (multimodal neighborhood signature) method is used. A well-organized database, consisting of car images with different known distances and viewing angels have been prepared to verify the performance of plate detection algorithm. This database can be used to establish a precise evaluation of the proposed method and any other related work. The results of experiments on different type of car images in complex scenes confirm the robustness of proposed method against severe imaging conditions.


Local Optimization and The Job-Shop Scheduling Problem


title: Inferring user knowledge level from eye movement patterns

abstract: The acquisition of information and the search interaction process is influenced strongly by a person's use of their knowledge of the domain and the task. In this paper we show that a user's level of domain knowledge can be inferred from their interactive search behaviors without considering the content of queries or documents. A technique is presented to model a user's information acquisition process during search using only measurements of eye movement patterns. In a user study (n=40) of search in the domain of genomics, a representation of the participant's domain knowledge was constructed using self-ratings of knowledge of genomics-related terms (n=409). Cognitive effort features associated with reading eye movement patterns were calculated for each reading instance during the search tasks. The results show correlations between the cognitive effort due to reading and an individual's level of domain knowledge. We construct exploratory regression models that suggest it is possible to build models that can make predictions of the user's level of knowledge based on real-time measurements of eye movement patterns during a task session.


title: Design of reconfigurable slot antenna using varactor diode

abstract: In this paper, reconfigurable slot antenna for LTE (2.6GHz), AMT-fixed service (4.5GHz), and WLAN (5.4GHz) applications is presented. There are two L-shaped slots and a U-shaped slot inserted in the ground plane. The size of the antenna is reduced as a result of these slots. There are three varactor diodes inserted in these slots. This is done for the purpose of frequency, directivity, and polarization reconfiguration. The directivities and polarization states of the antenna are changed at different frequencies i.e. at 2.6GHz, 5.4GHz, and at 4.5 GHz. These three different frequencies correspond to different states of the antenna. The states of the antenna are changed by switching the varactor diodes on and off. The varactor diodes are used in this paper as they have the property of fast frequency tuning.


title: Security Analysis of Vendor Customized Code in Firmware of Embedded Device

abstract: Despite the increased concerning about embedded system security, the security assessment of commodity embedded devices is far from being adequate. The lack of assessment is mainly due to the tedious, time-consuming, and the very ad hoc reverse engineering procedure of the embedded device firmware. To simplify this procedure, we argue that only a particular part of the entire embedded device’s firmware, as we called vendor customized code, should be thoroughly analyzed. Vendor customized code is usually developed to deal with external inputs and is especially sensitive to attacks compared to other parts of the system. Moreover, vendor customized code is often highly specific and proprietary, which lacks security implementation guidelines. Therefore, the security demands of analyzing this kind of code is urgent.


title: Millimeter-wave antenna-in-package solutions for WiGig and backhaul applications

abstract: This paper provides an overview of the authors' research work to develop low-cost antennas and packaging technologies for 60GHz and 120GHz high speed data rate communications. First, a WiGig module designed in a low cost High-Density-Interconnect organic packaging technology is briefly depicted. The linearly polarized antennas integrated in a 12×12×0.5 mm3 ball-grid-array module exhibit a realized gain higher than 5 dBi over the entire WiGig band. Then, in order to address the Millimeter-Wave backhaul applications while leveraging the previous achievement, a lens-casing solution is proposed. The achievable performance with a plastic (ABS-M30) lens manufactured using a rapid 3D printing prototyping technique is validated at both 60 GHz and 120 GHz.


title: Evaluation of Three Methods for MRI Brain Tumor Segmentation

abstract: Imaging plays a central role in the diagnosis and treatment planning of brain tumor. An accurate segmentation is critical, especially when the tumor morphological changes remain subtle, irregular and difficult to assess by clinical examination. Traditionally, segmentation is performed manually in clinical environment that is operator dependent and very tedious and time consuming labor intensive work. However, automated tumor segmentation in MRI brain tumor poses many challenges with regard to characteristics of an image. A comparison of three different semi-automated methods, viz., modified gradient magnitude region growing technique (MGRRGT), level set and a marker controlled watershed method is undertaken here for evaluating their relative performance in the segmentation of tumor. A study on 9 samples using MGRRGT reveals that all the errors are within 6 to 23% in comparison to other two methods.


title: Assessing Learning Outcomes in Web Search: A Comparison of Tasks and Query Strategies

abstract: Users make frequent use of Web search for learning-related tasks, but little is known about how different Web search interaction strategies affect outcomes for learning-oriented tasks, or what implicit or explicit indicators could reliably be used to assess search-related learning on the Web. We describe a lab-based user study in which we investigated potential indicators of learning in web searching, effective query strategies for learning, and the relationship between search behavior and learning outcomes. Using questionnaires, analysis of written responses to knowledge prompts, and search log data, we found that searchers' perceived learning outcomes closely matched their actual learning outcomes; that the amount searchers wrote in post-search questionnaire responses was highly correlated with their cognitive learning scores; and that the time searchers spent per document while searching was also highly and consistently correlated with higher-level cognitive learning scores. We also found that of the three query interaction conditions we applied, an intrinsically diverse presentation of results was associated with the highest percentage of users achieving combined factual and conceptual knowledge gains. Our study provides deeper insight into which aspects of search interaction are most effective for supporting superior learning outcomes, and the difficult problem of how learning may be assessed effectively during Web search.


title: Text detection in manga by combining connected-component-based and region-based classifications

abstract: As manga (Japanese comics) have become common content in many countries, it is necessary to search manga by text query or translate them automatically. For these applications, we must first extract texts from manga. In this paper, we develop a method to detect text regions in manga. Taking motivation from methods used in scene text detection, we propose an approach using classifiers for both connected components and regions. We have also developed a text region dataset of manga, which enables learning and detailed evaluations of methods used to detect text regions. Experiments using the dataset showed that our text detection method performs more effectively than existing methods.


title: A Variational Approach for Pan-Sharpening

abstract: Pan-sharpening is a process of acquiring a high resolution multispectral (MS) image by combining a low resolution MS image with a corresponding high resolution panchromatic (PAN) image. In this paper, we propose a new variational pan-sharpening method based on three basic assumptions: 1) the gradient of PAN image could be a linear combination of those of the pan-sharpened image bands; 2) the upsampled low resolution MS image could be a degraded form of the pan-sharpened image; and 3) the gradient in the spectrum direction of pan-sharpened image should be approximated to those of the upsampled low resolution MS image. An energy functional, whose minimizer is related to the best pan-sharpened result, is built based on these assumptions. We discuss the existence of minimizer of our energy and describe the numerical procedure based on the split Bregman algorithm. To verify the effectiveness of our method, we qualitatively and quantitatively compare it with some state-of-the-art schemes using QuickBird and IKONOS data. Particularly, we classify the existing quantitative measures into four categories and choose two representatives in each category for more reasonable quantitative evaluation. The results demonstrate the effectiveness and stability of our method in terms of the related evaluation benchmarks. Besides, the computation efficiency comparison with other variational methods also shows that our method is remarkable.


title: Implementation of the communication protocols SPI and I2C using a FPGA by the HDL-Verilog language

abstract: Currently, the most used serial communication protocols to exchange information between different electronic embedded devices are the SPI and I2C. This paper describes the development and implementation of these proto- cols using a FPGA card. For the implementation of each protocol, it was taken into account different modes of operation, such as master/slave mode sending or pending data mode. For the implementation of the I2C protocol was neces- sary to perform a tri-state buffer, which makes a bidirectional data line for a successful communication between devices, allowing to take advantage of these sources provided by the FPGA. Verilog is a hardware description lan- guage better known as HDL and it was used in the work to implement and sim- ulate these communication protocols with the software version 14.7 of Xilinx ISE Design Suite.


title: Teaching Aids: Struggling with/through student resistances in psychology curricula in South African universities

abstract: African universities have been called to respond to the social issues of trauma, adversity, injustice and inequality that trouble their embedding communities, their staff and their students. The need for South African universities to respond to HIV/Aids (in particular) includes the opening up of new knowledge about and ways of managing the impacts of the epidemic; and shaping a young generation of socio-politically literate subjects and citizens, who would be equipped to respond appropriately and creatively to social problems and issues. This article reflects on my own feminist poststructuralist pedagogical practice in incorporating issues related to HIV/Aids into two developmental psychology courses – Childhood & Adversity and Youth Risk – I have taught at two ‘historically white’ South African universities. These courses drew on traditional (western) psychological theories of human development, and located critique by engaging these theories from South African social scientific research on lived realiti...


title: A story of quid pro quo knowledge transfer

abstract: This research project (undertaken as part of a PhD thesis) exemplifies a proposed model for initiating and fostering knowledge transfer in local community health and social care services. The project stemmed from me contacting local service providers and commissioners (within one local authority in the South of England) with a view to establishing a quid pro quo relationship. In return for the opportunity to research how service users and providers experienced the transition to the ‘new age’ of social inclusion services (see research abstract below), I undertook a mixed methods independent evaluation of the current provision. The audit incorporated a formal internal report, with recommendations for practice, which has since been used to help restructure the social inclusion service. The report then formed the basis for a recently submitted research paper highlighting the practical, ethical and theoretical issues that emerge when translating contemporary social inclusion policy into practice. ::: ::: My presentation will begin by contextualising the project in terms of research aims and how initial community links were established. I will then briefly summarise the methodology, main research findings and research impact. I will finish by outlining my recent efforts to initiate a similar impact model local to UEL, and by opening up for discussion the opportunities for, and barriers to, extending this type of knowledge transfer initiative.


title: How Board Structure Influences the Corporate Social Responsibility Strategy of the Firm? Pakistan’s Perspective

abstract: The aim of this working paper is to identify the board structure types and variables that affect the corporate social responsibility strategy of the organizations. The main and core four types of board structures and determinants of corporate social responsibility strategy of the organizations have been identified in this working paper after studying and examining 35 research articles written by eminent scholars. These four types of board structure are: (1) having female directors on the board of directors greatly affects the corporate social responsibility strategy of the firms and such types of firms are appeared to be more philanthropic. (2) Outside or independent directors on board of directors also a major component in determining the organization’s corporate social responsibility strategy. (3) Tenure of directors also has positive effects on determination of corporate social responsibility strategy of the organizations and (4) Board size also plays an important role in planning and development of organization’s corporate social responsibility strategy. The other small variables and components of board structure also identified by the scholars but the main four are mentioned above. Substantial research gap exists for examining the affects of board composition and board structure in determining the corporate social responsibility strategy in the private and pubic sector organizations of Pakistan.


title: Assessing thesaurus-based query expansion using the UMLS metathesaurus

abstract: OBJECTIVES: Assess query expansion using thesaurus relationships and definitions in the UMLS Metathesaurus for improving searching performance. METHODS: The queries from a MEDLINE test collection (OHSUMED) were expanded using synonym, hierarchical, and related term information as well as term definitions from the UMLS Metathesaurus. Documents were retrieved from a word-statistical retrieval system and assessed for recall and precision based on relevance judgments from the test collection. RESULTS: All types of query expansion degraded aggregate retrieval performance as measured by recall and precision, although 38.6% of the queries with synonym expansion and up to 29.7% of the queries with hierarchical expansion showed improvement. CONCLUSIONS: Thesaurus-based query expansion causes a decline in retrieval performance generally but improves it in specific instances. Further research must focus on identifying instances where performance improves and how it can be exploited by real users.


title: TRAFFIC LIGHTS DETECTION IN ADVERSE CONDITIONS USING COLOR, SYMMETRY AND SPATIOTEMPORAL INFORMATION

abstract: This paper proposes the use of a monocular video camera for traffic lights detection, in a variety of conditions, including adverse weather and illumination. The system incorporates a color pre-processing module to enhance the discrimination of red and green regions in the image and handle the “blooming effect” that is often observed in such scenes. The fast radial symmetry transform is utilized for the detection of traffic light candidates and finally false positive results are minimized using spatiotemporal persistency verification. The system is qualitatively assessed in various conditions, including driving in the rain, at night and in city roads with dense traffic, as well as their synergy. It is also quantitatively assessed on a publicly available manually annotated database, scoring high detection rates.


Software defect prediction in large space systems through hybrid feature selection and classification.


title: Open access and Scopus: A new approach to scientific visibility from the standpoint of access

abstract: The last few years have seen the emergence of several open access (OA) options in scholarly communication, which can be grouped broadly into two areas referred to as gold and green roads. Several recent studies have shown how large the extent of OA is, but there have been few studies showing the impact of OA in the visibility of journals covering all scientific fields and geographical regions. This research presents a series of informative analyses providing a broad overview of the degree of proliferation of OA journals in a data sample of about 17,000 active journals indexed in Scopus. This study shows a new approach to scientific visibility from a systematic combination of four databases: Scopus, the Directory of Open Access Journals, Rights Metadata for Open Archiving © 2011 Wiley Periodicals, Inc.


title: Automated generation of activity and sequence diagrams from natural language requirements

abstract: Requirements analysis process involves developing abstract models for the envisioned or the proposed software system. These models are used to help refine and enrich the requirements for the system. Unified Modelling Language (UML) has become the standard for modelling software requirements. However, software requirements are captured in the form of Natural Language and, generating UML models from natural language requirements relies heavily on individual expertise. In this paper, we present an approach towards automated generation of behavioural UML models, namely activity diagrams and sequence diagrams. Our approach is based on transforming the requirements statements to intermediary structured representations — frames and then, translate them to the behavioural UML models. We are using Grammatical Knowledge Patterns and lexical and syntactic analysis of requirements statements to populate frames for the corresponding statements. Knowledge stored in frames is then used to automatically generate activity and sequence diagram. We present our approach through the case-studies performed.


title: Face Spoofing Detection Through Visual Codebooks of Spectral Temporal Cubes

abstract: Despite important recent advances, the vulnerability of biometric systems to spoofing attacks is still an open problem. Spoof attacks occur when impostor users present synthetic biometric samples of a valid user to the biometric system seeking to deceive it. Considering the case of face biometrics, a spoofing attack consists in presenting a fake sample (e.g., photograph, digital video, or even a 3D mask) to the acquisition sensor with the facial information of a valid user. In this paper, we introduce a low cost and software-based method for detecting spoofing attempts in face recognition systems. Our hypothesis is that during acquisition, there will be inevitable artifacts left behind in the recaptured biometric samples allowing us to create a discriminative signature of the video generated by the biometric sensor. To characterize these artifacts, we extract time-spectral feature descriptors from the video, which can be understood as a low-level feature descriptor that gathers temporal and spectral information across the biometric sample and use the visual codebook concept to find mid-level feature descriptors computed from the low-level ones. Such descriptors are more robust for detecting several kinds of attacks than the low-level ones. The experimental results show the effectiveness of the proposed method for detecting different types of attacks in a variety of scenarios and data sets, including photos, videos, and 3D masks.


title: Qualifying explore and exploit for efficient data dissemination in emergency adhoc networks

abstract: The literature of wireless adhoc networks includes a plethora of alternative dissemination strategies that aim for efficient delivery of data traffic from source to destination. Hence, the majority of these strategies assume that the source-destination pair is known prior to any data forwarding. On the contrary, an adhoc network opportunistically formed by mobile devices in the aftermath of a disaster, lacks any actual knowledge with respect to the network topology and the response units' availability. As such, distress nodes need to first understand their topological status in order to then be able to make informed forwarding decisions for alert message dissemi-nation. The Explore and Exploit strategy, described hereafter, is purposefully envisioned to solve the latter issues and meet the stringent requirements for highly efficient data dissemination.


title: Table-to-Text: Describing Table Region with Natural Language

abstract: In this paper, we present a generative model to generate a natural language sentence describing a table region, e.g., a row. The model maps a row from a table to a continuous vector and then generates a natural language sentence by leveraging the semantics of a table. To deal with rare words appearing in a table, we develop a flexible copying mechanism that selectively replicates contents from the table in the output sequence. Extensive experiments demonstrate the accuracy of the model and the power of the copying mechanism. On two synthetic datasets, WIKIBIO and SIMPLEQUESTIONS, our model improves the current state-of-the-art BLEU-4 score from 34.70 to 40.26 and from 33.32 to 39.12, respectively. Furthermore, we introduce an open-domain dataset WIKITABLETEXT including 13,318 explanatory sentences for 4,962 tables. Our model achieves a BLEU-4 score of 38.23, which outperforms template based and language model based approaches.


title: Learning in the presence of concept drift and hidden contexts

abstract: On-line learning in domains where the target concept depends on some hidden context poses serious problems. A changing context can induce changes in the target concepts, producing what is known as concept drift. We describe a family of learning algorithms that flexibly react to concept drift and can take advantage of situations where contexts reappear. The general approach underlying all these algorithms consists of (1) keeping only a window of currently trusted examples and hypotheses; (2) storing concept descriptions and reusing them when a previous context re-appears; and (3) controlling both of these functions by a heuristic that constantly monitors the system's behavior. The paper reports on experiments that test the systems' perfomance under various conditions such as different levels of noise and different extent and rate of concept drift.


title: Geometric context from a single image

abstract: Many computer vision algorithms limit their performance by ignoring the underlying 3D geometric structure in the image. We show that we can estimate the coarse geometric properties of a scene by learning appearance-based models of geometric classes, even in cluttered natural scenes. Geometric classes describe the 3D orientation of an image region with respect to the camera. We provide a multiple-hypothesis framework for robustly estimating scene structure from a single image and obtaining confidences for each geometric label. These confidences can then be used to improve the performance of many other applications. We provide a thorough quantitative evaluation of our algorithm on a set of outdoor images and demonstrate its usefulness in two applications: object detection and automatic single-view reconstruction.


title: Negative NAO and cold Eurasian winters: how exceptional was the winter of 1962/1963?

abstract: Since the mid 1980s frequent positive phases of the North Atlantic Oscillation (NAO) linked to a higher-than-normal pressure difference between the Azores high and Icelandic low pressure centres and stronger westerlies (Hurrell, 1995; Thompson and Wallace, 1998; Osborn, 2006) have led to higher winter temperatures over most of western Europe. Harsher winters were more frequent between the 1950s and the 1980s (see Figure 9 in Graham et al., 2006). Even though mild winters similar to the recent ones did occur they alternated with colder winter seasons. The best remembered is probably the winter of 1962/1963 when temperatures were well below zero across most of Europe. Figure 1 illustrates the unusual amounts of snow that occurred at lower elevations in Switzerland. With the exception of western Ireland, most of the British Isles was covered by a blanket of snow for most of the winter (Met Office, 2006a). On an anecdotal note, the harsh conditions prevailing during that winter led a then relatively unknown Bob Dylan, who was staying in a London flat at that time, to burn furniture in order to keep warm (Williamson, 2004). On mainland Europe it was the only time in the twentieth century that large lakes such as Lake Constance or Lake Zurich were completely ice-covered and it was only in March that the ice finally broke up (Pfister, 1999). Cold winters such as the one in 1962/1963 are generally associated with a negative NAO index characterized by weak westerlies (lower-than-normal pressure difference between Icelandic low and Azores high) and a reduced Atlantic influence in western Europe. The NAO index, and the late spring seasurface temperature pattern in the North Atlantic, can be used as indicators to forecast the conditions during the following winter (Graham et al., 2006; Met Office, 2006b). However, prediction of how much colder or warmer the winter season is going to be and in what regions the anomalies are likely to be most pronounced is subject to large uncertainties. A negative NAO index during the winter months does not always coincide with very cold winter conditions over the same regions. A recent example is the winter of 2005/2006. Despite the clearly negative NAO index the winter temperatures over most of Britain remained close to average values and were clearly higher than those experienced in 1962/1963. However, the winter 2005/2006 was the coldest winter since 1986 in parts of central, eastern and southern Europe. This illustrates that even if the NAO index gives an indication of whether a winter is warmer or colder than normal, the spatial pattern of the largest temperature anomalies can vary. In this study, we use reanalysis data from the National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP/NCAR, Kalnay, et al., 1996) for surface temperature and sealevel pressure to compare the winter of 1962/1963 with other winters associated with a negative NAO index and very low temperatures over other regions of Eurasia. The purpose is to highlight how exceptional the winter of 1962/1963 was compared to other harsh winters in terms of amplitude and spatial extent of the cold temperature anomaly. The fact that the winter of 1962/1963 is so well remembered is partly due to the character of an average winter over most areas of Britain and western Europe; generally temperatures are above freezing and long spells of snow are rare. However, a shift in temperature by a few degC can transform the traditional green British winter into a snowy (white) one, thus making the difference even more obvious. In comparison, other areas of Europe/ Eurasia are characterised by average winters where snow and ice are common. Therefore, negative temperature anomalies are more likely to go unnoticed; a winter may be colder than normal but it does not change its colour.


title: A path following algorithm for Sparse Pseudo-Likelihood Inverse Covariance Estimation (SPLICE)

abstract: Given n observations of a p-dimensional random vector, the covariance matrix and its inverse (precision matrix) are needed in a wide range of applications. Sample covariance (e.g. its eigenstructure) can misbehave when p is comparable to the sample size n. Regularization is often used to mitigate the problem. In this paper, we proposed an l1-norm penalized pseudo-likelihood estimate for the inverse covariance matrix. This estimate is sparse due to the l1-norm penalty, and we term this method SPLICE. Its regularization path can be computed via an algorithm based on the homotopy/LARS-Lasso algorithm. Simulation studies are carried out for various inverse covariance structures for p=15 and n=20, 1000. We compare SPLICE with the l1-norm penalized likelihood estimate and a l1-norm penalized Cholesky decomposition based method. SPLICE gives the best overall performance in terms of three metrics on the precision matrix and ROC curve for model selection. Moreover, our simulation results demonstrate that the SPLICE estimates are positive-definite for most of the regularization path even though the restriction is not enforced.


title: A Dual Theory of Inverse and Forward Light Transport

abstract: Inverse light transport seeks to undo global illumination effects, such as interreflections, that pervade images of most scenes. This paper presents the theoretical and computational foundations for inverse light transport as a dual of forward rendering. Mathematically, this duality is established through the existence of underlying Neumann series expansions. Physically, we show that each term of our inverse series cancels an interreflection bounce, just as the forward series adds them. While the convergence properties of the forward series are well-known, we show that the oscillatory convergence of the inverse series leads to more interesting conditions on material reflectance. Conceptually, the inverse problem requires the inversion of a large transport matrix, which is impractical for realistic resolutions. A natural consequence of our theoretical framework is a suite of fast computational algorithms for light transport inversion - analogous to finite element radiosity, Monte Carlo and wavelet-based methods in forward rendering - that rely at most on matrix-vector multiplications. We demonstrate two practical applications, namely, separation of individual bounces of the light transport and fast projector radiometric compensation to display images free of global illumination artifacts in real-world environments.


title: Efficient Relative Attribute Learning Using Graph Neural Networks

abstract: A sizable body of work on relative attributes provides evidence that relating pairs of images along a continuum of strength pertaining to a visual attribute yields improvements in a variety of vision tasks. In this paper, we show how emerging ideas in graph neural networks can yield a solution to various problems that broadly fall under relative attribute learning. Our main idea is the observation that relative attribute learning naturally benefits from exploiting the graph of dependencies among the different relative attributes of images, especially when only partial ordering is provided at training time. We use message passing to perform end to end learning of the image representations, their relationships as well as the interplay between different attributes. Our experiments show that this simple framework is effective in achieving competitive accuracy with specialized methods for both relative attribute learning and binary attribute prediction, while relaxing the requirements on the training data and/or the number of parameters, or both.


title: Evolving distortion function by exploiting the differences among comparable adaptive steganography

abstract: So far, the most effective model for adaptive steganography is to minimize a well-defined distortion function, in which the distortion function determines the modification probability (MP) of each pixel. We found that the MPs of some pixels calculated by a group of steganographic methods may be very different even though these methods have close performances in resisting the detection of steganalysis. We call such pixels as controversial pixels, and consider that steganalysis is not sensitive to such pixels. Therefore we can assign more payloads to the controversial pixels by increasing MPs on them to generate a new steganographic distortion function. We call this evolutionary strategy as the rule of Controversial Pixels Prior (CPP). Taking the state-of-art methods {WOW, UNIWARD} and {HILL, MVG} as two pairs of examples, we show that the principle of CPP can improve the security of state of the art steganographic algorithms for spatial images.


title: Threshold Signatures, Multisignatures and Blind Signatures Based on the Gap-Diffie-Hellman-Group Signature Scheme

abstract: We propose a robust proactive threshold signature scheme, a multisignature scheme and a blind signature scheme which work in any Gap Diffie-Hellman (GDH) group (where the Computational Diffie-Hellman problem is hard but the Decisional Diffie-Hellman problem is easy). Our constructions are based on the recently proposed GDH signature scheme of Boneh et al. [8]. Due to the instrumental structure of GDH groups and of the base scheme, it turns out that most of our constructions are simpler, more efficient and have more useful properties than similar existing constructions. We support all the proposed schemes with proofs under the appropriate computational assumptions, using the corresponding notions of security.


title: InScript: Narrative texts annotated with script information

abstract: This paper presents the InScript corpus (Narrative Texts Instantiating Script structure). InScript is a corpus of 1,000 stories centered around 10 different scenarios. Verbs and noun phrases are annotated with event and participant types, respectively. Additionally, the text is annotated with coreference information. The corpus shows rich lexical variation and will serve as a unique resource for the study of the role of script knowledge in natural language processing.


abstract: Advances in hardware and networking have made possible a wide use of augmented reality (AR) technologies. However, simply putting those hardware and technologies together does not make a good system for end users to use. New design principles and evaluation methods specific to this emerging area are urgently needed to keep up with the advance in technologies. Human Factors in Augmented Reality Environments is the first book on human factors in AR, addressing issues related to design, development, evaluation and application of AR systems. Topics include surveys, case studies, evaluation methods and metrics, HCI theories and design principles, human factors and lessons learned and experience obtained from developing, deploying or evaluating AR systems. The contributors for this cutting-edge volume are well-established researchers from diverse disciplines including psychologists, artists, engineers and scientists. Human Factors in Augmented Reality Environments is designed for a professional audience composed of practitioners and researchers working in the field of AR and human-computer interaction. Advanced-level students in computer science and engineering will also find this book useful as a secondary text or reference.


title: Efficient Far-Field Radio Frequency Energy Harvesting for Passively Powered Sensor Networks

abstract: An RF-DC power conversion system is designed to efficiently convert far-field RF energy to DC voltages at very low received power and voltages. Passive rectifier circuits are designed in a 0.25 mum CMOS technology using floating gate transistors as rectifying diodes. The 36-stage rectifier can rectify input voltages as low as 50 mV with a voltage gain of 6.4 and operates with received power as low as 5.5 muW(22.6 dBm). Optimized for far field, the circuit operates at a distance of 44 m from a 4 W EIRP source. The high voltage range achieved at low load current make it ideal for use in passively powered sensor networks.


title: Mathematical model for wide range gaze tracking system based on corneal reflections and pupil using stereo cameras

abstract: In this paper, we propose a mathematical model for a wide range gaze tracking system based on corneal reflections and pupil using calibrated stereo cameras and light sources. We demonstrate a general calculation method for estimating the optical axis of the eye for a combination of non-coaxial and coaxial configurations of many cameras and light sources. Gaze estimation is possible only when light is reflected from the spherical surface of the cornea. Moreover, we provide a method for calculating the eye rotation range where gaze tracking can be achieved, which is useful for positioning cameras and light sources in real world applications.


title: Nanoscale magnetometry with NV centers in diamond

abstract: Nitrogen-vacancy (NV) color centers in diamond are currently considered excellent solid-state magnetic field sensors. Their long coherence times at room temperature and their atomic size allow for achieving both high magnetic field sensitivity and nanoscale spatial resolution in ambient conditions. This article reviews recent progress in magnetic field imaging with NV centers. We focus on two topics: scanning probe techniques with single NV centers and their application in the imaging of nanoscale magnetic structures, as well as recent development of magnetometers with ensembles of NV centers, which image magnetic fields at micron-length scales with extremely high sensitivities.


title: Hand Hygiene: A Frequently Missed Lifesaving Opportunity During Patient Care

abstract: Health care-associated infections constitute one of the greatest challenges of modern medicine. Despite compelling evidence that proper hand washing can reduce the transmission of pathogens to patients and the spread of antimicrobial resistance, the adherence of health care workers to recommended hand-hygiene practices has remained unacceptably low. One of the key elements in improving hand-hygiene practice is the use of an alcohol-based hand rub instead of washing with soap and water. An alcohol-based hand rub requires less time, is microbiologically more effective, and is less irritating to skin than traditional hand washing with soap and water. Therefore, alcohol-based hand rubs should replace hand washing as the standard for hand hygiene in health care settings in all situations in which the hands are not visibly soiled. It is also important to change gloves between each patient contact and to use hand-hygiene procedures after glove removal. Reducing health care-associated infections requires that health care workers take responsibility for ensuring that hand hygiene becomes an everyday part of patient care.


title: LOG PERIODIC FRACTAL KOCH ANTENNA FOR UHF BAND APPLICATIONS

abstract: In this paper, the design of Log Periodic Fractal Koch Antennas (LPFKA) is proposed for Ultra High Frequency (UHF) band applications. The procedure to design the LPFKA with three different numbers of iterations in order to reduce the antenna size is discussed. The Computer Simulation Technology (CST) software has been used to analyze the performances of the designed antennas such as return loss, radiation patterns, current distribution and gain. The antennas have been fabricated using FR4 laminate board with wet etching technique. Using fractal Koch technique, the size of the antenna can be reduced up to 27% when the series iteration is applied to the antennas without degrading the overall performances. Both simulated and measured results are compared, analyzed and presented in this paper.


title: Pembrolizumab versus Ipilimumab in Advanced Melanoma

abstract: Background The immune checkpoint inhibitor ipilimumab is the standard-of-care treatment for patients with advanced melanoma. Pembrolizumab inhibits the programmed cell death 1 (PD-1) immune checkpoint and has antitumor activity in patients with advanced melanoma. Methods In this randomized, controlled, phase 3 study, we assigned 834 patients with advanced melanoma in a 1:1:1 ratio to receive pembrolizumab (at a dose of 10 mg per kilogram of body weight) every 2 weeks or every 3 weeks or four doses of ipilimumab (at 3 mg per kilogram) every 3 weeks. Primary end points were progressionfree and overall survival. Results The estimated 6-month progression-free-survival rates were 47.3% for pembrolizumab every 2 weeks, 46.4% for pembrolizumab every 3 weeks, and 26.5% for ipilimumab (hazard ratio for disease progression, 0.58; P<0.001 for both pembrolizumab regimens versus ipilimumab; 95% confidence intervals [CIs], 0.46 to 0.72 and 0.47 to 0.72, respectively). Estimated 12-month survival rates were 74.1%, 68.4%, and 58.2%, respectively (hazard ratio for death for pembrolizumab every 2 weeks, 0.63; 95% CI, 0.47 to 0.83; P = 0.0005; hazard ratio for pembrolizumab every 3 weeks, 0.69; 95% CI, 0.52 to 0.90; P = 0.0036). The response rate was improved with pembrolizumab administered every 2 weeks (33.7%) and every 3 weeks (32.9%), as compared with ipilimumab (11.9%) (P<0.001 for both comparisons). Responses were ongoing in 89.4%, 96.7%, and 87.9% of patients, respectively, after a median follow-up of 7.9 months. Efficacy was similar in the two pembrolizumab groups. Rates of treatment-related adverse events of grade 3 to 5 severity were lower in the pembrolizumab groups (13.3% and 10.1%) than in the ipilimumab group (19.9%). Conclusions The anti–PD-1 antibody pembrolizumab prolonged progression-free survival and overall survival and had less high-grade toxicity than did ipilimumab in patients with advanced melanoma. (Funded by Merck Sharp & Dohme; KEYNOTE-006 ClinicalTrials .gov number, NCT01866319.)


title: Action Temporal Localization in Untrimmed Videos via Multi-stage CNNs

abstract: We address action temporal localization in untrimmed long videos. This is important because videos in real applications are usually unconstrained and contain multiple action instances plus video content of background scenes or other activities. To address this challenging issue, we exploit the effectiveness of deep networks in action temporal localization via multi-stage segment-based 3D ConvNets: (1) a proposal stage identifies candidate segments in a long video that may contain actions; (2) a classification stage learns one-vs-all action classification model to serve as initialization for the localization stage; and (3) a localization stage fine-tunes on the model learnt in the classification stage to localize each action instance. We propose a novel loss function for the localization stage to explicitly consider temporal overlap and therefore achieve high temporal localization accuracy. On two large-scale benchmarks, our approach achieves significantly superior performances compared with other state-of-the-art systems: mAP increases from 1.7% to 7.4% on MEXaction2 and increased from 15.0% to 19.0% on THUMOS 2014, when the overlap threshold for evaluation is set to 0.5.


title: Why People Use Social Networking Sites

abstract: One of the recent popular social media platforms is the social networking site (SNS). Thus far, few previous studies have empirically investigated people's motivations for SNS usage, especially not outside the U.S. This study combines a large-scale quantitative and qualitative research design, by asking 1,200 SNS users an open question regarding their reasons for using SNSs. An important conclusion drawn from a preliminary content analysis is that people often report many motivational reasons for using SNSs . The most important reason is to get in contact with new people (31%). The second most valued was to keep in touch with their friends (21%), whereas the third was general socializing (14%). A total of 11 different reasons and several sub-reasons were identified; that all give insight into the personal incentives that drive people to use SNSs and thus contribute to our understanding of how to develop successful social networking online.


title: Scan statistics on Enron graphs

abstract: We introduce a theory of scan statistics on graphs and apply the ideas to the problem of anomaly detection in a time series of Enron email graphs.


title: A High-Speed CMOS Comparator with 8-b Resolution

abstract: A comparator consisting of a differential input stage, two regenerative flip-flops, and an S-R latch is presented. No offset cancellation is exploited, which reduces the power consumption as well as the die area and increases the comparison speed. An experimental version of the comparator has been integrated in a standard double-poly double-metal 1.5- mu m n-well process with a die area of only 140*100 mu m/sup 2/. This circuit, operating under a +2.5/-2.5-V power supply, performs comparison to a precision of 8 b with a symmetrical input dynamic range of 2.5 V (therefore +or-0.5 LSB resolution is equal to +or-4.9 mV). >


title: Attacking the bitlocker boot process

abstract: We discuss five attack strategies against BitLocker, which target the way BitLocker is using the TPM sealing mechanism. BitLocker is a disk encryption feature included in some versions of Microsoft Windows. It represents a state-of-the-art design, enhanced with TPM support for improved security. We show that, under certain assumptions, a dedicated attacker can circumvent the protection and break confidentiality with limited effort. Our attacks neither exploit vulnerabilities in the encryption itself nor do they directly attack the TPM. They rather exploit sequences of actions that Trusted Computing fails to prevent, demonstrating limitations of the technology.


title: Sparse coding with an overcomplete basis set: A strategy employed by V1?

abstract: The spatial receptive fields of simple cells in mammalian striate cortex have been reasonably well described physiologically and can be characterized as being localized, oriented, and ban@ass, comparable with the basis functions of wavelet transforms. Previously, we have shown that these receptive field properties may be accounted for in terms of a strategy for producing a sparse distribution of output activity in response to natural images. Here, in addition to describing this work in a more expansive fashion, we examine the neurobiological implications of sparse coding. Of particular interest is the case when the code is overcomplete--i.e., when the number of code elements is greater than the effective dimensionality of the input space. Because the basis functions are non-orthogonal and not linearly independent of each other, sparsifying the code will recruit only those basis functions necessary for representing a given input, and so the input-output function will deviate from being purely linear. These deviations from linearity provide a potential explanation for the weak forms of non-linearity observed in the response properties of cortical simple cells, and they further make predictions about the expected interactions among units in response to naturalistic stimuli. © 1997 Elsevier Science Ltd


title: TCD-TIMIT: An Audio-Visual Corpus of Continuous Speech

abstract: Automatic audio-visual speech recognition currently lags behind its audio-only counterpart in terms of major progress. One of the reasons commonly cited by researchers is the scarcity of suitable research corpora. This paper details the creation of a new corpus designed for continuous audio-visual speech recognition research . TCD-TIMIT consists of high-quality audio and video footage of 62 speakers reading a total of 6913 phonetically rich sentences. Three of the speakers are professionally-trained lipspeakers, recorded to test the hypothesis that lipspeakers may have an advantage over regular speakers in automatic visual speech recognition systems. Video footage was recorded from two angles: straight on, and at $30^{\circ}$ . The paper outlines the recording of footage, and the required post-processing to yield video and audio clips for each sentence. Audio, visual, and joint audio-visual baseline experiments are reported. Separate experiments were run on the lipspeaker and non-lipspeaker data, and the results compared. Visual and audio-visual baseline results on the non-lipspeakers were low overall. Results on the lipspeakers were found to be significantly higher. It is hoped that as a publicly available database, TCD-TIMIT will now help further state of the art in audio-visual speech recognition research.


title: Factors Influencing Emoji Usage in Smartphone Mediated Communications.

abstract: Emojis have become more and more popular in text-based online communication to express emotions. This indicates a potential to utilize emojis in sentiment analysis and emotion measurements. However, many factors could affect people’s emoji usage and need to be examined. Among them, age, gender, and relationship types may result in different interpretations of the same emoji due to the ambiguity of the iconic expression. In this paper, we aim to explore how these factors may affect the frequency, type, and sentiment of people’s emoji usage in communications. After analyzing 6,821 Wechat chatting messages from 158 participants, we found people between 26–35 had lowest frequency of emoji usage; younger and elder groups showed different sentiment levels for the same emojis; people chose emoji types based on relationships. These findings shed light on how people use emojis as a communication tool.


title: Deep Learning by Scattering

abstract: We introduce general scattering transforms as mathematical models of deep neural networks with l2 pooling. Scattering networks iteratively apply complex valued unitary operators, and the pooling is performed by a complex modulus. An expected scattering defines a contractive representation of a high-dimensional probability distribution, which preserves its mean-square norm. We show that unsupervised learning can be casted as an optimization of the space contraction to preserve the volume occupied by unlabeled examples, at each layer of the network. Supervised learning and classification are performed with an averaged scattering, which provides scattering estimations for multiple classes.


title: Screenmilker: How to Milk Your Android Screen for Secrets.

abstract: With the rapid increase in Android device popularity, the capabilities that the diverse user base demands from Android have significantly exceeded its original design. As a result, people have to seek ways to obtain the permissions not directly offered to ordinary users. A typical way to do that is using the Android Debug Bridge (ADB), a developer tool that has been granted permissions to use critical system resources. Apps adopting this solution have combined tens of millions of downloads on Google Play. However, we found that such ADB-level capabilities are not well guarded by Android. A prominent example we investigated is the apps that perform programmatic screenshots, a much-needed capability Android fails to support. We found that all such apps in the market inadvertently expose this ADB capability to any party with the INTERNET permission on the same device. With this exposure, a malicious app can be built to stealthily and intelligently collect sensitive user data through screenshots. To understand the threat, we built Screenmilker, an app that can detect the right moment to monitor the screen and pick up a user’s password when she is typing in real time. We show that this can be done efficiently by leveraging the unique design of smartphone user interfaces and its public resources. Such an understanding also informs Android developers how to protect this screenshot capability, should they consider providing an interface to let third-party developers use it in the future, and more generally the security risks of the ADB workaround, a standard technique gaining popularity in app development. Based on the understanding, we present a mitigation mechanism that controls the exposure of the ADB capabilities only to authorized apps.


title: Schematic surface reconstruction

abstract: This paper introduces a schematic representation for architectural scenes together with robust algorithms for reconstruction from sparse 3D point cloud data. The schematic models architecture as a network of transport curves, approximating a floorplan, with associated profile curves, together comprising an interconnected set of swept surfaces. The representation is extremely concise, composed of a handful of planar curves, and easily interpretable by humans. The approach also provides a principled mechanism for interpolating a dense surface, and enables filling in holes in the data, by means of a pipeline that employs a global optimization over all parameters. By incorporating a displacement map on top of the schematic surface, it is possible to recover fine details. Experiments show the ability to reconstruct extremely clean and simple models from sparse structure-from-motion point clouds of complex architectural scenes.


title: Modeling and simulation of porpoising for a multilink dolphin robot

abstract: This paper analytically analyzes the porpoising behavior of dolphins and other cetaceans in the context of bio-robotics. Based on the conventional assumption that porpoising saves energy at high speed, the “crossover” speed is recalculated. A dynamic model for a multilink dolphin-like robot within the framework of multibody dynamics is created. Simulations using the dynamic model have been extensively performed in MATLAB/Simulink. In terms of simulation results obtained, the primary assumption has been validated and a newly possible main cause of porpoising's energy economy is suggested that the route diversion action results in a significant speed increase while porpoising. Two contradictions between the observation and the theoretical prediction have been explained, agreeing well with the above finding.


title: Euler Number and Connectivity Indexes of a Three Dimensional Digital Picture

abstract: Fundamental properties of topological structure of a 3D digitized picture are presented including the concept of neighborhood and connectivity among volume cells (voxels) of 3D digitized binary pictures defined on a cubic grid, the concept of simplicial decomposition of a 3D digitized object, and two algorithms for calculating the Euler number (genus). First we define four types of connectivity. Second we present two algorithms to calculate the Euler number of a 3D figure. Thirdly we introduce new local features called the connectivity number (CN) and the connectivity index to study topological properties of a 3D object in a 3D digitized picture. The CN at a 1-voxel (=a voxel with the value 1) x is defined as the change in the Euler number of the object caused by changing the value of x into zero, that is, caused by deleting x. Finally, by using them, we prove a necessary and sufficient condition that a 1-voxel is deletable. A 1-voxel x is said to be deletable if deletion of x causes no decrease and no increase in the numbers of connected components, holes and cavities in a given 3D picture.


title: Compact microstrip feed networks for low-impedance quadrifillar helix antennas

abstract: Quadrifilar helix antennas (QHA) are widely used in various applications. QHA performance strongly depends on its feed network parameters. A proper network should equally divide power from input port between output ports and provide quadrature phases (0, 90, 180 and 270°) within antenna frequency band. Two microstrip feed networks are presented in the report. Both networks are based on Wilkinson power splitter and have similar shape — a 39 mm diameter circle. First one is designed for a small QHA with a low input impedance (12Ohm). It consists of planar two-port impedance-transforming Wilkinson splitters. Each splitter reduces output impedance by a factor of 2. Three splitters are cascaded to form a 4-port network. Proper phasing is achieved by means of microstrip delay lines. To equalize output signal amplitudes the splitters have been made non-symmetrical. Bandwidth of the network is much higher than required for a narrow-band (2–3%) QHA. Insertion loss is 1 dB higher than that of ideal network. Relatively high return loss (− 16 dB) results from interference with second stage dividers. Another drawback is high reflection from via holes which inevitably act as π-type filters. The second feed network is designed for a larger QHA with input impedance 28 Ohms. It is an impedance-transforming 4-port Wilkinson divider. Like in the first design, microstrip delay lines provide proper phasing. Network bandwidth and transmission losses are similar to the first one, but has the advantage of lower return losses due to absence of interference with second stage splitter. Simpler design allowed placing almost everything in the top plane of PCB except for splitter resistors. In a matched case, no current flows through these resistors, so the negative effects of via holes are negligible. Reducing complexity and demands for impedance transformation improved performance of this network compared to the first one. Return loss is lower than −25 dB within antenna operation range. Insertion loss is 0.7 dB higher than in case of ideal network and slightly lower than return loss of the first network.


title: Complexity Reduced MLD Based on QR Decomposition in OFDM MIMO Multiplexing with Frequency Domain Spreading and Code Multiplexing

abstract: This paper presents a new maximum likelihood detection (MLD)-based signal detection method for orthogonal frequency division multiplexing (OFDM) multiple input multiple output (MIMO) multiplexing with frequency domain spreading and code multiplexing. The proposed MLD reduces the computational complexity by utilizing signal orthogonalization based on QR decomposition of the product of the channel and spreading code matrices. Simulation results show that when the spreading factor and number of code multiplexing are 16, the proposed MLD reduces the average received signal energy per bit-to-noise spectrum density ratio (Eb/N0) for the average packet error rate (PER) of 10-2 by approximately 12 dB compared to the conventional minimum mean squared error (MMSE)-based filtering for 4-by-4 MIMO multiplexing (16QAM with rate-3/4 Turbo code is assumed).


NOVEL DEPLOYMENT SCHEMES FOR TARGET COVERAGE AND NETWORK CONNECTIVITY IN MOBILE SENSOR NETWORKS


Sentiment Analysis on Punjabi News Articles Using SVM


title: GATE - A General Architecture For Text Engineering

abstract: This paper presents the design, implementation and evaluation of GATE, a General Architecture for Text Engineering.GATE lies at the intersection of human language computation and software engineering, and constitutes aninfrastructural system supporting research and development of languageprocessing software.


title: The STAGE technique (superficial tunica albuginea geometric-based excision) for the correction of biplanar congenital penile curvature.

abstract: INTRODUCTION ::: Congenital penile curvature can present with both uniplanar and biplanar defects, the latter of which entails more technically demanding surgery. ::: ::: ::: AIM ::: The study aims to demonstrate the efficacy and safety of our novel superficial tunica albuginea geometric-based excision (STAGE) technique based on multiple, small, superficial elliptical tunica albuginea excisions and geometrical principles for correcting biplanar congenital penile curvature. ::: ::: ::: METHODS ::: The study represents a retrospective analysis of 145 patients with disabling congenital biplanar ventrolateral (n = 131; 90.3%) or dorsolateral (n = 14; 9.7%) curvature of the penis, which underwent stepwise STAGEs between June 2006 and March 2012. Multiple 3-mm elliptical excisions of the superficial tunica albuginea were performed without compromising the inner layer of the tunica albuginea, thus resulting in a stepwise correction of the curvature and improved distribution of the bending force of the curvature. ::: ::: ::: MAIN OUTCOME MEASURES ::: Functional outcome regarding penile straightening, erectile function, and patient satisfaction were evaluated. Furthermore, clinical data concerning the early postoperative outcome were analyzed retrospectively. ::: ::: ::: RESULTS ::: The mean follow-up period was 21 months (range 6-62 months). Mean age at surgery was 23.8 years (range 15-47 years). Mean degree of curvature was 65° (range 45-90°). There was no recurrent curvature. Complete correction of the penile axis was obtained in 98.6% (n = 143). No change in erectile function according to International Index of Erectile Function-5 score was visible (P = 0.748). The mean loss of penile length was 0.7 cm (range 0.3-0.9 cm). The excellent functional outcomes resulted in a high level of patient satisfaction, including improved self-esteem, libido, sexual intercourse, and psychosexual relief. Two patients had a residual curvature of up to 30° requiring a reoperation. No intra- or postoperative complications were encountered. ::: ::: ::: CONCLUSIONS ::: We recommend the STAGE technique as the optimal surgical intervention for correcting both uniplanar and biplanar congenital deviations.


title: Analysis of the Wikipedia Category Graph for NLP Applications

abstract: In this paper, we discuss two graphs in Wikipedia (i) the article graph, and (ii) the category graph. We perform a graphtheoretic analysis of the category graph, and show that it is a scale-free, small world graph like other well-known lexical semantic networks. We substantiate our findings by transferring semantic relatedness algorithms defined on WordNet to the Wikipedia category graph. To assess the usefulness of the category graph as an NLP resource, we analyze its coverage and the performance of the transferred semantic relatedness algorithms.


title: C3A: A Cognitive Collaborative Control Architecture For an Intelligent Wheelchair

abstract: Retention of residual skills for persons who partially lose their cognitive or physical ability is of utmost importance. Research is focused on developing systems that provide need-based assistance for retention of such residual skills. This paper describes a novel cognitive collaborative control architecture C3A, designed to address the challenges of developing need- based assistance for wheelchair navigation. Organization of C3A is detailed and results from simulation of the proposed architecture is presented. For simulation of our proposed architecture, we have used ROS (Robot Operating System) as a control framework and a 3D robotic simulator called USARSim (Unified System for Automation and Robot Simulation).


title: Exploring the space of adversarial images

abstract: Adversarial examples have raised questions regarding the robustness and security of deep neural networks. In this work we formalize the problem of adversarial images given a pretrained classifier, showing that even in the linear case the resulting optimization problem is nonconvex. We generate adversarial images using shallow and deep classifiers on the MNIST and ImageNet datasets. We probe the pixel space of adversarial images using noise of varying intensity and distribution. We bring novel visualizations that showcase the phenomenon and its high variability. We show that adversarial images appear in large regions in the pixel space, but that, for the same task, a shallow classifier seems more robust to adversarial images than a deep convolutional network.


title: Novel 3-dimensional Dual Control-gate with Surrounding Floating-gate (DC-SF) NAND flash cell for 1Tb file storage application

abstract: A novel 3-dimensional Dual Control-gate with Surrounding Floating-gate (DC-SF) NAND flash cell has been successfully developed, for the first time. The DC-SF cell consists of a surrounding floating gate with stacked dual control gate. With this structure, high coupling ratio, low voltage cell operation (program: 15V and erase: −11V), and wide P/E window (9.2V) can be obtained. Moreover, negligible FG-FG interference (12mV/V) is achieved due to the control gate shield effect. Then we propose 3D DC-SF NAND flash cell as the most promising candidate for 1Tb and beyond with stacked multi bit FG cell (2 ∼ 4bit/cell).


title: Iterative Parameter Mixing for Distributed Large-Margin Training of Structured Predictors for Natural Language Processing

abstract: The development of distributed training strategies for statistical prediction functions is important for applications of machine learning, generally, and the development of distributed structured prediction training strategies is important for natural language processing (NLP), in particular. With ever-growing data sets this is, first, because, it is easier to increase computational capacity by adding more processor nodes than it is to increase the power of individual processor nodes, and, second, because data sets are often collected and stored in different locations. Iterative parameter mixing (IPM) is a distributed training strategy in which each node in a network of processors optimizes a regularized average loss objective on its own subset of the total available training data, making stochastic (per-example) updates to its own estimate of the optimal weight vector, and communicating with the other nodes by periodically averaging estimates of the optimal vector across the network. This algorithm has been contrasted with a close relative, called here the singlemixture optimization algorithm, in which each node stochastically optimizes an average loss objective on its own subset of the training data, operating in isolation until convergence, at which point the average of the independently created estimates is returned. Recent empirical results have suggested that this IPM strategy produces better models than the single-mixture algorithm, and the results of this thesis add to this picture. The contributions of this thesis are as follows. The first contribution is to produce and analyze an algorithm for decentralized stochastic optimization of regularized average loss objective functions. This algorithm, which we call the distributed regularized dual averaging algorithm, improves over prior work on distributed dual averaging by providing a simpler algorithm (used in the rest of the thesis), better convergence bounds for the case of regularized average loss functions, and certain technical results that are used in the sequel. The central contribution of this thesis is to give an optimization-theoretic justification for the IPM algorithm. While past work has focused primarily on its empirical test-time performance, we give a novel perspective on this algorithm by showing that, in the context of the distributed dual averaging algorithm, IPM constitutes a convergent optimization algorithm for arbitrary convex functions, while the single-mixture distribution algorithm is not. Experiments indeed confirm that the superior test-time performance of models trained using IPM, compared to single-mixture, correlates with


title: A compiler technique for improving whole-program locality

abstract: Exploiting spatial and temporal locality is essential for obtaining high performance on modern computers. Writing programs that exhibit high locality of reference is difficult and error-prone. Compiler researchers have developed loop transformations that allow the conversion of programs to exploit locality. Recently, transformations that change the memory layouts of multi-dimensional arrays---called data transformations---have been proposed. Unfortunately, both data and loop transformations have some important draw-backs. In this work, we present an integrated framework that uses loop and data transformations in concert to exploit the benefits of both approaches while minimizing the impact of their disadvantages. Our approach works inter-procedurally on acyclic call graphs, uses profile data to eliminate layout conflicts, and is unique in its capability of resolving conflicting layout requirements of different references to the same array in the same nest and in different nests for regular array-based applications.The optimization technique presented in this paper has been implemented in a source-to-source translator. We evaluate its performance using standard benchmark suites and several math libraries (complete programs) with large input sizes. Experimental results show that our approach reduces the overall execution times of original codes by 17.5% on the average. This reduction comes from three important characteristics of the technique, namely, resolving layout conflicts between references to the same array in a loop nest, determining a suitable order to propagate layout modifications across loop nests, and propagating layouts between different procedures in the program --- all in a unified framework.


title: Self-schemata and processing information about the self.

abstract: Attempts to organize, summarize, or explain one's own behavior in a particular domain result in the formation of cognitive structures about the self or selfschemata. Self-schemata are cognitive generalizations about the self, derived from past experience, that organize and guide the processing of the self-related information contained in an individual's social experience. The role of schemata in processing information about the self is examined by linking self-schemata to a number of specific empirical referents. Female students with schemata in a particular domain and those without schemata are selected and their performance on a variety of cognitive tasks is compared. The results indicate that self-schemata facilitate the processing of information about the self (judgments and decisions about the self), contain easily retrievable behavioral evidence, provide a basis for the confident self-prediction of behavior on schema-related dimensions, and make individuals resistant to counterschematic information. The relationship of self-schemata to cross-situational consistency in behavior and the implications of self-schemata for attribution theory are discussed.


title: An updating method of self-adaptive background for moving objects detection in video

abstract: Background subtraction is a general and simple method in real-time detection of moving object. However, it requires the accurate current background image, and so far, no reasonable approach has been designed and implemented for automatic background updating along with the illumination variance, which limits its applications.To overcome the above problem, a new self-adaptive background approximating and updating algorithm based on static background and kalman self-adaptive background (KAB) is presented in this paper. Moreover, the two renewal rates in KAB are obtained by the cumulants of background subtraction in object region and background region. Experimental results demonstrate that the proposed new background updating method can update the background exactly and quickly along with the variance of illumination, the renewal rates can vary with the video automatically and they bring certain noise immunity to the new dynamic background.


title: Data-Driven Intelligent Transportation Systems: A Survey

abstract: For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D2ITS) : a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D2ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D2ITS, discussing the functionality of its key components and some deployment issues associated with D2ITS Future research directions for the development of D2ITS is also presented.