title: Career Paths in Higher Education Administration.

abstract: A national survey of administrators was conducted to explore career paths and barriers in higher education administration and differences between male and female administrators as well as differences in administrative ranks between the sexes. The sample consisted of 394 administrators at the dean and above level, 319 of whom were male. A majority were white. A majority of women and men held the doctorate degree; however, the majority of the women received their highest degree in education, while men were more spread out with degrees in education, social sciences, and other fields. The distribution of current administrative positions indicated that women were more likely than men to hold lower level positions, and were more likely to be at community colleges. Tv() areas were important in the career path to higher education administration: one's education, and whether an individual acquires any faculty experience. Barriers perceived and experienced by women include: lack of strong sponsorship for administrative positions, sex discrimination, the importance placed on family responsibilities in the early career, and the type of education received. The questionnaire is appended. (SW) *********************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. ***********************************************************************


title: Implementation of a vehicle tracking system using smartphone and SMS service

abstract: In this dynamic life where everyone is in a hurry to reach their destinations, waiting for bus is hectic and even many of us are unaware of the bus timing. To overcome this difficulty, an easy system is proposed in this paper to aid tracking real time bus location. The proposed solution takes advantages of the two main features in mobile platform nowadays which are location services, mainly GPS based, and basic telephony services, mainly SMS based. The system consists of two sides, server side and client side. The server device's main responsibility is to provide the exact location of the bus to the server, or to the user in case of SMS based query from client's device. On the other hand, client's device can find bus location either using SMS service or using internet service. If clients' device is an android based smartphone, he can install our application to track bus location using internet service. The server's device will be placed on the vehicle of interest with android application installed on it. Experiments were made with this system and found that it performs better in many ways than other similar vehicle tracking systems.


title: Biomedical Applications of Limulus Amebocyte Lysate

abstract: This year celebrates the 30th anniversary of the licensing of Limulus amebocyte lysate (LAL) by the US Food and Drug Administration (FDA) as a test for the presence of endotoxin in biologicals, pharmaceutical drugs, and medical devices. LAL is currently recognized by several major pharmacopoeias and is used worldwide. That a suitable alternative for the detection of endotoxin has not supplanted LAL is indicative of its superior reliability. Since its discovery, LAL has proven its usefulness not only to detect harmful levels of endotoxin (as pyrogens) in pharmaceutical products, but has become an indispensable tool in controlling endotoxin in processes and equipment used to produce pharmaceuticals. Indeed, the exquisite sensitivity of LAL compared to other assays for endotoxin/pyrogen has proven extremely useful in monitoring high-purity water used as a prime ingredient or processing agent for all biologicals, drugs, and devices. LAL has also become the assay of choice for researchers studying both the clinical and the environmental effects of endotoxin. To highlight its usefulness, various specific applications of LAL including modifications of the assay to allow testing of complex substances will be described. Finally, although horseshoe crab mortality associated with LAL production is low, the LAL industry has taken steps to find a synthetic substitute and to produce reagents and methods that use much less LAL than traditional assays. That the horseshoe crab has uniquely contributed a test that profoundly affects the safety of pharmaceuticals should be celebrated and rewarded by continuing to protect this valuable resource.


title: miR-7 Reverses Breast Cancer Resistance To Chemotherapy By Targeting MRP1 And BCL2

abstract: Background ::: MicroRNAs (miRNAs) are a class of non-coding RNAs that have been linked with breast cancer chemoresistance, which is a major clinical problem causing disease relapse and poor prognosis. miR-7 exerts several tumor suppressive activities. ::: ::: ::: Purpose ::: This study was designed to clarify whether and how miR-7 regulates breast cancer chemoresistance. ::: ::: ::: Methods ::: miR-7 level in breast cancer was determined by qRT-PCR analysis. Cell viability was assessed by MTS assay to quantify the IC50 value of paclitaxel and carboplatin. The targets of miR-7 were confirmed by luciferase reporter assay. ::: ::: ::: Results ::: Higher miR-7 expression predicts better pathological complete response (pCR) of breast cancer patients receiving paclitaxel/carboplatin chemotherapy. In vitro, miR-7 sensitizes breast cancer cell lines (MCF-7 and MDA-MB-231) to paclitaxel and carboplatin, alone and in combination. In addition, we reveal that both the multidrug resistance-associated protein 1 (MRP1) and anti-apoptotic B cell lymphoma 2 (BCL2) are targets of miR-7 in breast cancer cells. Furthermore, miR-7-induced sensitization of breast cancer to paclitaxel/carboplatin is markedly reversed by restoration of MRP1 and BCL2. ::: ::: ::: Conclusion ::: These findings show that miR-7 reverses breast cancer chemoresistance through suppressing MRP1 and BCL2, and also suggest that miR-7 may possess a predictive value and represent a therapeutic target in breast cancer chemotherapy.


title: Persuasive normative messages: The influence of injunctive and personal norms on using free plastic bags. Sustainability 2013

abstract: In this exploratory field-study, we examined how normative messages ( i.e. , activating an injunctive norm, personal norm, or both) could encourage shoppers to use fewer free plastic bags for their shopping in addition to the supermarket’s standard environmental message aimed at reducing plastic bags. In a one-way subjects-design ( N = 200) at a local supermarket, we showed that shoppers used significantly fewer free plastic bags in the injunctive, personal and combined normative message condition than in the condition where only an environmental message was present. The combined normative message did result in the smallest uptake of free plastic bags compared to the injunctive and personal normative-only message, although these differences were not significant. Our findings imply that re-wording the supermarket’s environmental message by including normative information could be a promising way to reduce the use of free plastic bags, which will ultimately benefit the environment.


title: The ICSI Meeting Corpus

abstract: We have collected a corpus of data from natural meetings that occurred at the International Computer Science Institute (ICSI) in Berkeley, California over the last three years. The corpus contains audio recorded simultaneously from head-worn and table-top microphones, word-level transcripts of meetings, and various metadata on participants, meetings, and hardware. Such a corpus supports work in automatic speech recognition, noise robustness, dialog modeling, prosody, rich transcription, information retrieval, and more. We present details on the contents of the corpus, as well as rationales for the decisions that led to its configuration. The corpus were delivered to the Linguistic Data Consortium (LDC).


title: Using Time-Sensitive Rooted PageRank to Detect Hierarchical Social Relationships

abstract: We study the problem of detecting hierarchical ties in a social network by exploiting the interaction patterns between the actors (members) involved in the network. Motivated by earlier work using a rank-based approach, i.e., Rooted-PageRank, we introduce a novel time-sensitive method, called T-RPR, that captures and exploits the dynamics and evolution of the interaction patterns in the network in order to identify the underlying hierarchical ties. Experiments on two real datasets demonstrate the performance of T-RPR in terms of recall and show its superiority over a recent competitor method.


title: Parameterized Complexity

abstract: An approach to complexity theory which offers a means of analysing algorithms in terms of their tractability. The authors consider the problem in terms of parameterized languages and taking "k-slices" of the language, thus introducing readers to new classes of algorithms which may be analysed more precisely than was the case until now. The book is as self-contained as possible and includes a great deal of background material. As a result, computer scientists, mathematicians, and graduate students interested in the design and analysis of algorithms will find much of interest.


title: Traps - Classifiers Of Temporal Patterns

abstract: The work proposes a radically di erent set of features for ASR where TempoRAl Patterns of spectral energies are used in place of the conventional spectral patterns. The approach has several inherent advantages, among them robustness to stationary or slowly varying disturbances.


title: A New Discriminative Common Spatial Pattern Method for Motor Imagery Brain–Computer Interfaces

abstract: Event-related desynchronization/synchronization patterns during right/left motor imagery (MI) are effective features for an electroencephalogram-based brain-computer interface (BCI). As MI tasks are subject-specific, selection of subject-specific discriminative frequency components play a vital role in distinguishing these patterns. This paper proposes a new discriminative filter bank (FB) common spatial pattern algorithm to extract subject-specific FB for MI classification. The proposed method enhances the classification accuracy in BCI competition III dataset IVa and competition IV dataset IIb. Compared to the performance offered by the existing FB-based method, the proposed algorithm offers error rate reductions of 17.42% and 8.9% for BCI competition datasets III and IV, respectively.


Wei Yang's Research Statement


title: A Tutorial on Runtime Verification

abstract: This tutorial presents an overview of the field referred as to runtime verification. Runtime Verification is the study of algorithms, data structures, and tools focused on analyzing executions of system. The performed analysis aims at improving the confidence in systems behavior, either by improving program understanding, or by checking conformance to specifications or algorithms. This chapter focuses specifically on checking execution traces against requirements formalized in terms of monitors. It is first shown on examples how such monitors can be written using aspect-oriented programming, exemplified by ASPECTJ. Subsequently four monitoring systems are illustrated on the same examples. The systems cover such formalisms as regular expressions, temporal logics, state machines, and rule-based programming, as well as the distinction between external and internal DSLs.


title: Design and Efficient FPGA Implementation of an RGB to YCrCb Color Space Converter Using Distributed Arithmetic

abstract: This paper presents two novel architectures for efficient implementation of a Color Space Converter (CSC) suitable for Field Programmable Gate Array (FPGAs) and VLSI. The proposed architectures are based on Distributed Arithmetic (DA) ROM accumulator principles. The architectures have been implemented and verified using the Celoxica RC1000-PP FPGA development board. In addition, they are platform independent and have a low latency (8 cycles). The first architecture has a throughput of height, while the second one is fully pipelined and has a throughput of one and capable of sustained data rate of over 234 mega-conversions/seconds.


title: AirLink: sharing files between multiple devices using in-air gestures

abstract: We introduce AirLink, a novel technique for sharing files between multiple devices. By waving a hand from one device towards another, users can directly transfer files between them. The system utilizes the devices' built-in speakers and microphones to enable easy file sharing between phones, tablets and laptops. We evaluate our system in an 11-participant study with 96.8% accuracy, showing the feasibility of using AirLink in a multiple-device environment. We also implemented a real-time system and demonstrate the capability of AirLink in various applications.


title: Impact of the Interdecadal Pacific Oscillation on Tropical Cyclone Activity in the North Atlantic and Eastern North Pacific

abstract: Tropical cyclones (TCs) are among the most devastating weather systems affecting the United States and Central America (USCA). Here we show that the Interdecadal Pacific Oscillation (IPO) strongly modulates TC activity over the North Atlantic (NA) and eastern North Pacific (eNP). During positive IPO phases, less (more) TCs were observed over NA (eNP), likely due to the presence of stronger (weaker) vertical wind shear and the resulting changes in genesis potential. Furthermore, TCs over NA tend to keep their tracks more eastward and recurve at lower latitudes during positive IPO phases. Such variations are largely determined by changes in steering flow instead of changes in genesis locations. Over the eNP, smaller track variations are observed at different IPO phases with stable, westward movements of TCs prevailing. These findings have substantial implications for understanding decadal to inter-decadal fluctuations in the risk of TC landfalls along USCA coasts.


title: Document understanding of graphical content in natively digital PDF documents

abstract: This paper presents an object-based method for analysing the content drawn by graphical operators in natively digital PDF documents. We propose that graphical content in a document can be classified either as structural or non-structural and present an output model for our analysis result. Heuristic techniques are used to group the instructions into regions and determine their logical role in the document's structure. Experimental results demonstrate the effectiveness of the algorithm.


title: Localization in Wireless Sensor Networks: A Survey on Algorithms, Measurement Techniques, Applications and Challenges

abstract: Localization is an important aspect in the field of wireless sensor networks (WSNs) that has developed significant research interest among academia and research community. Wireless sensor network is formed by a large number of tiny, low energy, limited processing capability and low-cost sensors that communicate with each other in ad-hoc fashion. The task of determining physical coordinates of sensor nodes in WSNs is known as localization or positioning and is a key factor in today’s communication systems to estimate the place of origin of events. As the requirement of the positioning accuracy for different applications varies, different localization methods are used in different applications and there are several challenges in some special scenarios such as forest fire detection. In this paper, we survey different measurement techniques and strategies for range based and range free localization with an emphasis on the latter. Further, we discuss different localization-based applications, where the estimation of the location information is crucial. Finally, a comprehensive discussion of the challenges such as accuracy, cost, complexity, and scalability are given.


title: Re-Evaluation The Role Of Bleu In Machine Translation Research

abstract: We argue that the machine translation community is overly reliant on the Bleu machine translation evaluation metric. We show that an improved Bleu score is neither necessary nor sufficient for achieving an actual improvement in translation quality, and give two significant counterexamples to Bleu’s correlation with human judgments of quality. This offers new potential for research which was previously deemed unpromising by an inability to improve upon Bleu scores.


title: Describing clothing by semantic attributes

abstract: Describing clothing appearance with semantic attributes is an appealing technique for many important applications. In this paper, we propose a fully automated system that is capable of generating a list of nameable attributes for clothes on human body in unconstrained images. We extract low-level features in a pose-adaptive manner, and combine complementary features for learning attribute classifiers. Mutual dependencies between the attributes are then explored by a Conditional Random Field to further improve the predictions from independent classifiers. We validate the performance of our system on a challenging clothing attribute dataset, and introduce a novel application of dressing style analysis that utilizes the semantic attributes produced by our system.


title: Bilinear dynamical systems

abstract: In this paper, we propose the use of bilinear dynamical systems (BDS)s for model-based deconvolution of fMRI time-series. The importance of this work lies in being able to deconvolve haemodynamic time-series, in an informed way, to disclose the underlying neuronal activity. Being able to estimate neuronal responses in a particular brain region is fundamental for many models of functional integration and connectivity in the brain. BDSs comprise a stochastic bilinear neurodynamical model specified in discrete time, and a set of linear convolution kernels for the haemodynamics. We derive an expectation-maximization (EM) algorithm for parameter estimation, in which fMRI time-series are deconvolved in an E-step and model parameters are updated in an M-Step. We report preliminary results that focus on the assumed stochastic nature of the neurodynamic model and compare the method to Wiener deconvolution.


title: Handling outliers in non-blind image deconvolution

abstract: Non-blind deconvolution is a key component in image deblurring systems. Previous deconvolution methods assume a linear blur model where the blurred image is generated by a linear convolution of the latent image and the blur kernel. This assumption often does not hold in practice due to various types of outliers in the imaging process. Without proper outlier handling, previous methods may generate results with severe ringing artifacts even when the kernel is estimated accurately. In this paper we analyze a few common types of outliers that cause previous methods to fail, such as pixel saturation and non-Gaussian noise. We propose a novel blur model that explicitly takes these outliers into account, and build a robust non-blind deconvolution method upon it, which can effectively reduce the visual artifacts caused by outliers. The effectiveness of our method is demonstrated by experimental results on both synthetic and real-world examples.


title: MOTIVATION, EMOTION, FEEDING, DRINKING NEUROREPORT The neural basis of romantic love

abstract: The neural correlates of many emotional states have been studied, most recently through the technique of fMRI. However, nothing is known about the neural substrates involved in evoking one of the most overwhelming of all affective states, that of romantic love, about which we report here. The activity in the brains of 17 subjects who were deeply in love was scanned using fMRI, while they viewed pictures of their partners, and compared with the activity produced by viewing pictures of three friends of similar age, sex and duration of friendship as their partners. The activity was restricted to foci in the medial insula and the anterior cingulate cortex and, subcortically, in the caudate nucleus and the putamen, all bilaterally. Deactivations were observed in the posterior cingulate gyrus and in the amygdala and were right-lateralized in the prefrontal, parietal and middle temporal cortices. The combination of these sites differs from those in previous studies of emotion, suggesting that a unique network of areas is responsible for evoking this affective state. This leads us to postulate that the principle of functional specialization in the cortex applies to affective states as well.


title: Recursive Automatic Bias Selection for Classifier Construction

abstract: The results of empirical comparisons of existing learning algorithms illustrate that each algorithm has a selective superiority; each is best for some but not all tasks. Given a data set, it is often not clear beforehand which algorithm will yield the best performance. In this article we present an approach that uses characteristics of the given data set, in the form of feedback from the learning process, to guide a search for a tree-structured hybrid classifier. Heuristic knowledge about the characteristics that indicate one bias is better than another is encoded in the rule base of the Model Class Selection (MCS) system. The approach does not assume that the entire instance space is best learned using a single representation language; for some data sets, choosing to form a hybrid classifier is a better bias, and MCS has the ability to determine these cases. The results of an empirical evaluation illustrate that MCS achieves classification accuracies equal to or higher than the best of its primitive learning components for each data set, demonstrating that the heuristic rules effectively select an appropriate learning bias.


title: Factors influence depositors’ withdrawal behavior in Islamic banks: a theory of reasoned action

abstract: Unlike its conventional counterpart, Islamic principles forbid Islamic banks to take any interest-related income and thus makes deposits from depositors as an important source of fund for its operational and financing. Consequently, the risk of deposit withdrawal by depositors is an important aspect that should be well- managed in Islamic banking. This paper aims to investigate factors that influence depositors' withdrawal behavior in Islamic banks, particularly in Malaysia, using the framework of theory of reasoned action. A total of 368 respondents from Klang valley are involved in the analysis. The paper finds that all the constructs variable i.e. normative beliefs, subjective norms, behavioral beliefs, and attitude towards behavior are perceived to be distinct by the respondents. In addition, the structural equation model is able to verify the structural relationships between subjective norms, attitude towards behavior and behavioral intention. Subjective norms gives more influence to depositors' decision on deposit withdrawal compared to attitude towards behavior. Keywords—Islamic bank, structural equation model, theory of reasoned action, withdrawal behavior


title: Information gathering problems in multinational banking

abstract: The banking sector provides an interesting focus for the problems of information provision to support decision making as it is undergoing a period of rapid intensification of competition and fundamental change where new products and services are both agents and consequences of change. This paper will introduce the banking sector in order to provide a background for the paper and an insight into the complexities of the business area. It will then go on to briefly discuss decision making and the associated role of information and communication technologies. This will lead into a review of information that is required to support decision making and the difficulties that arise in gathering and managing this information. Such issues will be briefly discussed before broad conclusions are presented.


title: Automatic Labeling for Entity Extraction in Cyber Security

abstract: Timely analysis of cyber-security information necessitates automated information extraction from unstructured text. While state-of-the-art extraction methods produce extremely accurate results, they require ample training data, which is generally unavailable for specialized applications, such as detecting security related entities; moreover, manual annotation of corpora is very costly and often not a viable solution. In response, we develop a very precise method to automatically label text from several data sources by leveraging related, domainspecific, structured data and provide public access to a corpus annotated with cyber-security entities. Next, we implement a Maximum Entropy Model trained with the average perceptron on a portion of our corpus ( 750,000 words) and achieve near perfect precision, recall, and accuracy, with training times under 17 seconds.


title: A Framework for Dynamic Semantic Web Services Management

abstract: The use of Web services as a means of dynamically discovering, negotiating, composing, executing and managing services to materialize enterprise-scale workflow is an active research topic. Existing approaches involve many disparate concepts, frameworks and technologies. What is needed is a comprehensive and overarching framework that handles the processing and workflow requirements of Virtual Organizations, maps them to a collection of service-oriented tasks, dynamically configures these tasks from available services, and manages the choreography and execution of these services. The goal is to add semantics to Web services to endow them with capabilities needed for their successful deployment in enterprise-scale systems for Virtual Organizations. This paper introduces such a framework, the Knowledge-based Dynamic Semantic Web Services (KDSWS) Framework that addresses in an integrated end-to-end manner, the life-cycle of activities involved in preparing, publishing, requesting, discovering, selecting, configuring, deploying, and delivering Semantic Web Services. In particular, the following issues are addressed with an emphasis on adaptability to rapidly changing environments and standards: (1) semantic specification of both service's and requestor's capabilities, constraints and preferences including quality of service, trust, and security; (2) transaction control and workflow management; and (3) resource management, interoperation and evolution of the Virtual Organization.


title: An Energy-Efficient Time-Domain Asynchronous 2 b/Step SAR ADC With a Hybrid R-2R/C-3C DAC Structure

abstract: This paper describes an energy-efficient SAR ADC for ultra-low power applications. The asynchronous 2 b/step scheme halves both conversion time and DAC/digital circuit's switching activities and hence likewise reduces static and dynamic energy consumption. A low-power sleep mode is engaged at the end of each clock period. The technical contributions of this work include: 1) an innovative 2 b/step reference scheme based on a hybrid R-2R/C-3C DAC to minimize DAC hardware, 2) an interpolation-assisted time-domain 2 b comparison scheme that saves 33% in comparator circuitry, and 3) a dual-edge-comparison mechanism that reduces the time-domain comparator's (TDC) switching activities by 50%. All these techniques help reduce circuit overhead and overall energy consumption. The prototype ADC was fabricated in 180 nm CMOS process with an active area of 0.103 mm 2 . With a single 0.6 V supply and reference, the ADC achieves an ENoB of 9.2 bits and a FoM of 6.7 fJ/conversion-step while sampling at 100-kS/s.


title: Security Aspects in Software Defined Radio and Cognitive Radio Networks: A Survey and A Way Ahead

abstract: Software Defined Radio (SDR) and Cognitive Radio (CR) are promising technologies, which can be used to alleviate the spectrum shortage problem or the barriers to communication interoperability in various application domains. The successful deployment of SDR and CR technologies will depend on the design and implementation of essential security mechanisms to ensure the robustness of networks and terminals against security attacks. SDR and CR may introduce entirely new classes of security threats and challenges including download of malicious software, licensed user emulation and selfish misbehaviors. An attacker could disrupt the basic functions of a CR network, cause harmful interference to licensed users or deny communication to other CR nodes. The research activity in this area has started only recently and many challenges are still to be resolved. This paper presents a survey of security aspects in SDR and CR. We identify the requirements for the deployment of SDR and CR, the main security threats and challenges and the related protection techniques. This paper provides an overview of the SDR and CR certification process and how it is related to the security aspects. Finally, this paper summarizes the most critical challenges in the context of the future evolution of SDR/CR technologies.


title: Simply Spinning: Extending Current Design Frameworks for Kinesthetic Empathy

abstract: We describe design considerations in Serpentine Dance, Refocused (SDR), an interactive movement installation that pays homage to Loie Fuller's mesmerizing creations of light and motion. Our design goals were inspired by kinesthetic empathy research. Fuller created the Serpentine Dance (1891) at a time when many artists turned to abstraction as a way for audiences to engage with the essence of motion rather than narrative plots. We sought to heighten the feeling of kinesthetic empathy through creating an interactive environment where audience members could physically engage and reflect on the sensation of spinning, a prominent action in the Serpentine Dance. Through our analysis of SDR we found that our design intentions relating to kinesthetic empathy were not addressed by current design frameworks for kinesthetic interactions. Based on kinesthetic empathy research, we restructure and extend these frameworks into an evaluative and generative framework for interactive systems. We propose that kinesthetic empathy is the center of all movement interactions. This broader definition of kinesthetic empathy can be used to evaluate and generate a wide variety of movement interactions. We discuss the design of SDR through the lens of our evaluative framework.


title: Model predictive control of a tilt-rotor UAV for load transportation

abstract: In this paper a model predictive control (MPC) is used to solve the path tracking problem of a tilt-rotor unmanned aerial vehicle while it carries a suspended load. The MPC is designed based on the linear error model of the system, which is linearized around a generic trajectory. The control action is calculated via an optimization problem, where a cost function is solved taking into account input and state constraints. Furthermore, the MPC considers a terminal cost in order to ensure stability, allowing the prediction horizon reduction. The multibody non-linear dynamic model is obtained via Euler-Lagrange formulation, assuming four rigid bodies and ten degrees of freedom (DOF) of the vehicle. Numerical simulations are performed with the objective to evaluate the controller considering constant disturbances at different instants of time, and modeling errors.


title: Long-term stability of anterior open-bite treatment by intrusion of maxillary posterior teeth.

abstract: INTRODUCTION ::: Anterior open bite results from the combined influences of skeletal, dental, functional, and habitual factors. The long-term stability of anterior open bite corrected with absolute anchorage has not been thoroughly investigated. The purpose of this study was to examine the long-term stability of anterior open-bite correction with intrusion of the maxillary posterior teeth. ::: ::: ::: METHODS ::: Nine adults with anterior open bite were treated by intrusion of the maxillary posterior teeth. Lateral cephalographs were taken immediately before and after treatment, 1 year posttreatment, and 3 years posttreatment to evaluate the postintrusion stability of the maxillary posterior teeth. ::: ::: ::: RESULTS ::: On average, the maxillary first molars were intruded by 2.39 mm (P<0.01) during treatment and erupted by 0.45 mm (P<0.05) at the 3-year follow-up, for a relapse rate of 22.88%. Eighty percent of the total relapse of the intruded maxillary first molars occurred during the first year of retention. Incisal overbite increased by a mean of 5.56 mm (P<0.001) during treatment and decreased by a mean of 1.20 mm (P<0.05) by the end of the 3-year follow-up period, for a relapse rate of 17.00%. Incisal overbite significantly relapsed during the first year of retention (P<0.05) but did not exhibit significant recurrence between the 1-year and 3-year follow-ups. ::: ::: ::: CONCLUSIONS ::: Most relapse occurred during the first year of retention. Thus, it is reasonable to conclude that the application of an appropriate retention method during this period clearly enhances the long-term stability of the treatment.


title: Data-Driven Event Triggering for IoT Applications

abstract: Event-triggering (ET) is an up-and-coming technological paradigm for monitoring, optimization, and control in the Internet of Things (IoT) that achieves improved levels of operational efficiency. This paper first defines the envisioned ET architecture for the IoT domain. It then classifies and reviews the various different ET approaches obtained from the available literature for the three phases of ET, namely behavior modeling, event detection, and event handling. Thereafter, a novel data-driven technique is developed to address all three phases of ET in an efficient and reliable manner. Finally, the applicability of the proposed data-driven technique is showcased in a real-world public transport scenario, demonstrating a substantial improvement in energy and spectrum efficiency compared to existing periodic techniques.


title: Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN

abstract: Text classification to a hierarchical taxonomy of topics is a common and practical problem. Traditional approaches simply use bag-of-words and have achieved good results.However, when there are a lot of labels with different topical granularities, bag-of-words representation may not be enough.Deep learning models have been proven to be effective to automatically learn different levels of representations for image data.It is interesting to study what is the best way to represent texts.In this paper, we propose a graph-CNN based deep learning model to first convert texts to graph-of-words, and then use graph convolution operations to convolve the word graph.Graph-of-words representation of texts has the advantage of capturing non-consecutive and long-distance semantics.CNN models have the advantage of learning different level of semantics.To further leverage the hierarchy of labels, we regularize the deep architecture with the dependency among labels.Our results on both RCV1 and NYTimes datasets show that we can significantly improve large-scale hierarchical text classification over traditional hierarchical text classification and existing deep models.


title: Coordinated Control of the Energy Router-Based Smart Home Energy Management System

abstract: Home area energy networks will be an essential part of the future Energy Internet in terms of energy saving, demand-side management and stability improvement of the distribution network, while an energy router will be the perfect choice to serve as an intelligent and multi-functional energy interface between the home area energy network and power grid. This paper elaborates on the design, analysis and implementation of coordinated control of the low-voltage energy router-based smart home energy management system (HEMS). The main contribution of this paper is to develop a novel solution to make the energy router technically feasible and practical for the HEMS to make full use of the renewable energy sources (RESs), while maintaining “operational friendly and beneficial” to the power grid. The behaviors of the energy router-based HEMS in correlation with the power grid are investigated, then the coordinated control scheme composed of a reference voltage and current compensation strategy and a fuzzy logic control-based power management strategy is developed. The system model is built on the MATLAB/Simulink platform, simulation results have demonstrated that the presented control scheme is a strong performer in making full use of the RES generations for the HEMS while maintaining the operational stability of the whole system, as well as in collaboration with the power grid to suppress the impact of RES output fluctuations and load consumption variations.


title: A Compact CNN-DBLSTM Based Character Model for Online Handwritten Chinese Text Recognition

abstract: Recently, character model based on integrated convolutional neural network (CNN) and deep bidirectional long short-term memory (DBLSTM) has been demonstrated to be effective for online handwritten Chinese text recognition (HCTR). However, the reported CNN-DBLSTM topologies are too complex to be practically useful. In this paper, we propose a compact CNN-DBLSTM which has small footprint and low computation cost yet be able to accommodate multiple receptive fields for CNN-based feature extraction. By using the training set of a popular benchmark database, namely CASIA-OLHWDB, we trained a compact CNN-DBLSTM by a connectionist temporal classification (CTC) criterion with a multi-step training strategy. Combined this character model with a character trigram language model, our online HCTR system with a WFSTbased decoder has achieved state-of-the-art performance on both CASIA and ICDAR-2013 Chinese handwriting recognition competition test sets.


title: Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer

abstract: The recent progress of computing, machine learning, and especially deep learning, for image recognition brings a meaningful effect for automatic detection of various diseases from chest X-ray images (CXRs). Here efficiency of lung segmentation and bone shadow exclusion techniques is demonstrated for analysis of 2D CXRs by deep learning approach to help radiologists identify suspicious lesions and nodules in lung cancer patients. Training and validation was performed on the original JSRT dataset (dataset #01), BSE-JSRT dataset, i.e. the same JSRT dataset, but without clavicle and rib shadows (dataset #02), original JSRT dataset after segmentation (dataset #03), and BSE-JSRT dataset after segmentation (dataset #04). The results demonstrate the high efficiency and usefulness of the considered pre-processing techniques in the simplified configuration even. The pre-processed dataset without bones (dataset #02) demonstrates the much better accuracy and loss results in comparison to the other pre-processed datasets after lung segmentation (datasets #02 and #03).


title: Super-Capacitor Stacks Management System With Dynamic Equalization Techniques

abstract: Super capacitor has the advantage of quick charge, large power density, and long cycle life. The shortage is the lower energy density compared with electrochemistry batteries. These features make it suitable for a short-distance electric bus used in the city. Because of the capacitance difference between the capacitor cells, after a number of deep discharging/charging cycles, the voltage difference between cells will be enlarged. This will accelerate the aging of the weak super capacitors and affect the output power. So, a management system with an equalization function is essential. In this paper, a practical super-capacitor stacks management system with dynamic equalization techniques is proposed. The function of the management system includes: monitoring the current, voltage, and temperature of the stacks, control of charge, and discharge with equalization online. A switched-capacitor equalization approach is adopted showing a low-cost way to meet the accuracy requirements. The dynamic equalized charging and discharging circuit is described. The algorithm to increase the operation speed and the precision is analyzed. By dynamically redistributing the current, the equalization procedure can be quicker and more efficient. This approach has been verified by experiments


title: Effect of age, weight, race and sex on blood pressure and erythrocyte sodium pump characteristics.

abstract: In order to examine whether erythrocyte membrane handling of sodium is influenced by factors other than hypertension, measurements of red cell sodium transport were studied in one hundred normotensive volunteers. Erythrocyte sodium content was found to increase with increasing age, body weight and mean arterial pressure (MAP). It is also significantly correlated with age, body weight and MAP. Total sodium efflux was found to be reduced and negatively correlated with age and body weight. A reduction in ouabain-sensitive sodium efflux was also observed with increasing age and body weight. In males, the rate of ouabain-sensitive sodium efflux is higher than in females. Race was found to have no effect on erythrocyte electrolyte content and cationic flux rates of subjects. These data suggest that when studies in hypertension are going to be carried out, control subjects carefully matched for age, body weight and sex should be used if confounding results are not to be obtained.


title: Causal Discovery of Experimental and

abstract: This paper describes a Bayesian method for combining an arbitrary mixture of observational and experimental data in order to learn causal Bayesian networks. Observational data are passively observed. Experimental data, such as that produced by randomized controlled trials, result from the exoerimenter manioulatine one or more variables (tipically randomiy) and observing the states of other variables. The paper presents a Bayesian method for learning the causal structure and parameters of the underlying causal process that is generating the data, given that (1) the data contains a mixture of observational and experimental case records, and (2) the causal process is modeled as a causal Bayesian network. This learning method was applied using as input various mixtures of experimental and observational data that were generated from the ALARM causal Bayesian network. In these experiments, the absolute and relative quantities of experimental and observational data were varied systematically. For each of these training datasets, the learning method was applied to predict the causal structure and to estimate the causal parameters that exist among randomly selected pairs of nodes in ALARM that are not confounded. The paper reports how these structure predictions and parameter estimates compare with the true causal structures and parameters as given by the ALARM network.


title: Energy Efficient Computational Offloading Framework for Mobile Cloud Computing

abstract: The latest developments in mobile computing technology have changed user preferences for computing. However, in spite of all the advancements in the recent years, Smart Mobile Devices (SMDs) are still low potential computing devices which are limited in memory capacity, CPU speed and battery power lifetime. Therefore, Mobile Cloud Computing (MCC) employs computational offloading for enabling computationally intensive mobile applications on SMDs. However, state-of-the-art computational offloading frameworks lack of considering the additional overhead of components migration at runtime. Therefore resources intensive and energy consuming distributed application execution platform is established. This paper proposes a novel distributed Energy Efficient Computational Offloading Framework (EECOF) for the processing of intensive mobile applications in MCC. The framework focuses on leveraging application processing services of cloud datacenters with minimal instances of computationally intensive component migration at runtime. As a result, the size of data transmission and energy consumption cost is reduced in computational offloading for MCC. We evaluate the proposed framework by benchmarking prototype application in the real MCC environment. Analysis of the results show that by employing EECOF the size of data transmission over the wireless network medium is reduced by 84 % and energy consumption cost is reduced by 69.9 % in offloading different components of the prototype application. Hence, EECOF provides an energy efficient application layer solution for computational offloading in MCC.


title: Exploring the Causes of Adverse Events in NHS Hospital Practice

abstract: In a previous paper we reported that 10.8% of patients admitted to two large hospitals in Greater London experienced one or more adverse events, of which half were deemed preventable. Here we examine the underlying causes of errors in clinical practice. Rather than identifying specific errors made by individuals, we have looked at possible faults in the organization of care. Adverse events were grouped according to stages in the care process: diagnosis, preoperative assessment and care, operative or invasive procedure (including anaesthesia), ward management, use of drugs and intravenous fluids and discharge from hospital. Less than 20% of preventable adverse events were directly related to surgical operations or invasive procedures and less than 10% to misdiagnoses. 53% of preventable adverse events occurred in general ward care (including initial assessment and the use of drugs and intravenous fluids) and 18% in care at the time of discharge. Probable contributory factors in these errors included dependence on diagnoses made by inexperienced clinicians, poor records, poor communication between professional carers, inadequate input by consultants into day-to-day care, and lack of detailed assessment of patients before discharge.


title: The Effects of Interactive Latency on Exploratory Visual Analysis

abstract: To support effective exploration, it is often stated that interactive visualizations should provide rapid response times. However, the effects of interactive latency on the process and outcomes of exploratory visual analysis have not been systematically studied. We present an experiment measuring user behavior and knowledge discovery with interactive visualizations under varying latency conditions. We observe that an additional delay of 500ms incurs significant costs, decreasing user activity and data set coverage. Analyzing verbal data from think-aloud protocols, we find that increased latency reduces the rate at which users make observations, draw generalizations and generate hypotheses. Moreover, we note interaction effects in which initial exposure to higher latencies leads to subsequently reduced performance in a low-latency setting. Overall, increased latency causes users to shift exploration strategy, in turn affecting performance. We discuss how these results can inform the design of interactive analysis tools.


title: Approximate Orthogonal Sparse Embedding for Dimensionality Reduction

abstract: Locally linear embedding (LLE) is one of the most well-known manifold learning methods. As the representative linear extension of LLE, orthogonal neighborhood preserving projection (ONPP) has attracted widespread attention in the field of dimensionality reduction. In this paper, a unified sparse learning framework is proposed by introducing the sparsity or L ::: 1 ::: -norm learning, which further extends the LLE-based methods to sparse cases. Theoretical connections between the ONPP and the proposed sparse linear embedding are discovered. The optimal sparse embeddings derived from the proposed framework can be computed by iterating the modified elastic net and singular value decomposition. We also show that the proposed model can be viewed as a general model for sparse linear and nonlinear (kernel) subspace learning. Based on this general model, sparse kernel embedding is also proposed for nonlinear sparse feature extraction. Extensive experiments on five databases demonstrate that the proposed sparse learning framework performs better than the existing subspace learning algorithm, particularly in the cases of small sample sizes.


title: Low cost implementation of filterless class D audio amplifier with constant switching frequency

abstract: A new and simple configuration of filterless class D audio amplifier is proposed in this paper. The proposed filterless class D audio amplifier provides the advantages of both low total system cost and generation of synchronized 3-level pulse-width modulated (PWM) output. Moreover, as a result of hysteresis window variation, a close to constant switching frequency of PWM output is obtained. The proposed filterless class D audio amplifier is verified by the PSpice simulation and prototype circuit experiment


title: Gbest-guided artificial bee colony algorithm for numerical function optimization

abstract: Abstract Artificial bee colony (ABC) algorithm invented recently by Karaboga is a biological-inspired optimization algorithm, which has been shown to be competitive with some conventional biological-inspired algorithms, such as genetic algorithm (GA), differential evolution (DE) and particle swarm optimization (PSO). However, there is still an insufficiency in ABC algorithm regarding its solution search equation, which is good at exploration but poor at exploitation. Inspired by PSO, we propose an improved ABC algorithm called gbest-guided ABC (GABC) algorithm by incorporating the information of global best (gbest) solution into the solution search equation to improve the exploitation. The experimental results tested on a set of numerical benchmark functions show that GABC algorithm can outperform ABC algorithm in most of the experiments.


title: An Analysis of the Convergence of Graph Laplacians

abstract: Existing approaches to analyzing the asymptotics of graph Laplacians typically assume a well-behaved kernel function with smoothness assumptions. We remove the smoothness assumption and generalize the analysis of graph Laplacians to include previously unstudied graphs including kNN graphs. We also introduce a kernel-free framework to analyze graph constructions with shrinking neighborhoods in general and apply it to analyze locally linear embedding (LLE). We also describe how, for a given limit operator, desirable properties such as a convergent spectrum and sparseness can be achieved by choosing the appropriate graph construction.


title: Supervised Discrete Hashing With Relaxation

abstract: Data-dependent hashing has recently attracted attention due to being able to support efficient retrieval and storage of high-dimensional data such as documents, images, and videos. In this paper, we propose a novel learning-based hashing method called "Supervised Discrete Hashing with Relaxation" (SDHR) based on "Supervised Discrete Hashing" (SDH). SDH uses ordinary least squares regression and traditional zero-one matrix encoding of class label information as the regression target (code words), thus fixing the regression target. In SDHR, the regression target is instead optimized. The optimized regression target matrix satisfies a large margin constraint for correct classification of each example. Compared with SDH, which uses the traditional zero-one matrix, SDHR utilizes the learned regression target matrix and, therefore, more accurately measures the classification error of the regression model and is more flexible. As expected, SDHR generally outperforms SDH. Experimental results on two large-scale image datasets (CIFAR-10 and MNIST) and a large-scale and challenging face dataset (FRGC) demonstrate the effectiveness and efficiency of SDHR.


title: LoRa Scalability: A Simulation Model Based on Interference Measurements

abstract: LoRa is a long-range, low power, low bit rate and single-hop wireless communication technology. It is intended to be used in Internet of Things (IoT) applications involving battery-powered devices with low throughput requirements. A LoRaWAN network consists of multiple end nodes that communicate with one or more gateways. These gateways act like a transparent bridge towards a common network server. The amount of end devices and their throughput requirements will have an impact on the performance of the LoRaWAN network. This study investigates the scalability in terms of the number of end devices per gateway of single-gateway LoRaWAN deployments. First, we determine the intra-technology interference behavior with two physical end nodes, by checking the impact of an interfering node on a transmitting node. Measurements show that even under concurrent transmission, one of the packets can be received under certain conditions. Based on these measurements, we create a simulation model for assessing the scalability of a single gateway LoRaWAN network. We show that when the number of nodes increases up to 1000 per gateway, the losses will be up to 32%. In such a case, pure Aloha will have around 90% losses. However, when the duty cycle of the application layer becomes lower than the allowed radio duty cycle of 1%, losses will be even lower. We also show network scalability simulation results for some IoT use cases based on real data.


title: Functionalizing bottom ash from biomass power plant for removing methylene blue from aqueous solution.

abstract: In order to seek a possible path for utilization bottom ash, the solid waste from biomass incineration for power generation, its basic characteristics were investigated, and removing methylene blue (MB) from aqueous solution was attempted as well. Results indicated bottom ash dominantly contained Si and Ca-related minerals with >35 mineral elements, meanwhile, it was typically characterized by alkalinity (pH of 9.5) and low specific surface area (14.5m2/g). As the only bottom ash was employed for removing MB, removal efficiency was lower than 44%, however, it was greatly improved to 100% as hydrogen peroxide was supplemented (final concentration of 4.0%). Based on the elucidated mechanisms, Fenton-like reaction was triggered by bottom ash, which was dominantly responsible for removing MB, rather than the adsorption by bottom ash. Besides, increasing reaction temperature and duration dramatically promoted MB removal by bottom ash with the aid of hydrogen peroxide (HBA). A great promotion on MB removal from 92.3mg/g to 143.9mg/g was achieved as pH was adjusted from 2.0 to 5.0, indicating MB removal was pH-dependent. The maximum removal of 260.9mg/g was achieved by HBA at a relatively high initial MB concentration of 2000.0mg/L, suggesting a great potential in treating dye wastewater by functionalizing bottom ash with the aid of hydrogen peroxide. Moreover, the released heavy metals and derived by-products from MB removal were acceptable in consideration of their potential environmental risks. Thus, the current work offers a new path to valorize the solid waste in biomass electricity generation plant.


title: Robust and Discriminative Concept Factorization for Image Representation

abstract: Concept Factorization (CF), as a variant of Nonnegative Matrix Factorization (NMF), has been widely used for learning compact representation for images because of its psychological and physiological interpretation of naturally occurring data. And graph regularization has been incorporated into the objective function of CF to exploit the intrinsic low-dimensional manifold structure, leading to better performance. But some shortcomings are shared by existing CF methods. 1) The squared loss used to measure the data reconstruction quality is sensitive to noise in image data. 2) The graph regularization may lead to trivial solution and scale transfer problems for CF such that the learned representation is meaningless. 3) Existing methods mostly ignore the discriminative information in image data. In this paper, we propose a novel method, called Robust and Discriminative Concept Factorization (RDCF) for image representation. Specifically, RDCF explicitly considers the influence of noise by imposing a sparse error matrix, and exploits the discriminative information by approximate orthogonal constraints which can also lead to nontrivial solution. We propose an iterative multiplicative updating rule for the optimization of RDCF and prove the convergence. Experiments on 5 benchmark image datasets show that RDCF can significantly out-perform several state-of-the-art related methods, which validates the effectiveness of RDCF.


title: Understanding Innovation Engines: Automated Creativity and Improved Stochastic Optimization via Deep Learning

abstract: The Achilles Heel of stochastic optimization algorithms is getting trapped on local optima. Novelty Search mitigates this problem by encouraging exploration in all interesting directions by replacing the performance objective with a reward for novel behaviors. This reward for novel behaviors has traditionally required a human-crafted, behavioral distance function. While Novelty Search is a major conceptual breakthrough and outperforms traditional stochastic optimization on certain problems, it is not clear how to apply it to challenging, high-dimensional problems where specifying a useful behavioral distance function is difficult. For example, in the space of images, how do you encourage novelty to produce hawks and heroes instead of endless pixel static? Here we propose a new algorithm, the Innovation Engine, that builds on Novelty Search by replacing the human-crafted behavioral distance with a Deep Neural Network DNN that can recognize interesting differences between phenotypes. The key insight is that DNNs can recognize similarities and differences between phenotypes at an abstract level, wherein novelty means interesting novelty. For example, a DNN-based novelty search in the image space does not explore in the low-level pixel space, but instead creates a pressure to create new types of images e.g., churches, mosques, obelisks, etc.. Here, we describe the long-term vision for the Innovation Engine algorithm, which involves many technical challenges that remain to be solved. We then implement a simplified version of the algorithm that enables us to explore some of the algorithm's key motivations. Our initial results, in the domain of images, suggest that Innovation Engines could ultimately automate the production of endless streams of interesting solutions in any domain: for example, producing intelligent software, robot controllers, optimized physical components, and art.


title: The use of Artificial Intelligence in digital forensics: An introduction

abstract: Dr Faye Mitchell argues that the use of Artificial Intelligence, which is a well-established area of modern computer science that is capable of dealing with computationally large or complex problems, could be useful for digital forensics. Digital forensics is becoming increasingly important, and often requires the intelligent analysis of large amounts of complex data. Artificial Intelligence could help to bridge the gap.


title: Accelerated singular value thresholding for matrix completion

abstract: Recovering a large matrix from a small subset of its entries is a challenging problem arising in many real world applications, such as recommender system and image in-painting. These problems can be formulated as a general matrix completion problem. The Singular Value Thresholding (SVT) algorithm is a simple and efficient first-order matrix completion method to recover the missing values when the original data matrix is of low rank. SVT has been applied successfully in many applications. However, SVT is computationally expensive when the size of the data matrix is large, which significantly limits its applicability. In this paper, we propose an Accelerated Singular Value Thresholding (ASVT) algorithm which improves the convergence rate from O(1/N) for SVT to O(1/N2), where N is the number of iterations during optimization. Specifically, the dual problem of the nuclear norm minimization problem is derived and an adaptive line search scheme is introduced to solve this dual problem. Consequently, the optimal solution of the primary problem can be readily obtained from that of the dual problem. We have conducted a series of experiments on a synthetic dataset, a distance matrix dataset and a large movie rating dataset. The experimental results have demonstrated the efficiency and effectiveness of the proposed algorithm.


title: Ceiling continuum arm with extensible pneumatic actuators for desktop workspace

abstract: We propose an extensible pneumatic continuum arm that elongates to perform reaching movements and object grasping, and is suspended on the ceiling to prevent interference with human workers in a desktop workspace. The selected actuators with bellows aim to enhance the arm motion capabilities. A single actuator can provide a maximum tension force of 150 N, and the proposed arm has a three-segment structure with a bundle of three actuators per segment. We measured the three-dimensional motion at the arm tip by using an optical motion-capture system. The corresponding results show that the arm can grasp objects with approximate radius of 80 mm and reach any point on the desktop. Furthermore, the maximum elongation ratio is 180%, with length varying between 0.75 m and 2.1 m. Experiments verified that the arm can grasp objects of various sizes and shapes. Moreover, we demonstrate the vertical transportation of objects taking advantage of the arm extensibility. We expect to apply the proposed arm for tasks such as grasping objects, illuminating desktops, and physically interacting with users.


title: Gaining efficiency and flexibility in the simple temporal problem

abstract: Deals with the problem of managing quantitative temporal networks without disjunctive constraints. This problem is known as the "simple temporal problem". Dynamic management algorithms are considered to be coupled with incremental constraint posting approaches for planning and scheduling. A basic algorithm for incremental propagation of a new time constraint is presented which is a modification of the Bellman-Ford algorithm for the single-source shortest-path problem. For this algorithm, a sufficient condition for inconsistency is given, based on cycle detection in the shortest-paths graph. Moreover, the problem of constraint retraction from a consistent situation is considered, and properties for repropagating the network locally are exploited. Some experiments are also presented that show the usefulness of these properties.


title: DOMPurify: Client-Side Protection Against XSS and Markup Injection

abstract: To prevent Cross-Site Scripting (XSS) and related attacks, sanitation of untrusted content is usually performed either on the server side, or by client-side filters like XSS Auditor or NoScript. However, modern web applications (including mobile apps) may not be able to rely on these mechanisms any more since untrusted content may pass these filters as ciphertext or may completely be processed within the DOM of the browser/app.


title: Development of a credible and integrated electronic voting machine based on contactless IC cards, biometrie fingerprint credentials and POS printer

abstract: In recent times there has been a decline in the confidence of common people over electronic voting machines (EVMs). More anticipated than what the reality actually is, today's automated vote casting methods have faced immense controversy related to being vulnerable to hacking and questions have been raised about their transparency and security. This paper proposes the design and development of a novel tamper resistant electronic voting system that aims to mitigate the recurring issues and flaws of existing voting machines of today. To elucidate the system in brief, multiple layered verification process would be carried out on a potential voter by the means of fingerprint recognition and a Near Field Communication (NFC) smart card entry in order to authenticate his or her identity. Subsequently, the person would cast the vote by pressing a button corresponding to a particular candidate which would be recorded in the system providing the vote caster a visual confirmation. The final vote would then be printed out spontaneously onto a ballot box using a POS (Point of Sales) printer for an added level of validation. This system not only prevents multiple vote casts, but also eliminates the discrepancies that commonly arise with a person claiming not to have voted, whereas his or her name is present in the list of vote casters. The project is an incremental extension of a previous research.


title: Mechatronic design of a transradial cybernetic hand

abstract: This paper presents the mechatronic design of an anthropomorphic 16 degree of freedom, 4 degree of mobility prosthetic hand for use by transradial amputees. Underactuated fingers and differential mechanisms have been used in order to fit all the functional components (actuators, sensors and electronics) in the size of a human hand. Moreover innovative non-back-drivable mechanisms have been designed in order to overcome some of the actual drawbacks of the mechanisms commonly used in prosthetic hands. The new hand is provided with a comprehensive sensory system embedded in the structure of the fingers composed of 40 proprioceptive and exteroceptive sensors. The large employment of sensors, combined with an actuation architecture that allows for grasping and gestures, will permit this hand to be used in clinical experimentation in the near future.


title: Local Deep Neural Networks for Age and Gender Classification

abstract: Local deep neural networks have been recently introduced for gender recognition. Although, they achieve very good performance they are very computationally expensive to train. In this work, we introduce a simplified version of local deep neural networks which significantly reduces the training time. Instead of using hundreds of patches per image, as suggested by the original method, we propose to use 9 overlapping patches per image which cover the entire face region. This results in a much reduced training time, since just 9 patches are extracted per image instead of hundreds, at the expense of a slightly reduced performance. We tested the proposed modified local deep neural networks approach on the LFW and Adience databases for the task of gender and age classification. For both tasks and both databases the performance is up to 1% lower compared to the original version of the algorithm. We have also investigated which patches are more discriminative for age and gender classification. It turns out that the mouth and eyes regions are useful for age classification, whereas just the eye region is useful for gender classification.


title: Developing language technology tools and resources for a resource-poor language: Sindhi

abstract: Sindhi, an Indo-Aryan language with more than 75 million native speakers 1 is a resourcepoor language in terms of the availability of language technology tools and resources. In this thesis, we discuss the approaches taken to develop resources and tools for a resourcepoor language with special focus on Sindhi. The major contributions of this work include raw and annotated datasets, a POS Tagger, a Morphological Analyser, a Transliteration and a Machine Translation System.


title: Adaptive activity learning with dynamically available context

abstract: Numerous methods have been proposed to address different aspects of human activity recognition. However, most of the previous approaches are static in terms of the data sources used for the recognition task. As sensors can be added or can fail and be replaced by different types of sensors, creating an activity recognition model that is able to leverage dynamically available sensors becomes important. In this paper, we propose methods for activity learning and activity recognition adaptation in environments with dynamic sensor deployments. Specifically, we propose sensor and activity context models to address the problem of sensor heterogeneity, so that sensor readings can be pre-processed and populated into the recognition system properly. Based on those context models, we propose the learning-to-rank method for activity learning and its adaptation. To model the temporal characteristics of the human behaviours, we add temporal regularization into the learning and prediction phases. We use comprehensive datasets to demonstrate effectiveness of the proposed method, and show its advantage over the conventional machine learning algorithms in terms of recognition accuracy. Our method outperforms hybrid models that combine typical machine learning methods with graphical models (i.e. HMM, CRF) for temporal smoothing.


title: Social tag prediction

abstract: In this paper, we look at the "social tag prediction" problem. Given a set of objects, and a set of tags applied to those objects by users, can we predict whether a given tag could/should be applied to a particular object? We investigated this question using one of the largest crawls of the social bookmarking system del.icio.us gathered to date. For URLs in del.icio.us, we predicted tags based on page text, anchor text, surrounding hosts, and other tags applied to the URL. We found an entropy-based metric which captures the generality of a particular tag and informs an analysis of how well that tag can be predicted. We also found that tag-based association rules can produce very high-precision predictions as well as giving deeper understanding into the relationships between tags. Our results have implications for both the study of tagging systems as potential information retrieval tools, and for the design of such systems.


title: Silicon microring resonators

abstract: An overview is presented of the current state-of-the-art in silicon nanophotonic ring resonators. Basic theory of ring resonators is discussed, and applied to the peculiarities of submicron silicon photonic wire waveguides: the small dimensions and tight bend radii, sensitivity to perturbations and the boundary conditions of the fabrication processes. Theory is compared to quantitative measurements. Finally, several of the more promising applications of silicon ring resonators are discussed: filters and optical delay lines, label-free biosensors, and active rings for efficient modulators and even light sources.


Teacher Preparation and Teacher Learning: A Changing Policy Landscape


title: Machine Learning-Based Temperature Prediction for Runtime Thermal Management Across System Components

abstract: Elevated temperatures limit the peak performance of systems because of frequent interventions by thermal throttling. Non-uniform thermal states across system nodes also cause performance variation within seemingly equivalent nodes leading to significant degradation of overall performance. In this paper we present a framework for creating a lightweight thermal prediction system suitable for run-time management decisions. We pursue two avenues to explore optimized lightweight thermal predictors. First, we use feature selection algorithms to improve the performance of previously designed machine learning methods. Second, we develop alternative methods using neural network and linear regression-based methods to perform a comprehensive comparative study of prediction methods. We show that our optimized models achieve improved performance with better prediction accuracy and lower overhead as compared with the Gaussian process model proposed previously. Specifically we present a reduced version of the Gaussian process model, a neural network–based model, and a linear regression–based model. Using the optimization methods, we are able to reduce the average prediction errors in the Gaussian process from $4.2^\circ$ C to $2.9^\circ$ C. We also show that the newly developed models using neural network and Lasso linear regression have average prediction errors of $2.9^\circ$ C and $3.8^\circ$ C respectively. The prediction overheads are 0.22, 0.097, and 0.026 ms per prediction for reduced Gaussian process, neural network, and Lasso linear regression models, respectively, compared with 0.57 ms per prediction for the previous Gaussian process model. We have implemented our proposed thermal prediction models on a two-node system configuration to help identify the optimal task placement. The task placement identified by the models reduces the average system temperature by up to $11.9^\circ$ C without any performance degradation. Furthermore, these models respectively achieve 75, 82.5, and 74.17 percent success rates in correctly pointing to those task placements with better thermal response, compared with 72.5 percent success for the original model in achieving the same objective. Finally, we extended our analysis to a 16-node system and we were able to train models and execute them in real time to guide task migration and achieve on average 17 percent reduction in the overall system cooling power.


title: Preventing loneliness and social isolation: interventions and outcomes

abstract: Key messages ::: • Older people are particularly vulnerable to ::: social isolation or loneliness owing to loss ::: of friends and family, mobility or income. ::: • Social isolation and loneliness impact upon ::: individuals’ quality of life and wellbeing, ::: adversely affecting health and increasing ::: their use of health and social care services. ::: • The interventions to tackle social isolation ::: or loneliness include: befriending, ::: mentoring, Community Navigators, social ::: group schemes. ::: • People who use befriending or Community ::: Navigator services reported that they were ::: less lonely and socially isolated following the ::: intervention. ::: • The outcomes from mentoring services are ::: less clear; one study reported improvements ::: in mental and physical health, another that ::: no difference was found. ::: • Where longitudinal studies recorded ::: survival rates, older people who were part ::: of a social group intervention had a greater ::: chance of survival than those who had not ::: received such a service. ::: • Users report high satisfaction with services, ::: benefiting from such interventions by ::: increasing their social interaction and ::: community involvement, taking up or going ::: back to hobbies and participating in wider ::: community activities. ::: • Users argued for flexibility and adaptation ::: of services. One-to-one services could be ::: more flexible, while enjoyment of group ::: activities would be greater if these could ::: be tailored to users’ preferences. ::: • When planning services to reduce social ::: isolation or loneliness, strong partnership ::: arrangements need to be in place between ::: organisations to ensure developed services ::: can be sustained. ::: • We need to invest in proven projects. ::: Community Navigator interventions ::: have been shown to be effective in ::: identifying those individuals who are ::: socially isolated. Befriending services can ::: be effective in reducing depression and ::: cost-effective. ::: • Research needs to be carried out on ::: interventions that include different ::: genders, populations and localities. ::: • There is an urgent need for more ::: longitudinal, randomised controlled ::: trials that incorporate standardised ::: quality-of-life and cost measures.


title: On combining Websensors and DTW distance for kNN Time Series Forecasting

abstract: In the pattern recognition field, different approaches have been proposed to improve time series forecasting models. In this sense, k-Nearest-Neighbour (kNN) with DTW (Dynamic Time Warping) distance is one of the most representative methods, due to its effectiveness, simplicity and intuitiveness. The great advantage of the DTW distance is the robustness to distortions in the time axis by allowing stretching and squeezing (time warping) of the time series, while traditional measures require a linear alignment between each data point. However, as well as other traditional measures, the DTW distance has the limitation of focusing only on historical time series data to predict future values, thereby not considering additional external knowledge of the problem domain. In this paper, we propose an approach called TSFW (Time Series Forecasting with Websensors) that incorporates Websensors into DTW distance to improve kNN time series forecasting. Websensors are models that represent knowledge extracted from news about the problem domain as well as the temporal evolution of this knowledge. In our proposed TSFW approach, we show that Websensors allow a more robust non-linear alignment of the time series by using similar events (extracted from news) that have occurred in the both time series. Thus, distortions in the time axis among the time series can be corrected more accurately compared to the traditional technique that uses only the original values of the time series.


title: Protruding labia minora: abnormal or just uncool?

abstract: There is a wide variety in the appearance of normal female external genitalia. Nevertheless a specific prototype is promoted by the media, leading to a false sense that all other appearances are abnormal. As adolescents become sexually aware at an earlier age, most of them are worried about the appearance of their genitalia, especially when labia minora protrude beyond labia majora. This is a prospective audit of adolescents presenting for assessment of their perceived abnormal genitalia. Sixteen girls aged 10.2 to 17.8 years presented between June 2009 and December 2010 to a specialist adolescent gynecology service. Their mean labial width was 36 mm (range: 20-55 mm). In six girls, the reason for attending the service was inequality of the size of labia ranging between 6 mm and 35 mm (mean of 20 mm). Among the remaining 10 girls, the concern had arisen through comparison with a prepubescent sibling (one case), change of genitalia during puberty (four cases), looking at internet pictures (four cases), and looking at an anatomy book (one case). Risks of Female Genital Cosmetic Surgery (FGCS) have not been adequately documented, especially with regards to sexual function and long-term patient satisfaction. External genitalia are likely to change during puberty and therefore, any genital operation in the absence of clear pathology should be deferred until adulthood. Even then, women should have clear expectations of what will be achieved with the operation in terms of appearance and function.


title: Investigation of deep neural networks (DNN) for large vocabulary continuous speech recognition: Why DNN surpasses GMMS in acoustic modeling

abstract: Recently, it has been reported that context-dependent deep neural network (DNN) has achieved some unprecedented gains in many challenging ASR tasks, including the well-known Switchboard task. In this paper, we first investigate DNN for several large vocabulary speech recognition tasks. Our results have confirmed that DNN can consistently achieve about 25–30% relative error reduction over the best discriminatively trained GMMs even in some ASR tasks with up to 700 hours of training data. Next, we have conducted a series of experiments to study where the unprecedented gain of DNN comes from. Our experiments show the gain of DNN is almost entirely attributed to DNN's feature vectors that are concatenated from several consecutive speech frames within a relatively long context window. At last, we have proposed a few ideas to reconfigure the DNN input features, such as using logarithm spectrum features or VTLN normalized features in DNN. Our results have shown that each of these methods yields over 3% relative error reduction over the traditional MFCC or PLP features in DNN.


title: Twitter Hash Tag Recommendation

abstract: The rise in popularity of microblogging services like Twitter has led to increased use of content annotation strategies like the hashtag. Hashtags provide users with a tagging mechanism to help organize, group, and create visibility for their posts. This is a simple idea but can be challenging for the user in practice which leads to infrequent usage. In this paper, we will investigate various methods of recommending hashtags as new posts are created to encourage more widespread adoption and usage. Hashtag recommendation comes with numerous challenges including processing huge volumes of streaming data and content which is small and noisy. We will investigate preprocessing methods to reduce noise in the data and determine an effective method of hashtag recommendation based on the popular classification algorithms.


title: Improving quality of training data for learning to rank using click-through data

abstract: In information retrieval, relevance of documents with respect to queries is usually judged by humans, and used in evaluation and/or learning of ranking functions. Previous work has shown that certain level of noise in relevance judgments has little effect on evaluation, especially for comparison purposes. Recently learning to rank has become one of the major means to create ranking models in which the models are automatically learned from the data derived from a large number of relevance judgments. As far as we know, there was no previous work about quality of training data for learning to rank, and this paper tries to study the issue. Specifically, we address three problems. Firstly, we show that the quality of training data labeled by humans has critical impact on the performance of learning to rank algorithms. Secondly, we propose detecting relevance judgment errors using click-through data accumulated at a search engine. Two discriminative models, referred to as sequential dependency model and full dependency model, are proposed to make the detection. Both models consider the conditional dependency of relevance labels and thus are more powerful than the conditionally independent model previously proposed for other tasks. Finally, we verify that using training data in which the errors are detected and corrected by our method, we can improve the performance of learning to rank algorithms.


title: A comparison of web privacy protection techniques

abstract: Abstract Tracking is pervasive on the web. Third party trackers acquire user data through information leak from websites, and user browsing history using cookies and device fingerprinting. In response, several privacy protection techniques (e.g. the Ghostery browser extension) have been developed. To the best of our knowledge, our work is the first study that proposes a reliable methodology for privacy protection comparison, and extensively compares a wide set of privacy protection techniques. Our contributions are the following. First, we propose a robust methodology to compare privacy protection techniques when crawling many websites, and quantify measurement error. To this end, we reuse the privacy footprint and apply the Kolmogorov–Smirnov test on browsing metrics. This test is likewise applied to HTML-based metrics to assess webpage quality degradation. To complement HTML-based metrics, we also design a manual analysis. Second, we study the overlap of blocking resources between most popular browser extensions, and compare the performances using the proposed methodology. We show that protection techniques have vastly different performances, and that the best of them exhibit a wide overlap. Next, we analyze the impact of privacy protection techniques on webpage quality. We show that automated HTML-based analysis sometimes fails to expose quality reduction perceived by users. Finally, we provide a set of usage recommendations for end-users and research recommendations for the scientific community. Ghostery and uBlock Origin provide the best trade-off between protection and webpage quality. Ghostery however requires a configuration step which is difficult for users. The RequestPolicy Continued and NoScript extensions exhibit the best performances but reduce webpage quality. Ghostery and uBlock Origin use manually built blocking lists which are cumbersome to maintain. Research efforts should focus on improving existing approaches that do not rely on blocking lists (such as Privacy badger or MyTrackingChoices), and automatically building reliable blocking lists.


Fuzzy Set Theory and Its Applications


title: Digital Fabrication Techniques for Cultural Heritage: A Survey

abstract: Digital fabrication devices exploit basic technologies in order to create tangible reproductions of 3D digital models. Although current 3D printing pipelines still suffer from several restrictions, accuracy in reproduction has reached an excellent level. The manufacturing industry has been the main domain of 3D printing applications over the last decade. Digital fabrication techniques have also been demonstrated to be effective in many other contexts, including the consumer domain. The Cultural Heritage is one of the new application contexts and is an ideal domain to test the flexibility and quality of this new technology. This survey overviews the various fabrication technologies, discussing their strengths, limitations and costs. Various successful uses of 3D printing in the Cultural Heritage are analysed, which should also be useful for other application contexts. We review works that have attempted to extend fabrication technologies in order to deal with the specific issues in the use of digital fabrication in the Cultural Heritage. Finally, we also propose areas for future research.


Linear Motor Powered Transportation


A pruning based method to learn both weights and connections for LSTM


title: The “Shoulds” and “Should Nots” of Moral Emotions: A Self-Regulatory Perspective on Shame and Guilt

abstract: A self-regulatory framework for distinguishing between shame and guilt was tested in three studies. Recently, two forms of moral regulation based on approach versus avoidance motivation have been proposed in the literature. Proscriptive regulation is sensitive to negative outcomes, inhibition based, and focused on what we should not do. Prescriptive regulation is sensitive to positive outcomes, activation based, and focused on what we should do. In the current research, consistent support was found for shame’s proscriptive and guilt’s prescriptive moral underpinnings. Study 1 found a positive association between avoidance orientation and shame proneness and between approach orientation and guilt proneness. In Study 2, priming a proscriptive orientation increased shame and priming a prescriptive orientation increased guilt. In Study 3, transgressions most apt to represent proscriptive and prescriptive violations predicted subsequent judgments of shame and guilt, respectively. This self-regulatory perspecti...


title: Analytical Calculation of Impedance Matching for Probe-Fed Microstrip Patch Antennas

abstract: The goal of this article is to present an analytical solution to the feeding mechanism for a microstrip patch antenna fed by a vertical probe. The feeding mechanism employs a circular capacitive structure to improve the impedance matching, specifically for microstrip patch antennas on thick dielectrics. A circuit model is introduced to find the design parameters. The circuit model includes a series inductor associated with the feeding probe, a parallel plate radial microstrip line, a series radial gap capacitor and an RLC resonator as the patch antenna. A closed form impedance transformation is generated for the parallel plate radial microstrip line. The result of a Schwarz-Christoffel mapping is used to find the radial gap capacitor. The full-wave simulation and measured results are compared with the model and display good agreement.


title: Interactive Comparison of Scalar Fields Based on Largest Contours with Applications to Flow Visualization

abstract: Understanding fluid flow data, especially vortices, is still a challenging task. Sophisticated visualization tools help to gain insight. In this paper, we present a novel approach for the interactive comparison of scalar fields using isosurfaces, and its application to fluid flow datasets. Features in two scalar fields are defined by largest contour segmentation after topological simplification. These features are matched using a volumetric similarity measure based on spatial overlap of individual features. The relationships defined by this similarity measure are ranked and presented in a thumbnail gallery of feature pairs and a graph representation showing all relationships between individual contours. Additionally, linked views of the contour trees are provided to ease navigation. The main render view shows the selected features overlapping each other. Thus, by displaying individual features and their relationships in a structured fashion, we enable exploratory visualization of correlations between similar structures in two scalar fields. We demonstrate the utility of our approach by applying it to a number of complex fluid flow datasets, where the emphasis is put on the comparison of vortex related scalar quantities.


title: Online user survey on current mobile augmented reality applications

abstract: Augmented reality (AR) as an emerging technology in the mobile computing domain is becoming mature enough to engender publicly available applications for end users. Various commercial applications have recently been emerging in the mobile consumer domain at an increasing pace — Layar, Junaio, Google Goggles, and Wikitude are perhaps the most prominent ones. However, the research community lacks an understanding of how well such timely applications have been accepted, what kind of user experiences they have evoked, and what the users perceive as the weaknesses of the various applications overall. During the spring of 2011 we conducted an online survey to study the overall acceptance and user experience of the mobile AR-like consumer applications currently existing on the market. This paper reports the first analyses of the qualitative and quantitative survey data of 90 respondents. We highlight an extensive set of user-oriented issues to be considered in developing the applications further, as well as in directing future user research in AR. The results indicate that the experiences have been inconsistent: generally positive evaluations are overshadowed by mentions of applications' pragmatic uselessness in everyday life and technical unreliability, as well as excessive or limited and irrelevant content.


title: A REVIEW ON IMAGE SEGMENTATION TECHNIQUES WITH REMOTE SENSING PERSPECTIVE

abstract: With the growing research on image segmentation, it has become important to categorise the research outcomes and provide readers with an overview of the existing segmentation techniques in each category. In this paper, different image segmentation techniques applied on optical remote sensing images are reviewed. The selection of papers include sources from image processing journals, conferences, books, dissertations and thesis out of more than 3000 journals, books and online research databases available at UNB. The conceptual details of the techniques are explained and mathematical details are avoided for simplicity. Both broad and detailed categorisations of reviewed segmentation techniques are provided. The state of art research on each category is provided with emphasis on developed technologies and image properties used by them. The categories defined are not always mutually independent. Hence, their interrelationships are also stated. Finally, conclusions are drawn summarizing commonly used techniques and their complexities in application


title: Microbiome and Anticancer Immunosurveillance

abstract: Anticancer immune responses can be considered a desirable form of autoimmunity that may be profoundly shaped by the microbiome. Here, we discuss evidence for the microbiome's influence on anti-tumor immunosurveillance, including those that are indirect and can act at a distance, and we put forward hypotheses regarding mechanisms of how these effects are implemented. These may involve cross-reactivity between microbial and tumor antigens shaping T cell repertoires and/or microbial products stimulating pattern recognition receptors that influence the type and intensity of immune responses. Understanding how the microbiome impacts natural cancer immunosurveillance as well as treatment-induced immune responses will pave the way for more effective therapies and prophylactics.


title: The Neglect of Power in Recent Framing Research

abstract: This article provides a critique of recent developments in research examining media frames and their influence. We contend that a number of trends in framing research have neglected the relationship between media frames and broader issues of political and social power. This neglect is a product of a number of factors, including conceptual problems in the definition of frames, the inattention to frames sponsorship, the failure to examine framing contests within wider political and social contexts, and the reduction of framing to a form of media effects. We conclude that framing research needs to be linked to the political and social questions regarding power central to the media hegemony thesis, and illustrate this focus by exploring how framing research can contribute to an understanding of the interaction between social movements and the news media. Examinations of the production, character, and influence of news stories represent an enduring focus of media scholarship. A variety of approaches, including gatekeeping, agenda setting, organizational studies of news work, and analyses of news bias, have explored either the gathering of news or journalism’s political role. In recent decades, a rapidly expanding research literature on news frames has sought to provide a comprehensive perspective on the production, reception, and influence of news texts. This article provides a critique of recent developments in research examining media frames and their influence. We contend that a number of trends in framing research have neglected the relationship between media frames and broader issues of political and social power. This neglect is a product of conceptual problems in the definition of frames, the inattention to frame sponsorship, the failure to examine framing contests within wider political and social contexts, and the reduction of framing to a form of media effects. In keeping with early sociological research on framing (Gitlin, 1980; Tuchman, 1978), we suggest that framing processes need to be examined within the contexts of the distribution of political and social power. We, therefore, call for the integration of framing research with the


title: A low-cost attack on a Microsoft captcha

abstract: CAPTCHA is now almost a standard security technology. The most widely deployed CAPTCHAs are text-based schemes, which typically require users to solve a text recognition task. The state of the art of CAPTCHA design suggests that such text-based schemes should rely on segmentation resistance to provide security guarantee, as individual character recognition after segmentation can be solved with a high success rate by standard methods such as neural networks. In this paper, we present new character segmentation techniques of general value to attack a number of text CAPTCHAs, including the schemes designed and deployed by Microsoft, Yahoo and Google. In particular, the Microsoft CAPTCHA has been deployed since 2002 at many of their online services including Hotmail, MSN and Windows Live. Designed to be segmentation-resistant, this scheme has been studied and tuned by its designers over the years. However, our simple attack has achieved a segmentation success rate of higher than 90% against this scheme. It took on average ~80 ms for the attack to completely segment a challenge on an ordinary desktop computer. As a result, we estimate that this CAPTCHA could be instantly broken by a malicious bot with an overall (segmentation and then recognition) success rate of more than 60%. On the contrary, the design goal was that automated attacks should not achieve a success rate of higher than 0.01%. For the first time, this paper shows that CAPTCHAs that are carefully designed to be segmentation-resistant are vulnerable to novel but simple attacks.


A survey of fingerprint classification Part I: Taxonomies on feature extraction methods and learning models


title: 2002 Special issue Dopamine: generalization and bonuses

abstract: In the temporal difference model of primate dopamine neurons, their phasic activity reports a prediction error for future reward. This model is supported by a wealth of experimental data. However, in certain circumstances, the activity of the dopamine cells seems anomalous under the model, as they respond in particular ways to stimuli that are not obviously related to predictions of reward. In this paper, we address two important sets of anomalies, those having to do with generalization and novelty. Generalization responses are treated as the natural consequence of partial information; novelty responses are treated by the suggestion that dopamine cells multiplex information about reward bonuses, including exploration bonuses and shaping bonuses. We interpret this additional role for dopamine in terms of the mechanistic attentional and psychomotor effects of dopamine, having the computational role of guiding exploration.


title: TS-CLOCK: temporal and spatial locality aware buffer replacement algorithm for NAND flash storages

abstract: NAND flash storage is widely adopted in all classes of computing devices. However, random write performance and lifetime issues remain to be addressed. In this paper, we propose a novel buffer replacement algorithm called TS-CLOCK that effectively resolves the remaining problems. Our experimental results show that TS-CLOCK outperforms state-of-the-art algorithms in terms of performance and lifetime.


title: On Calibration of Modern Neural Networks

abstract: Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike those from a decade ago, are poorly calibrated. Through extensive experiments, we observe that depth, width, weight decay, and Batch Normalization are important factors influencing calibration. We evaluate the performance of various post-processing calibration methods on state-of-the-art architectures with image and document classification datasets. Our analysis and experiments not only offer insights into neural network learning, but also provide a simple and straightforward recipe for practical settings: on most datasets, temperature scaling -- a single-parameter variant of Platt Scaling -- is surprisingly effective at calibrating predictions.