title: Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition

abstract: The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology - namely, Behaviorism and evolutionary psychology - that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls that have plagued previous theoretical movements.


title: Configuration system for simulation based design of vibratory bowl feeders

abstract: Vibratory bowl feeders are still among the most commonly used production equipment for automated part feeding, where parts are correctly oriented for further manipulation by being conveyed through a set of orienting devices. Designing vibratory bowl feeders involves selecting and sequencing a number of these devices that either reorients or rejects the part until a desirable orientation is achieved. To aid the designer in this task, this work presents a configuration system where knowledge of the behaviour for each device is acquired through dynamic simulation, and used to solve the configuration task. To test the approach, the configuration system is used to find three device sequences for feeding three parts in specific orientations. The sequences are validated through simulation and real world experiments, showing good consistency.


title: Adding secure deletion to your favorite file system

abstract: Files or even their names often contain confidential or secret information. Most users believe that such information is erased as soon as they delete a file. Even those who know that this is not true often ignore the issue. Nevertheless, recovering deleted files is trivial and can be performed even by novice hackers. The problem is exacerbated by the widespread of portable and mobile storage devices. This type of unwanted after-deletion data recovery is in part an education problem. Users believe that deleted files are erased, even though they are not. Retraining and educating users is difficult. Therefore, storage systems should behave appropriately - the data should be erased from the storage on a per-delete basis. We found that existing solutions are either inconvenient, inefficient, or insecure. We have designed Purgefs: a file system extension that transparently overwrites files on the per-delete basis. Purgefs can be automatically added to a number of existing and future file systems, including networked and stackable file systems. Purgefs supports multiple policies to trade-off performance with the level of purging guarantees. We demonstrate that Purgefs does not add overheads or perturb users' activity under typical user workloads


title: Pixel-wise Regression using U-Net and its Application on Pansharpening

abstract: Abstract Convolutional neural networks are widely used for solving image recognition and other classification problems in which the whole image is considered as a single object. In this paper, we take the pansharpening problem of remote sensing images as an example to discuss how to establish pixel-wise regression models using convolutional neural networks. In order to meet the requirements of pixel-wise analysis on both the localization accuracy and the abstraction ability of the regression process, a U-shaped architecture is applied in our study to construct the network model. By establishing direct connections between convolution layers at the front end and the back end of the network, image features corresponding to different resolution levels can be retained. Then a regression relationship between these multi-resolution image features and the target image pixel values can be obtained. Experimental results show that the proposed regression model can effectively accomplish pansharpening, with better performance in controlling geometric deformation and color distortion, as compared to some state of the art methods.


Compressed Sensing for Wireless Communications : A Few Tips and Tricks


title: Radiation Characteristics of a Microstrip Patch Over an Electromagnetic Bandgap Surface

abstract: Radiation characteristics of a microstrip patch over an electromagnetic bandgap (EBG) substrate are investigated in this paper. This paper focuses on a mushroom-type EBG structure, although the design is applicable to various EBG profiles. The patch antenna is modeled as a half-wavelength resonator of an EBG-loaded microstrip transmission line. Through a full-wave eigenmode analysis of a microstrip line on an EBG structure, it is found that the resonant patch will not see a high-impedance surface (HIS), but rather it is coupled to the EBG structure as an open cavity resonator. Due to strong near-field coupling, the propagation characteristics including the bandgap zones are very different with or without the patch cover. The use of an EBG structure as a bulk material for antennas is seen inappropriate. The EBG surface is found to have the effect of reducing the patch resonant length and bandwidth. A prototype of a microstrip line proximity fed to a patch antenna is fabricated and tested to verify the analysis.


title: The INTERSPEECH 2010 paralinguistic challenge

abstract: Abstract Most paralinguistic analysis tasks are lacking agreed-uponevaluation procedures and comparability, in contrast to more‘traditional’ disciplines in speech analysis. The INTERSPEECH2010 Paralinguistic Challenge shall help overcome the usuallylow compatibility of results, by addressing three selected sub-challenges. In the Age Sub-Challenge, the age of speakers hasto be determined in four groups. In the Gender Sub-Challenge,a three-class classiﬁcation task has to be solved and ﬁnally, theAffect Sub-Challenge asks for speakers’ interest in ordinal rep-resentation. This paper introduces the conditions, the Challengecorpora “aGender” and “TUM AVIC” and standard feature setsthat may be used. Further, baseline results are given.Index Terms: Paralinguistic Challenge, Age, Gender, Affect 1. Introduction Most paralinguistic analysis tasks resemble each other not onlyby means of processing and ever-present data sparseness, but bylacking agreed-upon evaluation procedures and comparability,in contrast to more traditional disciplines in speech analysis. Atthe same time, this is a rapidly emerging ﬁeld of research, dueto the constantly growing interest on applications in the ﬁeldsof Human-Machine Communication, Human-Robot Communi-cation, and Multimedia Retrieval. In these respects, the INTER-SPEECH 2010 Paralinguistic Challenge shall help bridging thegap between excellent research on paralinguistic information inspoken language and low compatibility of results, by address-ing three selected tasks. The “aGender” and the “TUM AVIC”corpora are provided by the organizers. The ﬁrst consists of 46hours of telephone speech, stemming from 954 speakers, andserves to evaluate features and algorithms for the detection ofspeaker age and gender. The second features 2 hours of humanconversational speech recording (21 subjects), annotated in 5differentlevelsofinterest. Thecorpusfurtherfeaturesauniquelydetailed transcription of spoken content with word boundaries byforced alignment, non-linguistic vocalizations, single annotatortracks, and the sequence of (sub-)speaker-turns. Both are given


title: Trajectory optimization under kinematical constraints for moving target search

abstract: Various recent events in the Mediterranean sea have shown the enormous importance of maritime search-and-rescue missions. By reducing the time to find floating victims, the number of casualties can be reduced. A major improvement can be achieved by employing unmanned aerial systems for autonomous search missions. In this context, the need for efficient search trajectory planning methods arises. Existing approaches either consider K-step-lookahead optimization without accounting for kinematics of fixed-wing platforms or propose a suboptimal myopic method. A few approaches consider both aspects, however only applicable to stationary target search. The contribution of this article consists of a novel method for Markovian target search-trajectory optimization. This is a unified method for fixed-wing and rotary-wing platforms, taking kinematical constraints into account. It can be classified as K-step-lookahead planning method, which allows for anticipation to the estimated future position and motion of the target. The method consists of a mixed integer linear program that optimizes the cumulative probability of detection. We show the applicability and effectiveness in computational experiments for three types of moving targets: diffusing, conditionally deterministic, and Markovian. This approach is the first K-step-lookahead method for Markovian target search under kinematical constraints.


title: Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations

abstract: We present a method for estimating articulated human pose from a single static image based on a graphical model with novel pairwise relations that make adaptive use of local image measurements. More precisely, we specify a graphical model for human pose which exploits the fact the local image measurements can be used both to detect parts (or joints) and also to predict the spatial relationships between them (Image Dependent Pairwise Relations). These spatial relationships are represented by a mixture model. We use Deep Convolutional Neural Networks (DCNNs) to learn conditional probabilities for the presence of parts and their spatial relationships within image patches. Hence our model combines the representational flexibility of graphical models with the efficiency and statistical power of DCNNs. Our method significantly outperforms the state of the art methods on the LSP and FLIC datasets and also performs very well on the Buffy dataset without any training.


title: Social Interactions by Visual Focus Of Attention in a three-dimensional environment

abstract: In human behaviour analysis, the visual focus of attention (VFOA) of a person is a very important cue. VFOA detection is difficult, though, especially in a unconstrained and crowded environment, typical of video surveillance scenarios. In this paper, we estimate the VFOA by defining the Subjective View Frustum, which approximates the visual field of a person in a three-dimensional representation of the scene. This opens up to several intriguing behavioural investigations. In particular, we propose the Inter-Relation Pattern Matrix, which suggests possible social interactions between the people present in a scene. Theoretical justifications and experimental results substantiate the validity and the goodness of the analysis performed.


title: Marginal Release Under Local Differential Privacy

abstract: Many analysis and machine learning tasks require the availability of marginal statistics on multidimensional datasets while providing strong privacy guarantees for the data subjects. Applications for these statistics range from finding correlations in the data to fitting sophisticated prediction models. In this paper, we provide a set of algorithms for materializing marginal statistics under the strong model of local differential privacy. We prove the first tight theoretical bounds on the accuracy of marginals compiled under each approach, perform empirical evaluation to confirm these bounds, and evaluate them for tasks such as modeling and correlation testing. Our results show that releasing information based on (local) Fourier transformations of the input is preferable to alternatives based directly on (local) marginals.


title: Vehicle color recognition using deep learning for hazy images

abstract: Recent years the number of vehicles increases tremendously. Because of that to identify the vehicle is significant task. Vehicle color and number plate recognition are various ways to identify the vehicle. So Vehicle color recognition essential part of an intelligent transportation system. There are several methods for recognizing the color of the vehicle like feature extract, template matching, convolutional neural network (CNN), etc. CNN is emerging technique within the field of Deep learning. The survey concludes that compared to other techniques CNN gives more accurate results with less training time even for large dataset. The images taken from roads or hill areas aren't visible because of haze. Consequently, removing haze may improve the color recognition. The proposed system combines both techniques and it adopts the dark channel prior technique to remove the haze, followed by feature learning using CNN. After feature learning, classification can be performed by effective classification technique like SVM.


title: It Just Feels Good: Customers' Affective Response to Touch and Its Influence on Persuasion

abstract: Prior research has assumed that touch has a persuasive effect only if it provides attribute or structural information about a product. Under this view, the role of touch as a persuasive tool is limited. The main purpose of this research is to investigate the persuasive influence of touch as an affective tool in the absence of useful product-related information. The authors find that for people who are motivated to touch because it is fun or interesting, a communication that incorporates touch leads to increased affective response and increased persuasion, particularly when the touch provides neutral or positive sensory feedback. People who are not motivated to touch for fun will also be persuaded by a communication that incorporates touch when they are able to make sense of how the touch is related to the message. The authors explore the effectiveness of different types of touch in generating an affective response, and they replicate the effects on attitudes and behavior in a real-world setting. This research suggests that the marketing implications of touch are more substantial than previously believed. The authors present research implications for direct marketing, product packaging, point-of-purchase displays, and print advertising.


title: Classifying without discriminating

abstract: Classification models usually make predictions on the basis of training data. If the training data is biased towards certain groups or classes of objects, e.g., there is racial discrimination towards black people, the learned model will also show discriminatory behavior towards that particular community. This partial attitude of the learned model may lead to biased outcomes when labeling future unlabeled data objects. Often, however, impartial classification results are desired or even required by law for future data objects in spite of having biased training data. In this paper, we tackle this problem by introducing a new classification scheme for learning unbiased models on biased training data. Our method is based on massaging the dataset by making the least intrusive modifications which lead to an unbiased dataset. On this modified dataset we then learn a non-discriminating classifier. The proposed method has been implemented and experimental results on a credit approval dataset show promising results: in all experiments our method is able to reduce the prejudicial behavior for future classification significantly without loosing too much predictive accuracy.


Feminist vs. Nonfeminist Therapy:: The Client's Perspective


title: On-chip supervised learning rule for ultra high density neural crossbar using memristor for synapse and neuron

abstract: The memristor-based neural learning network is considered as one of the candidates for future computing systems thanks to its low power, high density and defect-tolerance. However, its application is still hindered by the limitations of huge neuron structure and complicated learning cell. In this paper, we present a memristor-based neural crossbar circuit to implement on-chip supervised learning rule. In our work, activation function of neuron is implemented with simple CMOS inverter to save area overhead. Importantly, we propose a compact learning cell with a crossbar latch consisting of two anti-parallel oriented binary memristors. This scheme allows high density integration and could improve the reliability of learning circuit. We describe firstly the circuit architecture, memristor model and operation process of supervised learning rule. Afterwards we perform transient simulation with CMOS 40nm design kit to validate the function of proposed learning circuit. Analysis and evaluation demonstrate that our circuit show great potential in on-chip learning.


title: Temporally Factorized Network Modeling for Evolutionary Network Analysis

abstract: The problem of evolutionary network analysis has gained increasing attention in recent years, because of an increasing number of networks, which are encountered in temporal settings. For example, social networks, communication networks, and information networks continuously evolve over time, and it is desirable to learn interesting trends about how the network structure evolves over time, and in terms of other interesting trends. One challenging aspect of networks is that they are inherently resistant to parametric modeling, which allows us to truly express the edges in the network as functions of time. This is because, unlike multidimensional data, the edges in the network reflect interactions among nodes, and it is difficult to independently model the edge as a function of time, without taking into account its correlations and interactions with neighboring edges. Fortunately, we show that it is indeed possible to achieve this goal with the use of a matrix factorization, in which the entries are parameterized by time. This approach allows us to represent the edge structure of the network purely as a function of time, and predict the evolution of the network over time. This opens the possibility of using the approach for a wide variety of temporal network analysis problems, such as predicting future trends in structures, predicting links, and node-centric anomaly/event detection. This flexibility is because of the general way in which the approach allows us to express the structure of the network as a function of time. We present a number of experimental results on a number of temporal data sets showing the effectiveness of the approach.


title: Tracking word semantic change in biomedical literature

abstract: Abstract Up to this point, research on written scholarly communication has focused primarily on syntactic, rather than semantic, analyses. Consequently, we have yet to understand semantic change as it applies to disciplinary discourse. The objective of this study is to illustrate word semantic change in biomedical literature. To that end, we identify a set of representative words in biomedical literature based on word frequency and word-topic probability distributions. A word2vec language model is then applied to the identified words in order to measure word- and topic-level semantic changes. We find that for the selected words in PubMed, overall, meanings are becoming more stable in the 2000s than they were in the 1980s and 1990s. At the topic level, the global distance of most topics (19 out of 20 tested) is declining, suggesting that the words used to discuss these topics are stabilizing semantically. Similarly, the local distance of most topics (19 out of 20) is also declining, showing that the meanings of words from these topics are becoming more consistent with those of their semantic neighbors. At the word level, this paper identifies two different trends in word semantics, as measured by the aforementioned distance metrics: on the one hand, words can form clusters with their semantic neighbors, and these words, as a cluster, coevolve semantically; on the other hand, words can drift apart from their semantic neighbors while nonetheless stabilizing in the global context. In relating our work to language laws on semantic change, we find no overwhelming evidence to support either the law of parallel change or the law of conformity.


title: E-participation and enterprise architecture frameworks: an analysis

abstract: The success of innovative e-participation solutions depends heavily on the organizational planning and the incorporation of such initiatives into the different stages of the policy life-cycle. E-participation often demands to introduce new participation facilities into the traditional processes of policy formulation and decision making. Accommodating the various requirements from distinct perspectives calls for a holistic engineering approach for e-participation systems analysis and design. Enterprise Architectures (EA) have evolved in information systems research as an approach to give guidance in developing complex socio-technical systems. This paper analyzes the application of EA frameworks in the context of e-participation. Eparticipation domain and implementation models are investigated to identify crucial tasks and aspects in e-participation project development and implementation. Related to the tasks identified, two EA frameworks are analyzed: the Zachman Framework and TOGAF. We explain how EA frameworks can support the development and implementation of e-participation projects. Finally, the needs for a reference framework for e-participation are argued and a reference framework is presented.


title: An electronic walking stick for blinds

abstract: Independence is the building methodology in achieving dreams, goals and objectives in life. Visually impaired persons find themselves challenging to go out independently. There are millions of visually impaired or blind people in this world who are always in need of helping hands. For many years the white cane became a well-known attribute to blind person's navigation and later efforts have been made to improve the cane by adding remote sensor. Blind people have big problem when they walk on the street or stairs using white cane, but they have sharp haptic sensitivity. The electronic walking stick will help the blind person by providing more convenient means of life. The main aim of this paper is to contribute our knowledge and services to the people of blind and disable society.


title: Understanding Chinese Consumers’ Intention to Purchase Sustainable Fashion Products: The Moderating Role of Face-Saving Orientation

abstract: In a culture where collectivism is pervasive such as China, social norms can be one of the most powerful tools to influence consumers’ behavior. Individuals are driven to meet social expectations and fulfill social roles in collectivist cultures. Therefore, this study was designed to investigate how Chinese consumers’ concern with saving face affects sustainable fashion product purchase intention and how it also moderates consumers’ commitment to sustainable fashion. An empirical data set of 469 undergraduate students in Beijing and Shanghai was used to test our hypotheses. Results confirmed that face-saving is an important motivation for Chinese consumers’ purchase of sustainable fashion items, and it also attenuated the effect of general product value while enhancing the effect of products’ green value in predicting purchasing trends. The findings contribute to the knowledge of sustainable consumption in Confucian culture, and thus their managerial implications were also discussed.


title: DDoS attack detection under SDN context

abstract: Software Defined Networking (SDN) has recently emerged as a new network management platform. The centralized control architecture presents many new opportunities. Among the network management tasks, measurement is one of the most important and challenging one. Researchers have proposed many solutions to better utilize SDN for network measurement. Among them, how to detect Distributed Denial-of-Services (DDoS) quickly and precisely is a very challenging problem. In this paper, we propose methods to detect DDoS attacks leveraging on SDN's flow monitoring capability. Our methods utilize measurement resources available in the whole SDN network to adaptively balance the coverage and granularity of attack detection. Through simulations we demonstrate that our methods can quickly locate potential DDoS victims and attackers by using a constrained number of flow monitoring rules.


title: An unsupervised user identification algorithm using network embedding and scalable nearest neighbour

abstract: Most of the current studies on social network (SN) mainly focused on a single SN platform. Integration of SNs can provide more sufficient user behaviour data and more complete network structure, and thus is rewarding to an ocean of studies on social computing. Recognizing the identical users across SNs, or user identification, naturally bridges the SNs through users and has attracted extensive attentions. Due to the fragmentation, inconsistency and disruption of the accessible information among SNs, user identification is still an intractable problem. Different from the efforts implemented on user profiles and users’ content, many studies have noticed the accessibility and reliability of network structure in most of the SNs for addressing this issue. Although substantial achievements have been made, most of the current network structure-based solutions are supervised or semi-supervised and require some given identified users or seed users. In the scenarios where seed users are hard to obtain, it is laborious to label the seed users manually. In this study, we proposed an unsupervised scheme by employing the reliability and consistence of friend relationships in different SNs, termed Unsupervised Friend Relationship-based User Identification algorithm (UFRUI). The UFRUI first models the network structure and embeds the feature of each user into a vector using network embedding technique, and then converts the user identification problem into a nearest neighbour problem. Finally, the matching user is computed using the scalable nearest neighbour algorithm. Results of experiments demonstrated that UFRUI performs much better than current state-of-art network structure-based algorithm without seed users.


A Comparison of Two Paraphrase Models for Taxonomy Augmentation


title: How it Happened: Discovering and Archiving the Evolution of a Story Using Social Signals

abstract: Social networks like Twitter and Facebook are the largest sources of public opinion and real-time information on the Internet. If an event is of general interest, news articles follow and eventually a Wikipedia page. We propose the problem of automatic event story generation and archiving by combining social and news data to construct a new type of document in the form of a Wiki-like page structure. We introduce a technique that shows the evolution of a story as perceived by the crowd in social media, along with editorially authored articles annotated with examples of social media as supporting evidence. At the core of our research, is the temporally sensitive extraction of data that serve as context for retrieval purposes. Our approach includes a fine-grained vote counting strategy that is used for weighting purposes, pseudo-relevance feedback and query expansion with social data and web query logs along with a timeline algorithm as the base for a story. We demonstrate the effectiveness of our approach by processing a dataset comprising millions of English language tweets generated over a one year period and present a full implementation of our system.


title: Human female orgasm and mate fluctuating asymmetry

abstract: Abstract Human, Homo sapiens , female orgasm is not necessary for conception; hence it seems reasonable to hypothesize that orgasm is an adaptation for manipulating the outcome of sperm competition resulting from facultative polyandry. If heritable differences in male viability existed in the evolutionary past, selection could have favoured female adaptations (e.g. orgasm) that biased sperm competition in favour of males possessing heritable fitness indicators. Accumulating evidence suggests that low fluctuating asymmetry is a sexually selected male feature in a variety of species, including humans, possibly because it is a marker of genetic quality. Based on these notions, the proportion of a woman's copulations associated with orgasm is predicted to be associated with her partner's fluctuating asymmetry. A questionnaire study of 86 sexually active heterosexual couples supported this prediction. Women with partners possessing low fluctuating asymmetry and their partners reported significantly more copulatory female orgasms that were reported by women with partners possessing high fluctuating asymmetry and their partners, even with many potential confounding variables controlled. The findings are used to examine hypotheses for female orgasm other than selective sperm retention.


An Approach to Correction of Erroneous Links in Knowledge Graphs


title: Using deep learning for short text understanding

abstract: Classifying short texts to one category or clustering semantically related texts is challenging, and the importance of both is growing due to the rise of microblogging platforms, digital news feeds, and the like. We can accomplish this classifying and clustering with the help of a deep neural network which produces compact binary representations of a short text, and can assign the same category to texts that have similar binary representations. But problems arise when there is little contextual information on the short texts, which makes it difficult for the deep neural network to produce similar binary codes for semantically related texts. We propose to address this issue using semantic enrichment. This is accomplished by taking the nouns, and verbs used in the short texts and generating the concepts and co-occurring words with the help of those terms. The nouns are used to generate concepts within the given short text, whereas the verbs are used to prune the ambiguous context (if any) present in the text. The enriched text then goes through a deep neural network to produce a prediction label for that short text representing it’s category.


title: Selective activation of the superior frontal gyrus in task-switching: An event-related fNIRS study

abstract: Abstract In the task-switching paradigm, reaction time is longer and accuracy is worse in switch trials relative to repetition trials. This so-called switch cost has been ascribed to the engagement of control processes required to alternate between distinct stimulus–response mapping rules. Neuroimaging studies have reported an enhanced activation of the human lateral prefrontal cortex and the superior frontal gyrus during the task-switching paradigm. Whether neural activation in these regions is dissociable and associated with separable cognitive components of task switching has been a matter of recent debate. We used multi-channel near-infrared spectroscopy (fNIRS) to measure brain cortical activity in a task-switching paradigm designed to avoid task differences, order predictability, and frequency effects. The results showed a generalized bilateral activation of the lateral prefrontal cortex and the superior frontal gyrus in both switch trials and repetition trials. To isolate the activity selectively associated with the task-switch, the overall activity recorded during repetition trials was subtracted from the activity recorded during switch trials. Following subtraction, the remaining activity was entirely confined to the left portion of the superior frontal gyrus. The present results suggest that factors associated with load and maintenance of distinct stimulus–response mapping rules in working memory are likely contributors to the activation of the lateral prefrontal cortex, whereas only activity in the left superior frontal gyrus can be linked unequivocally to switching between distinct cognitive tasks.


title: Web-Service Clustering with a Hybrid of Ontology Learning and Information-Retrieval-Based Term Similarity

abstract: Organizing Web services into functionally similar clusters, is an efficient approach to discovering Web services efficiently. An important aspect of the clustering process is calculating the semantic similarity of Web services. Most current clustering approaches are based on similarity-distance measurement, including keyword, ontology and information-retrieval-based methods. Problems with these approaches include a shortage of high quality ontologies and a loss of semantic information. In addition, there has been little fine-grained improvement in existing approaches to service clustering. In this paper, we present a new approach to grouping Web services into functionally similar clusters by mining Web service documents and generating an ontology via hidden semantic patterns present within the complex terms used in service features to measure similarity. If calculating the similarity using the generated ontology fails, the similarity is calculated by using an information-retrieval-based term-similarity method that adopts term-similarity measuring techniques used by thesaurus and search engines. Another important aspect of high performance in clustering is identifying the most suitable cluster center. To improve the utility of clusters, we propose an approach to identifying the cluster center that combines service similarity with the term frequency-inverse document frequency values of service names. Experimental results show that our clustering approach performs better than existing approaches.


title: An Object-Based Bayesian Framework for Top-Down Visual Attention

abstract: We introduce a new task-independent framework to model top-down overt visual attention based on graphical models for probabilistic inference and reasoning. We describe a Dynamic Bayesian Network (DBN) that infers probability distributions over attended objects and spatial locations directly from observed data. Probabilistic inference in our model is performed over object-related functions which are fed from manual annotations of objects in video scenes or by state-of-theart object detection models. Evaluating over ∼3 hours (appx. 315;000 eye fixations and 12;600 saccades) of observers playing 3 video games (time-scheduling, driving, and flight combat), we show that our approach is significantly more predictive of eye fixations compared to: 1) simpler classifier-based models also developed here that map a signature of a scene (multimodal information from gist, bottom-up saliency, physical actions, and events) to eye positions, 2) 14 state-of-the-art bottom-up saliency models, and 3) brute-force algorithms such as mean eye position. Our results show that the proposed model is more effective in employing and reasoning over spatio-temporal visual data.


title: Fingerprinting encrypted network traffic types using machine learning

abstract: Internet applications rely on strong encryption techniques to protect the content of all communications between client and server. These encryption algorithms ensure that third parties are unable to obtain the plain text data but also make it hard for the network administrator to enforce restrictions on the types of traffic that are allowed. In this paper we show that we can train accurate machine learning models which can predict the type of traffic going through an IPsec or TOR tunnel based on features extracted from the encrypted streams. We use small, fast to execute machine learning models that work on small windows of data. This makes it possible to use our approach in real-time, for example as part of a Quality of Service (QoS) system.


title: Oral Cancer and Precancerous Lesions

abstract: In the United States, cancers of the oral cavity and oropharynx represent approximately three percent of all malignancies in men and two percent of all malignancies in women. The American Cancer Society estimates that 28,900 new cases of oral cancer will be diagnosed in 2002, and nearly 7,400 people will die from this disease. Over 90 percent of these tumors are squamous cell carcinomas, which arise from the oral mucosal lining. In spite of the ready accessibility of the oral cavity to direct examination, these malignancies still are often not detected until a late stage, and the survival rate for oral cancer has remained essentially unchanged over the past three decades. The purpose of this article is to review the clinical features of oral cancer and premalignant oral lesions, with an emphasis on early detection.


title: Authenticity and Learning: Implications for Reference Librarianship and Information Literacy Instruction

abstract: This article articulates and defends a student-centered approach to reference and instructional librarianship defined by authentic engagement with students’ interests. A review of the history of the construct of authenticity in philosophy, humanistic and existential psychology, and contemporary educational psychology is traced. Connections are drawn between the philosophy of authentic engagement and the tradition in librarianship of “Counselor Librarianship.” Recommendations for applications to the library context are then outlined.


title: Two Systems for the Detection of Melanomas in Dermoscopy Images Using Texture and Color Features

abstract: Melanoma is one of the deadliest forms of cancer; hence, great effort has been put into the development of diagnosis methods for this disease. This paper addresses two different systems for the detection of melanomas in dermoscopy images. The first system uses global methods to classify skin lesions, whereas the second system uses local features and the bag-of-features classifier. This paper aims at determining the best system for skin lesion classification. The other objective is to compare the role of color and texture features in lesion classification and determine which set of features is more discriminative. It is concluded that color features outperform texture features when used alone and that both methods achieve very good results, i.e., Sensitivity = 96% and Specificity = 80% for global methods against Sensitivity = 100% and Specificity = 75% for local methods. The classification results were obtained on a data set of 176 dermoscopy images from Hospital Pedro Hispano, Matosinhos.


title: License to evaluate: preparing learning analytics dashboards for educational practice

abstract: Learning analytics can bridge the gap between learning sciences and data analytics, leveraging the expertise of both fields in exploring the vast amount of data generated in online learning environments. A typical learning analytics intervention is the learning dashboard, a visualisation tool built with the purpose of empowering teachers and learners to make informed decisions about the learning process. Related work has investigated learning dashboards, yet none have explored the theoretical foundation that should inform the design and evaluation of such interventions. In this systematic literature review, we analyse the extent to which theories and models from learning sciences have been integrated into the development of learning dashboards aimed at learners. Our analysis revealed that very few dashboard evaluations take into account the educational concepts that were used as a theoretical foundation for their design. Furthermore, we report findings suggesting that comparison with peers, a common reference frame for contextualising information on learning analytics dashboards, was not perceived positively by all learners. We summarise the insights gathered through our literature review in a set of recommendations for the design and evaluation of learning analytics dashboards for learners.


title: Understanding Privacy Settings in Facebook with an Audience View

abstract: Users of online social networking communities are disclosing large amounts of personal information, putting themselves at a variety of risks. Our ongoing research investigates mechanisms for socially appropriate privacy management in online social networking communities. As a first step, we are examining the role of interface usability in current privacy settings. In this paper we report on our first iterative prototype, where presenting an audience-oriented view of profile information significantly improved the understanding of privacy settings.


title: The social camera: a case-study in contextual image recommendation

abstract: The digital camera revolution has changed the world of photography and now most people have access to, and even regularly carry, a digital camera. Often these cameras have been designed with simplicity in mind: they harness a variety of sophisticated technologies in order to automatically take care of all manner of complex settings (aperture, shutter speed, flash etc.) for point-and-shoot ease, these assistive features are usually incorporated directly into the cameras interface. However, there is little or no support for the end-user when it comes to helping them to compose or frame a scene. To this end we describe a novel recommendation process which uses a variety of intelligent and assistive interfaces to guide the user in taking relevant compositions given their current location and scene context. This application has been implemented on the Android platform and we describe its core user interaction, recommendation technologies and demonstrate its effectiveness in a number of real-world scenarios. Specifically we report on the results of a live-user trial of the technology in a real-world tourist setting.


title: Classifying digestive organs in wireless capsule endoscopy images based on deep convolutional neural network

abstract: This paper studies the classification problem of the digestive organs in wireless capsule endoscopy (WCE) images based on deep convolutional neural network (DCNN) framework. Essentially, DCNN proves having powerful ability to learn layer-wise hierarchy models with huge training data, which works similar to human biological visual systems. Classifying digestive organs in WCE images intuitively means to recognize higher semantic image features. To achieve this, an effective deep CNN-based WCE classification system has been constructed (DCNN-WCE-CS). With about 1 million real WCE images, intensive experiments are conducted to evaluate its performance by setting different network parameters. Results illustrate its superior performance compared to traditional classification methods, where about 95% classification accuracy can be achieved in average. Moreover, it is observed that the DCNN-WCE-CS is robust to the large variations of the WCE images due to the individuals and complex digestive tract circumstance, including the rotation, the luminance change of the WCE images.


title: VSIPL++: Parallel Performance

abstract: Abstract : VSIPL++(exp 1) is the object-oriented "next-generation" version of the Vector Signal and Image Processing Library (VSIPL). Like VISPL, VSIPL++ specifies an Application Programming Interface (API) for use in the development of high-performance numerical applications, with a particular focus on embedded real-time systems performing signal processing and image processing. VSIPL++ contains a number of improvements relative to VSIPL including a simpler, more intuitive programming model, simpler syntax, and greater flexibility and extensibility. This parallel support requires only that the user specify the way in which data should be distributed across processors. The VSIPL++ library automatically manages the transmission of data between the processors as necessary to effectively perform the desired computations. CodeSourcery was awarded funding under the Air Force Small Business Investment Research (SBIR) program to develop a prototype version of the parallel functionality described in the VSIPL++ specification and to obtain measurements of VSIPL++ performance when executing on parallel systems. Our prototype implementation achieves a near-linear speedup on multi-processor systems demonstrating that, despite the high level of abstraction present in VSIPL++, it is nevertheless possible to obtain excellent performance. Thus, VSIPL++ has the potential to allow programmers to easily and rapidly develop systems that are both highly portable and highly efficient. In our presentation, we will describe the parallel VSIPL++ programming model, our parallel performance benchmark, and the results we obtained.


title: Polysemy in sentence comprehension : Effects of meaning dominance

abstract: Words like church are polysemous, having two related senses (a building and an organization). Three experiments investigated how polysemous senses are represented and processed during sentence comprehension. On one view, readers retrieve an underspecified, core meaning, which is later specified more fully with contextual information. On another view, readers retrieve one or more specific senses. In a reading task, context that was neutral or biased towards a particular sense preceded a polysemous word. Disambiguating material consistent with only one sense followed, in a second sentence (Experiment 1) or the same sentence (Experiments 2 and 3). Reading the disambiguating material was faster when it was consistent with that context, and dominant senses were committed to more strongly than subordinate senses. Critically, following neutral context, the continuation was read more quickly when it selected the dominant sense, and the degree of sense dominance partially explained the reading time advantage. Similarity of the senses also affected reading times. Across experiments, we found that sense selection may not be completed immediately following a polysemous word but is completed at a sentence boundary. Overall, the results suggest that readers select an individual sense when reading a polysemous word, rather than a core meaning.


title: In search of an understandable consensus algorithm

abstract: Raft is a consensus algorithm for managing a replicated log. It produces a result equivalent to (multi-)Paxos, and it is as efficient as Paxos, but its structure is different from Paxos; this makes Raft more understandable than Paxos and also provides a better foundation for building practical systems. In order to enhance understandability, Raft separates the key elements of consensus, such as leader election, log replication, and safety, and it enforces a stronger degree of coherency to reduce the number of states that must be considered. Results from a user study demonstrate that Raft is easier for students to learn than Paxos. Raft also includes a new mechanism for changing the cluster membership, which uses overlapping majorities to guarantee safety.


title: Plasmons in Graphene: Fundamental Properties and Potential Applications

abstract: Plasmons in graphene have intriguing fundamental properties and hold great potential for applications. They enable strong confinement of electromagnetic energy at subwavelength scales, which can be tuned and controlled via gate voltage, providing an advantage for graphene's plasmons over surface plasmons (SPs) on a metal-dielectric interface. They have been described for a large span of frequencies from terahertz up to infrared and even in the visible. We provide a critical review of the current knowledge of graphene plasmon properties (dispersion and linewidth) with particular emphasis on plasmonic losses and the competition between different decay channels, which are not yet fully understood. Plasmons in graphene provide an insight into interesting many-body effects such as those arising from the electron-phonon interaction and electron-electron interactions, including hybrid plasmon-phonon collective excitations (either with intrinsic or substrate phonons) and plasmarons. We provide a comparison of SPs on a metal-dielectric interface with plasmons in graphene and 2-D metallic monolayers. We finally outline the potential for graphene's plasmons for applications.


title: Evolving System Bottlenecks in the as a Service Cloud

abstract: The web ecosystem is rapidly evolving with changing business and functional models. Cloud platforms are available in a SaaS, PaaS and IaaS model designed around commoditized Linux based servers. 10 billion users will be online and accessing the web and its various content. The industry has seen a convergence around IP based technology. Additionally, Linux based designs allow for a system wide profiling of application characteristics. The customer is an OEM who provides Linux based servers for telecom solutions. The end customer will develop business applications on the server. Customers are interested in a latency profiling mechanism which helps them to understand how the application behaves at run time. The latency profiler is supposed to find the code path which makes an application block on I/O, and other synchronization primitives. This will allow the customer to understand the performance bottleneck and tune the system and application parameters.


title: Annual Research Review: A meta-analysis of the worldwide prevalence of mental disorders in children and adolescents

abstract: Background ::: The literature on the prevalence of mental disorders affecting children and adolescents has expanded significantly over the last three decades around the world. Despite the field having matured significantly, there has been no meta-analysis to calculate a worldwide-pooled prevalence and to empirically assess the sources of heterogeneity of estimates. ::: ::: Methods ::: We conducted a systematic review of the literature searching in PubMed, PsycINFO, and EMBASE for prevalence studies of mental disorders investigating probabilistic community samples of children and adolescents with standardized assessments methods that derive diagnoses according to the DSM or ICD. Meta-analytical techniques were used to estimate the prevalence rates of any mental disorder and individual diagnostic groups. A meta-regression analysis was performed to estimate the effect of population and sample characteristics, study methods, assessment procedures, and case definition in determining the heterogeneity of estimates. ::: ::: Results ::: We included 41 studies conducted in 27 countries from every world region. The worldwide-pooled prevalence of mental disorders was 13.4% (CI 95% 11.3–15.9). The worldwide prevalence of any anxiety disorder was 6.5% (CI 95% 4.7–9.1), any depressive disorder was 2.6% (CI 95% 1.7–3.9), attention-deficit hyperactivity disorder was 3.4% (CI 95% 2.6–4.5), and any disruptive disorder was 5.7% (CI 95% 4.0–8.1). Significant heterogeneity was detected for all pooled estimates. The multivariate metaregression analyses indicated that sample representativeness, sample frame, and diagnostic interview were significant moderators of prevalence estimates. Estimates did not vary as a function of geographic location of studies and year of data collection. The multivariate model explained 88.89% of prevalence heterogeneity, but residual heterogeneity was still significant. Additional meta-analysis detected significant pooled difference in prevalence rates according to requirement of funcional impairment for the diagnosis of mental disorders. ::: ::: Conclusions ::: Our findings suggest that mental disorders affect a significant number of children and adolescents worldwide. The pooled prevalence estimates and the identification of sources of heterogeneity have important implications to service, training, and research planning around the world.


title: An Experimental Investigation of the Tradeoff between Switching Losses and EMI Generation With Hard-Switched All-Si, Si-SiC, and All-SiC Device Combinations

abstract: Silicon carbide (SiC) switching power devices (MOSFETs, JFETs) of 1200 V rating are now commercially available, and in conjunction with SiC diodes, they offer substantially reduced switching losses relative to silicon (Si) insulated gate bipolar transistors (IGBTs) paired with fast-recovery diodes. Low-voltage industrial variable-speed drives are a key application for 1200 V devices, and there is great interest in the replacement of the Si IGBTs and diodes that presently dominate in this application with SiC-based devices. However, much of the performance benefit of SiC-based devices is due to their increased switching speeds ( di/dt, dv/ dt), which raises the issues of increased electromagnetic interference (EMI) generation and detrimental effects on the reliability of inverter-fed electrical machines. In this paper, the tradeoff between switching losses and the high-frequency spectral amplitude of the device switching waveforms is quantified experimentally for all-Si, Si-SiC, and all-SiC device combinations. While exploiting the full switching-speed capability of SiC-based devices results in significantly increased EMI generation, the all-SiC combination provides a 70% reduction in switching losses relative to all-Si when operated at comparable dv/dt. It is also shown that the loss-EMI tradeoff obtained with the Si-SiC device combination can be significantly improved by driving the IGBT with a modified gate voltage profile.


title: A Dynamic Histogram Equalization for Image Contrast Enhancement

abstract: In this paper, a smart contrast enhancement technique based on conventional histogram equalization (HE) algorithm is proposed. This dynamic histogram equalization (DHE) technique takes control over the effect of traditional HE so that it performs the enhancement of an image without making any loss of details in it. DHE partitions the image histogram based on local minima and assigns specific gray level ranges for each partition before equalizing them separately. These partitions further go though a repartitioning test to ensure the absence of any dominating portions. This method outperforms other present approaches by enhancing the contrast well without introducing severe side effects, such as washed out appearance, checkerboard effects etc., or undesirable artifacts.


title: Streptomyces competition and co-evolution in relation to plant disease suppression

abstract: Abstract High densities of antagonistic Streptomyces are associated with plant disease suppression in many soils. Here we review use of inoculation and organic matter amendments for enriching antagonistic Streptomyces populations to reduce plant disease and note that effective and consistent disease suppression in response to management has been elusive. We argue that shifting the focus of research from short-term disease suppression to the population ecology and evolutionary biology of antagonistic Streptomyces in soil will enhance prospects for effective management. A framework is presented for considering the impacts of short- and long-term management on competitive and coevolutionary dynamics among Streptomyces populations in relation to disease suppression.


title: Understanding cultural and linguistic barriers to health literacy.

abstract: Abstract Nurses today are providing care, education, and case management to an increasingly diverse patient population that is challenged with a triad of cultural, linguistic, and health literacy barriers. For these patients, culture and language set the context for the acquisition and application of health literacy skills. Yet the nursing literature offers minimal help in integrating cultural and linguistic considerations into nursing efforts to address patient health literacy. Nurses are in an ideal position to facilitate the interconnections between patient culture, language, and health literacy in order to improve health outcomes for culturally diverse patients. In this article the authors begin by describing key terms that serve as background for the ensuing discussion explaining how culture and language need to be considered in any interaction designed to address health literacy for culturally diverse patients. The authors then discuss the interrelationships between health literacy, culture, and language. Next relevant cultural constructs are introduced as additional background. This is followed by a description of how literacy skills are affected by culture and language, a note about culturally diverse, native-born patients, and a presentation of case examples illustrating how culture and language barriers are seen in patients' healthcare experiences. The authors conclude by offering recommendations for promoting health literacy in the presence of cultural and language barriers and noting the need for nursing interventions that fully integrate health literacy, culture, and language. Citation: Singleton, K., Krause, E., (Sept. 30, 2009) "Understanding Cultural and Linguistic Barriers to Health Literacy" OJIN: The Online Journal of Issues in Nursing. Vol. 14, No. 3, Manuscript 4. Available: www.nursingworld.org/MainMenuCategories/ANAMarketplace/ANAPeriodicals/OJIN/TableofContents/Voll42009/No 3Sept09/Cultural-and-Linguistic-Barriers.aspx Keywords: health literacy, culture, cultural competence, interpretation, limited English proficiency (LEP), linguistic competence, transcultural nursing, communication, racial and ethnic health disparities Low health literacy, cultural barriers, and limited English proficiency have been coined the "triple threat" to effective health communication by The Joint Commission (Schvve, 2007). Nurses, who work with patients from increasingly diverse cultural groups, experience daily how these three threats offer a challenge to the effective provision of care at the system, provider, and patient levels. Over the past 15 years healthcare providers in the United States (US) have begun to address two of these threats to effective care, namely culture and language, and to demonstrate a growing awareness of the need for culturally and linguistically competent healthcare (Campinha-Bacote, 2003; Lester, 1998a, 1998b; Lockhart & Resick, 1997; Maier-Lorentz, 2008; Racher & Annis, 2007; Rees & Ruiz, 2003; Silva, 1994; Smith, 1998). However, health literacy, both conceptually and in practice, has often been siloed from interventions designed to overcome cultural and linguistic barriers. Because health literacy is an emerging field, examination of culture and language as determinants of patient health literacy has been limited (Andrulis & Brach, 2007; Chang & Kelly, 2007; Nguyen & Bowman, 2007; Zanchetta & Poureslami, 2006). To-date, strategies to address health literacy have often been distinct from, and at times inconsistent with, strategies to increase culturally and linguistically competent care (Andrulis & Brach). Integrating cultural and linguistic consideration with health literacy necessitates an expanded paradigm. The purpose of this conceptual article is twofold. The first aim is to help nurses appreciate how culture and language can affect patient health literacy. The second aim is to demonstrate the need for nursing interventions that fully integrate health literacy, language, and culture. …


title: Evaluation of indoor positioning based on Bluetooth Smart technology

abstract: Nowadays, positioning technologies are used in a wide variety of areas such as providing driving directions and tracking valuable goods to name a few. Since the introduction of the global positioning system(GPS), it has become a de facto standard for outdoor positioning applications. In contrast to this, no similar widespread technique is available for indoor positioning or in areas where GPS is unavailable. Extensive research have ::: been devoted to exploring the topic based on a variety of technologies such as Wi-Fi, Bluetooth, Zigbee and ultra wideband radio. None of these have successfully made ::: it into a widely accepted standard, although signi cant progress has been made. In 2010 a completely new Bluetooth technology, referred to as "Bluetooth low energy" or "Bluetooth smart" was released that promises among other things ultra low power consumption, changed radio frequency properties and a completely new software stack. No academic work can be found that explores the usability of this new technology in the context of indoor positioning. This Master thesis is the rst project to evaluate the suitability and the applicability of this new technology in the context of indoor positioning. A selection of algorithms and approaches are explored, tested, evaluated ::: and compared in a testbed scenario designed for this purpose. Furthermore, the results of these tests are analyzed by examining the received signal strength indicator (RSSI) behaviour, which is the parameter used as basis for the positioning approaches. The evaluation shows that Bluetooth smart is a viable alternative for indoor positioning which o ffers widespread availability in society, reasonable accuracy and low cost deployment. ::: The report also provides essential advice on some important pitfalls when using the technology for positioning applications.


title: Joint Spectrum and Power Allocation for D2D Communications Underlaying Cellular Networks

abstract: This paper addresses the joint spectrum sharing and power allocation problem for device-to-device (D2D) communications underlaying a cellular network (CN). In the context of orthogonal frequency-division multiple-access systems, with the uplink resources shared with D2D links, both centralized and decentralized methods are proposed. Assuming global channel state information (CSI), the resource allocation problem is first formulated as a nonconvex optimization problem, which is solved using convex approximation techniques. We prove that the approximation method converges to a suboptimal solution and is often very close to the global optimal solution. On the other hand, by exploiting the decentralized network structure with only local CSI at each node, the Stackelberg game model is then adopted to devise a distributed resource allocation scheme. In this game-theoretic model, the base station (BS), which is modeled as the leader, coordinates the interference from the D2D transmission to the cellular users (CUs) by pricing the interference. Subsequently, the D2D pairs, as followers, compete for the spectrum in a noncooperative fashion. Sufficient conditions for the existence of the Nash equilibrium (NE) and the uniqueness of the solution are presented, and an iterative algorithm is proposed to solve the problem. In addition, the signaling overhead is compared between the centralized and decentralized schemes. Finally, numerical results are presented to verify the proposed schemes. It is shown that the distributed scheme is effective for the resource allocation and could protect the CUs with limited signaling overhead.


title: Everyone's an influencer: quantifying influence on twitter

abstract: In this paper we investigate the attributes and relative influence of 1.6M Twitter users by tracking 74 million diffusion events that took place on the Twitter follower graph over a two month interval in 2009. Unsurprisingly, we find that the largest cascades tend to be generated by users who have been influential in the past and who have a large number of followers. We also find that URLs that were rated more interesting and/or elicited more positive feelings by workers on Mechanical Turk were more likely to spread. In spite of these intuitive results, however, we find that predictions of which particular user or URL will generate large cascades are relatively unreliable. We conclude, therefore, that word-of-mouth diffusion can only be harnessed reliably by targeting large numbers of potential influencers, thereby capturing average effects. Finally, we consider a family of hypothetical marketing strategies, defined by the relative cost of identifying versus compensating potential "influencers." We find that although under some circumstances, the most influential users are also the most cost-effective, under a wide range of plausible assumptions the most cost-effective performance can be realized using "ordinary influencers"---individuals who exert average or even less-than-average influence.


title: The role of strategic leadership in effective strategy implementation: Perceptions of South African strategic leaders

abstract: A review of the literature reveals that strategy implementation is an important component of the strategic management process. Research indicates that the ability to implement a strategy is viewed as considerably more important than strategy formulation, and that strategy implementation, rather than strategy formulation, is the key to superior organisational performance. However, the high failure rate of strategy implementation efforts is well documented, and many barriers to effective strategy implementation exist. A lack of leadership, and specifically strategic leadership, at the top of the organisation has been identified as one of the major barriers to effective strategy implementation. In turn, strategic leadership is also viewed as a key driver to effective strategy implementation. In the light of the identified problem, the primary objective of this study was to investigate the perceived role of strategic leadership in strategy implementation in South African organisations. The conclusion is that strategic leadership positively contributes to effective strategy implementation in South African organisations.


title: Locally Scale-Invariant Convolutional Neural Networks

abstract: Convolutional Neural Networks (ConvNets) have shown excellent results on many visual classification tasks. With the exception of ImageNet, these datasets are carefully crafted such that objects are well-aligned at similar scales. Naturally, the feature learning problem gets more challenging as the amount of variation in the data increases, as the models have to learn to be invariant to certain changes in appearance. Recent results on the ImageNet dataset show that given enough data, ConvNets can learn such invariances producing very discriminative features [1]. But could we do more: use less parameters, less data, learn more discriminative features, if certain invariances were built into the learning process? In this paper we present a simple model that allows ConvNets to learn features in a locally scale-invariant manner without increasing the number of model parameters. We show on a modified MNIST dataset that when faced with scale variation, building in scale-invariance allows ConvNets to learn more discriminative features with reduced chances of over-fitting.


title: Barrage relay networks for cooperative transport in tactical MANETs

abstract: Barrage relay networks (BRNs) are mobile ad-hoc networks (MANETs) based on an autonomous cooperative communication scheme that allows any number of radios to cooperate in sending a given message to one or more receivers without the need for coordination. Messages in a BRN propagate outward in spatial waves wherein the simultaneous reception of a single message from multiple sources results in capture rather than collision, obviating the need for traditional collision avoidance mechanisms. The result is a very low-latency, robust broadcast mechanism. In addition to being valuable on its own (e.g., multi-hop push-to-talk voice), the broadcast capability of BRNs can be leveraged to rapidly establish paths for robust multi-hop unicast and multicast transmission, thereby enabling low-state reactive protocol design. The resulting ldquocontrolled barrage regionsrdquo (CBRs) comprise a set of buffer radio nodes that isolate a set of cooperating BRN radios. The effectiveness of BRN for broadcast and unicast is illustrated via simulations.


title: LINE: Large-scale Information Network Embedding

abstract: This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction. Most existing graph embedding methods do not scale for real world information networks which usually contain millions of nodes. In this paper, we propose a novel network embedding method called the ``LINE,'' which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted. The method optimizes a carefully designed objective function that preserves both the local and global network structures. An edge-sampling algorithm is proposed that addresses the limitation of the classical stochastic gradient descent and improves both the effectiveness and the efficiency of the inference. Empirical experiments prove the effectiveness of the LINE on a variety of real-world information networks, including language networks, social networks, and citation networks. The algorithm is very efficient, which is able to learn the embedding of a network with millions of vertices and billions of edges in a few hours on a typical single machine. The source code of the LINE is available online\footnote{\url{https://github.com/tangjianpku/LINE}}.


title: Three paths for learning analytics and beyond: moving from rhetoric to reality

abstract: There is growing rhetoric about the potential of learning analytics in higher education. There is concern about what the growing hype around learning analytics will mean for the reality. Will learning analytics be a repeat of past mistakes where technology implementations fail to move beyond a transitory fad and provide meaningful and sustained contributions to learning and teaching? How can such a fate be avoided? This paper identifies three paths that learning analytics implementations might take, with particular consideration to their likely impact on learning and teaching. An ongoing learning analytics project – currently used by hundreds of teaching staff to support early interventions to improve student retention - at a regional Australian university is examined in relation to the three paths, and some implications, challenges and future directions are discussed.


title: EyeTell: Video-Assisted Touchscreen Keystroke Inference from Eye Movements

abstract: Keystroke inference attacks pose an increasing threat to ubiquitous mobile devices. This paper presents EyeTell, a novel video-assisted attack that can infer a victim's keystrokes on his touchscreen device from a video capturing his eye movements. EyeTell explores the observation that human eyes naturally focus on and follow the keys they type, so a typing sequence on a soft keyboard results in a unique gaze trace of continuous eye movements. In contrast to prior work, EyeTell requires neither the attacker to visually observe the victim's inputting process nor the victim device to be placed on a static holder. Comprehensive experiments on iOS and Android devices confirm the high efficacy of EyeTell for inferring PINs, lock patterns, and English words under various environmental conditions.


title: Power-converter universal-controller design using STM32

abstract: This paper illustrates the use of an STM32 based digital command to control different types of converters. The process is made by generating signals that manage functionality of the power switchers for each converter. The universal controller allows choosing the desired power converter by means of a mounted micro-switcher.


title: Online shopping drivers and barriers for older adults: Age and gender differences

abstract: To increase a better understanding of the drivers and barriers affecting older consumers' intention to shop online.The major factors driving older adults toward online shopping are performance expectation and social influence.The major barriers that keep older adults away from shopping online include value and tradition. The use of the Internet by older adults is growing at a substantial rate. They are becoming an increasingly important potential market for electronic commerce. However, previous researchers and practitioners have focused mainly on the youth market and paid less attention to issues related to the online behaviors of older consumers. To bridge the gap, the purpose of this study is to increase a better understanding of the drivers and barriers affecting older consumers' intention to shop online. To this end, this study is developed by integrating the Unified Theory of Acceptance and Use of Technology (UTAUT) and innovation resistance theory. By comparing younger consumers with their older counterparts, in terms of gender the findings indicate that the major factors driving older adults toward online shopping are performance expectation and social influence which is the same with younger. On the other hand, the major barriers include value, risk, and tradition which is different from younger. Consequently, it is notable that older adults show no gender differences in regards to the drivers and barriers.


title: DBSCAN: Past, present and future

abstract: Data Mining is all about data analysis techniques. It is useful for extracting hidden and interesting patterns from large datasets. Clustering techniques are important when it comes to extracting knowledge from large amount of spatial data collected from various applications including GIS, satellite images, X-ray crystallography, remote sensing and environmental assessment and planning etc. To extract useful pattern from these complex data sources several popular spatial data clustering techniques have been proposed. DBSCAN (Density Based Spatial Clustering of Applications with Noise) is a pioneer density based algorithm. It can discover clusters of any arbitrary shape and size in databases containing even noise and outliers. DBSCAN however are known to have a number of problems such as: (a) it requires user's input to specify parameter values for executing the algorithm; (b) it is prone to dilemma in deciding meaningful clusters from datasets with varying densities; (c) and it incurs certain computational complexity. Many researchers attempted to enhance the basic DBSCAN algorithm, in order to overcome these drawbacks, such as VDBSCAN, FDBSCAN, DD_DBSCAN, and IDBSCAN. In this study, we survey over different variations of DBSCAN algorithms that were proposed so far. These variations are critically evaluated and their limitations are also listed.


title: Analysis of security data from a large computing organization

abstract: This paper presents an in-depth study of the forensic data on security incidents that have occurred over a period of 5 years at the National Center for Supercomputing Applications at the University of Illinois. The proposed methodology combines automated analysis of data from security monitors and system logs with human expertise to extract and process relevant data in order to: (i) determine the progression of an attack, (ii) establish incident categories and characterize their severity, (iii) associate alerts with incidents, and (iv) identify incidents missed by the monitoring tools and examine the reasons for the escapes. The analysis conducted provides the basis for incident modeling and design of new techniques for security monitoring.


title: Swarm Intelligence: Foundations, Perspectives and Applications

abstract: Swarm Intelligence (SI) is an innovative distributed intelligent paradigm for solving optimization problems that originally took its inspiration from the biological examples by swarming, flocking and herding phenomena in vertebrates. Particle Swarm Optimization (PSO) incorporates swarming behaviors observed in flocks of birds, schools of fish, or swarms of bees, and even human social behavior, from which the idea is emerged [14, 7, 22]. PSO is a population-based optimization tool, which could be implemented and applied easily to solve various function optimization problems, or the problems that can be transformed to function optimization problems. As an algorithm, the main strength of PSO is its fast convergence, which compares favorably with many global optimization algorithms like Genetic Algorithms (GA) [13], Simulated Annealing (SA) [20, 27] and other global optimization algorithms. For applying PSO successfully, one of the key issues is finding how to map the problem solution into the PSO particle, which directly affects its feasibility and performance. Ant Colony Optimization (ACO) deals with artificial systems that is inspired from the foraging behavior of real ants, which are used to solve discrete


title: New Recommendation Techniques for Multicriteria Rating Systems

abstract: Personalization technologies and recommender systems help online consumers avoid information overload by making suggestions regarding which information is most relevant to them. Most online shopping sites and many other applications now use recommender systems. Two new recommendation techniques leverage multicriteria ratings and improve recommendation accuracy as compared with single-rating recommendation approaches. Taking full advantage of multicriteria ratings in personalization applications requires new recommendation techniques. In this article, we propose several new techniques for extending recommendation technologies to incorporate and leverage multicriteria rating information.


Teleoperator evolution at CERN


title: Hand gesture recognition with depth images: A review

abstract: This paper presents a literature review on the use of depth for hand tracking and gesture recognition. The survey examines 37 papers describing depth-based gesture recognition systems in terms of (1) the hand localization and gesture classification methods developed and used, (2) the applications where gesture recognition has been tested, and (3) the effects of the low-cost Kinect and OpenNI software libraries on gesture recognition research. The survey is organized around a novel model of the hand gesture recognition process. In the reviewed literature, 13 methods were found for hand localization and 11 were found for gesture classification. 24 of the papers included real-world applications to test a gesture recognition system, but only 8 application categories were found (and three applications accounted for 18 of the papers). The papers that use the Kinect and the OpenNI libraries for hand tracking tend to focus more on applications than on localization and classification methods, and show that the OpenNI hand tracking method is good enough for the applications tested thus far. However, the limitations of the Kinect and other depth sensors for gesture recognition have yet to be tested in challenging applications and environments.


title: Youth unemployment in Australia

abstract: Every year a large number of young Australians enter the labour market. But they quickly discover that getting a job - or even maintaining one - is not straightforward. And worse, the prospect is not getting easier. Youth unemployment has progressively taken a hit after the latest Global Financial Crisis (GFC), practically doubling since mid-2008. At an average rate of 13.5% in the past 12 months, the jobless rate among those aged 15 to 24 has reached worrying levels once thought to be left behind in a distant past. Currently, there are around 300,000 youth unemployed in Australia, accounting for over a third of total unemployment.


title: A fast and efficient projection-based approach for surface reconstruction

abstract: We present a fast and memory efficient algorithm that generates a manifold triangular mesh S with or without boundary passing through a set of unorganized points P/spl sub//spl Rscr//sup 3/ with no other additional information. Nothing is assumed about the geometry or topology of the sampled manifold model, except for its reasonable smoothness. The speed of our algorithm is derived from a projection-based approach we use to determine the incident faces on a point. Our algorithm has successfully reconstructed the surfaces of unorganized point clouds of sizes varying from 10,000 to 100,000 in about 3-30 seconds on a 250 MHz, R10000 SGI Onyx2. Our technique can be specialized for different kinds of input and applications. For example, our algorithm can be specialized to handle data from height fields like terrain and range scan, even in the presence of noise. We have successfully generated meshes for range scan data of size 900,000 points in less than 40 seconds.


Traffic Sign Recognition Using Neural Network on OpenCV : Toward Intelligent Vehicle / Driver Assistance System


A Software Framework for Musical Data Augmentation


title: Design of slotted H-shaped patch antenna for 2.4 GHz WLAN applications

abstract: Microstrip patch antennas are widely used in various wireless applications such as Wireless local area network (WLAN). WLAN is capable of providing maximum data transfer rate of 600 Mbps. The proposed antenna design has a slotted H-shaped patch and dumbbell (H) shaped Defected Ground Structure (DGS) on Flame Retardant-4 (FR-4) substrate with the microstrip feeding technique which radiates at 2.4GHz WLAN frequency. The overall size of the antenna is 39×47×1.56 mm. The simulation results are obtained using Advanced design software (ADS) which offers improved antenna parameters such as gain, directivity, bandwidth, return loss, voltage standing wave ratio (VSWR) and impedance matching.


title: Optimization tool for direct water cooling system of high power IGBT modules

abstract: Thermal management of power electronic devices is essential for reliable system performance especially at high power levels. Since even the most efficient electronic circuit becomes hot because of ohmic losses, it is clear that cooling is needed in electronics and even more as the power increases. One of the most important activities in the thermal management and reliability improvement is the cooling system design. As industries are developing smaller power devices with higher power densities, optimized design of cooling systems with minimum thermal resistance and pressure drop become important issue for thermal design engineers. This paper aims to present a user friendly optimization tool for direct water cooling system of a high power module which enables the cooling system designer to identify the optimized solution depending on customer load profiles and available pump power. CFD simulations are implemented to find best solution for each scenario.


title: Sham neurosurgical procedures in clinical trials for neurodegenerative diseases: scientific and ethical considerations

abstract: There have been several recent scientific advances in gene-based and cell-based therapies that might translate into novel therapeutic approaches for neurodegenerative disorders. Such therapies might need to be directly delivered into the CNS, and complex scientific and ethical assessment will be needed to determine whether a sham neurosurgical arm should be included in clinical trials assessing these agents. We have developed a framework of points for investigators to consider when designing trials that involve direct delivery of a therapeutic agent to the CNS. The inclusion of a sham neurosurgical arm will be guided in part by the objectives of the clinical study (preliminary safety, optimisation, and feasibility vs preliminary efficacy vs confirmatory efficacy) and the need to minimise bias and confounds. Throughout the clinical development process, the perspectives of researchers, ethicists, and patients must be considered, and risks should be minimised whenever possible in a manner that is consistent with good trial design.


title: Task versus relationship conflict, team performance, and team member satisfaction: A meta-analysis.

abstract: This study provides a meta-analysis of research on the associations between relationship conflict, task conflict, team performance, and team member satisfaction. Consistent with past theorizing, resultsrevealed strong and negative correlations between relationship conflict, team performance, and team member satisfaction. In contrast to what has been suggested in both academic research and introductorytextbooks, however, results also revealed strong and negative (instead of the predicted positive) correlations between task conflict, team performance, and team member satisfaction. As predicted, conflict had stronger negative relations with team performance in highly complex (decision making, project, mixed) than in less complex (production) tasks. Finally, task conflict was less negatively related to team performance when task conflict and relationship conflict were weakly, rather than strongly, correlated.


title: Event causality extraction based on connectives analysis

abstract: Causality is an important type of relation which is crucial in numerous tasks, such as predicting future events, generating scenario, question answering, textual entailment and discourse comprehension. Therefore, causality extraction is a fundamental task in text mining. Many efforts have been dedicated to extracting causality from texts utilizing patterns, constraints and machine learning techniques. This paper presents a new Restricted Hidden Naive Bayes model to extract causality from texts. Besides some commonly used features, such as contextual features, syntactic features, position features, we also utilize a new category feature of causal connectives. This new feature is obtained from the tree kernel similarity of sentences containing connectives. In previous studies, the features have been usually assumed to be independent, which is not the case in reality. The advantage of our model lies in its ability to cope with partial interactions among features so as to avoid over-fitting problem on Hidden Naive Bayes model, especially the interaction between the connective category and the syntactic structure of sentences. Evaluation on a public dataset shows that our method goes beyond all the baselines.


title: FMCW MIMO Radar System for Frequency-Division Multiple TX-Beamforming

abstract: In this paper, a prototype automotive radar sensor is presented that is capable of generating simultaneously multiple transmit (TX) beams. The system is based on a four-channel 77-GHz frequency-modulated continuous-wave (FMCW) radar system. The number of beams, their radiated power, steering angle, and beam pattern can be changed adaptively. This is achieved by the utilization of orthogonal waveforms applied to different beams in combination with digital beamforming on the receive side. Key components are vector modulators in the TX path controlled by digital-to-analog converters. The performance of the system is shown in measurements focused on beam pattern, signal-to-noise ratio, and susceptibility in case of interfering targets at cross-range. Measurement results are discussed and compared to theory and simulations. Furthermore, crest factor minimization of the vector modulator's control signals is introduced and used to increase the achievable TX power, which will be also shown in measurements.


title: Load-balanced clustering of wireless sensor networks

abstract: Wireless sensor networks have potential to monitor environments for both military and civil applications. Due to inhospitable conditions these sensors are not always deployed uniformly ion the area of interest. Since sensors are generally constrained in on-board energy supply, efficient management of the network is crucial to extend the life of the sensors. Sensors' energy cannot support long haul communication to reach a remote command site and thus requires many levels of hops or a gateway to forward the data on behalf of the sensor. In this paper, we propose an algorithm to network these sensors in to well define clusters with less energy-constrained gateway nodes acting as cluster-heads, and balance load among these gateways. Simulation results show how our approach can balance the load and improve the lifetime of the system.


title: A Survey on Smart Grid Communication Infrastructures: Motivations, Requirements and Challenges

abstract: A communication infrastructure is an essential part to the success of the emerging smart grid. A scalable and pervasive communication infrastructure is crucial in both construction and operation of a smart grid. In this paper, we present the background and motivation of communication infrastructures in smart grid systems. We also summarize major requirements that smart grid communications must meet. From the experience of several industrial trials on smart grid with communication infrastructures, we expect that the traditional carbon fuel based power plants can cooperate with emerging distributed renewable energy such as wind, solar, etc, to reduce the carbon fuel consumption and consequent green house gas such as carbon dioxide emission. The consumers can minimize their expense on energy by adjusting their intelligent home appliance operations to avoid the peak hours and utilize the renewable energy instead. We further explore the challenges for a communication infrastructure as the part of a complex smart grid system. Since a smart grid system might have over millions of consumers and devices, the demand of its reliability and security is extremely critical. Through a communication infrastructure, a smart grid can improve power reliability and quality to eliminate electricity blackout. Security is a challenging issue since the on-going smart grid systems facing increasing vulnerabilities as more and more automation, remote monitoring/controlling and supervision entities are interconnected.


title: Joint pose estimation and action recognition in image graphs

abstract: Human analysis in images and video is a hard problem due to the large variation in human pose, clothing, camera view-points, lighting and other factors. While the explicit modeling of this variability is difficult, the huge amount of available person images motivates for the implicit, data-driven approach to human analysis. In this work we aim to explore this approach using the large amount of images spanning a subspace of human appearance. We model this subspace by connecting images into a graph and propagating information through such a graph using a discriminatively-trained graphical model. We particularly address the problems of human pose estimation and action recognition and demonstrate how image graphs help solving these problems jointly. We report results on still images with human actions from the KTH dataset.


title: Expected Policy Gradients

abstract: We propose expected policy gradients (EPG), which unify stochastic policy gradients (SPG) and deterministic policy gradients (DPG) for reinforcement learning. Inspired by expected sarsa, EPG integrates across the action when estimating the gradient, instead of relying only on the action in the sampled trajectory. We establish a new general policy gradient theorem, of which the stochastic and deterministic policy gradient theorems are special cases. We also prove that EPG reduces the variance of the gradient estimates without requiring deterministic policies and, for the Gaussian case, with no computational overhead. Finally, we show that it is optimal in a certain sense to explore with a Gaussian policy such that the covariance is proportional to the exponential of the scaled Hessian of the critic with respect to the actions. We present empirical results confirming that this new form of exploration substantially outperforms DPG with the Ornstein-Uhlenbeck heuristic in four challenging MuJoCo domains.


title: QuickCut: An Interactive Tool for Editing Narrated Video

abstract: We present QuickCut, an interactive video editing tool designed to help authors efficiently edit narrated video. QuickCut takes an audio recording of the narration voiceover and a collection of raw video footage as input. Users then review the raw footage and provide spoken annotations describing the relevant actions and objects in the scene. QuickCut time-aligns a transcript of the annotations with the raw footage and a transcript of the narration to the voiceover. These aligned transcripts enable authors to quickly match story events in the narration with semantically relevant video segments and form alignment constraints between them. Given a set of such constraints, QuickCut applies dynamic programming optimization to choose frame-level cut points between the video segments while maintaining alignments with the narration and adhering to low-level film editing guidelines. We demonstrate QuickCut's effectiveness by using it to generate a variety of short (less than 2 minutes) narrated videos. Each result required between 14 and 52 minutes of user time to edit (i.e. between 8 and 31 minutes for each minute of output video), which is far less than typical authoring times with existing video editing workflows.


Analysis of Medical Image Processing and its Applications in Healthcare Industry


title: Improved document image segmentation algorithm using multiresolution morphology

abstract: Page segmentation into text and non-text elements is an essential preprocessing step before optical character ::: recognition (OCR) operation. In case of poor segmentation, an OCR classification engine produces garbage ::: characters due to the presence of non-text elements. This paper describes modifications to the text/non-text ::: segmentation algorithm presented by Bloomberg, 1 which is also available in his open-source Leptonica library. 2 The modifications result in significant improvements and achieved better segmentation accuracy than the original ::: algorithm for UW-III, UNLV, ICDAR 2009 page segmentation competition test images and circuit diagram ::: datasets.


title: Alternating Direction Method of Multipliers for Sparse Convolutional Neural Networks

abstract: The storage and computation requirements of Convolutional Neural Networks (CNNs) can be prohibitive for exploiting these models over low-power or embedded devices. This paper reduces the computational complexity of the CNNs by minimizing an objective function, including the recognition loss that is augmented with a sparsity-promoting penalty term. The sparsity structure of the network is identified using the Alternating Direction Method of Multipliers (ADMM), which is widely used in large optimization problems. This method alternates between promoting the sparsity of the network and optimizing the recognition performance, which allows us to exploit the two-part structure of the corresponding objective functions. In particular, we take advantage of the separability of the sparsity-inducing penalty functions to decompose the minimization problem into sub-problems that can be solved sequentially. Applying our method to a variety of state-of-the-art CNN models, our proposed method is able to simplify the original model, generating models with less computation and fewer parameters, while maintaining and often improving generalization performance. Accomplishments on a variety of models strongly verify that our proposed ADMM-based method can be a very useful tool for simplifying and improving deep CNNs.


title: Web user clustering analysis based on KMeans algorithm

abstract: As one of the most important tasks of Web Usage Mining (WUM), web user clustering, which establishes groups of users exhibiting similar browsing patterns, provides useful knowledge to personalized web services. In this paper, we cluster web users with KMeans algorithm based on web user log data. Given a set of web users and their associated historical web usage data, we study their behavior characteristic and cluster them. Experiment results show the feasibility and efficiency of such algorithm application. Web user clusters generated in this way can provide novel and useful knowledge for various personalized web applications.


title: A Multimodal Analysis of Floor Control in Meetings

abstract: The participant in a human-to-human communication who controls the floor bears the burden of moving the communication process along. Change in control of the floor can happen through a number of mechanisms, including interruptions, delegation of the floor, and so on. This paper investigates floor control in multiparty meetings that are both audio and video taped; hence, we are able to analyze patterns not only of speech (e.g., discourse markers) but also of visual cues (e.g, eye gaze exchanges) that are commonly involved in floor control changes. Identifying who has control of the floor provides an important focus for information retrieval and summarization of meetings. Additionally, without understanding who has control of the floor, it is impossible to identify important events such as challenges for the floor. In this paper, we analyze multimodal cues related to floor control in two different meetings involving five participants each.