title: Adrenal function and MC1R gene analysis in a prepubertal girl with generalized hyperpigmentation: case report

abstract: Generalized hyperpigmentation (GHPT) of the skin may occur as a primary defect of pigmentation or in combination with other variable manifestations. It is visible in a number of diseases such as Addison's disease (AD), haemochromatosis, porphyria cutanea tarda, scleroderma and neurofibromatosis, but it can also be associated with malignancy and the use of chemotherapeutics or it can be related to acanthosis nigricans in insulin resistance. Skin pigmentation depends on the differences in the amount, type and distribution of melanin produced during melanogenesis in skin melanocytes [1] and remains under the genetic control of more than 120 genes [2]. The most important one is the melanocortin 1 receptor (MC1R) gene [3] (OMIM ID: 155555) located on chromosome 16q24.3 and encoding for a 317-amino-acid G-protein coupled receptor. The MC1R receptor binds α-melanocyte-stimulating hormone (α-MSH) resulting in the activation of adenylyl cyclase, which produces cyclic adenosine monophosphate (cAMP). The increased cAMP concentration activates various intracellular molecular pathways, promotes melanin synthesis and increases the eumelanin to pheomelanin ratio [4]. MC1R receptor also binds ACTH, in this way contributing to the GHPT in AD. ::: ::: Upregulation of MC1R gene expression by UV radiation and α-MSH leads to enhancement of melanogenesis and melanin synthesis induction. Loss-of-function mutations in the MC1R gene are associated with fair skin, poor tanning, propensity to freckles and increased skin cancer risk due to a decrease in eumelanin synthesis and subsequently impaired protection against UV radiation [5–7]. To our knowledge, to date, no data are available considering gain-of-function mutations in the human MC1R gene which could lead to a constant activation of the MC1R receptor and subsequently cause GHPT. ::: ::: We present the case of a patient with a primary type of progressive GHPT in whom AD was suspected. ::: ::: An 11-year-old prepubertal girl with GHPT (Figures 1A--C)C) was born at term with normal birth weight and height and was first brought to our hospital at the age of 3 years with a suspicion of AD. She had a diffuse grey-brownish discoloration of the skin present since birth. Over the first few years of life she developed symmetrical hyperpigmentation most pronounced on her trunk and neck. Later, hyperpigmentation began to affect her hands and feet, and finally the whole body – sparing only the cheeks and finger tips. Her skin was very dry and atopic, and scars were not hyperpigmented. Her toenails as well as fingernails had no major alterations. The girl also experienced chronic diarrhoea, once a month on average, that started at the age of 10-12 months, always considered as allergic diarrhoea. She did not have any history of repeating infections. Her mother also reported a strong ammonia odour of the girl's urine. The remainder of the physical examination, mental development, abdominal ultrasound and magnetic resonance imaging (MRI) of the head were unremarkable. The girl's height and weight both are on the 25th percentile and the onset of puberty was noticed (thelarche Tanner 2). Metabolic diseases were ruled out by serum and urine analysis. Ophthalmologic and neurologic examinations showed no abnormalities. Familial pedigree analysis did not show any similar cases in the patient's family. The adrenocorticotropin (ACTH) stimulation test was performed several times by intravenous injection of synthetic ACTH (Table I). Cortisol levels were normal after ACTH stimulation, thus excluding AD. The patient acquired a chickenpox infection which presented as small white, unpigmented spots on the whole body, but hyperpigmentation was still present, especially on the girl's neck and trunk. A histopathological examination of a skin biopsy from the buttock revealed a strong pigmentation of the epidermal basal layer and melanin aggregates within melanophages of the stratum papillare. The number of melanocytes within the epidermis was unchanged and melanosis diffusa congenita was diagnosed (OMIM ID: 145250). ::: ::: ::: ::: Figure 1 ::: ::: The proband at the age of 11 manifesting a clinical feature of generalized hyperpigmentation (A) with the most intensive pigmentation on her trunk (A, B), neck (B), extremities (A, C) and hands (C) ::: ::: ::: ::: ::: ::: Table I ::: ::: Laboratory tests in blood samples (abnormal values are given in bold). A single measurement of oestradiol was elevated at the age of 3 years but with no breast development. Slightly elevated ACTH level at the age of 8 years was probably a result of the ... ::: ::: ::: ::: Written informed consent was obtained from parents and the study was approved by the local Ethics Committee of Poznan University of Medical Sciences. Blood samples were collected and frozen at –20°C until analysis. All laboratory tests were measured in the Central Laboratory of K. Jonscher's Clinical Hospital of the University using commercial kits. Luteinizing hormone (LH), follicle-stimulating hormone (FSH), thyroid-stimulating hormone (TSH), parathyroid hormone (PTH), prolactin, estradiol, free thyroxine (fT4) and insulin were assayed by MEIA, thyroid peroxidase antibody (TPO-Ab), 21-hydroxylase-antibody (21-hydroxylase-Ab), cortisol, aldosterone, dehydroepiandrosterone sulphate (DHEA-S) and C-peptide by RIA, and adrenocorticotropic hormone (ACTH) IRMA methods. ::: ::: Genomic DNA was isolated from peripheral blood leukocytes using QIAamp® DNA Blood Mini Kit (QIAGEN). Primer sequences used to amplify the MC1R gene as well as polymerase chain reaction (PCR) conditions are available on request. The samples were subjected to direct sequencing using BigDye Terminator v3.1 cycle sequencing kit (Applied Biosystems) and an ABI Prism 3130XL Genetic Analyzer (Applied Biosystems). We did not find any mutations within the whole MC1R gene. ::: ::: The most likely diagnosis of the patient with GHPT is primary adrenal insufficiency, subsequent ACTH excess and overstimulation of MC1R in the skin. Recurrent episodes of diarrhoea in the patient could suggest an adrenal crisis but several adrenal function tests, also performed during such episodes, excluded adrenal failure. ::: ::: Although we did not find any mutations in the coding region of the MC1R gene, or in the untranslated regions or the promoter region, it is possible that disorders in other genes are responsible for the skin pigmentation phenotype in our patient. Branicki et al. showed that HERC2 rs12913832 may be one of the genes responsible for masking the effect of MC1R polymorphisms and significantly affect the function of the MC1R receptor [8]. The phenotype of our patient was characterized by diffuse GHPT, which varied slightly in intensity in different parts of her body. Cafe-au-lait macules or larger hypopigmented macules were not present. This is contrary to patients previously described in the literature [9]. Therefore, we concluded that the pathogenesis is restricted only to melanogenesis-related functions in the skin. ::: ::: Familial progressive hyperpigmentation (FPH, OMIM ID: 145250) is a rare congenital diffuse hyperpigmentation disorder affecting the human skin. Familial progressive hyperpigmentation is inherited in an autosomally dominant [9] or recessive [10] manner and is characterized by patches of hyperpigmentation present at birth or in early infancy that increase in size and also in number with age. Ultimately, a large percentage of the skin becomes hyperpigmented. These skin changes always occur on the face, neck, trunk, limbs, lips, oral mucosa and on the palms and soles, but this is not the case for our patient, whose lips, oral mucosa and conjunctiva were not hyperpigmented. Histological examination reveals an increase in the amount of melanin pigment found throughout the epidermis, specifically within the stratum corneum. The skin is the only affected organ and patients do not normally suffer from any other systemic disease [11, 12]. The molecular background of the disease still remains unknown. Zhang et al. examined a three-generation Chinese family consisting of 17 individuals, 6 of whom were affected, including 3 males and 3 females. Contrary to the previous study and our patient, the first onset of the disease in this family was at the age of 5 years, not at birth. All 6 affected members had typical clinical manifestations of FPH. They had normal mental abilities and no systemic disease such as gastrointestinal, liver or kidney ailments. The pedigree of the family showed an autosomal dominant pattern of FPH inheritance [13]. This study gives new potential insight into the plausible bipotential role of ACTH in both adrenal stimulation via MC2R and in skin pigmentation via MC1R, both transduction processes effectively activated at different plasma concentrations of ACTH. ::: ::: Because there were no disease-causing gain-of-function mutations in the MC1R gene, we concluded that this gene is not responsible for the hyperpigmented phenotype in our patient. Further studies of other candidate genes crucial for human skin pigmentation as well as genetic analysis of the selected SNP polymorphisms will help us to understand the molecular background of the GHPT disorder present in our case.


title: Augmented Lagrangian and Alternating Direction Methods for Convex Optimization : A Tutorial and Some Illustrative Computational Results

abstract: The alternating direction of multipliers (ADMM) is a form of augmented Lagrangian algorithm that has experienced a renaissance in recent years due to its applicability to optimization problems arising from “big data” and image processing applications, and the relative ease with which it may be implemented in parallel and distributed computational environments. This chapter aims to provide an accessible introduction to the analytical underpinnings of the method, which are often obscured in treatments that do not assume knowledge of convex and set-valued analysis. In particular, it is tempting to view the method as an approximate version of the classical augmented Lagrangian algorithm, using one pass of block coordinate minimization to approximately minimize the augmented Lagrangian at each iteration. This chapter, assuming as little prior knowledge of convex analysis as possible, shows that the actual convergence mechanism of the algorithm is quite different, and then underscores this observations with some new computational results in which we compare the ADMM to algorithms that do indeed work by approximately minimizing the augmented Lagrangian. Acknowledgements: This material is based in part upon work supported by the National Science Foundation under Grant CCF-1115638. The author also thanks his student Wang Yao for his work on the computational experiments.Please insert the acknowledgement here.


title: Avoiding the disk bottleneck in the data domain deduplication file system

abstract: Disk-based deduplication storage has emerged as the new-generation storage system for enterprise data protection to replace tape libraries. Deduplication removes redundant data segments to compress data into a highly compact form and makes it economical to store backups on disk instead of tape. A crucial requirement for enterprise data protection is high throughput, typically over 100 MB/sec, which enables backups to complete quickly. A significant challenge is to identify and eliminate duplicate data segments at this rate on a low-cost system that cannot afford enough RAM to store an index of the stored segments and may be forced to access an on-disk index for every input segment. ::: ::: This paper describes three techniques employed in the production Data Domain deduplication file system to relieve the disk bottleneck. These techniques include: (1) the Summary Vector, a compact in-memory data structure for identifying new segments; (2) Stream-Informed Segment Layout, a data layout method to improve on-disk locality for sequentially accessed segments; and (3) Locality Preserved Caching, which maintains the locality of the fingerprints of duplicate segments to achieve high cache hit ratios. Together, they can remove 99% of the disk accesses for deduplication of real world workloads. These techniques enable a modern two-socket dual-core system to run at 90% CPU utilization with only one shelf of 15 disks and achieve 100 MB/sec for single-stream throughput and 210 MB/sec for multi-stream throughput.


title: Investigating event log analysis with minimum apriori information

abstract: This thesis proposes a hybrid log alert detection scheme, which incorporates anomaly detection and signature generation to accomplish its goal. Unlike previous work, minimum apriori knowledge of the system being analyzed is assumed. This assumption enhances the platform portability of the framework. The anomaly detection component works in a bottom-up manner on the contents of historical system log data to detect regions of the log, which contain anomalous (alert) behaviour. The identified anomalous regions (after inspection by a human administrator through a visualization system) are then passed to the signature generation component, which mines them for patterns. Consequently, future occurrences of the underlying alert in the anomalous log region, can be detected on a production system using the discovered patterns. The combination of anomaly detection and signature generation, which is novel when compared to previous work, ensures that a framework which is accurate while still being able to detect new and unknown alerts is attained.


title: Therapeutic effectiveness of instrument-assisted soft tissue mobilization for soft tissue injury: mechanisms and practical application

abstract: This article reviews the mechanism and effects of instrument-assisted soft tissue mobilization (IASTM), along with guidelines for its practical application. IASTM refers to a technique that uses instruments to remove scar tissues from injured soft tissues and facilitate healing process through formation of new extracellular matrix proteins such as collagen. Recently, frequent use of this instrument has increased in the fields of sports rehabilitation and athlete training. Some experimental studies and case reports have reported that IASTM can significantly improve soft tissue function and range of motion following sports injury, while also reducing pain. Based on the previous studies, it is thought that IASTM can help shorten the rehabilitation period and time to return to sports among athletes and ordinary people who have suffered sports injuries. However, few experimental studies of the mechanisms and effects of IASTM have examined, while case reports have accounted for the majority of articles. In the future, the scientific basis of IASTM and its reliability should be provided through well-designed experimental studies on humans. Moreover, IASTM studies that have mostly focused on tendons need to broaden their scope toward other soft tissues such as muscles and ligaments.


title: A Growing Long-term Episodic&Semantic Memory

abstract: The long-term memory of most connectionist systems lies entirely in the weights of the system. Since the number of weights is typically fixed, this bounds the total amount of knowledge that can be learned and stored. Though this is not normally a problem for a neural network designed for a specific task, such a bound is undesirable for a system that continually learns over an open range of domains. To address this, we describe a lifelong learning system that leverages a fast, though non-differentiable, content-addressable memory which can be exploited to encode both a long history of sequential episodic knowledge and semantic knowledge over many episodes for an unbounded number of domains. This opens the door for investigation into transfer learning, and leveraging prior knowledge that has been learned over a lifetime of experiences to new domains.


title: k-Sparse Autoencoders

abstract: Recently, it has been observed that when representations are learnt in a way that encourages sparsity, improved performance is obtained on classification tasks. These methods involve combinations of activation functions, sampling steps and different kinds of penalties. To investigate the effectiveness of sparsity by itself, we propose the k-sparse autoencoder, which is an autoencoder with linear activation function, where in hidden layers only the k highest activities are kept. When applied to the MNIST and NORB datasets, we find that this method achieves better classification results than denoising autoencoders, networks trained with dropout, and RBMs. k-sparse autoencoders are simple to train and the encoding stage is very fast, making them well-suited to large problem sizes, where conventional sparse coding algorithms cannot be applied.


title: A prioritized grid long short-term memory RNN for speech recognition

abstract: Recurrent neural networks (RNNs) are naturally suitable for speech recognition because of their ability of utilizing dynamically changing temporal information. Deep RNNs have been argued to be able to model temporal relationships at different time granularities, but suffer vanishing gradient problems. In this paper, we extend stacked long short-term memory (LSTM) RNNs by using grid LSTM blocks that formulate computation along not only the temporal dimension, but also the depth dimension, in order to alleviate this issue. Moreover, we prioritize the depth dimension over the temporal one to provide the depth dimension more updated information, since the output from it will be used for classification. We call this model the prioritized Grid LSTM (pGLSTM). Extensive experiments on four large datasets (AMI, HKUST, GALE, and MGB) indicate that the pGLSTM outperforms alternative deep LSTM models, beating stacked LSTMs with 4% to 7% relative improvement, and achieve new benchmarks among uni-directional models on all datasets.


title: Progressive Boosting for Class Imbalance

abstract: Pattern recognition applications often suffer from skewed data distributions between classes, which may vary during operations w.r.t. the design data. Two-class classification systems designed using skewed data tend to recognize the majority class better than the minority class of interest. Several data-level techniques have been proposed to alleviate this issue by up-sampling minority samples or under-sampling majority samples. However, some informative samples may be neglected by random under-sampling and adding synthetic positive samples through up-sampling adds to training complexity. In this paper, a new ensemble learning algorithm called Progressive Boosting (PBoost) is proposed that progressively inserts uncorrelated groups of samples into a Boosting procedure to avoid loss of information while generating a diverse pool of classifiers. Base classifiers in this ensemble are generated from one iteration to the next, using subsets from a validation set that grows gradually in size and imbalance. Consequently, PBoost is more robust to unknown and variable levels of skew in operational data, and has lower computation complexity than Boosting ensembles in literature. In PBoost, a new loss factor is proposed to avoid bias of performance towards the negative class. Using this loss factor, the weight update of samples and classifier contribution in final predictions are set based on the ability to recognize both classes. Using the proposed loss factor instead of standard accuracy can avoid biasing performance in any Boosting ensemble. The proposed approach was validated and compared using synthetic data, videos from the FIA dataset that emulates face re-identification applications, and KEEL collection of datasets. Results show that PBoost can outperform state of the art techniques in terms of both accuracy and complexity over different levels of imbalance and overlap between classes.


title: Automatic Argumentative-Zoning Using Word2vec

abstract: In comparison with document summarization on the articles from social media and newswire, argumentative zoning (AZ) is an important task in scientific paper analysis. Traditional methodology to carry on this task relies on feature engineering from different levels. In this paper, three models of generating sentence vectors for the task of sentence classification were explored and compared. The proposed approach builds sentence representations using learned embeddings based on neural network. The learned word embeddings formed a feature space, to which the examined sentence is mapped to. Those features are input into the classifiers for supervised classification. Using 10-cross-validation scheme, evaluation was conducted on the Argumentative-Zoning (AZ) annotated articles. The results showed that simply averaging the word vectors in a sentence works better than the paragraph to vector algorithm and by integrating specific cuewords into the loss function of the neural network can improve the classification performance. In comparison with the hand-crafted features, the word2vec method won for most of the categories. However, the hand-crafted features showed their strength on classifying some of the categories.


title: Data-driven Personas: Constructing Archetypal Users with Clickstreams and User Telemetry

abstract: User Experience (UX) research teams following a user centered design approach harness personas to better understand a user's workflow by examining that user's behavior, goals, needs, wants, and frustrations. To create target personas these researchers rely on workflow data from surveys, self-reports, interviews, and user observation. However, this data not directly related to user behavior, weakly reflects a user's actual workflow in the product, is costly to collect, is limited to a few hundred responses, and is outdated as soon as a persona's workflows evolve. To address these limitations we present a quantitative bottom-up data-driven approach to create personas. First, we directly incorporate user behavior via clicks gathered automatically from telemetry data related to the actual product use in the field; since the data collection is automatic it is also cost effective. Next, we aggregate 3.5 million clicks from 2400 users into 39,000 clickstreams and then structure them into 10 workflows via hierarchical clustering; we thus base our personas on a large data sample. Finally, we use mixed models, a statistical approach that incorporates these clustered workflows to create five representative personas; updating our mixed model ensures that these personas remain current. We also validated these personas with our product's user behavior experts to ensure that workflows and the persona goals represent actual product use.


title: DeepLink: A Novel Link Prediction Framework based on Deep Learning

abstract: Recently, link prediction has attracted more attention from various disciplines such as computer science, bioinformatics and economics. In link prediction, numerous information such as network topo...


title: Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization

abstract: Responses generated by neural conversational models tend to lack informativeness and diversity. We present Adversarial Information Maximization (AIM), an adversarial learning strategy that addresses these two related but distinct problems. To foster response diversity, we leverage adversarial training that allows distributional matching of synthetic and real responses. To improve informativeness, our framework explicitly optimizes a variational lower bound on pairwise mutual information between query and response. Empirical results from automatic and human evaluations demonstrate that our methods significantly boost informativeness and diversity.


title: PCANet: An energy perspective

abstract: The principal component analysis network (PCANet), which is one of the recently proposed deep learning architectures, achieves the state-of-the-art classification accuracy in various databases. However, the explanation of the PCANet is lacked. In this paper, we try to explain why PCANet works well from energy perspective point of view based on a set of experiments. The impact of various parameters on the error rate of PCANet is analyzed in depth. It was found that this error rate is correlated with the logarithm of energy of image. The proposed energy explanation approach can be used as a testing method for checking if every step of the constructed networks is necessary.


title: The Semantic Middleware for Networked Embedded Systems Applied in the Internet of Things and Services Domain

abstract: The paper presents the LinkSmart middleware platform that addresses the Internet of Things and Services approach. The platform was designed to support the interoperability and seamless integration of various external devices, sensors, and services into the mainstream enterprise systems. The design and development of LinkSmart goes across two integrated European research projects, namely the FP6 IST project Hydra and the FP7 ICT project EBBITS. Modular architecture and functionality of LinkSmart prototype, developed by combining the service-oriented architecture, peer-to-peer networking, and semantic web services technologies, is described with focus on semantic binding of networked devices by means of underlying ontologies and knowledge-based inference mechanisms. Extensions of the solution towards the service orchestration, complex event handling, business process modelling and workflow processing are discussed and described on a mechanism of context-aware processing of sensor data.


title: From Trailers to Storylines: An Efficient Way to Learn from Movies

abstract: The millions of movies produced in the human history are valuable resources for computer vision research. However, learning a vision model from movie data would meet with serious difficulties. A major obstacle is the computational cost -- the length of a movie is often over one hour, which is substantially longer than the short video clips that previous study mostly focuses on. In this paper, we explore an alternative approach to learning vision models from movies. Specifically, we consider a framework comprised of a visual module and a temporal analysis module. Unlike conventional learning methods, the proposed approach learns these modules from different sets of data -- the former from trailers while the latter from movies. This allows distinctive visual features to be learned within a reasonable budget while still preserving long-term temporal structures across an entire movie. We construct a large-scale dataset for this study and define a series of tasks on top. Experiments on this dataset showed that the proposed method can substantially reduce the training time while obtaining highly effective features and coherent temporal structures.


title: The zones of proximal flow: guiding students through a space of computational thinking skills and challenges

abstract: This paper presents a novel pedagogical framework, entitled the Zones of Proximal Flow, which integrates Vygotsky's Zone of Proximal Development theory with Csikszentmihalyi's ideas about Flow. Flow focuses on the individual-- an individual is in Flow when challenges are balanced with skills. The Zone of Proximal Development, on the other hand, brings in a social learning aspect focusing on a student's ability to learn concepts with external support. From our research experiences bringing game and simulation design into middle school classrooms, we attempt to provide students with appropriate challenges using a project-first based approach that aims to keep students in Flow. The project-first approach employs inquiry based scaffolding to guide students, with appropriate support by their teachers, through Vygotsky's Zone of Proximal Development, back in to Csikszentmihalyi's state of Flow for an ideal learning experience. We call this space the Zones of Proximal Flow. Data indicate that the Zones of Proximal Flow approach works, keeping classrooms engaged in the act of game design and enabling students to advance to more complex program creations.


title: Vision and leadership of principals in Hong Kong

abstract: Describes a study which investigated the relationship between the vision and five dimensions of leadership behaviour of principals in Hong Kong. A survey was carried out on 48 secondary schools involving 548 teachers. Half of the sample schools were in a pilot scheme of school‐based management, the school management initiative (SMI). Factor analysis and multiple regression were used to analyse the data. The results suggested that five dimensions of leadership behaviour were all significantly related to the vision of principals for both types of schools. Whether the school was participating in the SMI or not also had a significant effect on the relationship between the vision of principals and three dimensions of leadership behaviour. Contrary to the expectations of the SMI and the suggestions by some researchers that teachers need to be particularly empowered in schools undergoing reforms, the findings of this study demonstrated that for principals with average and below average scores on vision, the degree of empowerment perceived by teachers in schools under the SMI was lower than for schools not under the reform. However, principals with high vision in schools under the reform had the highest scores in all five dimensions of leadership behaviour. The SMI may provide opportunities for leaders with vision to bring about a better environment for school improvement. These results provide important insights for those responsible for the implementation and evaluation of the SMI in Hong Kong, and perhaps for other systems devolving decision‐making power to more self‐managing schools.


Density Ratio Estimation: A Comprehensive Review


title: Multimodal BCIs: Target Detection, Multidimensional Control, and Awareness Evaluation in Patients With Disorder of Consciousness

abstract: Despite rapid advances in the study of brain–computer interfaces (BCIs) in recent decades, two fundamental challenges, namely, improvement of target detection performance and multidimensional control, continue to be major barriers for further development and applications. In this paper, we review the recent progress in multimodal BCIs (also called hybrid BCIs), which may provide potential solutions for addressing these challenges. In particular, improved target detection can be achieved by developing multimodal BCIs that utilize multiple brain patterns, multimodal signals, or multisensory stimuli. Furthermore, multidimensional object control can be accomplished by generating multiple control signals from different brain patterns or signal modalities. Here, we highlight several representative multimodal BCI systems by analyzing their paradigm designs, detection/control methods, and experimental results. To demonstrate their practicality, we report several initial clinical applications of these multimodal BCI systems, including awareness evaluation/detection in patients with disorder of consciousness (DOC). As an evolving research area, the study of multimodal BCIs is increasingly requiring more synergetic efforts from multiple disciplines for the exploration of the underlying brain mechanisms, the design of new effective paradigms and means of neurofeedback, and the expansion of the clinical applications of these systems.


title: Automatic breast cancer grading in lymph nodes using a deep neural network

abstract: The progression of breast cancer can be quantified in lymph node whole-slide images (WSIs). We describe a novel method for effectively performing classification of whole-slide images and patient level breast cancer grading. Our method utilises a deep neural network. The method performs classification on small patches and uses model averaging for boosting. In the first step, region of interest patches are determined and cropped automatically by color thresholding and then classified by the deep neural network. The classification results are used to determine a slide level class and for further aggregation to predict a patient level grade. Fast processing speed of our method enables high throughput image analysis.


title: A defense-centric taxonomy based on attack manifestations

abstract: Many classifications of attacks have been tendered, often in taxonomic form, A common basis of these taxonomies is that they have been framed from the perspective of an attacker - they organize attacks with respect to the attacker's goals, such as privilege elevation from user to root (from the well known Lincoln taxonomy). Taxonomies based on attacker goals are attack-centric; those based on defender goals are defense-centric. Defenders need a way of determining whether or not their detectors will detect a given attack. It is suggested that a defense-centric taxonomy would suit this role more effectively than an attack-centric taxonomy. This paper presents a new, defense-centric attack taxonomy, based on the way that attacks manifest as anomalies in monitored sensor data. Unique manifestations, drawn from 25 attacks, were used to organize the taxonomy, which was validated through exposure to an intrusion-detection system, confirming attack detect ability. The taxonomy's predictive utility was compared against that of a well-known extant attack-centric taxonomy. The defense-centric taxonomy is shown to be a more effective predictor of a detector's ability to detect specific attacks, hence informing a defender that a given detector is competent against an entire class of attacks.


NUMERICAL SOLUTION OF NAVIER-STOKES EQUATIONS


title: Introduction to single carrier FDMA

abstract: Single carrier frequency division multiple access (SC-FDMA) which utilizes single carrier modulation at the transmitter and frequency domain equalization at the receiver is a technique that has similar performance and essentially the same overall structure as those of an OFDMA system. One prominent advantage over OFDMA is that the SC-FDMA signal has lower peak-to-average power ratio (PAPR). SC-FDMA has drawn great attention as an attractive alternative to OFDMA, especially in the uplink communications where lower PAPR greatly benefits the mobile terminal in terms of transmit power efficiency. SC-FDMA is currently a working assumption for the uplink multiple access scheme in 3GPP Long Term Evolution (LTE). In this paper, we give an in-depth overview of SC-FDMA with focus on physical layer and resource management aspects. We also show some research results on PAPR characteristics and channel-dependent resource scheduling of SC-FDMA.


Causes and Solutions When Adopting Continuous Delivery-A Systematic Literature


title: Magnetic integration of LCC compensated resonant converter for inductive power transfer applications

abstract: The aim of this paper is to present a novel magnetic integrated LCC series-parallel compensation topology for the design of both the primary and pickup pads in inductive power transfer (IPT) applications. A more compact structure can be realized by integrating the inductors of the compensation circuit into the coupled power-transmitting coils. The impact of the extra coupling between the compensated coils (inductors) and the power-transferring coils is modeled and analyzed. The basic characteristics of the proposed topology are studied based on the first harmonic approximation (FHA). High-order harmonics are taken into account to derive an analytical solution for the current at the switching instant, which is helpful for the design of soft-switching operation. An IPT system with up to 5.6kW output power for electric vehicles (EV) charger has been built to verify the validity of the proposed magnetic integrated compensation topology. A peak efficiency of 95.36% from DC power source to the battery load is achieved at rated operation condition.


title: Design preserving garment transfer

abstract: We present a fully automatic method for design-preserving transfer of garments between characters with different body shapes. For real-life garments, such transfer is performed through a knowledge intensive and time consuming process, known as pattern grading. Our first contribution is to reformulate the criteria used in professional pattern-grading as a set of geometric requirements, respectively expressing shape or design preservation, proportionality, and fit. We then propose a fully automatic garment transfer algorithm which satisfies all of these criteria while ensuring the physical plausibility of the result. Specifically, we formulate garment transfer as a constrained optimization problem and solve it efficiently through iterative quadratic minimization. As demonstrated by our results, our method is able to automatically generate design-preserving versions of existing garments for target characters whose proportions and body shape significantly differ from those of the source. The method correctly handles the transfer of multiple layers of garment. Lastly, when source 2D patterns are available, we output graded patterns suitable for manufacturing the transferred garments. Our fully automatic design-preserving transfer method leads to significant time savings for both computer artists and fashion designers.


title: Parallel and Distributed Collaborative Filtering: A Survey

abstract: Collaborative filtering is amongst the most preferred techniques when implementing recommender systems. Recently, great interest has turned towards parallel and distributed implementations of collaborative filtering algorithms. This work is a survey of the parallel and distributed collaborative filtering implementations, aiming not only to provide a comprehensive presentation of the field's development, but also to offer future research orientation by highlighting the issues that need to be further developed.


title: Human feelings: why are some more aware than others?

abstract: Abstract A recent article reports that human perception of heartbeat timing is mediated by right (non-dominant) anterior insular cortex, and that the activity and the size of this region is directly correlated with individuals' subjective awareness of inner body feelings and emotionality. These results support the somatic-marker hypothesis of consciousness (a modern successor to the James–Lange theory of emotion) and the neuroanatomical concept that human awareness is based on a phylogenetically distinct interoceptive pathway.


title: Neural Collaborative Filtering

abstract: In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback. ::: ::: Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in collaborative filtering --- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items. ::: ::: By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural network-based Collaborative Filtering. NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modelling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user-item interaction function. Extensive experiments on two real-world datasets show significant improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.


title: CULTURE, CORPORATE GOVERNANCE AND DISCLOSURE IN MALAYSIAN CORPORATIONS

abstract: Evidence from research conducted on corporate accounting indicates that the interaction of environmental factors in fluences disclosure practices. The purpose of this study is to examine the importance of various corporate governance and cultural (race and education) characteristics, in addition to firm–specific factors, as possible determinants of voluntary (non–mandatory accounting and non–accounting information) disclosures in the annual reports of Malaysian listed corporations. The results of the regression analysis indicate significant associations (at the 5 per cent level) between two corporate governance variables (viz. chair who is a non–executive director and domination of family members on boards) and the extent of voluntary disclosure. This finding has implications for corporate governance policy formulation by the Malaysian Institute of Corporate Governance (MISG). One cultural factor (proportion of Malay directors on the board) is significantly associated (at the 5 per cent level) with the extent of voluntary disclosure suggesting that governmental focus on culture may solicit a response to secrecy from those who feel threatened.


Flow motifs in soccer: What can passing behavior tell us?


title: SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection

abstract: Existing computational models for salient object detection primarily rely on hand-crafted features, which are only able to capture low-level contrast information. In this paper, we learn the hierarchical contrast features by formulating salient object detection as a binary labeling problem using deep learning techniques. A novel superpixelwise convolutional neural network approach, called SuperCNN, is proposed to learn the internal representations of saliency in an efficient manner. In contrast to the classical convolutional networks, SuperCNN has four main properties. First, the proposed method is able to learn the hierarchical contrast features, as it is fed by two meaningful superpixel sequences, which is much more effective for detecting salient regions than feeding raw image pixels. Second, as SuperCNN recovers the contextual information among superpixels, it enables large context to be involved in the analysis efficiently. Third, benefiting from the superpixelwise mechanism, the required number of predictions for a densely labeled map is hugely reduced. Fourth, saliency can be detected independent of region size by utilizing a multiscale network structure. Experiments show that SuperCNN can robustly detect salient objects and outperforms the state-of-the-art methods on three benchmark datasets.


title: Twitter data clustering and visualization

abstract: We present a system for the acquisition, analysis and visualisation of Twitter data. Twitter messages are harvested and stored in a distributed cluster, and the data is processed using algorithms implemented in a MapReduce framework. We present a clustering algorithm capable of identifying the main topics of interest in a tweet data set. Also, we designed a visualization method which allows to follow the intensity of Twitter activity at a given geographical location.


title: Modeling quantum entanglements in quantum language models

abstract: Recently, a Quantum Language Model (QLM) was proposed to model term dependencies upon Quantum Theory (QT) framework and successively applied in Information Retrieval (IR). Nevertheless, QLM's dependency is based on co-occurrences of terms and has not yet taken into account the Quantum Entanglement (QE), which is a key quantum concept and has a significant cognitive implication. In QT, an entangled state can provide a more complete description for the nature of realities, and determine intrinsic correlations of considered objects globally, rather than those co-occurrences on the surface. It is, however, a real challenge to decide and measure QE using the classical statistics of texts in a post-measurement configuration. In order to circumvent this problem, we theoretically prove the connection between QE and statistically Unconditional Pure Dependence (UPD). Since UPD has an implementable deciding algorithm, we can in turn characterize QE by extracting the UPD patterns from texts. This leads to a measurable QE, based on which we further advance the existing QLM framework. We empirically compare our model with related models, and the results demonstrate the effectiveness of our model.


title: Weighted least-squares estimation of phase errors for SAR/ISAR autofocus

abstract: A new method of phase error estimation that utilizes the weighted least-squares (WLS) algorithm is presented for synthetic aperture radar (SAR)/inverse SAR (ISAR) autofocus applications. The method does not require that the signal in each range bin be of a certain distribution model, and thus it is robust for many kinds of scene content. The most attractive attribute of the new method is that it can be used to estimate all kinds of phase errors, no matter whether they are of low order, high order, or random. Compared with other methods, the WLS estimation is optimal in the sense that it has the minimum variance of the estimation error. Excellent results have been obtained in autofocusing and imaging experiments on real SAR and ISAR data.


title: Decorrelated unbiased converted measurement Kalman filter

abstract: Converted measurement tracking is a technique that filters in the coordinate system where the underlying process of interest is linear and Gaussian and requires the measurements to be nonlinearly transformed to fit. The goal of the transformation is to allow for tracking in the coordinate system that is most natural for describing system dynamics. There are two potential issues that arise when performing converted measurement tracking. The first is conversion bias that occurs when the measurement transformation introduces a bias in the expected value of the converted measurement. The second is estimation bias that occurs because the estimate of the converted measurement error covariance is correlated with the measurement noise, leading to a biased Kalman gain. First, previously proposed unbiased conversions are examined. Following this, the decorrelated unbiased converted measurement approach is presented. Results show that to overcome conversion bias and estimation bias, an unbiased measurement conversion should be employed that calculates the converted measurement error covariance using the predicted measurement. The conversion approaches are evaluated in tracking scenarios relevant to radar and sonar measurements.


title: Oral Controlled Release Drug Delivery System: An Overview

abstract: Oral drug delivery is the most preferred and convenient option as the oral route provides maximum active surface area among all drug delivery system for administration of various drugs. The attractiveness of these dosage forms is due to awareness to toxicity and ineffectiveness of drugs when administered by oral conventional method in the form of tablets and capsules. Usually conventional dosage form produces wide range of fluctuation in drug concentration in the bloodstream and tissues with consequent undesirable toxicity and poor efficiency. The maintenance of concentration of drug in plasma within therapeutic index is very critical for effective treatment. These factors as well as factors such as repetitive dosing and unpredictable absorption lead to the concept of oral controlled release drug delivery systems. Controlled release drug delivery system works on many different mechanisms to control the release rate of drugs. Various mechanisms like osmotic pressure, matrix system, reservoir system, altered density system etc. have been utilized as formulation approaches. The present article contains brief review on various formulation approaches for controlled release drug delivery system.


Predicting Hotel Rating Based on User Reviews


title: A Multistage Processing Algorithm for Disturbance Removal and Target Detection in Passive Bistatic Radar

abstract: The paper examines the problem of cancellation of direct signal, multipath and clutter echoes in passive bistatic radar (PBR). This problem is exacerbated as the transmitted waveform is not under control of the radar designer and the sidelobes of the ambiguity function can mask targets including those displaced in either (or both) range and Doppler from the disturbance. A novel multistage approach is developed for disturbance cancellation and target detection based on projections of the received signal in a subspace orthogonal to both the disturbance and previously detected targets. The resulting algorithm is shown to be effective against typical simulated scenarios with a limited number of stages, and a version with computational savings is also introduced. Finally its effectiveness is demonstrated with the application to real data acquired with an experimental VHF PBR system.


title: Collecting and Analyzing Data from Smart Device Users with Local Differential Privacy

abstract: Organizations with a large user base, such as Samsung and Google, can potentially benefit from collecting and mining users' data. However, doing so raises privacy concerns, and risks accidental privacy breaches with serious consequences. Local differential privacy (LDP) techniques address this problem by only collecting randomized answers from each user, with guarantees of plausible deniability; meanwhile, the aggregator can still build accurate models and predictors by analyzing large amounts of such randomized data. So far, existing LDP solutions either have severely restricted functionality, or focus mainly on theoretical aspects such as asymptotical bounds rather than practical usability and performance. Motivated by this, we propose Harmony, a practical, accurate and efficient system for collecting and analyzing data from smart device users, while satisfying LDP. Harmony applies to multi-dimensional data containing both numerical and categorical attributes, and supports both basic statistics (e.g., mean and frequency estimates), and complex machine learning tasks (e.g., linear regression, logistic regression and SVM classification). Experiments using real data confirm Harmony's effectiveness.


title: Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function

abstract: Abstract Nowadays, several types of brain imaging device are available to provide images of the functional activity of the cerebral cortex based on hemodynamic, metabolic, or electromagnetic measurements. However, static images of brain regions activated during particular tasks do not convey the information of how these regions communicate with each other. In this study, advanced methods for the estimation of cortical connectivity from combined high-resolution electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data are presented. These methods include a subject's multicompartment head model (scalp, skull, dura mater, cortex) constructed from individual magnetic resonance images, multidipole source model, and regularized linear inverse source estimates of cortical current density. Determination of the priors in the resolution of the linear inverse problem was performed with the use of information from the hemodynamic responses of the cortical areas as revealed by block-designed (strength of activated voxels) fMRI. We estimate functional cortical connectivity by computing the directed transfer function (DTF) on the estimated cortical current density waveforms in regions of interest (ROIs) on the modeled cortical mantle. The proposed method was able to unveil the direction of the information flow between the cortical regions of interest, as it is directional in nature. Furthermore, this method allows to detect changes in the time course of information flow between cortical regions in different frequency bands. The reliability of these techniques was further demonstrated by elaboration of high-resolution EEG and fMRI signals collected during visually triggered finger movements in four healthy subjects. Connectivity patterns estimated for this task reveal an involvement of right parietal and bilateral premotor and prefrontal cortical areas. This cortical region involvement resembles that revealed in previous studies where visually triggered finger movements were analyzed with the use of separate EEG or fMRI measurements.


title: Empirical Evaluation of Fair Use Flat Rate Strategies for Mobile Internet

abstract: The fair use flat rate is a promising tariff concept for the mobile telecommunication industry. Similar to classical flat rates it allows unlimited usage at a fixed monthly fee. Contrary to classical flat rates it limits the access speed once a certain usage threshold is exceeded. Due to the current global roll-out of the LTE (Long Term Evolution) technology and the related economic changes for telecommunication providers, the application of fair use flat rates needs a reassessment. We therefore propose a simulation model to evaluate different pricing strategies and their contribution margin impact. The key input element of the model is provided by so-called discrete choice experiments that allow the estimation of customer preferences. Based on this customer information and the simulation results, the article provides the following recommendations. Classical flat rates do not allow profitable provisioning of mobile Internet access. Instead, operators should apply fair use flat rates with a lower usage threshold of 1 or 3 GB which leads to an improved contribution margin. Bandwidth and speed are secondary and do merely impact customer preferences. The main motivation for new mobile technologies such as LTE should therefore be to improve the cost structure of an operator rather than using it to skim an assumed higher willingness to pay of mobile subscribers. Copyright Gabler Verlag 2011


title: 2–10 GHz multisection 2-way Wilkinson power divider with enhanced port match and isolation

abstract: This paper presents two broadband Wilkinson power dividers (WPDs) using single layer microstrip lines. Operation of these WPDs from 2 GHz to 10 GHz could be achieved by using multisectional structure. Presented WPDs have 6 sections and differ in the size of the isolation resistors which have size of 0402 and 0603. For experimental verifications two devices were fabricated and the measurement results match with the simulation results well. Measurement results of the two independent samples show a bandwidth from 2 GHz to 10 GHz with an insertion loss of lower than 0.4 dB, isolation of greater than 25 dB, amplitude and phase imbalance of lower than ±0.3 dB and ±3°, respectively. The realized WPDs exhibit better matching, isolation and phase/amplitude imbalance compared with other wideband power dividers published so far.


title: Preparing a Qualitative Research-Based Dissertation: Lessons Learned

abstract: In this article, a newly minted Ph.D. shares seven lessons learned during the process of preparing a dissertation based on qualitative research methods. While most of the lessons may be applicable to any kind of research, the writer focuses on the special challenges of employing a qualitative methodology. The lessons are: (1) Read, read, read; (2) Consult the experts; (3) Adhere to university regulations; (4) Pay attention to rigor and trustworthiness; (5) Give details of the methodology; (6) Don't be afraid to include numerical data; and (7) Prepare to publish. Key Words: Confirmability, Credibility, Dependability, Grounded Theory, Inductive Analysis, Transferability, and Trustworthiness ********** As the new millennium dawned, I made a decision: I would reach for something seemingly beyond my grasp. That special something turned out to be a doctoral degree. However, even after I had fully embarked on that upward journey of discovery, I had no inkling of the methodological challenges that would mark many milestones on that journey. A social research neophyte, I have spent my adult life honing my skills and developing expertise as a journalist and public relations practitioner. Over the years, I have researched and written numerous news stories, feature articles, general-interest columns, and special reports for newspapers, magazines, and radio. Researching and writing a dissertation--particularly one based on qualitative research methods--demanded a different set of skills and offered some special challenges because of its nature and scope. In reflecting on that experience, I can identify various lessons learned along the way. During coursework, I learned all the quantitative stuff: descriptive statistics, t-test procedures, univariate and multivariate analyses of variance, chi-square test, regression analysis, factor analysis, structural equation modeling, and the like. What's more, I developed facility in Statistical Package for the Social Sciences (SPSS). Coursework also focused on basic matters like having a well-written research question; stating the purpose of the study (exploratory, descriptive, explanatory, or evaluative, or some combination); reviewing the literature thoroughly; and presenting a conceptual or theoretical framework for the study. Guiding me on the early part of my journey, my statistics professor emphasized the "power" of numbers and the precision of measures characterizing quantitative studies. Like so many numbers-crunching researchers, my stats professor viewed qualitative research with suspicion. It seemed the good professor considered a methodology in which the generation of hypotheses often replaces the testing thereof, explanation replaces measurement, and understanding replaces the making of generalizations as "airy fairy" (not "real") research (Labuschagne, 2003). Consequently, I became somewhat skeptical of this kind of research. In the end, though, I let the topic and goals of my research dictate the methodology. Fortunately, all four members of my dissertation committee (including the outgoing coordinator of the doctoral program) were enlightened enough to appreciate and support my choice of research methodology. Indeed, they emphasized the need for me to gather data reflecting the interactions and experiences of individuals and communities in relation to the research problem that I had identified. It was important to know quantitative research methodology and its assumptions, as well, so I could defend my choice of research design and methods. It is like preparing for a debate. To be effective, the debater had better know both (or all) sides of the issue. Before too long, it became clear to me that quantitative and qualitative research have distinct and complementary strengths. The main strength of qualitative research is that it yields data that provide depth and detail to create understanding of phenomena and lived experiences. …


title: Brain Tumor Type Classification via Capsule Networks

abstract: Brain tumor is considered as one of the deadliest and most common form of cancer both in children and in adults. Consequently, determining the correct type of brain tumor in early stages is of significant importance to devise a precise treatment plan and predict patient's response to the adopted treatment. In this regard, there has been a recent surge of interest in designing Convolutional Neural Networks (CNNs) for the problem of brain tumor type classification. However, CNNs typically require large amount of training data and can not properly handle input transformations. Capsule networks (referred to as CapsNets) are brand new machine learning architectures proposed very recently to overcome these shortcomings of CNNs, and posed to revolutionize deep learning solutions. Of particular interest to this work is that Capsule networks are robust to rotation and affine transformation, and require far less training data, which is the case for processing medical image datasets including brain Magnetic Resonance Imaging (MRI) images. In this paper, we focus to achieve the following four objectives: (i) Adopt and incorporate CapsNets for the problem of brain tumor classification to design an improved architecture which maximizes the accuracy of the classification problem at hand; (ii) Investigate the over-fitting problem of CapsNets based on a real set of MRI images; (iii) Explore whether or not CapsNets are capable of providing better fit for the whole brain images or just the segmented tumor, and; (iv) Develop a visualization paradigm for the output of the CapsNet to better explain the learned features. Our results show that the proposed approach can successfully overcome CNNs for the brain tumor classification problem.


title: Surgical smoke management for minimally invasive (micro)endoscopy: an experimental study

abstract: BACKGROUND ::: The aim of this study was to investigate the use of surgical smoke-producing procedures such as laser ablation or electrosurgery in minimally invasive microendoscopic procedures. This study proposes a technical solution to efficiently remove surgical smoke from very small endoscopic cavities using microports as small as 20 G (0.9 mm) in diameter. ::: ::: ::: METHODS ::: The experimental laboratory study used small, rigid, transparent plastic cavity models connected with tubes and pressure sensors to establish an endoscopic in vitro laboratory model. A Kalium-Titanyl-Phosphate (KTP) laser with a 0.5-mm fiber optic probe was used to produce smoke from bovine scleral tissue in the cavity. Endoscopic gas insufflation into the model was generated by pressurized air and a microvalve. A laboratory vacuum pump provided smoke and gas suction via a microvalve. A self-built control and steering system was utilized to control intracavital pressure during experimental insufflation and suction. ::: ::: ::: RESULTS ::: Problems related to smoke-generating processes, such as laser vaporization or electrocautery, in small closed cavities were first analyzed. A theoretical and mechatronic laboratory model was established and tested. Intracavital pressure and gas flow were measured first without and then with smoke generation. A new construction design for the suction tube was proposed due to rapid obstruction by smoke particles. ::: ::: ::: CONCLUSIONS ::: Surgical smoke evacuation from endoscopic cavities that are as small as 2 cm in diameter via minimally invasive ports as small as 20 G (0.9 mm) in diameter may be safe and efficient if sufficient gas exchange is provided during smoke generation by laser or electrosurgical instruments. However, maintaining a low and constant pressure in the cavity during gas exchange and adopting a special construction design for the suction tube are essential to provide an excellent view during the surgical maneuver and to minimize potential toxic side effects of the smoke.


title: MEmbER: Max-Margin Based Embeddings for Entity Retrieval

abstract: We propose a new class of methods for learning vector space embeddings of entities. While most existing methods focus on modelling similarity, our primary aim is to learn embeddings that are interpretable, in the sense that query terms have a direct geometric representation in the vector space. Intuitively, we want all entities that have some property (i.e. for which a given term is relevant) to be located in some well-defined region of the space. This is achieved by imposing max-margin constraints that are derived from a bag-of-words representation of the entities. The resulting vector spaces provide us with a natural vehicle for identifying entities that have a given property (or ranking them according to how much they have the property), and conversely, to describe what a given set of entities have in common. As we show in our experiments, our models lead to a substantially better performance in a range of entity-oriented search tasks, such as list completion and entity ranking.


title: Model Selection Through Sparse Maximum Likelihood Estimation

abstract: We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to solve a maximum likelihood problem with an added l1-norm penalty term. The problem as formulated is convex but the memory requirements and complexity of existing interior point methods are prohibitive for problems with more than tens of nodes. We present two new algorithms for solving problems with at least a thousand nodes in the Gaussian case. Our first algorithm uses block coordinate descent, and can be interpreted as recursive l1-norm penalized regression. Our second algorithm, based on Nesterov's first order method, yields a complexity estimate with a better dependence on problem size than existing interior point methods. Using a log determinant relaxation of the log partition function (Wainwright and Jordan, 2006), we show that these same algorithms can be used to solve an approximate sparse maximum likelihood problem for the binary case. We test our algorithms on synthetic data, as well as on gene expression and senate voting records data.


title: Robust Brightness Measurement and Exposure Control in Real-Time Video Recording

abstract: The auto exposure control (AEC) system in a video camera selects the suitable aperture size, gain setting, and exposure time such that the exposure best fits the scene brightness. Although a few approaches to AEC have been proposed, most of them lack smart mechanisms to quickly estimate the right exposure when users suddenly change the scene. The problem becomes more serious when some regions in the captured video frame are overexposed or underexposed. In this paper, we propose a robust AEC approach to real-time video recording. The proposed system consists of a well-designed control flow as well as some heuristic rules to scene brightness measurement and exposure adjustment. To get a more accurate estimation on scene brightness, the system adaptively selects suitable sensor operational modes. The proposed auto exposure also includes mechanisms to detect occasional moving objects and the flicker of the fluorescent light. Experimental results proved that the proposed AEC system has good performance under various conditions, and it helps to improve the recorded video quality.


title: Identifying extremism in social media with multi-view context-aware subset optimization

abstract: Cyber threat intelligence and security informatics play critical roles in the identification of the principal influencers and threats associated with criminal activity and extremism in web-based communities. This paper presents an effective, dynamic learning framework utilizing domain specific context information to design a novel classification model that can robustly identify malicious social media posts (e.g. enrollment propaganda for extremist groups) with expressions of extremism or criminal intent. Research towards automated identification of extreme online posts and their associated key suspects and threats faces numerous challenges, 1) Online data, particularly social media data, originated from numerous independent and heterogeneous sources are largely unstructured. 2) The tactics, techniques, and procedures (TTPs) of criminal activity and extremism are constantly evolving. 3) There are limited ground truth data to support the development of effective classification technologies. In this paper, we present a human-machine collaborative, semi-supervised learning system that can efficiently and effectively identify malicious social media posts in presence of these challenges. Our system and framework develops an initial classifier from limited annotated data and in an interactive manner evolves dynamically into a sophisticated model using shortlisted relevant samples, identified via a graph-based optimization method, solvable by maximum flow algorithm. This same method also may be used to refine the classifier as TTPs evolve. Under this framework, the classifier performance converges faster using roughly 1–2 orders of magnitude fewer annotated samples, as compared to fully supervised solutions, resulting in a reasonably acceptable accuracy of nearly 80%. We validate our framework using a large collection of English and non-English flagged words extracted from three web-based forums and manually verified by multiple independent annotators.


title: Multiagent Smart Grid Automation Architecture Based on IEC 61850/61499 Intelligent Logical Nodes

abstract: Universal, intelligent, and multifunctional devices controlling power distribution and measurement will become the enabling technology of the Smart Grid ICT. In this paper, we report on a novel automation architecture which supports distributed multiagent intelligence, interoperability, and configurability and enables efficient simulation of distributed automation systems. The solution is based on the combination of IEC 61850 object-based modeling and interoperable communication with IEC 61499 function block executable specification. Using the developed simulation environment, we demonstrate the possibility of multiagent control to achieve self-healing grid through collaborative fault location and power restoration.


title: Disturbances Attenuation of Permanent Magnet Synchronous Motor Drives Using Cascaded Predictive-Integral-Resonant Controllers

abstract: The performance of a standard model predictive controller (MPC) is directly related to its predictive model. If there are unmodeled periodic disturbances in the actual system, MPC will be difficult to suppress the disturbances, thus causing fluctuations of system output. To solve this problem, this paper proposes an improved MPC named predictive-integral-resonant control (PIRC). Compared with the standard MPC, the proposed PIRC could enhance the suppression ability for disturbances by embedding the internal model composing of the integral and resonant loop. Furthermore, this paper applies the proposed PIRC to PMSM drives, and proposes the PMSM control strategy based on the cascaded PIRC, which could suppress periodic disturbances caused by the dead time effects, current sampling errors, and so on. The experimental results show that the PIRC can suppress periodic disturbances in the drive system, thus ensuring good current and speed performance. Meanwhile, the PIRC could maintain the excellent dynamic performance as the standard MPC.


title: Learning Robust Features using Deep Learning for Automatic Seizure Detection

abstract: We present and evaluate the capacity of a deep neural network to learn robust features from EEG to automatically detect seizures. This is a challenging problem because seizure manifestations on EEG are extremely variable both inter- and intra-patient. By simultaneously capturing spectral, temporal and spatial information our recurrent convolutional neural network learns a general spatially invariant representation of a seizure. The proposed approach exceeds significantly previous results obtained on cross-patient classifiers both in terms of sensitivity and false positive rate. Furthermore, our model proves to be robust to missing channel and variable electrode montage.


title: Coupled inductor three-phase bidirectional buck/boost converter for energy storage application

abstract: This research designs and realizes a zero-voltage switching (ZVS) three-phase DC-DC buck/boost converter that reduces the current ripple, switching losses and increases converter efficiency. The size and cost can be reduced when the proposed converter is designed with the coupled inductor scheme. This paper describes a three-phase DC-DC buck/boost converter with the coupled inductor and ZVS soft switching operation under different inductor current conduction modes. Simulation and experimental results are employed compare the performance of the proposed three-phase bidirectional converter in the PV system under battery charge and discharge operating modes for energy storage systems.


ADAPTIVE COLOR FILTER ARRAY INTERPOLATION ALGORITHM BASED ON HUE TRANSITION AND EDGE


title: RoADS: a road pavement monitoring system for anomaly detection using smart phones

abstract: Monitoring the road pavement is a challenging task. Authorities spend time and finances to monitor the state and quality of the road pavement. This paper investigate road surface monitoring with smartphones equipped with GPS and inertial sensors: accelerometer and gyroscope. ::: In this study we describe the conducted experiments with data from the time domain, frequency domain and wavelet transformation, and a method to reduce the effects of speed, slopes and drifts from sensor signals. A new audiovisual data labelling technique is proposed. Our system named RoADS, implements wavelet decomposition analysis for signal processing of inertial sensor signals and Support Vector Machine (SVM) for anomaly detection and classification. Using these methods we are able to build a real time multiclass road anomaly detector. We obtained a consistent accuracy of ≈90% on detecting severe anomalies regardless of vehicle type and road location. Local road authorities and communities can benefit from this system to evaluate the state of their road network pavement in real time.


title: Graph theoretical analysis of complex networks in the brain

abstract: Since the discovery of small-world and scale-free networks the study of complex systems from a network perspective has taken an enormous flight. In recent years many important properties of complex networks have been delineated. In particular, significant progress has been made in understanding the relationship between the structural properties of networks and the nature of dynamics taking place on these networks. For instance, the 'synchronizability' of complex networks of coupled oscillators can be determined by graph spectral analysis. These developments in the theory of complex networks have inspired new applications in the field of neuroscience. Graph analysis has been used in the study of models of neural networks, anatomical connectivity, and functional connectivity based upon fMRI, EEG and MEG. These studies suggest that the human brain can be modelled as a complex network, and may have a small-world structure both at the level of anatomical as well as functional connectivity. This small-world structure is hypothesized to reflect an optimal situation associated with rapid synchronization and information transfer, minimal wiring costs, as well as a balance between local processing and global integration. The topological structure of functional networks is probably restrained by genetic and anatomical factors, but can be modified during tasks. There is also increasing evidence that various types of brain disease such as Alzheimer's disease, schizophrenia, brain tumours and epilepsy may be associated with deviations of the functional network topology from the optimal small-world pattern.


title: Explainable Reasoning over Knowledge Graphs for Recommendation

abstract: Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich and complementary information to user-item interactions. Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user’s interest. However, existing efforts have not fully explored this connectivity to infer user preferences, especially in terms of modeling the sequential dependencies within and holistic semantics of a path.In this paper, we contribute a new model named Knowledgeaware Path Recurrent Network (KPRN) to exploit knowledge graph for recommendation. KPRN can generate path representations by composing the semantics of both entities and relations. By leveraging the sequential dependencies within a path, we allow effective reasoning on paths to infer the underlying rationale of a user-item interaction. Furthermore, we design a new weighted pooling operation to discriminate the strengths of different paths in connecting a user with an item, endowing our model with a certain level of explainability. We conduct extensive experiments on two datasets about movie and music, demonstrating significant improvements over state-of-the-art solutions Collaborative Knowledge Base Embedding and Neural Factorization Machine.


title: IPSJ Transactions on Computer Vision and Applications

abstract: This paper presents a novel method for pair-wise range image registration, a backbone task in world modeling, parts inspection and manufacture, object recognition, pose estimation, robotic navigation, and reverse engineering. The method finds the most suitable homogeneous transformation matrix between two constructed range images to create a more complete 3D view of a scene. The proposed solution integrates a ray casting-based fitness estimation with a global optimization method called improved self-adaptive differential evolution. This method eliminates the fine registration steps of the well-known iterative closest point (ICP) algorithm used in previously proposed methods, and thus, is the first direct global registration algorithm. With its parallel implementation potential, the ray casting-based algorithm speeds up the fitness calculation for the global optimization method, which effectively exploits the search space to find the best transformation solution. The integration was successfully implemented in a parallel paradigm on a multi-core computer processor to solve a simultaneous 3D localization problem. The fast, accurate, and robust results show that the proposed algorithm significantly improves on the registration problem over state-of-the-art algorithms.


title: Development of a vision navigation system with Fuzzy Control Algorithm for Automated Guided Vehicle

abstract: Automated Guided Vehicle (AGV) has the abilities of flexibility, automation and reliability. It has been wildly used in the industry area. The needs of AGV in the medical area are also growing. Up to now, several AGV navigation methods has been proposed. As a modern method, the vision navigation has more benefits such as lower cost, easy installation, electromagnetic anti-interference and so on. In this paper, we will present the improved vision navigation with Fuzzy Control Algorithm. This navigation system combined the vehicle mechanism characters. Combining the navigation deviation parameters obtained from image processing with the motion and control characteristics of the AGV, the Fuzzy Control Algorithm is designed to navigate the AGV moving along the road marking line. In the paper, the detail of the system is introduced. The algorithm is examined with Matlab simulation. Finally, the real experiment with AGV is also performed to examine our proposed system. All the works leads to the meaningful works in the future.


title: Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks

abstract: Abstract Linking human whole-body motion and natural language is of great interest for the generation of semantic representations of observed human behaviors as well as for the generation of robot behaviors based on natural language input. While there has been a large body of research in this area, most approaches that exist today require a symbolic representation of motions (e.g. in the form of motion primitives), which have to be defined a-priori or require complex segmentation algorithms. In contrast, recent advances in the field of neural networks and especially deep learning have demonstrated that sub-symbolic representations that can be learned end-to-end usually outperform more traditional approaches, for applications such as machine translation. In this paper we propose a generative model that learns a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks (RNNs) and sequence-to-sequence learning. Our approach does not require any segmentation or manual feature engineering and learns a distributed representation, which is shared for all motions and descriptions. We evaluate our approach on 2 846 human whole-body motions and 6 187 natural language descriptions thereof from the KIT Motion-Language Dataset. Our results clearly demonstrate the effectiveness of the proposed model: We show that our model generates a wide variety of realistic motions only from descriptions thereof in form of a single sentence. Conversely, our model is also capable of generating correct and detailed natural language descriptions from human motions.


title: Sex Differences in Romantic Attachment: A Meta-Analysis

abstract: This article presents the first meta-analysis of sex differences in the avoidance and anxiety dimensions of adult romantic attachment, based on 113 samples (N = 66,132) from 100 studies employing two-dimensional romantic attachment questionnaires (Experiences in Close Relationships, Experiences in Close Relationships—Revised, and Adult Attachment Questionnaire). Overall, males showed higher avoidance and lower anxiety than females, with substantial between-study heterogeneity. Sex differences were much larger in community samples (bivariate D = .28) than in college samples (D = .12); web-based studies showed the smallest sex differences (D = .07) in the opposite direction. Sex differences also varied across geographic regions (overall Ds = .10 to .34). Sex differences in anxiety peaked in young adulthood, whereas those in avoidance increased through the life course. The relevance of these findings for evolutionary models of romantic attachment is discussed, and possible factors leading to underestimation ...


title: Lossy resonators for suppressing power-bus resonance of printed circuit boards

abstract: Power-bus resonance of printed circuit boards causes electromagnetic interference and detraction of power integrity. To suppress the power-bus resonance, we propose the application of lossy resonators in the power bus. Here, we first explain the concepts of the proposed resonators, focusing especially on the application based on the impedance characteristics. Then, we explain the full-wave simulation conducted to demonstrate that the proposed resonator effectively suppressed power-bus resonance.


title: ST-Hadoop: A MapReduce Framework for Big Spatio-temporal Data

abstract: This paper presents ST-Hadoop; the first full-fledged open-source MapReduce framework with a native support for spatio-temporal data. ST-Hadoop is a comprehensive extension to Hadoop and SpatialHadoop that injects spatio-temporal data awareness inside each of their layers, mainly, language, indexing, and operations layers. In the language layer, ST-Hadoop provides built in spatio-temporal data types and operations. In the indexing layer, ST-Hadoop spatiotemporally loads and divides data across computation nodes in Hadoop Distributed File System in a way that mimics spatio-temporal index structures, which result in achieving orders of magnitude better performance than Hadoop and SpatialHadoop when dealing with spatio-temporal data and queries. In the operations layer, ST-Hadoop shipped with support for two fundamental spatio-temporal queries, namely, spatio-temporal range and join queries. Extensibility of ST-Hadoop allows others to expand features and operations easily using similar approach described in the paper. Extensive experiments conducted on large-scale dataset of size 10 TB that contains over 1 Billion spatio-temporal records, to show that ST-Hadoop achieves orders of magnitude better performance than Hadoop and SpaitalHadoop when dealing with spatio-temporal data and operations. The key idea behind the performance gained in ST-Hadoop is its ability in indexing spatio-temporal data within Hadoop Distributed File System.


title: Inflammatory bowel disease and the risk of fracture after controlling for FRAX.

abstract: BACKGROUND: Subjects with inflammatory bowel disease (IBD) are at increased risk for hip and other major osteoporotic fractures. However, previous analyses have not fully accounted for differences in bone mineral density (BMD) and other clinical factors that affect the risk of fracture. The World Health Organization Fracture Risk Assessment tool (FRAX®) can be used to predict the 10-year fracture riskfrom BMD and clinical risk factors. METHODS: A population based database containing clinical information on all IBD subjects in the province of Manitoba, Canada, was linked with the Manitoba Bone Mineral Density Database, which contains results of all dual X-ray absorptiometry (DXA) scans in the province. FRAX probabilities were calculated for all subjects aged50 years or more undergoing baseline DXA testing. Subjects were followed for occurrence of major osteoporotic fractures (MOF; hip, clinical spine, wrist, humerus). Cox proportional hazards models were used to determine whether IBD was independently predictive of MOF or hip fracture. RESULTS: After controlling for FRAX fracture probability computed with BMD, IBD was not associated with a significantly increased risk for MOF (HR 1.12, 95%CI 0.83-1.55) but was associated with an increased risk for hip fracture (HR 2.14, 95%CI 1.26-3.65).The addition of femoral neck T-score to FRAX probability without knowledge of BMD had a negligible effect on the estimated hazard ratios for IBD, suggesting that IBD mediates any effect on fracture risk independently of femoral neck BMD CONCLUSION: After controlling for FRAX probability, subjects with IBD are not at an increased risk for overall MOF, but may be at increased risk of hip fracture. © 2012 American Society for Bone and Mineral Research.


title: Unsupervised texture segmentation using feature distributions

abstract: Abstract This paper presents an unsupervised texture segmentation method, which uses distributions of local binary patterns and pattern contrasts for measuring the similarity of adjacent image regions during the segmentation process. Nonparametric log-likelihood test, the G statistic, is engaged as a pseudo-metric for comparing feature distributions. A region-based algorithm is developed for coarse image segmentation and a pixelwise classification scheme for improving localization of region boundaries. The performance of the method is evaluated with various types of test images.


title: #FailedRevolutions: Using Twitter to Study the Antecedents of ISIS Support

abstract: During the last two years, the Islamic State of Iraq and Syria (ISIS) has managed to control large parts of Syria and Iraq. To better understand the roots of support for ISIS, we present a study using Twitter data. We collected a large number of Arabic tweets referring to ISIS and classified them as pro-ISIS and anti-ISIS. We then analyzed historic timelines of both users supporting and opposing and looked at their pre-ISIS period to gain insights into the antecedents of support. To achieve this, we built a classifier to “predict”, in retrospect, who will support or oppose the group. We show that ISIS supporters largely differ from ISIS opposition in that they referred a lot more to Arab Spring uprisings that failed. Our work not only contributes insights into what might be behind support for ISIS, but it also presents a methodology of how to use historic social media data to understand the root causes of political polarization.


title: Transfer Learning by Ranking for Weakly Supervised Object Annotation

abstract: Most existing approaches to training object detectors rely on fully supervised learning, which requires the tedious manual annotation of object location in a training set. Recently there has been an increasing interest in developing weakly supervised approach to detector training where the object location is not manually annotated but automatically determined based on binary (weak) labels indicating if a training image contains the object. This is a challenging problem because each image can contain many candidate object locations which partially overlaps the object of interest. Existing approaches focus on how to best utilise the binary labels for object location annotation. In this paper we propose to solve this problem from a very different perspective by casting it as a transfer learning problem. Specifically, we formulate a novel transfer learning based on learning to rank, which effectively transfers a model for automatic annotation of object location from an auxiliary dataset to a target dataset with completely unrelated object categories. We show that our approach outperforms existing state-of-the-art weakly supervised approach to annotating objects in the challenging VOC dataset.


title: The primordial anisotropy polarization pathfinder array (PAPPA): instrument overview and status

abstract: PAPPA is a balloon-based experiment designed to measure the polarization of the Cosmic Microwave Background using candidate technology for an eventual Einstein Inflation Probe mission. It will survey a 20° × 20° patch of sky with 0.5° angular resolution covering 3 passbands centered at 89, 212 and 302 GHz. Detection will be accomplished via antenna-coupled transition edge sensors (TESs) with SQUID-based readouts. In the eventual flight package, band defining filters and MEMS-based polarization modulators will be incorporated into the superconducting microstrip transmission lines that terminate in resistors that are thermally coupled to the TESs. The MEMS switches will allow on-chip polarization modulation that is faster than significant detector gain variations. The initial configuration will incorporate a simplified focal plane augmented by quasioptical polarization modulation. We describe the overall instrument design and present a summary of the current progress.


Seeing with Humans : Gaze-Assisted Neural Image


title: Collaborative Localization and Navigation in Heterogeneous UAV swarms: Demo Abstract

abstract: Resilient localization and navigation for autonomous Unmanned Aerial Vehicles (UAVs) still remains a challenge in certain scenarios, like GPS-deprived environments such as indoors or urban canyons. In this work, we explore a heterogeneous UAV swarm design, in which a small number of sensor and computationally powerful UAVs collaborate with the remaining resource-constrained UAVs to guarantee optimal localization accuracy.


title: Urban agriculture in the developing world: a review

abstract: The year 2007 marked a critical event in the world history. For the first time, more than half of the world population now lives in cities. In many developing countries, the urbanization process goes along with increasing urban poverty and polluted environment, growing food insecurity and malnutrition, especially for children, pregnant and lactating women; and increasing unemployment. Urban agriculture represents an opportunity for improving food supply, health conditions, local economy, social integration, and environmental sustainability altogether. Urban agriculture is present throughout the world in a diversity of farming systems. Urban dwellers ranging 25–30 % are involved worldwide in the agro-food sector. Urban agriculture will gain in recognition for its benefits and services because urban population and rural–urban migration are increasing. The actual scarcity of knowledge on urban agriculture has somehow hindered the relevance of this activity. Here, we review the social, cultural, technical, economic, environmental, and political factors affecting urban agriculture with examples taken in East Asia, South America, or East Africa. We discuss the definition, benefits, and limitations of urban agriculture. Food security benefit of urban agriculture is evidenced by 100–200 million urban farmers worldwide providing the city markets with fresh horticultural goods. Urban agriculture favors social improvement since the poors spend up to 85 % of their income in food purchase and most urban farmers belong to poorest populations. Sociologically urban farming favors both social inclusion and reduction of gender inequalities, as 65 % of urban farmers are women. Urban agriculture has ecological benefits by reducing the city waste, improving urban biodiversity and air quality, and overall reducing the environmental impact related to both food transport and storage. The production of horticultural goods shows the main benefits of urban agriculture. Fruit and vegetable crops give high yields, up to 50 kg m−2 year−1, a more efficient use of agricultural inputs, high added value, and rapidly perishable products that can easily substitute the rural production in the local market. Urban horticulture is the most competitive branch of urban farming due to the high cost of urban land and with the need of high water- and fertilizer-use efficiency. Traditional urban horticulture systems are classified in four types: allotment and family gardens, simplified extensive systems, shifting cultivation, and intensive systems. We describe also innovative systems including organoponics and simplified soilless cultures.


title: Ensemble Deep Learning for Biomedical Time Series Classification

abstract: Ensemble learning has been proved to improve the generalization ability effectively in both theory and practice. In this paper, we briefly outline the current status of research on it first. Then, a new deep neural network-based ensemble method that integrates filtering views, local views, distorted views, explicit training, implicit training, subview prediction, and Simple Average is proposed for biomedical time series classification. Finally, we validate its effectiveness on the Chinese Cardiovascular Disease Database containing a large number of electrocardiogram recordings. The experimental results show that the proposed method has certain advantages compared to some well-known ensemble methods, such as Bagging and AdaBoost.


title: Adaptive Multi-Scale Entropy Fusion De-Hazing Based on Fractional Order

abstract: This paper describes a proposed fractional filter-based multi-scale underwater and hazy image enhancement algorithm. The proposed system combines a modified global contrast operator with fractional order-based multi-scale filters used to generate several images, which are fused based on entropy and standard deviation. The multi-scale-global enhancement technique enables fully adaptive and controlled color correction and contrast enhancement without over exposure of highlights when processing hazy and underwater images. This in addition to the illumination/reflectance estimation coupled with global and local contrast enhancement. The proposed algorithm is also compared with the most recent available state-of-the-art multi-scale fusion de-hazing algorithm. Experimental comparisons indicate that the proposed approach yields a better edge and contrast enhancement results without a halo effect, without color degradation, and is faster and more adaptive than all other algorithms from the literature.


title: Inheritance and genetic mapping of tuber eye depth in cultivated diploid potatoes

abstract: Tuber eye depth of the potato (Solanum tuberosum L.) is an important trait for the processing quality and appearance of potatoes. In the present study, we used a cultivated diploid potato family (12601) of 107 plants to dissect the mode of inheritance and to map the gene(s) controlling the trait. The family segregated for both eye depth (deep vs shallow) and tuber shape (round vs long) traits. The deep eye (Eyd) phenotype was found to be associated with round tubers (Ro) in most progeny clones. Further evaluation of this population with molecular markers including simple sequence repeats, amplified fragment length polymorphism, and sequence-characterized amplified regions revealed that the primary locus for eye depth is located on chromosome 10. This map location was confirmed by evaluating a second diploid family (12586). The results of this study led to the following conclusions: (1) there is a major locus controlling the eye depth trait; (2) deep eye (Eyd) is dominant to shallow (eyd); (3) the Eyd/eyd locus is located on chromosome 10; and (4) the Eyd/eyd locus is closely linked with the major locus for tuber shape (Ro/ro), at a distance of about 4 cM.


title: Studies of malformation syndromes in man XXXX: Multiple congenital anomalies/mental retardation syndrome or variant familial developmental pattern; differential diagnosis and description of the McDonough syndrome (with XXY son from XY/XXY father)

abstract: The McDonough syndrome is a "new" MCA/MR syndrome which was found in 3 children (1 girl, 2 boys) of non-consanguineous parents. The affected children were mentally retarded (IQ 47--67) and had congenital heart defect, sternal deformity, kyphosis and craniofacila anomalies (anteverted auricles, upward slanted palpebral fissures, squint); cryptorchidism was present in the 2 boys. In addition a possible VFDP is postulated as the explanation for similar features in affected and unaffected siblings and parents. However, the McDonough syndrome may be an autosomal recessive trait with minor manifestations in heterozygotes. The klinefelter syndrome in one affected boy and a 46,XY/47,XXY chromosome constitution in the father was a coincidental finding.


title: Social mobile Augmented Reality for retail

abstract: Consumers are increasingly relying on web-based social content, such as product reviews, prior to making to a purchase. Recent surveys in the Retail Industry confirm that social content is indeed the #1 aid in a buying decision. Currently, accessing or adding to this valuable web-based social content repository is mostly limited to computers far removed from the site of the shopping experience itself. We present a mobile Augmented Reality application, which extends such social content from the computer monitor into the physical world through mobile phones, providing consumers with in situ information on products right when and where they need to make buying decisions.