title: Structural Clustering of Machine-Generated Mail

abstract: Several recent studies have presented different approaches for clustering and classifying machine-generated mail based on email headers. We propose to expand these approaches by considering email message bodies. We argue that our approach can help increase coverage and precision in several tasks, and is especially critical for mail extraction. We remind that mail extraction supports a variety of mail mining applications such as ad re-targeting, mail search, and mail summarization. We introduce new structural clustering methods that leverage the HTML structure that is common to messages generated by a same mass-sender script. We discuss how such structural clustering can be conducted at different levels of granularity, using either strict or flexible matching constraints, depending on the use cases. We present large scale experiments carried over real Yahoo mail traffic. For our first use case of automatic mail extraction, we describe novel flexible-matching clustering methods that meet the key requirements of high intra-cluster similarity, adequate clusters size, and relatively small overall number of clusters. We identify the precise level of flexibility that is needed in order to achieve extremely high extraction precision (close to 100%), while producing relatively small number of clusters. For our second use case, namely, mail classification, we show that strict structural matching is more adequate, achieving precision and recall rates between 85%-90%, while converging to a stable classification after a short learning cycle. This represents an increase of 10%-20% compared to the sender-based method described in previous work, when run over the same period length. Our work has been deployed in production in Yahoo mail backend.


title: Self-Mutilation of Tongue and Lip in a Patient with Simple Schizophrenia

abstract: Self-inflicted lesions that cause mutilation are observed in schizophrenic patients. This case report describes the diagnosis and treatment of simple schizophrenia in a 31-year-old male patient who bit his own tongue and lower lip. The dental treatment proposed included the construction of a splint to prevent new lesions and to allow healing of existing ones. The treatment afforded to avoid tooth extraction before the patient responded to psychiatric drug treatment. The importance of a transdisciplinary approach to self-mutilation due to psychiatric disorders is stressed. The interaction between the two teams, stomatology and psychiatry, was crucial for the improvement of the patient's condition.


CS224N Project: Let Computers Do Reading Comprehension


title: Bio-inspired group mobility model for mobile ad hoc networks based on bird-flocking behavior

abstract: In this paper, we propose a novel group mobility model for mobile ad hoc networks (MANETs), named as Bird-Flocking Behavior Inspired Group Mobility Model (BFBIGM), which takes inspiration from the mobility of a flock of birds, flying in a formation. Most existing modeling techniques are deficient in successfully addressing many aspects in terms of the application of realistic forces on the movement of mobile nodes (MNs), the interaction of MNs within a group, and collision avoidance within a group and with environmental obstacles. The results obtained through experiments show that in terms of connectivity metrics, such as link duration, BFBIGM performs around 50% better in comparison to the popular existing mobility models like Random Waypoint (RWP) Model (Johnson et al. in Ad hoc networking, Addison-Wesley, Menlo Park, pp. 139–172,2001) and the Reference Point Group Mobility (RPGM) Model (Hong et al. in: Proceedings of the 2nd ACM international workshop on modeling, analysis and simulation of wireless and mobile systems, Seattle, WA, pp. 53–60,1999).


Modelling Lean, Agile, Leagile Manufacturing Strategies: an Fuzzy Analytical Hierarchy Process Approach for Ready Made Ware (clothing) Industry in Mosul, Iraq


title: Fusion of Perception and V2P Communication Systems for the Safety of Vulnerable Road Users

abstract: With cooperative intelligent transportation systems, vulnerable road users (VRU) safety can be enhanced by multiple means. On the one hand, perception systems are based on embedded sensors to protect VRUs. However, such systems may fail due to the sensors’ visibility conditions and imprecision. On the other hand, vehicle-to-pedestrian (V2P) communication can contribute to the VRU safety by allowing vehicles and pedestrians to exchange information. This solution is, however, largely affected by the reliability of the exchanged information, which most generally is the GPS data. Since perception and communication have complementary features, we can expect that a fusion between these two approaches can be a solution to the VRU safety. In this paper, we propose a cooperative system that combines the outputs of communication and perception. After introducing theoretical models of both individual approaches, we develop a probabilistic association between perception and V2P communication information by means of multi-hypothesis tracking. Experimental studies are conducted to demonstrate the applicability of this approach in real-world environments. Our results show that the cooperative VRU protection system can benefit of the redundancy coming from the perception and communication technologies both in line-of-sight (LOS) and non-LOS conditions. We establish that the performances of this system are influenced by the classification performances of the perception system and by the accuracy of the GPS positioning transmitted by the communication system.


title: The FIGO classification of causes of abnormal uterine bleeding in the reproductive years.

abstract: At this juncture, clinical management, education for medical providers, and the design and interpretation of clinical trials have been hampered by the absence of a consensus system for nomenclature for the description of symptoms as well as classification of causes or potential causes of abnormal uterine bleeding (AUB). To address this issue, the Fédération Internationale de Gynécologie et d'Obstétrique (FIGO) has designed the PALM-COEIN (Polyp, Adenomyosis, Leiomyoma, Malignancy and Hyperplasia, Coagulopathy, Ovulatory Disorders, Endometrial Disorders, Iatrogenic Causes, and Not Classified) classification system for causes of AUB in the reproductive years.


Simple Resolver Demodulation


title: A complete planner design of microstrip patch antenna for a passive UHF RFID tag

abstract: A micro-strip patch antenna for a passive radio frequency identification (RFID) tag which can operate in the ultra high frequency (UHF) range from 865 MHz to 867 MHz is presented in this paper. The proposed antenna is designed and suitable for tagging the metallic boxes in the UK and Europe warehouse environment. The design is supplemented with the simulation results. In addition, the effect of the antenna substrate thickness and the ground plane on the performance of the proposed antenna is also investigated. The study shows that there is little affect by the antenna substrate thickness on the performance.


title: Which is more important in Internet shopping, perceived price or trust?

abstract: Price and trust are considered to be two important factors that influence customer purchasing decisions in Internet shopping. This paper examines the relative influence they have on online purchasing decisions for both potential and repeat customers. The knowledge of their relative impacts and changes in their relative roles over customer transaction experience is useful in developing customized sales strategies to target different groups of customers. The results of this study revealed that perceived trust exerted a stronger effect than perceived price on purchase intentions for both potential and repeat customers of an online store. The results also revealed that perceived price exerted a stronger influence on purchase decisions of repeat customers as compared to that of potential customers. Perceived trust exerted a stronger influence on purchase decisions of potential customers as compared to that of repeat customers.


title: Fairplay — a secure two-party computation system

abstract: Advances in modern cryptography coupled with rapid growth in processing and communication speeds make secure two-party computation a realistic paradigm. Yet, thus far, interest in this paradigm has remained mostly theoretical. ::: ::: This paper introduces Fairplay [28], a full-fledged system that implements generic secure function evaluation (SFE). Fairplay comprises a high level procedural definition language called SFDL tailored to the SFE paradigm; a compiler of SFDL into a one-pass Boolean circuit presented in a language called SHDL; and Bob/Alice programs that evaluate the SHDL circuit in the manner suggested by Yao in [39]. ::: ::: This system enables us to present the first evaluation of an overall SFE in real settings, as well as examining its components and identifying potential bottlenecks. It provides a test-bed of ideas and enhancements concerning SFE, whether by replacing parts of it, or by integrating with it. We exemplify its utility by examining several alternative implementations of oblivious transfer within the system, and reporting on their effect on overall performance.


title: Improving matrix factorization recommendations for examples in cold start

abstract: Novel framework for the imputation of missing values into the ratings matrix.Imputation of missing values significantly reduces matrix factorization prediction error.Increased matrix factorization performance in the cold start state. Recommender systems suggest items of interest to users based on their preferences (i.e. previous ratings). If there are no ratings for a certain user or item, it is said that there is a problem of a cold start, which leads to unreliable recommendations. We propose a novel approach for alleviating the cold start problem by imputing missing values into the input matrix. Our approach combines local learning, attribute selection, and value aggregation into a single approach; it was evaluated on three datasets and using four matrix factorization algorithms. The results showed that the imputation of missing values significantly reduces the recommendation error. Two tested methods, denoted with 25-FR-ME-? and 10-FR-ME-?, significantly improved performance of all tested matrix factorization algorithms, without the requirement to use a different recommendation algorithm for the users in the cold start state.


title: A list heuristic for vertex cover

abstract: We analyze a list heuristic for the vertex cover problem that handles the vertices in a given static order based on the degree sequence. We prove an approximation ratio of at most @D/2+32 for a nonincreasing degree sequence, and show that no ordering can achieve an approximation ratio of less than @D/2.


title: Attention based Sentence Extraction from Scientific Articles using Pseudo-Labeled data

abstract: In this work, we present a weakly supervised sentence extraction technique for identifying important sentences in scientific papers that are worthy of inclusion in the abstract. We propose a new attention based deep learning architecture that jointly learns to identify important content, as well as the cue phrases that are indicative of summary worthy sentences. We propose a new context embedding technique for determining the focus of a given paper using topic models and use it jointly with an LSTM based sequence encoder to learn attention weights across the sentence words. We use a collection of articles publicly available through ACL anthology for our experiments. Our system achieves a performance that is better, in terms of several ROUGE metrics, as compared to several state of art extractive techniques. It also generates more coherent summaries and preserves the overall structure of the document.


title: Time-Reversal UWB Wireless Communication-Based Train Control in Tunnel

abstract: This paper reports an evaluation of UWB radio technology and Time-Reversal (TR) technique in tunnel environments for train-to-wayside communication. UWB technology has the potential to offer simultaneous ground-totrain communication, train location and obstacle detection in front of the trains. Time-Reversal channel pre-filtering facilitates signal detection and helps reduce interference. Thus, UWB-TR combination provides a challenging, economically sensible, as well as technically effective alternative solution to existing signaling technologies used in urban transport systems. This paper deals with deterministic channel modeling and its characterization in tunnel environment. It reports simulation performance evaluation of UWB-TR combinations in the developed channel model.


title: Meeting demand for family planning within a generation: the post-2015 agenda

abstract: Expanding access to family planning has been a key aim of health and development programming for more than 40 years. During that time, significant gains have been made in reducing unmet need for family planning, increasing contraceptive prevalence, and preventing unintended pregnancies. Over the last two decades, however, the pace of these gains has slowed, especially in several countries in sub-Saharan Africa and South Asia. Not only does family planning enable individuals and couples to achieve their childbearing (family building?) goals, an ever growing body of research shows the wider beneficial impact of family planning on improving maternal and child health and survival, increasing economic well-being of individuals, families and communities, and empowering women.i,ii


title: Brief Announcement: Byzantine-Tolerant Machine Learning

abstract: We report on Krum, the first provably Byzantine-tolerant aggregation rule for distributed Stochastic Gradient Descent (SGD). Krum guarantees the convergence of SGD even in a distributed setting where (asymptotically) up to half of the workers can be malicious adversaries trying to attack the learning system.


title: Synthesizing Game Audio Using Deep Neural Networks

abstract: High quality audio plays an important role in gaming, contributing to player immersion during gameplay. Creating audio content which matches overall theme and aesthetic is essential, such that players can become fully engrossed in a game environment. Sound effects must also fit well with visual elements of a game so as not to break player immersion. Producing suitable, unique sound effects requires the use of a wide range of audio processing techniques. In this paper, we examine a method of generating in-game audio using Generative Adversarial Networks, and compare this to traditional methods of synthesizing and augmenting audio.


title: Spatial Correlation and Mobility-Aware Traffic Modeling for Wireless Sensor Networks

abstract: Recently, there has been a great deal of research on using mobility in wireless sensor networks (WSNs) to facilitate surveillance and reconnaissance in a wide deployment area. Besides providing an extended sensing coverage, node mobility along with spatial correlation introduces new network dynamics, which could lead to the traffic patterns fundamentally different from the traditional (Markovian) models. In this paper, a novel traffic modeling scheme for capturing these dynamics is proposed that takes into account the statistical patterns of node mobility and spatial correlation. The contributions made in this paper are twofold. First, it is shown that the joint effects of mobility and spatial correlation can lead to bursty traffic. More specifically, a high mobility variance and small spatial correlation can give rise to pseudo-long-range-dependent (LRD) traffic (high bursty traffic), whose autocorrelation function decays slowly and hyperbolically up to a certain cutoff time lag. Second, due to the ad hoc nature of WSNs, certain relay nodes may have several routes passing through them, necessitating local traffic aggregations. At these relay nodes, our model predicts that the aggregated traffic also exhibits the bursty behavior characterized by a scaled power-law decayed autocovariance function. According to these findings, a novel traffic shaping protocol using movement coordination is proposed to facilitate effective and efficient resource provisioning strategy. Finally, simulation results reveal a close agreement between the traffic pattern predicted by our theoretical model and the simulated transmissions from multiple independent sources, under specific bounds of the observation intervals.


title: A 2.4-GHz Low-Power Superregenerative RF Front-End for High Data Rate Applications

abstract: This paper describes the design and implementation of a low-power superregenerative receiver front-end operating in the 2.4-GHz ISM band. Specifically, the design is conceived to support high quench frequencies and, consequently, high data rates. The front-end is evaluated as a part of a novel architecture in which the superregenerative oscillator is quenched synchronously with the received data. This mode of operation allows higher data rates to be obtained and significantly overcomes the traditional problem of poor selectivity in this type or receivers. The implemented front-end achieves 10 Mbit/s, an RF bandwidth of 37 MHz and a total supply current of 870 ?A at 3-V supply voltage, offering an excellent trade-off between performance, simplicity and power consumption.


title: INDREX: In-database relation extraction

abstract: Abstract The management of text data has a long-standing history in the human mankind. A particular common task is extracting relations from text. Typically, the user performs this task with two separate systems, a relation extraction system and an SQL-based query engine for analytical tasks. During this iterative analytical workflow, the user must frequently ship data between these systems. Worse, the user must learn to manage both systems. Therefore, end users often desire a single system for both analytical and relation extraction tasks. We propose INDREX, a system that provides a single and comprehensive view of the whole process combining both relation extraction and later exploitation with SQL. The system permits a data warehouse style extract-transform-load of generic relations extracted from text documents and can support additional text mining analysis libraries or systems. Once generic relations are loaded, the user can define SQL queries on the extracted relations to discover higher level semantics or to join them with other relational data. For executing this powerful task, our system extends the SQL-based analytical capabilities of a columnar-based massively parallel query processing engine with a broad set of user-defined functions and a data model that supports this task. Our white-box approach permits INDREX to benefit from built-in query optimization and indexing techniques of the underlaying query execution engine. Applications that support both text mining and analytical workflows leverage new analytical platforms based on the MapReduce framework and its open source Hadoop implementation. We compare our system against this base line. We measure execution times for common workflows and demonstrate orders of magnitude improvement in execution time using INDREX.


title: Data fusion in intelligent transportation systems: Progress and challenges - A survey

abstract: In intelligent transportation systems (ITS), transportation infrastructure is complimented with information and communication technologies with the objectives of attaining improved passenger safety, reduced transportation time and fuel consumption and vehicle wear and tear. With the advent of modern communication and computational devices and inexpensive sensors it is possible to collect and process data from a number of sources. Data fusion (DF) is collection of techniques by which information from multiple sources are combined in order to reach a better inference. DF is an inevitable tool for ITS. This paper provides a survey of how DF is used in different areas of ITS.


title: Unsupervised models for morpheme segmentation and morphology learning

abstract: We present a model family called Morfessor for the unsupervised induction of a simple morphology from raw text data. The model is formulated in a probabilistic maximum a posteriori framework. Morfessor can handle highly inflecting and compounding languages where words can consist of lengthy sequences of morphemes. A lexicon of word segments, called morphs, is induced from the data. The lexicon stores information about both the usage and form of the morphs. Several instances of the model are evaluated quantitatively in a morpheme segmentation task on different sized sets of Finnish as well as English data. Morfessor is shown to perform very well compared to a widely known benchmark algorithm, in particular on Finnish data.


The Product Creation Process


title: EDHF: an update

abstract: The endothelium controls vascular tone not only by releasing NO and prostacyclin, but also by other pathways causing hyperpolarization of the underlying smooth muscle cells. This characteristic was at the origin of the term 'endothelium-derived hyperpolarizing factor' (EDHF). However, this acronym includes different mechanisms. Arachidonic acid metabolites derived from the cyclo-oxygenases, lipoxygenases and cytochrome P450 pathways, H(2)O(2), CO, H(2)S and various peptides can be released by endothelial cells. These factors activate different families of K(+) channels and hyperpolarization of the vascular smooth muscle cells contribute to the mechanisms leading to their relaxation. Additionally, another pathway associated with the hyperpolarization of both endothelial and vascular smooth muscle cells contributes also to endothelium-dependent relaxations (EDHF-mediated responses). These responses involve an increase in the intracellular Ca(2+) concentration of the endothelial cells, followed by the opening of SK(Ca) and IK(Ca) channels (small and intermediate conductance Ca(2+)-activated K(+) channels respectively). These channels have a distinct subcellular distribution: SK(Ca) are widely distributed over the plasma membrane, whereas IK(Ca) are preferentially expressed in the endothelial projections toward the smooth muscle cells. Following SK(Ca) activation, smooth muscle hyperpolarization is preferentially evoked by electrical coupling through myoendothelial gap junctions, whereas, following IK(Ca) activation, K(+) efflux can activate smooth muscle Kir2.1 and/or Na(+)/K(+)-ATPase. EDHF-mediated responses are altered by aging and various pathologies. Therapeutic interventions can restore these responses, suggesting that the improvement in the EDHF pathway contributes to their beneficial effect. A better characterization of EDHF-mediated responses should allow the determination of whether or not new drugable targets can be identified for the treatment of cardiovascular diseases.


title: The State of the Art in Sentiment Visualization

abstract: Visualization of sentiments and opinions extracted from or annotated in texts has become a prominent topic of research over the last decade. From basic pie and bar charts used to illustrate customer reviews to extensive visual analytics systems involving novel representations, sentiment visualization techniques have evolved to deal with complex multidimensional data sets, includingtemporal, relational, and geospatial aspects. This contribution presents a survey of sentiment visualization techniques based on a detailed categorization. We describe the background of sentiment analysis, introduce a categorization for sentiment visualization techniques that includes 7 groups with 35 categories in total, and discuss 132 techniques from peer-reviewed publications together with an interactive web-based survey browser. Finally, we discuss insights and opportunities for further research in sentiment visualization. We expect this survey to be useful for visualization researchers whose interests include sentiment or other aspects of text data as well as researchers and practitioners from other disciplines in search of efficient visualization techniques applicable to their tasks and data. (Less)


title: Sampling cube: a framework for statistical olap over sampling data

abstract: Sampling is a popular method of data collection when it is impossible or too costly to reach the entire population. For example, television show ratings in the United States are gathered from a sample of roughly 5,000 households. To use the results effectively, the samples are further partitioned in a multidimensional space based on multiple attribute values. This naturally leads to the desirability of OLAP (Online Analytical Processing) over sampling data. However, unlike traditional data, sampling data is inherently uncertain, i.e., not representing the full data in the population. Thus, it is desirable to return not only query results but also the confidence intervals indicating the reliability of the results. Moreover, a certain segment in a multidimensional space may contain none or too few samples. This requires some additional analysis to return trustable results. In this paper we propose a Sampling Cube framework, which efficiently calculates confidence intervals for any multidimensional query and uses the OLAP structure to group similar segments to increase sampling size when needed. Further, to handle high dimensional data, a Sampling Cube Shell method is proposed to effectively reduce the storage requirement while still preserving query result quality.


title: Turning Mobile Phones into 3D Scanners

abstract: In this paper, we propose an efficient and accurate scheme for the integration of multiple stereo-based depth measurements. For each provided depth map a confidence-based weight is assigned to each depth estimate by evaluating local geometry orientation, underlying camera setting and photometric evidence. Subsequently, all hypotheses are fused together into a compact and consistent 3D model. Thereby, visibility conflicts are identified and resolved, and fitting measurements are averaged with regard to their confidence scores. The individual stages of the proposed approach are validated by comparing it to two alternative techniques which rely on a conceptually different fusion scheme and a different confidence inference, respectively. Pursuing live 3D reconstruction on mobile devices as a primary goal, we demonstrate that the developed method can easily be integrated into a system for monocular interactive 3D modeling by substantially improving its accuracy while adding a negligible overhead to its performance and retaining its interactive potential.


title: Methods for Interpreting and Understanding Deep Neural Networks

abstract: Abstract This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions. It is based on a tutorial given at ICASSP 2017. As a tutorial paper, the set of methods covered here is not exhaustive, but sufficiently representative to discuss a number of questions in interpretability, technical challenges, and possible applications. The second part of the tutorial focuses on the recently proposed layer-wise relevance propagation (LRP) technique, for which we provide theory, recommendations, and tricks, to make most efficient use of it on real data.


title: Flow-Based Anomaly Detection Using Neural Network Optimized with GSA Algorithm

abstract: Reliable high-speed networks are essential to provide quality services to ever growing Internet applications. A Network Intrusion Detection System (NIDS) is an important tool to protect computer networks from attacks. Traditional packet-based NIDSs are time-intensive as they analyze all network packets. A state-of-the-art NIDS should be able to handle a high volume of traffic in real time. Flow-based intrusion detection is an effective method for high speed networks since it inspects only packet headers. The existence of new attacks in the future is another challenge for intrusion detection. Anomaly-based intrusion detection is a well-known method capable of detecting unknown attacks. In this paper, we propose a flow-based anomaly detection system. Artificial Neural Network (ANN) is an important approach for anomaly detection. We used a Multi-Layer Perceptron (MLP) neural network with one hidden layer. We investigate the use of a Gravitational Search Algorithm (GSA) in optimizing interconnection weights of a MLP network. Our proposed GSA-based flow anomaly detection system (GFADS) is trained with a flow-based data set. The trained system can classify benign and malicious flows with 99.43% accuracy. We compare the performance of GSA with traditional gradient descent training algorithms and a particle swarm optimization (PSO) algorithm. The results show that GFADS is effective in flow-based anomaly detection. Finally, we propose a four-feature subset as the optimal set of features.


title: A Linear-Time Graph Kernel

abstract: The design of a good kernel is fundamental for knowledge discovery from graph-structured data. Existing graph kernels exploit only limited information about the graph structures but are still computationally expensive. We propose a novel graph kernel based on the structural characteristics of graphs. The key is to represent node labels as binary arrays and characterize each node using logical operations on the label set of the connected nodes. Our kernel has a linear time complexity with respect to the number of nodes times the average number of neighboring nodes in the given graphs. The experimental result shows that the proposed kernel performs comparable and much faster than a state-of-the-art graph kernel for benchmark data sets and shows high scalability for new applications with large graphs.


title: Ontology-driven Requirements Engineering: Building the OntoREM Meta Model

abstract: The present paper gives an insight into the process of developing the metamodel of OntoREM, an Ontology-driven Requirements Engineering Methodology, which is currently being developed at the University of the West of England in close cooperation with Airbus [1]. The approach suggested by Noy and McGuinness (2000) has been followed to build a first version of the metamodel of OntoREM as an ontology specified in OWL using Protege-OWL [2]. The outcomes and shortcomings of applying this approach in the context of OntoREM have been assessed showing a major shift in the way that requirements engineering is practiced from process-driven to also knowledge-driven requirements engineering [1]. This, however, necessitates further empirical evaluation of the implementation of OntoREM.


title: SQUISH: an online approach for GPS trajectory compression

abstract: GPS-equipped mobile devices such as smart phones and in-car navigation units are collecting enormous amounts spatial and temporal information that traces a moving object's path. The popularity of these devices has led to an exponential increase in the amount of GPS trajectory data generated. The size of this data makes it difficult to transmit it over a mobile network and to analyze it to extract useful patterns. Numerous compression algorithms have been proposed to reduce the size of trajectory data sets; however these methods often lose important information essential to location-based applications such as object's position, time and speed. This paper describes the Spatial QUalIty Simplification Heuristic (SQUISH) method that demonstrates improved performance when compressing up to roughly 10% of the original data size, and preserves speed information at a much higher accuracy under aggressive compression. Performance is evaluated by comparison with three competing trajectory compression algorithms: Uniform Sampling, Douglas-Peucker and Dead Reckoning.


title: A case study of user-level spam filtering

abstract: There are number of Anti-Spam filters that have reduced the amount of email spam in the inbox but the problem still continues as the spammers circumvent these techniques. The problems need to be addressed from different aspects. Major problem for instance arises when these anti-spam techniques misjudge or misclassify legitimate emails as spam (false positive); or fail to deliver or block spam on the SMTP server (false negative); thus causing a staggering cost in loss of time, effort and finance. Though false positive are very harmful loss of important information for the user, false negatives defeat the purpose of the spam filtering. This paper makes an effort in proposing another aspect to address this problem. It discusses some of these anti-spam techniques, especially the filtering technological endorsements designed to prevent spam to entrench their capability enhancements, as well as analytical recommendations that will be subject to further research. Apart from applying anti-spam techniques, training of Spam control tool with relevant user preferences can reduce the chances of false positives, false negatives and spam email that land in the inbox. We identify the need for training the filter with domain specific data. This paper shows the decline in false negatives via results of a case study on training the Spam Bayes tool with carefully collected domain specific user preferred dataset for over a period of 12 months.


title: Clustering high dimensional categorical data via topographical features

abstract: Analysis of categorical data is a challenging task. In this paper, we propose to compute topographical features of high-dimensional categorical data. We propose an efficient algorithm to extract modes of the underlying distribution and their attractive basins. These topographical features provide a geometric view of the data and can be applied to visualization and clustering of real world challenging datasets. Experiments show that our principled method outperforms state-of-the-art clustering methods while also admits an embarrassingly parallel property.


title: FEPDF: A Robust Feature Extractor for Malicious PDF Detection

abstract: Due to rich characteristics and functionalities, PDF format has become the de facto standard for the electronic document exchange. As vulnerabilities in the major PDF viewers have been disclosed, a number of methods have been proposed to tame the increasing PDF threats. However, one recent evasion exploit is found to evade most of detections and renders all of the major static methods void. Moreover, many existing vulnerabilities identified before can now evade the detection through exploiting this evasion exploit. In this paper, we introduce this newly identified evasion exploit and propose a new feature extractor FEPDF to detect malicious PDFs. Based on the FEPDF and the JavaScript detection model, we test the performance of the proposed feature extractor FEPDF, and evaluation results show that FEPDF has a satisfactory performance in malicious PDF detection.


title: An Intelligent Tutoring System for Entity Relationship Modelling

abstract: The paper presents KERMIT, a Knowledge-based Entity Relationship Modelling Intelligent Tutor. KERMIT is a problem-solving environment for the university-level students, in which they can practise conceptual database design using the Entity-Relationship data model. KERMIT uses Constraint-Based Modelling (CBM) to model the domain knowledge and generate student models. We have used CBM previously in tutors that teach SQL and English punctuation rules. The research presented in this paper is significant because we show that CBM can be used to support students learning design tasks, which are very different from domains we dealt with in earlier tutors. The paper describes the system's architecture and functionality. The system observes students' actions and adapts to their knowledge and learning abilities. KERMIT has been evaluated in the context of genuine teaching activities. We present the results of two evaluation studies with students taking database courses, which show that KERMIT is an effective system. The students have enjoyed the system's adaptability and found it a valuable asset to their learning.


title: FasTC: accelerated fixed-rate texture encoding

abstract: We present a new algorithm for encoding low dynamic range images into fixed-rate texture compression formats. Our approach provides orders of magnitude improvements in speed over existing publicly-available compressors, while generating high quality results. The algorithm is applicable to any fixed-rate texture encoding scheme based on Block Truncation Coding and we use it to compress images into the OpenGL BPTC format. The underlying technique uses an axis-aligned bounding box to estimate the proper partitioning of a texel block and performs a generalized cluster fit to compute the endpoint approximation. This approximation can be further refined using simulated annealing. The algorithm is inherently parallel and scales with the number of processor cores. We highlight its performance on low-frequency game textures and the high frequency Kodak Test Image Suite.


title: When Collections of Creatives Become Creative Collectives: A Field Study of Problem Solving at Work

abstract: This paper introduces a model of collective creativity that explains how the locus of creative problem solving shifts, at times, from the individual to the interactions of a collective. The model is grounded in observations, interviews, informal conversations, and archival data gathered in intensive field studies of work in professional service firms. The evidence suggests that although some creative solutions can be seen as the products of individual insight, others should be regarded as the products of a momentary collective process. Such collective creativity reflects a qualitative shift in the nature of the creative process, as the comprehension of a problematic situation and the generation of creative solutions draw from---and reframe---the past experiences of participants in ways that lead to new and valuable insights. This research investigates the origins of such moments, and builds a model of collective creativity that identifies the precipitating roles played by four types of social interaction: help seeking, help giving, reflective reframing, and reinforcing. Implications of this research include shifting the emphasis in research and management of creativity from identifying and managing creative individuals to understanding the social context and developing interactive approaches to creativity, and from a focus on relatively constant contextual variables to the alignment of fluctuating variables and their precipitation of momentary phenomena.


title: Investigating age invariant face recognition based on periocular biometrics

abstract: In this paper, we will present a novel framework of utilizing periocular region for age invariant face recognition. To obtain age invariant features, we first perform preprocessing schemes, such as pose correction, illumination and periocular region normalization. And then we apply robust Walsh-Hadamard transform encoded local binary patterns (WLBP) on preprocessed periocular region only. We find the WLBP feature on periocular region maintains consistency of the same individual across ages. Finally, we use unsupervised discriminant projection (UDP) to build subspaces on WLBP featured periocular images and gain 100% rank-1 identification rate and 98% verification rate at 0.1% false accept rate on the entire FG-NET database. Compared to published results, our proposed approach yields the best recognition and identification results.


title: Multi-view Pictorial Structures for 3D Human Pose Estimation

abstract: Pictorial structure models are the de facto standard for 2D human pose estimation. Numerous refinements and improvements have been proposed such as discriminatively trained body part detectors, flexible body models, and local and global mixtures. While these techniques allow to achieve state-of-the-art performance for 2D pose estimation, they have not yet been extended to enable pose estimation in 3D. This paper thus proposes a multi-view pictorial structures model that builds on recent advances in 2D pose estimation and incorporates evidence across multiple viewpoints to allow for robust 3D pose estimation. We evaluate our multi-view pictorial structures approach on the HumanEva-I and MPII Cooking dataset. In comparison to related work for 3D pose estimation our approach achieves similar or better results while operating on single-frames only and not relying on activity specific motion models or tracking. Notably, our approach outperforms state-of-the-art for activities with more complex motions.


title: Modeling, design, and fabrication of ultra-high bandwidth 3D Glass Photonics (3DGP) in glass interposers

abstract: This paper presents, for the first time, the 3D Glass Photonics (3DGP) technology being developed by Georgia Tech, based on ultra-thin 3D glass interposer [1]. The 3DGP system integrates both optical and electrical interconnects in the same glass substrate using photo-sensitive polymer core, and polymer cladding within an ultra-thin glass substrate. The 3DGP processes are demonstrated using 180 & 100 um thick glass substrates with 30 um diameter via and 8 um wide waveguide structures. The optical vias are used as mode transformer and high-tolerance coupler between fibers and chips. Finite-difference analysis is performed to determine the alignment tolerances of such vias.


title: Multiscale, Multiphysics Numerical Modeling of Fusion Welding with Experimental Characterization and Validation

abstract: Various physical interfacial phenomena occur during the process of welding and influence the final properties of welded structures. As the features of such interfaces depend on physics that resolve at different spatial scales, a multiscale and multiphysics numerical modeling approach is necessary. In a collaborative research project Modeling of Interface Evolution in Advanced Welding, a novel strategy of model linking is employed in a multiscale, multiphysics computational framework for fusion welding. We only directly link numerical models that are on neighboring spatial scales instead of trying to link all submodels directly together through all available spatial scales. This strategy ensures that the numerical models assist one another via smooth data transfer, avoiding the huge difficulty raised by forcing models to attempt communication over many spatial scales. Experimental activities contribute to the modeling work by providing valuable input parameters and validation data. Representative examples of the results of modeling, linking and characterization are presented.


title: q-Space Novelty Detection with Variational Autoencoders

abstract: In machine learning, novelty detection is the task of identifying novel unseen data. During training, only samples from the normal class are available. Test samples are classified as normal or abnormal by assignment of a novelty score. Here we propose novelty detection methods based on training variational autoencoders (VAEs) on normal data. Since abnormal samples are not used during training, we define novelty metrics based on the (partially complementary) assumptions that the VAE is less capable of reconstructing abnormal samples well; that abnormal samples more strongly violate the VAE regularizer; and that abnormal samples differ from normal samples not only in input-feature space, but also in the VAE latent space and VAE output. These approaches, combined with various possibilities of using (e.g. sampling) the probabilistic VAE to obtain scalar novelty scores, yield a large family of methods. We apply these methods to magnetic resonance imaging, namely to the detection of diffusion-space (q-space) abnormalities in diffusion MRI scans of multiple sclerosis patients, i.e. to detect multiple sclerosis lesions without using any lesion labels for training. Many of our methods outperform previously proposed q-space novelty detection methods. We also evaluate the proposed methods on the MNIST handwritten digits dataset and show that many of them are able to outperform the state of the art.


title: Facilitating Natural Conversational Agent Interactions: Lessons from a Deception Experiment

abstract: This study reports the results of a laboratory experiment exploring interactions between humans and a conversational agent. Using the ChatScript language, we created a chat bot that asked participants to describe a series of images. The two objectives of this study were (1) to analyze the impact of dynamic responses on participants’ perceptions of the conversational agent, and (2) to explore behavioral changes in interactions with the chat bot (i.e. response latency and pauses) when participants engaged in deception. We discovered that a chat bot that provides adaptive responses based on the participant’s input dramatically increases the perceived humanness and engagement of the conversational agent. Deceivers interacting with a dynamic chat bot exhibited consistent response latencies and pause lengths while deceivers with a static chat bot exhibited longer response latencies and pause lengths. These results give new insights on social interactions with computer agents during truthful and deceptive interactions.


title: W-Net: A Deep Model for Fully Unsupervised Image Segmentation

abstract: While significant attention has been recently focused on designing supervised deep semantic segmentation algorithms for vision tasks, there are many domains in which sufficient supervised pixel-level labels are difficult to obtain. In this paper, we revisit the problem of purely unsupervised image segmentation and propose a novel deep architecture for this problem. We borrow recent ideas from supervised semantic segmentation methods, in particular by concatenating two fully convolutional networks together into an autoencoder--one for encoding and one for decoding. The encoding layer produces a k-way pixelwise prediction, and both the reconstruction error of the autoencoder as well as the normalized cut produced by the encoder are jointly minimized during training. When combined with suitable postprocessing involving conditional random field smoothing and hierarchical segmentation, our resulting algorithm achieves impressive results on the benchmark Berkeley Segmentation Data Set, outperforming a number of competing methods.


title: Fully Connected Network-Based Intra Prediction for Image Coding

abstract: This paper proposes a deep learning method for intra prediction. Different from traditional methods utilizing some fixed rules, we propose using a fully connected network to learn an end-to-end mapping from neighboring reconstructed pixels to the current block. In the proposed method, the network is fed by multiple reference lines. Compared with traditional single line-based methods, more contextual information of the current block is utilized. For this reason, the proposed network has the potential to generate better prediction. In addition, the proposed network has good generalization ability on different bitrate settings. The model trained from a specified bitrate setting also works well on other bitrate settings. Experimental results demonstrate the effectiveness of the proposed method. When compared with high efficiency video coding reference software HM-16.9, our network can achieve an average of 3.4% bitrate saving. In particular, the average result of 4K sequences is 4.5% bitrate saving, where the maximum one is 7.4%.


title: Infection Prevention and Control: Applied Microbiology for Healthcare

abstract: PART ONE: MICROBIOLOGY Micro-Organisms and Disease Response of the Body to Infection The Microbiology Laboratory Treating Infectious Disease PART TWO: PRINCIPLES OF INFECTION PREVENTION AND CONTROL Infection Control Policies Preventing Infection in Hospital PART THREE: APPLYING KNOWLEDGE TO PRACTICE Urinary Infection and Catheterisation Wound Infections Respiratory Infections Infections Associated with Intravascular Devices Enteric Infections Infection Risks from Blood and Body Fluids Sexually Transmitted Infections Epidemiology: Changing Trends of Infectious Diseases Glossary


title: Rigorous Performance Evaluation of Smartphone GNSS/IMU Sensors for ITS Applications

abstract: With the rapid growth in smartphone technologies and improvement in their navigation sensors, an increasing amount of location information is now available, opening the road to the provision of new Intelligent Transportation System (ITS) services. Current smartphone devices embody miniaturized Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU) and other sensors capable of providing user position, velocity and attitude. However, it is hard to characterize their actual positioning and navigation performance capabilities due to the disparate sensor and software technologies adopted among manufacturers and the high influence of environmental conditions, and therefore, a unified certification process is missing. This paper presents the analysis results obtained from the assessment of two modern smartphones regarding their positioning accuracy (i.e., precision and trueness) capabilities (i.e., potential and limitations) based on a practical but rigorous methodological approach. Our investigation relies on the results of several vehicle tracking (i.e., cruising and maneuvering) tests realized through comparing smartphone obtained trajectories and kinematic parameters to those derived using a high-end GNSS/IMU system and advanced filtering techniques. Performance testing is undertaken for the HTC One S (Android) and iPhone 5s (iOS). Our findings indicate that the deviation of the smartphone locations from ground truth (trueness) deteriorates by a factor of two in obscured environments compared to those derived in open sky conditions. Moreover, it appears that iPhone 5s produces relatively smaller and less dispersed error values compared to those computed for HTC One S. Also, the navigation solution of the HTC One S appears to adapt faster to changes in environmental conditions, suggesting a somewhat different data filtering approach for the iPhone 5s. Testing the accuracy of the accelerometer and gyroscope sensors for a number of maneuvering (speeding, turning, etc.,) events reveals high consistency between smartphones, whereas the small deviations from ground truth verify their high potential even for critical ITS safety applications.


title: SparkCL: A Unified Programming Framework for Accelerators on Heterogeneous Clusters

abstract: We introduce SparkCL, an open source unified programming framework based on Java, OpenCL and the Apache Spark framework. The motivation behind this work is to bring unconventional compute cores such as FPGAs/GPUs/APUs/DSPs and future core types into mainstream programming use. The framework allows equal treatment of different computing devices under the Spark framework and introduces the ability to offload computations to acceleration devices. The new framework is seamlessly integrated into the standard Spark framework via a Java-OpenCL device programming layer which is based on Aparapi and a Spark programming layer that includes new kernel function types and modified Spark transformations and actions. The framework allows a single code base to target any type of compute core that supports OpenCL and easy integration of new core types into a Spark cluster.


Medical image captioning : learning to describe medical image findings using multitask-loss CNN


Deriving Phonetic Transcriptions and Discovering Word Segmentations for Speech-to-Speech Translation in Low-Resource Settings.


title: Deep Convolution Neural Networks for Twitter Sentiment Analysis

abstract: Twitter sentiment analysis technology provides the methods to survey public emotion about the events or products related to them. Most of the current researches are focusing on obtaining sentiment features by analyzing lexical and syntactic features. These features are expressed explicitly through sentiment words, emoticons, exclamation marks, and so on. In this paper, we introduce a word embeddings method obtained by unsupervised learning based on large twitter corpora, this method using latent contextual semantic relationships and co-occurrence statistical characteristics between words in tweets. These word embeddings are combined with n-grams features and word sentiment polarity score features to form a sentiment feature set of tweets. The feature set is integrated into a deep convolution neural network for training and predicting sentiment classification labels. We experimentally compare the performance of our model with the baseline model that is a word n-grams model on five Twitter data sets, the results indicate that our model performs better on the accuracy and F1-measure for twitter sentiment classification.


title: Study of a Low-Profile 2.4-GHz Planar Dipole Antenna Using a High-Impedance Surface With 1-D Varactor Tuning

abstract: A theoretical and experimental study has been performed on a low-profile, 2.4-GHz dipole antenna that uses a frequency-selective surface (FSS) with varactor-tuned unit cells. The tunable unit cell is a square patch with a small aperture on either side to accommodate the varactor diodes. The varactors are placed only along one dimension to avoid the use of vias and simplify the dc bias network. An analytical circuit model for this type of electrically asymmetric unit cell is shown. The measured data demonstrate tunability from 2.15 to 2.63 GHz with peak gains at broadside that range from 3.7- to 5-dBi and instantaneous bandwidths of 50 to 280 MHz within the tuning range. It is shown that tuning for optimum performance in the presence of a human-core body phantom can be achieved. The total antenna thickness is approximately λ/45.


title: Monocular vision SLAM for indoor aerial vehicles

abstract: This paper presents a novel indoor navigation and ranging strategy by using a monocular camera. The proposed algorithms are integrated with simultaneous localization and mapping (SLAM) with a focus on indoor aerial vehicle applications. We experimentally validate the proposed algorithms by using a fully self-contained micro aerial vehicle (MAV) with on-board image processing and SLAM capabilities. The range measurement strategy is inspired by the key adaptive mechanisms for depth perception and pattern recognition found in humans and intelligent animals. The navigation strategy assumes an unknown, GPS-denied environment, which is representable via corner-like feature points and straight architectural lines. Experimental results show that the system is only limited by the capabilities of the camera and the availability of good corners.


Applying the Job Demands-Resources model : A ‘how to’ guide to measuring and tackling work engagement and burnout


title: A flat roof automobile antenna module for LTE, GPS and SDARS applications

abstract: A flat car-roof antenna module for LTE, GPS and SDARS applications is proposed. The module consists of a planar inverted-F antenna (PIFA), a square loop one and a circular loop one, for the LTE, GPS and SDARS operations, respectively. The three antennas are located in a same plane parallel to car roof and put close to each other so that the dimension of the module is only 70 × 70 mm2 in plane. Gap between the antenna plane and the car roof is only 20 mm. This low profile makes the module almost invisible when it is installed on a car roof.


title: Toward agile: an integrated analysis of quantitative and qualitative field data

abstract: As business and technology environments change at an unprecedented rate, software development agility to respond to changing user requirements has become increasingly critical for software development performance. Agile software development approaches, which emphasize sense-and-respond, self-organization, cross-functional teams, and continuous adaptation, have been adopted by an increasing number of organizations to improve their software development agility. However, the agile development literature is largely anecdotal and prescriptive, lacking empirical evidence and theoretical foundation to support the principles and practices of agile development. Little research has empirically examined the software development agility construct in terms of its dimensions, determinants, and effects on software development performance. As a result, there is a lack of understanding about how organizations can effectively implement an agile development approach. ::: ::: Using an integrated research approach that combines quantitative and qualitative data analyses, this research opens the black box of agile development by empirically examining the relationships among two dimensions of software development agility (software team response extensiveness and software team response efficiency), two antecedents that can be controlled (team autonomy and team diversity), and three aspects of software development performance (on-time completion, on-budget completion, and software functionality). Our PLS results of survey responses of 399 software project managers suggest that the relationships among these variables are more complex than what has been perceived by the literature. The results suggest a tradeoff relationship between response extensiveness and response efficiency. These two agility dimensions impact software development performance differently: response efficiency positively affects all of on-time completion, on-budget completion, and software functionality, whereas response extensiveness positively affects only software functionality. The results also suggest that team autonomy has a positive effect on response efficiency and a negative effect on response extensiveness, and that team diversity has a positive effect on response extensiveness. We conducted 10 post hoc case studies to qualitatively cross-validate our PLS results and provide rich, additional insights regarding the complex, dynamic interplays between autonomy, diversity, agility, and performance. The qualitative analysis also provides explanations for both supported and unsupported hypotheses. We discuss these qualitative analysis results and conclude with the theoretical and practical implications of our research findings for agile development approaches.


title: Harmful Help: The Costs of Backing-Up Behavior in Teams

abstract: Prior research on backing-up behavior has indicated that it is beneficial to teams (C. O. L. H. Porter, 2005; C. O. L. H. Porter et al., 2003). This literature has focused on how backing-up behavior aids backup recipients in tasks in which workload is unevenly distributed among team members. The authors of the present study examined different contexts of workload distribution and found that, in addition to the initial benefits to backup recipients, there are initial and subsequent costs. Backing-up behavior leads backup providers to neglect their own taskwork, especially when workload is evenly distributed. Team members who receive high amounts of backing-up behavior decrease their taskwork in a subsequent task, especially when a team member can observe their workload. These findings indicate that it is important to consider both the benefits and costs of engaging in backing-up behavior.


title: Active Semi-Supervision for Pairwise Constrained Clustering

abstract: Semi-supervised clustering uses a small amount of supervised data to aid unsupervised learning. One typical approach specifies a limited number of must-link and cannotlink constraints between pairs of examples. This paper presents a pairwise constrained clustering framework and a new method for actively selecting informative pairwise constraints to get improved clustering performance. The clustering and active learning methods are both easily scalable to large datasets, and can handle very high dimensional data. Experimental and theoretical results confirm that this active querying of pairwise constraints significantly improves the accuracy of clustering when given a relatively small amount of supervision.


title: Indonesian Twitter text authority classification for government in Bandung

abstract: Nowadays, social media based complaint management systems have been deployed in several countries and cities including Bandung. We proposed an automatic authority classification for Twitter text in Indonesian as part of the complaint management system. Our analysis showed that there are several Twitter message types raised in official account Twitter of the city government. The classification employed a statistical based multi-label text classification. Here, we compared several techniques in the classification such as the features, the algorithms and the classification schemes. In the features comparison, we examined several features such as the complaint word feature, n-gram feature, and the @username feature. In the algorithms comparison, we employed Decision Tree algorithm, Naive Bayes algorithm, and Support Vector Machine algorithm with multi-label classification techniques of Binary Relevance and Label Power Set. In the complaint classification schemes, we compared the direct classification and two steps classification. Using 2244 twitter texts from twitter of Bandung city government and 5-fold cross validation, the best experimental result of 70.90% accuracy was achieved by the feature combination of 1-gram and complaint word, with Support Vector Machine and Label Power Set as the algorithm, in the direct scheme of text classification.


title: Forward-look 2-D sonar image formation and 3-D reconstruction

abstract: Sonar imagery deals with transmitting acoustic signals and measuring the reflected sound from the scene surfaces. The recorded signal encodes information about the shape and material properties of these surfaces. The relationship of the scene geometry to the image irradiance can be established by modeling the physics of the sonar image formation process. The inherent ambiguities in the interpretation of the 3-D world based on visual cues in a 2-D forward-scan sonar image arise as a result of both operating as a ranging device and loss of elevation angle information due to the projection geometry. One aspect of this work is modeling the image formed by a new class of high-resolution 2-D forward-looking sonar systems which supports our complementary aim of recovering the unknown zenith angles from image brightness and thus reconstruct 3-D objects. Our method applies to a single forward-scan sonar image, assuming that the scene objects have smooth surfaces that vary monotonically in terms of distance from the sonar, and cast visible shadows on a flat background. We present the results of experiments with real data to demonstrate the performance of our 3-D reconstruction technique.


Autoregressive Attention for Parallel Sequence Modeling


title: Investigation of anthropometric and work-rate profiles of elite South American international soccer players.

abstract: BACKGROUND ::: The aim of the current investigation was to determine the movement profiles of elite South American soccer players during international competition and examine the relationship between anthropometric profile and work-rate variables. ::: ::: ::: METHODS ::: Seventeen full-time professional soccer players were filmed while competing for their countries. Anthropometric profiles were obtained for eleven of these players. Six full-time professional players from the English Premier League were also filmed for comparative purposes. ::: ::: ::: RESULTS ::: The South American international players covered significantly less (p<0.05) total distance during match-play than English Premier League players (International, 8638+/-1158 m; English Premier League, 10104+/-703 m). The total distance covered during the second half of the game was significantly reduced (p<0.05) compared to the first half distance for both groups of players (mean+/-SD first half 4605+/-625 m; mean+/-SD second half 4415+/-634 m). The data for both groups of players were combined to evaluate positional differences in the workrate profile. Midfield players covered a significantly greater (p<0.05) distance than forward players (midfield, 9826+/-1031 m; forwards, 7736+/-929 m) and defenders covered a greater (p<0.05) distance jogging backwards than forward players (defenders, 276+/-155 m; forwards, 68+/-25 m). Forwards sprinted a greater distance (p<0.05) than defensive players (defenders, 231+/-142 m; forwards, 557+/-142 m). Mean somatotype was a balanced mesomorph (2-5 1/2-2). Body mass and muscle mass was related to the total distance covered (r=0.43, r=0.53, p<0.05). ::: ::: ::: CONCLUSIONS ::: Based on these data, it seems that an individual's work-rate profile is dependent upon the type of competition and the playing position. Relationships between anthropometric profile and work-rate are complex due to the interaction between the variables that determine work-rate.


title: Analysis and Design of a Compact Dual-Band Directional Antenna

abstract: A new type of compact dual-band directional antenna is proposed for 2.4/5-GHz wireless access point and RFID reader applications. The dual-band antenna consists of a longer dipole for the lower band and a pair of shorter dipoles for the upper band. A simple coupling microstrip line is employed to excite the dipoles. Both the dual-band antenna and the feeding microstrip line are printed on the same substrate, leading to a fully planar structure. The length of the dipole for the lower band is shortened by a capacitive loading at each end of the dipole, offering a compact antenna configuration. The compact dual-band antenna achieves a desirable directional radiation pattern with an antenna gain of near 8 dBi for the lower band and 9-10 dBi for the upper band. An equivalent circuit analysis for the design of the dual-band directional antenna is presented.


title: Kinematic analysis of electroactive polymer actuators as soft and smart structures with more DoF than inputs

abstract: Electroactive polymer (EAP) actuators have been attracting the attention of researchers due to their muscle-like behaviour and unusual properties. Several modelling methods have been proposed to understand their mechanical, chemical, electrical behaviours or ‘electro-chemo-mechanical’ behaviour. However, estimating the whole shape or configuration of the EAP actuators has always been challenging due to their highly non-linear bending behaviour. This paper reports on an effective method to estimate the whole shape deflection of the EAP actuators by employing a so-called backbone approach. Tri-layer configured polypyrrole (PPy) based EAP actuators were used as a soft and smart structure with more degrees of freedom than its input. After deriving the inverse kinematic model of the actuator, its complete shape is estimated by solving the inverse kinematic model with an angle optimization (AngleOPT) method. The experimental results and numerical results have demonstrated the effectiveness of the method in estimating the highly non-linear bending behaviour of the PPy actuators and applicability of this modelling approach to other EAP actuators.


title: Measurement of $C\!P$ asymmetries in two-body $B_{(s)}^{0}$-meson decays to charged pions and kaons

abstract: The time-dependent CP asymmetries in B0→π+π− and B0s→K+K− decays are measured using a data sample of p p collisions corresponding to an integrated luminosity of 3.0 fb−1, collected with the LHCb detector at center-of-mass energies of 7 and 8 TeV. The same data sample is used to measure the time-integrated CP asymmetries in B0→K+π− and B0s→π+K− decays. The results are Cπ+π−=−0.34±0.06±0.01, Sπ+π−=−0.63±0.05±0.01, CK+K−=0.20±0.06±0.02, SK+K−=0.18±0.06±0.02, AΔΓK+K−=−0.79±0.07±0.10, AB0CP=−0.084±0.004±0.003, and AB0sCP=0.213±0.015±0.007, where the first uncertainties are statistical and the second systematic. Evidence for CP violation is found in the B0s→K+K− decay for the first time.


title: OpinionSeer: Interactive Visualization of Hotel Customer Feedback

abstract: The rapid development of Web technology has resulted in an increasing number of hotel customers sharing their opinions on the hotel services. Effective visual analysis of online customer opinions is needed, as it has a significant impact on building a successful business. In this paper, we present OpinionSeer, an interactive visualization system that could visually analyze a large collection of online hotel customer reviews. The system is built on a new visualization-centric opinion mining technique that considers uncertainty for faithfully modeling and analyzing customer opinions. A new visual representation is developed to convey customer opinions by augmenting well-established scatterplots and radial visualization. To provide multiple-level exploration, we introduce subjective logic to handle and organize subjective opinions with degrees of uncertainty. Several case studies illustrate the effectiveness and usefulness of OpinionSeer on analyzing relationships among multiple data dimensions and comparing opinions of different groups. Aside from data on hotel customer feedback, OpinionSeer could also be applied to visually analyze customer opinions on other products or services.


title: Incremental deployment and migration of geo-distributed situation awareness applications in the fog

abstract: Geo-distributed Situation Awareness applications are large in scale and are characterized by 24/7 data generation from mobile and stationary sensors (such as cameras and GPS devices); latency-sensitivity for converting sensed data to actionable knowledge; and elastic and bursty needs for computational resources. Fog computing [7] envisions providing computational resources close to the edge of the network, consequently reducing the latency for the sense-process-actuate cycle that exists in these applications. We propose Foglets, a programming infrastructure for the geo-distributed computational continuum represented by fog nodes and the cloud. Foglets provides APIs for a spatio-temporal data abstraction for storing and retrieving application generated data on the local nodes, and primitives for communication among the resources in the computational continuum. Foglets manages the application components on the Fog nodes. Algorithms are presented for launching application components and handling the migration of these components between Fog nodes, based on the mobility pattern of the sensors and the dynamic computational needs of the application. Evaluation results are presented for a Fog network consisting of 16 nodes using a simulated vehicular network as the workload. We show that the discovery and deployment protocol can be executed in 0.93 secs, and joining an already deployed application can be as quick as 65 ms. Also, QoS-sensitive proactive migration can be accomplished in 6 ms.


title: Predicting Breast Cancer Recurrence Using Machine Learning Techniques: A Systematic Review

abstract: Background: Recurrence is an important cornerstone in breast cancer behavior, intrinsically related to mortality. In spite of its relevance, it is rarely recorded in the majority of breast cancer datasets, which makes research in its prediction more difficult. Objectives: To evaluate the performance of machine learning techniques applied to the prediction of breast cancer recurrence. Material and Methods: Revision of published works that used machine learning techniques in local and open source databases between 1997 and 2014. Results: The revision showed that it is difficult to obtain a representative dataset for breast cancer recurrence and there is no consensus on the best set of predictors for this disease. High accuracy results are often achieved, yet compromising sensitivity. The missing data and class imbalance problems are rarely addressed and most often the chosen performance metrics are inappropriate for the context. Discussion and Conclusions: Although different techniques have been used, prediction of breast cancer recurrence is still an open problem. The combination of different machine learning techniques, along with the definition of standard predictors for breast cancer recurrence seem to be the main future directions to obtain better results.


title: Multifunctional Software-Defined Radar Sensor and Data Communication System

abstract: Orthogonal frequency-division multiplexing (OFDM) signal coding and system architecture were implemented to achieve radar and data communication functionalities. The resultant system is a software-defined unit, which can be used for range measurements, radar imaging, and data communications. Range reconstructions were performed for ranges up to 4 m using trihedral corner reflectors with approximately 203 m of radar cross section at the carrier frequency; range resolution of approximately 0.3 m was demonstrated. Synthetic aperture radar (SAR) image of a single corner reflector was obtained; SAR signal processing specific to OFDM signals is presented. Data communication tests were performed in radar setup, where the signal was reflected by the same target and decoded as communication data; bit error rate of was achieved at 57 Mb/s. The system shows good promise as a multifunctional software-defined sensor which can be used in radar sensor networks.


title: Adaptation of the Grasha Riechman Student Learning Style Survey and Teaching Style Inventory to assess individual teaching and learning styles in a quality improvement collaborative

abstract: Background ::: NIATx200, a quality improvement collaborative, involved 201 substance abuse clinics. Each clinic was randomized to one of four implementation strategies: (a) interest circle calls, (b) learning sessions, (c) coach only or (d) a combination of all three. Each strategy was led by NIATx200 coaches who provided direct coaching or facilitated the interest circle and learning session interventions.


title: Distances in Classification

abstract: The notion of distance is the most important basis for classification. This is especially true for unsupervised learning, i.e. clustering, since there is no validation mechanism by means of objects of known groups. But also for supervised learning standard distances often do not lead to appropriate results. For every individual problem the adequate distance is to be decided upon. This is demonstrated by means of three practical examples from very different application areas, namely social science, music science, and production economics. In social science, clustering is applied to spatial regions with very irregular borders. Then adequate spatial distances may have to be taken into account for clustering. In statistical musicology the main problem is often to find an adequate transformation of the input time series as an adequate basis for distance definition. Also, local modelling is proposed in order to account for different subpopulations, e.g. instruments. In production economics often many quality criteria have to be taken into account with very different scaling. In order to find a compromise optimum classification, this leads to a pre-transformation onto the same scale, called desirability.


title: The maximum clique problem

abstract: In this paper we present a survey of results concerning algorithms, complexity, and applications of the maximum clique problem. We discuss enumerative and exact algorithms, heuristics, and a variety of other proposed methods. An up to date bibliography on the maximum clique and related problems is also provided.


title: A Systematic Mapping Study in Microservice Architecture

abstract: The accelerating progress of network speed, reliability and security creates an increasing demand to move software and services from being stored and processed locally on users' machines to being managed by third parties that are accessible through the network. This has created the need to develop new software development methods and software architectural styles that meet these new demands. One such example in software architectural design is the recent emergence of the microservices architecture to address the maintenance and scalability demands of online service providers. As microservice architecture is a new research area, the need for a systematic mapping study is crucial in order to summarise the progress so far and identify the gaps and requirements for future studies. In this paper we present a systematic mapping study of microservices architectures and their implementation. Our study focuses on identifying architectural challenges, the architectural diagrams/views and quality attributes related to microsevice systems.


title: A Game-Theoretic Approach to Multi-Pedestrian Activity Forecasting

abstract: We develop predictive models of pedestrian dynamics by encoding the coupled nature of multi-pedestrian interaction using game theory, and deep learning-based visual analysis to estimate person-specific behavior parameters. Building predictive models for multi-pedestrian interactions however, is very challenging due to two reasons: (1) the dynamics of interaction are complex interdependent processes, where the predicted behavior of one pedestrian can affect the actions taken by others and (2) dynamics are variable depending on an individuals physical characteristics (e.g., an older person may walk slowly while the younger person may walk faster). To address these challenges, we (1) utilize concepts from game theory to model the interdependent decision making process of multiple pedestrians and (2) use visual classifiers to learn a mapping from pedestrian appearance to behavior parameters. We evaluate our proposed model on several public multiple pedestrian interaction video datasets. Results show that our strategic planning model explains human interactions 25% better when compared to state-of-the-art methods.


title: Towards Learning of Generic Skills for Robotic Manipulation

abstract: Learning versatile, reusable skills is one of the key prerequisites for autonomous robots. Imitation and reinforcement learning are among the most prominent approaches for learning basic robotic skills. However, the learned skills are often very specific and cannot be reused in different but related tasks. In the project "Behaviors for Mobile Manipulation", we develop hierarchical and transfer learning methods which allow a robot to learn a repertoire of versatile skills that can be reused in different situations. The development of new methods is closely integrated with the analysis of complex human behavior.


title: Filterless multi-level delta-sigma class-D amplifier for portable applications

abstract: A 2W filterless class-D amplifier using multi-level delta-sigma modulation is presented. LC filters consume large PCB space and add significant system cost, so filterless class-D solutions are preferred for portable applications. In this design, a multi-level, delta-sigma modulator is implemented to achieve high efficiency and high performance without the use of a filter. Although the modulator internally generates an 8MHz clock, the average switching frequency of output signal is dynamically reduced to 300kHz to minimize switching loss. Analog feedback from the output stages along with common-mode control yields 0.003% total harmonic distortion, and 103dB dynamic range.


title: A study on using Python vs Weka on dialysis data analysis

abstract: Health data has been drastically increasing in capacity and variety. Due to large and complex collection of datasets, it is difficult to process data using traditional data processing techniques. Machine Learning techniques, such as KNN and Naive Bayes, have been used. Python and Weka are tools that are widely used in the field of data analytics. Therefore, this paper gives the comprehensive comparison between both tools together with some machine learning algorithms on data analytic of Dialysis Dataset. The results show that using Python provides the better performance in term of correct/incorrect instances, precision, and recall.


title: The evolution of distributed systems towards microservices architecture

abstract: Applications developed to fulfil distributed systems needs have been growing rapidly. Major evolutions have happened beginning with basic architecture relying on initiated request by a client to a processing side referred to as the server. Such architectures were not enough to cope up with the fast ever-increasing number of requests and need to utilize network bandwidth. Mobile agents attempted to overcome such drawbacks but did cope up for so long with the growing technology platforms. Service Oriented Architecture (SOA) then evolved to be one of the most successful representations of the client-server architecture with an added business value that provides reusable and loosely coupled services. SOA did not meet customers and business expectations as it was still relying on monolithic systems. Resilience, scalability, fast software delivery and the use of fewer resources are highly desirable features. Microservices architecture came to fulfil those expectations of system development, yet it comes with many challenges. This paper illustrates how distributed systems evolved from the traditional client-server model to the recently proposed microservices architecture. All architectures are reviewed containing brief definitions, some related work and reasoning of why they had to evolve. A feature comparison of all architectures is also provided.


title: Visual inertial odometry for quadrotors on SE(3)

abstract: The combination of on-board sensors measurements with different statistical characteristics can be employed in robotics for localization and control, especially in GPS-denied environments. In particular, most aerial vehicles are packaged with low cost sensors, important for aerial robotics, such as camera, a gyroscope, and an accelerometer. In this work, we develop a visual inertial odometry system based on the Unscented Kalman Filter (UKF) acting on the Lie group SE(3), such to obtain an unique, singularity-free representation of a rigid body pose. We model this pose with the Lie group SE(3) and model the noise on the corresponding Lie algebra. Moreover, we extend the concepts used in the standard UKF formulation, such as state uncertainty and modeling, to correctly incorporate elements that do not belong to an Euclidean space such as the Lie group members. In this analysis, we use the parallel transport, which requires us to explicitly consider SE(3) as representing rigid bodies though the use of the affine connection. We present experimental results to show the effectiveness of the proposed approach for state estimation of a quadrotor platform.


title: Overlapping Community Detection via Local Spectral Clustering

abstract: Large graphs arise in a number of contexts and understanding their structure and extracting information from them is an important research area. Early algorithms on mining communities have focused on the global structure, and often run in time functional to the size of the entire graph. Nowadays, as we often explore networks with billions of vertices and find communities of size hundreds, it is crucial to shift our attention from macroscopic structure to microscopic structure in large networks. A growing body of work has been adopting local expansion methods in order to identify the community members from a few exemplary seed members. ::: In this paper, we propose a novel approach for finding overlapping communities called LEMON (Local Expansion via Minimum One Norm). The algorithm finds the community by seeking a sparse vector in the span of the local spectra such that the seeds are in its support. We show that LEMON can achieve the highest detection accuracy among state-of-the-art proposals. The running time depends on the size of the community rather than that of the entire graph. The algorithm is easy to implement, and is highly parallelizable. We further provide theoretical analysis on the local spectral properties, bounding the measure of tightness of extracted community in terms of the eigenvalues of graph Laplacian. ::: Moreover, given that networks are not all similar in nature, a comprehensive analysis on how the local expansion approach is suited for uncovering communities in different networks is still lacking. We thoroughly evaluate our approach using both synthetic and real-world datasets across different domains, and analyze the empirical variations when applying our method to inherently different networks in practice. In addition, the heuristics on how the seed set quality and quantity would affect the performance are provided.


title: Autopoiesis, free energy, and the life–mind continuity thesis

abstract: The life–mind continuity thesis is difficult to study, especially because the relation between life and mind is not yet fully understood, and given that there is still no consensus view neither on what qualifies as life nor on what defines mind. Rather than taking up the much more difficult task of addressing the many different ways of explaining how life relates to mind, and vice versa, this paper considers two influential accounts addressing how best to understand the life–mind continuity thesis: first, the theory of autopoiesis (AT) developed in biology and in enactivist theories of mind; and second, the recently formulated free energy principle in theoretical neurobiology, with roots in thermodynamics and statistical physics. This paper advances two claims. The first is that the free energy principle (FEP) should be preferred to the theory of AT, as classically formulated. The second is that the FEP and the recently formulated framework of autopoietic enactivism can be shown to be genuinely continuous on a number of central issues, thus raising the possibility of a joint venture when it comes to answering the life–mind continuity thesis.


title: Raindrop detection and removal using salient visual features

abstract: Raindrops on vehicles' windshields can degrade the performance of in-vehicle vision systems. In this paper, we present a novel approach that detects and removes raindrops in the captured image when using a single in-vehicle camera. When driving in light or moderate rainy conditions, raindrops appear as small circlets on the windshield in each image frame. Therefore, by analyzing the color, texture and shape characteristics of raindrops in images, we first identify possible raindrop candidates in the regions of interest (ROI), which are small locally salient droplets in a raindrop saliency map. Then, a learning-based verification algorithm is proposed to reduce the number of false alarms (i.e., clear regions mis-detected as raindrops). Finally, we fill in the regions occupied by the raindrops using image inpainting techniques. Numerical experiments indicate that the proposed method is capable of detecting and reducing raindrops in various rain and road scenarios. We also quantify the improvement offered by the proposed method over the state-of-the-art algorithms aimed at the same problem and the benefits to the in-vehicle vision applications like clear path detection.


title: Fog Computing based Radio Access Networks: Issues and Challenges

abstract: An F-RAN is presented in this article as a promising paradigm for the fifth generation wireless communication system to provide high spectral and energy efficiency. The core idea is to take full advantage of local radio signal processing, cooperative radio resource management, and distributed storing capabilities in edge devices, which can decrease the heavy burden on fronthaul and avoid large-scale radio signal processing in the centralized baseband unit pool. This article comprehensively presents the system architecture and key techniques of F-RANs. In particular, key techniques and their corresponding solutions, including transmission mode selection and interference suppression, are discussed. Open issues in terms of edge caching, software-defined networking, and network function virtualization are also identified.


title: Learning Phrase-Based Spelling Error Models from Clickthrough Data

abstract: This paper explores the use of clickthrough data for query spelling correction. First, large amounts of query-correction pairs are derived by analyzing users' query reformulation behavior encoded in the clickthrough data. Then, a phrase-based error model that accounts for the transformation probability between multi-term phrases is trained and integrated into a query speller system. Experiments are carried out on a human-labeled data set. Results show that the system using the phrase-based error model outperforms significantly its baseline systems.


title: Community-based topic modeling for social tagging

abstract: Exploring community is fundamental for uncovering the connections between structure and function of complex networks and for practical applications in many disciplines such as biology and sociology. In this paper, we propose a TTR-LDA-Community model which combines the Latent Dirichlet Allocation model (LDA) and the Girvan-Newman community detection algorithm with an inference mechanism. The model is then applied to data from Delicious, a popular social tagging system, over the time period of 2005-2008. Our results show that 1) users in the same community tend to be interested in similar set of topics in all time periods; and 2) topics may divide into several sub-topics and scatter into different communities over time. We evaluate the effectiveness of our model and show that the TTR-LDA-Community model is meaningful for understanding communities and outperforms TTR-LDA and LDA models in tag prediction.


title: Mining behavior models from user-intensive web applications

abstract: Many modern user-intensive applications, such as Web applications, must satisfy the interaction requirements of thousands if not millions of users, which can be hardly fully understood at design time. Designing applications that meet user behaviors, by efficiently supporting the prevalent navigation patterns, and evolving with them requires new approaches that go beyond classic software engineering solutions. We present a novel approach that automates the acquisition of user-interaction requirements in an incremental and reflective way. Our solution builds upon inferring a set of probabilistic Markov models of the users' navigational behaviors, dynamically extracted from the interaction history given in the form of a log file. We annotate and analyze the inferred models to verify quantitative properties by means of probabilistic model checking. The paper investigates the advantages of the approach referring to a Web application currently in use.


title: Processing flows of information: from data stream to complex event processing

abstract: An increasing number of distributed applications requires processing continuously flowing data from geographically distributed sources at unpredictable rate to obtain timely responses to complex queries. Examples of such applications come from the most disparate fields: from wireless sensor networks to financial tickers, from traffic management to click stream inspection. These requirements led to the development of a number of systems specifically designed to process information as a flow according to a set of pre-deployed processing rules. We collectively call them Information Flow Processing (IFP) Systems. Despite having a common goal, IFP systems differ in a wide range of aspects, including architectures, data models, rule languages, and processing mechanisms. In this tutorial we draw a general framework to analyze and compare the results achieved so far in the area of IFP systems. This allows us to offer a systematic overview of the topic, favoring the communication between different communities, and highlighting a number of open issue that still need to be addressed in research.


title: Putting the users center stage: role playing and low-fi prototyping enable end users to design mobile systems

abstract: This paper sums up lessons learned from a sequence of cooperative design workshops where end users were enabled to design mobile systems through scenario building, role playing, and low-fidelity prototyping. We present a resulting fixed workshop structure with well-chosen constraints that allows for end users to explore and design new technology and work practices. In these workshops, the systems developers get input to design from observing how users stage and act out current and future use scenarios and improvise new technology to fit their needs. A theoretical framework is presented to explain the creative processes involved and the workshop as a user-centered design method. Our findings encourage us to recommend the presented workshop structure for design projects involving mobility and computer-mediated communication, in particular project where the future use of the resulting products and services also needs to be designed.


title: The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer's disease

abstract: Abstract The National Institute on Aging and the Alzheimer's Association charged a workgroup with the task of revising the 1984 criteria for Alzheimer's disease (AD) dementia. The workgroup sought to ensure that the revised criteria would be flexible enough to be used by both general healthcare providers without access to neuropsychological testing, advanced imaging, and cerebrospinal fluid measures, and specialized investigators involved in research or in clinical trial studies who would have these tools available. We present criteria for all-cause dementia and for AD dementia. We retained the general framework of probable AD dementia from the 1984 criteria. On the basis of the past 27 years of experience, we made several changes in the clinical criteria for the diagnosis. We also retained the term possible AD dementia, but redefined it in a manner more focused than before. Biomarker evidence was also integrated into the diagnostic formulations for probable and possible AD dementia for use in research settings. The core clinical criteria for AD dementia will continue to be the cornerstone of the diagnosis in clinical practice, but biomarker evidence is expected to enhance the pathophysiological specificity of the diagnosis of AD dementia. Much work lies ahead for validating the biomarker diagnosis of AD dementia.


title: A Survey on Various Cyber Attacks and their Classification

abstract: The role of computers and the Internet in modern society is well recognized. Recent developments in the fields of networking and cyberspace have greatly benefited mankind, but the rapid growth of cyberspace has also contributed to unethical practices by individuals who are bent on using the technology to exploit others. Such exploitation of cyberspace for the purpose of accessing unauthorized or secure information, spying, disabling of networks and stealing both data and money is termed as cyber attack. Such attacks have been increasing in number and complexity over the past few years. There has been a dearth of knowledge about these attacks which has rendered many individuals/agencies/organizations vulnerable to these attacks. Hence there is a need to have comprehensive understanding of cyber attacks and its classification. The purpose of this survey is to do a comprehensive study of these attacks in order to create awareness about the various types of attacks and their mode of action so that appropriate defense measures can be initiated against such attacks.


title: Robust PCA for Anomaly Detection in Cyber Networks

abstract: This paper uses network packet capture data to demonstrate how Robust Principal Component Analysis (RPCA) can be used in a new way to detect anomalies which serve as cyber-network attack indicators. The approach requires only a few parameters to be learned using partitioned training data and shows promise of ameliorating the need for an exhaustive set of examples of different types of network attacks. For Lincoln Lab's DARPA intrusion detection data set, the method achieves low false-positive rates while maintaining reasonable true-positive rates on individual packets. In addition, the method correctly detected packet streams in which an attack which was not previously encountered, or trained on, appears.


title: Learning to Find Topic Experts in Twitter via Different Relations

abstract: Expert finding has become a hot topic along with the flourishing of social networks, such as micro-blogging services like Twitter. Finding experts in Twitter is an important problem because tweets from experts are valuable sources that carry rich information (e.g., trends) in various domains. However, previous methods cannot be directly applied to Twitter expert finding problem. Recently, several attempts use the relations among users and Twitter List s for expert finding. Nevertheless, these approaches only partially utilize such relations. To this end, we develop a probabilistic method to jointly exploit three types of relations (i.e., follower relation, user-list relation, and list-list relation) for finding experts. Specifically, we propose a S emi- S upervised G raph-based R anking approach ( $\sf{SSGR}$ ) to offline calculate the global authority of users. In $\sf{SSGR}$ , we employ a normalized Laplacian regularization term to jointly explore the three relations, which is subject to the supervised information derived from Twitter crowds. We then online compute the local relevance between users and the given query. By leveraging the global authority and local relevance of users, we rank all of users and find top-N users with highest ranking scores. Experiments on real-world data demonstrate the effectiveness of our proposed approach for topic-specific expert finding in Twitter .