title: Improving "Email Speech Acts" Analysis Via N-Gram Selection

abstract: In email conversational analysis, it is often useful to trace the the intents behind each message exchange. In this paper, we consider classification of email messages as to whether or not they contain certain intents or email-acts, such as "propose a meeting" or "commit to a task". We demonstrate that exploiting the contextual information in the messages can noticeably improve email-act classification. More specifically, we describe a combination of n-gram sequence features with careful message preprocessing that is highly effective for this task. Compared to a previous study (Cohen et al., 2004), this representation reduces the classification error rates by 26.4% on average. Finally, we introduce Ciranda: a new open source toolkit for email speech act prediction.


title: Encoding Data for HTM Systems

abstract: Hierarchical Temporal Memory (HTM) is a biologically inspired machine intelligence technology that mimics the architecture and processes of the neocortex. In this white paper we describe how to encode data as Sparse Distributed Representations (SDRs) for use in HTM systems. We explain several existing encoders, which are available through the open source project called NuPIC, and we discuss requirements for creating encoders for new types of data.


title: Stretchable Thermoelectric Generators Metallized with Liquid Alloy

abstract: Conventional thermoelectric generators (TEGs) are normally hard, rigid, and flat. However, most objects have curvy surfaces, which require soft and even stretchable TEGs for maximizing efficiency of thermal energy harvesting. Here, soft and stretchable TEGs using conventional rigid Bi2Te3 pellets metallized with a liquid alloy is reported. The fabrication is implemented by means of a tailored layer-by-layer fabrication process. The STEGs exhibit an output power density of 40.6 μW/cm2 at room temperature. The STEGs are operational after being mechanically stretched-and-released more than 1000 times, thanks to the compliant contact between the liquid alloy interconnects and the rigid pellets. The demonstrated interconnect scheme will provide a new route to the development of soft and stretchable energy-harvesting avenues for a variety of emerging electronic applications.


title: Anger and Its Direction in Collaborative Software Development

abstract: Recent research has provided evidence that software developers experience a wide range of emotions. We argue that among those emotions anger deserves special attention as it can serve as an onset for tools supporting collaborative software development. This, however, requires a fine-grained model of the anger emotion, able to distinguish between anger directed towards self, others, and objects. Detecting anger towards self could be useful to support developers experiencing difficulties; detection of anger towards others might be helpful for community management; detecting anger towards objects might be helpful to recommend and prioritize improvements. As a first step towards automatic identification of anger direction, we built a classifier for anger direction, based on a manually annotated gold standard of 723 sentences that were obtained by mining comments in Apache issue reports.


title: Micro Behaviors: A New Perspective in E-commerce Recommender Systems

abstract: The explosive popularity of e-commerce sites has reshaped users» shopping habits and an increasing number of users prefer to spend more time shopping online. This evolution allows e-commerce sites to observe rich data about users. The majority of traditional recommender systems have focused on the macro interactions between users and items, i.e., the purchase history of a customer. However, within each macro interaction between a user and an item, the user actually performs a sequence of micro behaviors, which indicate how the user locates the item, what activities the user conducts on the item (e.g., reading the comments, carting, and ordering) and how long the user stays with the item. Such micro behaviors offer fine-grained and deep understandings about users and provide tremendous opportunities to advance recommender systems in e-commerce. However, exploiting micro behaviors for recommendations is rather limited, which motivates us to investigate e-commerce recommendations from a micro-behavior perspective in this paper. Particularly, we uncover the effects of micro behaviors on recommendations and propose an interpretable Recommendation framework RIB, which models inherently the sequence of mIcro Behaviors and their effects. Experimental results on datasets from a real e-commence site demonstrate the effectiveness of the proposed framework and the importance of micro behaviors for recommendations.


Agents on Stage: Advancing the State of the Art of AI


title: Large Pages on Steroids: Small Ideas to Accelerate Big Memory Applications

abstract: Utilizing small (e.g., 4 KB) pages incurs frequent TLB misses on modern big memory applications, substantially degrading the performance of the system. Large (e.g., 1 GB) pages or direct segments can alleviate this penalty due to page table walks, but at the same time such a strategy exposes the organizational and operational details of modern DRAM-based memory systems to applications. Row-buffer conflicts caused by accesses heading to the same DRAM bank but different rows from multiple threads are regarded as the main culprits behind the very large gaps between peak and achieved main memory throughput, but hardware-based approaches in memory controllers have achieved only limited success whereas existing proposals that change memory allocators cannot be applied to large pages or direct segments. In this paper, we propose a set of application-level techniques to improve the effective main memory bandwidth. The techniques stem from the two key observations that 1) each thread of an application exclusively accesses certain datasets for a short or long period of time, and 2) superfluous memory reads originating from a cache’s write allocation policy can be avoided if scatters during the data shuffling pass through intermediate cache-friendly buffers. Experiments with a contemporary x86 server show that combining large pages with the proposed address linearization, bank coloring, and write streaming techniques improves the performance of the three big memory applications of high-throughput key-value store, fast-Fourier transform, and radix sort by 37.6, 22.9, and 68.1 percent, respectively.


title: Combining multiple evidence from different properties of weighting schemes

abstract: It has been known that using different representations of either queries or documents, or different retrieval techniques retrieves different sets of documents. Recent work suggests that significant improvements in retrieval performance can be achieved by combining multiple representations or multiple retrieval techniques. In this paper we propose a simple method for retrieving different documents within a single query representation, a single document representation and a single retrieval technique. We classify the types of documents, and describe the properties of weighting schemes. Then, we explain that different properties of weighting schemes may retrieve different types of documents. Experimental results show that significant improvements can be obtained by combining the retrieval results from different properties of weighting schemes.


title: Performance Evaluation of IEEE 802.15.4 Physical Layer Using MatLab/Simulink

abstract: IEEE 802.15.4 (Zigbee) is the new standard that has been developed for low cost, low data rate and low power consumption wireless network. This paper provides a brief description of this standard including the Physical (PHY) and medium access control (MAC) layer. It focuses on developing MatLab/Simulink models for the Zigbee protocol and the performance evaluation of these models. Simulation has been run using MatLab and the results have been analyzed. The results show how the relationship between the signal Bit Error Rate (BER) and Signal to Noise Ratio (SNR) is affected when varying the data rate and the input signal power to the noise channel.


title: Improved fraud detection in e-commerce transactions

abstract: Online transactions have gained popularity in the recent years with an impact of increasing fraud cases associated with it. Fraud increases as new technologies and weaknesses are found, resulting in tremendous losses each year. Since the transactions associated with e-commerce are large in number, the dataset associated with them is also large; therefore, it requires fast and efficient algorithms to identify fraudulent transactions. Most of the methods used for fraud detection are rule-based or are systems that require re-training when newer patterns of fraud occurs. Detecting fraud as it is happening or within a short time span is not easy and requires advanced techniques. As the demand has arisen for self-learning predictive systems, the main objective is to detect the fraudulent transactions by using Adaptive Neuro-Fuzzy Inference System, which is a hybrid of neural networks along with fuzzy inference, wherein the system can adapt to newer instances of fraud.


title: A combined confocal and magnetic resonance microscope for biological studies

abstract: Complementary data acquired with different microscopy techniques provide a basis for establishing a more comprehensive understanding of cell function in health and disease, particularly when results acquired with different methodologies can be correlated in time and space. In this article, a novel microscope is described for studying live cells simultaneously with both confocal scanning laser fluorescence optical microscopy and magnetic resonance microscopy. The various design considerations necessary for integrating these two complementary techniques are discussed, the layout and specifications of the instrument are given, and examples of confocal and magnetic resonance images of large frog cells and model tumor spheroids obtained with the compound microscope are presented.


title: HL7 and DICOM based integration of radiology departments with healthcare enterprise information systems.

abstract: Abstract Purpose Integration based on open standards, in order to achieve communication and information interoperability, is one of the key aspects of modern health care information systems. However, this requirement represents one of the major challenges for the Information and Communication Technology (ICT) solutions, as systems today use diverse technologies, proprietary protocols and communication standards which are often not interoperable. One of the main producers of clinical information in healthcare settings represent Radiology Information Systems (RIS) that communicate using widely adopted DICOM ( D igital I maging and CO mmunications in M edicine) standard, but in very few cases can efficiently integrate information of interest with other systems. In this context we identified HL7 standard as the world's leading medical ICT standard that is envisioned to provide the umbrella for medical data semantic interoperability, which amongst other things represents the cornerstone for the Croatia's National Integrated Healthcare Information System (IHCIS). The aim was to explore the ability to integrate and exchange RIS originated data with Hospital Information Systems based on HL7's CDA ( C linical D ocument A rchitecture) standard. Methods We explored the ability of HL7 CDA specifications and methodology to address the need of RIS integration HL7 based healthcare information systems. Results We introduced the use of WADO service interconnection to IHCIS and finally CDA rendering in widely used Internet explorers. The outcome of our pilot work proves our original assumption of HL7 standard being able to adopt radiology data into the integrated healthcare systems. Conclusion Uniform DICOM to CDA translation scripts and business processes within IHCIS is desired and cost effective regarding to use of supporting IHCIS services aligned to SOA.


Classifiers Fusion for Arabic Named Entity Recognition


title: Sex reassignment surgery for transsexuals.

abstract: PURPOSE OF REVIEW ::: The purpose of this article is to report on the most recent findings on the current surgical treatments for sex reassignment surgery for both male-to-female and female-to-male transsexuals. ::: ::: ::: RECENT FINDINGS ::: For male-to-female sex reassignment surgery, researches aim to refine the inverted peno-scrotal flap surgical technique in order to increase the neovaginal length and width, prevent complications and ameliorate the cosmetic outcome.Bowel segment vaginoplasty, used mainly for secondary cases, is becoming more and more common.For female-to-male sex reassignment surgery, the radial forearm flap is the only procedure that has proved to fulfil most of the patient expectations; however, because of a high complications rate, long recovery time and a large number of the required operations, and the absence of guarantee that patients will be satisfied, the free radial forearm flap is not the best option for every patient. Alternatives such as abdominal/groin flap, thigh flap and metaidoioplasty are very much used. ::: ::: ::: SUMMARY ::: In order to provide the best care for transsexual patients, centres performing sex reassignment surgery should co-operate and implement both the refinements and the new techniques offered and/or requested.


title: The Evolution of the Platform Concept: A Systematic Review

abstract: Platforms have been considered as a paradigm for managing new product development and innovation. Since their introduction, studies on platforms have introduced multiple conceptualizations, leading to a fragmentation of research and different perspectives. By systematically reviewing the platform literature and combining bibliometric and content analyses, this paper examines the platform concept and its evolution, proposes a thematic classification, and highlights emerging trends in the literature. Based on this hybrid methodological approach (bibliometric and content analyses), the results show that platform research has primarily focused on issues that are mainly related to firms’ internal aspects, such as innovation, modularity, commonality, and mass customization. Moreover, scholars have recently started to focus on new research themes, including managerial questions related to capability building, strategy, and ecosystem building based on platforms. As its main contributions, this paper improves the understanding of and clarifies the evolutionary trajectory of the platform concept, and identifies trends and emerging themes to be addressed in future studies.


title: Neural correlates of creative writing: An fMRI Study

abstract: r r Abstract: Cerebral activations involved in actual writing of a new story and the associated correlates with creative performance are still unexplored. To investigate the different aspects of the creative writ- ing process, we used functional magnetic resonance imaging while 28 healthy participants performed a new paradigm related to creative writing: ''brainstorming'' (planning a story) and ''creative writing'' (writing a new and creative continuation of a given literary text), as well as an additional control paradigm of ''reading'' and ''copying.'' Individual verbal creativity was assessed with a verbal creativ- ity test and creative performance with a qualitative rating of the creative products. ''brainstorming'' engaged cognitive, linguistic, and creative brain functions mainly represented in a parieto-frontal- temporal network, as well as writing preparation, and visual and imaginative processing. ''creative writing'' activated motor and visual brain areas for handwriting and additionally, cognitive and linguistic areas. Episodic memory retrieval, free-associative and spontaneous cognition, and semantic integration were observed in a right lateralized activation pattern in bilateral hippocampi, bilateral temporal poles (BA 38), and bilateral posterior cingulate cortex in a ''creative writing'' minus ''copy- ing'' comparison. A correlation analysis of ''creative writing'' minus ''copying'' with the creativity index revealed activation in the left inferior frontal gyrus (BA 45) and the left temporal pole (BA 38). Thus, verbal creativity during ''creative writing'' is associated with verbal and semantic memory as well as semantic integration. Hum Brain Mapp 00:000-000, 2011. V C 2011 Wiley Periodicals, Inc.


Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?


A hybrid particle swarm optimization – backpropagation algorithm for feedforward neural network training


title: A Review on Text Similarity Technique used in IR and its Application

abstract: With large number of documents on the web, there is a increasing need to be able to retrieve the best relevant document. There are different techniques through which we can retrieve most relevant document from the large corpus. Similarity between words, sentences, paragraphs and documents is an important component in various tasks such as information retrieval, document clustering, word-sense disambiguation, automatic essay scoring, short answer grading, machine translation and text summarization. Text similarity means user’s query text is matched with the document text and on the basis on this matching user retrieves the most relevant documents. Text similarity also plays an important role in the categorization of text as well as document. We can measure the similarity between sentences, words, paragraphs and documents to categorize them in an efficient way. On the basis of this categorization, we can retrieve the best relevant document corresponding to user’s query. This paper describes different types of similarity like lexical similarity, semantic similarity etc.


title: Adaptive algorithms for cache-efficient trie search

abstract: In this paper, we present cache-efficient algorithms for trie search. There are three key features of these algorithms. First, they use different data structures (partitioned-array, B-tree, hashtable, vectors) to represent different nodes in a trie. The choice of the data structure depends on cache characteristics as well as the fanout of the node. Second, they adapt to changes in the fanout at a node by dynamically switching the data structure used to represent the node. Third, the size and the layout of individual data structures is determined based on the size of the symbols in the alphabet as well as characteristics of the cache(s). We evaluate the performance of these algorithms on real and simulated memory hierarchies. Our evaluation indicates that these algorithms outperform alternatives that are otherwise efficient but do not take cache characteristics into consideration. A comparison of the number of instructions executed indicates that these algorithms derive their performance advantage primarily by making better use of the memory hierarchy.


title: Robust control and modeling a 2-DOF Inertial Stabilized Platform

abstract: This paper presents an uncertain linear model for an Inertial Stabilization Platform (ISP). The system has a 2 degree of freedom (DOF) gimbal which will be attached to a moving vehicle and the optical sensors mounted on stabilized gimbal. The main purpose of an ISP is to eliminate the various disturbance and body motion to make the line of sight (LOS), hold steady in an inertial space. Due to dynamical model of the 2-DOF gimbal, friction, cable restraint, noise, other disturbances from the outside environment and the motions of the vehicle's body (as a result of maneuvering or vibration), the pointing and tracking accuracy of the gimbaled system may strongly degrade. These disturbances are typically nonlinear. Modeling and controlling the stabilization loop with these nonlinearities are the main problem. The approach of this paper is considering a linear doubted model. Linearity makes the model simple and so linear classic controller can be used to meeting design requirements. Therefore the cost of the design and implementation stages will decrease. In addition uncertainty makes the model practical and so the nonlinear nature of mentioned disturbance can be interpreted by this model. So this built ISP by a PI controller which is capable enough to overcome the disturbances and to meet the implication requirements will be controlled. For decreasing the effect of these uncertainties and other probable variation in ISP's parameters on system performance, a determined simple structure for robust enhancer compensator is used and designed.


title: Kinematics and dynamics identification of a hyper-redundant, electromagnetically actuated manipulator

abstract: To overcome restrictions of passive, flexible endoscopes in technical tasks (e. g. inspection jobs) as well as in medical interventions, such as minimally invasive surgery, a hyper-redundant active shaft concept is proposed. Due to its unique binary, electromagnetic actuation concept it features good resistance with respect to manipulation forces. Thanks to an active control of each element of the hyper-redundant shaft, different shapes (configurations) can be commanded easily. As a matter of fact, the resulting shape and end effector errors highly depend on the accuracy of the kinematic model. Furthermore, a dynamic model — including a description of the electromagnetic behavior — is required to determine necessary individual poweron durations for the capacity discharge in the tilting circuit of the electromagnetic actuators. In this paper, the dynamics and kinematics for the hyperredundant manipulator are determined. A simplified model for the tilting actuator torque is deduced from the electromagnetic behavior. Subsequently, model parameters are identified and evaluated for a prototypical manipulator based on measurements of the end effector movements.


title: A tutorial introduction to Bayesian models of cognitive development

abstract: We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive development. Our goal is to provide an intuitive and accessible guide to the what, the how, and the why of the Bayesian approach: what sorts of problems and data the framework is most relevant for, and how and why it may be useful for developmentalists. We emphasize a qualitative understanding of Bayesian inference, but also include information about additional resources for those interested in the cognitive science applications, mathematical foundations, or machine learning details in more depth. In addition, we discuss some important interpretation issues that often arise when evaluating Bayesian models in cognitive science.


title: Planning in the Presence of Cost Functions Controlled by an Adversary

abstract: We investigate methods for planning in a Markov Decision Process where the cost function is chosen by an adversary after we fix our policy. As a running example, we consider a robot path planning problem where costs are influenced by sensors that an adversary places in the environment. We formulate the problem as a zero-sum matrix game where rows correspond to deterministic policies for the planning player and columns correspond to cost vectors the adversary can select. For a fixed cost vector, fast algorithms (such as value iteration) are available for solving MDPs. We develop efficient algorithms for matrix games where such best response oracles exist. We show that for our path planning problem these algorithms are at least an order of magnitude faster than direct solution of the linear programming formulation.


title: General and Interval Type-2 Fuzzy Face-Space Approach to Emotion Recognition

abstract: Facial expressions of a person representing similar emotion are not always unique. Naturally, the facial features of a subject taken from different instances of the same emotion have wide variations. In the presence of two or more facial features, the variation of the attributes together makes the emotion recognition problem more complicated. This variation is the main source of uncertainty in the emotion recognition problem, which has been addressed here in two steps using type-2 fuzzy sets. First a type-2 fuzzy face space is constructed with the background knowledge of facial features of different subjects for different emotions. Second, the emotion of an unknown facial expression is determined based on the consensus of the measured facial features with the fuzzy face space. Both interval and general type-2 fuzzy sets (GT2FS) have been used separately to model the fuzzy face space. The interval type-2 fuzzy set (IT2FS) involves primary membership functions for m facial features obtained from n-subjects, each having l-instances of facial expressions for a given emotion. The GT2FS in addition to employing the primary membership functions mentioned above also involves the secondary memberships for individual primary membership curve, which has been obtained here by formulating and solving an optimization problem. The optimization problem here attempts to minimize the difference between two decoded signals: the first one being the type-1 defuzzification of the average primary membership functions obtained from the n-subjects, while the second one refers to the type-2 defuzzified signal for a given primary membership function with secondary memberships as unknown. The uncertainty management policy adopted using GT2FS has resulted in a classification accuracy of 98.333% in comparison to 91.667% obtained by its interval type-2 counterpart. A small improvement (approximately 2.5%) in classification accuracy by IT2FS has been attained by pre-processing measurements using the well-known interval approach.


title: A Self-Adaptive Deep Learning-Based System for Anomaly Detection in 5G Networks

abstract: The upcoming fifth-generation (5G) mobile technology, which includes advanced communication features, is posing new challenges on cybersecurity defense systems. Although innovative approaches have evolved in the last few years, 5G will make existing intrusion detection and defense procedures become obsolete, in case they are not adapted accordingly. In this sense, this paper proposes a novel 5G-oriented cyberdefense architecture to identify cyberthreats in 5G mobile networks efficient and quickly enough. For this, our architecture uses deep learning techniques to analyze network traffic by extracting features from network flows. Moreover, our proposal allows adapting, automatically, the configuration of the cyberdefense architecture in order to manage traffic fluctuation, aiming both to optimize the computing resources needed in each particular moment and to fine tune the behavior and the performance of analysis and detection processes. Experiments using a well-known botnet data set depict how a neural network model reaches a sufficient classification accuracy in our anomaly detection system. Extended experiments using diverse deep learning solutions analyze and determine their suitability and performance for different network traffic loads. The experimental results show how our architecture can self-adapt the anomaly detection system based on the volume of network flows gathered from 5G subscribers’ user equipments in real-time and optimizing the resource consumption.


title: Single View Stereo Matching

abstract: Previous monocular depth estimation methods take a single view and directly regress the expected results. Though recent advances are made by applying geometrically inspired loss functions during training, the inference procedure does not explicitly impose any geometrical constraint. Therefore these models purely rely on the quality of data and the effectiveness of learning to generalize. This either leads to suboptimal results or the demand of huge amount of expensive ground truth labelled data to generate reasonable results. In this paper, we show for the first time that the monocular depth estimation problem can be reformulated as two sub-problems, a view synthesis procedure followed by stereo matching, with two intriguing properties, namely i) geometrical constraints can be explicitly imposed during inference; ii) demand on labelled depth data can be greatly alleviated. We show that the whole pipeline can still be trained in an end-to-end fashion and this new formulation plays a critical role in advancing the performance. The resulting model outperforms all the previous monocular depth estimation methods as well as the stereo block matching method in the challenging KITTI dataset by only using a small number of real training data. The model also generalizes well to other monocular depth estimation benchmarks. We also discuss the implications and the advantages of solving monocular depth estimation using stereo methods.


Emotion Analysis as a Regression Problem - Dimensional Models and Their Implications on Emotion Representation and Metrical Evaluation.


title: An anatomical substrate for integration among functional networks in human cortex.

abstract: The human brain shows several characteristics of an efficient communication network architecture, including short communication paths and the existence of modules interlinked by a small set of highly connected regions. Studies of structural networks comprising macroscopic white matter projections have shown that these putative hubs are densely interconnected, giving rise to a spatially distributed and topologically central collective called the "rich club." In parallel, studies of intrinsic brain activity have consistently revealed distinct functional communities or resting-state networks (RSNs), indicative of specialized processing and segregation of neuronal information. However, the pattern of structural connectivity interconnecting these functional RSNs and how such inter-RSN structural connections might bring about functional integration between RSNs remain largely unknown. Combining high-resolution diffusion weighted imaging with resting-state fMRI, we present novel evidence suggesting that the rich club structure plays a central role in cross-linking macroscopic RSNs of the human brain. Rich club hub nodes were present in all functional networks, accounted for a large proportion of "connector nodes," and were found to coincide with regions in which multiple networks overlap. In addition, a large proportion of all inter-RSN connections were found to involve rich club nodes, and these connections participated in a disproportionate number of communication paths linking nodes in different RSNs. Our findings suggest that the brain's rich club serves as a macroscopic anatomical substrate to cross-link functional networks and thus plays an important role in the integration of information between segregated functional domains of the human cortex.


title: Pathfinding in Computer Games

abstract: One of the greatest challenges in the design of realistic Artificial Intelligence (AI) in computer games is agent movement. Pathfinding strategies are usually employed as the core of any AI movement system. This report will highlight pathfinding algorithms used presently in games and their shortcomings especially when dealing with real-time pathfinding. With the advances being made in other components, such as physics engines, it is AI that is impeding the next generation of computer games. This report will focus on how machine learning techniques such as Artificial Neural Networks and Genetic Algorithms can be used to enhance an agents ability to handle pathfinding in real-time.


Inorganic Compounds of Carbon, Nitrogen, and Oxygen


title: The Social Connectedness of Older Adults: A National Profile*

abstract: For decades, scholars have wrestled with the assumption that old age is characterized by social isolation. However, there has been no systematic, nationally representative evaluation of this possibility in terms of social network connectedness. In this article, we develop a profile of older adults' social integration with respect to nine dimensions of interpersonal networks and voluntary associations. We use new data from the National Social Life, Health, and Aging Project (NSHAP), a population-based study of noninstitutionalized older Americans ages 57 to 85, conducted in 2005 to 2006. Results suggest that among older adults, age is negatively related to network size, closeness to network members, and number of non-primary-group ties. On the other hand, age is positively related to frequency of socializing with neighbors, religious participation, and volunteering. In addition, age has a U-shaped relationship with volume of contact with network members. These findings are inconsistent with the view that o...


title: Rotman-lens-feeding double-layer low-profile multibeam millimeter-wave microstrip antenna using bow-tie waveguide microstrip connections

abstract: A Rotman-lens-feeding double-layer microstrip antenna is in the development for a compact and low-profile multi-beam antenna. For the connection of the microstrip lines to the microstrip antenna and to the backed Rotman-lens feeding circuit, a broadband bow-tie waveguide microstrip connection circuit is designed in this work. The structures and the principles of the connection circuit and the whole multibeam antenna are explained. The broadband characteristics and the multi-beam performance are demonstrated by simulation in the millimeter-wave band.


title: Web Search of Fashion Items with Multimodal Querying

abstract: In this paper, we introduce a novel multimodal fashion search paradigm where e-commerce data is searched with a multimodal query composed of both an image and text. In this setting, the query image shows a fashion product that the user likes and the query text allows to change certain product attributes to fit the product to the user's desire. Multimodal search gives users the means to clearly express what they are looking for. This is in contrast to current e-commerce search mechanisms, which are cumbersome and often fail to grasp the customer's needs. Multimodal search requires intermodal representations of visual and textual fashion attributes which can be mixed and matched to form the user's desired product, and which have a mechanism to indicate when a visual and textual fashion attribute represent the same concept. With a neural network, we induce a common, multimodal space for visual and textual fashion attributes where their inner product measures their semantic similarity. We build a multimodal retrieval model which operates on the obtained intermodal representations and which ranks images based on their relevance to a multimodal query. We demonstrate that our model is able to retrieve images that both exhibit the necessary query image attributes and satisfy the query texts. Moreover, we show that our model substantially outperforms two state-of-the-art retrieval models adapted to multimodal fashion search.


title: Alterations of the CD4+, CD8+ T Cell Subsets, Interleukins-1β, IL-10, IL-17, Tumor Necrosis Factor-α and Soluble Intercellular Adhesion Molecule-1 in Rheumatoid Arthritis and Osteoarthritis: Preliminary Observations

abstract: Rheumatoid arthritis is a multisystem disease with underlying immune mechanisms. Osteoarthritis is a debilitating, progressive disease of diarthrodial joints associated with the aging process. Although much is known about the pathogenesis of rheumatoid arthritis and osteoarthritis, our understanding of some immunologic changes remains incomplete. This study tries to examine the numeric changes in the T cell subsets and the alterations in the levels of some cytokines and adhesion molecules in these lesions. To accomplish this goal, peripheral blood and synovial fluid samples were obtained from 24 patients with rheumatoid arthritis, 15 patients with osteoarthritis and six healthy controls. The counts of CD4 (+) and CD8 (+) T lymphocytes were examined using flow cytometry. The levels of some cytokines (TNF-alpha, IL1-beta, IL-10, and IL-17) and a soluble intercellular adhesion molecule-1 (sICAM-1) were measured in the sera and synovial fluids using enzyme linked immunosorbant assay. We found some variations in the counts of T cell subsets, the levels of cytokines and sICAM-1 adhesion molecule between the healthy controls and the patients with arthritis. High levels of IL-1beta, IL-10, IL-17 and TNF-alpha (in the serum and synovial fluid) were observed in arthritis compared to the healthy controls. In rheumatoid arthritis, a high serum level of sICAM-1 was found compared to its level in the synovial fluid. A high CD4(+)/CD8(+) T cell ratio was found in the blood of the patients with rheumatoid arthritis. In rheumatoid arthritis, the cytokine levels correlated positively with some clinicopathologic features. To conclude, the development of rheumatoid arthritis and osteoarthritis is associated with alteration of the levels of some cytokines. The assessment of these immunologic changes may have potential prognostic roles.


title: Abstract T MP17: Electrically Preconditioned Neural Stem Cells Improve Stroke Recovery

abstract: Background: Neural stem cells (NSCs) improve functional recovery after stroke, but the optimal method and conditions for delivery remain elusive. Tissue engineered scaffolds offer a unique method to manipulate and improve NSC function. We have developed a novel NSC delivery system utilizing an electrically conductive polymer scaffold to improve recovery and further elucidate repair mechanisms. ::: ::: Methods: Human NSCs were seeded onto an electrically conductive polymer scaffold. Electrical fields were applied to the NSCs to electrically “precondition” the cells prior to transplantation. One day after in vitro stimulation, the polymer scaffold was implanted into a distal MCA occlusion rat model 1 week post stroke, and functional recovery was assessed weekly for 4 weeks post-transplantation using the vibrissae-paw test. Control groups included unstimulated NSCs on the polymer and polymer without cells. To assess how electrical stimulation altered the cells, we performed qPCR analysis of genes of interest in stimulated and unstimulated cells on the polymer, and on NSCs on a plastic petri dish. ::: ::: Results: AC preconditioned NSCs improved the rate of neurologic functional recovery compared to unstimulated NSCs on the polymer (p<0.05) and significantly improved recovery compared to DC preconditioned cells and polymer alone (p<0.01). Preliminary results demonstrate that VEGF-A, -B and thrombospondin (TSP) -3 gene expression were upregulated in AC preconditioned cells compared to other groups. ::: ::: Conclusions: Our results demonstrate that electrically preconditioned NSCs enhance stroke recovery. Gene expression of the thrombospondins (a family of matricellular proteins thought to be involved with angiogenesis and inflammation) and VEGF (proteins critical to angiogenesis) were found to be modified by electrically stimulating NSCs, suggesting possible mechanisms for functional recovery.


title: Theory and Practice of Bloom Filters for Distributed Systems

abstract: Many network solutions and overlay networks utilize probabilistic techniques to reduce information processing and networking costs. This survey article presents a number of frequently used and useful probabilistic techniques. Bloom filters and their variants are of prime importance, and they are heavily used in various distributed systems. This has been reflected in recent research and many new algorithms have been proposed for distributed systems that are either directly or indirectly based on Bloom filters. In this survey, we give an overview of the basic and advanced techniques, reviewing over 20 variants and discussing their application in distributed systems, in particular for caching, peer-to-peer systems, routing and forwarding, and measurement data summarization.


title: A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies

abstract: The popularity of computer games has exploded in recent years, yet methods of evaluating user emotional state during play experiences lag far behind. There are few methods of assessing emotional state, and even fewer methods of quantifying emotion during play. This paper presents a novel method for continuously modeling emotion using physiological data. A fuzzy logic model transformed four physiological signals into arousal and valence. A second fuzzy logic model transformed arousal and valence into five emotional states relevant to computer game play: boredom, challenge, excitement, frustration, and fun. Modeled emotions compared favorably with a manual approach, and the means were also evaluated with subjective self-reports, exhibiting the same trends as reported emotions for fun, boredom, and excitement. This approach provides a method for quantifying emotional states continuously during a play experience.


title: iPathology: Robotic Applications and Management of Plants and Plant Diseases

abstract: The rapid development of new technologies and the changing landscape of the online world (e.g., Internet of Things (IoT), Internet of All, cloud-based solutions) provide a unique opportunity for developing automated and robotic systems for urban farming, agriculture, and forestry. Technological advances in machine vision, global positioning systems, laser technologies, actuators, and mechatronics have enabled the development and implementation of robotic systems and intelligent technologies for precision agriculture. Herein, we present and review robotic applications on plant pathology and management, and emerging agricultural technologies for intra-urban agriculture. Greenhouse advanced management systems and technologies have been greatly developed in the last years, integrating IoT and WSN (Wireless Sensor Network). Machine learning, machine vision, and AI (Artificial Intelligence) have been utilized and applied in agriculture for automated and robotic farming. Intelligence technologies, using machine vision/learning, have been developed not only for planting, irrigation, weeding (to some extent), pruning, and harvesting, but also for plant disease detection and identification. However, plant disease detection still represents an intriguing challenge, for both abiotic and biotic stress. Many recognition methods and technologies for identifying plant disease symptoms have been successfully developed; still, the majority of them require a controlled environment for data acquisition to avoid false positives. Machine learning methods (e.g., deep and transfer learning) present promising results for improving image processing and plant symptom identification. Nevertheless, diagnostic specificity is a challenge for microorganism control and should drive the development of mechatronics and robotic solutions for disease management.


title: Do infants detect indirect reciprocity?

abstract: Abstract In social interactions involving indirect reciprocity, agent A acts prosocially towards B and this prompts C to act prosocially towards A. This happens because A’s actions enhanced its reputation in the eyes of third parties. Indirect reciprocity may have been of central importance in the evolution of morality as one of the major mechanisms leading to the selection of helping and fair attitudes. Here we show that 10-month-old infants expect third parties to act positively towards fair donors who have distributed attractive resources equally between two recipients, rather than toward unfair donors who made unequal distributions. Infants’ responses were dependent on the reciprocator’s perceptual exposure to previous relevant events: they expected the reciprocator to reward the fair donor only when it had seen the distributive actions performed by the donors. We propose that infants were able to generate evaluations of agents that were based on the fairness of their distributive actions and to generate expectations about the social preferences of informed third parties.


PSVM: Parallelizing Support Vector Machines on Distributed Computers


title: Content-Aware Partial Compression for Big Textual Data Analysis Acceleration

abstract: Analysing text-based data has become increasingly important due to the importance of text from sources such as social media, web contents, web searches. The growing volume of such data creates challenges for data analysis including efficient and scalable algorithm, effective computing platforms and energy efficiency. Compression is a standard method for reducing data size but current standard compression algorithms are destructive to the organisation of data contents. This work introduces Content-aware, Partial Compression (CaPC) for text using a dictionary-based approach. We simply use shorter codes to replace strings while maintaining the original data format and structure, so that the compressed contents can be directly consumed by analytic platforms. We evaluate our approach with a set of real-world datasets and several classical MapReduce jobs on Hadoop. We also provide a supplementary utility library for Hadoop, hence, existing MapReduce programs can be used directly on the compressed datasets with little or no modification. In evaluation, we demonstrate that CaPC works well with a wide variety of data analysis scenarios, experimental results show ~30% average data size reduction, and up to ~32% performance increase on some I/O intensive jobs on an in-house Hadoop cluster. While the gains may seem modest, the point is that these gains are 'for free' and act as supplementary to all other optimizations.


title: A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition

abstract: This paper proposes a facial expression recognition system using evolutionary particle swarm optimization (PSO)-based feature optimization. The system first employs modified local binary patterns, which conduct horizontal and vertical neighborhood pixel comparison, to generate a discriminative initial facial representation. Then, a PSO variant embedded with the concept of a micro genetic algorithm (mGA), called mGA-embedded PSO, is proposed to perform feature optimization. It incorporates a nonreplaceable memory, a small-population secondary swarm, a new velocity updating strategy, a subdimension-based in-depth local facial feature search, and a cooperation of local exploitation and global exploration search mechanism to mitigate the premature convergence problem of conventional PSO. Multiple classifiers are used for recognizing seven facial expressions. Based on a comprehensive study using within- and cross-domain images from the extended Cohn Kanade and MMI benchmark databases, respectively, the empirical results indicate that our proposed system outperforms other state-of-the-art PSO variants, conventional PSO, classical GA, and other related facial expression recognition models reported in the literature by a significant margin.


title: Genetical and Taxonomic Investigations in the Genus Oenothera

abstract: The present study is the result of combining genetics with taxonomy in the investigation of a polymorphic group of wild plants. It shows a degree of multiformity which was hitherto unsuspected in the genus. All the early genetical work on Oenothera was done with species which had been naturalized in Europe and whose North American home was unknown. Later, de vries (1913) introduced various American forms into cultivation and used them in genetic experiment, but without full taxonomic descriptions. Bartlett was mainly concerned in describing about twenty-five new species from wild plants of eastern North America brought into cultivation, and the present writer has previously described five, all but one of them from Eastern Canada. Professional taxonomists have paid little attention to the Onagra section of the genus except for the occasional description of a new species from western North America, and the whole number of species now recognized and described is about 70, not counting the 17 new species and 15 new varieties described in the present paper. The reason for the neglect of the taxonomists, even after the mutation work concentrated a great deal of attention on the genus, was no doubt the difficulty that many of the characters are not well shown in ordinary herbarium material. Indeed, cultures are necessary in order to study adequately the characters of these forms ; but, on the other hand, species once clearly delimited in this way can be recognized in the field, at least when well-developed plants are available, and frequently from the rosette stage alone.


title: Private Equality Test Using Ring-LWE Somewhat Homomorphic Encryption

abstract: We propose two secure protocols namely private equality test (PET) for single comparison and private batch equality test (PriBET) for batch comparisons of l-bit integers. We ensure the security of these secure protocols using somewhat homomorphic encryption (SwHE) based on ring learning with errors (ring-LWE) problem in the semi-honest model. In the PET protocol, we take two private integers input and produce the output denoting their equality or non-equality. Here the PriBET protocol is an extension of the PET protocol. So in the PriBET protocol, we take single private integer and another set of private integers as inputs and produce the output denoting whether single integer equals at least one integer in the set of integers or not. To serve this purpose, we also propose a new packing method for doing the batch equality test using few homomorphic multiplications of depth one. Here we have done our experiments at the 140-bit security level. For the lattice dimension 2048, our experiments show that the PET protocol is capable of doing any equality test of 8-bit to 2048-bit that require at most 107 milliseconds. Moreover, the PriBET protocol is capable of doing about 600 (resp., 300) equality comparisons per second for 32-bit (resp., 64-bit) integers. In addition, our experiments also show that the PriBET protocol can do more computations within the same time if the data size is smaller like 8-bit or 16-bit.


title: Pulse width modulation with resonant DC link converters

abstract: A technique for realizing pulse width modulation (PWM) capability in resonant DC link converters is presented. This technique eliminates the subharmonics present in the conventional discrete pulse modulated converters without requiring any additional switching devices. Detailed design-oriented analysis enumerating the tradeoffs involved are presented. Control techniques for simultaneously accomplishing link and output control are considered. Simulation results verifying the principle of operation are presented and backed up by experiments. >


title: Underwater Image Enhancement by Dehazing With Minimum Information Loss and Histogram Distribution Prior

abstract: Images captured under water are usually degraded due to the effects of absorption and scattering. Degraded underwater images show some limitations when they are used for display and analysis. For example, underwater images with low contrast and color cast decrease the accuracy rate of underwater object detection and marine biology recognition. To overcome those limitations, a systematic underwater image enhancement method, which includes an underwater image dehazing algorithm and a contrast enhancement algorithm, is proposed. Built on a minimum information loss principle, an effective underwater image dehazing algorithm is proposed to restore the visibility, color, and natural appearance of underwater images. A simple yet effective contrast enhancement algorithm is proposed based on a kind of histogram distribution prior, which increases the contrast and brightness of underwater images. The proposed method can yield two versions of enhanced output. One version with relatively genuine color and natural appearance is suitable for display. The other version with high contrast and brightness can be used for extracting more valuable information and unveiling more details. Simulation experiment, qualitative and quantitative comparisons, as well as color accuracy and application tests are conducted to evaluate the performance of the proposed method. Extensive experiments demonstrate that the proposed method achieves better visual quality, more valuable information, and more accurate color restoration than several state-of-the-art methods, even for underwater images taken under several challenging scenes.


title: Fingertip detection for virtual keyboard based on camera

abstract: Tablet producers offer many utilities for their tablets to attract the users. The virtual keyboard based on camera can be such a utility in the near future. We have designed the strategy for an estimation of the finger positions in a virtual keyboard space and detection of clicking events. This strategy includes the processes: picture capturing, edge detection (finger contour detection), fingertip localization, fingertip tracing and click detection. A key step in the whole strategy is the exact localization of fingertip. In this article we describe in detail three proposed solutions to this task. The first algorithm is based on finding local maxima of the finger contours, and this maximum has to meet certain criteria. The second proposed method uses a curvature function calculated from a traced hand contour - a curvature function has the local maxima at the fingertips. The third method is based on angle between two vectors created from a traced hand contour - angle has a minimum at the point of fingertip. In our article we describe the principles of the three proposed methods as well as the practical experiences that we gained in real conditions.


title: The diagnosis of head injury requires a classification based on computed axial tomography.

abstract: The introduction of structural imaging of the brain by computed tomography (CT) scans and magnetic resonance imaging (MRI) has further refined classification of head injury for prognostic, diagnosis, and treatment purposes. We describe a new classification scheme to be used both as a research and a clinical tool in association with other predictors of neurologic status.


A Logistic Regression Model of Irony Detection in Chinese Internet Texts


title: Identifying bilingual Multi-Word Expressions for Statistical Machine Translation

abstract: MultiWord Expressions (MWEs) repesent a key issue for numerous applications in Natural Language Processing (NLP) especially for Machine Translation (MT). In this paper, we describe a strategy for detecting translation pairs of MWEs in a French-English parallel corpus. In addition we introduce three methods aiming to integrate extracted bilingual MWES in MOSES, a phrase based Statistical Machine Translation (SMT) system. We experimentally show that these textual units can improve translation quality.


title: Causality reasoning about network events for detecting stealthy malware activities

abstract: Malicious software activities have become more and more clandestine, making them challenging to detect. Existing security solutions rely heavily on the recognition of known code or behavior signatures, which are incapable of detecting new malware patterns. We propose to discover the triggering relations on network requests and leverage the structural information to identify stealthy malware activities that cannot be attributed to a legitimate cause. The triggering relation is defined as the temporal and causal relationship between two events. We design and compare rule- and learning-based methods to infer the triggering relations on network data. We further introduce a user-intention based security policy for pinpointing stealthy malware activities based on a triggering relation graph. We extensively evaluate our solution on a DARPA dataset and 7?GB real-world network traffic. Results indicate that our dependence analysis successfully detects various malware activities including spyware, data exfiltrating malware, and DNS bots on hosts. With good scalability for large datasets, the learning-based method achieves better classification accuracy than the rule-based one. The significance of our traffic reasoning approach is its ability to detect new and stealthy malware activities.


title: Uncovering network tarpits with degreaser

abstract: Network tarpits, whereby a single host or appliance can masquerade as many fake hosts on a network and slow network scanners, are a form of defensive cyber-deception. In this work, we develop degreaser, an efficient fingerprinting tool to remotely detect tarpits. In addition to validating our tool in a controlled environment, we use degreaser to perform an Internet-wide scan. We discover tarpits of non-trivial size in the wild (prefixes as large as/16), and characterize their distribution and behavior. We then show how tarpits pollute existing network measurement surveys that are tarpit-naive, e.g. Internet census data, and how degreaser can improve the accuracy of such surveys. Lastly, our findings suggest several ways in which to advance the realism of current network tarpits, thereby raising the bar on tarpits as an operational security mechanism.


title: Consilience and Life History Theory: From Genes to Brain to Reproductive Strategy.

abstract: We describe an integrated theory of individual diVerences that traces the behavioral development of life history from genes to brain to reproductive strategy. We provide evidence that a single common factor, the K-Factor, underpins a variety of life-history parameters, including an assortment of sexual, reproductive, parental, familial, and social behaviors. We explore the psychometrics and behavioral genetics of the K-Factor and oVer a speculative account of the proximate mediation of this adaptive patterning of behavior as instantiated in well-established functions of speciWc areas of the human brain, including the frontal lobes, amygdala, and hippocampus. We then apply Life History Theory to predict patterns of development within the brain that are paedomorphic (i.e., development begins later, proceeds at a slower rate, and has an earlier cessation) and peramorphic (i.e., development begins early, proceeds at a faster rate, and has a later cessation).


title: The assessment of body image investment: An extensive revision of the appearance schemas inventory

abstract: Objective ::: The Appearance Schemas Inventory (ASI) is a 14-item instrument that assesses body image investment in relation to certain beliefs or assumptions about the importance, meaning, and influence of appearance in one's life. Despite empirical support of the ASI, critical examination evinces several limitations of this assessment. These problems entail the inclusion of explicitly self-evaluative items and social stereotypes, few behavioral items, and a repeated failure to find expected gender differences on the ASI. ::: ::: Method ::: We initially constructed a 45-item measure (40 new items plus 5 original items) and administered it, along with the original ASI and other validational assessments, to 603 college students (468 women and 135 men). ::: ::: Results ::: The end result was a 20-item revision of the inventory (ASI-R), which included two factors: Self-Evaluative Salience and Motivational Salience. For both genders, the composite ASI-R and its two factors had high internal consistency and were significantly convergent with other pertinent measures of body image and psychosocial functioning. The ASI-R and its two subscales showed significant gender differences, whereas the original ASI did not. We also examined racial differences on the ASI-R, its correlations with body mass, and its unique contribution to the prediction of disturbed eating attitudes. ::: ::: Discussion ::: We offer this measure as an improved, psychometrically sound replacement for the ASI. © 2004 by Wiley Periodicals, Inc. Int J Eat Disord 35: 305–316, 2004.


title: Quad-switch push-pull (QSPP) RF amplifier with direct, simultaneous modulation of phase and pulse position for spread-spectrum power applications

abstract: We present a new RF power amplifier for spread spectrum applications, including wireless power transfer with low electromagnetic interference (EMI). The amplifier delivers half sine wave voltage pulses of either polarity through direct push-pull modulation of phase and pulse position. EMI is a concern not only from a regulatory perspective, but also in medical and other critical environments. The voltage pulses can be optimized by a variety of methods for specific spectral characteristics to create a current, and hence a high frequency magnetic field with a spread spectrum. The topology takes advantage of the inherent characteristics of GaN FET devices.


title: Internet of Things based home automation system

abstract: The internet has been connecting people and making life simpler by providing all kinds of information with the click of a button. The next big wave in this domain is the internet of things which will enable physical objects used in day to day life to connect to the internet and exchange data. In this paper a home automation system based on internet of things is developed. This allows the user to automate all the devices and appliances of home and integrate them to provide seamless control over every aspect of their home. The designed system not only monitors the sensor data, but also actuates a process according to the requirement, for example switching on the light when it gets dark and it allows the user to control the household devices from anywhere.


title: Interpreting and Unifying Outlier Scores

abstract: Outlier scores provided by different outlier models differ widely in their meaning, range, and contrast between different outlier models and, hence, are not easily comparable or interpretable. We propose a unification of outlier scores provided by various outlier models and a translation of the arbitrary “outlier factors” to values in the range [0, 1] interpretable as values describing the probability of a data object of being an outlier. As an application, we show that this unification facilitates enhanced ensembles for outlier detection.


title: A 2.5-GHz asymmetric multilevel outphasing power amplifier in 65-nm CMOS

abstract: We present a high-efficiency transmitter based on asymmetric multilevel outphasing (AMO). AMO transmitters improve their efficiency over LINC (linear amplification using nonlinear components) transmitters by switching the output envelopes of the power amplifiers among a discrete set of levels. This minimizes the occurrence of large outphasing angles, reducing the energy lost in the power combiner. We demonstrate this concept with a 2.5-GHz, 20-dBm peak output power transmitter using 2-level AMO designed in a 65-nm CMOS process. To the authors' knowledge, this IC is the first integrated implementation of the AMO concept. At peak output power, the measured power-added efficiency is 27.8%. For a 16-QAM signal with 6.1dB peak-to-average power ratio, the AMO prototype improves the average efficiency from 4.7% to 10.0% compared to the standard LINC system.


title: Chinese Handwriting Recognition: An Algorithmic Perspective

abstract: Designing machines that can read handwriting like human beings has been an ambitious goal for more than half a century, driving talented researchers to explore diverse approaches. Obstacles have often been encountered that at first appeared insurmountable but were indeed overcome before long. Yet some open issues remain to be solved. As an indispensable branch, Chinese handwriting recognition has been termed as one of the most difficult Pattern Recognition tasks. Chinese handwriting recognition poses its own unique challenges, such as huge variations in strokes, diversity of writing styles, and a large set of confusable categories. With ever-increasing training data, researchers have pursued elaborate algorithms to discern characters from different categories and compensate for the sample variations within the same category. As a result, Chinese handwriting recognition has evolved substantially and amazing achievements can be seen. This book introduces integral algorithms used in Chinese handwriting recognition and the applications of Chinese handwriting recogniers. The first part of the book covers both widespread canonical algorithms to a reliable recognizer and newly developed scalable methods in Chinese handwriting recognition. The recognition of Chinese handwritten text is presented systematically, including instructive guidelines for collecting samples, novel recognition paradigms, distributed discriminative learning of appearance models and distributed estimation of contextual models for large categories, in addition to celebrated methods, e.g. Gradient features, MQDF and HMMs. In the second part of this book, endeavors are made to create a friendlier human-machine interface through application of Chinese handwriting recognition. Four scenarios are exemplified: grid-assisted input, shortest moving input, handwritten micro-blog, and instant handwriting messenger. All the while, the book moves from basic to more complex approaches, also providing a list for further reading with literature comments.


title: Flow-Based Propagators for the SEQUENCE and Related Global Constraints

abstract: We propose new filtering algorithms for the Sequence constraint and some extensions of the Sequence constraint based on network flows. We enforce domain consistency on the Sequence constraint in O(n2) time down a branch of the search tree. This improves upon the best existing domain consistency algorithm by a factor of O(logn). The flows used in these algorithms are derived from a linear program. Some of them differ from the flows used to propagate global constraints like Gcc since the domains of the variables are encoded as costs on the edges rather than capacities. Such flows are efficient for maintaining bounds consistency over large domains and may be useful for other global constraints.


title: Hedonic tone and activation level in the mood-creativity link: toward a dual pathway to creativity model.

abstract: To understand when and why mood states influence creativity, the authors developed and tested a dual pathway to creativity model; creative fluency (number of ideas or insights) and originality (novelty) are functions of cognitive flexibility, persistence, or some combination thereof. Invoking work on arousal, psychophysiological processes, and working memory capacity, the authors argue that activating moods (e.g., angry, fearful, happy, elated) lead to more creative fluency and originality than do deactivating moods (e.g., sad, depressed, relaxed, serene). Furthermore, activating moods influence creative fluency and originality because of enhanced cognitive flexibility when tone is positive and because of enhanced persistence when tone is negative. Four studies with different mood manipulations and operationalizations of creativity (e.g., brainstorming, category inclusion tasks, gestalt completion tests) support the model.


title: Self-healing soft pneumatic robots

abstract: Inspired by the compliance found in many organisms, soft robots are made almost entirely out of flexible, soft material, making them suitable for applications in uncertain, dynamic task environments, including safe human-robot interactions. Their intrinsic compliance absorbs shocks and protects them against mechanical impacts. However, the soft materials used for their construction are highly susceptible to damage, such as cuts and perforations caused by sharp objects present in the uncontrolled and unpredictable environments they operate in. In this research, we propose to construct soft robotics entirely out of self-healing elastomers. On the basis of healing capacities found in nature, these polymers are given the ability to heal microscopic and macroscopic damage. Diels-Alder polymers, being thermoreversible covalent networks, were used to develop three applications of self-healing soft pneumatic actuators (a soft gripper, a soft hand, and artificial muscles). Soft pneumatic actuators commonly experience perforations and leaks due to excessive pressures or wear during operation. All three prototypes were designed using finite element modeling and mechanically characterized. The manufacturing method of the actuators exploits the self-healing behavior of the materials, which can be recycled. Realistic macroscopic damage could be healed entirely using a mild heat treatment. At the location of the scar, no weak spots were created, and the full performance of the actuators was nearly completely recovered after healing.


title: Customer Satisfaction Cues To Support Market Segmentation and Explain Switching Behavior

abstract: Abstract In this paper, customer satisfaction cues in retail banking services in Greece are examined. The study proposes an instrument of customer satisfaction that contains service quality and such other attributes as price, convenience, and innovation. The proposed framework of customer satisfaction was verified empirically yielding four distinct facets for business customers and five for individual customers. The performance implications of the customer satisfaction instrument are also explored. What is shown is that customer segments, in fact, yield statistically different satisfaction scores, which verifies the managerial value of customer segmentation practices. Finally, the facets of customer satisfaction as explanatory cues for the switching behavior of individual and business customers were tested successfully.


title: A source independent framework for research paper recommendation

abstract: As the number of research papers available on the Web has increased enormously over the years, paper recommender systems have been proposed to help researchers on automatically finding works of interest. The main problem with the current approaches is that they assume that recommending algorithms are provided with a rich set of evidence (e.g., document collections, citations, profiles) which is normally not widely available. In this paper we propose a novel source independent framework for research paper recommendation. The framework requires as input only a single research paper and generates several potential queries by using terms in that paper, which are then submitted to existing Web information sources that hold research papers. Once a set of candidate papers for recommendation is generated, the framework applies content-based recommending algorithms to rank the candidates in order to recommend the ones most related to the input paper. This is done by using only publicly available metadata (i.e., title and abstract). We evaluate our proposed framework by performing an extensive experimentation in which we analyzed several strategies for query generation and several ranking strategies for paper recommendation. Our results show that good recommendations can be obtained with simple and low cost strategies.


title: Comparison of two Types of Surgery for Thoraco-Lumbar Burst Fractures: Combined Anterior and Posterior Stabilisation vs. Posterior Instrumentation Only

abstract: This retrospective study compares clinical outcome following two different types of surgery for thoracolumbar burst fractures. Forty-six patients with thoracolumbar burst fractures causing encroachment of the spinal canal greater than 50% were operated on within 30 days performing either: combined anterior decompression and stabilisation and posterior stabilisation (Group 1) or posterior distraction and stabilisation using pedicle instrumentation (AO internal fixator) (Group 2). We evaluated: neurological status (Frankel Grade), spinal deformities, residual pain, and complications. The average follow-up was 6 years. There were no significant differences between the patients in both groups concerning age, sex, cause of injury and the presence of other severe injuries. Neurological dysfunction was present in 39% of all cases. Bony union occurred in all patients. Loss of reduction greater than 5 degrees and instrumentation failure occurred significantly more often in Group 2 compared to Group 1, but the kyphosis angle at late follow-up did not differ between groups, due to some degree of overcorrection initially after surgery in Group 2. The clinical outcome was similar in both groups, and all but one patient with neurological deficits improved by at least one Frankel grade. Indirect decompression of the spinal canal by posterior distraction and short-segment stabilisation with AO internal fixator is considered appropriate treatment for the majority of unstable thoracolumbar burst fractures. This is a less extensive surgical procedure than a combined anterior and posterior approach.


title: Exploratory security analytics for anomaly detection

abstract: The huge number of alerts generated by network-based defense systems prevents detailed manual inspections of security events. Existing proposals for automatic alerts analysis work well in relatively stable and homogeneous environments, but in modern networks, that are characterized by extremely complex and dynamic behaviors, understanding which approaches can be effective requires exploratory data analysis and descriptive modeling. We propose a novel framework for automatically investigating temporal trends and patterns of security alerts with the goal of understanding whether and which anomaly detection approaches can be adopted for identifying relevant security events. Several examples referring to a real large network show that, despite the high intrinsic dynamism of the system, the proposed framework is able to extract relevant descriptive statistics that allow to determine the effectiveness of popular anomaly detection approaches on different alerts groups.


title: Cyber Threat Intelligence Model: An Evaluation of Taxonomies, Sharing Standards, and Ontologies within Cyber Threat Intelligence

abstract: Threat intelligence is the provision of evidence-based knowledge about existing or potential threats. Benefits of threat intelligence include improved efficiency and effectiveness in security operations in terms of detective and preventive capabilities. Successful threat intelligence within the cyber domain demands a knowledge base of threat information and an expressive way to represent this knowledge. This purpose is served by the use of taxonomies, sharing standards, and ontologies.This paper introduces the Cyber Threat Intelligence (CTI) model, which enables cyber defenders to explore their threat intelligence capabilities and understand their position against the ever-changing cyber threat landscape. In addition, we use our model to analyze and evaluate several existing taxonomies, sharing standards, and ontologies relevant to cyber threat intelligence. Our results show that the cyber security community lacks an ontology covering the complete spectrum of threat intelligence. To conclude, we argue the importance of developing a multi-layered cyber threat intelligence ontology based on the CTI model and the steps should be taken under consideration, which are the foundation of our future work.


title: An Ultra-Low-Power 9T SRAM Cell Based on Threshold Voltage Techniques

abstract: This paper presents a new nine-transistor (9T) SRAM cell operating in the subthreshold region. In the proposed 9T SRAM cell, a suitable read operation is provided by suppressing the drain-induced barrier lowering effect and controlling the body---source voltage dynamically. Proper usage of low-threshold voltage (L-$$V_{\mathrm{t}}$$Vt) transistors in the proposed design helps to reduce the read access time and enhance the reliability in the subthreshold region. In the proposed cell, a common bit-line is used in the read and write operations. This design leads to a larger write margin without using extra circuits. The simulation results at 90 nm CMOS technology demonstrate a qualified performance of the proposed SRAM cell in terms of power dissipation, power---delay product, write margin, read access time and sensitivity to process, voltage and temperature variations as compared to the other most efficient low-voltage SRAM cells previously presented in the literature.


title: Illustrating smooth surfaces

abstract: We present a new set of algorithms for line-art rendering of smooth surfaces. We introduce an efficient, deterministic algorithm for finding silhouettes based on geometric duality, and an algorithm for segmenting the silhouette curves into smooth parts with constant visibility. These methods can be used to find all silhouettes in real time in software. We present an automatic method for generating hatch marks in order to convey surface shape. We demonstrate these algorithms with a drawing style inspired by A Topological Picturebook by G. Francis.


title: An Open-Source Hardware/Software Architecture for Quadrotor UAVs

abstract: In this paper, we illustrate an open-source ready-to-use hardware/software archi- tecture for a quadrotor UAV. The presented platform is price effective, highly customizable, and easily exploitable by other researchers involved in high-level UAV control tasks and for educational purposes as well. The use of object-oriented programming and full support of Robot Operating System (ROS) and Matlab Simulink allows for an efficient customization, code reuse, functionality expansion and rapid prototyping of new algorithms. We provide an extensive illustration of the various UAV components and a thorough description of the main basic algorithms and calibration procedures. Finally, we present some experimental case studies aimed at showing the effectiveness of the proposed architecture.


Deep Multi-Class Segmentation Without Ground-Truth Labels


title: The relativistic discriminator: a key element missing from standard GAN

abstract: In standard generative adversarial network (SGAN), the discriminator estimates the probability that the input data is real. The generator is trained to increase the probability that fake data is real. We argue that it should also simultaneously decrease the probability that real data is real because 1) this would account for a priori knowledge that half of the data in the mini-batch is fake, 2) this would be observed with divergence minimization, and 3) in optimal settings, SGAN would be equivalent to integral probability metric (IPM) GANs. ::: We show that this property can be induced by using a relativistic discriminator which estimate the probability that the given real data is more realistic than a randomly sampled fake data. We also present a variant in which the discriminator estimate the probability that the given real data is more realistic than fake data, on average. We generalize both approaches to non-standard GAN loss functions and we refer to them respectively as Relativistic GANs (RGANs) and Relativistic average GANs (RaGANs). We show that IPM-based GANs are a subset of RGANs which use the identity function. ::: Empirically, we observe that 1) RGANs and RaGANs are significantly more stable and generate higher quality data samples than their non-relativistic counterparts, 2) Standard RaGAN with gradient penalty generate data of better quality than WGAN-GP while only requiring a single discriminator update per generator update (reducing the time taken for reaching the state-of-the-art by 400%), and 3) RaGANs are able to generate plausible high resolutions images (256x256) from a very small sample (N=2011), while GAN and LSGAN cannot; these images are of significantly better quality than the ones generated by WGAN-GP and SGAN with spectral normalization.


title: Determinants of malaria infection in Dembia district, Northwest Ethiopia: a case-control study

abstract: Despite the progress in reducing malaria infections and related deaths, the disease remains a major global public health problem. The problem is among the top five leading causes of outpatient visits in Dembia district of the northwest Ethiopia. Therefore, this study aimed to assess the determinants of malaria infections in the district. An institution-based case-control study was conducted in Dembia district from October to November 2016. Out of the ten health centers in the district, four were randomly selected for the study in which 370 participants (185 cases and 185 controls) were enrolled. Data were collected using a pretested structured questionnaire. Factors associated with malaria infections were determined using logistic regression analysis. Odds ratio with 95% CI was used as a measure of association, and variables with a p-value of ≤0.05 were considered as statistically significant. The median age of all participants was 26 years, while that of cases and controls was 22 and 30 with a range of 1 to 80 and 2 to 71, respectively. In the multivariable logistic regression, over 15 years of age adjusted odds ratio(AOR) and confidence interval (CI) of (AOR = 18; 95% CI: 2.1, 161.5), being male (AOR = 2.2; 95% CI: 1.2, 3.9), outdoor activities at night (AOR = 5.7; 95% CI: 2.5, 12.7), bed net sharing (AOR = 3.9; 95% CI: 2.0, 7.7), and proximity to stagnant water sources (AOR = 2.7; 95% CI: 1.3, 5.4) were independent predictors. Being in over 15 years of age group, male gender, night time activity, bed net sharing and proximity to stagnant water sources were determinant factors of malaria infection in Dembia district. Additional interventions and strategies which focus on men, outdoor work at night, household net utilization, and nearby stagnant water sources are essential to reduce malaria infections in the area.


title: Evolution of fashion brands on Twitter and Instagram.

abstract: Social media has become a popular platform for marketing and brand advertisement especially for fashion brands. To promote their products and gain popularity, different brands post their latest products and updates as photos on different social networks. Little has been explored on how these fashion brands use different social media to reach out to their customers and obtain feedback on different products. Understanding this can help future consumers to focus on their interested brands on specific social media for better information and also can help the marketing staff of different brands to understand how the other brands are utilizing the social media. In this article we focus on the top-20 fashion brands and comparatively analyze how they target their current and potential future customers on Twitter and Instagram. Using both linguistic and deep image features, our work reveals an increasing diversification of trends accompanied by a simultaneous concentration towards a few selected trends. It provides insights about brand marketing strategies and their respective competencies. Our investigations show that the brands are using Twitter and Instagram in a distinctive manner.


title: Noise robust classification of moving vehicles via micro-Doppler signatures

abstract: For robust classification of moving wheeled and tracked vehicles using returned micro-Doppler signals within short dwell time, the influence of receiver white noise and low spectrum resolution are encountered. In this paper, noise reduction and super-resolution are realized simultaneously via a redundant dictionary based l1 -norm optimization method. Experiments based on the measured data are presented, including the analysis of noise reduction performance, and the evaluation of classification robustness for different signal-to-noise ratio cases. The experimental results are also compared with related methods.


title: Flexible smart sensor framework for autonomous structural health monitoring

abstract: Wireless smart sensors enable new approaches to improve structural health monitoring (SHM) practices through the use of distributed data processing. Such an approach is scalable to the large number of sensor nodes required for high-fidelity modal analysis and damage detection. While much of the technology associated with smart sensors has been available for nearly a decade, there have been limited numbers of full- scale implementations due to the lack of critical hardware and software elements. This research develops a flexible wireless smart sensor framework for full-scale, autonomous SHM that integrates the necessary software and hardware while addressing key implementation requirements. The Imote2 smart sensor platform is employed, providing the computation and communication resources that support demanding sensor network applications such as SHM of civil infrastructure. A multi-metric Imote2 sensor board with onboard signal processing specifically designed for SHM applications has been designed and validated. The framework software is based on a service-oriented architecture that is modular, reusable and extensible, thus allowing engineers to more readily realize the potential of smart sensor technology. Flexible network management software combines a sleep/wake cycle for enhanced power efficiency with threshold detection for triggering network wide operations such as synchronized sensing or decentralized modal analysis. The framework developed in this research has been validated on a full-scale a cable-stayed bridge in South Korea.


title: Performance Analysis and Modeling of a Tubular Staggered-Tooth Transverse-Flux PM Linear Machine

abstract: This paper investigates the performance analysis and mathematical modeling of a staggered-tooth transverse-flux permanent magnet linear synchronous machine (STTF-PMLSM), which is characterized by simple structure and low flux leakage. Firstly, the structure advantages and operation principle of the STTF-PMLSM are introduced, and a simplified one phase model is established to investigate the performance of the machine in order to save the computation time. Then, the electromagnetic characteristics, including no-load flux linkage, electromotive force (EMF), inductance, detent force and thrust force, are simulated and analyzed in detail. After that, the theoretical analysis of the detent force, thrust force, and power factor are carried out. And the theoretical analysis results are validated with 3-D finite-element method (FEM). Finally, an improved mathematical model of the machine based on d-q rotating coordinate system is proposed, in which inductance harmonics and coupling between d - and q -axis inductance is considered. The results from the proposed mathematical model are in accordance with the results from 3-D FEM, which proves the validity and effectiveness of the proposed mathematical model. This provides a powerful foundation for the control of the machine.


title: Exploration of Quantum Interference in Document Relevance Judgement Discrepancy

abstract: Quantum theory has been applied in a number of fields outside physics, e.g., cognitive science and information retrieval (IR). Recently, it has been shown that quantum theory can subsume various key IR models into a single mathematical formalism of Hilbert vector spaces. While a series of quantum-inspired IR models has been proposed, limited effort has been devoted to verify the existence of the quantum-like phenomenon in real users’ information retrieval processes, from a real user study perspective. In this paper, we aim to explore and model the quantum interference in users’ relevance judgement about documents, caused by the presentation order of documents. A user study in the context of IR tasks have been carried out. The existence of the quantum interference is tested by the violation of the law of total probability and the validity of the order effect. Our main findings are: (1) there is an apparent judging discrepancy across different users and document presentation orders, and empirical data have violated the law of total probability; (2) most search trials recorded in the user study show the existence of the order effect, and the incompatible decision perspectives in the quantum question (QQ) model are valid in some trials. We further explain the judgement discrepancy in more depth, in terms of four effects (comparison, unfamiliarity, attraction and repulsion) and also analyse the dynamics of document relevance judgement in terms of the evolution of the information need subspace.


title: Breakup of drops in a microfluidic T junction

abstract: We propose a mechanism of droplet breakup in a symmetric microfluidic T junction driven by pressure decrement in a narrow gap between the droplet and the channel wall. This mechanism works in a two-dimensional setting where the capillary (Rayleigh–Plateau) instability of a cylindrical liquid thread, suggested earlier [D. Link, S. Anna, D. Weitz, and H. Stone, Phys. Rev. Lett. 92, 054503 (2004)] as the cause of breakup, is not operative, but it is likely to be responsible for the breakup also in three dimensions. We derive a dependence of the critical droplet extension on the capillary number Ca by combining a simple geometric construction for the interface shape with lubrication analysis in a narrow gap where the surface tension competes with the viscous drag. The theory, formally valid for Ca1/5⪡1, shows a very good agreement with numerical results when it is extrapolated to moderate values of Ca.


title: A coordinated transmitter and receiver pre-coding scheme in multi-user MIMO downlink

abstract: Coordinated transmitter and receiver processing, in the multi-user multi-input multi-output downlink system, is a better scheme for suppressing the multi-user interferences than only employing pre-coding at the transmitter. In this paper, we propose a pre-coding scheme to joint the zero forcing and minimum mean-square error pre-coding based on the generalized zero-forcing channel inversion pre-coding. Zero forcing pre-coding is employed at the transmitter to eliminate the interferences among users. At the receiver, we investigate minimum mean-square error filter for the purpose of optimizing the system. A non-iterative algorithm is studied instead of common iterative algorithms. Simulation results show that the proposed method can obtain higher sum rate and better tradeoff between complexity and performance.


title: Correlation for user confidence in predictive decision making

abstract: Despite the recognized value of Machine Learning (ML) techniques and high expectation of applying ML techniques within various applications, significant barriers to the widespread adoption and local implementation of ML approaches still exist in the areas of trust (of ML results), comprehension (of ML processes), as well as confidence (in decision making) by users. This paper investigates the effects of correlation between features and target values on user confidence in data analytics-driven decision making. Our user study found that revealing the correlation between features and target variables affected user confidence in decision making significantly. Moreover, users felt more confident in decision making when correlation shared the same trend with the prediction model performance. These findings would help design intelligent user interfaces and evaluate the effectiveness of machine learning models in applications.


Adaptive ε-greedy Exploration in Reinforcement Learning Based on Value Differences


title: A Quantum-Theoretic Approach to Distributional Semantics

abstract: In this paper we explore the potential of quantum theory as a formal framework for capturing lexical meaning. We present a novel semantic space model that is syntactically aware, takes word order into account, and features key quantum aspects such as superposition and entanglement. We define a dependency-based Hilbert space and show how to represent the meaning of words by density matrices that encode dependency neighborhoods. Experiments on word similarity and association reveal that our model achieves results competitive with a variety of classical models.


title: A Simple and Efficient Replay Attack Prevention Scheme for LoRaWAN

abstract: In recent years, LPWAN technology, designed to realize low-power and long distance communication, has attracted much attention. Among several LPWAN technologies, Long Range (LoRa) is one of the most competitive physical layer protocol. Long Range Wide Area Network (LoRaWAN) is an upper layer protocol used with LoRa, and it provides several security functions including random number-based replay attack prevention system. According to recent studies, current replay attack prevention of LoRaWAN can mislead benign messages into replay attacks. To resolve this problem, several new replay attack prevention schemes have been proposed. However, existing schemes have limitations such as not being compatible with the existing packet structure or not considering an exceptional situation such as device reset. Therefore, in this paper, we propose a new LoRaWAN replay attack prevention scheme that resolves these problems. Our scheme follows the existing packet structure and is designed to cope with exceptional situations such as device reset. As a result of calculations, in our scheme, the probability that a normal message is mistaken for a replay attack is 60-89% lower than the current LoRaWAN. Real-world experiments also support these results.


title: Joint DOD and DOA Estimation for Bistatic MIMO Radar in Unknown Correlated Noise

abstract: In this paper, joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation for bistatic multiple-input–multi-output (MIMO) radar in an unknown spatially correlated noise environment is investigated. The signal model is based on the assumption that the waveforms are transmitted by two separated subarrays having $M_1$ and $M_2$ sensors and received by two separated subarrays having $N_1$ and $N_2$ sensors, respectively. The received data are pulse-compressed using a matching matrix consisting of $M=M_1+M_2$ orthogonally transmitted waveforms. The joint covariance matrix of unknown correlated noise is analyzed. A novel algorithm is proposed by jointly estimating the DOD and DOA with transmitter and receiver subarrays in unknown noise. The joint estimation algorithm is based on the canonical correlation decomposition (CCD) and exploits the shift-invariance properties in the Kronecker product structure of each column of the various steering matrices. The estimated DOA and DOD can be automatically paired correspondingly. In addition, the formulas of stochastic Cramer–Rao bounds (CRB) for DOD and DOA estimation are derived. Simulations show that our method can effectively improve the performance of estimation in unknown correlated noise environments and is insensitive to having different noise environments in the two subarrays.


title: GPU-based NFA implementation for memory efficient high speed regular expression matching

abstract: Regular expression pattern matching is the foundation and core engine of many network functions, such as network intrusion detection, worm detection, traffic analysis, web applications and so on. DFA-based solutions suffer exponentially exploding state space and cannot be remedied without sacrificing matching speed. Given this scalability problem of DFA-based methods, there has been increasing interest in NFA-based methods for memory efficient regular expression matching. To achieve high matching speed using NFA, it requires potentially massive parallel processing, and hence represents an ideal programming task on Graphic Processor Unit (GPU). Based on in-depth understanding of NFA properties as well as GPU architecture, we propose effective methods for fitting NFAs into GPU architecture through proper data structure and parallel programming design, so that GPU's parallel processing power can be better utilized to achieve high speed regular expression matching. Experiment results demonstrate that, compared with the existing GPU-based NFA implementation method [9], our proposed methods can boost matching speed by 29~46 times, consistently yielding above 10Gbps matching speed on NVIDIA GTX-460 GPU. Meanwhile, our design only needs a small amount of memory space, growing exponentially more slowly than DFA size. These results make our design an effective solution for memory efficient high speed regular expression matching, and clearly demonstrate the power and potential of GPU as a platform for memory efficient high speed regular expression matching.


title: Probability of error, equivocation and the chernoff bound

abstract: Relationships between the probability of error, the equivocation, and the Chernoff bound are examined for the two-hypothesis decision problem. The effect of rejections on these bounds is derived. Finally, the results are extended to the case of any finite number of hypotheses.


title: System dynamics modeling applied to software outsourcing decision support

abstract: Requirements in the software market for reduced costs, reduced development cycle time, as well as shortages of software developers, have motivated software organizations to outsource product development processes or components. The primary objective of this research is to determine whether software organizations can improve their software outsourcing strategies and processes in response to these forces. This research utilizes simulation modeling to explore the dynamics of outsourcing relationships, including both positive and negative outcomes, as well as to provide potential decision support for strategic outsourcing decisions. The model's current implementation, applicability and usefulness are demonstrated with an example use case and analysis of simulation results. We also present suggestions for future research directions on software outsourcing strategies and processes. Copyright © 2000 John Wiley & Sons Ltd


title: Twitter sentiment analysis of online transportation service providers

abstract: GO-JEK and Grab are two most popular online transportation service providers in Indonesia. The competition between the two is tight to acquire new customers and provide the best service. No official survey has been conducted in measuring customer satisfaction at both companies to give insight which company offers better service quality. This study aims to measure GO-JEK and Grab customer satisfaction through sentiment analysis of Twitter's data. Both companies use Twitter to reach their customers and promote their service. We collect 126,405 tweets from February to March 2016 containing GO-JEK and Grab keywords. Then, we pre-process the tweets and label manually before they are classified using three algorithms: Support Vector Machine, Naive Bayes, and Decision Tree. We made and compared several classification schemes with different keywords, dataset, and k-fold cross validation techniques. Finally, we calculate Net Sentiment Score which correlates with customer satisfaction using classification results. The experiments shows that Grab's customer satisfaction is higher than GO-JEK's. The study also shows that customers tend to mention both companies Twitter account for bad experiences and not mentioning company's account for positive comments.