title: Introduction: Aspects of Artificial General Intelligence

abstract: This introductory chapter sets the stage for the book as a whole. First, the purpose of the conference that led to the book is reviewed. Then, the notion of “Artificial General Intelligence” (AGI) is clarified, including a brief survey of the past and present situation of the field, an analysis and refutation of some common objections and doubts regarding the AGI area of research, and a discussion of what needs to be addressed by the field as a whole in the near future. Finally, there is a summary of the contents of the other chapters in the book.


title: Code Mixing: A Challenge for Language Identification in the Language of Social Media

abstract: In social media communication, multilingual speakers often switch between languages, and, in such an environment, automatic language identification becomes both a necessary and challenging task. In this paper, we describe our work in progress on the problem of automatic language identification for the language of social media. We describe a new dataset that we are in the process of creating, which contains Facebook posts and comments that exhibit code mixing between Bengali, English and Hindi. We also present some preliminary word-level language identification experiments using this dataset. Different techniques are employed, including a simple unsupervised dictionary-based approach, supervised word-level classification with and without contextual clues, and sequence labelling using Conditional Random Fields. We find that the dictionary-based approach is surpassed by supervised classification and sequence labelling, and that it is important to take contextual clues into consideration.


Drebin : � Efficient and Explainable Detection of Android Malware in Your Pocket


title: The development of summary components for the Disablement in the Physically Active scale in collegiate athletes

abstract: Purpose ::: The Disablement in the Physically Active scale (DPA) is a generic patient-reported outcome designed to evaluate constructs of disability in physically active populations. The purpose of this study was to analyze the DPA scale structure for summary components.


title: Causality on longitudinal data: Stable specification search in constrained structural equation modeling

abstract: A typical problem in causal modeling is the instability of model structure learning, i.e., small changes in finite data can result in completely different optimal models. The present work introduces a novel causal modeling algorithm for longitudinal data, that is robust for finite samples based on recent advances in stability selection using subsampling and selection algorithms. Our approach uses exploratory search but allows incorporation of prior knowledge, e.g., the absence of a particular causal relationship between two specific variables. We represent causal relationships using structural equation models. Models are scored along two objectives: the model fit and the model complexity. Since both objectives are often conflicting, we apply a multi-objective evolutionary algorithm to search for Pareto optimal models. To handle the instability of small finite data samples, we repeatedly subsample the data and select those substructures (from the optimal models) that are both stable and parsimonious. These substructures can be visualized through a causal graph. Our more exploratory approach achieves at least comparable performance as, but often a significant improvement over state-of-the-art alternative approaches on a simulated data set with a known ground truth. We also present the results of our method on three real-world longitudinal data sets on chronic fatigue syndrome, Alzheimer disease, and chronic kidney disease. The findings obtained with our approach are generally in line with results from more hypothesis-driven analyses in earlier studies and suggest some novel relationships that deserve further research


title: Storytelling with robots: Learning companions for preschool children's language development

abstract: Children's oral language skills in preschool can predict their academic success later in life. As such, increasing children's skills early on could improve their success in middle and high school. To this end, we propose that a robotic learning companion could supplement children's early language education. The robot targets both the social nature of language learning and the adaptation necessary to help individual children. The robot is designed as a social character that interacts with children as a peer, not as a tutor or teacher. It will play a storytelling game, during which it will introduce new vocabulary words, and model good story narration skills, such as including a beginning, middle, and end; varying sentence structure; and keeping cohesion across the story. We will evaluate whether adapting the robot's level of language to the child's - so that, as children improve their storytelling skills, so does the robot - influences (i) whether children learn new words from the robot, (ii) the complexity and style of stories children tell, (iii) the similarity of children's stories to the robot's stories. We expect children will learn more from a robot that adapts to maintain an equal or greater ability than the children, and that they will copy its stories and narration style more than they would with a robot that does not adapt (a robot of lesser ability). However, we also expect that playing with a robot of lesser ability could prompt teaching or mentoring behavior from children, which could also be beneficial to language learning.


Semi-Supervised Learning with Auxiliary Evaluation Component for Large Scale e-Commerce Text Classification


Baseline: A Library for Rapid Modeling, Experimentation and Development of Deep Learning Algorithms targeting NLP


title: SLPA: Uncovering Overlapping Communities in Social Networks via A Speaker-listener Interaction Dynamic Process

abstract: Overlap is one of the characteristics of social networks, in which a person may belong to more than one social group. For this reason, discovering overlapping structures is necessary for realistic social analysis. In this paper, we present a novel, general framework to detect and analyze both individual overlapping nodes and entire communities. In this framework, nodes exchange labels according to dynamic interaction rules. A specific implementation called Speaker-listener Label Propagation Algorithm (SLPA1) demonstrates an excellent performance in identifying both overlapping nodes and overlapping communities with different degrees of diversity.


title: Plagiorhynchidae Meyer, 1931 (Acanthocephala) from Australasian birds and mammals, with descriptions of Plagiorhynchus (Plagiorhynchus) menurae (Johnston, 1912) and P. (P.) allisonae n. sp.

abstract: New host and locality records are given for Plagiorhynchus (Plagiorhynchus) charadrii (Yamaguti, 1939) Van Cleave, 1951 and P. (Prosthorhynchus) cylindraceus (Goeze, 1782) Schmidt & Kuntz, 1966. The uncertainty of identification of a plover host of P. (P.) charadrii as well as the origins of P. (P.) cylindraceus (found in Australia but not New Zealand) and its occurrence in both bird and mammal hosts are discussed. P. (P.) menurae (Johnston, 1912) Golvan, 1956 is redescribed, including the male, and new host, Menura alberti Bonaparte, and locality records are given. P. (P.) allisonae n. sp. is described from Haematopus ostralegus finschi (Martens) and H. unicolor (Forster) from the South Island of New Zealand. P. (P.) allisonae can be differentiated from its congeners by having a proboscis armature of 18-23 rows of 14-20 hooks, thorns of hooks shorter than simple roots with short manubria, eight tubular cement glands and eggs of 134-154 × 33-36 μm in size. The presence of P. (P.) gracilis Petrochenko, 1958 in Australia is questioned. New host and locality records are given for Porrorchis hylae (Johnston, 1914) Schmidt & Kuntz, 1967 and the northern distribution of P. hydromuris (Edmonds, 1957) Schmidt & Kuntz, 1967 confirmed.


title: Emotional Context Influences Micro-Expression Recognition

abstract: Micro-expressions are often embedded in a flow of expressions including both neutral and other facial expressions. However, it remains unclear whether the types of facial expressions appearing before and after the micro-expression, i.e., the emotional context, influence micro-expression recognition. To address this question, the present study used a modified METT (Micro-Expression Training Tool) paradigm that required participants to recognize the target micro-expressions presented briefly between two identical emotional faces. The results of Experiments 1 and 2 showed that negative context impaired the recognition of micro-expressions regardless of the duration of the target micro-expression. Stimulus-difference between the context and target micro-expression was accounted for in Experiment 3. Results showed that a context effect on micro-expression recognition persists even when the stimulus similarity between the context and target micro-expressions was controlled. Therefore, our results not only provided evidence for the context effect on micro-expression recognition but also suggested that the context effect might result from both the stimulus and valence differences.


title: FPGA Design Methodology for Industrial Control Systems—A Review

abstract: This paper reviews the state of the art of field- programmable gate array (FPGA) design methodologies with a focus on industrial control system applications. This paper starts with an overview of FPGA technology development, followed by a presentation of design methodologies, development tools and relevant CAD environments, including the use of portable hardware description languages and system level programming/design tools. They enable a holistic functional approach with the major advantage of setting up a unique modeling and evaluation environment for complete industrial electronics systems. Three main design rules are then presented. These are algorithm refinement, modularity, and systematic search for the best compromise between the control performance and the architectural constraints. An overview of contributions and limits of FPGAs is also given, followed by a short survey of FPGA-based intelligent controllers for modern industrial systems. Finally, two complete and timely case studies are presented to illustrate the benefits of an FPGA implementation when using the proposed system modeling and design methodology. These consist of the direct torque control for induction motor drives and the control of a diesel-driven synchronous stand-alone generator with the help of fuzzy logic.


title: Tile Based Procedural Terrain Generation in Real-Time : A Study in Performance

abstract: Context. Procedural Terrain Generation refers to the algorithmical creation of terrains with limited or no user input. Terrains are an important piece of content in many video games and other forms of simulations. Objectives. In this study a tile-based approach to creating endless terrains is investigated. The aim is to find if real-time performance is possible using the proposed method and possible performance increases from utilization of the GPU. Methods. An application that allows the user to walk around on a seemingly endless terrain is created in two versions, one that exclusively utilizes the CPU and one that utilizes both CPU and GPU. An experiment is then conducted that measures performance of both versions of the application. Results. Results showed that real-time performance is indeed possible for smaller tile sizes on the CPU. They also showed that the application benefits significantly from utilizing the GPU. Conclusions. It is concluded that the tile-based approach works well and creates a functional terrain. However performance is too poor for the technique to be utilized in e.g. a video game.


title: Adaptive Neural Fuzzy Inference System for Accurate Localization of Wireless Sensor Network in Outdoor and Indoor Cycling Applications

abstract: When localizing wireless sensor networks, estimating the distances of sensor nodes according to the known locations of the anchor nodes remains a challenge. As nodes may transfer from one place to another, a localization technique that can measure or determine the location of a mobile node is necessary. In this paper, the distance between a bicycle when moves on the cycling track and a coordinator node (i.e., coach), which positioned on the middle of the cycling field was estimated for the indoor and outdoor velodromes. The distance was determined based on two methods. First, the raw estimate is done by using the log-normal shadowing model (LNSM) and later, the intelligence technique, based on adaptive neural fuzzy inference system (ANFIS) is applied to improve the distance estimation accuracy, especially in an indoor environment, which the signal is severely dominated by the effect of wireless multipath impairments. The received signal strength indicator from anchor nodes based on ZigBee wireless protocol are employed as inputs to the ANFIS and LNSM. In addition, the parameters of the propagation channel, such as standard deviation and path loss exponent were measured. The results shown that the distance estimation accuracy was improved by 84% and 99% for indoor and outdoor velodromes, respectively, after applying the ANFIS optimization, relative to the rough estimate by the LNSM method. Moreover, the proposed ANFIS technique outperforms the previous studies in terms of errors of estimated distance with minimal mean absolute error of 0.023 m (outdoor velodrome) and 0.283 m (indoor velodrome).


A Computational Approach to Feature Extraction for Identification of Suicidal Ideation in Tweets


title: Phenomenology of men with body dysmorphic disorder concerning penis size compared to men anxious about their penis size and to men without concerns: A cohort study

abstract: Abstract Men with body dysmorphic disorder (BDD) may be preoccupied with the size or shape of the penis, which may be causing significant shame or impairment. Little is known about the characteristics and phenomenology of such men and whether they can be differentiated from men with small penis anxiety (SPA) (who do not have BDD), and men with no penile concerns. Twenty-six men with BDD, 31 men with SPA, and 33 men without penile concerns were compared on psychopathology, experiences of recurrent imagery, avoidance and safety-seeking behaviours. Men with BDD had significantly higher scores than both the SPA group and no penile concern group for measures of imagery, avoidance, safety seeking and general psychopathology. The groups differed on the phenomenology of BDD specific to penile size preoccupation clearly from the worries of SPA, which in turn were different to those of the men without concerns. The common avoidance and safety seeking behaviours were identified in such men that may be used clinically.


title: Deep optimization for spectrum repacking

abstract: Over 13 months in 2016-17 the FCC conducted an "incentive auction" to repurpose radio spectrum from broadcast television to wireless internet. In the end, the auction yielded $19.8 billion, $10.05 billion of which was paid to 175 broadcasters for voluntarily relinquishing their licenses across 14 UHF channels. Stations that continued broadcasting were assigned potentially new channels to fit as densely as possible into the channels that remained. The government netted more than $7 billion (used to pay down the national debt) after covering costs. A crucial element of the auction design was the construction of a solver, dubbed SATFC, that determined whether sets of stations could be "repacked" in this way; it needed to run every time a station was given a price quote. This paper describes the process by which we built SATFC. We adopted an approach we dub "deep optimization", taking a data-driven, highly parametric, and computationally intensive approach to solver design. More specifically, to build SATFC we designed software that could pair both complete and local-search SAT-encoded feasibility checking with a wide range of domain-specific techniques. We then used automatic algorithm configuration techniques to construct a portfolio of eight complementary algorithms to be run in parallel, aiming to achieve good performance on instances that arose in proprietary auction simulations. To evaluate the impact of our solver in this paper, we built an open-source reverse auction simulator. We found that within the short time budget required in practice, SATFC solved more than 95% of the problems it encountered. Furthermore, the incentive auction paired with SATFC produced nearly optimal allocations in a restricted setting and substantially outperformed other alternatives at national scale.


title: Constructionist Design Methodology for Interactive Intelligences

abstract: We present a methodology for designing and implementing interactive intelligences. The constructionist design methodology (CDM) -- so called because it advocates modular building blocks and incorporation of prior work -- addresses factors that we see as key to future advances in AI, including support for interdisciplinary collaboration, coordination of teams, and large-scale systems integration. We test the methodology by building an interactive multifunctional system with a real-time perception- action loop. The system, whose construction relied entirely on the methodology, consists of an embodied virtual agent that can perceive both real and virtual objects in an augmented-reality room and interact with a user through coordinated gestures and speech. Wireless tracking technologies give the agent awareness of the environment and the user's speech and communicative acts. User and agent can communicate about things in the environment, their placement, and their function, as well as about more abstract topics, such as current news, through situated multimodal dialogue. The results demonstrate the CDM's strength in simplifying the modeling of complex, multifunctional systems that require architectural experimentation and exploration of unclear subsystem boundaries, undefined variables, and tangled data flow and control hierarchies.


title: Designing multicore ECU architecture in vehicle networks using AUTOSAR

abstract: The number of electronic control units (ECUs) in modern vehicles has continuously increased in last few decades. In addition, advanced functionalities antilock braking systems, adaptive cruise control and climate control put higher computational demand on ECUs, which further increases the design complexity of automotive control systems. Multicore processors having multiple processing units are integrated on a single chip, have emerged to be the main computing controllers not only for high-end servers but also for embedded control systems. Using multicore processors, more centralized architecture designs can be adopted for automotive control systems. The recent initiative on AUTOmotive Open System ARchitecture (AUTOSAR) has established several standards for automotive software and hardware designs, which include guidelines for designing centralized architecture with multicore ECUs for automotive control systems. With AUTOSAR, it is expected that computational control tasks of different functions can share one ECU or run on any ECU connected with in-vehicle network like CAN and FlexRay. Also, In this paper, we describe some key analytical components that together characterize the hierarchical bus structures composed of FlexRay, CANbus and LINbus. Our analysis shows that the practical constraints imposed by standards such as AUTOSAR can lead to higher levels of schedulable resource utilization. This reduces both the overall component count and cost, while facilitating easy enhancements.


title: Epidemiology and natural history of urinary incontinence in women

abstract: Abstract Understanding the epidemiology (distribution and determinants) of urinary incontinence (UI), as well as its natural history is a very important issue. In this article, we discuss prevalence, incidence, natural history, and the variations that may be related to race and ethnicity. We focus on epidemiologic population comprising community-dwelling women who are not institutionalized. Our review clearly shows that there is a lack of advanced epidemiologic analyses. Variables that better characterize UI include frequency measure, quantity of urine loss, duration, type, and severity. These factors should be incorporated into basic study design so that more advanced and informative analyses may be conducted.


title: Diagnosis and treatment of bulbar symptoms in amyotrophic lateral sclerosis

abstract: Amyotrophic lateral sclerosis (ALS) is the most common neurodegenerative disease of the motor system. Bulbar symptoms such as dysphagia and dysarthria are frequent features of ALS and can result in reductions in life expectancy and quality of life. These dysfunctions are assessed by clinical examination and by use of instrumented methods such as fiberendoscopic evaluation of swallowing and videofluoroscopy. Laryngospasm, another well-known complication of ALS, commonly comes to light during intubation and extubation procedures in patients undergoing surgery. Laryngeal and pharyngeal complications are treated by use of an array of measures, including body positioning, compensatory techniques, voice and breathing exercises, communication devices, dietary modifications, various safety strategies, and neuropsychological assistance. Meticulous monitoring of clinical symptoms and close cooperation within a multidisciplinary team (physicians, speech and language therapists, occupational therapists, dietitians, caregivers, the patients and their relatives) are vital.


title: BWS: balanced work stealing for time-sharing multicores

abstract: Running multithreaded programs in multicore systems has become a common practice for many application domains. Work stealing is a widely-adopted and effective approach for managing and scheduling the concurrent tasks of such programs. Existing work-stealing schedulers, however, are not effective when multiple applications time-share a single multicore---their management of steal-attempting threads often causes unbalanced system effects that hurt both workload throughput and fairness. In this paper, we present BWS (Balanced Work Stealing), a work-stealing scheduler for time-sharing multicore systems that leverages new, lightweight operating system support. BWS improves system throughput and fairness via two means. First, it monitors and controls the number of awake, steal-attempting threads for each application, so as to balance the costs (resources consumed in steal attempts) and benefits (available tasks get promptly stolen) of such threads. Second, a steal-attempting thread can yield its core directly to a peer thread with an unfinished task, so as to retain the core for that application and put it to better use. We have implemented a prototype of BWS based on Cilk++, a state-of-the-art work-stealing scheduler. Our performance evaluation with various sets of concurrent applications demonstrates the advantages of BWS over Cilk++, with average system throughput increased by 12.5% and average unfairness decreased from 124% to 20%.


title: LOCI: Fast Outlier Detection Using the Local Correlation Integral

abstract: Outlier detection is an integral part of data mining and has attracted much attention recently [M. Breunig et al., (2000)], [W. Jin et al., (2001)], [E. Knorr et al., (2000)]. We propose a new method for evaluating outlierness, which we call the local correlation integral (LOCI). As with the best previous methods, LOCI is highly effective for detecting outliers and groups of outliers (a.k.a. micro-clusters). In addition, it offers the following advantages and novelties: (a) It provides an automatic, data-dictated cutoff to determine whether a point is an outlier-in contrast, previous methods force users to pick cut-offs, without any hints as to what cut-off value is best for a given dataset. (b) It can provide a LOCI plot for each point; this plot summarizes a wealth of information about the data in the vicinity of the point, determining clusters, micro-clusters, their diameters and their inter-cluster distances. None of the existing outlier-detection methods can match this feature, because they output only a single number for each point: its outlierness score, (c) Our LOCI method can be computed as quickly as the best previous methods, (d) Moreover, LOCI leads to a practically linear approximate method, aLOCI (for approximate LOCI), which provides fast highly-accurate outlier detection. To the best of our knowledge, this is the first work to use approximate computations to speed up outlier detection. Experiments on synthetic and real world data sets show that LOCI and aLOCI can automatically detect outliers and micro-clusters, without user-required cut-offs, and that they quickly spot both expected and unexpected outliers.


title: Advance Driver Assistance System (ADAS) - Speed bump detection

abstract: In Intelligent Transportation System, Advance Driver Assistance Systems (ADAS) plays a vital role. In ADAS, many research works are done in the area of traffic sign recognition, Forward Collision Warning, Automotive navigation system, Lane departure warning system but an another important area to look through is speed bumps detection. The recognition of speed bump is a safety to a human and a vehicle. Early research in speed bump detection is done with the help of sensors, accelerometer and GPS. In this paper, a novel method is presented to achieve speed bump detection and recognition either to alert or to interact directly with the vehicle. Detection of speed bump is recognized with a help of image processing concepts. This methodology is effortless and simple to implement without the investment of special sensors, hardware, Smartphone and GPS. This procedure suits very well for the roads constructed with proper marking, and can be used in self-driving car.


title: Circular generalized cylinder fitting for 3D reconstruction in endoscopic imaging based on MRF

abstract: Endoscopy has become an established procedure for the diagnosis and therapy of various gastrointestinal (GI) ailments, and has also emerged as a commonly-used technique for minimally-invasive surgery. Most existing endoscopes are monocular, with stereo-endoscopy facing practical difficulties, preventing the physicians/surgeons from having a desired, realistic 3D view. Traditional monocular 3D reconstruction approaches (e.g., structure from motion) face extraordinary challenges for this application due to issues including noisy data, lack of textures supporting robust feature matching, nonrigidity of the objects, and glare artifacts from the imaging process, etc. In this paper, we propose a method to automatically reconstruct 3D structure from a monocular endoscopic video. Our approach attempts to address the above challenges by incorporating a circular generalized cylinder (CGC) model in 3D reconstruction. The CGC model is decomposed as a series of 3D circles. To reconstruct this model, we formulate the problem as one of maximum a posteriori estimation within a Markov random field framework, so as to ensure the smoothness constraints of the CGC model and to support robust search for the optimal solution, which is achieved by a two-stage heuristic search scheme. Both simulated and real data experiments demonstrate the effectiveness of the proposed approach.


title: Modeling Civil Violence: An Evolutionary Multi-Agent, Game Theoretic Approach

abstract: This paper focuses on the design and development of a spatial evolutionary multi-agent social network (EMAS) to investigate the underlying emergent macroscopic behavioral dynamics of civil violence, as a result of the microscopic local movement and game-theoretic interactions between multiple goal-oriented agents. Agents are modeled from multi-disciplinary perspectives and their behavioral strategies are evolved over time via collective co-evolution and independent learning. Experimental results reveal the onset of fascinating global emergent phenomenon as well as interesting patterns of group movement and behavioral development. Analysis of the results provides new insights into the intricate behavioral dynamics that arises in civil upheavals. Collectively, EMAS serves as a vehicle to facilitate the behavioral development of autonomous agents as well as a platform to verify the effectiveness of various violence management policies which is paramount to the mitigation of casualties.


title: Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation

abstract: An originally chaotic system can be controlled into various periodic dynamics. When it is implemented into a legged robot's locomotion control as a central pattern generator (CPG), sophisticated gait patterns arise so that the robot can perform various walking behaviors. However, such a single chaotic CPG controller has difficulties dealing with leg malfunction. Specifically, in the scenarios presented here, its movement permanently deviates from the desired trajectory. To address this problem, we extend the single chaotic CPG to multiple CPGs with learning. The learning mechanism is based on a simulated annealing algorithm. In a normal situation, the CPGs synchronize and their dynamics are identical. With leg malfunction or disability, the CPGs lose synchronization leading to independent dynamics. In this case, the learning mechanism is applied to automatically adjust the remaining legs' oscillation frequencies so that the robot adapts its locomotion to deal with the malfunction. As a consequence, the trajectory produced by the multiple chaotic CPGs resembles the original trajectory far better than the one produced by only a single CPG. The performance of the system is evaluated first in a physical simulation of a quadruped as well as a hexapod robot and finally in a real six-legged walking machine called AMOSII. The experimental results presented here reveal that using multiple CPGs with learning is an effective approach for adaptive locomotion generation where, for instance, different body parts have to perform independent movements for malfunction compensation.


title: Magnetism from Fe2O3 nanoparticles embedded in amorphous SiO2 matrix

abstract: Fe2O3 nanoparticles are embedded in amorphous SiO2 matrix by coprecipitation method with varying concentrations. Conditions are optimized to get almost monodispersed Fe2O3 nanoparticles with high chemical stability. Microstructure of synthesized nanoparticles is well characterized and found that Fe2O3 is in nanocrystalline form and embedded uniformly in amorphous SiO2 matrix. Enhanced surface reactivity is found for nanoparticles which influences physical properties of the SiO2 supported Fe2O3 system due to adsorption. In oxide nanoparticles, significant number of defect sites at the surface is expected but when supported medium such as SiO2 it reduces this defect concentration. Field- and temperature-dependent magnetisation studies on these samples show superparamagnetic behaviour. Superparamagnetic behaviour is seen in all the concentration systems but the coercivity observed in the lower concentration systems is found to be anomalous compared to that of higher concentrations. The observed magnetic behaviour comes from either unsaturated bond existing due to the absence of anions at the surface of nanoparticles or reconstruction of atomic orbitals taking place at interface of Fe2O3–SiO2 system.


title: End-to-End Representation Learning for Correlation Filter Based Tracking

abstract: The Correlation Filter is an algorithm that trains a linear template to discriminate between images and their translations. It is well suited to object tracking because its formulation in the Fourier domain provides a fast solution, enabling the detector to be re-trained once per frame. Previous works that use the Correlation Filter, however, have adopted features that were either manually designed or trained for a different task. This work is the first to overcome this limitation by interpreting the Correlation Filter learner, which has a closed-form solution, as a differentiable layer in a deep neural network. This enables learning deep features that are tightly coupled to the Correlation Filter. Experiments illustrate that our method has the important practical benefit of allowing lightweight architectures to achieve state-of-the-art performance at high framerates.


title: Complementary and alternative medicine use among adults and children

abstract: Objective—This report presents selected estimates of complementary and alternative medicine (CAM) use among U.S. adults and children, using data from the 2007 National Health Interview Survey (NHIS), conducted by the Centers for Disease Control and Prevention’s (CDC) National Center for Health Statistics (NCHS). Trends in adult use were assessed by comparing data from the 2007 and 2002 NHIS. Methods—Estimates were derived from the Complementary and Alternative Medicine supplements and Core components of the 2007 and 2002 NHIS. Estimates were generated and comparisons conducted using the SUDAAN statistical package to account for the complex sample design. Results—In 2007, almost 4 out of 10 adults had used CAM therapy in the past 12 months, with the most commonly used therapies being nonvitamin, nonmineral, natural products (17.7%) and deep breathing exercises (12.7%). American Indian or Alaska Native adults (50.3%) and white adults (43.1%) were more likely to use CAM than Asian adults (39.9%) or black adults (25.5%). Results from the 2007 NHIS found that approximately one in nine children (11.8%) used CAM therapy in the past 12 months, with the most commonly used therapies being nonvitamin, nonmineral, natural products (3.9%) and chiropractic or osteopathic manipulation (2.8%). Children whose parent used CAM were almost five times as likely (23.9%) to use CAM as children whose parent did not use CAM (5.1%). For both adults and children in 2007, when worry about cost delayed receipt of conventional care, individuals were more likely to use CAM than when the cost of conventional care was not a worry. Between 2002 and 2007 increased use was seen among adults for acupuncture, deep breathing exercises, massage therapy, meditation, naturopathy, and yoga. CAM use for head or chest colds showed a marked decrease from 2002 to 2007 (9.5% to 2.0%).


title: Simplifying software-defined network optimization using SOL

abstract: Realizing the benefits of SDN for many network management applications (e.g., traffic engineering, service chaining, topology reconfiguration) involves addressing complex optimizations that are central to these problems. Unfortunately, such optimization problems require (a) significant manual effort and expertise to express and (b) non-trivial computation and/or carefully crafted heuristics to solve. Our goal is to simplify the deployment of SDN applications using general high-level abstractions for capturing optimization requirements from which we can efficiently generate optimal solutions. To this end, we present SOL, a framework that demonstrates that it is possible to simultaneously achieve generality and efficiency. The insight underlying SOL is that many SDN applications can be recast within a unifying path-based optimization abstraction. Using this, SOL can efficiently generate near-optimal solutions and device configurations to implement them. We show that SOL provides comparable or better scalability than custom optimization solutions for diverse applications, allows a balancing of optimality and route churn per reconfiguration, and interfaces with modern SDN controllers.


The influence of hydrostatic pressure upon ciliary frequency


2008-01-0201 Route Prediction from Trip Observations


title: Deep Learning to Predict Patient Future Diseases from the Electronic Health Records

abstract: The increasing cost of health care has motivated the drive towards preventive medicine, where the primary concern is recognizing disease risk and taking action at the earliest stage. We present an application of deep learning to derive robust patient representations from the electronic health records and to predict future diseases. Experiments showed promising results in different clinical domains, with the best performances for liver cancer, diabetes, and heart failure.


title: Genitourinary injuries in the newborn

abstract: Abstract Background: Circumcisions and cesarian sections are common procedures. Although complications to the newborn child fortunately are rare, it is important to emphasize the potential significance of this problem and its frequent iatrogenic etiology. The authors present 7 cases of genitourinary trauma in newborns, including surgical management and follow-up. Methods: The authors relate 7 recent cases of genitourinary trauma in newborns from a children's hospital in a major metropolitan area. Results: Case 1 and 2: Two infants suffered degloving injuries to both the prepuce and penile shaft from a Gomco clamp. Successful full-thickness skin grafting using the previously excised foreskin was used in 1 child. Case 3, 4, and 5: A Mogen clamp caused glans injuries in 3 infants. In 2, hemorrhage from the severed glans was controlled with topical epinephrine; the glans healed with a flattened appearance. Another infant sustained a laceration ventrally, requiring a delayed modified meatal advancement glanoplasty to correct the injury. Case 6: A male infant suffered a ventral slit and division of the ventral urethra before placement of a Gomco clamp. Formal hypospadias repair was required. Case 7: An emergent cesarean section resulted in a grade 4-perineal laceration in a female infant. The vaginal tear caused by the surgeon's finger, extended up to the posterior insertion of the cervix and into the rectum. The infant successfully underwent an emergent multilayered repair. Conclusions: Genitourinary trauma in the newborn is rare but often necessitates significant surgical intervention. Circumcision often is the causative event. There has been only 1 prior report of a perineal injury similar to case 7, with a fatal outcome. J Pediatr Surg 36:235-239. Copyright © 2001 by W.B. Saunders Company.


title: Wasserstein Propagation for Semi-Supervised Learning

abstract: Probability distributions and histograms are natural representations for product ratings, traffic measurements, and other data considered in many machine learning applications. Thus, this paper introduces a technique for graph-based semi-supervised learning of histograms, derived from the theory of optimal transportation. Our method has several properties making it suitable for this application; in particular, its behavior can be characterized by the moments and shapes of the histograms at the labeled nodes. In addition, it can be used for histograms on non-standard domains like circles, revealing a strategy for manifold-valued semi-supervised learning. We also extend this technique to related problems such as smoothing distributions on graph nodes.


title: Trisomy 21 causes changes in the circulating proteome indicative of chronic autoinflammation

abstract: Trisomy 21 (T21) causes Down syndrome (DS), but the mechanisms by which T21 produces the different disease spectrum observed in people with DS are unknown. We recently identified an activated interferon response associated with T21 in human cells of different origins, consistent with overexpression of the four interferon receptors encoded on chromosome 21, and proposed that DS could be understood partially as an interferonopathy. However, the impact of T21 on systemic signaling cascades in living individuals with DS is undefined. To address this knowledge gap, we employed proteomics approaches to analyze blood samples from 263 individuals, 165 of them with DS, leading to the identification of dozens of proteins that are consistently deregulated by T21. Most prominent among these proteins are numerous factors involved in immune control, the complement cascade, and growth factor signaling. Importantly, people with DS display higher levels of many pro-inflammatory cytokines (e.g. IL-6, MCP-1, IL-22, TNF-α) and pronounced complement consumption, resembling changes seen in type I interferonopathies and other autoinflammatory conditions. Therefore, these results are consistent with the hypothesis that increased interferon signaling caused by T21 leads to chronic immune dysregulation, and justify investigations to define the therapeutic value of immune-modulatory strategies in DS.


title: IBM POWER7 multicore server processor

abstract: The IBM POWER® processor is the dominant reduced instruction set computing microprocessor in the world today, with a rich history of implementation and innovation over the last 20 years. In this paper, we describe the key features of the POWER7® processor chip. On the chip is an eight-core processor, with each core capable of four-way simultaneous multithreaded operation. Fabricated in IBM's 45-nm silicon-on-insulator (SOI) technology with 11 levels of metal, the chip contains more than one billion transistors. The processor core and caches are significantly enhanced to boost the performance of both single-threaded response-time-oriented, as well as multithreaded, throughput-oriented applications. The memory subsystem contains three levels of on-chip cache, with SOI embedded dynamic random access memory (DRAM) devices used as the last level of cache. A new memory interface using buffered double-data-rate-three DRAM and improvements in reliability, availability, and serviceability are discussed


title: The combinatorial aspect of motion planning: Maneuver variants in structured environments

abstract: Motion planning plays a key role in autonomous driving. In this work, we introduce the combinatorial aspect of motion planning which tackles the fact that there are usually many possible and locally optimal solutions to accomplish a given task. Those options we call maneuver variants. We argue that by partitioning the trajectory space into discrete solution classes, such that local optimization methods yield an optimum within each discrete class, we can improve the chance of finding the global optimum as the optimum trajectory among the manuever variants. This work provides methods to enumerate the maneuver variants as well as constraints to enforce them. The return of the effort put into the problem modification as suggested is gaining assuredness in the convergency behaviour of the optimization algorithm. We show an experiment where we identify three local optima that would not have been found with local optimization methods.


title: Deep convolutional encoder-decoder for myelin and axon segmentation

abstract: We propose a fully automatic method for segmenting myelin and axon from microscopy images of excised mouse spinal cord based on Convolutional Neural Networks (CNNs) and Deep Convolutional Encoder-Decoder. We compare a two-class CNN, multi-class CNN, and multi-class deep convolutional encoder-decoder with traditional methods. The CNN method gives a pixel-wise accuracy of 79.7% whereas an Active Contour method gives 59.4%. The encoder-decoder shows better performance with 82.3% and noticeably shorter classification time than CNN methods.


title: A Machine Learning Based Framework for Sub-Resolution Assist Feature Generation

abstract: Sub-Resolution Assist Feature (SRAF) generation is a very important resolution enhancement technique to improve yield in modern semiconductor manufacturing process. Model- based SRAF generation has been widely used to achieve high accuracy but it is known to be time consuming and it is hard to obtain consistent SRAFs on the same layout pattern configurations. This paper proposes the first ma- chine learning based framework for fast yet consistent SRAF generation with high quality of results. Our technical con- tributions include robust feature extraction, novel feature compaction, model training for SRAF classification and pre- diction, and the final SRAF generation with consideration of practical mask manufacturing constraints. Experimental re- sults demonstrate that, compared with commercial Calibre tool, our machine learning based SRAF generation obtains 10X speed up and comparable performance in terms of edge placement error (EPE) and process variation (PV) band.


title: Characterization of the rubber tree metallothionein family reveals a role in mitigating the effects of reactive oxygen species associated with physiological stress

abstract: Metallothioneins (MTs) as reactive oxygen species (ROS) scavengers play important roles in stress response and heavy metal homeostasis. In Hevea brasiliensis (the para rubber tree that is the source of commercial natural rubber) and in other trees, the functions of MTs are not well understood. Latex exudes when the rubber tree is tapped. The flow of latex and its regeneration can be enhanced by tapping, wounding and ethylene treatment, all of which produce ROS as a by-product. Here, we show the presence of four MT genes in H. brasiliensis, comprising three Type 2 (HbMT2, -2a and -2b) and one Type 3 (HbMT3L) isoforms, representing one of the smallest MT gene families among angiosperms. The four HbMTs exhibited distinct tissue expression patterns: HbMT2 and HbMT3L mainly in leaves, HbMT2a specifically in flowers and HbMT2b in diverse tissues. The expression of HbMT2b, an isoform present in latex, decreased significantly in the latex following the stress-inducing treatments of tapping, wounding and ethephon (an ethylene generator). The expressions of the leaf-abundant isoforms, HbMT2 and -3L were up-regulated following pathogenic fungus infection and high-temperature stress, but down-regulated by low-temperature stress. These reactions were consistent with multiple defense- and hormone-responsive cis-acting elements in the HbMT promoters. Nine transcription factors were shown to implicate in the high-temperature responsiveness of HbMT2 and -3L in leaves. Overexpression of HbMT2 in Escherichia coli enhanced the bacterium's tolerance to heavy metals and ROS, consistent with its predicted role as an ROS scavenger. Taken together, our results, along with other relevant studies, suggest an important role of HbMTs in latex regeneration as well as species adaptation via the regulation of ROS homeostasis.


title: A Modular Framework for Versatile Conversational Agent Building

abstract: This paper illustrates a web-based infrastructure of an architecture for conversational agents equipped with a modular knowledge base. This solution has the advantage to allow the building of specific modules that deal with particular features of a conversation (ranging from its topic to the manner of reasoning of the chatbot). This enhances the agent interaction capabilities. The approach simplifies the chatbot knowledge base design process: extending, generalizing or even restricting the chatbot knowledge base in order to suit it to manage specific dialoguing tasks as much as possible.


title: Trends in Interactive Knowledge Discovery for Personalized Medicine: Cognitive Science meets Machine Learning

abstract: A grand goal of future medicine is in modelling the complexity of patients to tailor medical decisions, health practices and therapies to the individual patient. This trend towards personalized medicine produces unprecedented amounts of data, and even though the fact that human experts are excellent at pattern recognition in dimensions of ≤ 3, the problem is that most biomedical data is in dimensions much higher than 3, making manual analysis difficult and often impossible. Experts in daily medical routine are decreasingly capable of dealing with the complexity of such data. Moreover, they are not interested the data, they need knowledge and insight in order to support their work. Consequently, a big trend in computer science is to provide efficient, useable and useful computational methods, algorithms and tools to discover knowledge and to interactively gain insight into high-dimensional data. A synergistic combination of methodologies of two areas may be of great help here: Human–Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human intelligence with machine learning. A trend in both disciplines is the acquisition and adaptation of representations that support efficient learning. Mapping higher dimensional data into lower dimensions is a major task in HCI, and a concerted effort of computational methods including recent advances from graphtheory and algebraic topology may contribute to finding solutions. Moreover, much biomedical data is sparse, noisy and timedependent, hence entropy is also amongst promising topics. This paper provides a rough overview of the HCI-KDD approach and focuses on three future trends: graph-based mining, topological data mining and entropy-based data mining.


title: Exemplar-Based Sparse Representation With Residual Compensation for Voice Conversion

abstract: We propose a nonparametric framework for voice conversion, that is, exemplar-based sparse representation with residual compensation. In this framework, a spectrogram is reconstructed as a weighted linear combination of speech segments, called exemplars, which span multiple consecutive frames. The linear combination weights are constrained to be sparse to avoid over-smoothing, and high-resolution spectra are employed in the exemplars directly without dimensionality reduction to maintain spectral details. In addition, a spectral compression factor and a residual compensation technique are included in the framework to enhance the conversion performances. We conducted experiments on the VOICES database to compare the proposed method with a large set of state-of-the-art baseline methods, including the maximum likelihood Gaussian mixture model (ML-GMM) with dynamic feature constraint and the partial least squares (PLS) regression based methods. The experimental results show that the objective spectral distortion of ML-GMM is reduced from 5.19 dB to 4.92 dB, and both the subjective mean opinion score and the speaker identification rate are increased from 2.49 and 73.50% to 3.15 and 79.50%, respectively, by the proposed method. The results also show the superiority of our method over PLS-based methods. In addition, the subjective listening tests indicate that the naturalness of the converted speech by our proposed method is comparable with that by the ML-GMM method with global variance constraint.


title: An Autonomous Multi-UAV System for Search and Rescue

abstract: This paper proposes and evaluates a modular architecture of an autonomous unmanned aerial vehicle (UAV) system for search and rescue missions. Multiple multicopters are coordinated using a distributed control system. The system is implemented in the Robot Operating System (ROS) and is capable of providing a real-time video stream from a UAV to one or more base stations using a wireless communications infrastructure. The system supports a heterogeneous set of UAVs and camera sensors. If necessary, an operator can interfere and reduce the autonomy. The system has been tested in an outdoor mission serving as a proof of concept. Some insights from these tests are described in the paper.


title: Training word embeddings for deep learning in biomedical text mining tasks

abstract: Most word embedding methods are proposed with general purpose which take a word as a basic unit and learn embeddings according to words' external contexts. However, in biomedical text mining, there are many biomedical entities and syntactic chunks which contain rich domain information, and the semantic meaning of a word is also strongly related to those information. Hence, we present a biomedical domain-specific word embedding model by incorporating stem, chunk and entity to train word embeddings. We also present two deep learning architectures respectively for two biomedical text mining tasks, by which we evaluate our word embeddings and compare them with other models. Experimental results show that our biomedical domain-specific word embeddings overall outperform other general-purpose word embeddings in these deep learning methods for biomedical text mining tasks.


title: Social media and the police: tweeting practices of british police forces during the August 2011 riots

abstract: With this paper we take a first step to understand the appropriation of social media by the police. For this purpose we analyzed the Twitter communication by the London Metropolitan Police (MET) and the Greater Manchester Police (GMP) during the riots in August 2011. The systematic comparison of tweets demonstrates that the two forces developed very different practices for using Twitter. While MET followed an instrumental approach in their communication, in which the police aimed to remain in a controlled position and keep a distance to the general public, GMP developed an expressive approach, in which the police actively decreased the distance to the citizens. In workshops and interviews, we asked the police officers about their perspectives, which confirmed the identified practices. Our study discusses benefits and risks of the two approaches and the potential impact of social media on the evolution of the role of police in society.


title: Optimizing an ANSI C interpreter with superoperators

abstract: This paper introduces superoperators , an optimization technique for bytecoded interpreters. Superoperators are virtual machine operations automatically synthesized from smaller operations to avoid costly per-operation overheads. Superoperators decrease executable size and can double or triple the speed of interpreted programs. The paper describes a simple and effective heuristic for inferring powerful superoperators from the usage patterns of simple operators. The paper describes the design and implementation of a hybrid translator/interpreter that employs superoperators. From a specification of the superoperators (either automatically inferred or manually chosen), the system builds an efficient implementation of the virtual machine in assembly language. The system is easily retargetable and currently runs on the MIPS R3000 and the SPARC.


title: Embedded Hypervisor Xvisor: A Comparative Analysis

abstract: Virtualization technology has shown immense popularity within embedded systems due to its direct relationship with cost reduction, better resource utilization, and higher performance measures. Efficient hypervisors are required to achieve such high performance measures in virtualized environments, while taking into consideration the low memory footprints as well as the stringent timing constraints of embedded systems. Although there are a number of open-source hypervisors available such as Xen, Linux KVM and OKL4 Micro visor, this is the first paper to present the open-source embedded hypervisor Extensible Versatile hyper Visor (Xvisor) and compare it against two of the commonly used hypervisors KVM and Xen in-terms of comparison factors that affect the whole system performance. Experimental results on ARM architecture prove Xvisor's lower CPU overhead, higher memory bandwidth, lower lock synchronization latency and lower virtual timer interrupt overhead and thus overall enhanced virtualized embedded system performance.


title: Credit Scoring Models for a Tunisian Microfinance Institution: Comparison between Artificial Neural Network and Logistic Regression

abstract: This paper compares, for a microfinance institution, the performance of two individual classification models: Logistic Regression (Logit) and Multi-Layer Perceptron Neural Network (MLP), to evaluate the credit risk problem and discriminate good creditors from bad ones. Credit scoring systems are currently in common use by numerous financial institutions worldwide. However, credit scoring using a non-parametric statistical technique with the microfinance industry, is a relatively recent application. In Tunisia, no model which employs a non-parametric statistical technique has yet, as far as we know, been published. This lack is surprising since the implementation of credit scoring should contribute towards the efficiency of microfinance institutions, thereby improving their competitiveness. This paper builds a non-parametric credit scoring model based on the Multi-Layer perceptron approach (MLP) and benchmarks its performance against Logistic Regression (LR) techniques. Based on a sample of 300 borrowers from a Tunisian microfinance institution, the results reveal that Logistic Regression outperforms neural network models.


title: An empirical study of gamification impact on e-Learning environment

abstract: Effective e-Learning requires students to be active, thus, creating an environment that engages the students is one of the challenges. One of the ways to build an engaging e-Learning environment is by applying gamification concept. It improves user engagement by enriching the system with the game design elements. This research aims to analyze the impact of gamification environment on e-Learning system by looking at student's quantitative interaction. 38 students from different high schools were voluntarily joined to test out two different system. This experiment focus on observing the impact of removing and adding gamification on the existing e-Learning system. The result shows that removing gamification causes significant decrement on student participation while adding gamification had no significant impact.


title: The Mechanical Design and Experiments of HIT/DLR Prosthetic Hand

abstract: Designed based on the underactuated mechanism, HIT/DLR Prosthetic Hand is a multi-sensory flve-flngered bio- prosthetic hand. Similarly with adult's hand, it is simple constructed and comprises 13 joints. Three motors actuate the thumb, index finger and the other three fingers each. Actuated by a motor, the thumb can move along cone surface, which resembles human thumb and is superior in the appearance. Driven by another motor and transmitted by springs, the mid finger, ring finger and little finger can envelop objects with complex shape. The appearance designation and sensory system are introduced. The grasp experiments are presented in detail. The hand has been greatly improved from HIT-ARhand. It was verified from experimentations, the hand has strong capability of self adaptation, can accomplish precise and power grasp for objects with complex shape.


title: The Factor Structure of the Presence Questionnaire

abstract: Constructing a valid measure of presence and discovering the factors that contribute to presence have been much sought after goals of presence researchers and at times have generated controversy among them. This paper describes the results of principal-components analyses of Presence Questionnaire (PQ) data from 325 participants following exposure to immersive virtual environments. The analyses suggest that a 4-factor model provides the best fit to our data. The factors are Involvement, Adaptation/Immersion, Sensory Fidelity, and Interface Quality. Except for the Adaptation/Immersion factor, these factors corresponded to those identified in a cluster analysis of data from an earlier version of the questionnaire. The existence of an Adaptation/Immersion factor leads us to postulate that immersion is greater for those individuals who rapidly and easily adapt to the virtual environment. The magnitudes of the correlations among the factors indicate moderately strong relationships among the 4 factors. Within these relationships, Sensory Fidelity items seem to be more closely related to Involvement, whereas Interface Quality items appear to be more closely related to Adaptation/Immersion, even though there is a moderately strong relationship between the Involvement and Adaptation/Immersion factors.


title: Integral Human Pose Regression

abstract: State-of-the-art human pose estimation methods are dominated by complex multi-stage architectures using a heat map representation. The representation has a few issues in nature, such as no end-to-end training, quantization error and sensitivity to multiple modes. While pose estimation is essentially a regression problem, existing regression approaches are not as effective. This work proposes an integral regression approach that unifies the heat map representation and joint regression, thus possessing the merits of both paradigms. It is differentiable and allows end-to-end training. It is simple, fast, and compatible with any heat map based methods. Comprehensive experiments verify its efficacy. Building upon simple baseline networks, its performance on benchmarks is strong and competitive to state-of-the-art.


title: The relationship between HS-CRP serum levels with the results of cardiac perfusion SPECT imaging in patients with suspected coronary artery disease

abstract: Background: Coronary heart disease is known as the leading cause of death in industrialized and developing countries. Measuring the CRP in the study of cardiovascular disease, the cost is reasonable and one study has shown that it can have a strong and independent prognostic value of myocardial infarction and unstable angina. So, we decided to study the relationship between hs-CRP serum levels and the results of cardiac perfusion SPECT imaging in patients with suspected coronary disease. Materials and methods: This cross-sectional study was conducted in 1393 at Shafa hospital in Kerman; Patients with suspected coronary heart disease , candidates for perfusion scan to confirm or reject the ischemic heart disease were recruited into the study. hs-CRP levels in both groups were compared by normal and abnormal perfusion scans. In the end, the level of hs-CRP in both groups, and the correlation between hs-CRP and sex, age, and risk factors were analyzed. Findings: The mean age of patients with normal perfusion scan was 64.05 and 59.68 in the group with abnormal perfusion scan. (P=0.744). Average CRP in the group with normal perfusion scan was 1.96 and in the group with abnormal perfusion scan, 3.3. The difference in these two groups was statistically significant. (P=0.012). There was no significant difference between CRP levels and age, sex and risk factors. (P-value respectively: 0.727, 0.924, 0.329) Conclusion: In general, given the correlation between hs-CRP in patients with abnormal perfusion scan and the role of hs-CRP in infection and inflammation as causes of atherosclerosis inducer, the serum levels of hs-CRP test before ETT, the impact of scan results enhanced accuracy, especially in cases of suspected attenuation. Keywords: Coronary artery disease, hs-CRP serum levels, perfusion scan.


title: A Statistical Approach to Texture Classification from Single Images

abstract: We investigate texture classification from single images obtained under unknown viewpoint and illumination. A statistical approach is developed where textures are modelled by the joint probability distribution of filter responses. This distribution is represented by the frequency histogram of filter response cluster centres (textons). Recognition proceeds from single, uncalibrated images and the novelty here is that rotationally invariant filters are used and the filter response space is low dimensional.Classification performance is compared with the filter banks and methods of Leung and Malik [IJCV, 2001], Schmid [CVPR, 2001] and Cula and Dana [IJCV, 2004] and it is demonstrated that superior performance is achieved here. Classification results are presented for all 61 materials in the Columbia-Utrecht texture database.We also discuss the effects of various parameters on our classification algorithm--such as the choice of filter bank and rotational invariance, the size of the texton dictionary as well as the number of training images used. Finally, we present a method of reliably measuring relative orientation co-occurrence statistics in a rotationally invariant manner, and discuss whether incorporating such information can enhance the classifier’s performance.


title: Project Success Prediction in Crowdfunding Environments

abstract: Crowdfunding has gained widespread attention in recent years. Despite the huge success of crowdfunding platforms, the percentage of projects that succeed in achieving their desired goal amount is only around 40%. Moreover, many of these crowdfunding platforms follow "all-or-nothing" policy which means the pledged amount is collected only if the goal is reached within a certain predefined time duration. Hence, estimating the probability of success for a project is one of the most important research challenges in the crowdfunding domain. To predict the project success, there is a need for new prediction models that can potentially combine the power of both classification (which incorporate both successful and failed projects) and regression (for estimating the time for success). In this paper, we formulate the project success prediction as a survival analysis problem and apply the censored regression approach where one can perform regression in the presence of partial information. We rigorously study the project success time distribution of crowdfunding data and show that the logistic and log-logistic distributions are a natural choice for learning from such data. We investigate various censored regression models using comprehensive data of 18K Kickstarter (a popular crowdfunding platform) projects and 116K corresponding tweets collected from Twitter. We show that the models that take complete advantage of both the successful and failed projects during the training phase will perform significantly better at predicting the success of future projects compared to the ones that only use the successful projects. We provide a rigorous evaluation on many sets of relevant features and show that adding few temporal features that are obtained at the project's early stages can dramatically improve the performance.


title: Gender and Smile Classification Using Deep Convolutional Neural Networks

abstract: Facial gender and smile classification in unconstrained environment is challenging due to the invertible and large variations of face images. In this paper, we propose a deep model composed of GNet and SNet for these two tasks. We leverage the multi-task learning and the general-to-specific fine-tuning scheme to enhance the performance of our model. Our strategies exploit the inherent correlation between face identity, smile, gender and other face attributes to relieve the problem of over-fitting on small training set and improve the classification performance. We also propose the tasks-aware face cropping scheme to extract attribute-specific regions. The experimental results on the ChaLearn 16 FotW dataset for gender and smile classification demonstrate the effectiveness of our proposed methods.


title: Mapping the ‘early salinity response’ triggered proteome adaptation in contrasting rice genotypes using iTRAQ approach

abstract: BackgroundTo delineate the adaptive mechanisms operative under salinity stress, it is essential to study plant responses at the very early stages of stress which are very crucial for governing plant survival and adaptation. We believe that it is the initial perception and response phase which sets the foundation for stress adaptation in rice seedlings where plants can be considered to be in a state of osmotic shock and ion buildup.ResultsAn isobaric Tags for Relative and Absolute Quantitation (iTRAQ) approach was used to analyze the pre-existing differences as well as the very early salt shock responsive changes in the proteome of seedlings of contrasting rice genotypes, viz salt-sensitive IR64 and salt-tolerant Pokkali. In response to a quick salt shock, shoots of IR64 exhibited hyperaccumulation of Na+, whereas in Pokkali, these ions accumulated more in roots. Interestingly, we could find 86 proteins to be differentially expressed in shoots of Pokkali seedlings under non-stress conditions whereas under stress, 63 proteins were differentially expressed in Pokkali shoots in comparison to IR64. However, only, 40 proteins under non-stress and eight proteins under stress were differentially expressed in Pokkali roots. A higher abundance of proteins involved in photosynthesis (such as, oxygen evolving enhancer proteins OEE1 & OEE3, PsbP) and stress tolerance (such as, ascorbate peroxidase, superoxide dismutase, peptidyl-prolyl cis-trans isomerases and glyoxalase II), was observed in shoots of Pokkali in comparison to IR64. In response to salinity, selected proteins such as, ribulose bisphosphate carboxylase/oxygenase activase, remained elevated in Pokkali shoots. Glutamate dehydrogenase - an enzyme which serves as an important link between Krebs cycle and metabolism of amino acids was found to be highly induced in Pokkali in response to stress. Similarly, other enzymes such as peroxidases and triose phosphate isomerase (TPI) were also altered in roots in response to stress.ConclusionWe conclude that Pokkali rice seedlings are primed to face stress conditions where the proteins otherwise induced under stress in IR64, are naturally expressed in high abundance. Through specific alterations in its proteome, this proactive stress machinery contributes towards the observed salinity tolerance in this wild rice germplasm.


title: Efficient rijndael encryption implementation with composite field arithmetic

abstract: We explore the use of subfield arithmetic for efficient implementations of Galois Field arithmetic especially in the context of the Rijndael block cipher. Our technique involves mapping field elements to a composite field representation. We describe how to select a representation which minimizes the computation cost of the relevant arithmetic, taking into account the cost of the mapping as well. Our method results in a very compact and fast gate circuit for Rijndael encryption. ::: ::: In conjunction with bit-slicing techniques applied to newly proposed parallelizable modes of operation, our circuit leads to a high-performance software implementation for Rijndael encryption which offers significant speedup compared to previously reported implementations.


Bipartite Graph Sampling Methods for Sampling Recommendation Data


title: Prototype-Based Discriminative Feature Learning for Kinship Verification

abstract: In this paper, we propose a new prototype-based discriminative feature learning (PDFL) method for kinship verification. Unlike most previous kinship verification methods which employ low-level hand-crafted descriptors such as local binary pattern and Gabor features for face representation, this paper aims to learn discriminative mid-level features to better characterize the kin relation of face images for kinship verification. To achieve this, we construct a set of face samples with unlabeled kin relation from the labeled face in the wild dataset as the reference set. Then, each sample in the training face kinship dataset is represented as a mid-level feature vector, where each entry is the corresponding decision value from one support vector machine hyperplane. Subsequently, we formulate an optimization function by minimizing the intraclass samples (with a kin relation) and maximizing the neighboring interclass samples (without a kin relation) with the mid-level features. To better use multiple low-level features for mid-level feature learning, we further propose a multiview PDFL method to learn multiple mid-level features to improve the verification performance. Experimental results on four publicly available kinship datasets show the superior performance of the proposed methods over both the state-of-the-art kinship verification methods and human ability in our kinship verification task.


title: Three Decades of Driver Assistance Systems: Review and Future Perspectives

abstract: This contribution provides a review of fundamental goals, development and future perspectives of driver assistance systems. Mobility is a fundamental desire of mankind. Virtually any society strives for safe and efficient mobility at low ecological and economic costs. Nevertheless, its technical implementation significantly differs among societies, depending on their culture and their degree of industrialization. A potential evolutionary roadmap for driver assistance systems is discussed. Emerging from systems based on proprioceptive sensors, such as ABS or ESC, we review the progress incented by the use of exteroceptive sensors such as radar, video, or lidar. While the ultimate goal of automated and cooperative traffic still remains a vision of the future, intermediate steps towards that aim can be realized through systems that mitigate or avoid collisions in selected driving situations. Research extends the state-of-the-art in automated driving in urban traffic and in cooperative driving, the latter addressing communication and collaboration between different vehicles, as well as cooperative vehicle operation by its driver and its machine intelligence. These steps are considered important for the interim period, until reliable unsupervised automated driving for all conceivable traffic situations becomes available. The prospective evolution of driver assistance systems will be stimulated by several technological, societal and market trends. The paper closes with a view on current research fields.


title: doi:10.1006/nimg.2002.1132 Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images

abstract: Linear registration and motion correction are important components of structural and functional brain image analysis. Most modern methods optimize some intensity-based cost function to determine the best registration. To date, little attention has been focused on the optimization method itself, even though the success of most registration methods hinges on the quality of this optimization. This paper examines the optimization process in detail and demonstrates that the commonly used multiresolution local optimization methods can, and do, get trapped in local minima. To address this problem, two approaches are taken: (1) to apodize the cost function and (2) to employ a novel hybrid global-local optimization method. This new optimization method is specifically designed for registering whole brain images. It substantially reduces the likelihood of producing misregistrations due to being trapped by local minima. The increased robustness of the method, compared to other commonly used methods, is demonstrated by a consistency test. In addition, the accuracy of the registration is demonstrated by a series of experiments with motion correction. These motion correction experiments also investigate how the results are affected by different cost functions and interpolation methods.


How Feasible is Officiating Technology in Football


title: Building a mobile manipulator from off-the-shelf components

abstract: A number of mobile manipulators have been custom engineered and built in labs worldwide. Mobile manipulators could become much more prevalent if they were assembled from commercially available components instead of being locally engineered and built. In this paper, we describe some tests and the integration steps in putting together power source, computers, and distributed software systems for integrating two Barrett WAM arms on top of a Segway RMP mobile base. The description is meant to be accessible so that researchers without engineering lab access or experience can make use of them. We conclude by showing initial experiments with two arm manipulation.


title: Processing of Structurally Heterogeneous Cryo-EM Data in RELION.

abstract: This chapter describes algorithmic advances in the RELION software, and how these are used in high-resolution cryo-electron microscopy (cryo-EM) structure determination. Since the presence of projections of different three-dimensional structures in the dataset probably represents the biggest challenge in cryo-EM data processing, special emphasis is placed on how to deal with structurally heterogeneous datasets. As such, this chapter aims to be of practical help to those who wish to use RELION in their cryo-EM structure determination efforts.


title: Robotic-Assisted Microsurgery for an Elective Microsurgical Practice

abstract: Robotic-assisted microsurgery can be utilized for either intracorporal or extracorporeal surgical procedures. Three-dimensional high-definition magnification, a stable ergonomic platform, elimination of physiologic tremor, and motion scaling make the robotic platform attractive for microsurgeons for complex procedures. Additionally, robotic assistance enables the microsurgeon to take microsurgery to challenging intracorporeal locations in a minimally invasive manner. Recent adjunctive technological developments offer the robotic platform enhanced optical magnification, improved intraoperative imaging, and more precise ablation techniques for microsurgical procedures. The authors present the current state-of-the art tools available in the robotic-assisted microsurgical platform.


title: Propagation measurements and models for wireless communications channels

abstract: The authors describe the type of signals that occur in various environments and the modeling of the propagation parameters. Models are essentially of two classes. The first class consists of parametric statistical models that on average describe the phenomenon within a given error. They are simple to use, but relatively coarse. In the last few years a second class of environment-specific models has been introduced. These models are of a more deterministic nature, characterizing a specific street, building, etc. They are necessarily more time consuming to use, but are also more revealing concerning physical details and hopefully more accurate. Some key parameters and the measurement of them are discussed and then the different wireless environments are treated. The latter topic is divided into outdoor environments, indoor environments, and radio penetration from outdoor to indoor environments. >


title: THE COMBINED EFFECT OF ELECTRICAL STIMULATION AND BLADE TENDERIZATION ON SOME BOVINE MUSCLES

abstract: The electrical stimulation (ES) period was a very important factor determining the tenderness of three bovine muscles: Biceps femoris (BF), Longissimus dorsi (LD), and Semimembranosus (SM). Tenderness (shear force values) increased in most cases with increasing the ES period. An ES period of 15 min was optimal for obtaining a tenderness equal to that obtained by conventional processing of beef. The shortest ES period used (1 min) was adequate in most cases to set the muscles in rigor mortis and in preventing cold shortening. The tenderness of blade tenderized muscles was in most cases independent from the ES period. Blade tenderization was very effective to tenderize the BF muscle followed in decreasing order of efficacy by the LD and SM muscles. The cooking times and cooking losses of the three muscles were not affected by either the ES period or by blade tenderization.


title: MONITORING FOR NEWBORN HEARING SCREENING PROGRAMMES: AN INTEGRATIVE REVIEW Acompanhamento em programas de saúde auditiva infantil: uma revisão integrativa

abstract: In Brazil, hearing loss diagnosis does not occur in early years. Among various factors that could explain this fact is that the parents do not know well about hearing health for their children, and then do not attend for the follow-up. This literature review had as main goal to describe how the follow up has been done in Brazil, especially regarding to the audiologic exams, regarding to the follow up by the family, and regarding to educations activities for the health professionals. In order to achieve that, an integrative review of the literature was done, leaded by the question: How the audiologic follow up has been done for the newborn hearing screening programs? It was used the the following databases: Lilacs, Medline, IBECS and CidSaude using combinations of the terms “follow-up studies”, “hearing” and “newborn screening”. Initially, 1130 articles were screened by titles and abstracts. Then, 21 full articles were pre-selected by title and summary. Twelve articles answered the question of this review. The results indicated that tests for hearing screening and audiologic were used more Evoked otoacoustic emissions and auditory brainstem response, and behavioral assessment, tympanometry and acoustic reflexes. Three studies reported the presence of educational programs and only one specified professionals realized that, being the nurse what else made these activities. The percentage of families who joined the monitoring varied considerably. It was interesting to see that those recommendations from local and international entities to perform the audiologic, seeking a standard of quality and effectiveness in the programs and the quality of the audiological evaluation.


title: A current-starved inverter-based differential amplifier design for ultra-low power applications

abstract: As silicon feature sizes decrease, more complex circui try arrays can now be contrived on a single die. This increase in the number of on-chip devices per unit area results in increased power dissipation per unit area. In order to meet certain power and operating temperature specifications, circuit design necessitates a focus on power efficiency, which is especially important in systems employing hundreds or thousands of instances of the same device. In large arrays, a slight increase in the power efficiency of a single component is heightened by the number of instances of the device in the system. This paper proposes a fully differential, low-power current-starving inverter-based amplifier topology designed in a commercial 0.18μm process. This design achieves 46dB DC gain and a 464 kHz uni ty gain frequency with a power consumption of only 145.32nW at 700mV power supply vol tage for ultra-low power, low bandwidth applications. Higher bandwidth designs are also proposed, including a 48dB DC gain, 2.4 MHz unity-gain frequency amplifier operating at 900mV wi th only 3.74μW power consumption.


title: Governance in Global Value Chains

abstract: This book opens a fresh chapter in the debate on local enterprise clusters and their strategies for upgrading in the global economy. The authors employ a novel conceptual framework in their research on industrial clusters in Europe, Latin America and Asia and provide new perspectives and insights for researchers and policymakers alike.


title: Accurate iris segmentation based on novel reflection and eyelash detection model

abstract: The authors propose a novel noise detection model for accurate segmentation of an iris. Eyelash, eyelid and reflection are three main noises. Eyelid has already been solved by a traditional eye model; however, eyelash and reflection have not been tackled. To determinate a pixel in an eyelash, the model presented follows the three criteria: 1) separable eyelash condition; 2) non-informative condition; and 3) connective criterion. The first and second conditions handle separable and multiple eyelashes respectively. The last criterion avoids misclassification of strong iris texture as a single and separable eyelash. For reflection, strong reflection points are detected by a threshold and the weak reflection points around the strong points are determined by connective criterion and statistical test. A number of images are selected to evaluate the accuracy and necessity of our noise detection model and the results are encouraging.


title: Ultra-Fast and High-Reliability SOT-MRAM: From Cache Replacement to Normally-Off Computing

abstract: This paper deals with a new MRAM technology whose writing scheme relies on the Spin Orbit Torque (SOT). Compared to Spin Transfer Torque (STT) MRAM, it offers a very fast switching, a quasi-infinite endurance and improves the reliability by solving the issue of “read disturb”, thanks to separate reading and writing paths. These properties allow introducing SOT at all-levels of the memory hierarchy of systems and adressing applications which could not be easily implemented by STT-MRAM. We present this emerging technology and a full design framework, allowing to design and simulate hybrid CMOS/SOT complex circuits at any level of abstraction, from device to system. The results obtained are very promising and show that this technology leads to a reduced power consumption of circuits without notable penalty in terms of performance.


title: A Survey of Prediction Using Social Media

abstract: Social media comprises interactive applications and platforms for creating, sharing and exchange of user-generated contents. The past ten years have brought huge growth in social media, especially online social networking services, and it is changing our ways to organize and communicate. It aggregates opinions and feelings of diverse groups of people at low cost. Mining the attributes and contents of social media gives us an opportunity to discover social structure characteristics, analyze action patterns qualitatively and quantitatively, and sometimes the ability to predict future human related events. In this paper, we firstly discuss the realms which can be predicted with current social media, then overview available predictors and techniques of prediction, and finally discuss challenges and possible future directions.


title: Predicting Action Tubes

abstract: In this work, we present a method to predict an entire `action tube' (a set of temporally linked bounding boxes) in a trimmed video just by observing a smaller subset of it. Predicting where an action is going to take place in the near future is essential to many computer vision based applications such as autonomous driving or surgical robotics. Importantly, it has to be done in real-time and in an online fashion. We propose a Tube Prediction network (TPnet) which jointly predicts the past, present and future bounding boxes along with their action classification scores. At test time TPnet is used in a (temporal) sliding window setting, and its predictions are put into a tube estimation framework to construct/predict the video long action tubes not only for the observed part of the video but also for the unobserved part. Additionally, the proposed action tube predictor helps in completing action tubes for unobserved segments of the video. We quantitatively demonstrate the latter ability, and the fact that TPnet improves state-of-the-art detection performance, on one of the standard action detection benchmarks - J-HMDB-21 dataset.


title: Understanding how deep neural networks learn face expressions

abstract: Deep neural networks have been used successfully for several different computer vision-related tasks, including facial expression recognition. In spite of the good results, it is still not clear why these networks achieve such good recognition rates. One way to learn more about deep neural networks is to visualise and understand what they are learning, and to do so techniques such as deconvolution could play a significant role. In this paper, we train a Convolutional Neural Network (CNN) and Lateral Inhibition Pyramidal Neural Network (LIPNet) to learn facial expressions. Then, we use the deconvolution process to visualise the learned features of the CNN and we introduce a novel mechanism for visualising the internal representation of the LIPNet. We perform a series of experiments, training our networks with the Cohn-Kanade data set and show what kind of facial structures compose the learned emotion expression representation. Then, we use the trained networks to recognise images from the Jaffe data set and demonstrate that the learned representations are present in different face images, emphasizing the generalization aspects of these networks. We discuss the different representations that each network learns and how they differ from each other. We also discuss how each learned representation contributes to the recognition process and how they can be compared to the emotional notation Facial Action Coding System - Facs. Finally, we explain how the principles of invariance, redundancy and filtering, common for deep networks, contribute to the learned features and to the facial expression recognition task in general.


title: OCR and RFID enabled vehicle identification and parking allocation system

abstract: The available parking management systems require human efforts for recording entries of coming and leaving vehicles from parking in sheets. For huge parking it is difficult to keep track of the information. Use of Radio Frequency Identification known as RFID technology reduces human efforts and Optical Character Recognition known as OCR, enabled system will provide an automated system for parking management. And also it will provide the control over the access of parking space by the use of boom barriers. For huge parking it will be an effective system. Parking can be a commercial one or can be a Very Important Person (VIP) parking. Depending on the usage system can be used. Vehicle cannot be parked in parking if it is not registered. OCR acts as a solution for vehicles which are not registered. To keep track of the happenings in parking cameras are used. Places where automated systems are required, the proposed system is an answer. System combines the two strong technologies RFID and OCR.


title: The best clustering algorithms in data mining

abstract: In data mining, Clustering is the most popular, powerful and commonly used unsupervised learning technique. It is a way of locating similar data objects into clusters based on some similarity. Clustering algorithms can be categorized into seven groups, namely Hierarchical clustering algorithm, Density-based clustering algorithm, Partitioning clustering algorithm, Graph-based algorithm, Grid-based algorithm, Model-based clustering algorithm and Combinational clustering algorithm. These clustering algorithms give different result according to the conditions. Some clustering techniques are better for large data set and some gives good result for finding cluster with arbitrary shapes. This paper is planned to learn and relates various data mining clustering algorithms. Algorithms which are under exploration as follows: K-Means algorithm, K-Medoids, Distributed K-Means clustering algorithm, Hierarchical clustering algorithm, Grid-based Algorithm and Density based clustering algorithm. This paper compared all these clustering algorithms according to the many factors. After comparison of these clustering algorithms I describe that which clustering algorithms should be used in different conditions for getting the best result.


title: Parseweb: a programmer assistant for reusing open source code on the web

abstract: Programmers commonly reuse existing frameworks or libraries to reduce software development efforts. One common problem in reusing the existing frameworks or libraries is that the programmers know what type of object that they need, but do not know how to get that object with a specific method sequence. To help programmers to address this issue, we have developed an approach that takes queries of the form "Source object type → Destination object type" as input, and suggests relevant method-invocation sequences that can serve as solutions that yield the destination object from the source object given in the query. Our approach interacts with a code search engine (CSE) to gather relevant code samples and performs static analysis over the gathered samples to extract required sequences. As code samples are collected on demand through CSE, our approach is not limited to queries of any specific set of frameworks or libraries. We have implemented our approach with a tool called PARSEWeb, and conducted four different evaluations to show that our approach is effective in addressing programmer's queries. We also show that PARSEWeb performs better than existing related tools: Prospector and Strathcona


title: An offset-cancelling four-phase voltage sense amplifier for resistive memories in 14nm CMOS

abstract: This paper presents a low-offset read sensing scheme for resistive memories. Due to increasing device variations in sub-32 nm CMOS processes, it becomes very challenging to design a high yield and low-offset read-sensing scheme. In this work we address these issues by using a pseudo-differential sensing scheme to get 2× signal margin and by full offset cancellation of the sense-amplifier, making it more suitable to tolerate variation from the memory array due to storage device resistance variation. Measurement results show the sense-amplifier can work with a 20mV input, which makes it ideal for small-signal sensing for resistive memories.


REU: RSSI-based rendezvous on the tiny terrestrial robotic platform (TinyTeRP)


title: Automatic Detection and Language Identification of Multilingual Documents

abstract: Language identification is the task of automatically detecting the language(s) present in a document based on the content of the document. In this work, we address the problem of detecting documents that contain text from more than one language ( multilingual documents). We introduce a method that is able to detect that a document is multilingual, identify the languages present, and estimate their relative proportions. We demonstrate the effectiveness of our method over synthetic data, as well as real-world multilingual documents collected from the web.


title: Ensembles of Recurrent Neural Networks for Robust Time Series Forecasting

abstract: Time series forecasting is a problem that is strongly dependent on the underlying process which generates the data sequence. Hence, finding good model fits often involves complex and time consuming tasks such as extensive data preprocessing, designing hybrid models, or heavy parameter optimization. Long Short-Term Memory (LSTM), a variant of recurrent neural networks (RNNs), provide state of the art forecasting performance without prior assumptions about the data distribution. LSTMs are, however, highly sensitive to the chosen network architecture and parameter selection, which makes it difficult to come up with a one-size-fits-all solution without sophisticated optimization and parameter tuning. To overcome these limitations, we propose an ensemble architecture that combines forecasts of a number of differently parameterized LSTMs to a robust final estimate which, on average, performs better than the majority of the individual LSTM base learners, and provides stable results across different datasets. The approach is easily parallelizable and we demonstrate its effectiveness on several real-world data sets.


title: Dietary Polyunsaturated Fatty Acids and Inflammation: The Role of Phospholipid Biosynthesis

abstract: The composition of fatty acids in the diets of both human and domestic animal species can regulate inflammation through the biosynthesis of potent lipid mediators. The substrates for lipid mediator biosynthesis are derived primarily from membrane phospholipids and reflect dietary fatty acid intake. Inflammation can be exacerbated with intake of certain dietary fatty acids, such as some ω-6 polyunsaturated fatty acids (PUFA), and subsequent incorporation into membrane phospholipids. Inflammation, however, can be resolved with ingestion of other fatty acids, such as ω-3 PUFA. The influence of dietary PUFA on phospholipid composition is influenced by factors that control phospholipid biosynthesis within cellular membranes, such as preferential incorporation of some fatty acids, competition between newly ingested PUFA and fatty acids released from stores such as adipose, and the impacts of carbohydrate metabolism and physiological state. The objective of this review is to explain these factors as potential obstacles to manipulating PUFA composition of tissue phospholipids by specific dietary fatty acids. A better understanding of the factors that influence how dietary fatty acids can be incorporated into phospholipids may lead to nutritional intervention strategies that optimize health.


title: Information technology for sustainable supply chain management: a literature survey

abstract: ABSTRACTIn supply chain management (SCM), two topics have gained importance over the last years. On the one hand, sustainable SCM (SSCM) has become increasingly relevant and many publications have contributed to the topic. On the other hand, information technology (IT) is being progressively considered as a key enabler for efficiency in supply chains. Several research efforts have contributed to the field of IT for SSCM. However, this paper is the first recent attempt to summarise the current state of the art of how IT can affect SSCM in any structured way and to compare it with IT for ‘general’ SCM to give guidance for future research. This paper surveys 55 peer-reviewed articles that were retrieved through keyword searches (until May 2014). The analysis identifies research deficits as well as a lack of scientific discourse employing empirical techniques and a lack of investigations on the social sustainability. Additionally, possible topics for further research were derived by comparing the survey’s res...


title: How Can We Explore Patterns of Customer Segments' Structural Changes? A Sequential Rule Mining Approach

abstract: In real world situations, customer behavior is changing and evolving over time. It is necessary to consider this dynamism in customer segmentation analysis and other business-related activities to develop effective marketing strategies. The main aim of this study is to explore the patterns of customer segments' structural changes. Up to now, there has been no research on this particular topic. This is the first study that investigates the impact of customer dynamics on segments' structural changes. This paper tries to develop a method to describe and explain this issue. A new method is proposed based on the clustering and sequential rule mining techniques. Furthermore, a new definition and framework for finding distinguishing sequential rules is developed. The proposed method is implemented on the customer data of a telecommunication service provider. The results show that the method is capable in this domain. The findings reveal a group of customers whose dynamic behavior causes structural changes. We name this group as "structure breakers". The marketing managers of this company can use the obtained results to improve their marketing strategies and decision-making. This method can be used in every other organization to mine the patterns of segments' structural changes.