title: Efficient resampling methods for training support vector machines with imbalanced datasets

abstract: Random undersampling and oversampling are simple but well-known resampling methods applied to solve the problem of class imbalance. In this paper we show that the random oversampling method can produce better classification results than the random undersampling method, since the oversampling can increase the minority class recognition rate by sacrificing less amount of majority class recognition rate than the undersampling method. However, the random oversampling method would increase the computational cost associated with the SVM training largely due to the addition of new training examples. In this paper we present an investigation carried out to develop efficient resampling methods that can produce comparable classification results to the random oversampling results, but with the use of less amount of data. The main idea of the proposed methods is to first select the most informative data examples located closer to the class boundary region by using the separating hyperplane found by training an SVM model on the original imbalanced dataset, and then use only those examples in resampling. We demonstrate that it would be possible to obtain comparable classification results to the random oversampling results through two sets of efficient resampling methods which use 50% less amount of data and 75% less amount of data, respectively, compared to the sizes of the datasets generated by the random oversampling method.


title: Collecting community-based mappings in an ontology repository

abstract: Several ontology repositories provide access to the growing collection of ontologies on the Semantic Web. Some repositories collect ontologies automatically by crawling the Web; in other repositories, users submit ontologies themselves. In addition to providing search across multiple ontologies, the added value of ontology repositories lies in the metadata that they may contain. This metadata may include information provided by ontology authors, such as ontologies' scope and intended use; feedback provided by users such as their experiences in using the ontologies or reviews of the content; and mapping metadata that relates concepts from different ontologies. In this paper, we focus on the ontology-mapping metadata and on community-based method to collect ontology mappings. More specifically, we develop a model for representing mappings collected from the user community and the metadata associated with the mapping. We use the model to bring together more than 30,000 mappings from 7 sources. We also validate the model by extending BioPortal---a repository of biomedical ontologies that we have developed--to enable users to create single concept-to-concept mappings in its graphical user interface, to upload and download mappings created with other tools, to comment on the mappings and to discuss them, and to visualize the mappings and the corresponding metadata.


title: Learning Efficient Single-Stage Pedestrian Detectors by Asymptotic Localization Fitting

abstract: Though Faster R-CNN based two-stage detectors have witnessed significant boost in pedestrian detection accuracy, it is still slow for practical applications. One solution is to simplify this working flow as a single-stage detector. However, current single-stage detectors (e.g. SSD) have not presented competitive accuracy on common pedestrian detection benchmarks. This paper is towards a successful pedestrian detector enjoying the speed of SSD while maintaining the accuracy of Faster R-CNN. Specifically, a structurally simple but effective module called Asymptotic Localization Fitting (ALF) is proposed, which stacks a series of predictors to directly evolve the default anchor boxes of SSD step by step into improving detection results. As a result, during training the latter predictors enjoy more and better-quality positive samples, meanwhile harder negatives could be mined with increasing IoU thresholds. On top of this, an efficient single-stage pedestrian detection architecture (denoted as ALFNet) is designed, achieving state-of-the-art performance on CityPersons and Caltech, two of the largest pedestrian detection benchmarks, and hence resulting in an attractive pedestrian detector in both accuracy and speed. Code is available at https://github.com/VideoObjectSearch/ALFNet.


title: Linear RGB-D SLAM for Planar Environments

abstract: We propose a new formulation for including orthogonal planar features as a global model into a linear SLAM approach based on sequential Bayesian filtering. Previous planar SLAM algorithms estimate the camera poses and multiple landmark planes in a pose graph optimization. However, since it is formulated as a high dimensional nonlinear optimization problem, there is no guarantee the algorithm will converge to the global optimum. To overcome these limitations, we present a new SLAM method that jointly estimates camera position and planar landmarks in the map within a linear Kalman filter framework. It is rotations that make the SLAM problem highly nonlinear. Therefore, we solve for the rotational motion of the camera using structural regularities in the Manhattan world (MW), resulting in a linear SLAM formulation. We test our algorithm on standard RGB-D benchmarks as well as additional large indoors environments, demonstrating comparable performance to other state-of-the-art SLAM methods without the use of expensive nonlinear optimization.


title: Quantitative detection of acyclovir by surface enhanced Raman spectroscopy using a portable Raman spectrometer coupled with multivariate data analysis

abstract: Abstract Acyclovir (ACV) is a synthetic antiviral agent with serious side effect, particularly its nephrotoxicity, so this study was to explore the ultrasensitive detection of ACV by surface-enhanced Raman scattering (SERS). The enhancement capability of nanoparticles prepared by different chemical reduction were compared, and Ag nanoparticles reduced by citrate are the most propriate enhanced substrate for acyclovir. In addition, comparison between prominent SERS-enhanced bands and the precise mode descriptions predicted through density functional theory (DFT) simulations is used to understand the mechanisms between ACV and metallic surface. 130 different levels of ACV concentrations in a range from 10−1∼10-7 were used to build quantitative prediction models by two different modeling methods, partial least-squares (PLS) regression and artificial neural network (ANN). Under the optimal conditions, the performance of the PLS model was much better than ANN. The results demonstrated that SERS imaging with multivariate analysis holds great potential for the sensitive and cost effective clinic test of ACV and its metabolites in biological fluids.


title: Building Generic Scalable Middlebox Services Over Encrypted Protocols

abstract: The trends of the increasing middleboxes make the middle network more and more complex. Today, many middleboxes work on application layer and offer significant network services by the plain-text traffic, such as firewalling, intrusion detecting and application layer gateways. At the same time, more and more network applications are encrypting their data transmission to protect security and privacy. It is becoming a critical task and hot topic to continue providing application-layer middlebox services in the encrypted Internet, however, the state of the art is far from being able to be deployed in the real network. In this paper, we propose a practical architecture, named PlainBox, to enable session key sharing between the communication client and the middleboxes in the network path. It employs Attribute-Based Encryption (ABE) in the key sharing protocol to support multiple chaining middleboxes efficiently and securely. We develop a prototype system and apply it to popular security protocols such as TLS and SSH. We have tested our prototype system in a lab testbed as well as real-world websites. Our result shows PlainBox introduces very little overhead and the performance is practically deployable.


title: Joint Representation Classification for Collective Face Recognition

abstract: Sparse representation based classification (SRC) is popularly used in many applications such as face recognition, and implemented in two steps: representation coding and classification. For a given set of testing images, SRC codes every image over the base images as a sparse representation then classifies it to the class with the least representation error. This scheme utilizes an individual representation rather than the collective one to classify such a set of images, doing so obviously ignores the correlation among the given images. In this paper, a joint representation classification (JRC) for collective face recognition is proposed. JRC takes the correlation of multiple images as well as a single representation into account. Under the assumption that the given face images are generally related to each other, JRC codes all the testing images over the base images simultaneously to facilitate recognition. To this end, the testing inputs are aligned into a matrix and the joint representation coding is formulated to a generalized $l_{2,q}-l_{2,p}$-minimization problem. To uniformly solve the induced optimization problems for any $q\in[1,2]$ and $p\in (0,2]$, an iterative quadratic method (IQM) is developed. IQM is proved to be a strict descent algorithm with convergence to the optimal solution. Moreover, a more practical IQM is proposed for large-scale case. Experimental results on three public databases show that the JRC with practical IQM no only saves much computational cost but also achieves better performance in collective face recognition than the state-of-the-arts.


title: A Study on the Customer-Based Brand Equity of Online Retailers

abstract: Different from traditional firms, online retailers do not often produce common goods on their own. On the contrary, they sell merchandises that traditional firms produce. Furthermore, online retailers are also different from traditional retailers because the former do not often have frontline employees, physical outlets, or displaying layouts. Therefore, when building a strong online retailer brand, it is not enough to only consider the traditional dimensions of customer-based brand equity, namely brand awareness, perceived quality, brand associations, and brand loyalty. Some dimensions, i.e. trust associations and emotional connection need to be distinguished or included in the context of online business. Besides, the willingness to pay a price premium is always being viewed as a good indicator to measure brand equity, but for online retailers, it is conditional on some premises. By structural equation modeling, it was verified that for online retailers, the importance of brand awareness and trust associations decreased but perceived quality remained its strong influence. Brand loyalty was the better indicator for measuring brand equity, and emotional connection became so critical that if without it, customers would lack willingness to pay a price premium.


Digitization as a Means of Preservation of Manuscripts : Case study of Osmania University Library


title: Abstract 4606: Involvement of an angiotensin II type 2 receptor (AT2R) signalling in human pancreatic ductal adenocarcinoma (PDAC): a novel AT2R agonist effectively attenuates growth of PDAC grafts in mice

abstract: Pancreatic cancer, primarily PDAC, is the most difficult cancer to treat. Therapeutic interventions currently available for PDAC are ineffective. Therefore, obtaining knowledge about developmental mechanisms associated with this cancer could be valuable in the development of early detection and effective treatments for pancreatic cancer. Angiotensin II is the key effecter of the renin-angiotensin system which plays an important role in maintaining blood pressure, body fluid and electrolyte homeostasis, and collagen deposition in the stroma. Expression of the angiotensin II type 1 receptor (AT1R) is shown to be associated with the progression of multiple cancers including PDAC. On the contrary, the expression of AT2R is shown to be involved in the inhibition of the growth of multiple cancers in mice. Accordingly, the objectives of this study were to determine the potential involvement of angiotensin II receptor expression in human PDAC and to evaluate the effect of a novel AT2R agonist on the growth of murine PDAC in syngeneic mouse models. Expression of AT1R and AT2R in human PDAC and adjacent normal tissue was evaluated by immunohistochemistry and real time PCR using 28 surgically dissected human PDAC specimens. In this study, though a strong AT1R expression was detected consistently in both normal pancreas and PDAC areas, AT1R expression in the PDAC area was stronger than its expression in the normal area. A moderate AT2R expression was detected in 71% of the PDAC specimens and normal area of the pancreas, and its expression levels in the two areas were similar. Both AT1R and the AT2R mRNA levels were significantly higher in the PDAC area than in the normal pancreas tissue. Cell culture studies clarified that the AT2R agonist significantly attenuated both murine and human PDAC cells with negligible cytotoxicity in normal epithelial cells. Administrations of the AT2R agonist, but not control saline, in tumor surrounding connective tissue markedly attenuated growth of only AT2R expressing PAN02 murine PDAC grafts in the syngeneic mice. Immunohistochemical analysis of the PDAC grafts revealed that the agonist treatment induced apoptosis in tumor cells but had no effect on stromal cells. Taken together, our findings offer clinical and preclinical evidence for the involvement of AT2R signaling in PDAC development. In addition, the novel AT2R agonist is suggested to be an effective therapeutic for the treatment of PDAC. This work was supported by the Kansas State University (KSU) Johnson Cancer Research Center, NIH RR017686, RR15563, Kansas Bioscience Authority research grant and the Greek Ministry of Education and Research and program ARISTEIA II to AGT Citation Format: Susumu Ishiguro, Kiyoshi Yoshimura, Sonshin Takao, Atsushi Kawabata, Terrahn Wall, Ryouichi Tsunedomi, Masaaki Oka, Makoto Inui, Charalambos Pappas, Andreas G. Tzakos, Masaaki Tamura. Involvement of an angiotensin II type 2 receptor (AT2R) signalling in human pancreatic ductal adenocarcinoma (PDAC): a novel AT2R agonist effectively attenuates growth of PDAC grafts in mice. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4606. doi:10.1158/1538-7445.AM2014-4606


title: 3D-TV R&D Activities in Europe

abstract: 3D-TV is a topic that has been studied for many years in Europe. Through the research frameworks of the European Commission in particular, a number of long-term issues have been addressed to overcome limitations of the traditional two-view stereoscopy. This article gives a brief overview of the goals and achievements of some completed European projects starting in the 1990s. It then reviews the topics related to 3D-TV in recent European research. Finally an overview with a selection of recent projects is presented.


Large-scale JPEG steganalysis using hybrid deep-learning framework


title: Lazygraph: lazy data coherency for replicas in distributed graph-parallel computation

abstract: Replicas 1 of a vertex play an important role in existing distributed graph processing systems which make a single vertex to be parallel processed by multiple machines and access remote neighbors locally without any remote access. However, replicas of vertices introduce data coherency problem. Existing distributed graph systems treat replicas of a vertex v as an atomic and indivisible vertex, and use an eager data coherency approach to guarantee replicas atomicity. In eager data coherency approach, any changes to vertex data must be immediately communicated to all replicas of v, thus leading to frequent global synchronizations and communications. In this paper, we propose a lazy data coherency approach, called LazyAsync, which treats replicas of a vertex as independent vertices and maintains the data coherency by computations, rather than communications in existing eager approach. Our approach automatically selects some data coherency points from the graph algorithm, and maintains all replicas to share the same global view only at such points, which means the replicas are enabled to maintain different local views between any two adjacent data coherency points. Based on PowerGraph, we develop a distributed graph processing system LazyGraph to implement the LazyAsync approach and exploit graph-aware optimizations. On a 48-node EC2-like cluster, LazyGraph outperforms PowerGraph on four widely used graph algorithms across a variety of real-world graphs, with a speedup ranging from 1.25x to 10.69x.


title: Bridging gaps between developers and testers in globally-distributed software development

abstract: One of the main challenges in distributed development is ensuring effective communication and coordination among the distributed teams. In this context, little attention has been paid so far to coordination in software testing. In distributed development environments, testing is often performed by specialized teams that operate as independent quality assurance centers. The use of these centers can be advantageous from both an economic and a software quality perspective. These benefits, however, are offset by severe difficulties in coordination between testing and software development centers. Test centers operate as isolated silos and have little to no interactions with developers, which can result in multiple problems that lead to poor quality of software. Based on our preliminary investigation, we claim that we need to rethink the way testing is performed in distributed development environments. We then present a possible research agenda that would help address the identified issues and discuss the main challenges involved.


title: RotBoost: a technique for combining rotation forest and AdaBoost

abstract: This paper presents a novel ensemble classifier generation technique RotBoost, which is constructed by combining Rotation Forest and AdaBoost. The experiments conducted with 36 real-world data sets available from the UCI repository, among which a classification tree is adopted as the base learning algorithm, demonstrate that RotBoost can generate ensemble classifiers with significantly lower prediction error than either Rotation Forest or AdaBoost more often than the reverse. Meanwhile, RotBoost is found to perform much better than Bagging and MultiBoost. Through employing the bias and variance decompositions of error to gain more insight of the considered classification methods, RotBoost is seen to simultaneously reduce the bias and variance terms of a single tree and the decrement achieved by it is much greater than that done by the other ensemble methods, which leads RotBoost to perform best among the considered classification procedures. Furthermore, RotBoost has a potential advantage over AdaBoost of suiting parallel execution.


title: Fast time intervals mining using the transitivity of temporal relations

abstract: We introduce an algorithm, called KarmaLego, for the discovery of frequent symbolic time interval-related patterns (TIRPs). The mined symbolic time intervals can be part of the input, or can be generated by a temporal-abstraction process from raw time-stamped data. The algorithm includes a data structure for TIRP-candidate generation and a novel method for efficient candidate-TIRP generation, by exploiting the transitivity property of Allen's temporal relations. Additionally, since the non-ambiguous definition of TIRPs does not specify the duration of the time intervals, we propose to pre-cluster the time intervals based on their duration to decrease the variance of the supporting instances. Our experimental comparison of the KarmaLego algorithm's runtime performance with several existing state of the art time intervals pattern mining methods demonstrated a significant speed-up, especially with large datasets and low levels of minimal vertical support. Furthermore, pre-clustering by time interval duration led to an increase in the homogeneity of the duration of the discovered TIRP's supporting instances' time intervals components, accompanied, however, by a corresponding decrease in the number of discovered TIRPs.


title: Decoupled Direct Memory Access: Isolating CPU and IO Traffic by Leveraging a Dual-Data-Port DRAM

abstract: Memory channel contention is a critical performance bottleneck in modern systems that have highly parallelized processing units operating on large data sets. The memory channel is contended not only by requests from different user applications (CPU access) but also by system requests for peripheral data (IO access), usually controlled by Direct Memory Access (DMA) engines. Our goal, in this work, is to improve system performance byeliminating memory channel contention between CPU accesses and IO accesses. To this end, we propose a hardware-software cooperative data transfer mechanism, Decoupled DMA (DDMA) that provides a specialized low-cost memory channel for IO accesses. In our DDMA design, main memoryhas two independent data channels, of which one is connected to the processor (CPU channel) and the other to the IO devices (IO channel), enabling CPU and IO accesses to be served on different channels. Systemsoftware or the compiler identifies which requests should be handled on the IO channel and communicates this to the DDMA engine, which then initiates the transfers on the IO channel. By doing so, our proposal increasesthe effective memory channel bandwidth, thereby either accelerating data transfers between system components, or providing opportunities to employ IO performance enhancement techniques (e.g., aggressive IO prefetching)without interfering with CPU accessesWe demonstrate the effectiveness of our DDMA framework in two scenarios: (i) CPU-GPU communication and (ii) in-memory communication (bulk datacopy/initialization within the main memory). By effectively decoupling accesses for CPU-GPU communication and in-memory communication from CPU accesses, our DDMA-based design achieves significant performanceimprovement across a wide variety of system configurations (e.g., 20% average performance improvement on a typical 2-channel 2-rank memory system).


title: Towards quality discourse in online news comments

abstract: With the growth in sociality and interaction around online news media, news sites are increasingly becoming places for communities to discuss and address common issues spurred by news articles. The quality of online news comments is of importance to news organizations that want to provide a valuable exchange of community ideas and maintain credibility within the community. In this work we examine the complex interplay between the needs and desires of news commenters with the functioning of different journalistic approaches toward managing comment quality. Drawing primarily on newsroom interviews and reader surveys, we characterize the comment discourse of SacBee.com, discuss the relationship of comment quality to both the consumption and production of news information, and provide a description of both readers' and writers' motivations for usage of news comments. We also examine newsroom strategies for dealing with comment quality as well as explore tensions and opportunities for value-sensitive innovation within such online communities.


title: Quaternion-based trajectory tracking control of VTOL-UAVs using command filtered backstepping

abstract: This paper discusses trajectory tracking control for Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicles (UAVs) using the command filtered backstepping technique. Quaternions are used to represent the attitude of the vehicle to ensure the global attitude tracking without singularities. Since the quaternions have their own unique algebra, they cannot be filtered by a vector-based command filter; therefore, a second-order quaternion filter is developed to filter the quaternion and automatically compute its derivative, which determines the commanded angular rate vector. A quadrotor vehicle is used as an example to show the performance of the proposed controller.


title: Enhanced frequent itemsets based on topic modeling in information filtering

abstract: In order to generate user's information needs from a collection of documents, many term-based and pattern-based approaches have been used in Information Filtering. In these approaches, the documents in the collection are all about one topic. However, user's interests can be diverse and the documents in the collection often involve multiple topics. Topic modeling is useful for the area of machine learning and text mining. It generates models to discover the hidden multiple topics in a collection of documents and each of these topics are presented by distribution of words. But its effectiveness in information filtering has not been so well explored. Patterns are always thought to be more discriminative than single terms for describing documents. The major challenge found in frequent pattern mining is a large number of result patterns. As the minimum threshold becomes lower, an exponentially large number of patterns are generated. To deal with the above mentioned limitations and problems, in this paper, a novel information filtering model, EFITM (Enhanced Frequent Itemsets based on Topic Model) model is proposed. Experimental results using the CRANFIELD dataset for the task of information filtering show that the proposed model outperforms over state-of-the-art models.


title: Protection motivation and deterrence: a framework for security policy compliance in organisations

abstract: AbstractEnterprises establish computer security policies to ensure the security of information resources; however, if employees and end-users of organisational information systems (IS) are not keen or are unwilling to follow security policies, then these efforts are in vain. Our study is informed by the literature on IS adoption, protection-motivation theory, deterrence theory, and organisational behaviour, and is motivated by the fundamental premise that the adoption of information security practices and policies is affected by organisational, environmental, and behavioural factors. We develop an Integrated Protection Motivation and Deterrence model of security policy compliance under the umbrella of Taylor-Todd's Decomposed Theory of Planned Behaviour. Furthermore, we evaluate the effect of organisational commitment on employee security compliance intentions. Finally, we empirically test the theoretical model with a data set representing the survey responses of 312 employees from 78 organisations. Our r...


title: Personalized Approach Based on SVM and ANN for Detecting Credit Card Fraud

abstract: A novel personalized approach has recently been presented to prevent credit card fraud. This new approach proposes to prevent fraud before initial use of a new card, even users without any real transaction data. This approach shows potential, nevertheless, there are some problems needed solving. A main issue is how to predict accurately with only few data, since it collects quasi-real transaction data via an online questionnaire system and thus respondents are commonly unwilling to spend too much time to reply questionnaires. This study employs both support vector machines (SVM) and artificial neural networks (ANN) to investigate the time-varying fraud problem. The performance of ANN is compared with that from SVM. Results show that SVM and ANN are comparable in training but ANN can have highest training accuracy. However, ANN seems to overfit training data and thus has worse performance of predicting the future data when data number is small


title: Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform

abstract: Reliable and accurate forecasts of river flow is needed in many water resources planning, design development, operation and maintenance activities. In this study, the relative accuracy of artificial neural network (ANN) and support vector regression (SVR) models coupled with wavelet transform in monthly river flow forecasting is investigated, and compared to regular ANN and SVR models, respectively. The relative performance of regular ANN and SVR models is also compared to each other. For this, monthly river flow data of Kharjegil and Ponel stations in Northern Iran are used. The comparison of the results reveals that both ANN and SVR models coupled with wavelet transform, are able to provide more accurate forecasting results than the regular ANN and SVR models. However, it is found that SVR models coupled with wavelet transform provide better forecasting results than ANN models coupled with wavelet transform. The results also indicate that regular SVR models perform slightly better than regular ANN models.


title: Testing the Generalization Power of Neural Network Models Across NLI Benchmarks

abstract: Neural network models have been very successful for natural language inference, with the best models reaching 90% accuracy in some benchmarks. However, the success of these models turns out to be largely benchmark specific. We show that models trained on natural language inference dataset drawn from one benchmark fail to perform well in others, even if the notion of inference assumed in these benchmark tasks is the same or similar. We train five state-of-the-art neural network models on different datasets and show that each one of these fail to generalize outside of the respective benchmark. In light of these results we conclude that the current neural network models are not able to generalize in capturing the semantics of natural language inference, but seem to be overfitting to the specific dataset.


title: An innovation diffusion model of TQM implementation

abstract: Researchers and practitioners have acknowledged the need to understand causal relationships among various elements of total quality management (TQM). In this paper, the authors model TQM as an organizational innovation. Using the innovation diffusion perspective from the information systems and organizational innovation literature, they theorize that TQM implementation translates top management's quality intent into plant-level operational performance through a four-stage process of adoption, adaptation, acceptance, and use. The top management adopts the TQM philosophy in the first stage. This commitment influences the adaptation of the organizational members' ability and attitude to the new quality management philosophy. In the acceptance stage, the organizational members demonstrate acceptance of the new quality focus through cooperative teamwork, relationships with suppliers, and quality-related learning. The diffusion of the new philosophy is confirmed through routinization of core quality improvement through effective design, tracking, assurance, and improvement of quality. The four-stage transformation process leads to plant-level measures of product and process quality. The authors tested this framework on a sample of 407 plants in the automobile parts suppliers industry and found good support for the model. Their results suggest that firms should ensure appropriate technical and behavioral preparation of employees and suppliers before and concurrent to actual TQM implementation. Furthermore, the results also suggest that firms should implement TQM in an integrated fashion covering all sociotechnical elements detailed in their framework. Implications of these findings for TQM practice and research are discussed.


title: A background subtraction algorithm for detecting and tracking vehicles

abstract: An innovative system for detecting and extracting vehicles in traffic surveillance scenes is presented. This system involves locating moving objects present in complex road scenes by implementing an advanced background subtraction methodology. The innovation concerns a histogram-based filtering procedure, which collects scatter background information carried in a series of frames, at pixel level, generating reliable instances of the actual background. The proposed algorithm reconstructs a background instance on demand under any traffic conditions. The background reconstruction algorithm demonstrated a rather robust performance in various operating conditions including unstable lighting, different view-angles and congestion.


title: Development of the extrinsic sympathetic innervation to the enteric neurones of the rat small intestine

abstract: Abstract The development of the sympathetic control of motility of the small intestine of the rat has been studied over the early postnatal period. An inhibition of spontaneous motility was recorded in response to stimulation of the mesenteric paravascular nerve bundles as early as 3–4 days postnatal. At this time, the ganglia of the myenteric plexus were well supplied with noradrenergic nerve fibres, while not all of the ganglia of the submucous plexus were contacted by fibres until 6 days postnatal. The sympathetic innervation to the submucous arteries developed even later and at 9 days postnatal was still less dense than in adults. The onset of sympathetic function in the gut preceded that in the mesenteric arteries by several days. These results further support the hypothesis that the sympathetic neurones supplying the enteric ganglia are a subpopulation of cells distinct from those supplying the blood vessels of the mesentery and submucosa.


Hacking Mobile Phones Using 2 D Printed Fingerprints


DEMAND FORECASTING USING NEURAL NETWORK FOR SUPPLY CHAIN MANAGEMENT


title: Resilience and survivability in communication networks : Strategies , principles , and survey of disciplines

abstract: The Internet has become essential to all aspects of modern life, and thus the consequences of network disruption have become increasingly severe. It is widely recognised that the Internet is not sufficiently resilient, survivable, and dependable, and that significant research, development, and engineering is necessary to improve the situation. This paper provides an architectural framework for resilience and survivability in communication networks and provides a survey of the disciplines that resilience encompasses, along with significant past failures of the network infrastructure. A resilience strategy is presented to defend against, detect, and remediate challenges, a set of principles for designing resilient networks is presented, and techniques are described to analyse network resilience.


title: Scalable Offline Monitoring

abstract: We propose an approach to monitoring IT systems offline, where system actions are logged in a distributed file system and subsequently checked for compliance against policies formulated in an expressive temporal logic. The novelty of our approach is that monitoring is parallelized so that it scales to large logs. Our technical contributions comprise a formal framework for slicing logs, an algorithmic realization based on MapReduce, and a high-performance implementation. We evaluate our approach analytically and experimentally, proving the soundness and completeness of our slicing techniques and demonstrating its practical feasibility and efficiency on real-world logs with 400 GB of relevant data.


title: Understanding Nomophobia: Structural Equation Modeling and Semantic Network Analysis of Smartphone Separation Anxiety

abstract: Abstract This study explicates nomophobia by developing a research model that identifies several determinants of smartphone separation anxiety and by conducting semantic network analyses on smartphone users' verbal descriptions of the meaning of their smartphones. Structural equation modeling of the proposed model indicates that personal memories evoked by smartphones encourage users to extend their identity onto their devices. When users perceive smartphones as their extended selves, they are more likely to get attached to the devices, which, in turn, leads to nomophobia by heightening the phone proximity-seeking tendency. This finding is also supplemented by the results of the semantic network analyses revealing that the words related to memory, self, and proximity-seeking are indeed more frequently used in the high, compared with low, nomophobia group.


Data Mining and Knowledge Discovery: Applications, Techniques, Challenges and Process Models in Healthcare


title: Cerebral asymmetry and the effects of sex and handedness on brain structure: A voxel-based morphometric analysis of 465 normal adult human brains

abstract: We used voxel-based morphometry (VBM) to examine human brain asymmetry and the effects of sex and handedness on brain structure in 465 normal adults. We observed significant asymmetry of cerebral grey and white matter in the occipital, frontal, and temporal lobes (petalia), including Heschl's gyrus, planum temporale (PT) and the hippocampal formation. Males demonstrated increased leftward asymmetry within Heschl's gyrus and PT compared to females. There was no significant interaction between asymmetry and handedness and no main effect of handedness. There was a significant main effect of sex on brain morphology, even after accounting for the larger global volumes of grey and white matter in males. Females had increased grey matter volume adjacent to the depths of both central sulci and the left superior temporal sulcus, in right Heschl's gyrus and PT, in right inferior frontal and frontomarginal gyri and in the cingulate gyrus. Females had significantly increased grey matter concentration extensively and relatively symmetrically in the cortical mantle, parahippocampal gyri, and in the banks of the cingulate and calcarine sulci. Males had increased grey matter volume bilaterally in the mesial temporal lobes, entorhinal and perirhinal cortex, and in the anterior lobes of the cerebellum, but no regions of increased grey matter concentration.


title: Determination of the soil microbial biomass carbon using the method of substrate-induced respiration

abstract: Specific features of determining the carbon content in the soil microbial biomass using the method of substrate-induced respiration (MBSIR) were studied as related to the conditions of the incubation (the glucose concentration and temperature) and pre-incubation (the duration and temperature) of the soil samples collected in the summer (tundra gley and soddy-podzolic soils and chernozems) and in different seasons (for the gray forest soil). The glucose concentration providing the highest substrate-induced respiration (SIR) in the soils studied was shown to be 2–15 mg/g. The MBSIR in the soil samples collected in summer and in the soils pre-incubated for 10 and 22°C (7 days) did not significantly differ. The MBSIR in the gray forest soil pre-incubated at 3, 6, and 10°C (winter, spring/autumn, and summer, respectively) and at 22°C (recommended by the authors of the SIR method) was similar for the cropland in all the seasons. For the meadow, it was the same in the winter, summer, and autumn, and, in summer, it did not differ only for the forest. For the comparative assessment of the MBSIR, soil samples from different ecosystems are recommended to be collected in the autumn or in the summer. Soil samples of 100–500 g should be pre-incubated for 7 days at 22°C and moisture of 60% of the total water capacity; then, 1-2 g soil should be incubated with glucose (10 mg/g) at 22°C for 3–5 hours.


title: Highly discriminative statistical features for email classification

abstract: This paper reports on email classification and filtering, more specifically on spam versus ham and phishing versus spam classification, based on content features. We test the validity of several novel statistical feature extraction methods. The methods rely on dimensionality reduction in order to retain the most informative and discriminative features. We successfully test our methods under two schemas. The first one is a classic classification scenario using a 10-fold cross-validation technique for several corpora, including four ground truth standard corpora: Ling-Spam, SpamAssassin, PU1, and a subset of the TREC 2007 spam corpus, and one proprietary corpus. In the second schema, we test the anticipatory properties of our extracted features and classification models with two proprietary datasets, formed by phishing and spam emails sorted by date, and with the public TREC 2007 spam corpus. The contributions of our work are an exhaustive comparison of several feature selection and extraction methods in the frame of email classification on different benchmarking corpora, and the evidence that especially the technique of biased discriminant analysis offers better discriminative features for the classification, gives stable classification results notwithstanding the amount of features chosen, and robustly retains their discriminative value over time and data setups. These findings are especially useful in a commercial setting, where short profile rules are built based on a limited number of features for filtering emails.


Feature Extraction of Ballistocardiogram Signal


title: ERP systems adoption: An exploratory study of the organizational factors and impacts of ERP success

abstract: We examined the relationships between the success of ERP system adoption, extent of business process improvement (BPI), and organizational performance and investigated the associations between the outcomes of these initiatives and such organizational factors as strategic intent, senior management support, and the status of the IT function within a company. A correlation analysis of 96 firms was made to test our hypothesis that the strategic intent to use ERP was closely related to the success of BPI, ERP, and organizational performance. The results also demonstrated that CEO-IT distance may have little direct bearing on the outcomes of ERP and BPI initiatives. A closer CEO-IT reporting relationship, however, was found to be associated with higher levels of senior management support for both types of enterprise projects. This empirical evidence reinforced the long-held assumption that organization-level benefits, BPI, and ERP success were closely related; and that these relationships were subject to the influence of the organizational variables.


title: Design of a Hybrid Directional Coupler in Empty Substrate Integrated Waveguide (ESIW)

abstract: This letter shows the procedure of design of a hybrid directional coupler based on Empty Substrate Integrated Waveguide (ESIW). Once proved in previous works the feasibility of ESIW and the good performance indexes this technology can achieve, the following step is designing useful devices to be integrated in planar circuits. The proposed hybrid coupler, which is the first ESIW 4-port available circuit, is designed at 15 GHz with 3 dB coupling factor and a minimum of 20 dB of isolation over 30% bandwidth. After manufacturing the circuit, simulated and measured results are presented and discussed.


title: Power assist control for walking aid with HAL-3 based on EMG and impedance adjustment around knee joint

abstract: This paper describes the power assist control for walking aid based on EMG and impedance adjustment with HAL-3 we have developed. Virtual torque derived from EMG is adopted as a basic control method, and the motion assist control as to operator's intention can be realized by this method. And we suggest the impedance adjustment around knee joint for more effective power assist control. Experiments for simple motion and walking motion were performed to verify the proposed approach, with impedance parameters found by RLS (recursive least square) method. The evaluation of assisted motion was done by a calculation based on EMG in nearly proportion to the operator's muscle force. The results showed the amplitudes of EMG were reduced significantly, the operator was able to swing the leg lighter by reducing the inertia around knee, and the strain of knee in foot-grounding could be alleviated by adding the stiffness to joint.


The Utility of Neuropsychological Measures on the Differential Diagnosis of ADHD-Inattentive Type Versus Anxiety in a Pediatric Outpatient Behavioral Health Population


title: Interlayer Exciton Optoelectronics in a 2D Heterostructure p–n Junction

abstract: Semiconductor heterostructures are backbones for solid-state-based optoelectronic devices. Recent advances in assembly techniques for van der Waals heterostructures have enabled the band engineering of semiconductor heterojunctions for atomically thin optoelectronic devices. In two-dimensional heterostructures with type II band alignment, interlayer excitons, where Coulomb bound electrons and holes are confined to opposite layers, have shown promising properties for novel excitonic devices, including a large binding energy, micron-scale in-plane drift-diffusion, and a long population and valley polarization lifetime. Here, we demonstrate interlayer exciton optoelectronics based on electrostatically defined lateral p–n junctions in a MoSe2–WSe2 heterobilayer. Applying a forward bias enables the first observation of electroluminescence from interlayer excitons. At zero bias, the p–n junction functions as a highly sensitive photodetector, where the wavelength-dependent photocurrent measurement allows the dir...


title: A Better Way to Pretrain Deep Boltzmann Machines

abstract: We describe how the pretraining algorithm for Deep Boltzmann Machines (DBMs) is related to the pretraining algorithm for Deep Belief Networks and we show that under certain conditions, the pretraining procedure improves the variational lower bound of a two-hidden-layer DBM. Based on this analysis, we develop a different method of pretraining DBMs that distributes the modelling work more evenly over the hidden layers. Our results on the MNIST and NORB datasets demonstrate that the new pretraining algorithm allows us to learn better generative models.


A Survey of Truth Discovery in Information Extraction


title: Intelligent Traffic Control System

abstract: Traffic is a major concern for most of the metropolitan cities of the world. Efficient traffic management can have a major impact on the country's economy. This paper proposes a new digital-logic based system which is more efficient than currently used traffic control systems. The intelligent traffic control system (ITSC) is based on a simple principle; the principle being that "a car can only move ahead if there is space for it" and "the signal remains green until the present cars have passed". By placing sensors at every entry and exit of a junction and monitoring the number of cars present at the junction, it is possible to make traffic very efficient, which is a good application of digital signal processing. However, absolute advantage of such a system will only be felt if every junction in a city is controlled by this system.


title: An Overview of Warehousing Applications based on Cable Robot Technology in Logistics

abstract: Robotics belongs to the megatrends radically changing the logistics industry which has long time been reluctant to the adoption of technological innovation. Facing aggravated competition by ever new and stronger actors in the industry, labor shortage due to demographic shifts, and constant cost pressure, logistics robots are considered as an effective means to tackle those problems. By taking a look at the market of logistics robots, the market condition for cable-driven parallel robots for storage purposes can be studied and a successful introduction into the market of material handling devices initiated. Hence, this paper examines the prevalence of robotics solutions in general and cable-robot applications in particular in the logistics industry.


title: Enhancement of Underwater Images With Statistical Model of Background Light and Optimization of Transmission Map

abstract: Underwater images often have severe quality degradation and distortion due to light absorption and scattering in the water medium. A hazed image formation model is widely used to restore the image quality. It depends on two optical parameters: the background light and the transmission map. Underwater images can also be enhanced by color and contrast correction from the perspective of image processing. In this paper, we propose an effective underwater image enhancement method for underwater images in composition of underwater image restoration and color correction. Firstly, a manually annotated background lights (MABLs) database is developed. With reference to the relationship between MABLs and the histogram distributions of various underwater images, robust statistical models of BLs estimation are provided. Next, the TM of R channel is roughly estimated based on the new underwater dark channel prior via the statistic of clear and high resolution underwater images, then a scene depth map based on the underwater light attenuation prior and an adjusted reversed saturation map are applied to compensate and modify the coarse TM of R channel. Next, TMs of G-B channels are estimated based on the difference of attenuation ratios between R channel and G-B channels. Finally, to improve the color and contrast of the restored image with a natural appearance, a variation of white balance is introduced as post-processing. In order to guide the priority of underwater image enhancement, sufficient evaluations are conducted to discuss the impacts of the key parameters including BL and TM, and the importance of the color correction. Comparisons with other state-of-the-art methods demonstrate that our proposed underwater image enhancement method can achieve higher accuracy of estimated BLs, less computation time, more superior performance, and more valuable information retention.


STUDENTS ’ PERCEPTION ON THE USE OF WHATSAPP AS A LEARNING TOOL IN ESL


title: Real-Time Business Intelligence for the Utilities Industry

abstract: In today’s competitive environment with rapid innovation in smart metering and smart grids, there is an increased need for real-time business intelligence (RTBI) in the utilities industry. Giving the fact that this industry is an environment where decisions are time sensitive, RTBI solutions will help utilities improve customer experiences and operational efficiencies. The focus of this paper is on the importance of real-time business intelligence (RTBI) in the utilities industry, outlining our vision of real-time business intelligence for this industry. Besides the analysis in this area, the article presents as a case study the Oracle Business Intelligence solution for utilities.


Fast NP Chunking Using Memory-Based Learning Techniques


title: Two-Stage Approach for Detection and Reduction of Motion Artifacts in Photoplethysmographic Data

abstract: Corruption of photopleythysmograms (PPGs) by motion artifacts has been a serious obstacle to the reliable use of pulse oximeters for real-time, continuous state-of-health monitoring. In this paper, we propose an automated, two-stage PPG data processing method to minimize the effects of motion artifacts. The technique is based on our prior work related to motion artifact detection (stage 1) [R. Krishnan, B. Natarajan, and S. Warren, ``Analysis and detection of motion artifacts in photoplethysmographic data using higher order statistics,'' in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP 2008), Las Vegas, Nevada, Apr. 2008, pp. 613-616] and motion artifact reduction (stage 2) [R. Krishnan, B. Natarajan, and S. Warren, ``Motion artifact reduction in photoplethysmography using magnitude-based frequency domain independent component analysis,'' in Proc. 17th Int. Conf. Comput. Commun. Network, St. Thomas, Virgin Islands, Aug. 2008, pp. 1-5]. Regarding stage 1, we present novel and consistent techniques to detect the presence of motion artifact in PPGs given higher order statistical information present in the data. We analyze these data in the time and frequency domains (FDs) and identify metrics to distinguish between clean and motion-corrupted data. A Neyman-Pearson detection rule is formulated for each of the metrics. Furthermore, by treating each of the metrics as observations from independent sensors, we employ hard fusion and soft fusion techniques presented in [Z. Chair and P. Varshney, ``Optimal data fusion in multiple sensor detection systems,'' IEEE Trans. Aerosp. Electron. Syst., AES, vol. 1, no. 1, pp. 98-101, Jan. 1986] and [C. C. Lee and J. J. Chao, ``Optimum local decision space partitioning for distributed detection,'' IEEE Trans. Aerosp. Electron. Syst., AES, vol. 25, no. 7, pp. 536-544, Jul. 1989], respectively, in order to fuse individual decisions into a global system decision. For stage two, we propose a motion artifact reduction method that is effective even in the presence of severe subject movement. The approach involves an enhanced preprocessing unit consisting of a motion detection unit (MDU, developed in this paper), period estimation unit, and Fourier series reconstruction unit. The MDU identifies clean data frames versus those corrupted with motion artifacts. The period estimation unit determines the fundamental frequency of a corrupt frame. The Fourier series reconstruction unit reconstructs the final preprocessed signal by utilizing the spectrum variability of the pulse waveform. Preprocessed data are then fed to a magnitude-based FD independent component analysis unit. This helps reduce motion artifacts present at the frequencies of the reconstruction components. Experimental results are presented to demonstrate the efficacy of the overall motion artifact reduction method.


title: School burnout and engagement in the context of demands-resources model

abstract: Background. A four-wave longitudinal study tested the demands–resources model in the school context. Aim. To examine the applicability of the demands–resources to the school context. Method. Data of 1,709 adolescents were gathered, once during the transition from comprehensive to post-comprehensive education, twice during post-comprehensive education, and once 2 years later. Results. The hypotheses were supported, path analysis showing that study demands were related to school burnout a year later, while study resources were related to schoolwork engagement. Self-efficacy was positively related to engagement and negatively to burnout. School burnout predicted schoolwork engagement negatively 1 year later. Engagement was positively related to life satisfaction 2 years later, while burnout was related to depressive symptoms. Finally, burnout mediated the relationship between study demands and mental health outcomes. Conclusions. The demands–resources model can usefully be applied to the school context, including the associations between school-related burnout and engagement among adolescents. The model comprises two processes, the energy-depleting process and the motivational process.


title: High-level event recognition in unconstrained videos

abstract: The goal of high-level event recognition is to automatically detect complex high-level events in a given video sequence. This is a difficult task especially when videos are captured under unconstrained conditions by non-professionals. Such videos depicting complex events have limited quality control, and therefore, may include severe camera motion, poor lighting, heavy background clutter, and occlusion. However, due to the fast growing popularity of such videos, especially on the Web, solutions to this problem are in high demands and have attracted great interest from researchers. In this paper, we review current technologies for complex event recognition in unconstrained videos. While the existing solutions vary, we identify common key modules and provide detailed descriptions along with some insights for each of them, including extraction and representation of low-level features across different modalities, classification strategies, fusion techniques, etc. Publicly available benchmark datasets, performance metrics, and related research forums are also described. Finally, we discuss promising directions for future research.


title: Does Gamification Work? -- A Literature Review of Empirical Studies on Gamification

abstract: This paper reviews peer-reviewed empirical studies on gamification. We create a framework for examining the effects of gamification by drawing from the definitions of gamification and the discussion on motivational affordances. The literature review covers results, independent variables (examined motivational affordances), dependent variables (examined psychological/behavioral outcomes from gamification), the contexts of gamification, and types of studies performed on the gamified systems. The paper examines the state of current research on the topic and points out gaps in existing literature. The review indicates that gamification provides positive effects, however, the effects are greatly dependent on the context in which the gamification is being implemented, as well as on the users using it. The findings of the review provide insight for further studies as well as for the design of gamified systems.


title: A control system for autonomous vehicle valet parking

abstract: The autonomous vehicle valet parking service (AVP) enables a vehicle to drive and park without any human interaction. The driverless vehicle has to follow the scheduled route and be parked in a parking lot automatically. Most fundamental technologies for autonomous vehicle valet parking service are controlling vehicle driving and parking. For autonomous driving, the path to near goal parking lot has to be generated and control commands to follow the path have to be generated continuously. For autonomous parking, the controlling vehicle steer value has to be found to park the vehicle at a parking lot well. We have used two turning radius circles to find vehicle steering control value about perpendicular and parallel parking method basically. These two vehicle turning radius circles are minimum radius circles in initial and are updated in real time as vehicle stop location in parking mode. In this paper, we have developed the control system for autonomous vehicle valet parking service in parking area with low speed limited.


title: CURE: Curvature Regularization For Missing Data Recovery

abstract: Missing data recovery is an important and yet challenging problem in imaging and data science. Successful models often adopt certain carefully chosen regularization. Recently, the low dimension manifold model (LDMM) was introduced by S.Osher et al. and shown effective in image inpainting. They observed that enforcing low dimensionality on image patch manifold serves as a good image regularizer. In this paper, we observe that having only the low dimension manifold regularization is not enough sometimes, and we need smoothness as well. For that, we introduce a new regularization by combining the low dimension manifold regularization with a higher order Curvature Regularization, and we call this new regularization CURE for short. The key step of solving CURE is to solve a biharmonic equation on a manifold. We further introduce a weighted version of CURE, called WeCURE, in a similar manner as the weighted nonlocal Laplacian (WNLL) method. Numerical experiments for image inpainting and semi-supervised learning show that the proposed CURE and WeCURE significantly outperform LDMM and WNLL respectively.


title: Trapdoors for hard lattices and new cryptographic constructions

abstract: We show how to construct a variety of "trapdoor" cryptographic tools assuming the worst-case hardness of standard lattice problems (such as approximating the length of the shortest nonzero vector to within certain polynomial factors). Our contributions include a new notion of trapdoor function with preimage sampling, simple and efficient "hash-and-sign" digital signature schemes, and identity-based encryption. A core technical component of our constructions is an efficient algorithm that, given a basis of an arbitrary lattice, samples lattice points from a discrete Gaussian probability distribution whose standard deviation is essentially the length of the longest Gram-Schmidt vector of the basis. A crucial security property is that the output distribution of the algorithm is oblivious to the particular geometry of the given basis.


title: Spatial Task Assignment for Crowd Sensing with Cloaked Locations

abstract: Distributed mobile crowd sensing is becoming a valuable paradigm, enabling a variety of novel applications built on mobile networks and smart devices. However, this trend brings several challenges, including the need for crowd sourcing platforms to manage interactions between applications and the crowd (participants or workers). One of the key functions of such platforms is spatial task assignment which assigns sensing tasks to participants based on their locations. Task assignment becomes critical when participants are hesitant to share their locations due to privacy concerns. In this paper, we examine the problem of spatial task assignment in crowd sensing when participants utilize spatial cloaking to obfuscate their locations. We investigate methods for assigning sensing tasks to participants, efficiently managing location uncertainty and resource constraints. We propose a novel two-stage optimization approach which consists of global optimization using cloaked locations followed by a local optimization using participants' precise locations without breaching privacy. Experimental results using both synthetic and real data show that our methods achieve high sensing coverage with low cost using cloaked locations.


title: A design for automotive CAN bus monitoring system

abstract: This system is designed to test automotive CAN (controller area network) controlled devices or entire network. This system not only can display CAN frames (CAN 2.0A/B) received from or sent onto CAN bus, but also can record data on log files for off-line evaluation. Users can configure several monitoring modes and CAN channel features of the system with PC application. Furthermore the USB technology adopted in the system make it a more practical and convenient CAN bus testing system.


title: The Visual Analysis of Human Movement: A Survey

abstract: The ability to recognize humans and their activities by vision is key for a machine to interact intelligently and effortlessly with a human-inhabited environment. Because of many potentially important applications, “looking at people” is currently one of the most active application domains in computer vision. This survey identifies a number of promising applications and provides an overview of recent developments in this domain. The scope of this survey is limited to work on whole-body or hand motion; it does not include work on human faces. The emphasis is on discussing the various methodologies; they are grouped in 2-D approaches with or without explicit shape models and 3-D approaches. Where appropriate, systems are reviewed. We conclude with some thoughts about future directions.


title: Probabilistic Label Relation Graphs with Ising Models

abstract: We consider classification problems in which the label space has structure. A common example is hierarchical label spaces, corresponding to the case where one label subsumes another (e.g., animal subsumes dog). But labels can also be mutually exclusive (e.g., dog vs cat) or unrelated (e.g., furry, carnivore). To jointly model hierarchy and exclusion relations, the notion of a HEX (hierarchy and exclusion) graph was introduced in [8]. This combined a conditional random field (CRF) with a deep neural network (DNN), resulting in state of the art results when applied to visual object classification problems where the training labels were drawn from different levels of the ImageNet hierarchy (e.g., an image might be labeled with the basic level category "dog", rather than the more specific label "husky"). In this paper, we extend the HEX model to allow for soft or probabilistic relations between labels, which is useful when there is uncertainty about the relationship between two labels (e.g., an antelope is "sort of" furry, but not to the same degree as a grizzly bear). We call our new model pHEX, for probabilistic HEX. We show that the pHEX graph can be converted to an Ising model, which allows us to use existing off-the-shelf inference methods (in contrast to the HEX method, which needed specialized inference algorithms). Experimental results show significant improvements in a number of large-scale visual object classification tasks, outperforming the previous HEX model.


title: Using laser range data for 3D SLAM in outdoor environments

abstract: Traditional simultaneous localization and mapping (SLAM) algorithms have been used to great effect in flat, indoor environments such as corridors and offices. We demonstrate that with a few augmentations, existing 2D SLAM technology can be extended to perform full 3D SLAM in less benign, outdoor, undulating environments. In particular, we use data acquired with a 3D laser range finder. We use a simple segmentation algorithm to separate the data stream into distinct point clouds, each referenced to a vehicle position. The SLAM technique we then adopt inherits much from 2D delayed state (or scan-matching) SLAM in that the state vector is an ever growing stack of past vehicle positions and inter-scan registrations are used to form measurements between them. The registration algorithm used is a novel combination of previous techniques carefully balancing the need for maximally wide convergence basins, robustness and speed. In addition, we introduce a novel post-registration classification technique to detect matches which have converged to incorrect local minima


title: A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images

abstract: Goal: In this work, we present an extensive description and evaluation of our method for blood vessel segmentation in fundus images based on a discriminatively trained fully connected conditional random field model. Methods: Standard segmentation priors such as a Potts model or total variation usually fail when dealing with thin and elongated structures. We overcome this difficulty by using a conditional random field model with more expressive potentials, taking advantage of recent results enabling inference of fully connected models almost in real time. Parameters of the method are learned automatically using a structured output support vector machine, a supervised technique widely used for structured prediction in a number of machine learning applications. Results: Our method, trained with state of the art features, is evaluated both quantitatively and qualitatively on four publicly available datasets: DRIVE, STARE, CHASEDB1, and HRF. Additionally, a quantitative comparison with respect to other strategies is included. Conclusion: The experimental results show that this approach outperforms other techniques when evaluated in terms of sensitivity, F1-score, G-mean, and Matthews correlation coefficient. Additionally, it was observed that the fully connected model is able to better distinguish the desired structures than the local neighborhood-based approach. Significance: Results suggest that this method is suitable for the task of segmenting elongated structures, a feature that can be exploited to contribute with other medical and biological applications.


title: From Rational to Emotional Agents

abstract: To date, most research on multiagent systems has focused on rational utilitymaximizing agents. However, theories show that emotions have a strong effect on human's physical states, motivations, beliefs, and desires. The details have not been explicated clearly so far. In artificial intelligence, emotions have begun to receive more attention, but mostly in human-robot/computer interaction. The research on applying emotions to agents' decision-making is still very limited. ::: Can agents be intelligent without emotions? We believe that, whether for humanlike or non-human-like agents, the effect of emotions on decision-making cannot be ignored, since agents with high emotional quotients (EQs) can be built to have better performance in complex dynamic environments than purely rational agents. ::: This research focuses on the effects of emotions on decision-making. Taking into account the incompleteness of emotion theories and emotional differences among individuals, I describe EBDI, a common architecture for emotional agents, which specifies a separate emotion mechanism within an agent, instead of trying to model emotion mechanisms to reflect the reasoning process specifically, like most researchers have done. It reflects the practical reasoning process, and one can select and apply part of an emotion theory into the architecture as needed. Sample agents in Tileworld are presented and the results show that an EBDI agent can have better performance than traditional BDI agents. ::: To apply EBDI in negotiation; a plug-in is designed, which modifies the OCC model, a standard model for emotion synthesis, to generate emotions. Considering the possibility of incorporating emotions into negotiation, I generate EWOD (Emotional Worth-Oriented Domain), which requires numerical emotions. Thus, a mapping from 22 OCC emotions to 3-dimension numerical PAD emotions is given. Finally, I describe how PAD emotions affect the negotiation strategy and provide an evaluation which shows that it can be used to implement emotional agents that mimic human emotions during negotiation. Thus we can design high EQ agents for negotiation according to specific design purposes. Since negotiation is used widely in many different domains, this research, based on a general process of negotiation, can also be widely applied to other areas.


title: Structural Mechanism for STI-571 Inhibition of Abelson Tyrosine Kinase

abstract: The inadvertent activation of the Abelson tyrosine kinase (Abl) causes chronic myelogenous leukemia (CML). A small-molecule inhibitor of Abl (STI-571) is effective in the treatment of CML. We report the crystal structure of the catalytic domain of Abl, complexed to a variant of STI-571. Critical to the binding of STI-571 is the adoption by the kinase of an inactive conformation, in which a centrally located “activation loop” is not phosphorylated. The conformation of this loop is distinct from that in active protein kinases, as well as in the inactive form of the closely related Src kinases. These results suggest that compounds that exploit the distinctive inactivation mechanisms of individual protein kinases can achieve both high affinity and high specificity.


title: Robust set invariance for implicit robot force control in presence of contact model uncertainty

abstract: The present paper exploits set invariance theory to address the problem of robot implicit force control in presence of stiffness uncertainty in the interaction model. A numerical approach is introduced to compute the invariance function for constraints with arbitrary relative degree. The method is then applied to robot force control, enhancing force regulation performance, in terms of steady state error and convergence speed, despite model mismatch and measurement noise. Its effectiveness is experimentally validated and compared to previous results of set invariance control on a hybrid force/position task performed with a 6 degrees of freedom industrial robot equipped with a force/torque sensor.


title: Multi-objective approach for robot motion planning in search tasks

abstract: This work addresses the problem of single robot coverage and exploration in an environment with the goal of finding a specific object previously known to the robot. As limited time is a constraint of interest we cannot search from an infinite number of points. Thus, we propose a multi-objective approach for such search tasks in which we first search for a good set of positions to place the robot sensors in order to acquire information from the environment and to locate the desired object. Given the interesting properties of the Generalized Voronoi Diagram, we restrict the candidate search points along this roadmap. We redefine the problem of finding these search points as a multi-objective optimization one. NSGA-II is used as the search engine and ELECTRE I is applied as a decision making tool to decide among the trade-off alternatives. We also solve a Chinese Postman Problem to optimize the path followed by the robot in order to visit the computed search points. Simulation results show a comparison between the solution found by our method and solutions defined by other known approaches. Finally, a real robot experiment indicates the applicability of our method in practical scenarios.


title: Scene understanding — A survey

abstract: In recent times, scene understanding holds a great position in computer vision due to its real time perceiving, analyzing and elaborating an interpretation of dynamic scene which leads to new discoveries. A scene is a view of real world environment with multiple objects and surfaces in a meaningful way. Objects are compact and act upon whereas scene are extended in space and act within. The visual information can be given with many features such as Colors, Luminance and contours or in the form of Shapes, Parts and Textures or through semantic context. The goal of scene understanding is to make machines look like humans, to have a complete understanding of visual scenes. Scene understanding is influenced by cognitive vision with an involvement of major areas like computer vision, cognitive engineering and software engineering. Due to its enormous growth many outstanding universities like Boston University, Stafford Vision lab, Scene grammar lab, air lab, Laboratory Machine Vision and Pattern Recognition have been perseveringly working for added improvements in this area. This paper discusses an extensive survey of scene understanding with various strategies and methods.


title: Machine learning for medical diagnosis: history, state of the art and perspective

abstract: The paper provides an overview of the development of intelligent data analysis in medicine from a machine learning perspective: a historical view, a state-of-the-art view, and a view on some future trends in this subfield of applied artificial intelligence. The paper is not intended to provide a comprehensive overview but rather describes some subareas and directions which from my personal point of view seem to be important for applying machine learning in medical diagnosis. In the historical overview, I emphasize the naive Bayesian classifier, neural networks and decision trees. I present a comparison of some state-of-the-art systems, representatives from each branch of machine learning, when applied to several medical diagnostic tasks. The future trends are illustrated by two case studies. The first describes a recently developed method for dealing with reliability of decisions of classifiers, which seems to be promising for intelligent data analysis in medicine. The second describes an approach to using machine learning in order to verify some unexplained phenomena from complementary medicine, which is not (yet) approved by the orthodox medical community but could in the future play an important role in overall medical diagnosis and treatment.


title: What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities

abstract: Current social media research mainly focuses on temporal trends of the information flow and on the topology of the social graph that facilitates the propagation of information. In this paper we study the effect of the content of the idea on the information propagation. We present an efficient hybrid approach based on a linear regression for predicting the spread of an idea in a given time frame. We show that a combination of content features with temporal and topological features minimizes prediction error. Our algorithm is evaluated on Twitter hashtags extracted from a dataset of more than 400 million tweets. We analyze the contribution and the limitations of the various feature types to the spread of information, demonstrating that content aspects can be used as strong predictors thus should not be disregarded. We also study the dependencies between global features such as graph topology and content features.


title: Doctors and nurses: doing it differently

abstract: Additional articles on doctors and nurses working together appear in this week9s Nursing Times and on its website www.nursingtimes.net


title: Rail defect detection using Gabor filters with texture analysis

abstract: Railways is the major means of transport in most of the countries. Rails are the backbone of the track structure and should be protected from defects. Surface defects are irregularities in the rails caused due to the shear stresses between the rails and wheels of the trains. This type of defects should be detected to avoid rail fractures. The objective of this paper is to propose an innovative technique to detect the surface defect on rail heads. In order to identify the defects, it is essential to extract the rails from the background and further enhance the image for thresholding. The proposed method uses Binary Image Based Rail Extraction (BIBRE) algorithm to extract the rails from the background. The extracted rails are enhanced to achieve uniform background with the help of direct enhancement method. The direct enhancement method enhance the image by enhancing the brightness difference between objects and their backgrounds. The enhanced rail image uses Gabor filters to identify the defects from the rails. The Gabor filters maximizes the energy difference between defect and defect less surface. Thresholding is done based on the energy of the defects. From the thresholded image the defects are identified and a message box is generated when there is a presence of defects.


title: Reliable smoke detection in the domains of image energy and color

abstract: Smoke detection calls for a reliable and fast distinction between background, moving objects and variable shapes that are recognizable as smoke. In our system we propose a stable background suppression module joined with a smoke detection module working on segmented objects. It exploits two features: the energy variation in wavelet model and a color model of the smoke. The decrease of energy ratio in wavelet domain between background and current image is a clue to detect smoke representing the variations of texture level. A mixture of Gaussians models this texture ratio for temporal evolution. The color model is used as reference to measure the deviation of the current pixel color from the model. The two features have been combined using a Bayesian classifier to detect smoke in the scene. Experiments on real data and a comparison between our background model and Gaussian mixture (MOG) model for smoke detection are presented.


title: GO-SPADE: Mining Sequential Patterns over Datasets with Consecutive Repetitions

abstract: Databases of sequences can contain consecutive repetitions of items. This is the case in particular when some items represent discretized quantitative values. We show that on such databases, a typical algorithm like the SPADE algorithm tends to loose its efficiency. SPADE is based on the used of lists containing the localization of the occurrences of a pattern in the sequences and these lists are not appropriated in the case of data with repetitions. We introduce the concept of generalized occurrences and the corresponding primitive operators to manipulate them. We present an algorithm called GO-SPADE that extends SPADE to incorporate generalized occurrences. Finally we present experiments showing that GO-SPADE can handle sequences containing consecutive repetitions at nearly no extra cost.


title: A Transfer-Learnable Natural Language Interface for Databases

abstract: Relational database management systems (RDBMSs) are powerful because they are able to optimize and answer queries against any relational database. A natural language interface (NLI) for a database, on the other hand, is tailored to support that specific database. In this work, we introduce a general purpose transfer-learnable NLI with the goal of learning one model that can be used as NLI for any relational database. We adopt the data management principle of separating data and its schema, but with the additional support for the idiosyncrasy and complexity of natural languages. Specifically, we introduce an automatic annotation mechanism that separates the schema and the data, where the schema also covers knowledge about natural language. Furthermore, we propose a customized sequence model that translates annotated natural language queries to SQL statements. We show in experiments that our approach outperforms previous NLI methods on the WikiSQL dataset and the model we learned can be applied to another benchmark dataset OVERNIGHT without retraining.


title: Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders

abstract: Deep generative models have achieved remarkable success in various data domains, including images, time series, and natural languages. There remain, however, substantial challenges for combinatorial structures, including graphs. One of the key challenges lies in the difficulty of ensuring semantic validity in context. For example, in molecular graphs, the number of bonding-electron pairs must not exceed the valence of an atom; whereas in protein interaction networks, two proteins may be connected only when they belong to the same or correlated gene ontology terms. These constraints are not easy to be incorporated into a generative model. In this work, we propose a regularization framework for variational autoencoders as a step toward semantic validity. We focus on the matrix representation of graphs and formulate penalty terms that regularize the output distribution of the decoder to encourage the satisfaction of validity constraints. Experimental results confirm a much higher likelihood of sampling valid graphs in our approach, compared with others reported in the literature.


title: The Skiplist-Based LSM Tree

abstract: Log-Structured Merge (LSM) Trees provide a tiered data storage and retrieval paradigm that is attractive for write-optimized data systems. Maintaining an efficient buffer in memory and deferring updates past their initial write-time, the structure provides quick operations over hot data. Because each layer of the structure is logically separate from the others, the structure is also conducive to opportunistic and granular optimization. In this paper, we introduce the Skiplist-Based LSM Tree (sLSM), a novel system in which the memory buffer of the LSM is composed of a sequence of skiplists. We develop theoretical and experimental results that demonstrate that the breadth of tuning parameters inherent to the sLSM allows it broad flexibility for excellent performance across a wide variety of workloads.


title: A Deep Convolutional Auto-Encoder with Embedded Clustering

abstract: In this paper, we propose a clustering approach embedded in a deep convolutional auto-encoder (DCAE). In contrast to conventional clustering approaches, our method simultaneously learns feature representations and cluster assignments through DCAEs. DCAEs have been effective in image processing as it fully utilizes the properties of convolutional neural networks. Our method consists of clustering and reconstruction objective functions. All data points are assigned to their new corresponding cluster centers during the optimization, after that, clustering centers are iteratively updated to obtain a stable performance of clustering. The experimental results on the MNIST dataset show that the proposed method substantially outperforms deep clustering models in term of clustering quality.


title: Abnormal Event Detection in Videos Using Hybrid Spatio-Temporal Autoencoder

abstract: The LSTM Encoder-Decoder framework is used to learn representation of video sequences and applied for detect abnormal event in complex environment. However, it generally fails to account for the global context of the learned representation with a fixed dimension representation and the learned representation is crucial for decoder phase. Based on the LSTM Encoder-Decoder and the Convolutional Autoencoder, we explore a hybrid autoencoder architecture, which not only extracts better spatio-temporal context, but also improves the extrapolate capability of the corresponding decoder with the shortcut connection. The experimental results demonstrate that our approach outperforms lots of state-of-the-art methods on benchmark datasets.


title: Natural Language Processing: State of The Art, Current Trends and Challenges

abstract: Natural language processing (NLP) has recently gained much attention for representing and analysing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. The paper distinguishes four phases by discussing different levels of NLP and components of Natural Language Generation (NLG) followed by presenting the history and evolution of NLP, state of the art presenting the various applications of NLP and current trends and challenges.


Improving Demand Prediction in Bike Sharing System by Learning Global Features


El-Gayar and Moran College students ’ acceptance of Tablet PCs COLLEGE STUDENTS ’ ACCEPTANCE OF TABLET PCS: AN APPLICATION OF THE UTAUT MODEL


title: The Effectiveness of the Size Matters Handwriting Program

abstract: &NA; Date Presented 3/31/2017 With school‐based occupational therapists reporting up to 75% of their caseload related to handwriting, the urgency to identify a proven and efficient instructional program is paramount. Effective, embeddable, measurable, easy, and fast, the Size Matters Handwriting Program promotes collaboration in the natural environment and the Workload model. Primary Author and Speaker: Beverly Moskowitz Additional Authors and Speakers: Beth Carswell, Jennifer Kitzmiller, Moira Bushell, Laura Neikrug, Chaya Gottesman Contributing Authors: Beth Pfeiffer, Gillian Rai, Tammy Murray


title: A new scalable parallel DBSCAN algorithm using the disjoint-set data structure

abstract: DBSCAN is a well-known density based clustering algorithm capable of discovering arbitrary shaped clusters and eliminating noise data. However, parallelization of Dbscan is challenging as it exhibits an inherent sequential data access order. Moreover, existing parallel implementations adopt a master-slave strategy which can easily cause an unbalanced workload and hence result in low parallel efficiency. We present a new parallel Dbscan algorithm (Pdsdbscan) using graph algorithmic concepts. More specifically, we employ the disjoint-set data structure to break the access sequentiality of Dbscan. In addition, we use a tree-based bottom-up approach to construct the clusters. This yields a better-balanced workload distribution. We implement the algorithm both for shared and for distributed memory. Using data sets containing up to several hundred million high-dimensional points, we show that Pdsdbscan significantly outperforms the master-slave approach, achieving speedups up to 25.97 using 40 cores on shared memory architecture, and speedups up to 5,765 using 8,192 cores on distributed memory architecture.


title: Document page segmentation and layout analysis using soft ordering

abstract: This paper presents a novel algorithm for layout analysis of document images. A major component of this algorithm is the independent segmentation algorithm that identifies text and graphics regions. The segmentation algorithm first locates document patterns and then performs classification using run-length characteristics, spread analysis and adjacency relations. A key feature of the layout analysis algorithm is soft ordering which provides a means of ordering regions in a more logical way, and allows for some overlapping between separate regions. This is very useful for processing documents that are slightly skewed or irregular in layout. The algorithm has been tested on many different documents, and can successfully recognise single and multicolumn documents, even when the column format varies several times on one page. Furthermore, it can process documents with text tightly wrapped around graphics and documents that are slightly skewed.


title: Brain potentials in affective picture processing: covariation with autonomic arousal and affective report

abstract: Abstract Emotionally arousing picture stimuli evoked scalp-recorded event-related potentials. A late, slow positive voltage change was observed, which was significantly larger for affective than neutral stimuli. This positive shift began 200–300 ms after picture onset, reached its maximum amplitude ≈1 s after picture onset, and was sustained for most of a 6-s picture presentation period. The positive increase was not related to local probability of content type, but was accentuated for pictures that prompted increased autonomic responses and reports of greater affective arousal (e.g. erotic or violent content). These results suggest that the late positive wave indicates a selective processing of emotional stimuli, reflecting the activation of motivational systems in the brain.


title: A survey of security issues in wireless sensor networks

abstract: Wireless Sensor Networks (WSNs) are used in many applications in military, ecological, and health-related areas. These applications often include the monitoring of sensitive information such as enemy movement on the battlefield or the location of personnel in a building. Security is therefore important in WSNs. However, WSNs suffer from many constraints, including low computation capability, small memory, limited energy resources, susceptibility to physical capture, and the use of insecure wireless communication channels. These constraints make security in WSNs a challenge. In this article we present a survey of security issues in WSNs. First we outline the constraints, security requirements, and attacks with their corresponding countermeasures in WSNs. We then present a holistic view of security issues. These issues are classified into five categories: cryptography, key management, secure routing, secure data aggregation, and intrusion detection. Along the way we highlight the advantages and disadvantages of various WSN security protocols and further compare and evaluate these protocols based on each of these five categories. We also point out the open research issues in each subarea and conclude with possible future research directions on security in WSNs.


title: Decoding, Calibration and Rectification for Lenselet-Based Plenoptic Cameras

abstract: Plenoptic cameras are gaining attention for their unique light gathering and post-capture processing capabilities. We describe a decoding, calibration and rectification procedure for lenselet-based plenoptic cameras appropriate for a range of computer vision applications. We derive a novel physically based 4D intrinsic matrix relating each recorded pixel to its corresponding ray in 3D space. We further propose a radial distortion model and a practical objective function based on ray reprojection. Our 15-parameter camera model is of much lower dimensionality than camera array models, and more closely represents the physics of lenselet-based cameras. Results include calibration of a commercially available camera using three calibration grid sizes over five datasets. Typical RMS ray reprojection errors are 0.0628, 0.105 and 0.363 mm for 3.61, 7.22 and 35.1 mm calibration grids, respectively. Rectification examples include calibration targets and real-world imagery.


title: The Polar Parallel Coordinates Method for Time-Series Data Visualization

abstract: Parallel coordinates is a very important visualization method, but the dimensions it can express are limited by the length or width of the screen. In this paper, we present the polar parallel coordinates method. First, we define the polar parallel coordinates as the coordinate whose axes exist between the same degree and show the expression of the axis. We give the algorithms for the polar parallel coordinates generation and data shown in the polar parallel coordinates. Then we point out two feathers of the polar parallel coordinates. It can express more axes than parallel coordinates and reflect the characteristics of time-series data. Based on this, we compare the parallel coordinates with the polar parallel coordinates by the time-series data visualization.


CircleView : Scalable Visualization and Navigation of Citation Networks


title: GRATIN: Accelerating Graph Traversals in Main-Memory Column Stores

abstract: Native graph query and processing capabilities have become indispensable for modern business applications in enterprise-critical operations on data that is stored in relational database management systems. Traversal operations are a basic ingredient of graph algorithms and graph queries. As a consequence, they are fundamental for querying graph data in a relational database management system. In this paper we present gratin, a concise secondary index structure to speedup graph traversals in main-memory column stores. Conventional approaches for graph traversals rely on repeated full column scans, making it an inefficient approach for deep traversals on very large graphs. To tackle this challenge, we devise a novel and adaptive block-based index to handle graphs efficiently. Most importantly, gratin is updateable in constant time and allows supporting evolving graphs with frequent updates to the graph topology. We conducted an extensive evaluation on real-world data sets from different domains for a large variety of traversal queries. Our experiments show improvements of up to an order of magnitude compared to a scan-based traversal algorithm.


title: IEEE TRANSACTIONS ON SIGNAL PROCESSING VOL 41 NO I2 DECEMBtR 1991 Embedded Image Coding Using Zerotrees of Wavelet Coefficients

abstract: The embedded zerotree wavelet algorithm (EZW) is a simple, yet remarkably effective, image compression algorithm, having the property that the bits in the bit stream are generated in order of importance, yielding a fully embedded code. The embedded code represents a sequence of binary decisions that distinguish an image from the "null" image. Using an embedded coding algorithm, an encoder can terminate the encoding at any point thereby allowing a target rate or target distortion metric to be met exactly. Also, given a bit stream, the decoder can cease decoding at any point in the bit stream and still produce exactly the same image that would have been encoded at the bit rate corresponding to the truncated bit stream. In addition to producing a fully embedded bit stream, the EZW consistently produces compression results that are competitive with virtually all known compression algorithms on standard test images. Yet this performance is achieved with a technique that requires absolutely no training, no pre-stored tables or codebooks, and requires no prior knowledge of the image source. The EZW algorithm is based on four key concepts: (1) a discrete wavelet transform or hierarchical subband decomposition, (2) prediction of the absence of significant information across scales by exploiting the self-similarity inherent in images, (3) entropy-coded successive-approximation quantization, and (4) universal lossless data compression which is achieved via adaptive arithmetic coding. >


title: DC-to-DC Converters With Continuous Input and Output Power

abstract: This paper presents three topological classes of dc-to-dc converters, totaling nine converters (each class with three buck, boost, and buck-boost voltage transfer function topologies), which offer continuous input and output energy flow, applicable and mandatory for renewable energy source, maximum power point tracking and maximum source energy extraction. A current sourcing output caters for converter module output parallel connection. The first class of three topologies employs both series input and output inductance, while anomalously the other two classes of six related topologies employ only either series input (three topologies) or series output (three topologies) inductance. All nine converter topologies employ the same elements, while additional load shunting capacitance creates a voltage sourcing output. Converter time-domain simulations and experimental results for the converters support and extol the concepts and analysis presented.


title: Hesitant Fuzzy Sets: State of the Art and Future Directions

abstract: The necessity of dealing with uncertainty in real world problems has been a long-term research challenge that has originated different methodologies and theories. Fuzzy sets along with their extensions, such as type-2 fuzzy sets, interval-valued fuzzy sets, and Atanassov's intuitionistic fuzzy sets, have provided a wide range of tools that are able to deal with uncertainty in different types of problems. Recently, a new extension of fuzzy sets so-called hesitant fuzzy sets has been introduced to deal with hesitant situations, which were not well managed by the previous tools. Hesitant fuzzy sets have attracted very quickly the attention of many researchers that have proposed diverse extensions, several types of operators to compute with such types of information, and eventually some applications have been developed. Because of such a growth, this paper presents an overview on hesitant fuzzy sets with the aim of providing a clear perspective on the different concepts, tools and trends related to this extension of fuzzy sets.