title: WFIM: Weighted Frequent Itemset Mining with a Weight Range and a Minimum Weight

abstract: Researchers have proposed weighted frequent itemset mining algorithms that reflect the importance of items. The main focus of weighted frequent itemset mining concerns satisfying the downward closure property. All weighted association rule mining algorithms suggested so far have been based on the Apriori algorithm. However, pattern growth algorithms are more efficient than Apriori based algorithms. Our main approach is to push the weight constraints into the pattern growth algorithm while maintaining the downward closure property. In this paper, a weight range and a minimum weight constraint are defined and items are given different weights within the weight range. The weight and support of each item are considered separately for pruning the search space. The number of weighted frequent itemsets can be reduced by setting a weight range and a minimum weight, allowing the user to balance support and weight of itemsets. WFIM generates more concise and important weighted frequent itemsets in large databases, particularly dense databases with low minimum support, by adjusting a minimum weight and a weight range.


title: Enhancing the stability and efficiency of spectral ordering with partial supervision and feature selection

abstract: Several studies have demonstrated the prospects of spectral ordering for data mining. One successful application is seriation of paleontological findings, i.e. ordering the sites of excavation, using data on mammal co-occurrences only. However, spectral ordering ignores the background knowledge that is naturally present in the domain: paleontologists can derive the ages of the sites within some accuracy. On the other hand, the age information is uncertain, so the best approach would be to combine the background knowledge with the information on mammal co-occurrences. Motivated by this kind of partial supervision we propose a novel semi-supervised spectral ordering algorithm that modifies the Laplacian matrix such that domain knowledge is taken into account. Also, it performs feature selection by discarding features that contribute most to the unwanted variability of the data in bootstrap sampling. Moreover, we demonstrate the effectiveness of the proposed framework on the seriation of Usenet newsgroup messages, where the task is to find out the underlying flow of discussion. The theoretical properties of our algorithm are thoroughly analyzed and it is demonstrated that the proposed framework enhances the stability of the spectral ordering output and induces computational gains.


title: Mad City Mystery: Developing scientific argumentation skills with a place-based augmented reality game on handheld computers

abstract: While the knowledge economy has reshaped the world, schools lag behind in producing appropriate learning for this social change. Science education needs to prepare students for a future world in which multiple representations are the norm and adults are required to “think like scientists.” Location-based augmented reality games offer an opportunity to create a “post-progressive” pedagogy in which students are not only immersed in authentic scientific inquiry, but also required to perform in adult scientific discourses. This cross-case comparison as a component of a design-based research study investigates three cases (roughly 28 students total) where an Augmented Reality curriculum, Mad City Mystery, was used to support learning in environmental science. We investigate whether augmented reality games on handhelds can be used to engage students in scientific thinking (particularly argumentation), how game structures affect students’ thinking, the impact of role playing on learning, and the role of the physical environment in shaping learning. We argue that such games hold potential for engaging students in meaningful scientific argumentation. Through game play, players are required to develop narrative accounts of scientific phenomena, a process that requires them to develop and argue scientific explanations. We argue that specific game features scaffold this thinking process, creating supports for student thinking non-existent in most inquiry-based learning environments.


title: Learning structural SVMs with latent variables

abstract: We present a large-margin formulation and algorithm for structured output prediction that allows the use of latent variables. Our proposal covers a large range of application problems, with an optimization problem that can be solved efficiently using Concave-Convex Programming. The generality and performance of the approach is demonstrated through three applications including motiffinding, noun-phrase coreference resolution, and optimizing precision at k in information retrieval.


title: Review on flexible photonics/electronics integrated devices and fabrication strategy

abstract: In recent years, to meet the greater demand for next generation electronic devices that are transplantable, lightweight and portable, flexible and large-scale integrated electronics attract much more attention have been of interest in both industry and academia. Organic electronics and stretchable inorganic electronics are the two major branches of flexible electronics. With the semiconductive and flexible properties of the organic semiconductor materials, flexible organic electronics have become a mainstay of our technology. Compared to organic electronics, stretchable and flexible inorganic electronics are fabricated via mechanical design with inorganic electronic components on flexible substrates, which have stretchability and flexibility to enable very large deformations without degradation of performance. This review summarizes the recent progress on fabrication strategies, such as hydrodynamic organic nanowire printing and inkjet-assisted nanotransfer printing of flexible organic electronics, and screen printing, soft lithography and transfer printing of flexible inorganic electronics. In addition, this review considers large-scale organic and inorganic flexible electronic systems and the future applications of flexible and stretchable electronics.


title: Expert Finding in Heterogeneous Bibliographic Networks with Locally-trained Embeddings

abstract: Expert finding is an important task in both industry and academia. It is challenging to rank candidates with appropriate expertise for various queries. In addition, different types of objects interact with one another, which naturally forms heterogeneous information networks. We study the task of expert finding in heterogeneous bibliographical networks based on two aspects: textual content analysis and authority ranking. Regarding the textual content analysis, we propose a new method for query expansion via locally-trained embedding learning with concept hierarchy as guidance, which is particularly tailored for specific queries with narrow semantic meanings. Compared with global embedding learning, locally-trained embedding learning projects the terms into a latent semantic space constrained on relevant topics, therefore it preserves more precise and subtle information for specific queries. Considering the candidate ranking, the heterogeneous information network structure, while being largely ignored in the previous studies of expert finding, provides additional information. Specifically, different types of interactions among objects play different roles. We propose a ranking algorithm to estimate the authority of objects in the network, treating each strongly-typed edge type individually. To demonstrate the effectiveness of the proposed framework, we apply the proposed method to a large-scale bibliographical dataset with over two million entries and one million researcher candidates. The experiment results show that the proposed framework outperforms existing methods for both general and specific queries.


title: Marketing through Instagram influencers: the impact of number of followers and product divergence on brand attitude

abstract: ABSTRACTFindings of two experimental studies show that Instagram influencers with high numbers of followers are found more likeable, partly because they are considered more popular. Important, only in limited cases, perceptions of popularity induced by the influencer's number of followers increase the influencer's perceived opinion leadership. However, if the influencer follows very few accounts him-/herself, this can negatively impact popular influencers’ likeability. Also, cooperating with influencers with high numbers of followers might not be the best marketing choice for promoting divergent products, as this decreases the brand's perceived uniqueness and consequently brand attitudes.


title: Computational models and heuristic methods for Grid scheduling problems

abstract: In this paper we survey computational models for Grid scheduling problems and their resolution using heuristic and meta-heuristic approaches. Scheduling problems are at the heart of any Grid-like computational system. Different types of scheduling based on different criteria, such as static versus dynamic environment, multi-objectivity, adaptivity, etc., are identified. Then, heuristic and meta-heuristic methods for scheduling in Grids are presented. The paper reveals the complexity of the scheduling problem in Computational Grids when compared to scheduling in classical parallel and distributed systems and shows the usefulness of heuristic and meta-heuristic approaches for the design of efficient Grid schedulers. We also discuss on requirements for a modular Grid scheduling and its integration with Grid architecture.


title: PredictingRice crop yield using Bayesian networks

abstract: Rice crop production plays a vital role in food security of India, contributing more than 40% to overall crop production. High crop production is dependent on suitable climatic conditions. Detrimental seasonal climate conditions such as low rainfall or temperature extremes can dramatically reduce crop yield. Developing better techniques to predict crop productivity in different climatic conditions can assist farmer and other stakeholders in important decision making in terms of agronomy and crop choice. This paper reports on the use of Bayesian Networks to predict rice crop yield for Maharashtra state, India. For this study, 27 districts of Maharashtra were selected on the basis of available data from publicly available Indian Government records with various climate and crop parameters selected. The parameters selected for the study were precipitation, minimum temperature, average temperature, maximum temperature, reference crop evapotranspiration, area, production and yield for the Kharif season (June to November) for the years 1998 to 2002. The dataset was processed using the WEKA tool. The classifiers used in the study were BayesNet and NaiveBayes. The experimental results showed that the performance of BayesNet was much better compared with NaiveBayes for the dataset.


title: Self Organizing Neural Networks for the Identification Problem

abstract: This work introduces a new method called Self Organizing Neural Network (SONN) algorithm and demonstrates its use in a system identification task. The algorithm constructs the network, chooses the neuron functions, and adjusts the weights. It is compared to the Back-Propagation algorithm in the identification of the chaotic time series. The results shows that SONN constructs a simpler, more accurate model, requiring less training data and epochs. The algorithm can be applied and generalized to appilications as a classifier.


title: ERP in action — Challenges and benefits for management control in SME context

abstract: ERP systems have fundamentally re-shaped the way business data is collected, stored, disseminated and used throughout the world. However, the existing research in accounting has provided only relatively few empirical findings on the implications for management control when companies implement ERP systems as the technological platform. Especially scarce are the findings concerning the production phase, after implementation, when the information processes, related work practices and the new information contents can be seen as established. In this paper we explored and theorized the benefits, challenges and problems for management control when an ERP system is in use, four years after the implementation. Our findings also illustrate why and under what circumstances these challenges and benefits may exist. For a holistic view of the organization our findings, based on a qualitative case study, are constructed from the viewpoints of people at different levels and functions of the organization. Top management expected a new strategic control system, but due to the many challenges it ended up with merely financial accounting based control. At the operational level, serious challenges lead to inadequate usage of the ERP system. Management control produces the financial basic data and must contend with many practical problems caused by ERP implementation.


title: Deep Learning for Launching and Mitigating Wireless Jamming Attacks

abstract: An adversarial machine learning approach is introduced to launch jamming attacks on wireless communications and a defense strategy is presented. A cognitive transmitter uses a pre-trained classifier to predict the current channel status based on recent sensing results and decides whether to transmit or not, whereas a jammer collects channel status and ACKs to build a deep learning classifier that reliably predicts the next successful transmissions and effectively jams them. This jamming approach is shown to reduce the transmitter’s performance much more severely compared with random or sensing-based jamming. The deep learning classification scores are used by the jammer for power control subject to an average power constraint. Next, a generative adversarial network is developed for the jammer to reduce the time to collect the training dataset by augmenting it with synthetic samples. As a defense scheme, the transmitter deliberately takes a small number of wrong actions in spectrum access (in form of a causative attack against the jammer) and therefore prevents the jammer from building a reliable classifier. The transmitter systematically selects when to take wrong actions and adapts the level of defense to mislead the jammer into making prediction errors and consequently increase its throughput.


title: Passive os fingerprinting methods in the jungle of wireless networks

abstract: Operating system fingerprinting methods are well- known in the domain of static networks and managed environments. Yet few studies tackled this challenge in real networks, where users can bring and connect any device. We evaluate the performance of three OS fingerprinting methods on a large dataset collected from university wireless network. Our results show that method based on HTTP User-agents is the most accurate but can identify only low portion of the traffic. TCP/IP parameters method proved to be the opposite with high coverage but low accuracy. We also implemented a new method based on detection of communication to OS-specific domains. Its performance is comparable to the two established ones. Next, we discuss the impacts of traffic encryption and embracing new protocols such as IPv6 or HTTP/2.0 on OS fingerprinting. Our findings suggest that OS identification based on specific domain detection is viable and corresponds to the current directions of network traffic evolution, while methods based on TCP/IP parameters and User-agents will become ineffective in the future.


title: Predicting Serial Killers' Home Base Using a Decision Support System

abstract: The effectiveness of a geographical decision support tool (Dragnet) forlocating the base of serial offenders was compared across 570 modelscomprised of a range of negative exponential functions, buffer zonecomponents, and normalization parameters. The models were applied to thebody disposal locations within each series for 70 U.S. serial killers. Twonormalization parameters were compared for all functions. The test ofeffectiveness was a specifically defined measure of search cost. Whenapplied to the Dragnet predictions it was found that the specially developednormalization parameter (QRange) produced the optimal search costs. Theoptimal search cost was also found to be for a function that did not includeany buffer zone. The optimal, average search cost across the whole samplewas 11% of the defined search area. Fifty-one percent of the offendersresided in the first 5% of the search area, with 87% in the first 25%. Allresided in the total defined search area. These results support thepotential for operational tools using such procedures as well ascontributing to our understanding of criminal's geographicalbehavior. The applicability to other forms of serial crime is considered.


title: Concurrent software testing in practice: a catalog of tools

abstract: The testing of concurrent programs is very complex due to the non-determinism present in those programs. They must be subjected to a systematic testing process that assists in the identification of defects and guarantees quality. Although testing tools have been proposed to support the concurrent program testing, to the best of our knowledge, no study that concentrates all testing tools to be used as a catalog for testers is available in the literature. This paper proposes a new classification for a set of testing tools for concurrent programs, regarding attributes, such as testing technique supported, programming language, and paradigm of development. The purpose is to provide a useful categorization guide that helps testing practitioners and researchers in the selection of testing tools for concurrent programs. A systematic mapping was conducted so that studies on testing tools for concurrent programs could be identified. As a main result, we provide a catalog with 116 testing tools appropriately selected and classified, among which the following techniques were identified: functional testing, structural testing, mutation testing, model based testing, data race and deadlock detection, deterministic testing and symbolic execution. The programming languages with higher support were Java and C/C++. Although a large number of tools have been categorized, most of them are academic and only few are available on a commercial scale. The classification proposed here can contribute to the state-of-the-art of testing tools for concurrent programs and also provides information for the exchange of knowledge between academy and industry.


title: From Deterministic to Generative: Multimodal Stochastic RNNs for Video Captioning

abstract: Video captioning, in essential, is a complex natural process, which is affected by various uncertainties stemming from video content, subjective judgment, and so on. In this paper, we build on the recent progress in using encoder-decoder framework for video captioning and address what we find to be a critical deficiency of the existing methods that most of the decoders propagate deterministic hidden states. Such complex uncertainty cannot be modeled efficiently by the deterministic models. In this paper, we propose a generative approach, referred to as multimodal stochastic recurrent neural networks (MS-RNNs), which models the uncertainty observed in the data using latent stochastic variables. Therefore, MS-RNN can improve the performance of video captioning and generate multiple sentences to describe a video considering different random factors. Specifically, a multimodal long short-term memory (LSTM) is first proposed to interact with both visual and textual features to capture a high-level representation. Then, a backward stochastic LSTM is proposed to support uncertainty propagation by introducing latent variables. Experimental results on the challenging data sets, microsoft video description and microsoft research video-to-text, show that our proposed MS-RNN approach outperforms the state-of-the-art video captioning benchmarks.


title: SimplE Embedding for Link Prediction in Knowledge Graphs

abstract: Knowledge graphs contain knowledge about the world and provide a structured representation of this knowledge. Current knowledge graphs contain only a small subset of what is true in the world. Link prediction approaches aim at predicting new links for a knowledge graph given the existing links among the entities. Tensor factorization approaches have proved promising for such link prediction problems. Proposed in 1927, Canonical Polyadic (CP) decomposition is among the first tensor factorization approaches. CP generally performs poorly for link prediction as it learns two independent embedding vectors for each entity, whereas they are really tied. We present a simple enhancement of CP (which we call SimplE) to allow the two embeddings of each entity to be learned dependently. The complexity of SimplE grows linearly with the size of embeddings. The embeddings learned through SimplE are interpretable, and certain types of background knowledge can be incorporated into these embeddings through weight tying. We prove SimplE is fully expressive and derive a bound on the size of its embeddings for full expressivity. We show empirically that, despite its simplicity, SimplE outperforms several state-of-the-art tensor factorization techniques. SimplE's code is available on GitHub at this https URL.


title: EMG analysis of the scapular muscles during a shoulder rehabilitation program

abstract: The purpose of this study was to determine which exercises most effectively use the scapular muscles. Eight muscles in 9 healthy subjects were studied with indwelling electromyographic electrodes and cinematography while performing 16 exercises. The 8 muscles studied were the upper, middle, and lower trapezius; levator scapula; rhomboids; pectoralis minor; and the middle and lower serratus anterior. Each exercise was divided into arcs of motion and the electromyographic activity was quantified as a percentage of the maximal manual muscle test. The optimal exercises for each muscle were identified based on intensity (greater than 50% maximal manual muscle test) and duration (over at least 3 consecutive arcs of motion) of the muscle activity. Twelve of the exercises qualified as top exercises for all of the muscles. On further analysis, a group of 4 exercises was shown to make up the core of a scapular muscle strengthening program. Those 4 exercises include scaption (scapular plane elevation), rowing, push-up with a plus, and press-up.


title: The OoO VLIW JIT Compiler for GPU Inference

abstract: Current trends in Machine Learning~(ML) inference on hardware accelerated devices (e.g., GPUs, TPUs) point to alarmingly low utilization. As ML inference is increasingly time-bounded by tight latency SLOs, increasing data parallelism is not an option. The need for better efficiency motivates GPU multiplexing. Furthermore, existing GPU programming abstractions force programmers to micro-manage GPU resources in an early-binding, context-free fashion. We propose a VLIW-inspired Out-of-Order (OoO) Just-in-Time (JIT) compiler that coalesces and reorders execution kernels at runtime for throughput-optimal device utilization while satisfying latency SLOs. We quantify the inefficiencies of space-only and time-only multiplexing alternatives and demonstrate an achievable 7.7x opportunity gap through spatial coalescing.


title: TGAN: Deep Tensor Generative Adversarial Nets for Large Image Generation

abstract: Deep generative models have been successfully applied to many applications. However, existing works experience limitations when generating large images (the literature usually generates small images, e.g. 32 * 32 or 128 * 128). In this paper, we propose a novel scheme, called deep tensor adversarial generative nets (TGAN), that generates large high-quality images by exploring tensor structures. Essentially, the adversarial process of TGAN takes place in a tensor space. First, we impose tensor structures for concise image representation, which is superior in capturing the pixel proximity information and the spatial patterns of elementary objects in images, over the vectorization preprocess in existing works. Secondly, we propose TGAN that integrates deep convolutional generative adversarial networks and tensor super-resolution in a cascading manner, to generate high-quality images from random distributions. More specifically, we design a tensor super-resolution process that consists of tensor dictionary learning and tensor coefficients learning. Finally, on three datasets, the proposed TGAN generates images with more realistic textures, compared with state-of-the-art adversarial autoencoders. The size of the generated images is increased by over 8.5 times, namely 374 * 374 in PASCAL2.


title: A Systematic Review for Smart City Data Analytics

abstract: Smart cities (SCs) are becoming highly sophisticated ecosystems at which innovative solutions and smart services are being deployed. These ecosystems consider SCs as data production and sharing engines, setting new challenges for building effective SC architectures and novel services. The aim of this article is to “connect the pieces” among Data Science and SC domains, with a systematic literature review which identifies the core topics, services, and methods applied in SC data monitoring. The survey focuses on data harvesting and data mining processes over repeated SC data cycles. A survey protocol is followed to reach both quantitative and semantically important entities. The review results generate useful taxonomies for data scientists in the SC context, which offers clear guidelines for corresponding future works. In particular, a taxonomy is proposed for each of the main SC data entities, namely, the “D Taxonomy” for the data production, the “M Taxonomy” for data analytics methods, and the “S Taxonomy” for smart services. Each of these taxonomies clearly places entities in a classification which is beneficial for multiple stakeholders and for multiple domains in urban smartness targeting. Such indicative scenarios are outlined and conclusions are quite promising for systemizing.


title: A Survey on Bayesian Nonparametric Learning

abstract: Bayesian (machine) learning has been playing a significant role in machine learning for a long time due to its particular ability to embrace uncertainty, encode prior knowledge, and endow interpretability. On the back of Bayesian learning’s great success, Bayesian nonparametric learning (BNL) has emerged as a force for further advances in this field due to its greater modelling flexibility and representation power. Instead of playing with the fixed-dimensional probabilistic distributions of Bayesian learning, BNL creates a new “game” with infinite-dimensional stochastic processes. BNL has long been recognised as a research subject in statistics, and, to date, several state-of-the-art pilot studies have demonstrated that BNL has a great deal of potential to solve real-world machine-learning tasks. However, despite these promising results, BNL has not created a huge wave in the machine-learning community. Esotericism may account for this. The books and surveys on BNL written by statisticians are overcomplicated and filled with tedious theories and proofs. Each is certainly meaningful but may scare away new researchers, especially those with computer science backgrounds. Hence, the aim of this article is to provide a plain-spoken, yet comprehensive, theoretical survey of BNL in terms that researchers in the machine-learning community can understand. It is hoped this survey will serve as a starting point for understanding and exploiting the benefits of BNL in our current scholarly endeavours. To achieve this goal, we have collated the extant studies in this field and aligned them with the steps of a standard BNL procedure—from selecting the appropriate stochastic processes through manipulation to executing the model inference algorithms. At each step, past efforts have been thoroughly summarised and discussed. In addition, we have reviewed the common methods for implementing BNL in various machine-learning tasks along with its diverse applications in the real world as examples to motivate future studies.


title: Assimilation of oceanographic observations with estimates of vertical background-error covariances by a Bayesian hierarchical model

abstract: A new method to estimate the vertical part of the background-error covariance matrix for an ocean variational data assimilation system is presented and tested in the Mediterranean operational daily analysis system. The operational, seasonally varying error covariances are compared with high-frequency estimates from a Bayesian Hierarchical Model (BHM) which estimates distributions for the vertical error covariances from two data-stage inputs: model anomalies and differences between model background and observations, i.e. so-called misfits. It is found that the posterior mean BHM-error covariance estimates that vary on 5-day time-scales reduce the misfits root mean square of the analysis vertical profiles of temperature and salinity by 10–20% versus analyses arising from covariances that vary on seasonal time-scales or those from the BHM given only model anomalies as data stage inputs.


title: Robust classification of EEG signal for brain-computer interface

abstract: We report the implementation of a text input application (speller) based on the P300 event related potential. We obtain high accuracies by using an SVM classifier and a novel feature. These techniques enable us to maintain fast performance without sacrificing the accuracy, thus making the speller usable in an online mode. In order to further improve the usability, we perform various studies on the data with a view to minimizing the training time required. We present data collected from nine healthy subjects, along with the high accuracies (of the order of 95% or more) measured online. We show that the training time can be further reduced by a factor of two from its current value of about 20 min. High accuracy, fast learning, and online performance make this P300 speller a potential communication tool for severely disabled individuals, who have lost all other means of communication and are otherwise cut off from the world, provided their disability does not interfere with the performance of the speller.


title: u-BabSang: a context-aware food recommendation system

abstract: In this paper, we propose a context-aware food recommendation system for well-being care applications. The proposed system, called u-BabSang, provides individualized food recommendation lists at the dining table, and is based dietary advice in the typical Korean medical text. Our proposed system receives a user's profile, physiological signals, and environmental information around the dining table in real time. To operate our system, we present a method for user specified analysis, and also describe time-division layered context integration which integrates the multiple contexts obtained from the sensors. Thus, our system recommends appropriate foods for each individual's health at the table in real time.


title: Whole Brain Segmentation Automated Labeling of Neuroanatomical Structures in the Human Brain

abstract: An icecraft has a base provided with a plurality of ice-engaging runners. A sail-surfing rig including an articulated mast carrying a triangular sail held in place by means of a bow-type boom is fitted to this support plate so that a user can stand on the support plate and ice sail in much the same manner as in surf sailing. A standard surfboard can constitute the base plate for such an icecraft, and the runners can be replaced by wheels or the like if desired for summer use of the arrangement on land.


title: MR imaging of the retrorectal–presacral tumors: an algorithmic approach

abstract: The retrorectal–presacral space is located posterior to the mesorectum and anterior to the sacrum, and can harbor a heterogeneous group of uncommon masses. Retrorectal–presacral tumors may be classified as congenital, neurogenic, osseous, and miscellaneous. Magnetic resonance imaging (MRI) plays a crucial role in directing appropriate management through accurate diagnosis, detection of complications and anatomic extent. MRI aids in the selection of optimal surgical approach such as anterior, posterior, or combined—based on the lesion extent and relationship to adjacent structures. This article reviews the anatomy of the retrorectal–presacral space and the related tumors, optimal MRI protocol, MRI-based approach to differential diagnosis, and finally pertinent reporting pointers and implications of MR imaging findings for surgical management.


title: On the Accuracy, Repeatability, and Degree of Influence of Kinematics Parameters for Industrial Robots

abstract: In this article, we discuss industrial robot characteristics of accuracy and repeatability. The factors that affect these characteristics are identified, and an error tree is developed. Subsequently, the accuracy and repeatability are investigated utilizing the Denavit-Hartenberg kinematics parameters, the homogeneous transformation matrix, and the differential transformation matrix theory, and corresponding measures are developed. The formulation indicates that the influence matrices associated with joint variables are constant. A new measure called degree of influence is established that qualitatively assesses the relative contribution of each kinematic parameter variation to the accuracy and repeatability of rigid manipulators. The developed formulation provides for easy evaluation of the degree of influence measures for rigid manipulators in either numerical or symbolic form. A numerical example is included in which the degree of influence of the kinematics parameters for an articulated manipulator, PUMA 560, are evaluated and analysed.


title: A two-phase algorithm for fast discovery of high utility itemsets

abstract: Traditional association rules mining cannot meet the demands arising from some real applications. By considering the different values of individual items as utilities, utility mining focuses on identifying the itemsets with high utilities. In this paper, we present a Two-Phase algorithm to efficiently prune down the number of candidates and precisely obtain the complete set of high utility itemsets. It performs very efficiently in terms of speed and memory cost both on synthetic and real databases, even on large databases that are difficult for existing algorithms to handle.


title: Validation of Decentralised Smart Contracts Through Game Theory and Formal Methods

abstract: Decentralised smart contracts represent the next step in the development of protocols that support the interaction of independent players without the presence of a coercing authority. Based on protocols i la BitCoin for digital currencies, smart contracts are believed to be a potentially enabling technology for a wealth of future applications. The validation of such an early developing technology is as necessary as it is complex. In this paper we combine game theory and formal models to tackle the new challenges posed by the validation of such systems.


title: Differentially Private Frequent Sequence Mining

abstract: In this paper, we study the problem of mining frequent sequences under the rigorous differential privacy model. We explore the possibility of designing a differentially private frequent sequence mining (FSM) algorithm which can achieve both high data utility and a high degree of privacy. We found, in differentially private FSM, the amount of required noise is proportionate to the number of candidate sequences. If we could effectively prune those unpromising candidate sequences, the utility and privacy tradeoff can be significantly improved. To this end, by leveraging a sampling-based candidate pruning technique, we propose PFS $^2$ , a novel differentially private FSM algorithm. It is the first algorithm that supports the general gap-constrained FSM in the context of differential privacy. The gap constraints in FSM can be used to limit the mining results to a controlled set of frequent sequences. In our PFS $^2$ algorithm, the core is to utilize sample databases to prune the candidate sequences generated based on the downward closure property. In particular, we use the noisy local support of candidate sequences in the sample databases to estimate which candidate sequences are potentially frequent. To improve the accuracy of such private estimations, a gap-aware sequence shrinking method is proposed to enforce the length constraint on the sample databases. Moreover, to calibrate the amount of noise required by differential privacy, a gap-aware sensitivity computation method is proposed to obtain the sensitivity of the local support computations with different gap constraints. Furthermore, to decrease the probability of misestimating frequent sequences as infrequent, a threshold relaxation method is proposed to relax the user-specified threshold for the sample databases. Through formal privacy analysis, we show that our PFS $^2$ algorithm is $\epsilon$ -differentially private. Extensive experiments on real datasets illustrate that our PFS $^2$ algorithm can privately find frequent sequences with high accuracy.


title: Survey on Fault Operation on Multilevel Inverters

abstract: This paper is related to faults that can appear in multilevel (ML) inverters, which have a high number of components. This is a subject of increasing importance in high-power inverters. First, methods to identify a fault are classified and briefly described for each topology. In addition, a number of strategies and hardware modifications that allow for operation in faulty conditions are also presented. As a result of the analyzed works, it can be concluded that ML inverters can significantly increase their availability and are able to operate even with some faulty components.


Bayesian Estimation of Multi-Trap RTN Parameters Using Markov Chain Monte Carlo Method


title: Data Offloading Techniques in Cellular Networks: A Survey

abstract: One of the most engaging challenges for mobile operators today is how to manage the exponential data traffic increase. Mobile data offloading stands out as a promising and low-cost solution to reduce the burden on the cellular network. To make this possible, we need a new hybrid network paradigm that leverages the existence of multiple alternative communication channels. This entails significant modifications in the way data are handled, affecting also the behavior of network protocols. In this paper, we present a comprehensive survey of data offloading techniques in cellular networks and extract the main requirements needed to integrate data offloading capabilities into today's mobile networks. We classify existing strategies into two main categories, according to their requirements in terms of content delivery guarantees: delayed and nondelayed offloading. We overview the technical aspects and discuss the state of the art in each category. Finally, we describe in detail the novel functionalities needed to implement mobile data offloading in the access network, as well as current and future research challenges in the field, with an eye toward the design of hybrid architectures.


title: A Framework to Analyse IS Alignment Approaches: Towards the Definition of Underlying Alignment Mechanisms

abstract: Today, companies are immersed in extremely competitive worldwide markets that change continuously. Thus, companies have to evolve introducing strategic and structural changes as a response to the external forces of the environment. This implies to consider the IS alignment from a global point of view integrating the classical “internal” strategic alignment with two other levels: the alignments with the environment and with uncertain evolutions. In this boarder, approaches that support and operationalise IS alignment are numerous but remain fuzzy towards the kind of alignment tackled. Therefore, it is proposed to build an analysis framework taking the global alignment problematic into account. It is composed of four elements and corresponding attributes detailing each aspect of alignment. This framework is applied to nine current alignment approaches in order to get a wide picture of the research in the domain. The corresponding analysis emphasises possible new work perspectives.


title: Support vector machine parameter tuning using firefly algorithm

abstract: Performance of the support vector machine strongly depends on parameters settings. One of the most common algorithms for parameter tuning is grid search, combined with cross validation. This algorithm is often time consuming and inaccurate. In this paper we propose the use of stochastic metaheuristic algorithm, firefly algorithm, for effective support vector machine parameter tuning. The experimental results on 13 standard benchmark datasets show that our proposed method achieve better results compared to other state-of-the-art algorithms from literature.


title: A new boosting algorithm for improved time-series forecasting with recurrent neural networks

abstract: Ensemble methods for classification and regression have focused a great deal of attention in recent years. They have shown, both theoretically and empirically, that they are able to perform substantially better than single models in a wide range of tasks. We have adapted an ensemble method to the problem of predicting future values of time series using recurrent neural networks (RNNs) as base learners. The improvement is made by combining a large number of RNNs, each of which is generated by training on a different set of examples. This algorithm is based on the boosting algorithm where difficult points of the time series are concentrated on during the learning process however, unlike the original algorithm, we introduce a new parameter for tuning the boosting influence on available examples. We test our boosting algorithm for RNNs on single-step-ahead and multi-step-ahead prediction problems. The results are then compared to other regression methods, including those of different local approaches. The overall results obtained through our ensemble method are more accurate than those obtained through the standard method, backpropagation through time, on these datasets and perform significantly better even when long-range dependencies play an important role.


title: Android Forensics: Simplifying Cell Phone Examinations

abstract: It is hardly appropriate to call the devices many use to receive the occasional phone call a telephone any more. The capability of these devices is growing, as is the number of people utilizing them. By the end of 2009, 46.3% of mobile phones in use in the United States were reported to be smart phones (AdMob, 2010). With the increased availability of these powerful devices, there is also a potential increase for criminals to use this technology as well. Criminals could use smart phones for a number of activities such as committing fraud over e-mail, harassment through text messages, trafficking of child pornography, communications related to narcotics, etc. The data stored on smart phones could be extremely useful to analysts through the course of an investigation. Indeed, mobile devices are already showing themselves to have a large volume of probative information that is linked to an individual with just basic call history, contact, and text message data; smart phones contain even more useful information, such as e-mail, browser history, and chat logs. Mobile devices probably have more probative information that can be linked to an individual per byte examined than most computers -and this data is harder to acquire in a forensically proper fashion. Part of the problem lies in the plethora of cell phones available today and a general lack of hardware, software, and/or interface standardization within the industry. These differences range from the media on which data is stored and the file system to the operating system and the effectiveness of certain tools. Even different model cell phones made by the same manufacture may require different data cables and software to access the phone's information. The good news is there are numerous people in the field working on making smart phone forensics easier. Already there is material available on how to conduct an examination on Blackberry phones and a growing number of resources about the iPhone. However, there is a new smart phone OS on the market named Android and it will likely gain in appeal and market share over the next year. While Android initially launched with only one phone on T-Mobile, phones are now available on Sprint, Verizon and AT&T as well.


title: A Class-$E$ RF Power Amplifier With a Flat-Top Transistor-Voltage Waveform

abstract: This paper shows a new class- $E$ amplifier topology with the objective to increase the nominal class- $E$ output power for a given voltage and current stress on the power transistor. To obtain that result, a parallel LC resonator is added to the load network, tuned to the second harmonic of the switching frequency. A class- $E$ power amplifier is obtained whose transistor-voltage waveform peak value is 81% of the peak value of the voltage of a nominal class- $E$ amplifier using the same dc supply voltage. In this amplifier, the peak voltage across the transistor is 3.0 times the dc supply voltage, instead of the 3.6 times associated with nominal class- $E$ amplifiers. A normalized design is presented, and the behavior of the circuit is analyzed with simulation showing that the ratio of output power versus transistor peak voltage times peak current is 20.4% better than the nominal class $E$ . The proposed converter and normalized design approach are verified by simulations and measurements done on an experimental prototype.


title: Real-Time American Sign Language Recognition System Using Surface EMG Signal

abstract: Sign Language Recognition (SLR) system is a method which allow deaf people to communicate with society. In this study, Real-Time Sign Language recognition system was proposed by using the surface Electromyography (sEMG). To this purpose, sEMG data acquired from subject right forearm for all twenty six American Sign Language gestures. Raw sEMG data was filtered, feature extracted and fed into classification. Support Vector Machine (SVM) with one vs. all approach was used for multi class classification. The experiment result of offline system is reaching a recognition rate of 91.% accuracy and real-time system has a recognition rate of 82.3% accuracy. The results of the proposed system shows that sEMG signal can be used for Real-Time SLR systems.


title: In-Depth Survey of Digital Advertising Technologies

abstract: Some of the world’s most well-known IT companies are in fact advertising companies deriving their primary revenues through digital advertising. For this reason, these IT giants are able to continually drive the evolutions of information technology in ways that serve to enhance our everyday lives. The benefits of this relationship include free web browsers with powerful search engines and mobile applications. Still, it turns out that “free” comes at a cost that is paid through our interactions within a digital advertising ecosystem. Digital advertising is not without its challenges. Issues originate from the complex platforms utilized to support advertising over web and mobile application interfaces. This is especially true for advertising links. Additionally, as new methods for advertising develop so too does the potential for impacting its underlying ecosystem for good or ill. Accordingly, researchers are interested in understanding this ecosystem, the factors that impact it, and the strategies for improving it. The major contribution of this survey is that it is the first review of the digital advertising ecosystem as it applies to online websites and mobile applications. In doing so, we explain the digital advertising relationships within this ecosystem along with their technical, social, political, and physical implications. Furthermore, advertising principles along with a variation of other advertising approaches, both legitimate and malicious, are explored in order to compare and contrast competing digital advertising methods.


A Constructivist Approach to Online Learning: The Community of Inquiry Framework


title: On the merits of helical tendon routing in continuum robots

abstract: Tendon-driven continuum robots possess versatile application capabilities and have a robust design. The actuation of such robots with non-straight tendons that wrap around the backbone, described by a variable function, offers a lot of untapped potentials. While it has been shown that these continuum robots are able to take up complex shapes using only one actuated segment, the merits of non-straight tendon routing have not been quantified in terms of workspace and motion. In this paper, we show that one additional helically routed tendon can greatly benefit the robot's reachable workspace. For instance, the reachable workspace of a one-segment robot with 3 conventional straight tendons increases by 400 % by adding one helically routed tendon. Furthermore, the dexterity of such a continuum robot is improved, i.e. motion sequences to avoid obstacles or to twine an object for grasping. For the first time, the potential of tendon-driven continuum robots with two segments and helically routed tendons is investigated. The general findings on the merits of helical tendon routing are supported with both simulation and experimental results.


title: Fire detection using infrared images for UAV-based forest fire surveillance

abstract: Unmanned aerial vehicle (UAV) based computer vision system, as a more and more promising option for forest fires surveillance and detection, is now widely employed. In this paper, an image processing method for the application to UAV is presented for the automatic detection of forest fires in infrared (IR) images. The presented algorithm makes use of brightness and motion clues along with image processing techniques based on histogram-based segmentation and optical flow approach for fire pixels detection. First, the histogram-based segmentation is used to extract the hot objects as fire candidate regions. Then, the optical flow method is adopted to calculate motion vectors of the candidate regions. The motion vectors are also further analyzed to distinguish fires from other fire analogues. Through performing morphological operations and blob counter method, a fire can be finally tracked in each IR image. Experimental results verified that the designed method can effectively extract and track fire pixels in IR video sequences.


title: LIE GROUPS AND LIE ALGEBRAS IN ROBOTICS

abstract: In this lecture the group of rigid body motions is introduced via its representation on standard three dimensional Euclidian space. The relevance for robotics is that the links of a robot are usually modelled as rigid bodies. Moreover the payload of a robot is also usually a rigid body and hence much of robotics is concerned with understanding rigid transformations and sequences of these transformations. Chasles’s theorem is presented, that is: a general rigid body motion is a screw motion, a rotation about a line in space followed by a translation along the line.


title: Online Adaptation of a c-VEP Brain-Computer Interface(BCI) Based on Error-Related Potentials and Unsupervised Learning

abstract: The goal of a Brain-Computer Interface (BCI) is to control a computer by pure brain activity. Recently, BCIs based on code-modulated visual evoked potentials (c-VEPs) have shown great potential to establish high-performance communication. In this paper we present a c-VEP BCI that uses online adaptation of the classifier to reduce calibration time and increase performance. We compare two different approaches for online adaptation of the system: an unsupervised method and a method that uses the detection of error-related potentials. Both approaches were tested in an online study, in which an average accuracy of 96% was achieved with adaptation based on error-related potentials. This accuracy corresponds to an average information transfer rate of 144 bit/min, which is the highest bitrate reported so far for a non-invasive BCI. In a free-spelling mode, the subjects were able to write with an average of 21.3 error-free letters per minute, which shows the feasibility of the BCI system in a normal-use scenario. In addition we show that a calibration of the BCI system solely based on the detection of error-related potentials is possible, without knowing the true class labels.


title: A miniature low-insertion-loss, high-power CMOS SPDT switch using floating-body technique for 2.4- and 5.8-GHz applications

abstract: A low insertion loss SPDT switch in a standard 0.18-/spl mu/m CMOS process was developed for 2.4- and 5.8-GHz WLAN applications. In order to reduce the insertion loss and increase the P/sub 1dB/, the floating-body circuit topology is proposed. The series-shunt switch achieves a measured P/sub 1dB/ of 20 dBm, an insertion loss of 1.1 dB, and an isolation of 27 dB at 5.8 GHz. It also achieves a measured insertion loss of 0.65 dB and an isolation of 35 dB at 2.4 GHz. The effective chip size is only 0.03 mm/sup 2/. The measured data agree well with the simulation results. This work presents low insertion loss, high isolation and good power performance with the smallest chip size compared to previously reported 2.4- and 5.8-GHz CMOS switches.


title: A cooperative intrusion detection system for ad hoc networks

abstract: Mobile ad hoc networking (MANET) has become an exciting and important technology in recent years because of the rapid proliferation of wireless devices. MANETs are highly vulnerable to attacks due to the open medium, dynamically changing network topology, cooperative algorithms, lack of centralized monitoring and management point, and lack of a clear line of defense. In this paper, we report our progress in developing intrusion detection (ID) capabilities for MANET. Building on our prior work on anomaly detection, we investigate how to improve the anomaly detection approach to provide more details on attack types and sources. For several well-known attacks, we can apply a simple rule to identify the attack type when an anomaly is reported. In some cases, these rules can also help identify the attackers. We address the run-time resource constraint problem using a cluster-based detection scheme where periodically a node is elected as the ID agent for a cluster. Compared with the scheme where each node is its own ID agent, this scheme is much more efficient while maintaining the same level of effectiveness. We have conducted extensive experiments using the ns-2 and MobiEmu environments to validate our research.


title: Feature hashing for large scale multitask learning

abstract: Empirical evidence suggests that hashing is an effective strategy for dimensionality reduction and practical nonparametric estimation. In this paper we provide exponential tail bounds for feature hashing and show that the interaction between random subspaces is negligible with high probability. We demonstrate the feasibility of this approach with experimental results for a new use case --- multitask learning with hundreds of thousands of tasks.


title: The Meaning Factory: Formal Semantics for Recognizing Textual Entailment and Determining Semantic Similarity

abstract: Shared Task 1 of SemEval-2014 comprised two subtasks on the same dataset of sentence pairs: recognizing textual entailment and determining textual similarity. We used an existing system based on formal semantics and logical inference to participate in the first subtask, reaching an accuracy of 82%, ranking in the top 5 of more than twenty participating systems. For determining semantic similarity we took a supervised approach using a variety of features, the majority of which was produced by our system for recognizing textual entailment. In this subtask our system achieved a mean squared error of 0.322, the best of all participating systems.


title: The resting microstate networks (RMN): cortical distributions, dynamics, and frequency specific information flow

abstract: A brain microstate is characterized by a unique, fixed spatial distribution of electrically active neurons with time varying amplitude. It is hypothesized that a microstate implements a functional/physiological state of the brain during which specific neural computations are performed. Based on this hypothesis, brain electrical activity is modeled as a time sequence of non-overlapping microstates with variable, finite durations (Lehmann and Skrandies 1980, 1984; Lehmann et al 1987). In this study, EEG recordings from 109 participants during eyes closed resting condition are modeled with four microstates. In a first part, a new confirmatory statistics method is introduced for the determination of the cortical distributions of electric neuronal activity that generate each microstate. All microstates have common posterior cingulate generators, while three microstates additionally include activity in the left occipital/parietal, right occipital/parietal, and anterior cingulate cortices. This appears to be a fragmented version of the metabolically (PET/fMRI) computed default mode network (DMN), supporting the notion that these four regions activate sequentially at high time resolution, and that slow metabolic imaging corresponds to a low-pass filtered version. In the second part of this study, the microstate amplitude time series are used as the basis for estimating the strength, directionality, and spectral characteristics (i.e., which oscillations are preferentially transmitted) of the connections that are mediated by the microstate transitions. The results show that the posterior cingulate is an important hub, sending alpha and beta oscillatory information to all other microstate generator regions. Interestingly, beyond alpha, beta oscillations are essential in the maintenance of the brain during resting state.


title: Fisheye lens distortion correction on multicore and hardware accelerator platforms

abstract: Wide-angle (fisheye) lenses are often used in virtual reality and computer vision applications to widen the field of view of conventional cameras. Those lenses, however, distort images. For most real-world applications the video stream needs to be transformed, at real-time (20 frames/sec or better), back to the natural-looking, central perspective space. This paper presents the implementation, optimization and characterization of a fisheye lens distortion correction application on three platforms: a conventional, homogeneous multicore processor by Intel, a heterogeneous multicore (Cell BE), and an FPGA implementing an automatically generated streaming accelerator. We evaluate the interaction of the application with those architectures using both high- and low-level performance metrics. In macroscopic terms, we find that todays mainstream conventional multicores are not effective in supporting real-time distortion correction, at least not with the currently commercially available core counts. Architectures, such as the Cell BE and FPGAs, offer the necessary computational power and scalability, at the expense of significantly higher development effort. Among these three platforms, only the FPGA and a fully optimized version of the code running on the Cell processor can provide realtime processing speed. In general, FPGAs meet the expectations of performance, flexibility, and low overhead. General purpose multicores are, on the other hand, much easier to program.


A cross-cultural examination of presenteeism and supervisory support


title: Speeding Up the Douglas-Peucker Line-Simplification Algorithm

abstract: We analyze the line simplification algorithm reported by Douglas and Peucker and show that its worst case is quadratic in n, the number of input points. Then we give a algorithm, based on path hulls, that uses the geometric structure of the problem to attain a worst-case running time proportional to n log_2(n), which is the best case of the Douglas algorithm. We give complete C code and compare the two algorithms theoretically, by operation counts, and practically, by machine timings.


title: SENTIMENT ANALYSIS OF RESTAURANT REVIEWS USING HYBRID CLASSIFICATION METHOD

abstract: The area of sentiment mining (also called sentiment extraction, opinion mining, opinion extraction, sentiment analysis, etc.) has seen a large increase in academic interest in the last few years. Researchers in the areas of natural language processing, data mining, machine learning, and others have tested a variety of methods of automating the sentiment analysis process. In this research work, new hybrid classification method is proposed based on coupling classification methods using arcing classifier and their performances are analyzed in terms of accuracy. A Classifier ensemble was designed using Naive Bayes (NB), Support Vector Machine (SVM) and Genetic Algorithm (GA). In the proposed work, a comparative study of the effectiveness of ensemble technique is made for sentiment classification. The feasibility and the benefits of the proposed approaches are demonstrated by means of restaurant review that is widely used in the field of sentiment classification. A wide range of comparative experiments are conducted and finally, some in-depth discussion is presented and conclusions are drawn about the effectiveness of ensemble technique for sentiment classification.


title: Adolescent simulated gambling via digital and social media: An emerging problem

abstract: Recently, there has been significant expansion in the range of gambling activities supported by digital technology. The convergence of gambling and digital media is of particular concern with respect to the immense potential for earlier age of gambling involvement, and development of positive attitudes and/or behavioral intentions toward gambling. This study examined the prevalence of adolescent involvement in a range of digital and social media gambling activities, and the association between exposure to, and involvement in, simulated gambling and monetary gambling and indicators of pathological gambling risk. A total of 1287 adolescents aged 12-17years were recruited from seven secondary schools in Adelaide, South Australia. The results indicated that a significant proportion of young people engage in a range of simulated gambling activities via internet gambling sites, social media, smartphone applications, and video-games. A logistic regression analysis showed that adolescents with a history of engagement in simulated gambling activities appear to be at greater risk of endorsing indicators of pathological gambling. These findings highlight the need for further research on the potential risks of early exposure to simulated gambling activities, as well as greater consideration of the need for regulation and monitoring of gambling activity via digital technologies.


title: Disease Trajectory Maps

abstract: Medical researchers are coming to appreciate that many diseases are in fact complex, heterogeneous syndromes composed of subpopulations that express different variants of a related complication. Time series data extracted from individual electronic health records (EHR) offer an exciting new way to study subtle differences in the way these diseases progress over time. In this paper, we focus on answering two questions that can be asked using these databases of time series. First, we want to understand whether there are individuals with similar disease trajectories and whether there are a small number of degrees of freedom that account for differences in trajectories across the population. Second, we want to understand how important clinical outcomes are associated with disease trajectories. To answer these questions, we propose the Disease Trajectory Map (DTM), a novel probabilistic model that learns low-dimensional representations of sparse and irregularly sampled time series. We propose a stochastic variational inference algorithm for learning the DTM that allows the model to scale to large modern medical datasets. To demonstrate the DTM, we analyze data collected on patients with the complex autoimmune disease, scleroderma. We find that DTM learns meaningful representations of disease trajectories and that the representations are significantly associated with important clinical outcomes.


title: Jumping robots: a biomimetic solution to locomotion across rough terrain

abstract: This paper introduces jumping robots as a means to traverse rough terrain; such terrain can pose problems for traditional wheeled, tracked and legged designs. The diversity of jumping mechanisms found in nature is explored to support the theory that jumping is a desirable ability for a robot locomotion system to incorporate, and then the size-related constraints are determined from first principles. A series of existing jumping robots are presented and their performance summarized. The authors present two new biologically inspired jumping robots, Jollbot and Glumper, both of which incorporate additional locomotion techniques of rolling and gliding respectively. Jollbot consists of metal hoop springs forming a 300 mm diameter sphere, and when jumping it raises its centre of gravity by 0.22 m and clears a height of 0.18 m. Glumper is of octahedral shape, with four 'legs' that each comprise two 500 mm lengths of CFRP tube articulating around torsion spring 'knees'. It is able to raise its centre of gravity by 1.60 m and clears a height of 1.17 m. The jumping performance of the jumping robot designs presented is discussed and compared against some specialized jumping animals. Specific power output is thought to be the performance-limiting factor for a jumping robot, which requires the maximization of the amount of energy that can be stored together with a minimization of mass. It is demonstrated that this can be achieved through optimization and careful materials selection.


title: USING THE ANALYTIC HIERARCHY PROCESS FOR DECISION MAKING IN ENGINEERING APPLICATIONS: SOME CHALLENGES

abstract: In many industrial engineering applications the final decision is based on the evaluation of a number of alternatives in terms of a number of criteria. This problem may become a very difficult one when the criteria are expressed in different units or the pertinent data are difficult to be quantified. The Analytic Hierarchy Process (AHP) is an effective approach in dealing with this kind of decision problems. This paper examines some of the practical and computational issues involved when the AHP method is used in engineering applications. Significance: In many engineering applications the final decision depends on the evaluation of a set of alternatives in terms of a number of decision criteria. This may be a difficult task and the Analytic Hierarchy Process seems to provide an effective way for properly quantifying the pertinent data. However, there are many critical issues that a decision maker needs to be aware of.


title: Deep Object Co-Segmentation

abstract: This work presents a deep object co-segmentation (DOCS) approach for segmenting common objects of the same class within a pair of images. This means that the method learns to ignore common, or uncommon, background stuff and focuses on objects. If multiple object classes are presented in the image pair, they are jointly extracted as foreground. To address this task, we propose a CNN-based Siamese encoder-decoder architecture. The encoder extracts high-level semantic features of the foreground objects, a mutual correlation layer detects the common objects, and finally, the decoder generates the output foreground masks for each image. To train our model, we compile a large object co-segmentation dataset consisting of image pairs from the PASCAL VOC dataset with common objects masks. We evaluate our approach on commonly used datasets for co-segmentation tasks and observe that our approach consistently outperforms competing methods, for both seen and unseen object classes.


A Probabilistic Framework for Real-time 3D Segmentation using Spatial, Temporal, and Semantic Cues.


title: Stereo image feature matching using Harris corner detection algorithm

abstract: Stereo image matching is one of the research areas in computer vision. In stereo image matching, technological developments advances from area based matching techniques to the feature based matching techniques. In this paper, we present a Harris corner detection algorithm for stereo image feature matching. This is an intensity based feature matching algorithm and it controls the strong and weak corners with the help of threshold value. Generally image processing algorithms are simulated in software but to do hardware co-simulation, here the model based design is implemented in Xilinx System Generator. Further the architecture is synthesized on the Xilinx Virtex - 5 FPGA. Simulation results are included in this paper to verify the performance of proposed system. Complexity level is minimized in model based design than that of script level design.


title: Mechanisms linking obesity to insulin resistance and type 2 diabetes

abstract: Obesity is associated with an increased risk of developing insulin resistance and type 2 diabetes. In obese individuals, adipose tissue releases increased amounts of non-esterified fatty acids, glycerol, hormones, pro-inflammatory cytokines and other factors that are involved in the development of insulin resistance. When insulin resistance is accompanied by dysfunction of pancreatic islet beta-cells - the cells that release insulin - failure to control blood glucose levels results. Abnormalities in beta-cell function are therefore critical in defining the risk and development of type 2 diabetes. This knowledge is fostering exploration of the molecular and genetic basis of the disease and new approaches to its treatment and prevention.


title: Task-Oriented Query Reformulation with Reinforcement Learning

abstract: Search engines play an important role in our everyday lives by assisting us in finding the information we need. When we input a complex query, however, results are often far from satisfactory. In this work, we introduce a query reformulation system based on a neural network that rewrites a query to maximize the number of relevant documents returned. We train this neural network with reinforcement learning. The actions correspond to selecting terms to build a reformulated query, and the reward is the document recall. We evaluate our approach on three datasets against strong baselines and show a relative improvement of 5-20% in terms of recall. Furthermore, we present a simple method to estimate a conservative upper-bound performance of a model in a particular environment and verify that there is still large room for improvements.


title: Why Have Divorce Rates Fallen?: The Role of Women’s Age at Marriage

abstract: Explores the extent to which the rise in age at marriage can explain the rapid decrease in divorce rates for cohorts marrying from 1980 to 2004.


title: Medical Image Enhancement Using Morphological Transformation

abstract: Medical imaging includes different modalities and processes to visualize the interior of human body for diagnostic and treatment purpose. However, one of the most common degradations in medical images is their poor contrast quality and noise. The existence of several objects and the close proximity of adjacent pixels values make the diagnostic process a daunting task. The idea of image enhancement techniques is to improve the quality of an image. In this study, morphological transform operation is carried out on medical images to enhance the contrast and quality. A disk shaped mask is used in Top-Hat and Bottom-Hat transform and this mask plays a vital role in the operation. Different types and sizes of medical images need different masks so that they can be successfully enhanced. The method shown in this study takes a mask of an arbitrary size and keeps changing its size until an optimum enhanced image is obtained from the transformation operation. The enhancement is achieved via an iterative exfoliation process. The results indicate that this method improves the contrast of medical images and can help with better diagnosis.


title: Power to the People: The Role of Humans in Interactive Machine Learning

abstract: Intelligent systems that learn interactively from their end-users are quickly becoming widespread. Until recently, this progress has been fueled mostly by advances in machine learning; however, more and more researchers are realizing the importance of studying users of these systems. In this article we promote this approach and demonstrate how it can result in better user experiences and more effective learning systems. We present a number of case studies that characterize the impact of interactivity, demonstrate ways in which some existing systems fail to account for the user, and explore new ways for learning systems to interact with their users. We argue that the design process for interactive machine learning systems should involve users at all stages: explorations that reveal human interaction patterns and inspire novel interaction methods, as well as refinement stages to tune details of the interface and choose among alternatives. After giving a glimpse of the progress that has been made so far, we discuss the challenges that we face in moving the field forward.


title: SESA: Supervised Explicit Semantic Analysis

abstract: In recent years supervised representation learning has provided state of the art or close to the state of the art results in semantic analysis tasks including ranking and information retrieval. The core idea is to learn how to embed items into a latent space such that they optimize a supervised objective in that latent space. The dimensions of the latent space have no clear semantics, and this reduces the interpretability of the system. For example, in personalization models, it is hard to explain why a particular item is ranked high for a given user profile. We propose a novel model of representation learning called Supervised Explicit Semantic Analysis (SESA) that is trained in a supervised fashion to embed items to a set of dimensions with explicit semantics. The model learns to compare two objects by representing them in this explicit space, where each dimension corresponds to a concept from a knowledge base. This work extends Explicit Semantic Analysis (ESA) with a supervised model for ranking problems. We apply this model to the task of Job-Profile relevance in LinkedIn in which a set of skills defines our explicit dimensions of the space. Every profile and job are encoded to this set of skills their similarity is calculated in this space. We use RNNs to embed text input into this space. In addition to interpretability, our model makes use of the web-scale collaborative skills data that is provided by users for each LinkedIn profile. Our model provides state of the art result while it remains interpretable.


title: Unsupervized Image Clustering With SIFT-Based Soft-Matching Affinity Propagation

abstract: It is known that affinity propagation can perform exemplar-based unsupervised image clustering by taking as input similarities between pairs of images and producing a set of exemplars that best represent the images, and then assigning each nonexemplar image to its most appropriate exemplar. However, the clustering performance of affinity propagation is largely limited by the adopted similarity between any pair of images. As the scale invariant feature transform (SIFT) has been widely employed to extract image features, the nonmetric similarity between any pair of images was proposed by “hard” matching of SIFT features (e.g., counting the number of matching SIFT features). In this letter, we notice, however, that the decision of hard matching of SIFT features is binary, which is not necessary for deriving similarities. Hence, we propose a novel measure of similarities by replacing hard matching with the so-called soft matching. Experimental examples show that significant performance gains can be achieved by the resulting affinity propagation algorithm.


title: An intelligent fast sales forecasting model for fashion products

abstract: Research highlights? We devise a fast and efficient sales forecasting model for fashion products. ? We propose a systematic framework to determine the suitable parameters of the model. ? We illustrate the features and the efficiency of the model with two sets of real data. Sales forecasting is crucial in fashion business because of all the uncertainty associated with demand and supply. Many models for forecasting fashion products are proposed in the literature over the past few decades. With the emergence of artificial intelligence models, artificial neural networks (ANN) are widely used in forecasting. ANN models have been revealed to be more efficient and effective than many traditional statistical forecasting models. Despite the reported advantages, it is relatively more time-consuming for ANN to perform forecasting. In the fashion industry, sales forecasting is challenging because there are so many product varieties (i.e., SKUs) and prompt forecasting result is needed. As a result, the existing ANN models would become inadequate. In this paper, a new model which employs both the extreme learning machine (ELM) and the traditional statistical methods is proposed. Experiments with real data sets are conducted. A comparison with other traditional methods has shown that this ELM fast forecasting (ELM-FF) model is quick and effective.


title: What makes an image popular

abstract: Hundreds of thousands of photographs are uploaded to the internet every minute through various social networking and photo sharing platforms. While some images get millions of views, others are completely ignored. Even from the same users, different photographs receive different number of views. This begs the question: What makes a photograph popular? Can we predict the number of views a photograph will receive even before it is uploaded? These are some of the questions we address in this work. We investigate two key components of an image that affect its popularity, namely the image content and social context. Using a dataset of about 2.3 million images from Flickr, we demonstrate that we can reliably predict the normalized view count of images with a rank correlation of 0.81 using both image content and social cues. In this paper, we show the importance of image cues such as color, gradients, deep learning features and the set of objects present, as well as the importance of various social cues such as number of friends or number of photos uploaded that lead to high or low popularity of images.


title: Domain-Independent Unsupervised Text Segmentation for Data Management

abstract: In this study, we have proposed a domain-independent unsupervised text segmentation method, which is applicable to even if unseen single document. This proposed method segments text documents by evaluating similarity between sentences. It is generally difficult to calculate semantic similarity between words that comprise sentences when the domain knowledge is insufficient. This problem influences segmentation accuracy. To address this problem, we use word 2 vec to calculate semantic similarity between words. Using word 2 vec, we embed semantic relationships between words in a vector space by training with large domain-independent corpora. Furthermore, we combine semantic and collocation similarities, i.e., The features between words within a document. The proposed method applies this combined similarity to affinity propagation clustering. Similarity between sentences is defined based on the earth mover's distance between the frequencies of the obtained topical clusters. After calculating similarity between sentences, segmentation boundaries are automatically optimized using dynamic programming. The experimental results obtained using two datasets show that the proposed method clearly outperforms state-of-the-art domain-independent approaches and obtains equal performance with state-of-the-art domain-dependent approaches such as those that use topic modeling.


title: SELF- ENUCLEATION OF EYES IN SCHIZOPHRENIA- AN UNUSUAL MODE OF DELIBERATE SELF HARM

abstract: Self inflicted injury to the eyes including self-enucleation is a rare phenomenon. The authors describe a case of bilateral self-enucleation in a patient with paranoid schizophrenia. A review of literature shows that self-enucleation is correlated with moral, religious and psychodynamic paradigms and various unconscious mechanisms have been proposed to explain this unusual behaviour.


title: A naturalistic open source movie for optical flow evaluation

abstract: Ground truth optical flow is difficult to measure in real scenes with natural motion. As a result, optical flow data sets are restricted in terms of size, complexity, and diversity, making optical flow algorithms difficult to train and test on realistic data. We introduce a new optical flow data set derived from the open source 3D animated short film Sintel. This data set has important features not present in the popular Middlebury flow evaluation: long sequences, large motions, specular reflections, motion blur, defocus blur, and atmospheric effects. Because the graphics data that generated the movie is open source, we are able to render scenes under conditions of varying complexity to evaluate where existing flow algorithms fail. We evaluate several recent optical flow algorithms and find that current highly-ranked methods on the Middlebury evaluation have difficulty with this more complex data set suggesting further research on optical flow estimation is needed. To validate the use of synthetic data, we compare the image- and flow-statistics of Sintel to those of real films and videos and show that they are similar. The data set, metrics, and evaluation website are publicly available.


Clustering by Authorship Within and Across Documents


Physician Well-Being: A Powerful Way to Improve the Patient Experience


title: The impact of total productive maintenance practices on manufacturing performance

abstract: In this paper we investigate the relationship between Total Productive Maintenance (TPM) and manufacturing performance (MP) through Structural Equation Modeling (SEM). We find that TPM has a positive and significant relationship with low cost (as measured by higher inventory turns), high levels of quality (as measured by higher levels of conformance to specifications), and strong delivery performance (as measured by higher percentage of on-time deliveries and by faster speeds of delivery). We also find that the relationship between TPM and MP can be explained by both direct and indirect relationships. In particular, there is a significant and positive indirect relationship between TPM and MP through Just-In-Time (JIT) practices. © 2001 Elsevier Science B.V. All rights reserved.


title: SMART on FHIR: a standards-based, interoperable apps platform for electronic health records

abstract: Objective In early 2010, Harvard Medical School and Boston Children’s Hospital began an interoperability project with the distinctive goal of developing a platform to enable medical applications to be written once and run unmodified across different healthcare IT systems. The project was called Substitutable Medical Applications and Reusable Technologies (SMART). ::: ::: Methods We adopted contemporary web standards for application programming interface transport, authorization, and user interface, and standard medical terminologies for coded data. In our initial design, we created our own openly licensed clinical data models to enforce consistency and simplicity. During the second half of 2013, we updated SMART to take advantage of the clinical data models and the application-programming interface described in a new, openly licensed Health Level Seven draft standard called Fast Health Interoperability Resources (FHIR). Signaling our adoption of the emerging FHIR standard, we called the new platform SMART on FHIR. ::: ::: Results We introduced the SMART on FHIR platform with a demonstration that included several commercial healthcare IT vendors and app developers showcasing prototypes at the Health Information Management Systems Society conference in February 2014. This established the feasibility of SMART on FHIR, while highlighting the need for commonly accepted pragmatic constraints on the base FHIR specification. ::: ::: Conclusion In this paper, we describe the creation of SMART on FHIR, relate the experience of the vendors and developers who built SMART on FHIR prototypes, and discuss some challenges in going from early industry prototyping to industry-wide production use.


title: Control of an LLC Resonant Converter Using Load Feedback Linearization

abstract: LLC resonant converter is a nonlinear system, limiting the use of typical linear control methods. This paper proposed a new nonlinear control strategy, using load feedback linearization for an LLC resonant converter. Compared with the conventional PI controllers, the proposed feedback linearized control strategy can achieve better performance with elimination of the nonlinear characteristics. The LLC resonant converter's dynamic model is built based on fundamental harmonic approximation using extended describing function. By assuming the dynamics of resonant network is much faster than the output voltage and controller, the LLC resonant converter's model is simplified from seven-order state equations to two-order ones. Then, the feedback linearized control strategy is presented. A double loop PI controller is designed to regulate the modulation voltage. The switching frequency can be calculated as a function of the load, input voltage, and modulation voltage. Finally, a 200 W laboratory prototype is built to verify the proposed control scheme. The settling time of the LLC resonant converter is reduced from 38.8 to 20.4 ms under the positive load step using the proposed controller. Experimental results prove the superiority of the proposed feedback linearized controller over the conventional PI controller.


title: Motion Prediction of Traffic Actors for Autonomous Driving using Deep Convolutional Networks.

abstract: Recent algorithmic improvements and hardware breakthroughs resulted in a number of success stories in the field of AI impacting our daily lives. However, despite its ubiquity AI is only just starting to make advances in what may arguably have the largest impact thus far, the nascent field of autonomous driving. In this work we discuss this important topic and address one of crucial aspects of the emerging area, the problem of predicting future state of autonomous vehicle's surrounding necessary for safe and efficient operations. We introduce a deep learning-based approach that takes into account current state of traffic actors and produces rasterized representations of each actor's vicinity. The raster images are then used by deep convolutional models to infer future movement of actors while accounting for inherent uncertainty of the prediction task. Extensive experiments on real-world data strongly suggest benefits of the proposed approach. Moreover, following successful tests the system was deployed to a fleet of autonomous vehicles.


title: CARP: A Channel-aware routing protocol for underwater acoustic wireless networks

abstract: The paper concerns the definition and performance evaluation of a new multi-hop routing protocol for underwater wireless sensor networks. Our solution, termed CARP for Channel-aware Routing Protocol, exploits link quality information for data forwarding, in that nodes are selected as relays if they exhibit recent history of successful transmissions to their neighbors. CARP avoids loops and can successfully route around connectivity voids and shadow zones by using simple topology information, such as hop count. The protocol is also designed to take advantage of power control for selecting robust links. The performance of CARP has been compared with that of two other protocols for underwater routing, namely, the Focused Beam Routing (FBR) and a flooding-based solution (EFlood). Metrics of interest include packet delivery ratio, end-to-end packet latency and energy consumption, which have been investigated through ns2-based simulations and experiments at sea. The in-field trials have been conducted at two European locations, namely, a Norwegian fjord and the Mediterranean Sea. The tests in the Mediterranean Sea have been performed jointly with the NATO Science and Technology Organization Centre for Maritime Research and Experimentation (STO CMRE), under a collaboration agreement between the University of Roma and CMRE. 1 Our results show that CARP robust mechanism for relay selection doubles the packet delivery


title: e-Government in Africa Promise and Practice

abstract: e-Government has already arrived in Africa, though it is essentially an imported concept based on imported designs. There are growing numbers of e-government projects, some of which are contributing to public sector reform and delivering gains of efficiency and/or effectiveness across a broad agenda. However, this positive picture must be set alongside significant challenges. e-Government is only slowly diffusing within Africa because of a lack of 'e-readiness for e-government' that can be charted along six dimensions. There is widespread recognition that this challenge must be met by strategic building of national infrastructure. Where e-government projects are introduced, they mainly end in failure; either partial or total. To address this tactical challenge, stakeholders must be sensitised to the large gaps that often exist between project design and African public sector reality. These large `design - reality gaps' can be seen to underlie failure. They arise particularly because e-government concepts and designs have their origins in the West; origins that are significantly different from African realities. Some best practices are outlined that may help to close design - reality gaps and, hence, may help to improve project success rates. This will only happen, though, if they too are appropriate to African realities.


High Performance , Low Cost FPGA Correlator for Wideband CDMA and Other Wireless Applications


title: On combining classifiers

abstract: We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision. An experimental comparison of various classifier combination schemes demonstrates that the combination rule developed under the most restrictive assumptions-the sum rule-outperforms other classifier combinations schemes. A sensitivity analysis of the various schemes to estimation errors is carried out to show that this finding can be justified theoretically.


title: Secrets of GrabCut and Kernel K-Means

abstract: The log-likelihood energy term in popular model-fitting segmentation methods, e.g. [39, 8, 28, 10], is presented as a generalized "probabilistic K-means" energy [16] for color space clustering. This interpretation reveals some limitations, e.g. over-fitting. We propose an alternative approach to color clustering using kernel K-means energy with well-known properties such as non-linear separation and scalability to higher-dimensional feature spaces. Our bound formulation for kernel K-means allows to combine general pair-wise feature clustering methods with image grid regularization using graph cuts, similarly to standard color model fitting techniques for segmentation. Unlike histogram or GMM fitting [39, 28], our approach is closely related to average association and normalized cut. But, in contrast to previous pairwise clustering algorithms, our approach can incorporate any standard geometric regularization in the image domain. We analyze extreme cases for kernel bandwidth (e.g. Gini bias) and demonstrate effectiveness of KNN-based adaptive bandwidth strategies. Our kernel K-means approach to segmentation benefits from higher-dimensional features where standard model fitting fails.


title: SQLUnitGen: SQL Injection Testing Using Static and Dynamic Analysis

abstract: This paper proposes an approach to facilitate the identification of actual input manipulation vulnerabilities via automated testing based on static analysis. We implemented a prototype of a SQL injection vulnerability detection tool, SQLUnitGen, which we compared to a static analysis tool, FindBugs. The evaluation results show that our approach can be used to locate precise vulnerable locations of source code and help to identify false positives that are caused by static analysis tools.


title: Improving Data Association in Vision-based SLAM

abstract: This paper presents an approach to vision-based simultaneous localization and mapping (SLAM). Our approach uses the scale invariant feature transform (SIFT) as features and applies a rejection technique to concentrate on a reduced set of distinguishable, stable features. We track detected SIFT features over consecutive frames obtained by a stereo camera and select only those features that appear to be stable from different views. Whenever a feature is selected, we compute a representative feature given the previous observations. This approach is applied within a Rao-Blackwellized particle filter to make the data association easier and furthermore to reduce the number of landmarks that need to be maintained in the map. Our system has been implemented and tested on data gathered with a mobile robot in a typical office environment. Experiments presented in this paper demonstrate that our method improves the data association and in this way leads to more accurate maps.


title: Application of kernels to link analysis

abstract: The application of kernel methods to link analysis is explored. In particular, Kandola et al.'s Neumann kernels are shown to subsume not only the co-citation and bibliographic coupling relatedness but also Kleinberg's HITS importance. These popular measures of relatedness and importance correspond to the Neumann kernels at the extremes of their parameter range, and hence these kernels can be interpreted as defining a spectrum of link analysis measures intermediate between co-citation/bibliographic coupling and HITS. We also show that the kernels based on the graph Laplacian, including the regularized Laplacian and diffusion kernels, provide relatedness measures that overcome some limitations of co-citation relatedness. The property of these kernel-based link analysis measures is examined with a network of bibliographic citations. Practical issues in applying these methods to real data are discussed, and possible solutions are proposed.


title: Radiographic markers of acetabular retroversion: correlation of the cross-over sign, ischial spine sign and posterior wall sign.

abstract: Radiological diagnosis of acetabular retroversion is based on the presence of the cross-over sign (COS), the posterior wall sign (PWS), and prominence of the ischial spine (PRISS). The primary purpose of the study was to correlate the quantitative cross-over sign with the presence or absence of the PRISS and PWS signs. The hypothesis was that both, PRISS and PWS are associated with a higher cross-over sign ratio or higher amount of acetabular retroversion. A previous study identified 1417 patients with a positive acetabular cross-over sign. Among these, three radiological parameters were assessed: (1) the amount of acetabular retroversion, quantified as a cross-over sign ratio; (2) the presence of the PRISS sign; (3) the presence of the PWS sign. The relation of these three parameters was analysed using Fisher's exact test, ANOVA, and linear regression analysis. In hips with cross-over sign, the PRISS was present in 61.7%. A direct association between PRISS and the cross-over sign ratio (p < 0.001) was seen. The PWS was positive in 31% of the hips and was also significantly related with the cross-over sign ratio (p < 0.001). In hips with a PRISS, 39.7% had a PWS sign, which was a significant relation (p < 0.001). In patients with positive PWS, 78.8% of the cases also had a PRISS (p < 0.001). Both the PRISS and PWS signs were significantly associated with higher grade cross-over values. Both the PRISS and PWS signs as well as the coexistence of COS, PRISS, and PWS are significantly associated with higher grade of acetabular retroversion. In conjunction with the COS, the PRISS and PWS signs indicate severe acetabular retroversion. Presence and recognition of distinct radiological signs around the hip joint might raise the awareness of possible femoroacetabular impingement (FAI).


title: Minimizing DNF formulas and AC/sup 0//sub d/ circuits given a truth table

abstract: For circuit classes R, the fundamental computational problem Min-R asks for the minimum R-size of a Boolean function presented as a truth table. Prominent examples of this problem include Min-DNF, which asks whether a given Boolean function presented as a truth table has a k-term DNF, and Min-Circuit (also called MCSP), which asks whether a Boolean function presented as a truth table has a size k Boolean circuit. We present a new reduction proving that Min-DNF is NP-complete. It is significantly simpler than the known reduction of Masek (1979), which is from Circuit-SAT. We then give a more complex reduction, yielding the result that Min-DNF cannot be approximated to within a factor smaller than (log N)/sup /spl Upsi//, for some constant /spl Upsi/ > 0, assuming that NP is not contained in quasipolynomial time. The standard greedy algorithm for set cover is often used in practice to approximate Min-DNF. The question of whether Min-DNF can be approximated to within a factor of o(log N) remains open, but we construct an instance of Min-DNF on which the solution produced by the greedy algorithm is /spl Omega/(log N) larger than optimal. Finally, we extend known hardness results for Min-TC/sup 0//sub d/ to obtain new hardness results for Min-AC/sup 0//sub d/, under cryptographic assumptions.