title: InPrivate Digging: Enabling Tree-based Distributed Data Mining with Differential Privacy

abstract: Data mining has heralded the major breakthrough in data analysis, serving as a “super cruncher” to discover hidden information and valuable knowledge in big data systems. For many applications, the collection of big data usually involves various parties who are interested in pooling their private data sets together to jointly train machine-learning models that yield more accurate prediction results. However, data owners may not be willing to disclose their own data due to privacy concerns, making it imperative to provide privacy guarantee in collaborative data mining over distributed data sets. In this paper, we focus on tree-based data mining. To begin with, we design novel privacy-preserving schemes for two most common tasks: regression and binary classification, where individual data owners can perform training locally in a differentially private manner. Then, for the first time, we design and implement a privacy-preserving system for gradient boosting decision tree (GBDT), where different regression trees trained by multiple data owners can be securely aggregated into an ensemble. We conduct extensive experiments to evaluate the performance of our system on multiple real-world data sets. The results demonstrate that our system can provide a strong privacy protection for individual data owners while maintaining the prediction accuracy of the original trained model.


title: Effects of Intraruminal versus Intravenous Infusions of Acetone on the Ruminating and Masticating Behavior of Goats

abstract: Acetone, which is produced from butyric acid when it passes through the rumen wall, was infused into the rumen and jugular veins of three female goats to investigate the role of acetone in ruminating and masticating behavior. The ruminating behavior, as measured by the number of boli and the ruminating time, decreased (p<0.05) with intraruminal acetone infusion. However, the ruminating behavior did not change significantly in response to intravenous acetone infusion. Feed intake significantly decreased with intraruminal acetone infusion, but not with intravenous acetone infusion. The concentrations of acetone in the plasma increased significantly (p<0.05) with both acetone infusion regions. Ruminal fluid acetone, and isopropyl alcohol (IPA), which is one of the ketone bodies, produced from acetone by bacterial action in rumen, concentrations were significantly increased (p<0.05) with both acetone infusion regions. These results suggest that the chemoreceptors sensitive to acetone are more likely to be in the rumen epithelium, portal system, or liver, where they can respond to acetone levels. (Asian-Aust. J. Anim. Sci. 2003. Vol 16, No. 2 : 198-203)


title: On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products

abstract: Machine learning algorithms increasingly influence our decisions and interact with us in all parts of our daily lives. Therefore, just as we consider the safety of power plants, highways, and a variety of other engineered socio-technical systems, we must also take into account the safety of systems involving machine learning. Heretofore, the definition of safety has not been formalized in a machine learning context. In this paper, we do so by defining machine learning safety in terms of risk, epistemic uncertainty, and the harm incurred by unwanted outcomes. We then use this definition to examine safety in all sorts of applications in cyber-physical systems, decision sciences, and data products. We find that the foundational principle of modern statistical machine learning, empirical risk minimization, is not always a sufficient objective. Finally, we discuss how four different categories of strategies for achieving safety in engineering, including inherently safe design, safety reserves, safe fail, and procedural safeguards can be mapped to a machine learning context. We then discuss example techniques that can be adopted in each category, such as considering interpretability and causality of predictive models, objective functions beyond expected prediction accuracy, human involvement for labeling difficult or rare examples, and user experience design of software and open data.


title: Anomaly detection using Support Vector Machine classification with k-Medoids clustering

abstract: Anomaly based Intrusion Detection System, in the recent years, has become more dependent on learning methods — specially on classifications schemes. To make the classification more accurate and effective, hybrid approaches of combining with clustering techniques are often introduced. In this paper, a better combination is proposed to address problems of the previously proposed hybrid approach of combining k-Means/k-Medoids clustering technique with Naive Bayes classification. In this new approach, the need of large samples by the previous approach is reduced by using Support Vector Machine while maintaining the high quality clustering of k-Medoids. Simulations have been carried out by using Kyoto2006+ data sets in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Experiments and analyses show that the new approach is better in increasing the detection rate as well as in decreasing the false positive rate.


title: The VOT2013 challenge: overview and additional results

abstract: Visual tracking has attracted a significant attention in the last few decades. The recent surge in the number of publications on tracking-related problems have made it almost impossible to follow the developments in the field. One of the reasons is that there is a lack of commonly accepted annotated data-sets and standardized evaluation protocols that would allow objective comparison of different tracking methods. To address this issue, the Visual Object Tracking (VOT) challenge and workshop was organized in conjunction with ICCV2013. Researchers from academia as well as industry were invited to participate in the first VOT2013 challenge which aimed at single-object visual trackers that do not apply pre-learned models of object appearance (model-free). In this paper we provide an overview of the VOT2013 challenge, point out its main results and document the additional previously unpublished experiments and results.


title: Sexual Arousal Patterns of Bisexual Men

abstract: There has long been controversy about whether bisexual men are substantially sexually aroused by both sexes. We investigated genital and self-reported sexual arousal to male and female sexual stimuli in 30 heterosexual, 33 bisexual, and 38 homosexual men. In general, bisexual men did not have strong genital arousal to both male and female sexual stimuli. Rather, most bisexual men appeared homosexual with respect to genital arousal, although some appeared heterosexual. In contrast, their subjective sexual arousal did conform to a bisexual pattern. Male bisexuality appears primarily to represent a style of interpreting or reporting sexual arousal rather than a distinct pattern of genital sexual arousal.


title: Understanding the Heat Shock Response in the Sea Cucumber Apostichopus japonicus, Using iTRAQ-Based Proteomics

abstract: The sea cucumber Apostichopus japonicus is exploited as a commercial species owing to their high nutritive and medicinal value. Recent high summer temperatures have caused high mortality rates in A. japonicus. In this study, we applied the isobaric tag for relative and absolute quantitation (iTRAQ) technique to investigate the global protein expression profile under an acute short-term (48 h) heat stress. In total, 3432 proteins were identified, and 127 proteins showed significant heat stress responses, with 61 upregulated proteins and 66 downregulated proteins. Our results suggest that heat stress influenced the expression of proteins involved in various biological processes, such as tissue protection and detoxification, lipid and amino acid metabolism, energy production and usage, transcription and translation, cell apoptosis, and cell proliferation. These findings provide a better understanding about the response and thermo-tolerance mechanisms of A. japonicus under heat stress.


title: A Modeling Framework for Supply Chain Simulation: Opportunities for Improved Decision Making

abstract: Owing to its inherent modeling flexibility, simulation is often regarded as the proper means for supporting decision making on supply chain design. The ultimate success of supply chain simulation, however, is determined by a combination of the analyst's skills, the chain members' involvement, and the modeling capabilities of the simulation tool. This combination should provide the basis for a realistic simulation model, which is both transparent and complete. The need for transparency is especially strong for supply chains as they involve (semi)autonomous parties each having their own objectives. Mutual trust and model effectiveness are strongly influenced by the degree of completeness of each party's insight into the key decision variables. Ideally, visual interactive simulation models present an important communicative means for realizing the required overview and insight. Unfortunately, most models strongly focus on physical transactions, leaving key decision variables implicit for some or all of the parties involved. This especially applies to control structures, that is, the managers or systems responsible for control, their activities and their mutual attuning of these activities. Control elements are, for example, dispersed over the model, are not visualized, or form part of the time-indexed scheduling of events. In this article, we propose an alternative approach that explicitly addresses the modeling of control structures. First, we will conduct a literature survey with the aim of listing simulation model qualities essential for supporting successful decision making on supply chain design. Next, we use this insight to define an object-oriented modeling framework that facilitates supply chain simulation in a more realistic manner. This framework is meant to contribute to improved decision making in terms of recognizing and understanding opportunities for improved supply chain design. Finally, the use of the framework is illustrated by a case example concerning a supply chain for chilled salads.


title: A tool for understanding object-oriented program dependencies

abstract: We present a tool for understanding and re engineering C++ programs called OO!CARE (Object-Oriented Computer Aided Re Engineering). OO!CARE demonstrates some practical solutions to the problem of extracting and visualizing object oriented program dependencies (i.e. data objects and their relationships). It is an extension of an earlier tool for maintaining C programs called CARE (Computer Aided Re engineering). We also discuss some early experiences acquired from using the tool. For instance, an important observation made during a re engineering exercise is that some characteristics of the object-oriented programming paradigm such as inheritance and polymorphism contribute significantly to the complexity of understanding program dependencies. Moreover, we discuss how object-oriented program dependencies differ from the procedural ones and explain how they can be visualized within the same environment. >


POS Tagger and Chunker for Tamil Language


title: An efficient graph-mining method for complicated and noisy data with real-world applications

abstract: In this paper, we present a novel graph database-mining method called APGM (APproximate Graph Mining) to mine useful patterns from noisy graph database. In our method, we designed a general framework for modeling noisy distribution using a probability matrix and devised an efficient algorithm to identify approximate matched frequent subgraphs. We have used APGM to both synthetic data set and real-world data sets on protein structure pattern identification and structure classification. Our experimental study demonstrates the efficiency and efficacy of the proposed method.


title: V8 Storming: How Far Should Two Ideas Be?

abstract: All innovative ideas are said to be pairings of existing ideas. In collaborative ideation, various methods are used to suggest words for stimulating ideation based on the theory that suggesting strongly related words to the ideation topic will activate and enhance the ideation. However, the relationship between the words suggested by the system and the words that users associate remains unclear. Solving this issue will help selecting words suggestion in computer-supported ideation systems, in which the information flow is fluid and, therefore, users have strictly limited time for interaction. To address this issue, we studied the relationship between pairs of words suggested by computer and the quantity of associated ideas by users. We also conducted a user study on practical applications.


title: A Robust Printed Antenna for UHF Wearable Applications

abstract: A novel printed antenna is designed for wearable and RFID applications in the UHF band. The antenna design is performed following energy-based design guidelines available in the recent literature. High robustness with respect to the human body coupling is achieved by confining the electric energy density in a region far from the antenna borders. The proposed configuration also provides a compact realization, and a high versatility, since it can be easily matched to different commercial chips. A prototype of this wearable antenna has been manufactured and tested in an anechoic environment. Experimental measurements of the read range confirm the results of the numerical simulations and demonstrate the antenna robustness with respect to the variations of the body–antenna coupling scenario.


title: Pixel-level Semantics Guided Image Colorization

abstract: While many image colorization algorithms have recently shown the capability of producing plausible color versions from gray-scale photographs, they still suffer from the problems of context confusion and edge color bleeding. To address context confusion, we propose to incorporate the pixel-level object semantics to guide the image colorization. The rationale is that human beings perceive and distinguish colors based on the object's semantic categories. We propose a hierarchical neural network with two branches. One branch learns what the object is while the other branch learns the object's colors. The network jointly optimizes a semantic segmentation loss and a colorization loss. To attack edge color bleeding we generate more continuous color maps with sharp edges by adopting a joint bilateral upsamping layer at inference. Our network is trained on PASCAL VOC2012 and COCO-stuff with semantic segmentation labels and it produces more realistic and finer results compared to the colorization state-of-the-art.


title: Truth and deception at the rhetorical structure level

abstract: This paper furthers the development of methods to distinguish truth from deception in textual data. We use rhetorical structure theory RST as the analytic framework to identify systematic differences between deceptive and truthful stories in terms of their coherence and structure. A sample of 36 elicited personal stories, self-ranked as truthful or deceptive, is manually analyzed by assigning RST discourse relations among each story's constituent parts. A vector space model VSM assesses each story's position in multidimensional RST space with respect to its distance from truthful and deceptive centers as measures of the story's level of deception and truthfulness. Ten human judges evaluate independently whether each story is deceptive and assign their confidence levels 360 evaluations total, producing measures of the expected human ability to recognize deception. As a robustness check, a test sample of 18 truthful stories with 180 additional evaluations is used to determine the reliability of our RST-VSM method in determining deception. The contribution is in demonstration of the discourse structure analysis as a significant method for automated deception detection and an effective complement to lexicosemantic analysis. The potential is in developing novel discourse-based tools to alert information users to potential deception in computer-mediated texts.


title: NTFS Directory Index Analysis for Computer Forensics

abstract: This work provides a forensic analysis method for a directory index in NTFS file system. NTFS employed B-tree indexing for providing efficient storage of many files and fast lookups, which changes in a structure of the directory index when files are operated. As a forensic view point, we observe behaviors of the B-tree to analyze files that once existed in the directory. However, it is difficult to analyze the allocated index entry when the file commands are executed. So, this work treats a forensic method for a directory index, especially when there are a large number of files in the directory. The index entry records are naturally expanded, then we examine how the index entry records are configured in the index tree. And we provide information that how the directory index nodes are changed and how the index entries remain traces in the index entry record with a computer forensic point of view when the files are deleted.


title: Visual Question Answering with Memory-Augmented Networks

abstract: In this paper, we exploit a memory-augmented neural network to predict accurate answers to visual questions, even when those answers occur rarely in the training set. The memory network incorporates both internal and external memory blocks and selectively pays attention to each training exemplar. We show that memory-augmented neural networks are able to maintain a relatively long-term memory of scarce training exemplars, which is important for visual question answering due to the heavy-tailed distribution of answers in a general VQA setting. Experimental results on two large-scale benchmark datasets show the favorable performance of the proposed algorithm with a comparison to state of the art.


Chapter 2 Bacteria for Plant Growth Promotion and Disease Management


title: Managing information technology (IT) for one-to-one customer interaction

abstract: A one-to-one marketing paradigm has emerged that suggests organizations will be more successful if they concentrate on obtaining and maintaining a share of each customer rather than a share of the entire market, with information technology (IT) being the enabling factor. This paper presents four key elements that provide the necessary steps that allow an organization to position its people, business processes, and information systems to establish and take advantage of this emerging paradigm. The key elements are: (1) business process analysis, (2) integration and redesign of customer data, (3) IT-enabled customer interaction, and (4) accessibility/transmission of organizational information. Further, this paper discusses the importance of integrating these four IT elements for achieving effective customer interaction.


title: Translating into Morphologically Rich Languages with Synthetic Phrases

abstract: Translation into morphologically rich languages is an important but recalcitrant problem in MT. We present a simple and effective approach that deals with the problem in two phases. First, a discriminative model is learned to predict inflections of target words from rich source-side annotations. Then, this model is used to create additional sentencespecific word- and phrase-level translations that are added to a standard translation model as “synthetic” phrases. Our approach relies on morphological analysis of the target language, but we show that an unsupervised Bayesian model of morphology can successfully be used in place of a supervised analyzer. We report significant improvements in translation quality when translating from English to Russian, Hebrew and Swahili.


title: Terramechanics-based wheel-terrain interaction model and its applications to off-road wheeled mobile robots

abstract: This paper presents a wheel-terrain interaction model, which enables efficient modeling of wheeled locomotion in soft soil and numerical simulations of off-road mobile robots. This modular model is derived based on wheel kinematics and terramechanics and the main focus is on describing the stress distributions along the wheel-terrain interface and the reaction forces exerted on the wheel by the soil. When the wheels are steered, the shear stresses underneath the wheel were modeled based on isotropic assumptions. The forces and torques contributed by the bulldozing effect of the side surfaces is also considered in the proposed model. Furthermore, the influence of grousers, commonly used on smaller mobile robots, was modeled by (1) averaging the normal pressures contributed by the grousers and the wheel concave portion, and (2) assuming that the shear phenomenon takes places along the grouser tips. By integrating the model with multibody system code for vehicle dynamics, simulation studies of various off-road conditions in three-dimensional environments can be conducted. The model was verified by using field experiment data, both for a single-wheel vehicle and a whole vehicle.


title: Using Hidden Semi-Markov Models for Effective Online Failure Prediction

abstract: A proactive handling of faults requires that the risk of upcoming failures is continuously assessed. One of the promising approaches is online failure prediction, which means that the current state of the system is evaluated in order to predict the occurrence of failures in the near future. More specifically, we focus on methods that use event-driven sources such as errors. We use hidden semi-Markov models (HSMMs)for this purpose and demonstrate effectiveness based on field data of a commercial telecommunication system. For comparative analysis we selected three well-known failure prediction techniques: a straightforward method that is based on a reliability model, dispersion frame technique by Lin and Siewiorek and the eventset-based method introduced by Vilalta et al. We assess and compare the methods in terms of precision, recall, F-measure, false-positive rate, and computing time. The experiments suggest that our HSMM approach is very effective with respect to online failure prediction.


Chapter 2 Motion Analysis : Past , Present and Future


title: Event detection and popularity prediction in microblogging

abstract: Abstract As one of the most influential social media platforms, microblogging is becoming increasingly popular in the last decades. Each day a large amount of events appear and spread in microblogging. The spreading of events and corresponding comments on them can greatly influence the public opinion. It is practical important to discover new emerging events in microblogging and predict their future popularity. Traditional event detection and information diffusion models cannot effectively handle our studied problem, because most existing methods focus only on event detection but ignore to predict their future trend. In this paper, we propose a new approach to detect burst novel events and predict their future popularity simultaneously. Specifically, we first detect events from online microblogging stream by utilizing multiple types of information, i.e., term frequency, and user׳s social relation. Meanwhile, the popularity of detected event is predicted through a proposed diffusion model which takes both the content and user information of the event into account. Extensive evaluations on two real-world datasets demonstrate the effectiveness of our approach on both event detection and their popularity prediction.


title: Integration and evaluation of intrusion detection for CoAP in smart city applications

abstract: Abstract — The Constrained Application Protocol (CoAP) is a promising candidate for future smart city applications that run on resource-constrained devices. However, additional security means are mandatory to cope with the high security requirements of smart city applications. We present a framework to evaluate lightweight intrusion detection techniques for CoAP applications. This framework combines an OMNeT++ simulation with C/C++ application code that also runs on real hardware. As the result of our work, we used our framework to evaluate intrusion detection techniques for a smart public transport application that uses CoAP. Our first evaluations indicate that a hybrid IDS approach is a favorable choice for smart city applications. Index Terms — Smart city; Critical Infrastructure protection; Intrusion detection; Simulation; OMNeT++ I. I NTRODUCTION Smart city applications such as smart buildings, or smart public transport can improve our everyday life and comfort. City administrations, businesses, and citizens will benefit from easier information sharing as well as optimized processes applied to smart city applicationand control. Nevertheless, security and safety are essential requirements Application Protocol (CoAPfor smart city applications since they may operate on Critical Infrastructures. For their task, smart city applications interact with various networks and devices. A smart city application may send data over public networks such as the internet, cellular networks, networks that belong to different operators, or self-organized wireless sensor networks. The devices used in these networks vary from large cloud and network servers, through customer equipment, down to small sensor nodes. This heterogeneous structure poses a variety of challenges, in particular applying security to such systems. Therefore, the protection of smart city applications is essential since they will form parts of Critical Infrastructures. Protection must preferably be made equally available on small sensor nodes as well as on powerful servers.


title: Designing mobile health technology for bipolar disorder: a field trial of the monarca system

abstract: An increasing number of pervasive healthcare systems are being designed, that allow people to monitor and get feedback on their health and wellness. To address the challenges of self-management of mental illnesses, we have developed the MONARCA system - a personal monitoring system for bipolar patients. We conducted a 14 week field trial in which 12 patients used the system, and we report findings focusing on their experiences. The results were positive; compared to using paper-based forms, the adherence to self-assessment improved; the system was considered very easy to use; and the perceived usefulness of the system was high. Based on this study, the paper discusses three HCI questions related to the design of personal health technologies; how to design for disease awareness and self-treatment, how to ensure adherence to personal health technologies, and the roles of different types of technology platforms.


title: Image-based synthesis of Chinese landscape painting

abstract: This paper describes a new framework for synthesizing Chinese landscape painting using an image-based approach. The framework involves two stages: a preprocessing phase, in which a few brush stroke texture primitivities (BSTP) are collected from samples of handmade Chinese paintings, and the control picture is constructed to provide color IDs of mountains, and the on-line phases, in which the fog image is synthesized and mountains are "drawn" by mapping multiple layers of BSTP guided by the control picture. When more complex shading is needed, the shading picture is constructed and used during the BSTP mapping phase. Finally, the synthesized Chinese landscape paintings of a variety of styles are given and they look more close to the handmade work than those produced with previous modeling methods.


title: Privacy-preserving access control model for big data cloud

abstract: Due to the proliferation of advanced analytic applications built on a massive scale of data from several data sources, big data technology has emerged to shift the paradigm of data management. Big data management is usually taken into data outsourcing environment such as cloud computing. According to the outsourcing environment, security and privacy management becomes one of the critical issues for business decision. Typically, cryptographic-based access control is employed to support privacy-preserving authentication and authorization for data outsourcing scenario. In this paper, we propose a novel access control model combining Role-based Access Control (RBAC) model, symmetric encryption, and ciphertext attribute-based encryption (CP-ABE) to support finegrained access control for big data outsourced in cloud storage systems. We also demonstrate the efficiency and performance of our proposed scheme through the implementation.


title: Vertical versus dynamic menus on the world wide web: Eye tracking study measuring the influence of menu design and task complexity on user performance and subjective preference

abstract: Web sites need fast and effective navigation systems. An eye tracking laboratory study with n=120 participants was conducted to compare the influence of different navigation designs (vertical versus dynamic menus) and task complexity (simple versus complex navigation tasks) on user performance, navigation strategy, and subjective preference. With vertical menus, users needed less eye fixations, were faster and more successful. We conclude that, firstly, vertical menus fit better to perception and cognition than dynamic menus, where the navigation items are hidden and must be accessed by an additional mouse click. Secondly, navigation systems should be extended with different kinds of navigation items adapted to the complexity of the users' navigation tasks, because users tend to switch their navigation strategy when confronted with complex tasks.


title: Error Mechanisms of the Oscillometric Fixed-Ratio Blood Pressure Measurement Method

abstract: The oscillometric fixed-ratio method is widely employed for non-invasive measurement of systolic and diastolic pressures (SP and DP) but is heuristic and prone to error. We investigated the accuracy of this method using an established mathematical model of oscillometry. First, to determine which factors materially affect the errors of the method, we applied a thorough parametric sensitivity analysis to the model. Then, to assess the impact of the significant parameters, we examined the errors over a physiologically relevant range of those parameters. The main findings of this model-based error analysis of the fixed-ratio method are that: (1) SP and DP errors drastically increase as the brachial artery stiffens over the zero trans-mural pressure regime; (2) SP and DP become overestimated and underestimated, respectively, as pulse pressure (PP) declines; (3) the impact of PP on SP and DP errors is more obvious as the brachial artery stiffens over the zero trans-mural pressure regime; and (4) SP and DP errors can be as large as 58 mmHg. Our final and main contribution is a comprehensive explanation of the mechanisms for these errors. This study may have important implications when using the fixed-ratio method, particularly in subjects with arterial disease.


title: Proper imputation techniques for missing values in data sets

abstract: Data mining requires a pre-processing task in which the data are prepared and cleaned for ensuring the quality. Missing value occurs when no data value is stored for a variable in an observation. This has a significant effect on the results especially when it leads to biased parameter estimates. It will not only diminish the quality of the result, but also disqualify for analysis purposes. Hence there are risks associated with missing values in a dataset. Imputation is a technique of replacing missing data with substituted values. This research presents a comparison of imputation techniques such as Mean\Mode, K-Nearest Neighbor, Hot-Deck, Expectation Maximization and C5.0 for missing data. The choice of proper imputation method is based on datatypes, missing data mechanisms, patterns and methods. Datatype can be numerical, categorical or mixed. Missing data mechanism can be missing completely at random, missing at random, or not missing at random. Patterns of missing data can be with respect to cases or attributes. Methods can be a pre-replace or an embedded method. These five imputation techniques are used to impute artificially created missing data from different data sets of varying sizes. The performance of these techniques are compared based on the classification accuracy and the results are presented.


title: A Guide to Recurrent Neural Networks and Backpropagation

abstract: This paper provides guidance to some of the concepts surrounding recurrent neural networks. Contrary to feedforward networks, recurrent networks can be sensitive, and be adapted to past inputs. Backpropagation learning is described for feedforward networks, adapted to suit our (probabilistic) modeling needs, and extended to cover recurrent networks. The aim of this brief paper is to set the scene for applying and understanding recurrent neural networks.


title: Heart Track: Automated ECG Analysis for Detecting Myocardial Infarction

abstract: The paper presents a system, Heart Track, which aims for automated ECG (Electrocardiogram) analysis. Different modules and algorithms which are proposed and used for implementing the system are discussed. The ECG is the recording of the electrical activity of the heart and represents the depolarization and repolarization of the heart muscle cells and the heart chambers. The electrical signals from the heart are measured non-invasively using skin electrodes and appropriate electronic measuring equipment. ECG is measured using 12 leads which are placed at specific positions on the body [2]. The required data is converted into ECG curve which possesses a characteristic pattern. Deflections from this normal ECG pattern can be used as a diagnostic tool in medicine in the detection of cardiac diseases. Diagnosis of large number of cardiac disorders can be predicted from the ECG waves wherein each component of the ECG wave is associated with one or the other disorder. This paper concentrates entirely on detection of Myocardial Infarction, hence only the related components (ST segment) of the ECG wave are analyzed.


title: More Effective Synchronization Scheme in ML Using Stale Parameters

abstract: In Machine learning (ML) the model we use is increasingly important, and the model's parameters, the key point of the ML, are adjusted through iteratively processing a training dataset until convergence. Although data-parallel ML systems often engage a perfect error tolerance when synchronizing the model parameters for maximizing parallelism, the synchronization of model parameters may delay in completion, a problem that generally gets worse at a large scale. This paper presents a Bounded Asynchronous Parallel (BAP) model of computation that allows computations using stale model parameters in order to reduce synchronization overheads. In the meanwhile, our BAP model ensures theoretical convergence guarantees for large scale data-parallel ML applications. This model permits distributed workers to use the stale parameters storing in the local cache, instead of waiting until the Parameter Server (PS) produces a new version. This expressively reduces the time workers spend on waiting. Furthermore, the BAP model guarantees the convergence of ML algorithm by bounding the maximum distance of the stale parameters. Experiments conducted on 4 cluster nodes with up to 32 GPUs showed that our model significantly improved the proportion of computing time relative to the waiting time and led to 1.2–2×speedup. Besides, we elaborated how to choose the staleness threshold when considering the tradeoff between Efficiency and Speed.


title: A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests

abstract: We propose a new scoring function for learning Bayesian networks from data using score+search algorithms. This is based on the concept of mutual information and exploits some well-known properties of this measure in a novel way. Essentially, a statistical independence test based on the chi-square distribution, associated with the mutual information measure, together with a property of additive decomposition of this measure, are combined in order to measure the degree of interaction between each variable and its parent variables in the network. The result is a non-Bayesian scoring function called MIT (mutual information tests) which belongs to the family of scores based on information theory. The MIT score also represents a penalization of the Kullback-Leibler divergence between the joint probability distributions associated with a candidate network and with the available data set. Detailed results of a complete experimental evaluation of the proposed scoring function and its comparison with the well-known K2, BDeu and BIC/MDL scores are also presented.


title: Energy Management System of Fuel-Cell-Battery Hybrid Tramway

abstract: This paper describes the configuration, modeling, and control of a fuel cell (FC)-battery-powered hybrid system for the Metro Centro tramway in Seville, Spain. The proposed hybrid system presents a polymer electrolyte membrane FC as the primary energy source and a nickel-metal hydride cell battery as the secondary energy source, which supplements the output of the FC during tramway acceleration or whenever else needed and cruise and for energy recovery during braking. The tramway presents a traction system which is composed of four traction induction motor drives. The hybrid system also supplies the power for the auxiliary services. The power conditioning system is composed of two converters: 1) a boost-type unidirectional dc/dc converter for the FC and 2) a boost-type bidirectional dc/dc converter for the battery. The energy management system (EMS) of the hybrid tramway determines the reference signals for the electric motor drives, FC, and power converters in order to regulate accurately the power from the two electrical sources. EMS also determines the reference signal for energy dissipation in braking chopper when required during regenerative braking. In this paper, the proposed hybrid system is evaluated for the real driving cycle of the tramway. The results demonstrate the hybrid system capability to meet appropriate driving cycle.


title: Design and implementation of multiple-rotorcraft-flying-robot testbed

abstract: In this paper, a multiple-rotorcraft-flying-robot (MRFR) testbed is introduced. Firstly, physical structure, as well as hardware and software system, is described in detail. Subsequently, in order to realize the autonomous flight of each single rotorcraft-flying-robot (RFR), modeling, identification and flight control experiments are conducted. Then, formation flying demonstration is presented to verify the basic functions of the new-designed MRFR testbed. Finally, two points should be emphasized: 1) both cooperation/coordination algorithm of MRFR and advanced flight control algorithm of single RFR can be verified on this testbed; 2) experiments on this testbed can approach real flight test much better because three real RFR systems are used.


A Survey of Clustering with Instance Level Constraints 1 2


title: Characterizing ELL Students' Behavior During MOOC Videos Using Content Type

abstract: Making MOOCs accessible to English Language Learners (ELLs) requires that students understand the language of instruction, and that instructional strategies address their unique learning challenges. Through the analysis of clickstream log data gathered from two MOOC courses deployed on Coursera, Introduction to Psychology and Statistical Thermodynamics, we show that ELL students exhibit distinct struggle behaviors in video portions without visual aids e.g., narrations without slides. Our findings challenge widely accepted multimedia design principles such as the split attention effect, provide insights into designing MOOC videos, and emphasize the need for adaptivity to increase MOOC access for ELLs.


title: Performing computation offloading on multiple platforms

abstract: An offloading framework designed for supporting multiple platforms is proposed.The solution supports Android and Windows Phone mobile applications.Developers can use static or dynamic offloading decision.The offloading technique improves the performance of mobile applications.The type of serialization used impacts the offloading performance. Mobile devices such as smart phones and tablets are increasingly important tools in daily routine. These devices generally interact with more powerful machines usually hosted on public clouds. In this context, this paper presents MpOS (Multiplatform Offloading System), a framework that supports a method-based offloading technique for applications of different mobile platforms (Android and Windows Phone). In addition, details of MpOS main components and services as well as code examples are presented. To evaluate the proposed solution and to analyse the impact of different serialization types on the offloading performance, we developed two applications and performed several experiments on both Android and Windows Phone platforms using WiFi and 4G/LTE connections to access the remote execution environments. Our results state that offloading to a cloudlet has provided the best performance for both Android and Windows Phone platforms, beyond showing that the type of serialization used by the framework directly impacts on the offloading performance.


title: Statistical mechanics of complex networks

abstract: The emergence of order in natural systems is a constant source of inspiration for both physical and biological sciences. While the spatial order characterizing for example the crystals has been the basis of many advances in contemporary physics, most complex systems in nature do not offer such high degree of order. Many of these systems form complex networks whose nodes are the elements of the system and edges represent the interactions between them. ::: Traditionally complex networks have been described by the random graph theory founded in 1959 by Paul Erdohs and Alfred Renyi. One of the defining features of random graphs is that they are statistically homogeneous, and their degree distribution (characterizing the spread in the number of edges starting from a node) is a Poisson distribution. In contrast, recent empirical studies, including the work of our group, indicate that the topology of real networks is much richer than that of random graphs. In particular, the degree distribution of real networks is a power-law, indicating a heterogeneous topology in which the majority of the nodes have a small degree, but there is a significant fraction of highly connected nodes that play an important role in the connectivity of the network. ::: The scale-free topology of real networks has very important consequences on their functioning. For example, we have discovered that scale-free networks are extremely resilient to the random disruption of their nodes. On the other hand, the selective removal of the nodes with highest degree induces a rapid breakdown of the network to isolated subparts that cannot communicate with each other. ::: The non-trivial scaling of the degree distribution of real networks is also an indication of their assembly and evolution. Indeed, our modeling studies have shown us that there are general principles governing the evolution of networks. Most networks start from a small seed and grow by the addition of new nodes which attach to the nodes already in the system. This process obeys preferential attachment: the new nodes are more likely to connect to nodes with already high degree. We have proposed a simple model based on these two principles wich was able to reproduce the power-law degree distribution of real networks. Perhaps even more importantly, this model paved the way to a new paradigm of network modeling, trying to capture the evolution of networks, not just their static topology.


title: An Approximate, Efficient Solver for LP Rounding

abstract: Many problems in machine learning can be solved by rounding the solution of an appropriate linear program (LP). This paper shows that we can recover solutions of comparable quality by rounding an approximate LP solution instead of the exact one. These approximate LP solutions can be computed efficiently by applying a parallel stochastic-coordinate-descent method to a quadratic-penalty formulation of the LP. We derive worst-case runtime and solution quality guarantees of this scheme using novel perturbation and convergence analysis. Our experiments demonstrate that on such combinatorial problems as vertex cover, independent set and multiway-cut, our approximate rounding scheme is up to an order of magnitude faster than Cplex (a commercial LP solver) while producing solutions of similar quality.


title: Design and Algorithm Optimization of P2P Mobile Monitoring Network-Based Facial Recognition System

abstract: To meet the demand of provisional emergency monitoring in large-scale outdoor rallies and emergency situations, this paper designs a P2P mobile monitoring system which would be based P2P wireless network infrastructure. The mobile terminal function of the system is designed to be able to make monitoring recording, play videos and realize P2P video session in the form of streaming media. The embedded design and algorithm optimization of the system’s facial recognition function gives more well-rounded performance to the system in terms of safeguarding, dispatch and commanding. This paper would design the system’s P2P network infrastructure and the function module of the mobile terminal software and propose the optimization of the facial recognition algorithm based on this system.


title: A new alternating minimization algorithm for total variation image reconstruction

abstract: We propose, analyze, and test an alternating minimization algorithm for recovering images from blurry and noisy observations with total variation (TV) regularization. This algorithm arises from a new half-quadratic model applicable to not only the anisotropic but also the isotropic forms of TV discretizations. The per-iteration computational complexity of the algorithm is three fast Fourier transforms. We establish strong convergence properties for the algorithm including finite convergence for some variables and relatively fast exponential (or $q$-linear in optimization terminology) convergence for the others. Furthermore, we propose a continuation scheme to accelerate the practical convergence of the algorithm. Extensive numerical results show that our algorithm performs favorably in comparison to several state-of-the-art algorithms. In particular, it runs orders of magnitude faster than the lagged diffusivity algorithm for TV-based deblurring. Some extensions of our algorithm are also discussed.


title: Friendship and mobility: user movement in location-based social networks

abstract: Even though human movement and mobility patterns have a high degree of freedom and variation, they also exhibit structural patterns due to geographic and social constraints. Using cell phone location data, as well as data from two online location-based social networks, we aim to understand what basic laws govern human motion and dynamics. We find that humans experience a combination of periodic movement that is geographically limited and seemingly random jumps correlated with their social networks. Short-ranged travel is periodic both spatially and temporally and not effected by the social network structure, while long-distance travel is more influenced by social network ties. We show that social relationships can explain about 10% to 30% of all human movement, while periodic behavior explains 50% to 70%. Based on our findings, we develop a model of human mobility that combines periodic short range movements with travel due to the social network structure. We show that our model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance than present models of human mobility.


title: Generating test cases to expose concurrency bugs in Android applications

abstract: Mobile systems usually support an event-based model of concurrent programming. This model, although advantageous to maintain responsive user interfaces, may lead to subtle concurrency errors due to unforeseen threads interleaving coupled with non-deterministic reordering of asynchronous events. These bugs are very difficult to reproduce even by the same user action sequences that trigger them, due to the undetermined schedules of underlying events and threads. In this paper, we proposed RacerDroid, a novel technique that aims to expose concurrency bugs in android applications by actively controlling event schedule and thread interleaving, given the test cases that have potential data races. By exploring the state model of the application constructed dynamically, our technique starts first to generate a test case that has potential data races based on the results obtained from existing static or dynamic race detection technique. Then it reschedules test cases execution by actively controlling event dispatching and thread interleaving to determine whether such potential races really lead to thrown exceptions or assertion violations. Our preliminary experiments show that RacerDroid is effective, and it confirms real data races, while at the same time eliminates false warnings for Android apps found in the wild.


title: Differential power analysis

abstract: Cryptosystem designers frequently assume that secrets will be manipulated in closed, reliable computing environments. Unfortunately, actual computers and microchips leak information about the operations they process. This paper examines specific methods for analyzing power consumption measurements to find secret keys from tamper resistant devices. We also discuss approaches for building cryptosystems that can operate securely in existing hardware that leaks information.


title: An integrated hybrid power system based on renewable energy sources with electric wind MPPT

abstract: This paper presents the behaviour of a hybrid system based on renewable sources (wind and solar) with their stochastic behaviour and a pre-programmed timed load profile. The goal is the analysis of a small hybrid system in the context of new services that can be offered to the grids as a power generator. Both the sources have continuous maximum power extraction where the Maximum Power Point Tracking (MPPT) of the wind generator is based on its DC power output. The structure of the system is presented and the local and global control is developed. Simulation and conclusions about the behaviour of the system are presented.


The Reasonable Effectiveness of Synthetic Visual Data


title: Explaining consumer satisfaction of services : The role of innovativeness and emotion in an electronic mediated environment

abstract: The services section has started to dominate economic activity in many industrialized economies since the last decade. The growth of services in Electronic Mediated Environment (EME) has changed the manner in which firms and consumers interact. Drawing on such theoretical foundations as trust, risk perceptions, trying, emotion, and satisfaction, this study proposes an integrative research model to investigate the relationships among these constructs in the EME context. We collected data from 415 consumers and found that consumer innovativeness and emotion are significant antecedents of trust, risk perception of service, perceived benefits, and service quality. These important factors will influence consumer satisfaction in EME. This empirical investigation breaks a new ground for future studies to understand and gauge the influence of innovativeness and emotion in emerging IT artifacts such as EME services. Innovativeness and emotions are antecedents of risk perception, trust, service quality, and perceived benefits.Interactions exist among consumer innovativeness, emotion, service quality, benefits, trust, and risks of EME services.EME service satisfaction is determined by service quality, service benefits, consumer's trust, and perceived risks


End-to-End Sentiment Analysis of Twitter Data


title: Developing an expert system based on association rules and predicate logic for earthquake prediction

abstract: Expert systems (ES) are a branch of applied artificial intelligence. The basic idea behind ES is simply that expertise, which is the vast body of task-specific knowledge, is transferred from a human to a computer. ES provide powerful and flexible means for obtaining solutions to a variety of problems that often cannot be dealt with by other, more traditional and orthodox methods. Thus, their use is proliferating to many sectors of our social and technological life, where their applications are proving to be critical in the process of decision support and problem solving. Earthquake professionals for many decades have recognized the benefits to society from reliable earthquake predictions, but uncertainties regarding source initiation, rupture phenomena, and accuracy of both the timing and magnitude of the earthquake occurrence have often times seemed either very difficult or impossible to overcome. This research proposes and implements an expert system to predict earthquakes from previous data. This is achieved by applying association rule mining on earthquake data from 1972 to 2013. These associations are polished using predicate-logic techniques to draw stimulating production-rules to be used with a rule-based expert system. The proposed expert system was able to predict all earthquakes which actually occurred within 12h at-most.


Context-Sensitive Inference Rule Discovery: A Graph-Based Method


title: Multilevel thresholding image segmentation using memetic algorithm

abstract: Memetic algorithms (MAs) are hybrid algorithms aimed to improve traditional evolutionary algorithms such as genetic algorithms (GAs). Recently, MAs have been widely used in the image processing field. This paper proposes an image segmentation approach using MA. The proposed approach employs local search as an improvement operator added to the GA in order to speed up the searching process and generate the best solutions faster. The results of the experiments that were conducted on eight different images have shown that MA converges to the solutions faster than GA. In terms of processing time, MA has recorded significant improvement ranging from 17.5% to 79.8%. The comparison results have also shown that MA always achieves the same or better quality of the segmented images.


title: Type-2 fuzzy logic controller for conical AMB-rotor system

abstract: In the present work, the design of controller for complete support of a rotor by Conical Active Magnetic Bearings (AMBs) is presented. These bearings eliminate the requirement of an axial AMB which results in reduction of cost and required mounting space. The presented controller is designed with the aim to reduce the requirement of exact system modelling. Fuzzy logic controller (FLC) satisfies this requirement since it can be designed by the knowledge of system behaviour or performance. However, the determination of different parameters of FLC is based on the designer knowledge and experience about the operational characteristics of the system. In the present work, efforts are made to enhance the system performance by designing the Interval Type-2 fuzzy logic controller (IT2FLC) with uncertain bound algorithm. This structure is better suited for handling parameters uncertainty. The simulations are carried out and performance of the designed controller is validated by comparing it with that of normal FLC.


title: Preparation of zinc oxide particles by using layered basic zinc acetate as a precursor

abstract: Abstract TEA and NaOH are applied to transform layered basic zinc acetate into zinc oxide particles by a simple aqueous solution process (


title: Songle: A Web Service for Active Music Listening Improved by User Contributions

abstract: This paper describes a public web service for active music listening, Songle, that enriches music listening experiences by using music-understanding technologies based on signal processing. Although various research-level interfaces and technologies have been developed, it has not been easy to get people to use them in everyday life. Songle serves as a showcase to demonstrate how people can benefit from music-understanding technologies by enabling people to experience active music listening interfaces on the web. Songle facilitates deeper understanding of music by visualizing music scene descriptions estimated automatically, such as music structure, hierarchical beat structure, melody line, and chords. When using music-understanding technologies, however, estimation errors are inevitable. Songle therefore features an efficient error correction interface that encourages people to contribute by correcting those errors to improve the web service. We also propose a mechanism of collaborative training for music-understanding technologies, in which corrected errors will be used to improve the music-understanding performance through machine learning techniques. We hope Songle will serve as a research platform where other researchers can exhibit results of their music-understanding technologies to jointly promote the popularization of the field of music information research.


title: Sprobes: Enforcing Kernel Code Integrity on the TrustZone Architecture

abstract: Many smartphones now deploy conventional operating systems, so the rootkit attacks so prevalent on desktop and server systems are now a threat to smartphones. While researchers have advocated using virtualization to detect and prevent attacks on operating systems (e.g., VM introspection and trusted virtual domains), virtualization is not practical on smartphone systems due to the lack of virtualization support and/or the expense of virtualization. Current smartphone processors do have hardware support for running a protected environment, such as the ARM TrustZone extensions, but such hardware does not control the operating system operations sufficiently to enable VM introspection. In particular, a conventional operating system running with TrustZone still retains full control of memory management, which a rootkit can use to prevent traps on sensitive instructions or memory accesses necessary for effective introspection. In this paper, we present SPROBES, a novel primitive that enables introspection of operating systems running on ARM TrustZone hardware. Using SPROBES, an introspection mechanism protected by TrustZone can instrument individual operating system instructions of its choice, receiving an unforgeable trap whenever any SPROBE is executed. The key challenge in designing SPROBES is preventing the rootkit from removing them, but we identify a set of five invariants whose enforcement is sufficient to restrict rootkits to execute only approved, SPROBE-injected kernel code. We implemented a proof-of-concept version of SPROBES for the ARM Fast Models emulator, demonstrating that in Linux kernel 2.6.38, only 12 SPROBES are sufficient to enforce all five of these invariants. With SPROBES we show that it is possible to leverage the limited TrustZone extensions to limit conventional kernel execution to approved code comprehensively.


title: Computer Aided Business Model Design: Analysis of Key Features Adopted by Users

abstract: Business model innovation comprises many theoretical models, frameworks and methodologies; however, there is still little in the way of digital support for them using Computer Aided Design software. In this paper, we present an analysis of real-world usage data from the first generation of such tools that were designed to support the business model canvas. We first present how two artifacts implement features such as colors for grouping, custom attributes and positioning of elements. An examination of how digital support brings about new opportunities in business modeling allows us to compare this with a paper-based version. We then analyze how variations of these features have been used in the real world. Finally, we examine the implications of our observations for the next generation of tools and the advancement of research for dedicated Computer Aided Design tools to support strategic objects such as business models.


title: Micro-grid energy dispatch optimization and predictive control algorithms; A UC Irvine case study

abstract: Distributed power and energy resources are now being used to meet the combined electric power, heating, and cooling demands of many buildings. The addition of on-site renewables and their accompanying intermittency and non-coincidence requires even greater dynamic performance from the distributed power and energy system. Load following generators, energy storage devices, and predictive energy management are increasingly important to achieve the simultaneous goals of increased efficiency, reduced emissions, and sustainable economics. This paper presents two optimization strategies for the dispatch of a multi-chiller cooling plant with cold-water thermal storage. The optimizations aim to reduce both costs and emissions while considering real operational constraints of a plant. The UC Irvine campus micro-grid operation between January 2009 and December 2013 serves as a case study for how improved utilization of energy storage can buffer demand transients, reduce costs and improve plant efficiency. A predictive control strategy which forecasts campus demands from weather predictions, optimizes the plant dispatch, and applies feedback control to modify the plant dispatch in real-time is compared to best-practices manual operation. The dispatch optimization and predictive control algorithms are shown to reduce annual utility bill costs by 12.0%, net energy costs by 3.61%, and improve energy efficiency by 1.56%.


title: Adaptation and learning over networks for nonlinear system modeling

abstract: Abstract In this chapter, we analyze nonlinear filtering problems in distributed environments, e.g., sensor networks or peer-to-peer protocols. In these scenarios, the agents in the environment receive measurements in a streaming fashion, and they are required to estimate a common (nonlinear) model by alternating local computations and communications with their neighbors. We focus on the important distinction between single-task problems, where the underlying model is common to all agents, and multitask problems, where each agent might converge to a different model due to, e.g., spatial dependencies. Currently, most of the literature on distributed learning in the nonlinear case has focused on the single-task case, which may be a strong limitation in real-world scenarios. After introducing the problem and reviewing the existing approaches, we describe a simple kernel-based algorithm tailored for the multitask case. We evaluate the proposal on a simulated benchmark task, and we conclude by detailing currently open problems and lines of research.


title: A Zero-Voltage Switching Three-Phase Inverter

abstract: A new modulation scheme for the active clamping zero-voltage switching (ZVS) inverter is proposed. With the proposed modulation scheme, the inverter can realize ZVS operation in all switching devices and can remarkably suppress the reverse recovery current in insulated-gate bipolar transistor's antiparallel diodes as well. All the switches can operate at a fixed frequency in the new modulation scheme. Also, with the new modulation scheme, the ZVS inverter has some voltage boost capability, which is suitable for renewable energy generation. The inverter can achieve ZVS in all the switches under the grid current power factor angle from zero to ±π/6. The soft-switching condition of this inverter is analyzed. The operation principal and design guideline are described in detail. The previous theory is verified in a 30-kW inverter prototype.


A quantitative study of petal size and shape inSaxifraga granulata L.


title: Tangles and plaques in nondemented aging and “preclinical” Alzheimer’s disease

abstract: The distribution and density of neurofibrillary tangles and amyloid plaques was studied in a unique series of cases whose premortem cognitive status had been assessed with the Clinical Dementia Rating (CDR), including 39 nondemented cases (CDR = 0; age, 51-88 years), 15 very mildly demented cases (CDR = 0.5), and 8 severely demented (CDR = 3) cases. The initial formation of tangles and plaques in healthy aging appeared to be independent of each other. Tangles were found in all the nondemented cases, especially in hippocampal and parahippocampal areas; the average tangle concentration increased exponentially with age. In contrast, plaques were absent in some brains up to age 88, and the earliest plaque formation in other cases occurred in the neocortex, in patches of diffuse plaques. Widely distributed neuritic as well as diffuse plaques throughout neocortex and limbic structures characterized a further group of nondemented cases. In these cases there was also a substantial increase over other nondemented cases, both in the number of tangles and in the rate of increase in tangles with age, suggesting an interaction between amyloid and neurofibrillary change at this stage. Such cases closely resemble CDR = 0.5 cases, and it is proposed they represent "preclinical" Alzheimer's disease.


Feature Selection and Weighting Methods in Sentiment Analysis


title: Novel Construction of Distinct Perfect Polyphase Sequences with Large Family Size

abstract: In a wireless communication system, sequences with impulsive periodic autocorrelation correlation, known as perfect sequences, are widely applied to, e.g., such sequences can be made use of as synchronous signals. In this paper, based on interleaving technique, a novel construction of distinct perfect polyphase sequences (PSs) is presented. The proposed perfect PSs possess the same phase alphabet size and sequence length as Frank perfect PSs, however, the former includes the latter as a special case and its family size is much larger than the latter's.


title: Exploring threats and vulnerabilities in hacker web: Forums, IRC and carding shops

abstract: Cybersecurity is a problem of growing relevance that impacts all facets of society. As a result, many researchers have become interested in studying cybercriminals and online hacker communities in order to develop more effective cyber defenses. In particular, analysis of hacker community contents may reveal existing and emerging threats that pose great risk to individuals, businesses, and government. Thus, we are interested in developing an automated methodology for identifying tangible and verifiable evidence of potential threats within hacker forums, IRC channels, and carding shops. To identify threats, we couple machine learning methodology with information retrieval techniques. Our approach allows us to distill potential threats from the entirety of collected hacker contents. We present several examples of identified threats found through our analysis techniques. Results suggest that hacker communities can be analyzed to aid in cyber threat detection, thus providing promising direction for future work.


Reasoning about Consensus when Opinions Diffuse through Majority Dynamics


title: Evaluating and Complementing Vision-to-Language Technology for People who are Blind with Conversational Crowdsourcing

abstract: We study how real-time crowdsourcing can be used both for evaluating the value provided by existing automated approaches and for enabling workflows that provide scalable and useful alt text to blind users. We show that the shortcomings of existing AI image captioning systems frequently hinder a user's understanding of an image they cannot see to a degree that even clarifying conversations with sighted assistants cannot correct. Based on analysis of clarifying conversations collected from our studies, we design experiences that can effectively assist users in a scalable way without the need for real-time interaction. Our results provide lessons and guidelines that the designers of future AI captioning systems can use to improve labeling of social media imagery for blind users.


title: Pruning based Distance Sketches with Provable Guarantees on Random Graphs

abstract: Measuring the distances between vertices on graphs is one of the most fundamental components in network analysis. Since finding shortest paths requires traversing the graph, it is challenging to obtain distance information on large graphs very quickly. In this work, we present a preprocessing algorithm that is able to create landmark based distance sketches efficiently, with strong theoretical guarantees. When evaluated on a diverse set of social and information networks, our algorithm significantly improves over existing approaches by reducing the number of landmarks stored, preprocessing time, or stretch of the estimated distances. On Erdos-Renyi graphs and random power law graphs with degree distribution exponent 2


title: BARS: A Blockchain-Based Anonymous Reputation System for Trust Management in VANETs

abstract: The public key infrastructure (PKI) based authentication protocol provides the basic security services for vehicular ad-hoc networks (VANETs). However, trust and privacy are still open issues due to the unique characteristics of vehicles. It is crucial for VANETs to prevent internal vehicles from broadcasting forged messages while simultaneously protecting the privacy of each vehicle against tracking attacks. In this paper, we propose a blockchain-based anonymous reputation system (BARS) to break the linkability between real identities and public keys to preserve privacy. The certificate and revocation transparency is implemented efficiently using two blockchains. We design a trust model to improve the trustworthiness of messages relying on the reputation of the sender based on both direct historical interactions and indirect opinions about the sender. Experiments are conducted to evaluate BARS in terms of security and performance and the results show that BARS is able to establish distributed trust management, while protecting the privacy of vehicles.


title: The relation between social network site usage and loneliness and mental health in community-dwelling older adults

abstract: Background ::: Loneliness is expected to become an even bigger social problem in the upcoming decades, because of the growing number of older adults. It has been argued that the use of social network sites can aid in decreasing loneliness and improving mental health. The purpose of this study was to examine whether and how social network sites usage is related to loneliness and mental health in community-dwelling older adults. ::: ::: Method ::: The study population included community-dwelling older adults aged 60 and over residing in the Netherlands (n = 626) collected through the LISS panel (www.lissdata.nl). Univariate and multivariate linear regression analyses, adjusted for potentially important confounders, were conducted in order to investigate the relation between social network sites usage and (emotional and social) loneliness and mental health. ::: ::: Results ::: More than half of the individuals (56.2%) reported to use social network sites at least several times per week. Social network sites usage appeared unrelated to loneliness in general, and to emotional and social loneliness in particular. Social network sites usage also appeared unrelated to mental health. Several significant associations between related factors and the outcomes at hand were detected. ::: ::: Conclusion ::: In this sample, which was representative for the Dutch population, social network sites usage was unrelated to loneliness and/or mental health. The results indicate that a simple association between social network site usage and loneliness and mental health as such, cannot automatically be assumed in community-dwelling older adults. Copyright © 2014 John Wiley & Sons, Ltd.


Knowledge Via An Explanatory Graph


title: DEMN: Distilled-Exposition Enhanced Matching Network for Story Comprehension

abstract: This paper proposes a Distilled-Exposition Enhanced Matching Network (DEMN) for story-cloze test, which is still a challenging task in story comprehension. We divide a complete story into three narrative segments: an \textit{exposition}, a \textit{climax}, and an \textit{ending}. The model consists of three modules: input module, matching module, and distillation module. The input module provides semantic representations for the three segments and then feeds them into the other two modules. The matching module collects interaction features between the ending and the climax. The distillation module distills the crucial semantic information in the exposition and infuses it into the matching module in two different ways. We evaluate our single and ensemble model on ROCStories Corpus \cite{Mostafazadeh2016ACA}, achieving an accuracy of 80.1\% and 81.2\% on the test set respectively. The experimental results demonstrate that our DEMN model achieves a state-of-the-art performance.


title: Which Way Am I Facing: Inferring Horizontal Device Orientation from an Accelerometer Signal

abstract: We present a method to infer the orientation of mobile device carried in a pocket from the acceleration signal acquired when the user is walking. Whereas previous work has shown how to determine the the orientation in the vertical plane (angle towards earth gravity), we demonstrate how to compute the orientation within the horizontal plane. To validate our method we compare the output of our method with GPS heading information when walking in a straight line. On a total of 16 different orientations and traces we have a mean difference of 5 degrees with 2.5 degrees standard deviation.


title: A supply chain traceability system for food safety based on HACCP, blockchain & Internet of things

abstract: In recent times food safety has drawn upsurge of academic and commercial concerns. In supply chain area, with the rapid growth of internet technologies, a lot of emerging technologies have been applied in traceability systems. However, to date, nearly all of these systems are centralized which are monopolistic, asymmetric and opaque that could result in the trust problem, such as fraud, corruption, tampering and falsifying information. Besides, centralized system is vulnerable to collapse, since a single point of breakdown will lead the whole system to be crashed. Today, a new technology called the blockchain which is a ground-breaking innovation in decentralized information technology presents a whole new approach. However, since this technology is still in its early stages, it has some inherent defects, in which scalability become a primary and urgent one when we face the mass data in the real world. In this paper we will build a food supply chain traceability system for real-time food tracing based on HACCP (Hazard Analysis and Critical Control Points), blockchain and Internet of things, which could provide an information platform for all the supply chain members with openness, transparency, neutrality, reliability and security. Furthermore, we introduce a new concept BigchainDB to fill the gap in the decentralized systems at scale. The paper concludes with a description of a use case and the challenges to adopt blockchain technology in the future food supply chain traceability systems are discussed.


title: Predicting Students Drop Out: A Case Study

abstract: The monitoring and support of university freshmen is considered very important at many educational institutions. In this paper we describe the results of the educational data mining case study aimed at predicting the Electrical Engineering (EE) students drop out after the first semester of their studies or even before they enter the study program as well as identifying success-factors specific to the EE program. Our experimental results show that rather simple and intuitive classifiers (decision trees) give a useful result with accuracies between 75 and 80%. Besides, we demonstrate the usefulness of cost-sensitive learning and thorough analysis of misclassifications, and show a few ways of further prediction improvement without having to collect additional data about the students.


Annotating meta-discourse in academic lectures from different disciplines.


title: From Properties to Links: Deep Network Embedding on Incomplete Graphs

abstract: As an effective way of learning node representations in networks, network embedding has attracted increasing research interests recently. Most existing approaches use shallow models and only work on static networks by extracting local or global topology information of each node as the algorithm input. It is challenging for such approaches to learn a desirable node representation on incomplete graphs with a large number of missing links or on dynamic graphs with new nodes joining in. It is even challenging for them to deeply fuse other types of data such as node properties into the learning process to help better represent the nodes with insufficient links. In this paper, we for the first time study the problem of network embedding on incomplete networks. We propose a Multi-View Correlation-learning based Deep Network Embedding method named MVC-DNE to incorporate both the network structure and the node properties for more effectively and efficiently perform network embedding on incomplete networks. Specifically, we consider the topology structure of the network and the node properties as two correlated views. The insight is that the learned representation vector of a node should reflect its characteristics in both views. Under a multi-view correlation learning based deep autoencoder framework, the structure view and property view embeddings are integrated and mutually reinforced through both self-view and cross-view learning. As MVC-DNE can learn a representation mapping function, it can directly generate the representation vectors for the new nodes without retraining the model. Thus it is especially more efficient than previous methods. Empirically, we evaluate MVC-DNE over three real network datasets on two data mining applications, and the results demonstrate that MVC-DNE significantly outperforms state-of-the-art methods.


Building Brand Equity through Advertising Africa Makasi


title: C?aSH: Structural Descriptions of Synchronous Hardware Using Haskell

abstract: CλaSH is a functional hardware description language that borrows both its syntax and semantics from the functional programming language Haskell. Polymorphism and higher-order functions provide a level of abstraction and generality that allow a circuit designer to describe circuits in a more natural way than possible with the language elements found in the traditional hardware description languages. Circuit descriptions can be translated to synthesizable VHDL using the prototype CλaSH compiler. As the circuit descriptions, simulation code, and test input are also valid Haskell, complete simulations can be done by a Haskell compiler or interpreter, allowing high-speed simulation and analysis.


title: The Human Genome Contracts Again

abstract: Summary: The number of human genomes that have been sequenced completely for different individuals has increased rapidly in recent years. Storing and transferring complete genomes between computers for the purpose of applying various applications and analysis tools will soon become a major hurdle, hindering the analysis phase. Therefore, there is a growing need to compress these data efficiently. Here, we describe a technique to compress human genomes based on entropy coding, using a reference genome and known Single Nucleotide Polymorphisms (SNPs). Furthermore, we explore several intrinsic features of genomes and information in other genomic databases to further improve the compression attained. Using these methods, we compress James Watson’s genome to 2.5megabytes (MB), improving on recent work by 37%. Similar compression is obtained for most genomes available from the 1000 Genomes Project. Our biologically inspired techniques promise even greater gains for genomes of lower organisms and for human genomes as more genomic data become available. Availability: Code is available at sourceforge.net/projects/genomezip/


title: Towards Automatic Visual Obstacle Avoidance

abstract: This report describes ongoing research on a working system which drives a vehicle through cluttered environments under computer control, guided by images perceived through an onboard television camera. The emphasis is on reliable and fast low level visual techniques which determine the existence and location of objects in the world, but do not identify them


title: Sentiment Classification of Chinese Microblogging Texts with Global RNN

abstract: Microblogging websites such as twitter and Sina Weibo have attracted many users to share their experiences and express their opinions on a variety of topics. Sentiment classification of microblogging texts is of great significance in analyzing users' opinion on products, persons and hot topics. However, conventional bag-of-words-based sentiment classification methods may meet some problems in processing Chinese microblogging texts because they does not consider semantic meanings of texts. In this paper, we proposed a global RNN-based sentiment method, which use the outputs of all the time-steps as features to extract the global information of texts, for sentiment classification of Chinese microblogging texts and explored different RNN-models. The experiments on two Chinese microblogging datasets show that the proposed method achieves better performance than conventional bag-of-words-based methods.


title: A multiscale theory for the dynamical evolution of sentiment in novels

abstract: Recent work in literary sentiment analysis has suggested that shifts in emotional valence may serve as a reliable proxy for plot movement in novels. The raw sentiment time series of a novel can now be extracted using a variety of different methods, and after extraction, filtering is commonly used to smooth the irregular sentiment time series. Using an adaptive filter, which is among the most effective in determining trends of a signal, reducing noise, and performing fractal and multifractal analysis, we show that the energy of the smoothed sentiment signals decays with the smoothing parameter as a power-law, characterized by a Hurst parameter H of 1/2 < H < 1, which signifies long-range correlations. We further show that a smoothed sentiment arc corresponds to the sentiment of fast playing mode or sentiment retained in one's memory, and that for a novel to be both captivating and rich, H has to be larger than 1/2 but cannot be too close to 1.


title: Super-Resolution Sparse MIMO-OFDM Channel Estimation Based on Spatial and Temporal Correlations

abstract: This letter proposes a parametric sparse multiple input multiple output (MIMO)-OFDM channel estimation scheme based on the finite rate of innovation (FRI) theory, whereby super-resolution estimates of path delays with arbitrary values can be achieved. Meanwhile, both the spatial and temporal correlations of wireless MIMO channels are exploited to improve the accuracy of the channel estimation. For outdoor communication scenarios, where wireless channels are sparse in nature, path delays of different transmit-receive antenna pairs share a common sparse pattern due to the spatial correlation of MIMO channels. Meanwhile, the channel sparse pattern is nearly unchanged during several adjacent OFDM symbols due to the temporal correlation of MIMO channels. By simultaneously exploiting those MIMO channel characteristics, the proposed scheme performs better than existing state-of-the-art schemes. Furthermore, by joint processing of signals associated with different antennas, the pilot overhead can be reduced under the framework of the FRI theory.


title: Adversarial training of quantum Born machine

abstract: Generative adversarial network (GAN) is an effective machine learning framework to train unsupervised generative models, and has drawn lots of attention in recent years. In the meantime, there are some researches that use parameterized quantum circuits to generate simple patterns. In this paper, we present a quantum version of GAN, where parameterized quantum circuits are trained by an adversarial discriminator, to generate new samples that follows the probability distribution of a given training dataset. Two families of quantum circuits, both composed of simple one-qubit rotation and two-qubit controlled-phase gates, are considered. The parameters are learned through classical gradient descent optimization. The results of a small-scale proof-of-principle numerical experiment demonstrate that quantum circuits can be trained in an adversarial way for generative tasks.


title: A BROADBAND SUSPENDED MICROSTRIP ANTENNA FOR CIRCULAR POLARIZATION

abstract: In this paper we propose a circularly polarized (CP) microstrip antenna on a suspended substrate with a coplanar capacitive feed and a slot within the rectangular patch. The antenna has an axial ratio bandwidth (< 3 dB) of 7.1%. The proposed antenna exhibits a much higher impedance bandwidth of about 49% (S11 < -10 dB) and also yields return loss better than -15 dB in the useful range of circular polarization. Measured characteristics of the antenna are in good agreement with the simulated results. The radiation patterns indicate good cross polarization rejection and low back lobe radiations. The design proposed here can be scaled to any frequency of interest.


title: Texture Mapping for 3D Reconstruction with RGB-D Sensor

abstract: Acquiring realistic texture details for 3D models is important in 3D reconstruction. However, the existence of geometric errors, caused by noisy RGB-D sensor data, always makes the color images cannot be accurately aligned onto reconstructed 3D models. In this paper, we propose a global-to-local correction strategy to obtain more desired texture mapping results. Our algorithm first adaptively selects an optimal image for each face of the 3D model, which can effectively remove blurring and ghost artifacts produced by multiple image blending. We then adopt a non-rigid global-to-local correction step to reduce the seaming effect between textures. This can effectively compensate for the texture and the geometric misalignment caused by camera pose drift and geometric errors. We evaluate the proposed algorithm in a range of complex scenes and demonstrate its effective performance in generating seamless high fidelity textures for 3D models.


title: Analysis of key-exchange protocols and their use for building secure channels

abstract: We present a formalism for the analysis of key-exchange protocols that combines previous definitional approaches and results in a definition of security that enjoys some important analytical benefits: (i) any key-exchange protocol that satisfies the security definition can be composed with symmetric encryption and authentication functions to provide provably secure communication channels (as defined here); and (ii) the definition allows for simple modular proofs of security: one can design and prove security of key-exchange protocols in an idealized model where the communication links are perfectly authenticated, and then translate them using general tools to obtain security in the realistic setting of adversary-controlled links. ::: ::: We exemplify the usability of our results by applying them to obtain the proof of two classes of key-exchange protocols, Diffie-Hellman and key-transport, authenticated via symmetric or asymmetric techniques.


title: Position Estimation for Mobile Robots in Dynamic Environments

abstract: For mobile robots to be successful, they have to navigate safely in populated and dynamic environments. While recent research has led to a variety of localization methods that can track robots well in static environments, we still lack methods that can robustly localize mobile robots in dynamic environments, in which people block the robot's sensors for extensive periods of time or the position of furniture may change. This paper proposes extensions to Markov localization algorithms enabling them to localize mobile robots even in densely populated environments. Two different filters for determining the "believability" of sensor readings are employed. These filters are designed to detect sensor readings that are corrupted by humans or unexpected changes in the environment. The technique was recently implemented and applied as part of an installation, in which a mobile robot gave interactive tours to visitors of the "Deutsches Museum Bonn." Extensive empirical tests involving datasets recorded during peak traffic hours in the museum demonstrate that this approach is able to accurately estimate the robot's position in more than 98% of the cases even in such highly dynamic environments.


title: CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure

abstract: Motivation: Clustering of individuals into populations on the basis of multilocus genotypes is informative in a variety of settings. In population-genetic clustering algorithms, such as BAPS, STRUCTURE and TESS, individual multilocus genotypes are partitioned over a set of clusters, often using unsupervised approaches that involve stochastic simulation. As a result, replicate cluster analyses of the same data may produce several distinct solutions for estimated cluster membership coefficients, even though the same initial conditions were used. Major differences among clustering solutions have two main sources: (1) ‘label switching’ of clusters across replicates, caused by the arbitrary way in which clusters in an unsupervised analysis are labeled, and (2) ‘genuine multimodality,’ truly distinct solutions across replicates. Results: To facilitate the interpretation of population-genetic clustering results, we describe three algorithms for aligning multiple replicate analyses of the same data set. We have implemented these algorithms in the computer program CLUMPP (CLUster Matching and Permutation Program). We illustrate the use of CLUMPP by aligning the cluster membership coefficients from 100 replicate cluster analyses of 600 chickens from 20 different breeds. Availability: CLUMPP is freely available at http://rosenberglab.


The Effect of Organic Compounds in Pot Experiments


title: PAL, a tool for Pre-annotation and Active Learning

abstract: Many natural language processing systems rely on machine learning models that are trained on large amounts of manually annotated text data. The lack of sufficient amounts of annotated data is, however, a common obstacle for such systems, since manual annotation of text is often expensive and time-consuming. The aim of “PAL", a tool for Pre-annotation and Active Learning” is to provide a ready-made package that can be used to simplify annotation and to reduce the amount of annotated data required to train a machine learning classifier. The package provides support for two techniques that have been shown to be successful in previous studies, namely active learning and pre-annotation. The output of the pre-annotation is provided in the annotation format of the annotation tool BRAT, but PAL is a stand-alone package that can be adapted to other formats. (Less)


title: Interactive whiteboards: boon or bandwagon? A critical review of the literature

abstract: This article reviews the literature concerning the introduction of interactive whiteboards (IWBs) in educational settings. It identifies common themes to emerge from a burgeoning and diverse literature, which includes reports and summaries available on the Internet. Although the literature reviewed is overwhelmingly positive about the impact and the potential of IWBs, it is primarily based on the views of teachers and pupils. There is insufficient evidence to identify the actual impact of such technologies upon learning either in terms of classroom interaction or upon attainment and achievement. This article examines this issue in light of varying conceptions of interactivity and research into the effects of learning with verbal and visual information.


title: A role-based distributed publish/subscribe system in IoT

abstract: With the emerging of IoT (Internet of Things), the scale of Internet is extended extremely and large amounts of information are produced. Most previous work only focuses on the interconnection and interoperability problems among different entities and enterprise application systems in a dynamic interaction environment. However, the problems of how to efficiently disseminate the sensing information among the information providers and consumers on-demand still have not been comprehensively explored. In order to delivery various types of data in IoT on-demand, this paper proposed a role-based distributed publish/subscribe system (RBDS). RBDS is based on a hierarchy routing strategy of Grid. Firstly, the subscribers are mapped to appropriate role broker node by the organization of IoT data. Secondly, divide broker nodes into different groups based on role. In each group, broker nodes are separated into super nodes and common nodes according to their ability. Those super nodes are organized in upper Grid structure to distribute events in different groups. And the common nodes are organized in lower Grid structure to distribute events in the same group. It improves the scalability and decreases latency. At last, calculate the load of each broker node dynamically based on its capacity and its subscribers. Thereby the total number of subscribers on each broker node is efficiently balanced. Experimental results show that the RBDS can improve scalability, decrease latency and balance load effectively.


title: Looking at LTE in Practice: A Performance Analysis of the LTE System Based on Field Test Results

abstract: This article introduces a practical performance analysis of the long-term evolution (LTE) cellular system based on field test results from a commercially deployed Third-Generation Partnership Project's (3GPP?s) Release 8 LTE network in Band-3 (i.e., 1,800 MHz, sometimes termed LTE1,800 MHz) with 20-MHz channel bandwidth. The presented analysis demonstrates the downlink (DL) and uplink (UL) throughputs in mobility conditions in addition to other aspects of the LTE system such as link budget, cell coverage, spectral efficiency, and handover performance. LTE's key features such as multiple-input, multiple-output (MIMO) and higher-order modulation [i.e., 64 quadrature amplitude modulation (QAM)] are assessed in different radio frequency (RF) conditions. A comparison is presented between LTE and 3GPP Release 8 evolution of high-speed packet access (HSPA+) with a dual-cell high-speed DL packet access (DC-HSDPA) feature based on the universal mobile telecommunications system (UMTS) 2,100-MHz commercial network with 10-MHz bandwidth (i.e., 2x5-MHz adjacent carriers). The presented LTE network performance analysis and the comparison with the HSPA+ network can be used to benchmark the LTE system performance and evaluate the gains and merits of deploying the LTE network. It has been demonstrated that the LTE system outperforms the HSPA+ system in terms of spectrum efficiency, 64-QAM utilization, cell coverage, and handover delay. The LTE system offers a 34% improvement in spectrum efficiency, 40% 64-QAM utilization versus 7.4% for the HSPA+ system, and a 60% reduction in data interruption time during handover.


title: Theory and Circuit Model of Brush Commutation in Helical Coil Electromagnetic Launchers

abstract: A model is developed to investigate the commutation process of a helical coil electromagnetic launcher (HCEML) structure. When armature moves, the motion of the projectile coils' magnetic energy would supply reactive power to move the leakage into and out of the coil windings of the launcher to accomplish the commutation. The energy conversion of the coil electromagnetic launcher can be considered as the process of energy transfer from the back commutation coils to the excited coils and then from the excited turns to the drive coils in front. In this study, governing equations of the commutation was deduced theoretically and Finite Element Model of HCEML was established in ANSOFT. Then a deeper circuit investigation on the overall performance of commutation was carried out by means of PSPICE software. The validity of the theoretical analysis and circuit network model were finally verified by experiments. The results show that during the commutation process, the current of back commutation coil drops to zero with no energy remaining in the internal; the current of front commutation coils and drive — projectile coils system changes to a stable value, which is related to the geometric parameters of the launcher; drive- projectile coils system releases energy overall that is mainly used to increase the kinetic energy of the projectile; the power supply system does little effect on the energy conversion and transformation in the commutation instant and just keeps the voltage and current not decaying from impedance and inductance of launching system.


Programming an Artificial Neural Network Tool for Spatial Interpolation in GIS - A Case Study for Indoor Radio Wave Propagation of WLAN


title: Every Prime Has a Succinct Certificate

abstract: To prove that a number n is composite, it suffices to exhibit the working for the multiplication of a pair of factors. This working, represented as af string, is of length bounded by a polynomial in $\log _2 n$. We show that the same property holds for the primes. It is noteworthy that almost no other set is known to have the property that short proofs for membership or nonmembership exist for all candidates without being known to have the property that such proofs are easy to come by. It remains an open problem whether a prime n can be recognized in only $\log _2^\alpha n$ operations of a Turing machine for any fixed $\alpha $.The proof system used for certifying primes is as follows.Axiom. $(x,y,1)$.Inference Rules. \[ R_1 :\quad(p,x,a),q \vdash (p,x,qa)\quad\text{provided }x^{(p - 1)/q} \not\equiv 1(\bmod p)\text{ and }q | (p - 1). \]\[ R_2 :\quad(p,x,p - 1) \vdash p\quad\text{provided } x^{p - 1} \equiv 1(\bmod p). \]Theorem 1. pis a theorem$\equiv p$is a prime.Theorem 2. pis a theorem$\supset p$has a...