title: Growing random forest on deep convolutional neural networks for scene categorization

abstract: Random forests are grown on convolutional neural networks for scene categorization.Features from multi-layers of deep convolutional neural networks are utilized.A feature selection method is proposed to use random forests to categorize scenes. Breakthrough performances have been achieved in computer vision by utilizing deep neural networks. In this paper we propose to use random forest to classify image representations obtained by concatenating multiple layers of learned features of deep convolutional neural networks for scene classification. Specifically, we first use deep convolutional neural networks pre-trained on the large-scale image database Places to extract features from scene images. Then, we concatenate multiple layers of features of the deep neural networks as image representations. After that, we use random forest as the classifier for scene classification. Moreover, to reduce feature redundancy in image representations we derived a novel feature selection method for selecting features that are suitable for random forest classification. Extensive experiments are conducted on two benchmark datasets, i.e. MIT-Indoor and UIUC-Sports. Obtained results demonstrated the effectiveness of the proposed method. The contributions of the paper are as follows. First, by extracting multiple layers of deep neural networks, we can explore more information of image contents for determining their categories. Second, we proposed a novel feature selection method that can be used to reduce redundancy in features obtained by deep neural networks for classification based on random forest. In particular, since deep learning methods can be used to augment expert systems by having the systems essentially training themselves, and the proposed framework is general, which can be easily extended to other intelligent systems that utilize deep learning methods, the proposed method provide a potential way for improving performances of other expert and intelligent systems.


title: TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions

abstract: Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the entangled geometric complexity and biological complexity. We introduce topology, i.e., element specific persistent homology (ESPH), to untangle geometric complexity and biological complexity. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains crucial biological information via a multichannel image representation. It is able to reveal hidden structure-function relationships in biomolecules. We further integrate ESPH and convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the limitations to deep learning arising from small and noisy training sets, we present a multitask topological convolutional neural network (MT-TCNN). We demonstrate that the present TopologyNet architectures outperform other state-of-the-art methods in the predictions of protein-ligand binding affinities, globular protein mutation impacts, and membrane protein mutation impacts.


title: Understanding Machine Learning: From Theory to Algorithms

abstract: Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.


title: Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking

abstract: The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on. These limitations add significantly to development costs and make cross-domain, multi-lingual dialogue systems intractable. Moreover, human languages are context-aware. The most natural response should be directly learned from data rather than depending on predefined syntaxes or rules. This paper presents a statistical language generator based on a joint recurrent and convolutional neural network structure which can be trained on dialogue act-utterance pairs without any semantic alignments or predefined grammar trees. Objective metrics suggest that this new model outperforms previous methods under the same experimental conditions. Results of an evaluation by human judges indicate that it produces not only high quality but linguistically varied utterances which are preferred compared to n-gram and rule-based systems.


title: TecLines: A MATLAB-Based Toolbox for Tectonic Lineament Analysis from Satellite Images and DEMs, Part 2: Line Segments Linking and Merging

abstract: Extraction and interpretation of tectonic lineaments is one of the routines for mapping large areas using remote sensing data. However, this is a subjective and time-consuming process. It is difficult to choose an optimal lineament extraction method in order to reduce subjectivity and obtain vectors similar to what an analyst would manually extract. The objective of this study is the implementation, evaluation and comparison of Hough transform, segment merging and polynomial fitting methods towards automated tectonic lineament mapping. For this purpose we developed a new MATLAB-based toolbox (TecLines). The proposed toolbox capabilities were validated using a synthetic Digital Elevation Model (DEM) and tested along in the Andarab fault zone (Afghanistan) where specific fault structures are known. In this study, we used filters in both frequency and spatial domains and the tensor voting framework to produce binary edge maps. We used the Hough transform to extract linear image discontinuities. We used B-spline as a polynomial curve fitting method to eliminate artificial line segments that are out of interest and to link discontinuous segments with similar trends. We performed statistical analyses in order to compare the final image discontinuities maps with existing references map.


title: Semantic Processing using the Hidden Vector State Model

abstract: This paper discusses semantic processing using the Hidden Vector State (HVS) model. The HVS model extends the basic discrete Markov model by encoding context in each state as a vector. State transitions are then factored into a stack shift operation similar to those of a push-down automaton followed by a push of a new preterminal semantic category label. The key feature of the model is that it can capture hierarchical structure without the use of treebank data for training. ::: ::: Experiments have been conducted in the travel domain using the relatively simple ATIS corpus and the more complex DARPA Communicator Task. The results show that the HVS model can be robustly trained from only minimally annotated corpus data. Furthermore, when measured by its ability to extract attribute-value pairs from natural language queries in the travel domain, the HVS model outperforms a conventional finite-state semantic tagger by 4.1% in F-measure for ATIS and by 6.6% in F-measure for Communicator, suggesting that the benefit of the HVS model's ability to encode context increases as the task becomes more complex.


title: Git can facilitate greater reproducibility and increased transparency in science

abstract: BackgroundReproducibility is the hallmark of good science. Maintaining a high degree of transparency in scientific reporting is essential not just for gaining trust and credibility within the scientific community but also for facilitating the development of new ideas. Sharing data and computer code associated with publications is becoming increasingly common, motivated partly in response to data deposition requirements from journals and mandates from funders. Despite this increase in transparency, it is still difficult to reproduce or build upon the findings of most scientific publications without access to a more complete workflow.FindingsVersion control systems (VCS), which have long been used to maintain code repositories in the software industry, are now finding new applications in science. One such open source VCS, Git, provides a lightweight yet robust framework that is ideal for managing the full suite of research outputs such as datasets, statistical code, figures, lab notes, and manuscripts. For individual researchers, Git provides a powerful way to track and compare versions, retrace errors, explore new approaches in a structured manner, while maintaining a full audit trail. For larger collaborative efforts, Git and Git hosting services make it possible for everyone to work asynchronously and merge their contributions at any time, all the while maintaining a complete authorship trail. In this paper I provide an overview of Git along with use-cases that highlight how this tool can be leveraged to make science more reproducible and transparent, foster new collaborations, and support novel uses.


Visualizing and Understanding Neural Machine Translation


title: Fast Guided Filter

abstract: The guided filter is a technique for edge-aware image filtering. Because of its nice visual quality, fast speed, and ease of implementation, the guided filter has witnessed various applications in real products, such as image editing apps in phones and stereo reconstruction, and has been included in official MATLAB and OpenCV. In this note, we remind that the guided filter can be simply sped up from O(N) time to O(N/s^2) time for a subsampling ratio s. In a variety of applications, this leads to a speedup of >10x with almost no visible degradation. We hope this acceleration will improve performance of current applications and further popularize this filter. Code is released.


title: Secure Operation, Control, and Maintenance of Future E-Enabled Airplanes

abstract: Commercial aviation is at the threshold of the era of the e-enabled airplane, brought about by the convergence of rapidly expanding worldwide data communication infrastructures, network-centric information processing, and commoditized lightweight computational hardware. With advanced avionics, processing, and wireless communication capabilities, the e-enabled airplane can revolutionize the current air transportation system. However, the use of unregulated information technology and wireless technologies introduces vulnerabilities that can be exploited to provide unauthorized access to the onboard aviation information systems and impede their operation. The emerging security threats are not covered by current aviation guidance, and regulations, hence, remain to be addressed. This paper presents a comprehensive survey of security of the e-enabled airplane with applications such as electronic distribution of loadable software and data, as well as future directions such as wireless health monitoring, networked control, and airborne ad hoc networks.


title: A Real-time Intrusion Detection System Based on PSO-SVM

abstract: The success of any Intrusion Detection System (IDS) is a complicated problem due to its nonlinearity and the quantitative or qualitative network traffic data stream with irrelevant and redundant features. How to choose the effective and key features to IDS is very important topic in information security. Support vector machine (SVM) has been employed to provide potential solutions for the IDS problem. However, the practicability of SVM is affected due to the difficulty of selecting appropriate SVM parameters. Particle swarm optimization (PSO) is an optimization method, which is not only has strong global search capability, but also is very easy to implement. Thus, the proposed PSO-SVM model is applied to an intrusion detection problem, the KDD Cup 99 data set. The standard PSO is used to determine free parameters of support vector machine and the binary PSO is to obtain the optimum feature subset at building intrusion detection system. The experimental results indicate that the PSO-SVM method can achieve higher detection rate than regular SVM algorithms in the same time. Index Terms—Intrusion detection system, Support vector machines (SVM), PSO, Feature selection


title: An elementary proof of linear programming optimality conditions without using Farkas' lemma

abstract: Although it is easy to prove the sufficient conditions for optimality of a linear program, the necessary conditions pose a pedagogical challenge. A widespread practice in deriving the necessary conditions is to invoke Farkas’ lemma, but proofs of Farkas’ lemma typically involve “nonlinear” topics such as separating hyperplanes between disjoint convex sets, or else more advanced LP-related material such as duality and anti-cycling strategies in the simplex method. An alternative approach taken previously by several authors is to avoid Farkas’ lemma through a direct proof of the necessary conditions. In that spirit, this paper presents what we believe to be an “elementary” proof of the necessary conditions that does not rely on Farkas’ lemma and is independent of the simplex method, relying only on linear algebra and a perturbation technique published in 1952 by Charnes. No claim is made that the results are new, but we hope that the proofs may be useful for those who teach linear programming.


Hybrid DWT-DCT algorithm for image and video compression applications


title: Chitchat: Navigating tradeoffs in device-to-device context sharing

abstract: Acquiring local context information and sharing it among co-located devices is critical for emerging pervasive computing applications. The devices belonging to a group of co-located people may need to detect a shared activity (e.g., a meeting) to adapt their devices to support the activity. Today's devices are almost universally equipped with device-to-device communication that easily enables direct context sharing. While existing context sharing models tend not to consider devices' resource limitations or users' constraints, enabling devices to directly share context has significant benefits for efficiency, cost, and privacy. However, as we demonstrate quantitatively, when devices share context via device-to-device communication, it needs to be represented in a size-efficient way that does not sacrifice its expressiveness or accuracy. We present Chitchat, a suite of context representations that allows application developers to tune tradeoffs between the size of the representation, the flexibility of the application to update context information, the energy required to create and share context, and the quality of the information shared. We can substantially reduce the size of context representation (thereby reducing applications' overheads when they share their contexts with one another) with only a minimal reduction in the quality of shared contexts.


title: Modeling Correlation between QoS Attributes for Trust Computation in Cloud Computing Environments

abstract: This paper introduces an approach that handles with the trustworthy cloud service selection issue in Cloud computing environments. Despite the fact that most of the existing trust systems consider several QoS attributes for trust computing, none of them did consider the correlation that may exist among these attributes. However, we demonstrate in this paper that the integration of correlation between QoS attributes in trust computing significantly helps to solve many issues such as predicting missing assessment, detecting malicious feedbacks and improving the accuracy of trust values. The main goal of this paper is to show the significance of correlation between those attributes for trust computing. To do that, we propose combining both the popular Naive Bayes model and the n-gram Markov model to design a more efficient trust model for cloud environments. Our proposed trust model also takes into account the user's requirements, aggregates both the qualitative and quantitative assessment, and considers several sources when computing cloud services' trust values. Experimental results show that our proposed approach outperforms the traditional Naive Bayes trust models.


title: Photobook: tools for content-based manipulation of image databases

abstract: We describe the Photobook system, which is a set of interactive tools for browsing and searching images and image sequences. These tools differ from those used in standard image databases in that they make direct use of the image content rather than relying on annotations. Direct search on image content is made possible by use of semantics-preserving image compression, which reduces images to a small set of perceptually-significant coefficients. We describe three Photobook tools in particular: one that allows search based on grey-level appearance, one that uses 2D shape, and a third that allows search based on textural properties.


title: Policy Iteration Algorithm for Online Design of Robust Control for a Class of Continuous-Time Nonlinear Systems

abstract: In this paper, a novel strategy is established to design the robust controller for a class of continuous-time nonlinear systems with uncertainties based on the online policy iteration algorithm. The robust control problem is transformed into the optimal control problem by properly choosing a cost function that reflects the uncertainties, regulation, and control. An online policy iteration algorithm is presented to solve the Hamilton-Jacobi-Bellman (HJB) equation by constructing a critic neural network. The approximate expression of the optimal control policy can be derived directly. The closed-loop system is proved to possess the uniform ultimate boundedness. The equivalence of the neural-network-based HJB solution of the optimal control problem and the solution of the robust control problem is established as well. Two simulation examples are provided to verify the effectiveness of the present robust control scheme.


title: Dual Level Security for Key Exchange using Modified RSA Public Key Encryption in Playfair Technique

abstract: The objective of this paper is to provide the dual level security for the key in playfair cipher using modified RSA digital signature scheme. This work has two phases, in the first phase the key is converted into the ASCII code and in the second phase, this work is using the concept of modified RSA digital signature scheme in which user authentication, data integrity and non-repudiation services are provided but as all know that in playfair technique, key exchange process is most important so there is a need to secure the key during key transfer that’s why this work is using the concept of modified RSA public key encryption technique to encrypt the key before sending it to the receiver for decryption. This work use a 10*8 matrix which contain all alphabetic in the uppercase as well as in lowercase, the numeric value from 0 to 9, list of operator and list of bracket. General Terms Key Security.


PHANTOM: A Scalable BlockDAG Protocol.


title: Modified leakage-based transmit filter designs for multi-user MIMO systems

abstract: This paper proposes two modified definitions of signal-to-leakage-plus-noise ratio (mSLNR) as criteria for linear transmit filter design in Multi-User MIMO systems. The proposed criteria incorporate receiver structures and priority weight matrices into the precoder design, which can effectively exploit unused receive signal spaces, in the case that available eigenmodes are not fully transmitted, and can potentially prioritise users' data streams. Iterative SLNR (iSLNR) precoding algorithms based on the proposed mSLNR definitions are also presented. Further, the variations of the iSLNR algorithms are thoroughly studied. In particular, the impact of the mSLNR definitions, choices of receive filters and iteration types on the convergence property and sum-rate performance is discussed. Moreover, the effect of weight matrices on users' prioritisation is elaborated. For extreme weight values, the proposed algorithms are shown to converge to either eigenbeamforming or null-space decomposition techniques. Further, a robust design of the proposed schemes for the case of imperfect channel state information (CSI) is presented. It is shown that adequate knowledge of effective receive subspaces can be attained by simply assuming matched filters (MF) in the iterative precoding process, while further improvement can be obtained by an actual implementation of interference-mitigation-capable receivers.


title: A Lexicon-Based Sentiment Analysis for Amazon Web Review

abstract: The development of internet more quickly over time as well as the development of e-commerce one of them is amazon.com. The amazon.com is one of the largest e-commerce in the world by providing various needs that can be accessed by internet. Review feature provided to make amazon.com party can know the various responses from consumers. However amazon.com difficulties in summarizing the various kinds of reviews that are positive or negative. By using one natural language processing the main purpose is to help the amazon.com in knowing the most responses from consumers to improve the quality service. In this research we will be using the dataset that was obtained from UCI Machine Learning that contain 1000 set of data which has 478 of negative data and 522 positive data and this will be combine a variety of classification methods for comparison and in preprocessing process will be added with lexicon technique to improve the quality of preprocessing. The result of this research is K-Nearest Neighbor with lexicon technique get highest accuracy with value 92.67% followed by SVM with lexicon get 91.33% accuracy and last Decision tree with 82% accuracy.


title: EmotionX-DLC: Self-Attentive BiLSTM for Detecting Sequential Emotions in Dialogue

abstract: In this paper, we propose a self-attentive bidirectional long short-term memory (SA-BiLSTM) network to predict multiple emotions for the EmotionX challenge. The BiLSTM exhibits the power of modeling the word dependencies, and extracting the most relevant features for emotion classification. Building on top of BiLSTM, the self-attentive network can model the contextual dependencies between utterances which are helpful for classifying the ambiguous emotions. We achieve 59.6 and 55.0 unweighted accuracy scores in the \textit{Friends} and the \textit{EmotionPush} test sets, respectively.


title: Collaborative gaming in augmented reality

abstract: We introduce a local collaborative environment for gaming. In our setup multiple users can interact with the virtual game and the real surroundings at the same time. They are able to communicate with other players during the game. We describe an augmented reality setup for multiple users with see-trough head-mounted displays, allowing dedicated stereoscopic views and individualized interaction for each user. We use face-snapping for fast and precise direct object manipulation. With face snapping and the subdivision of the gaming space into spatial regions, the semantics of actions can be derived out of geometric actions of the user. Further, we introduce a layering concept allowing individual views onto the common data structure. The layer concept allows to make privacy management very easy by simply manipulating the common data structure. Moreover, assigning layers to spatial regions carefully, a special privacy management is often not necessary. Moving objects from one region into another will automatically change their visibility and privacy for each participant. We demonstrate our system with two example board-games: Virtual Roulette and MahJongg, both relying heavily on social communication and the need of a private space.


title: The Protection of Information in Computer Systems

abstract: This tutorial paper explores the mechanics of protecting computer-stored information from unauthorized use or modification. It concentrates on those architectural structures-whether hardware or software-that are necessary to support information protection. The paper develops in three main sections. Section I describes desired functions, design principles, and examples of elementary protection and authentication mechanisms. Any reader familiar with computers should find the first section to be reasonably accessible. Section II requires some familiarity with descriptor-based computer architecture. It examines in depth the principles of modern protection architectures and the relation between capability systems and access control list systems, and ends with a brief analysts of protected subsystems and protected objects. The reader who is dismayed by either the prerequisites or the level of detail in the second section may wish to skip to Section III, which reviews the state of the art and current research projects and provides suggestions for further reading.


title: A Variable Step Size INC MPPT Method for PV Systems

abstract: Maximum power point tracking (MPPT) techniques are employed in photovoltaic (PV) systems to make full utilization of PV array output power which depends on solar irradiation and ambient temperature. Among all the MPPT strategies, the incremental conductance (INC) algorithm is widely used due to the high tracking accuracy at steady state and good adaptability to the rapidly changing atmospheric conditions. In this paper, a modified variable step size INC MPPT algorithm is proposed, which automatically adjusts the step size to track the PV array maximum power point. Compared with the conventional fixed step size method, the proposed approach can effectively improve the MPPT speed and accuracy simultaneously. Furthermore, it is simple and can be easily implemented in digital signal processors. A theoretical analysis and the design principle of the proposed method are provided and its feasibility is also verified by simulation and experimental results.


title: Reducing Wrong Labels in Distant Supervision for Relation Extraction

abstract: In relation extraction, distant supervision seeks to extract relations between entities from text by using a knowledge base, such as Freebase, as a source of supervision. When a sentence and a knowledge base refer to the same entity pair, this approach heuristically labels the sentence with the corresponding relation in the knowledge base. However, this heuristic can fail with the result that some sentences are labeled wrongly. This noisy labeled data causes poor extraction performance. In this paper, we propose a method to reduce the number of wrong labels. We present a novel generative model that directly models the heuristic labeling process of distant supervision. The model predicts whether assigned labels are correct or wrong via its hidden variables. Our experimental results show that this model detected wrong labels with higher performance than baseline methods. In the experiment, we also found that our wrong label reduction boosted the performance of relation extraction.


title: A Novel Fault-Prognostic Approach Based on Interacting Multiple Model Filters and Fuzzy Systems

abstract: An interacting multiple model (IMM) filter is a recognized method for adaptive estimation of states that is often necessary to characterize the behavior of dynamic systems with a multiple-mode operation. The traditional IMM filter adopts the measurement set to update the information about the active models. However, when this approach is used for fault-prognostic applications, it can lead to misleading state estimation. To overcome this problem, this paper proposes a novel fault-prognostic technique based on a modified IMM filter with fuzzy systems. The main methodological contribution of this paper is to build an approach based on an IMM filter able to make long-term fault predictions without measurements. This is done by incorporating a fuzzy system into the proposed IMM filter with the objective of modeling the system's dynamics and updating the probabilities of the observed modes. Furthermore, the proposed method and the standard IMM filter are compared in a numerical example and a real experimental platform PRONOSTIA for validation. The results analysis indicates a better prediction performance than the conventional IMM filter considering the failure time predictions and a measurement model not properly adjusted.


title: Uncovering divergent linguistic information in word embeddings with lessons for intrinsic and extrinsic evaluation

abstract: Following the recent success of word embeddings, it has been argued that there is no such thing as an ideal representation for words, as different models tend to capture divergent and often mutually incompatible aspects like semantics/syntax and similarity/relatedness. In this paper, we show that each embedding model captures more information than directly apparent. A linear transformation that adjusts the similarity order of the model without any external resource can tailor it to achieve better results in those aspects, providing a new perspective on how embeddings encode divergent linguistic information. In addition, we explore the relation between intrinsic and extrinsic evaluation, as the effect of our transformations in downstream tasks is higher for unsupervised systems than for supervised ones.


title: Standard Implementation for Privacy Framework and Privacy Reference Architecture for Protecting Personally Identifiable Information

abstract: Personal Identifiable Information (PII) is considered information that identifies or can be used to identify, contact, or locate a person to whom such information pertains or that is or might be linked to a natural person directly or indirectly. In order to recognize such data processed within information and communication technologies such as PII, it should be determined at which stage the information identifies, or can be associated with, an individual. For this, there has been ongoing research for privacy protection mechanism to protect PII, which now becomes one of hot issues in the International Standard as privacy framework and privacy reference architecture. Data processing flow models should be developed as an integral component of privacy risk assessments. Such diagrams are also the basis for categorizing PII. The data processing flow may not only show areas where the PII has a certain level of sensitivity or importance and, as a consequence, requires the implementation of stronger safeguarding measures. This paper propose a standard format for satisfying the ISO/IEC 29100 “Privacy Framework” and shows an implementation example for privacy reference architecture implementing privacy controls for the processing of PII in information and communication technology.


title: MODIFYING LISIANTHUS TRAITS BY GENETIC ENGINEERING

abstract: The control of flowering time is an essential issue for most ornamentals. The molecular basis of this trait has been extensively studied in model plants, yielding useful information on the role of various genes which could potentially be used in heterologous systems. For instance, overexpression of the meristem-identity gene LEAFY (LFY) from Arabidopsis has previously been shown to be sufficient to reduce flowering time in plants as diverse as Arabidopsis itself, tobacco and aspen. We tested the effect of LFY overexpression on flower development in lisianthus plants transformed with LFY cDNA under constitutive control of the 35S CaMV promoter (35S::LFY, provided by Dr. Weigel). F1 progenies from two different transformed plants flowered in average one to two weeks earlier than control plants at two consecutive years. The number of nodes to the first flower as well as the number of flowers per inflorescence were reduced in the early-flowering plants, suggesting a faster flower development. Normal fertile flowers were obtained from all plants. These results indicate that flowering time can me manipulated by transgenic means. Lisianthus transformations with genes acting upstream to LFY are currently being made. The possibility of introducing a fragrance to lisianthus flowers by genetic engineering is being investigated using genes from the scented plant Clarkia breweri: Acetyl CoA benzyl alcohol acetyl transferase (BEAT) and S-linalool synthase (LIS). These two genes code for key enzymes in the synthesis of the aroma compounds benzyl acetate and S-linalool respectively. Aroma modification of the fruit of transgenic tomato plants carrying a LIS construct has already been demonstrated. The effect of BEAT and LIS transgenes on tobacco and lisianthus fragrance is under investigation.


title: Solar photovoltaic array fed canonical switching cell converter based BLDC motor drive for water pumping system

abstract: This paper deals with the analysis, design and control of solar photovoltaic (SPV) array fed permanent magnet brushless DC (BLDC) motor drive for water pumping system which utilizes a canonical switching cell (CSC) converter as an intermediate DC-DC converter between the SPV array and the voltage source inverter (VSI). The CSC converter performs the functions of solar PV array power optimization and the safe starting of the BLDC motor by an adequate control. In addition to MPPT (Maximum Power Point Tracking) of SPV array, the CSC converter provides a variable output DC voltage for the electronic commutation of VSI facilitating its fundamental switching for reduction in its losses and an elimination of its current sensors. The detailed performance of the proposed water pumping system is demonstrated through simulated results obtained using MATLAB/Simulink based developed model subjected to various operating conditions such as the variations in the solar insolation level.


abstract: AbstractVigotsky, AD, Bryanton, MA, Nuckols, G, Beardsley, C, Contreras, B, Evans, J, and Schoenfeld, BJ. Biomechanical, anthropometric, and psychological determinants of barbell back squat strength. J Strength Cond Res 33(7S): S26–S35, 2019—Previous investigations of strength have only focused on b


title: Automatic semantic edge labeling over legal citation graphs

abstract: A large number of cross-references to various bodies of text are used in legal texts, each serving a different purpose. It is often necessary for authorities and companies to look into certain types of these citations. Yet, there is a lack of automatic tools to aid in this process. Recently, citation graphs have been used to improve the intelligibility of complex rule frameworks. We propose an algorithm that builds the citation graph from a document and automatically labels each edge according to its purpose. Our method uses the citing text only and thus works only on citations who’s purpose can be uniquely identified by their surrounding text. This framework is then applied to the US code. This paper includes defining and evaluating a standard gold set of labels that cover a vast majority of citation types which appear in the “US Code” but are still short enough for practical use. We also proposed a novel linear-chain conditional random field model that extracts the features required for labeling the citations from the surrounding text. We then analyzed the effectiveness of different clustering methods such as K-means and support vector machine to automatically label each citation with the corresponding label. Besides this, we talk about the practical difficulties of this task and give a comparison of human accuracy compared to our end-to-end algorithm.


title: Grounding English Commands to Reward Functions.

abstract: As intelligent robots become more prevalent, methods to make interaction with the robots more accessible are increasingly important. Communicating the tasks that a person wants the robot to carry out via natural language, and training the robot to ground the natural language through demonstration, are especially appealing approaches for interaction, since they do not require a technical background. However, existing approaches map natural language commands to robot command languages that directly express the sequence of actions the robot should execute. This sequence is often specific to a particular situation and does not generalize to new situations. To address this problem, we present a system that grounds natural language commands into reward functions using demonstrations of different natural language commands being carried out in the environment. Because language is grounded to reward functions, rather than explicit actions that the robot can perform, commands can be high-level, carried out in novel environments autonomously, and even transferred to other robots with different action spaces. We demonstrate that our learned model can be both generalized to novel environments and transferred to a robot with a different action space than the action space used during training.


title: Coarse to fine K nearest neighbor classifier

abstract: In this paper, we propose a coarse to fine K nearest neighbor (KNN) classifier (CFKNNC). CFKNNC differs from the conventional KNN classifier (CKNNC) as follows: CFKNNC first coarsely determines a small number of training samples that are ''close'' to the test sample and then finely identifies the K nearest neighbors of the test sample. The main difference between CFKNNC and CKNNC is that they exploit the ''representation-based distances'' and Euclidean distances to determine the nearest neighbors of the test sample from the set of training samples, respectively. The analysis shows that the ''representation-based distances'' are able to take into account the dependent relationship between different training samples. Actually, the nearest neighbors determined by the proposed method are optimal from the point of view of representing the test sample. Moreover, the nearest neighbors obtained using our method contain less redundant information than those obtained using CKNNC. The experimental results show that CFKNNC can classify much more accurately than CKNNC and various improvements to CKNNC such as the nearest feature line (NFL) classifier, the nearest feature space (NFS) classifier, nearest neighbor line classifier (NNLC) and center-based nearest neighbor classifier (CBNNC).


title: Distributed computing with rules of thumb

abstract: We present our recent work (ICS 2011) on dynamic environments in which computational nodes, or decision makers, follow simple and unsophisticated rules of behavior (e.g., repeatedly "best replying" to others' actions, and minimizing "regret") that have been extensively studied in game theory and economics. We aim to understand when convergence of the resulting dynamics to an equilibrium point is guaranteed if nodes' interaction is not synchronized (e.g., as in Internet protocols and large-scale markets). We take the first steps of this research agenda. We exhibit a general non-convergence result and consider its implications across a wide variety of interesting and timely applications: routing, congestion control, game theory, social networks and circuit design. We also consider the relationship between classical nontermination results in distributed computing theory and our result, explore the impact of scheduling on convergence, study the computational and communication complexity of asynchronous dynamics and present some basic observations regarding the effects of asynchrony on no-regret dynamics.


title: Word Channel Based Multiscale Pedestrian Detection without Image Resizing and Using Only One Classifier

abstract: Most pedestrian detection approaches that achieve high accuracy and precision rate and that can be used for real-time applications are based on histograms of gradient orientations. Usually multiscale detection is attained by resizing the image several times and by recomputing the image features or using multiple classifiers for different scales. In this paper we present a pedestrian detection approach that uses the same classifier for all pedestrian scales based on image features computed for a single scale. We go beyond the low level pixel-wise gradient orientation bins and use higher level visual words organized into Word Channels. Boosting is used to learn classification features from the integral Word Channels. The proposed approach is evaluated on multiple datasets and achieves outstanding results on the INRIA and Caltech-USA benchmarks. By using a GPU implementation we achieve a classification rate of over 10 million bounding boxes per second and a 16 FPS rate for multiscale detection in a 640×480 image.


title: Preliminary brain region segmentation using FCM and graph cut for CT scan images

abstract: Brain segmentation is important in the field of neuropsychiatric disorders. With Computed Tomography (CT) scan being the gold standard in brain scan, brain segmentation in CT images is also very important in the detection of many pathology related to the brain. Fuzzy c-Means (FCM) is a popular method in data clustering and also in image segmentation due to it being robust. Graph cut is a segmentation algorithm that is able to separate the image into several partitions based on the similarity between each nodes in the image. In this paper, the CT scan images were first processed with FCM optimization and are separated into clusters based on pixel intensity. After that the post-FCM images were then loaded into the graph cut algorithm to separate the images into partitions, allowing users to manually select the appropriate partitions that best represent the brain region. The results showed that the images are less erroneous when they are clustered first with FCM before going through the graph cut algorithm.


title: Perception of Risk and Terrorism-Related Behavior Change: Dual Influences of Probabilistic Reasoning and Reality Testing

abstract: The present study assessed the degree to which probabilistic reasoning performance and thinking style influenced perception of risk and self-reported levels of terrorism-related behaviour change. A sample of 263 respondents, recruited via convenience sampling, completed a series of measures comprising probabilistic reasoning tasks (perception of randomness, base rate, probability, and conjunction fallacy), the Reality Testing subscale of the Inventory of Personality Organization (IPO-RT), the Domain-Specific Risk-Taking Scale, and a terrorism-related behaviour change scale. Structural equation modelling examined three progressive models. Firstly, the Independence Model assumed that probabilistic reasoning, perception of risk and reality testing independently predicted terrorism-related behaviour change. Secondly, the Mediation Model supposed that probabilistic reasoning and reality testing correlated, and indirectly predicted terrorism-related behaviour change through perception of risk. Lastly, the Dual-Influence Model proposed that probabilistic reasoning indirectly predicted terrorism-related behaviour change via perception of risk, independent of reality testing. Results indicated that performance on probabilistic reasoning tasks most strongly predicted perception of risk, and preference for an intuitive thinking style (measured by the IPO-RT) best explained terrorism-related behaviour change. The combination of perception of risk with probabilistic reasoning ability in the Dual-Influence Model enhanced the predictive power of the analytical-rational route, with conjunction fallacy having a significant indirect effect on terrorism-related behaviour change via perception of risk. The Dual-Influence Model possessed superior fit and reported similar predictive relations between intuitive-experiential and analytical-rational routes and terrorism-related behaviour change. The discussion critically examines these findings in relation to dual-processing frameworks. This includes considering the limitations of current operationalisations and recommendations for future research that align outcomes and subsequent work more closely to specific dual-process models.


A Lossless Watermarking Based Authentication System For Medical Images


title: A reputation-based trust management system for P2P networks

abstract: The open and anonymous nature of a P2P network makes it an ideal medium for attackers to spread malicious content. In this paper we describe a reputation-based trust management protocol for P2P networks where users rate the reliability of parties they deal with, and share this information with their peers. The protocol helps establish trust among good peers as well as identifying the malicious ones. Results of various simulation experiments show that the proposed system can be highly effective in preventing the spread of malicious content in P2P networks.


title: Amazon in the Air: Innovating with Big Data at Lufthansa

abstract: Big data offers great opportunities for innovation. However, at the end of 2014, at the peak of its hype, big data deployment was still scarce and failures abounded. This article presents a case of how Lufthansa, the largest airline in Europe, successfully tackled the task of discovering value from big data, addressing the inherent technical complexities, and transforming their business model of selling airline tickets to customers to one that we call "Amazon in the Air" where service-dominant logic prevails. This case demonstrates that IT innovation is the business imperative to survive and exemplifies the complex business environment and rapid changes for which big data is being considered. Our case study also sheds light on the challenges and critical success factors for innovating with big data and how to navigate through uncharted waters, employing new thinking and new approaches to seize innovation with big data.


title: Design and Implementation of a Process Migration System for the Linux Environment

abstract: This paper reviews the field of process migration by summarizing the key concepts and giving an overview of a high level process migration algorithm and also provides a unique alternative implementation of our own for the linux environment. Design and implementation issues of process migration are analyzed in general, and these are used as pointers in describing our implementation. The primary aim of this paper is to build a user space process migration tool which would obviate the need for kernel support. This paper aims to provide an insight into the difficult task of actual migration for performance gain.


title: Gender swapping and user behaviors in online social games

abstract: Modern Massively Multiplayer Online Role-Playing Games (MMORPGs) provide lifelike virtual environments in which players can conduct a variety of activities including combat, trade, and chat with other players. While the game world and the available actions therein are inspired by their offline counterparts, the games' popularity and dedicated fan base are testaments to the allure of novel social interactions granted to people by allowing them an alternative life as a new character and persona. In this paper we investigate the phenomenon of "gender swapping," which refers to players choosing avatars of genders opposite to their natural ones. We report the behavioral patterns observed in players of Fairyland Online, a globally serviced MMORPG, during social interactions when playing as in-game avatars of their own real gender or gender-swapped. We also discuss the effect of gender role and self-image in virtual social situations and the potential of our study for improving MMORPG quality and detecting online identity frauds.


title: Efficient backprojection-based synthetic aperture radar computation with many-core processors

abstract: Tackling computationally challenging problems with high efficiency often requires the combination of algorithmic innovation, advanced architecture, and thorough exploitation of parallelism. We demonstrate this synergy through synthetic aperture radar (SAR) via backprojection, an image reconstruction method that can require hundreds of TFLOPS. Computation cost is significantly reduced by our new algorithm of approximate strength reduction; data movement cost is economized by software locality optimizations facilitated by advanced architecture support; parallelism is fully harnessed in various patterns and granularities. We deliver over 35 billion backprojections per second throughput per compute node on an Intel® Xeon® processor E5-2670-based cluster, equipped with Intel® Xeon Phi coprocessors. This corresponds to processing a 3K×3K image within a second using a single node. Our study can be extended to other settings: backprojection is applicable elsewhere including medical imaging, approximate strength reduction is a general code transformation technique, and many-core processors are emerging as a solution to energy-efficient computing.


title: SQL-to-NoSQL Schema Denormalization and Migration: A Study on Content Management Systems

abstract: Content management systems (CMSs) are able to let people, who have no technical skill to manage websites, rapidly create, edit, organize and publish online contents. For example, CMSs are often used to establish e-commerce online shops, community portals, personal blogs, or organization websites. Thus, CMSs play the role of collecting data and then can be easily extended to become intelligent Internet systems. Most popular CMSs, e.g., Word Press and Joomla, rely on relational databases or SQL databases, e.g., MySQL, PostgreSQL, etc. However, due to the explosive growth of huge data, the well-structured characteristic of SQL databases may limit the scalability to handle horizontal scaling. Therefore, regarding the flexibility and the feasibility of parallel processing, cloud computing takes Not Only SQL (NoSQL) databases into consideration. How to migrate the original CMS software from SQL databases to NoSQL databases becomes one emerging and critical research issue. This paper is motivated to propose an autonomous SQL-to-NoSQL schema denormalization and migration. At the same time, this paper also evaluates the proposed mechanism on the realistic CMS software. Based on our experimental results, our mechanism not only helps the current CMS software migrate into the cloud platform without re-designing their database schemas, but also improves at least 45% access performance after schema migration.


title: Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10–20 system oriented for transcranial functional brain mapping

abstract: Abstract The recent advent of multichannel near-infrared spectroscopy (NIRS) has expanded its technical potential for human brain mapping. However, NIRS measurement has a technical drawback in that it measures cortical activities from the head surface without anatomical information of the object to be measured. This problem is also found in transcranial magnetic stimulation (TMS) that transcranially activates or inactivates the cortical surface. To overcome this drawback, we examined cranio-cerebral correlation using magnetic resonance imaging (MRI) via the guidance of the international 10–20 system for electrode placement, which had originally been developed for electroencephalography. We projected the 10–20 standard cranial positions over the cerebral cortical surface. After examining the cranio-cerebral correspondence for 17 healthy adults, we normalized the 10–20 cortical projection points of the subjects to the standard Montreal Neurological Institute (MNI) and Talairach stereotactic coordinates and obtained their probabilistic distributions. We also expressed the anatomical structures for the 10–20 cortical projection points probabilistically. Next, we examined the distance between the cortical surface and the head surface along the scalp and created a cortical surface depth map. We found that the locations of 10–20 cortical projection points in the standard MNI or Talairach space could be estimated with an average standard deviation of 8 mm. This study provided an initial step toward establishing a three-dimensional probabilistic anatomical platform that enables intra- and intermodal comparisons of NIRS and TMS brain imaging data.


title: Discourse markers as signals (or not) of rhetorical relations

abstract: Abstract Ever since the formulation of Rhetorical Structure Theory (RST) by Mann and Thompson, researchers have debated about what is the ‘right’ number of relations. One proposal is based on the discourse markers (connectives) signalling the presence of a particular relationship. In this paper, I discuss the adequacy of such a proposal, in the light of two different corpus studies: a study of conversations, and a study of newspaper articles. The two corpora were analysed in terms of rhetorical relations, and later coded for external signals of those relations. The conclusion in both studies is that a high number of relations (between 60 and 70% of the total, on average) are not signalled. A comparison between the two corpora suggests that genre-specific factors may affect which relations are signalled, and which are not.


title: Spatial prediction of species distribution: an interface between ecological theory and statistical modelling

abstract: Neglect of ecological knowledge is a limiting factor in the use of statistical modelling to predict species distribution. Three components are needed for statistical modelling, an ecological model concerning the ecological theory to be used or tested, a data model concerning the collection and measurement of the data, and a statistical model concerning the statistical theory and methods used. This component framework is reviewed with emphasis on ecological theory. The expected shape of a species response curv et o an environmental gradient is a central assumption on which agreement has yet to be reached. The nature of the environmental predictors whether indirect variables, e.g. latitude that hav en o physiological impact on plants, or direct variables, e.g. temperature also influence the type of response expected. Straight-line relationships between organisms and environment are often used uncritically. Many users of canonical correlation analysis use linear (straight-line) functions to relate ordination axes to variables such as slope and aspect though this is not a necessary part of the method. Some statisticians have used straight lines for species/environment relationships without testing, when evaluating new statistical procedures. Assumptions used in one component often conflict with those in another component. Statistical models can be used to explore ecological theory. Skewed species response curves predominate contrary to the symmetric unimodal curves assumed by some statistical methods. Improvements in statistical modelling can be achieved based on ecological concepts. Examples include incorporating interspecific competition from dominant species; more proximal predictors based on water balance models and spatial autocorrelation procedures to accommodate non-equilibrium vegetation. # 2002 Elsevier Science B.V. All rights reserved.


title: Finger force vectors in multi-finger prehension.

abstract: In a majority of studies on grasp, only normal forces were measured and only when a zero torque was exerted on a hand-held object. This study concerns finger force vectors during the torque production tasks. Subjects (n=8) stabilized a handle with an attachment that allowed for change of external torque from -1.5 to 1.5 Nm. Forces and moments exerted by the digit tips on the object were recorded. At the large (>-0.375 Nm) supination torques the index/middle and ring/little pairs of fingers generated oppositely directed tangential forces. The index and middle finger produced forces in a downward direction and therefore did not support the load. At a zero torque and pronation torques, the middle, ring and little fingers produced forces along nearly the same direction. The vector of the index finger force was always directed differently from the vectors of other finger forces, the angles ranged from 19 degrees 30' to 47 degrees 40'. The points of force application were systematically displaced with the torque, with the exception of the little finger. Tangential finger forces contributed substantially to the total torque exerted on the hand-held object.


title: 28 GHz Downlink Multi-User MIMO Experimental Verification Using 360 Element Digital AAS for 5G Massive MIMO

abstract: This paper describes the indoor experimental verification testing results and discussion of a 28 GHz multi-user (MU) multiple-input multiple-output (MIMO) using the 360 element full-digital massive MIMO active antenna system (AAS) for 5G base station applications. AAS accommodates 24 channels 28 GHz transceiver chains and FPGA based on 1-bit direct-IF digital transmitters. The antenna array consists of full digitally controlled 24 streams of 15 elements inline sub-arrays. The simultaneous four beams are generated and the average Effective Isotropically Radiated Power (EIRP) is 59.4 dBm. The throughputs are measured by maximum-ratio-combining (MRC) method and zero-forcing (ZF) method by steering control. The achieved user-equipment (UE) throughputs are 0.996 Gbps with single beam, 0.655 Gbps to 0.857 Gbps with four users by MRC, and 0.673 Gbps to 0.873 Gbps by ZF. The four user cell throughputs are 2.884 Gbps and 3.134 Gbps by MRC and ZF respectively. The achieved spectrum efficiency is 10.4 bps/Hz/cell using 300 MHz OFDM signal. As par author's knowledge, this is a first report for experimental verification of multi-user MIMO using 28 GHz digital AAS.


title: Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes

abstract: Synchronized stochastic gradient descent (SGD) optimizers with data parallelism are widely used in training large-scale deep neural networks. Although using larger mini-batch sizes can improve the system scalability by reducing the communication-to-computation ratio, it may hurt the generalization ability of the models. To this end, we build a highly scalable deep learning training system for dense GPU clusters with three main contributions: (1) We propose a mixed-precision training method that significantly improves the training throughput of a single GPU without losing accuracy. (2) We propose an optimization approach for extremely large mini-batch size (up to 64k) that can train CNN models on the ImageNet dataset without losing accuracy. (3) We propose highly optimized all-reduce algorithms that achieve up to 3x and 11x speedup on AlexNet and ResNet-50 respectively than NCCL-based training on a cluster with 1024 Tesla P40 GPUs. On training ResNet-50 with 90 epochs, the state-of-the-art GPU-based system with 1024 Tesla P100 GPUs spent 15 minutes and achieved 74.9\% top-1 test accuracy, and another KNL-based system with 2048 Intel KNLs spent 20 minutes and achieved 75.4\% accuracy. Our training system can achieve 75.8\% top-1 test accuracy in only 6.6 minutes using 2048 Tesla P40 GPUs. When training AlexNet with 95 epochs, our system can achieve 58.7\% top-1 test accuracy within 4 minutes, which also outperforms all other existing systems.


Biomedical Event Extraction Using Convolutional Neural Networks and Dependency Parsing


title: Association of Therapy for Autoimmune Disease With Myelodysplastic Syndromes and Acute Myeloid Leukemia

abstract: Importance Therapy-related myeloid neoplasms are a potentially life-threatening consequence of treatment for autoimmune disease (AID) and an emerging clinical phenomenon. Objective To query the association of cytotoxic, anti-inflammatory, and immunomodulating agents to treat patients with AID with the risk for developing myeloid neoplasm. Design, Setting, and Participants This retrospective case-control study and medical record review included 40 011 patients with an International Classification of Diseases, Ninth Revision, coded diagnosis of primary AID who were seen at 2 centers from January 1, 2004, to December 31, 2014; of these, 311 patients had a concomitant coded diagnosis of myelodysplastic syndrome (MDS) or acute myeloid leukemia (AML). Eighty-six cases met strict inclusion criteria. A case-control match was performed at a 2:1 ratio. Main Outcomes and Measures Odds ratio (OR) assessment for AID-directed therapies. Results Among the 86 patients who met inclusion criteria (49 men [57%]; 37 women [43%]; mean [SD] age, 72.3 [15.6] years), 55 (64.0%) had MDS, 21 (24.4%) had de novo AML, and 10 (11.6%) had AML and a history of MDS. Rheumatoid arthritis (23 [26.7%]), psoriasis (18 [20.9%]), and systemic lupus erythematosus (12 [14.0%]) were the most common autoimmune profiles. Median time from onset of AID to diagnosis of myeloid neoplasm was 8 (interquartile range, 4-15) years. A total of 57 of 86 cases (66.3%) received a cytotoxic or an immunomodulating agent. In the comparison group of 172 controls (98 men [57.0%]; 74 women [43.0%]; mean [SD] age, 72.7 [13.8] years), 105 (61.0%) received either agent (P = .50). Azathioprine sodium use was observed more frequently in cases (odds ratio [OR], 7.05; 95% CI, 2.35- 21.13; P < .001). Notable but insignificant case cohort use among cytotoxic agents was found for exposure to cyclophosphamide (OR, 3.58; 95% CI, 0.91-14.11) followed by mitoxantrone hydrochloride (OR, 2.73; 95% CI, 0.23-33.0). Methotrexate sodium (OR, 0.60; 95% CI, 0.29-1.22), mercaptopurine (OR, 0.62; 95% CI, 0.15-2.53), and mycophenolate mofetil hydrochloride (OR, 0.66; 95% CI, 0.21-2.03) had favorable ORs that were not statistically significant. No significant association between a specific length of time of exposure to an agent and the drug’s category was observed. Conclusions and Relevance In a large population with primary AID, azathioprine exposure was associated with a 7-fold risk for myeloid neoplasm. The control and case cohorts had similar systemic exposures by agent category. No association was found for anti–tumor necrosis factor agents. Finally, no timeline was found for the association of drug exposure with the incidence in development of myeloid neoplasm.


title: Formal methods: state of the art and future directions

abstract: Hardware and software systems will inevitably grow in scale and functionality. Because of this increase in complexity, the likelihood of subtle errors is much greater. Moreover, some of these errors may cause catastrophic loss of money, time, or even human life. A major goal of software engineering is to enable developers to construct systems that operate reliably despite this complexity. One way of achieving this goal is by using formal methods, which are mathematically based languages, techniques, and tools for specifying and verifying such systems. Use of formal methods does not a priori guarantee correctness. However, they can greatly increase our understanding of a system by revealing inconsistencies, ambiguities, and incompleteness that might otherwise go undetected. The first part of this report assesses the state of the art in specification and verification. For verification, we highlight advances in model checking and theorem proving. In the three sections on specification, model checking, and theorem proving, we explain what we mean by the general technique and briefly describe some successful case studies and well-known tools. The second part of this report outlines future directions in fundamental concepts, new methods and tools, integration of methods, and education and technology transfer. We close with summary remarks and pointers to resources for more information.


title: Why/AutoTutor: A Test of Learning Gains from a Physics Tutor with Natural Language Dialog

abstract: Author(s): Graesser, A.C.; Jackson, G.T.; Matthews, E.C.; Mitchell, H.H.; Olney, A.; Ventura, M.; Chipman, P.; Franceschetti, D.; Hu, X.; Louwerse, M.M.; Person, N.K.; Tutoring Research Group


title: Cytoscape tools for the web age: D3.js and Cytoscape.js exporters

abstract: In this paper we present new data export modules for Cytoscape 3 that can generate network files for Cytoscape.js and D3.js. Cytoscape.js exporter is implemented as a core feature of Cytoscape 3, and D3.js exporter is available as a Cytoscape 3 app. These modules enable users to seamlessly export network and table data sets generated in Cytoscape to popular JavaScript library readable formats. In addition, we implemented template web applications for browser-based interactive network visualization that can be used as basis for complex data visualization applications for bioinformatics research. Example web applications created with these tools demonstrate how Cytoscape works in modern data visualization workflows built with traditional desktop tools and emerging web-based technologies. This interactivity enables researchers more flexibility than with static images, thereby greatly improving the quality of insights researchers can gain from them.


title: Model-Based Compressive Sensing

abstract: Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for the acquisition of sparse or compressible signals that can be well approximated by just K ? N elements from an N -dimensional basis. Instead of taking periodic samples, CS measures inner products with M < N random vectors and then recovers the signal via a sparsity-seeking optimization or greedy algorithm. Standard CS dictates that robust signal recovery is possible from M = O(K log(N/K)) measurements. It is possible to substantially decrease M without sacrificing robustness by leveraging more realistic signal models that go beyond simple sparsity and compressibility by including structural dependencies between the values and locations of the signal coefficients. This paper introduces a model-based CS theory that parallels the conventional theory and provides concrete guidelines on how to create model-based recovery algorithms with provable performance guarantees. A highlight is the introduction of a new class of structured compressible signals along with a new sufficient condition for robust structured compressible signal recovery that we dub the restricted amplification property, which is the natural counterpart to the restricted isometry property of conventional CS. Two examples integrate two relevant signal models-wavelet trees and block sparsity-into two state-of-the-art CS recovery algorithms and prove that they offer robust recovery from just M = O(K) measurements. Extensive numerical simulations demonstrate the validity and applicability of our new theory and algorithms.


title: POCAD: A novel pay load-based one-class classifier for anomaly detection

abstract: In this paper, we propose a novel Payload-based One-class Classifier for Anomaly Detection called POCAD, which combines a generalized 2v-gram feature extractor and a one-class SVM classifier to effectively detect network intrusion attacks. We extensively evaluate POCAD with real-world datasets of HTTP-based attacks. Our experiment results show that POCAD can quickly detect malicious payload and achieves a high detection rate as well as a low false positive rate. The experiment results also show that POCAD outperforms state of the art payload-based detection schemes such as McPAD [4] and PAYL [8].


title: A new family of growth factors produced by the fat body and active on Drosophila imaginal disc cells.

abstract: By fractionating conditioned medium (CM) from Drosophila imaginal disc cell cultures, we have identified a family of Imaginal Disc Growth Factors (IDGFs), which are the first polypeptide growth factors to be reported from invertebrates. The active fraction from CM, as well as recombinant IDGFs, cooperate with insulin to stimulate the proliferation, polarization and motility of imaginal disc cells. The IDGF family in Drosophila includes at least five members, three of which are encoded by three genes in a tight cluster. The proteins are structurally related to chitinases, but they show an amino acid substitution that is known to abrogate catalytic activity. It therefore seems likely that they have evolved from chitinases but acquired a new growth-promoting function. The IDGF genes are expressed most strongly in the embryonic yolk cells and in the fat body of the embryo and larva. The predicted molecular structure, expression patterns, and mitogenic activity of these proteins suggest that they are secreted and transported to target tissues via the hemolymph. However, the genes are also expressed in embryonic epithelia in association with invagination movements, so the proteins may have local as well as systemic functions. Similar proteins are found in mammals and may constitute a novel class of growth factors.


title: Tower Crane Remote Wireless Monitoring System Based on Modbus/Tcp Protocol

abstract: The wireless monitoring and management system of tower crane based on Modbus / TCP protocol is designed for the distributed, efficient and safe operation of tower crane. Designing monitor surveillance terminal with Cortex A8 and WinCE6 as platform for real-time monitoring of state information of tower crane operation. And then, Using Modbus / TCP protocol to design a wireless communication message, through GPRS and the two-way communication of remote management center, and to achieve the tower group of remote monitoring and unified management. The field operation proves that the system performance is stable and reliable, and has good application prospect.


title: The Blockchain Anomaly

abstract: Most popular blockchain solutions, like Bitcoin, rely on proof-of-work, guaranteeing that the output of the consensus is agreed upon with high probability. However, this probability depends on the delivery of messages and that the computational power of the system is sufficiently scattered among pools of nodes in the network so that no pool can mine more blocks faster than the crowd. New approaches, like Ethereum, generalise the proof-of-work approach by letting individuals deploy their own private blockchain with high transaction throughput. As companies are starting to deploy private chains, it has become crucial to better understand the guarantees blockchains offer in such a small and controlled environment. ::: In this paper, we present the \emph{Blockchain Anomaly}, an execution that we experienced when building our private chain at NICTA/Data61. Even though this anomaly has never been acknowledged before, it may translate into dramatic consequences for the user of blockchains. Named after the infamous Paxos anomaly, this anomaly makes dependent transactions, like "Bob sends money to Carole after he received money from Alice" impossible. This anomaly relies on the fact that existing blockchains do not ensure consensus safety deterministically: there is no way for Bob to make sure that Alice actually sent him coins without Bob using an external mechanism, like converting these coins into a fiat currency that allows him to withdraw. We also explore smart contracts as a potential alternative to transactions in order to freeze coins, and show implementations of smart contract that can suffer from the Blockchain anomaly and others that may cope with it.


title: Target Detection and Localization Using MIMO Radars and Sonars

abstract: In this paper, we propose a new space-time coding configuration for target detection and localization by radar or sonar systems. In common active array systems, the transmitted signal is usually coherent between the different elements of the array. This configuration does not allow array processing in the transmit mode. However, space-time coding of the transmitted signals allows to digitally steer the beam pattern in the transmit in addition to the received signal. The ability to steer the transmitted beam pattern, helps to avoid beam shape loss. We show that the configuration with spatially orthogonal signal transmission is equivalent to additional virtual sensors which extend the array aperture with virtual spatial tapering. These virtual sensors can be used to form narrower beams with lower sidelobes and, therefore, provide higher performance in target detection, angular estimation accuracy, and angular resolution. The generalized likelihood ratio test for target detection and the maximum likelihood and Cramer-Rao bound for target direction estimation are derived for an arbitrary signal coherence matrix. It is shown that the optimal performance is achieved for orthogonal transmitted signals. Target detection and localization performances are evaluated and studied theoretically and via simulations


title: Computing payment for ecosystem services in watersheds: an analysis of the Middle Route Project of South-to-North Water Diversion in China.

abstract: Payment for ecosystem services (PES) has attracted considerable attention as an economic incentive for promoting natural resource management recently. As emphasis has been placed on using the incentive-based mechanism by the central government, rapid progress on PES research and practice has been achieved. However PES still faces many difficulties. A key issue is the lack of a fully-fledged theory and method to clearly define the design scope, accounting and feasibility of PES criteria. An improved watershed criteria model was developed in light of research on PES practices in China, investigations on the water source area for the Middle Route Project of South-to-North Water Diversion and ecosystem services outflows theory. The basic principle of assessment is the direct and opportunity cost for ecological conservation and environmental protection in the water source area deduct nationally-financed PES and internal effect. Then the scope and the criteria methods were determined, and internal effect was put forward to define benefits brought from water source area. Finally, Shiyan City, which is the main water source area for the Project of Water Diversion, was analyzed by this model and its payment was calculated. The results showed that: (1) during 2003-2050, the total direct cost and opportunity cost would reach up to 262.70 billion and 256.33 billion Chinese Yuan (CNY, 2000 constant prices), i.e., 50.61% and 49.38% of total cost, respectively; (2) Shiyan City would gain 0.23, 0.06 and 0.03 CNY/m3 in 2014-2020, 2021-2030, and 2031-2050, respectively.


Big Data Categorization for Arabic Text Using Latent Semantic Indexing and Clustering


title: md 5 bloom : Forensic filesystem hashing revisited

abstract: Hashing is a fundamental tool in digital forensic analysis used both to ensure data integrity and to efficiently identify known data objects. However, despite many years of practice, its basic use has advanced little. Our objective is to leverage advanced hashing techniques in order to improve the efficiency and scalability of digital forensic analysis. Specifically, we explore the use of Bloom filters as a means to efficiently aggregate and search hashing information. In this paper, we present md5bloom-an actual Bloom filter manipulation tool that can be incorporated into forensic practice, along with example uses and experimental results. We also provide a basic theoretical foundation, which quantifies the error rates associated with the various Bloom filter uses along with a simulation-based verification. We provide a probabilistic framework that allows the interpretation of direct, bitwise comparison of Bloom filters to infer similarity and abnormality. Using the similarity interpretation, it is possible to efficiently identify versions of a known object, whereas the notion of abnormality could aid in identifying tampered hash sets.


title: Interventions to prevent and reduce physician burnout: a systematic review and meta-analysis

abstract: BACKGROUND ::: Physician burnout has reached epidemic levels, as documented in national studies of both physicians in training and practising physicians. The consequences are negative effects on patient care, professionalism, physicians' own care and safety, and the viability of health-care systems. A more complete understanding than at present of the quality and outcomes of the literature on approaches to prevent and reduce burnout is necessary. ::: ::: ::: METHODS ::: In this systematic review and meta-analysis, we searched MEDLINE, Embase, PsycINFO, Scopus, Web of Science, and the Education Resources Information Center from inception to Jan 15, 2016, for studies of interventions to prevent and reduce physician burnout, including single-arm pre-post comparison studies. We required studies to provide physician-specific burnout data using burnout measures with validity support from commonly accepted sources of evidence. We excluded studies of medical students and non-physician health-care providers. We considered potential eligibility of the abstracts and extracted data from eligible studies using a standardised form. Outcomes were changes in overall burnout, emotional exhaustion score (and high emotional exhaustion), and depersonalisation score (and high depersonalisation). We used random-effects models to calculate pooled mean difference estimates for changes in each outcome. ::: ::: ::: FINDINGS ::: We identified 2617 articles, of which 15 randomised trials including 716 physicians and 37 cohort studies including 2914 physicians met inclusion criteria. Overall burnout decreased from 54% to 44% (difference 10% [95% CI 5-14]; p<0·0001; I2=15%; 14 studies), emotional exhaustion score decreased from 23·82 points to 21·17 points (2·65 points [1·67-3·64]; p<0·0001; I2=82%; 40 studies), and depersonalisation score decreased from 9·05 to 8·41 (0·64 points [0·15-1·14]; p=0·01; I2=58%; 36 studies). High emotional exhaustion decreased from 38% to 24% (14% [11-18]; p<0·0001; I2=0%; 21 studies) and high depersonalisation decreased from 38% to 34% (4% [0-8]; p=0·04; I2=0%; 16 studies). ::: ::: ::: INTERPRETATION ::: The literature indicates that both individual-focused and structural or organisational strategies can result in clinically meaningful reductions in burnout among physicians. Further research is needed to establish which interventions are most effective in specific populations, as well as how individual and organisational solutions might be combined to deliver even greater improvements in physician wellbeing than those achieved with individual solutions. ::: ::: ::: FUNDING ::: Arnold P Gold Foundation Research Institute.


Factors associated with contraceptive use among sexually active Nepalese youths in the Kathmandu Valley


title: A Reconfigurable and Scalable FPGA Architecture for Bilateral Filtering

abstract: Bilateral filter is an edge-preserving smoother that has applications in image processing, computer vision, and computational photography. In the past, field-programmable gate array (FPGA) implementations of the filter have been proposed that can achieve high throughput using parallelization and pipelining. An inherent limitation with direct implementations is that their complexity scales as $O(\omega ^2)$ with the filter width $\omega$ . In this paper, we propose an FPGA implementation of a fast bilateral filter that requires just $O(1)$ operations for any arbitrary $\omega$ . The attractive feature of the FPGA implementation is that it is both scalable and reconfigurable. To the best of our knowledge, this is the first scalable FPGA implementation of the bilateral filter. As an application, we use the FPGA implementation for image denoising.


title: Volumetric 3D mapping in real-time on a CPU

abstract: In this paper we propose a novel volumetric multi-resolution mapping system for RGB-D images that runs on a standard CPU in real-time. Our approach generates a textured triangle mesh from a signed distance function that it continuously updates as new RGB-D images arrive. We propose to use an octree as the primary data structure which allows us to represent the scene at multiple scales. Furthermore, it allows us to grow the reconstruction volume dynamically. As most space is either free or unknown, we allocate and update only those voxels that are located in a narrow band around the observed surface. In contrast to a regular grid, this approach saves enormous amounts of memory and computation time. The major challenge is to generate and maintain a consistent triangle mesh, as neighboring cells in the octree are more difficult to find and may have different resolutions. To remedy this, we present in this paper a novel algorithm that keeps track of these dependencies, and efficiently updates corresponding parts of the triangle mesh. In our experiments, we demonstrate the real-time capability on a large set of RGB-D sequences. As our approach does not require a GPU, it is well suited for applications on mobile or flying robots with limited computational resources.


The Modelica Flight Dynamics Library


title: MetaSync: file synchronization across multiple untrusted storage services

abstract: Cloud-based file synchronization services, such as Drop-box, are a worldwide resource for many millions of users. However, individual services often have tight resource limits, suffer from temporary outages or even shutdowns, and sometimes silently corrupt or leak user data. ::: ::: We design, implement, and evaluate MetaSync, a secure and reliable file synchronization service that uses multiple cloud synchronization services as untrusted storage providers. To make MetaSync work correctly, we devise a novel variant of Paxos that provides efficient and consistent updates on top of the unmodified APIs exported by existing services. Our system automatically redistributes files upon reconfiguration of providers. ::: ::: Our evaluation shows that MetaSync provides low update latency and high update throughput while being more trustworthy and available. MetaSync outperforms its underlying cloud services by 1.2-10×on three realistic workloads.


abstract: The Convolutional Neural Network (CNN) has been successfully applied in many fields during recent decades; however it lacks the ability to utilize prior domain knowledge when dealing with many realistic problems. We present a framework called Geometric Operator Convolutional Neural Network (GO-CNN) that uses domain knowledge, wherein the kernel of the first convolutional layer is replaced with a kernel generated by a geometric operator function. This framework integrates many conventional geometric operators, which allows it to adapt to a diverse range of problems. Under certain conditions, we theoretically analyze the convergence and the bound of the generalization errors between GO-CNNs and common CNNs. Although the geometric operator convolution kernels have fewer trainable parameters than common convolution kernels, the experimental results indicate that GO-CNN performs more accurately than common CNN on CIFAR-10/100. Furthermore, GO-CNN reduces dependence on the amount of training examples and enhances adversarial stability. In the practical task of medically diagnosing bone fractures, GO-CNN obtains 3% improvement in terms of the recall.


title: Pediatric in vivo cross-calibration between the GE Lunar Prodigy and DPX-L bone densitometers

abstract: Dual energy x-ray absorptiometry (DXA) machine cross-calibration is an important consideration when upgrading from old to new technology. In a recent cross-calibration study using adult subjects, close agreement between GE Lunar DPX-L and GE Lunar Prodigy scanners was reported. The aim of this work was to cross-calibrate the two machines for bone and body composition parameters for pediatrics from age 5 years onwards. One-hundred ten healthy volunteers aged 5-20 years had total body and lumbar spine densitometry performed on DPX-L and Prodigy densitometers. Cross-calibration was achieved using linear regression and Bland-Altman analysis. There was close agreement between the machines, with r2 ranging from 0.85 to 0.99 for bone and body composition parameters. Paired t-tests demonstrated significant differences between machines that were dependent on scan acquisition mode, with the greatest differences reported for the smallest children. At the lumbar spine, Prodigy bone mineral density (BMD) values were on average 1.6% higher compared with DPX-L. For the total body, there were no significant differences in BMD; however, there were significant differences in bone mineral content (BMC) and bone area. For small children, the Prodigy measured lower BMC (9.4%) and bone area (5.8%), whereas for larger children the Prodigy measured both higher BMC (3.1%) and bone area (3.0%). A similar contrasting pattern was also observed for the body composition parameters. Prodigy values for lean body mass were higher (3.0%) for small children and lower (0.5%) for larger children, while fat body mass was lower (16.4%) for small children and higher (2.0%) for large children. Cross-calibration coefficients ranged from 0.84 to 1.12 and were significantly different from 1 (p<0.0001) for BMC and bone area. Bland-Altman plots showed that within the same scan acquisition modes, the magnitude of the difference increased with body weight. The results from this study suggest that the differences between machines are mainly due to differences in bone detection algorithms and that they vary with body weight and scan mode. In general, for population studies the differences are not clinically significant. However, for individual children being measured longitudinally, cross-over scanning may be required.


title: Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy

abstract: We evaluate the prediction accuracy of models designed using different classification methods depending on the technique used to select variables, and we study the relationship between the structure of the models and their ability to correctly predict financial failure. We show that a neural network based model using a set of variables selected with a criterion that it is adapted to the network leads to better results than a set chosen with criteria used in the financial literature. We also show that the way in which a set of variables may represent the financial profiles of healthy companies plays a role in Type I error reduction.


title: Unsupervised Learning of Sparse Features for Scalable Audio Classification

abstract: In this work we present a system to automatically learn features from audio in an unsupervised manner. Our method first learns an overcomplete dictionary which can be used to sparsely decompose log-scaled spectrograms. It then trains an efficient encoder which quickly maps new inputs to approximations of their sparse representations using the learned dictionary. This avoids expensive iterative procedures usually required to infer sparse codes. We then use these sparse codes as inputs for a linear Support Vector Machine (SVM). Our system achieves 83.4% accuracy in predicting genres on the GTZAN dataset, which is competitive with current state-of-the-art approaches. Furthermore, the use of a simple linear classifier combined with a fast feature extraction system allows our approach to scale well to large datasets.


title: A new interface using image-based foot tracking for motion sensing devices

abstract: Foot interfacing has applications in games that require foot manipulation control using motion sensing devices. However, when part of a foot overlaps itself (selfocclusion), is out of the line of sight of the infrared sensor and it is then difficult to accurately capture foot pose. To overcome this problem, we propose a real-time motion sensing method based on 3-dimensional motion estimation. Our method employs pose parameters that are acquired using the ground plane and camera coordinates. This enables an assessment of the positions of the two joints that comprise a skeletal foot model. The proposed approach can capture a foot's 3-dimensional information that an infrared sensor may miss; therefore, motion sensing device's pose estimation results improve. According to the tracking and experimental results, the proposed method is applicable for use by motion sensing devices.


title: A Novel Deployment Scheme for Green Internet of Things

abstract: The Internet of Things (IoT) has been realized as one of the most promising networking paradigms that bridge the gap between the cyber and physical world. Developing green deployment schemes for IoT is a challenging issue since IoT achieves a larger scale and becomes more complex so that most of the current schemes for deploying wireless sensor networks (WSNs) cannot be transplanted directly in IoT. This paper addresses this challenging issue by proposing a deployment scheme to achieve green networked IoT. The contributions made in this paper include: 1) a hierarchical system framework for a general IoT deployment, 2) an optimization model on the basis of proposed system framework to realize green IoT, and 3) a minimal energy consumption algorithm for solving the presented optimization model. The numerical results on minimal energy consumption and network lifetime of the system indicate that the deployment scheme proposed in this paper is more flexible and energy efficient compared to typical WSN deployment scheme; thus is applicable to the green IoT deployment.


title: Codesurfer/x86-a platform for analyzing x86 executables

abstract: CodeSurfer/x86 is a prototype system for analyzing x86 executables. It uses a static-analysis algorithm called value-set analysis (VSA) to recover intermediate representations that are similar to those that a compiler creates for a program written in a high-level language. A major challenge in building an analysis tool for executables is in providing useful information about operations involving memory. This is difficult when symbol-table and debugging information is absent or untrusted. CodeSurfer/x86 overcomes these challenges to provide an analyst with a powerful and flexible platform for investigating the properties and behaviors of potentially malicious code (such as COTS components, plugins, mobile code, worms, Trojans, and virus-infected code) using (i) CodeSurfer/x86's GUI, (ii) CodeSurfer/x86's scripting language, which provides access to all of the intermediate representations that CodeSurfer/x86 builds for the executable, and (iii) GrammaTech's Path Inspector, which is a tool that uses a sophisticated pattern-matching engine to answer questions about the flow of execution in a program.


title: Resilient individuals use positive emotions to bounce back from negative emotional experiences

abstract: Theory indicates that resilient individuals “bounce back” from stressful experiences quickly and effectively. Few studies, however, have provided empirical evidence for this theory. The broaden-and-build theory of positive emotions (B. L. Fredrickson, 1998, 2001) is used as a framework for understanding psychological resilience. The authors used a multimethod approach in 3 studies to predict that resilient people use positive emotions to rebound from, and find positive meaning in, stressful encounters. Mediational analyses revealed that the experience of positive emotions contributed, in part, to participants’ abilities to achieve efficient emotion regulation, demonstrated by accelerated cardiovascular recovery from negative emotional arousal (Studies 1 and 2) and by finding positive meaning in negative circumstances (Study 3). Implications for research on resilience and positive emotions are discussed.


title: PEOPLE DETECTION AND RE-IDENTIFICATION FOR MULTI SURVEILLANCE CAMERAS

abstract: Re-identifying people in a network of non overlapping cameras requires people to be accurately detected and tracked in order to build a strong visual signature of people appearances. Traditional surveillance cameras do not provide high enough image resolution to iris recognition algorithms. State of the art face recognition can not be easily applied to surveillance videos as people need to be facing the camera at a close range. The different lighting environment contained in each camera scene and the strong illumination variability occurring as people walk throughout a scene induce great variability in their appearance. %In addition, surveillance scene often display people whose images occlud each other onto the image plane making people detection difficult to achieve. In addition, people images occlud each other onto the image plane making people detection difficult to achieve. We propose a novel simplified Local Binary Pattern features to detect people, head and faces. A Mean Riemannian Covariance Grid (MRCG) is used to model appearance of tracked people to obtain highly discriminative human signature. The methods are evaluated and compared with the state of the art algorithms. We have created a new dataset from a network of 2 cameras showing the usefulness of our system to detect, track and re-identify people using appearance and face features.


title: Wires: a geometric deformation technique

abstract: Finding effective interactive deformation techniques for complex geometric objects continues to be a challenging problem in modeling and animation. We present an approach that is inspired by armatures used by sculptors, in which wire curves give definition to an object and shape its deformable features. We also introduce domain curves that define the domain of deformation about an object. A wire together with a collection of domain curves provide a new basis for an implicit modeling primitive. Wires directly reflect object geometry, and as such they provide a coarse geometric representation of an object that can be created through sketching. Furthermore, the aggregate deformation from several wires is easy to define. We show that a single wire is an appealing direct manipulation deformation technique; we demonstrate that the combination of wires and domain curves provide a new way to outline the shape of an implicit volume in space; and we describe techniques for the aggregation of deformations resulting from multiple wires, domain curves and their interaction with each other and other deformation techniques. The power of our approach is illustrated using applications of animating figures with flexible articulations, modeling wrinkled surfaces and stitching geometry together.


title: A New Blended Learning Concept for Medical Students in OtolaryngologyA New Blended Learning Concept in Otolaryngology

abstract: Objective To evaluate students' overall assessment and effectiveness of the web-based blended learning conception “Unified Patient Project” (UPP) for medical students rotating on their otolaryngology internship (ear, nose, and throat [ENT] tertiary). Design Prospective comparison group design of the quasiexperimental type. Setting Medical education. Subjects The experimental group (preintervention test [pretest], intervention, and postintervention test [posttest]) comprised 117 students, and the comparison group (pretest, alternative intervention, and posttest), 119. Interventions In the experimental group, lecturing of case studies was replaced by the blended learning concept UPP. A standardized questionnaire evaluated students' overall assessment of teaching otolaryngology. A pretest and posttest using multiple choice questions was administered to clarify whether the UPP has led to a knowledge gain. Results The comparison group was more satisfied with their teaching; however, this was not statistically significant (P = .26) compared with the UPP. Students with higher preknowledge benefitted from the UPP, while students with lower preknowledge did not (P = .01). On average, posttest results in the experimental group exceeded those of the comparison group by 8.7 percentage points for a 75% preknowledge of the maximum attainable score, while they fell below those of the comparison group by 8.1 percentage points for a 25% preknowledge. Conclusions Students' satisfaction with the blended learning concept UPP was lower than in the face-to-face teaching, although this was not statistically significant. The new web-based UPP leads to an improved knowledge in clinical otolaryngology for all students. Students with lower preknowledge benefitted more from face-to-face teaching than from the UPP, while students with higher preknowledge benefitted more from the UPP. This implies students with poor preknowledge need special promotion programs.


title: The effect of user engagement on system success: A meta-analytical integration of research findings

abstract: The effect of user involvement on system success is an important topic yet empirical results have been controversial. Many methodological and theoretical differences among prior studies have been suggested as possible causes for inconsistent findings. This research is an attempt to resolve inconsistent data despite differences that may exist in prior studies. Data from 25 studies were meta-analyzed to test the separate effects of user participation and user involvement on six system success variables. Results showed that user participation had a moderate positive correlation with four success measures: system quality, use, user satisfaction, and organizational impact. The correlation between user participation and individual impact was minimum. User involvement generally had a larger correlation with system success than did user participation. Overall, these findings indicate that both user involvement and user participation are beneficial, but the magnitude of these benefits much depends on how involvement and its effect are defined.