query: Event Pattern Analysis and Prediction at Sentence Level using Neuro-Fuzzy Model for Crime Event Detection

positive: Topic Detection and Tracking Pilot Study Final Report Topic Detection and Tracking (TDT) is a DARPA-sponsored initiative to investigate the state of the art in finding and following new events in a stream of broadcast news stories. The TDT problem consists of three major tasks: (1) segmenting a stream of data, especially recognized speech, into distinct stories; (2) identifying those news stories that are the first to discuss a new event occurring in the news; and (3) given a small number of sample news stories about an event, finding all following stories in the stream. The TDT Pilot Study ran from September 1996 through October 1997. The primary participants were DARPA, Carnegie Mellon University, Dragon Systems, and the University of Massachusetts at Amherst. This report summarizes the findings of the pilot study. The TDT work continues in a new project involving larger training and test corpora, more active participants, and a more broadly defined notion of “topic” than was used in the pilot study. The following individuals participated in the research reported.

positive: Event Extraction with Complex Event Classification Using Rich Features Biomedical Natural Language Processing (BioNLP) attempts to capture biomedical phenomena from texts by extracting relations between biomedical entities (i.e. proteins and genes). Traditionally, only binary relations have been extracted from large numbers of published papers. Recently, more complex relations (biomolecular events) have also been extracted. Such events may include several entities or other relations. To evaluate the performance of the text mining systems, several shared task challenges have been arranged for the BioNLP community. With a common and consistent task setting, the BioNLP'09 shared task evaluated complex biomolecular events such as binding and regulation.Finding these events automatically is important in order to improve biomedical event extraction systems. In the present paper, we propose an automatic event extraction system, which contains a model for complex events, by solving a classification problem with rich features. The main contributions of the present paper are: (1) the proposal of an effective bio-event detection method using machine learning, (2) provision of a high-performance event extraction system, and (3) the execution of a quantitative error analysis. The proposed complex (binding and regulation) event detector outperforms the best system from the BioNLP'09 shared task challenge.

positive: Message Understanding Conference- 6: A Brief History We have recently completed the sixth in a series of "Message Understanding Conferences" which are designed to promote and evaluate research in information extraction. MUC-6 introduced several innovations over prior MUCs, most notably in the range of different tasks for which evaluations were conducted. We describe some of the motivations for the new format and briefly discuss some of the results of the evaluations. 1 The M U C Evaluations We have just completed the sixth in a series of Message Understanding Conferences, which have been organized by NRAD, the RDT&E division of the Naval Command, Control and Ocean Surveillance Center (formerly NOSC, the Naval Ocean Systems Center) with the support of DARPA, the Defense Advanced Research Projects Agency. This paper looks briefly at the history of these Conferences and then examines the considerations which led to the structure of MUC-6} The Message Understanding Conferences were initiated by NOSC to assess and to foster research on the automated analysis of mili tary messages containing textual information. Although called "conferences", the distinguishing characteristic of the MUCs are not the conferences themselves, but the evaluations to which participants must submit in order to be permit ted to at tend the conference. For each MUC, participating groups have been given sample messages and instructions on the type of information to be extracted, and have developed a system to process such messages. Then, shortly before the conference, participants are given a set of test messages to be run through their system (without making any changes to the system); the output of each part icipant 's system 1The full proceedings of the conference are to be distributed by Morgan Kaufmann Publishers, San Mateo, California; earlier MUC proeeedings~ for MUC-3, 4, and 5, are also available from Morgan Kaufmann. Beth Sundhe im Naval Command, Control and Ocean Surveillance Center Research, Development, Test and Evaluation Division (NRaD) Code 44208 53140 Gatchell Road San Diego, CMifornia 92152-7420 s u n d h e i m @ p o j k e . n o s c . m i l is then evaluated against a manual ly-prepared answer key. The MUCs are remarkable in part because of the degree to which these evaluations have defined a prograin of research and development. DARPA has a number of information science and technology programs which are driven in large part, by regular evaluations. The MUCs are notable, however, in that they in large par t have shaped the research program in information extraction and brought it to its current s ta te}

positive: Inducing Features of Random Fields We present a technique for constructing random elds from a set of training samples. The learning paradigm builds increasingly complex elds by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the Kullback-Leibler divergence between the model and the empirical distribution of the training data. A greedy algorithm determines how features are incrementally added to the eld and an iterative scaling algorithm is used to estimate the optimal values of the weights. The random eld models and techniques introduced in this paper di er from those common to much of the computer vision literature in that the underlying random elds are nonMarkovian and have a large number of parameters that must be estimated. Relations to other learning approaches including decision trees and Boltzmann machines are given. As a demonstration of the method, we describe its application to the problem of automatic word classi cation in natural language processing.

negative: Memory consolidation and the hippocampus: further evidence from studies of autobiographical memory in semantic dementia and frontal variant frontotemporal dementia Studies of autobiographical memory in semantic dementia have found relative preservation of memories for recent rather than remote events. As semantic dementia is associated with progressive atrophy to temporal neocortex, with early asymmetric sparing of the hippocampus, this neuropsychological pattern suggests that the hippocampal complex plays a role in the acquisition and retrieval of recent memories, but is not necessary for the recall of older episodic events. In an alternative view of memory consolidation, however, the hippocampus plays a role in the retrieval of all autobiographical memories, regardless of the age of the memory [Curr. Opin. Neurobiol. 7(1997)217]. This 'multiple trace theory' predicts that patients with semantic dementia should show no effects of time in their autobiographical recall. In this article, we ask whether it is possible to reconcile the data from semantic dementia with the multiple trace theory by investigating whether the time-dependent pattern of autobiographical retrieval seen in the disease is due to (i) patients showing this effect being exceptional in their presentation; and/or (ii) patients with semantic dementia exhibiting impaired strategic retrieval from concomitant frontal damage. A series of experiments in patients with semantic dementia, the frontal variant of frontotemporal dementia and Alzheimer's disease clearly demonstrates that neither of these two factors can explain the documented effect of time seen in semantic dementia. Nonetheless, we discuss how damage to semantic knowledge could result in an autobiographical memory deficit and suggest that data from semantic dementia may be consistent with both views of hippocampal involvement in long-term memory.

negative:
An integrated GPU power and performance model GPU architectures are increasingly important in the multi-core era due to their high number of parallel processors. Performance optimization for multi-core processors has been a challenge for programmers. Furthermore, optimizing for power consumption is even more difficult. Unfortunately, as a result of the high number of processors, the power consumption of many-core processors such as GPUs has increased significantly.
 Hence, in this paper, we propose an integrated power and performance (IPP) prediction model for a GPU architecture to predict the optimal number of active processors for a given application. The basic intuition is that when an application reaches the peak memory bandwidth, using more cores does not result in performance improvement.
 We develop an empirical power model for the GPU. Unlike most previous models, which require measured execution times, hardware performance counters, or architectural simulations, IPP predicts execution times to calculate dynamic power events. We then use the outcome of IPP to control the number of running cores. We also model the increases in power consumption that resulted from the increases in temperature.
 With the predicted optimal number of active cores, we show that we can save up to 22.09%of runtime GPU energy consumption and on average 10.99% of that for the five memory bandwidth-limited benchmarks.

negative:
Growth of the flickr social network Online social networking sites like MySpace, Orkut, and Flickr are among the most popular sites on the Web and continue to experience dramatic growth in their user population. The popularity of these sites offers a unique opportunity to study the dynamics of social networks at scale. Having a proper understanding of how online social networks grow can provide insights into the network structure, allow predictions of future growth, and enable simulation of systems on networks of arbitrary size. However, to date, most empirical studies have focused on static network snapshots rather than growth dynamics.
 In this paper, we collect and examine detailed growth data from the Flickr online social network, focusing on the ways in which new links are formed. Our study makes two contributions. First, we collect detailed data covering three months of growth, encompassing 950,143 new users and over 9.7 million new links, and we make this data available to the research community. Second, we use a first-principles approach to investigate the link formation process. In short, we find that links tend to be created by users who already have many links, that users tend to respond to incoming links by creating links back to the source, and that users link to other users who are already close in the network.

negative: Characterizing Data Structures for Volatile Forensics Volatile memory forensic tools can extract valuable evidence from latent data structures present in memory dumps. However, current techniques are generally limited by a lack of understanding of the underlying data without the use of expert knowledge. In this paper, we characterize the nature of such evidence by using deep analysis techniques to better understand the life-cycle and recoverability of latent program data in memory. We have developed Cafegrind, a tool that can systematically build an object map and track the use of data structures as a program is running. Statistics collected by our tool can show which data structures are the most numerous, which structures are the most frequently accessed and provide summary statistics to guide forensic analysts in the evidence gathering process. As programs grow increasingly complex and numerous, the ability to pinpoint specific evidence in memory dumps will be increasingly helpful. Cafegrind has been tested on a number of real-world applications and we have shown that it can successfully map up to 96% of heap accesses.

negative: Sparse Model-based Image Alignment Feature Alignment Pose & Structure Refinement Motion Estimation Thread New Image Last Frame Map Frame Queue Feature Extraction Initialize Depth-Filters Direct methods for Visual Odometry (VO) have gained popularity due to their capability to exploit information from all intensity gradients in the image. However, low computational speed as well as missing guarantees for optimality and consistency are limiting factors of direct methods, where established feature-based methods instead succeed at. Based on these considerations, we propose a Semi-direct VO (SVO) that uses direct methods to track and triangulate pixels that are characterized by high image gradients but relies on proven feature-based methods for joint optimization of structure and motion. Together with a robust probabilistic depth estimation algorithm, this enables us to efficiently track pixels lying on weak corners and edges in environments with little or high-frequency texture. We further demonstrate that the algorithm can easily be extended to multiple cameras, to track edges, to include motion priors, and to enable the use of very large field of view cameras, such as fisheye and catadioptric ones. Experimental evaluation on benchmark datasets shows that the algorithm is significantly faster than the state of the art while achieving highly competitive accuracy. SUPPLEMENTARY MATERIAL Video of the experiments: https://youtu.be/hR8uq1RTUfA

negative: Autonomic nervous system activity in emotion: A review Autonomic nervous system (ANS) activity is viewed as a major component of the emotion response in many recent theories of emotion. Positions on the degree of specificity of ANS activation in emotion, however, greatly diverge, ranging from undifferentiated arousal, over acknowledgment of strong response idiosyncrasies, to highly specific predictions of autonomic response patterns for certain emotions. A review of 134 publications that report experimental investigations of emotional effects on peripheral physiological responding in healthy individuals suggests considerable ANS response specificity in emotion when considering subtypes of distinct emotions. The importance of sound terminology of investigated affective states as well as of choice of physiological measures in assessing ANS reactivity is discussed.

negative: Dude, where's my card?: RFID positioning that works with multipath and non-line of sight RFIDs are emerging as a vital component of the Internet of Things. In 2012, billions of RFIDs have been deployed to locate equipment, track drugs, tag retail goods, etc. Current RFID systems, however, can only identify whether a tagged object is within radio range (which could be up to tens of meters), but cannot pinpoint its exact location. Past proposals for addressing this limitation rely on a line-of-sight model and hence perform poorly when faced with multipath effects or non-line-of-sight, which are typical in real-world deployments. This paper introduces the first fine-grained RFID positioning system that is robust to multipath and non-line-of-sight scenarios. Unlike past work, which considers multipath as detrimental, our design exploits multipath to accurately locate RFIDs. The intuition underlying our design is that nearby RFIDs experience a similar multipath environment (e.g., reflectors in the environment) and thus exhibit similar multipath profiles. We capture and extract these multipath profiles by using a synthetic aperture radar (SAR) created via antenna motion. We then adapt dynamic time warping (DTW) techniques to pinpoint a tag's location. We built a prototype of our design using USRP software radios. Results from a deployment of 200 commercial RFIDs in our university library demonstrate that the new design can locate misplaced books with a median accuracy of 11~cm.

negative: Performance evaluation and model checking join forces A call for the perfect marriage between classical performance evaluation and state-of-the-art verification techniques.

negative: Activity Recognition with Smartphone Sensors Second Exam Literature Review The ubiquity of smartphones together with their ever-growing computing, networking and sensing powers have been changing the landscape of people’s daily life. Among others, activity recoginition, which takes the raw sensor reading as inputs and predicts a user’s motion activity, has become an active research area in recent years. It is the core building block in many high-impact applications, ranging from health and fitness monitoring, personal biometric signature, urban computing, assistive technology and elder-care, to indoor localization and navigation, etc. This paper presents a comprehensive survey of the recent advances in activity recognition with smartphones’ sensors. We start with the basic concepts such as sensors, activity types, etc. We review the core data mining techniques behind the main stream activity recognition algorithms, analyze their major challenges and introduce a variety of real applications enabled by activity recognition. Professor: Dr. Hanghang Tong Signature: Date: 06-12-2014 Professor: Dr. Ping Ji Signature: Date: 06-12-2014 Professor: Dr. Ted Brown Signature: Date: 06-12-2014 Advisor: Dr. Hanghang Tong

negative: Chinese character CAPTCHA recognition based on convolution neural network CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) are increasingly used in many applications for machine and human identification. Compared with traditional English and digital characters based CAPTCHAs, Chinese characters contain more complicated characters which greatly enhance difficulty of automatic recognition. To solve that problem, we proposed a Convolution Neural Network (CNN) based approach. This approach greatly improves the recognition accuracy of Chinese Character CAPTCHAs with distortion, rotation and background noise. Our experiment results show that this approach achieves more than 95% accuracy for single character and 84% accuracy for three types of Chinese Character CAPTCHAs with four characters. This encouraging result indicates that deep neural network is useful in complicated structure perception of Chinese Character CAPTCHAs.

negative: Deep learning with support vector data description One of the most critical problems for machine learning methods is overfitting. The overfitting problem is a phenomenon in which the accuracy of the model on unseen data is poor whereas the training accuracy is nearly perfect. This problem is particularly severe in complex models that have a large set of parameters. In this paper, we propose a deep learning neural network model that adopts the support vector data description (SVDD). The SVDD is a variant of the support vector machine, which has high generalization performance by acquiring a maximal margin in one-class classification problems. The proposed model strives to obtain the representational power of deep learning. Generalization performance is maintained using the SVDD. The experimental results showed that the proposed model can learn multiclass data without severe overfitting problems.

negative: Building Footprints Extraction from Oblique Imagery Nowadays, multi-camera aerial platforms combining nadir and oblique cameras are experiencing a revival and several companies have proposed new image acquisition systems. Due to their various advantages, oblique imagery have found their place in numerous companies and civil applications. However, the automatic processing of such image blocks still remains a topic of research. Camera configuration indeed poses a challenge on the traditional photogrammetric pipeline used in commercial software but, on the other hand, gives the opportunity to exploit the additional information provided by the oblique views and allows a more reliable feature extraction. In particular, the information that can be provided in correspondence of building façades can open new possibilities for the building detection and footprint extraction. In this paper, a methodology for the automated extraction of building footprints from oblique imagery is presented. The extraction is performed using dense point clouds generated using an image matching algorithm. The developed methodology and the achieved results are described in detail showing the advantages and opportunities offered by oblique aerial systems for cartographic and mapping purposes.

negative: Multi-View Dimensionality Reduction via Canonical Correlation Analysis We analyze the multi-view regression problem where we have two views X = (X, X) of the input data and a target variableY of interest. We provide sufficient conditions under which we can reduce the dimensionality of X (via a projection) without loosing predictive power of Y . Crucially, this projection can be computed via a Canonical Correlation Analysis only on the unlabeled data. The algorithmic template is as f ollows: with unlabeled data, perform CCA and construct a certain projection; with the labeled data, do least squa res regression in this lower dimensional space. We show how, under certain natural assumptions, the number o f labeled samples could be significantly reduced (in comparison to the single view setting) — in particular, we show how this dimen sionality reduction does not loose predictive power ofY (thus it only introduces little bias but could drastically reduce the variance). We explore two separate assumptions under which this is possible and show how, under either assumption alone, dimensionality reduction could reduce the labeled sample complexity. The two assumptions we consider are a conditional independenceassumption and a redundancyassumption. The typical conditional independence assumption is that conditioned onY the viewsX andX are independent — we relax this assumption to: conditioned on some hidden state H the viewsX andX are independent. Under the redundancy assumption, we show that the best predictor from each view is roughly as good as the best predictor u sing both views.

negative: High Performance Ternary Multiplier Using CNTFET Ternary logic is a promising alternative to the conventional binary logic in VLSI design as it provides the advantages of reduced interconnects, higher operating speeds and smaller chip area. This work presents a ternary multiplier using carbon nanotube field effect transistors (CNTFETs). The proposed designs use in-depth analysis of addition required for designing a two trit multiplier. Based on this analysis two efficient adders are proposed. These adders are used to optimize the multiplier design. The proposed circuits are extensively simulated using HSPICE to obtain power, delay and power delay product. The circuit performances are compared with designs reported in recent literature. This circuit demonstrates a power delay product improvement up to 16.8%, with lesser transistor count of 16%. So, the use of these circuits in complex arithmetic circuits will be advantageous.

negative: Informative Path Planning and Mapping with Multiple UAVs in Wind Fields Informative path planning (IPP) is used to design paths for robotic sensor platforms to extract the best/maximum possible information about a quantity of interest while operating under a set of constraints, such as dynamic feasibility of vehicles. The key challenges of IPP are the strong coupling in multiple layers of decisions: the selection of locations to visit, the allocation of sensor platforms to those locations; and the processing of the gathered information along the paths. This paper presents an systematic procedure for IPP and environmental mapping using multiple UAV sensor platforms. It (a) selects the best locations to observe, (b) calculates the cost and finds the best paths for each UAV, and (c) estimates the measurement value within a given region using the Gaussian process (GP) regression framework. An illustrative example of RF intensity field mapping is presented to demonstrate the validity and applicability of the proposed approach.

negative: A new pedestrian detection method based on combined HOG and LSS features Pedestrian detection is a critical issue in computer vision, with several feature descriptors can be adopted. Since the ability of various kinds of feature descriptor is different in pedestrian detection and there is no basis in feature selection, we analyze the commonly used features in theory and compare them in experiments. It is desired to find a new feature with the strongest description ability from their pair-wise combinations. In experiments, INRIA database and Daimler database are adopted as the training and testing set. By theoretic analysis, we find the HOG–LSS combined feature have more comprehensive description ability. At first, Adaboost is regarded as classifier and the experimental results show that the description ability of the new combination features is improved on the basis of the single feature and HOG–LSS combined feature has the strongest description ability. For further verifying this conclusion, SVM classifier is used in the experiment. The detection performance is evaluated by miss rate, the false positives per window, and the false positives per image. The results of these indicators further prove that description ability of HOG–LSS feature is better than other combination of these

negative: Visfer: Camera-based visual data transfer for cross-device visualization Going beyond the desktop to leverage novel devices—such as smartphones, tablets, or large displays—for visual sensemaking typically requires supporting extraneous operations for device discovery, interaction sharing, and view management. Such operations can be time-consuming and tedious, and distract the user from the actual analysis. Embodied interaction models in these multi-device environments can take advantage of the natural interaction and physicality afforded by multimodal devices and help effectively carry out these operations in visual sensemaking. In this paper, we present cross-device interaction models for visualization spaces, that are embodied in nature, by conducting a user study to elicit actions from participants that could trigger a portrayed effect of sharing visualizations (and therefore information) across devices. We then explore one common interaction style from this design elicitation called Visfer, a technique for effortlessly sharing visualizations across devices using the visual medium. More specifically, this technique involves taking pictures of visualizations, or rather the QR codes augmenting them, on a display using the built-in camera on a handheld device. Our contributions include a conceptual framework for cross-device interaction and the Visfer technique itself, as well as transformation guidelines to exploit the capabilities of each specific device and a web framework for encoding visualization components into animated QR codes, which capture multiple frames of QR codes to embed more information. Beyond this, we also present the results from a performance evaluation for the visual data transfer enabled by Visfer. We end the paper by presenting the application examples of our Visfer framework.

negative:
CS Teacher Experiences with Educational Technology, Problem-BasedLearning, and a CS Principles Curriculum Little is known about how K-12 Computer Science (CS) teachers use technology and problem-based learning (PBL) to teach CS content in the context of CS Principles curricula. Significantly, little qualitative research has been conducted in these areas in computer science education, so we lack an in-depth understanding of the complicated realities of CS teachers' experiences. This paper describes the practices and experiences of six teachers' use of technology that was implemented to support PBL in the context of a dual enrollment CS Principles course.
 Results from an early offering of this course suggest that (1) while CS teachers used technology, they did not appear to use it to support student inquiry, (2) local adaptations to the curriculum were largely teacher-centric, and (3) the simultaneous adoption of new instructional practices, technologies, and curricula was overwhelming to teachers. This paper then describes how these results were used to modify the curriculum and professional development, leading to increased teacher satisfaction and student success in the course.

negative:
Resin composite--state of the art. OBJECTIVES
The objective is to review the current state of the art of dental composite materials.

METHODS
An outline of the most important aspects of dental composites was created, and a subsequent literature search for articles related to their formulation, properties and clinical considerations was conducted using PubMed followed by hand searching citations from relevant articles.

RESULTS
The current state of the art of dental composites includes a wide variety of materials with a broad range of mechanical properties, handling characteristics, and esthetic possibilities. This highly competitive market continues to evolve, with the major emphasis in the past being to produce materials with adequate strength, and high wear resistance and polishability retention. The more recent research and development efforts have addressed the issue of polymerization shrinkage and its accompanying stress, which may have a deleterious effect on the composite/tooth interfacial bond. Current efforts are focused on the delivery of materials with potentially therapeutic benefits and self-adhesive properties, the latter leading to truly simplified placement in the mouth.

SIGNIFICANCE
There is no one ideal material available to the clinician, but the commercial materials that comprise the current armamentarium are of high quality and when used appropriately, have proven to deliver excellent clinical outcomes of adequate longevity.

negative: On-road driver eye movement tracking using head-mounted devices It is now evident from anecdotal evidence and preliminary research that distractions can hinder the task of operating a vehicle, and consequently reduce driver safety. However with increasing wireless connectivity and the portability of office devices, the vehicle of the future is visualized as an extension of the static work place - i.e. an office-on-the-move, with a phone, a fax machine and a computer all within the reach of the vehicle operator. For this research a Head mounted Eye-tracking Device (HED), is used for tracking the eye movements of a driver navigating a test route in an automobible while completing various driving tasks. Issues arising from data collection of eye movements during the completion of various driving tasks as well as the analysis of this data are discussed. Methods for collecting video and scan-path data, as well as difficulties and limitations are also reported.

negative: State-space model with deep learning for functional dynamics estimation in resting-state fMRI Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach.

negative: Computationally modeling human emotion Computer models of emotion inform theories of human intelligence and advance human-centric applications.

negative: LEARNING PHYSICAL DYNAMICS We present the Neural Physics Engine (NPE), a framework for learning simulators of intuitive physics that naturally generalize across variable object count and different scene configurations. We propose a factorization of a physical scene into composable object-based representations and a neural network architecture whose compositional structure factorizes object dynamics into pairwise interactions. Like a symbolic physics engine, the NPE is endowed with generic notions of objects and their interactions; realized as a neural network, it can be trained via stochastic gradient descent to adapt to specific object properties and dynamics of different worlds. We evaluate the efficacy of our approach on simple rigid body dynamics in two-dimensional worlds. By comparing to less structured architectures, we show that the NPE’s compositional representation of the structure in physical interactions improves its ability to predict movement, generalize across variable object count and different scene configurations, and infer latent properties of objects such as mass.

negative: An Ontology-based Context Model in Intelligent Environments Computing becomes increasingly mobile and pervasive today; these changes imply that applications and services must be aware of and adapt to their changing contexts in highly dynamic environments. Today, building context-aware systems is a complex task due to lack of an appropriate infrastructure support in intelligent environments. A context-aware infrastructure requires an appropriate context model to represent, manipulate and access context information. In this paper, we propose a formal context model based on ontology using OWL to address issues including semantic context representation, context reasoning and knowledge sharing, context classification, context dependency and quality of context. The main benefit of this model is the ability to reason about various contexts. Based on our context model, we also present a Service-Oriented Context-Aware Middleware (SOCAM) architecture for building of context-aware services.

negative:
Parental vaccine safety concerns in 2009. OBJECTIVE
Vaccine safety concerns can diminish parents' willingness to vaccinate their children. The objective of this study was to characterize the current prevalence of parental vaccine refusal and specific vaccine safety concerns and to determine whether such concerns were more common in specific population groups.

METHODS
In January 2009, as part of a larger study of parents and nonparents, 2521 online surveys were sent to a nationally representative sample of parents of children who were aged </=17 years. The main outcome measures were parental opinions on vaccine safety and whether the parent had ever refused a vaccine that a doctor recommended for his or her child.

RESULTS
The response rate was 62%. Most parents agreed that vaccines protect their child(ren) from diseases; however, more than half of the respondents also expressed concerns regarding serious adverse effects. Overall, 11.5% of the parents had refused at least 1 recommended vaccine. Women were more likely to be concerned about serious adverse effects, to believe that some vaccines cause autism, and to have ever refused a vaccine for their child(ren). Hispanic parents were more likely than white or black parents to report that they generally follow their doctor's recommendations about vaccines for their children and less likely to have ever refused a vaccine. Hispanic parents were also more likely to be concerned about serious adverse effects of vaccines and to believe that some vaccines cause autism.

CONCLUSIONS
Although parents overwhelmingly share the belief that vaccines are a good way to protect their children from disease, these same parents express concerns regarding the potential adverse effects and especially seem to question the safety of newer vaccines. Although information is available to address many vaccine safety concerns, such information is not reaching many parents in an effective or convincing manner.

negative: Neuro-fuzzy rule generation: survey in soft computing framework The present article is a novel attempt in providing an exhaustive survey of neuro-fuzzy rule generation algorithms. Rule generation from artificial neural networks is gaining in popularity in recent times due to its capability of providing some insight to the user about the symbolic knowledge embedded within the network. Fuzzy sets are an aid in providing this information in a more human comprehensible or natural form, and can handle uncertainties at various levels. The neuro-fuzzy approach, symbiotically combining the merits of connectionist and fuzzy approaches, constitutes a key component of soft computing at this stage. To date, there has been no detailed and integrated categorization of the various neuro-fuzzy models used for rule generation. We propose to bring these together under a unified soft computing framework. Moreover, we include both rule extraction and rule refinement in the broader perspective of rule generation. Rules learned and generated for fuzzy reasoning and fuzzy control are also considered from this wider viewpoint. Models are grouped on the basis of their level of neuro-fuzzy synthesis. Use of other soft computing tools like genetic algorithms and rough sets are emphasized. Rule generation from fuzzy knowledge-based networks, which initially encode some crude domain knowledge, are found to result in more refined rules. Finally, real-life application to medical diagnosis is provided.

negative: Local sensor system for badminton smash analysis This paper presents a development of a sensory system for analysis of badminton smashes. During a badminton game, the ability to execute a powerful smash is fundamental for a player to be competitive. In most games, the winning factor for the game is often attributed to a high shuttle speed during the execution of a smash. It was envisioned that the shuttle speed can be correlated from the speed of the rackets head. To help analyze the key relationship between the racket speed and the shuttle ball speed, a series of sensors were integrated into a conventional racket. The aim of this investigation is to develop a sensor system that will facilitate the quantifiable analysis of the badminton smash. It was determined in a previous investigation that a single accelerometer is insufficient to determine the two or three dimensional trajectory of the racket. Therefore a low mass compact, 2-axes piezoelectric accelerometers was applied to the head of the racket. An acoustic shock sensor was also mounted on the racket head to identify the instant of the contact event. It was hypothesized that the magnitude of the acoustic signal, associated with the hitting event, and the final speed of the racket when fused could provide a good correlation with the shuttle speed. The fuzzy inference system and ANFIS (Adaptive Neuro-Fuzzy Inferior System) sensor fusion techniques was investigated. It has been demonstrated that it is possible to analyze the performance of the smashing stroke based on the system developed in this investigation which in turn may, with further develop by a useful aid to badminton coaches and the methods developed may also be applicable to other applications.

negative: Classification of rice grain varieties using two artificial neural networks (MLP and neuro-fuzzy). Artificial neural networks (ANNs) have many applications in various scientific areas such as identification, prediction and image processing. This research was done at the Islamic Azad University, Shahr-e-Rey Branch, during 2011 for classification of 5 main rice grain varieties grown in different environments in Iran. Classification was made in terms of 24 color features, 11 morphological features and 4 shape factors that were extracted from color images of each grain of rice. The rice grains were then classified according to variety by multi layer perceptron (MLP) and neuro-fuzzy neural networks. The topological structure of the MLP model contained 39 neurons in the input layer, 5 neurons (Khazar, Gharib, Ghasrdashti, Gerdeh and Mohammadi) in the output layer and two hidden layers; neuro-fuzzy classifier applied the same structure in input and output layers with 60 rules. Average accuracy amounts for classification of rice grain varieties computed 99.46% and 99.73% by MLP and neuro-fuzzy classifiers alternatively. The accuracy of MLP and neuro-fuzzy networks changed after feature selections were 98.40% and 99.73 % alternatively.

negative: Capturing and analyzing low-level events from the code editor In this paper, we present FLUORITE, a publicly available event logging plug-in for Eclipse which captures all of the low-level events when using the Eclipse code editor. FLUORITE captures not only what types of events occurred in the code editor, but also more detailed information such as the inserted and deleted text and the specific parameters for each command. This enables the detection of many usage patterns that could otherwise not be recognized, such as "typo correction" that requires knowing that the entered text is immediately deleted and replaced. Moreover, the snapshots of each source code file that has been opened during the session can be completely reproduced using the collected information. We also provide analysis and visualization tools which report various statistics about usage patterns, and we provide the logs in an XML format so others can write their own analyzers. FLUORITE can be used for not only evaluating existing tools, but also for discovering issues that motivate new tools.

negative: Modeling Response Time in Digital Human Communication Our daily lives increasingly involve interactions with other individuals via different communication channels, such as email, text messaging, and social media. In this paper we focus on the problem of modeling and predicting how long it takes an individual to respond to an incoming communication event, such as receiving an email or a text. In particular, we explore the effect on response times of an individual’s temporal pattern of activity, such as circadian and weekly patterns which are typically present in individual data. A probabilistic time-warping approach is used, considering linear time to be a transformation of “effective time,” where the transformation is a function of an individual’s activity rate. We apply this transformation of time to two different types of temporal event models, the first for modeling response times directly, and the second for modeling event times via a Hawkes process. We apply our approach to two different sets of real-world email histories. The experimental results clearly indicate that the transformation-based approach produces systematically better models and predictions compared to simpler methods that ignore circadian and weekly patterns. Current technology allows us to collect large quantities of time-stamped individual-level event data characterizing our “digital behavior” in contexts such as texting, email activity, microblogging, social media interactions, and more — and the volume and variety of this type of data is continually increasing. The resulting time-series of events are rich in behavioral information about our daily lives. Tools for obtaining and visualizing such information are becoming increasingly popular, such as the ability to download your entire email history for mail applications such as Gmail, and various software packages for tracking personal fitness using data from devices such as Fitbit. This paper is focused on modeling the temporal aspects of how an individual (also referred to as the “ego”) responds to others, given a sequence of timestamped events (e.g. communication messages via email or text). What can we learn from the way we respond to others? Are there systematic ∗Currently employed at Google. The research described in this paper was conducted while the author was a graduate student at UC Irvine. Copyright c © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Mon Tue Wed Thu Fri Sat Sun 0 1 a (t ) Mon Tue Wed Thu Fri Sat Sun 0 1 a (t ) Figure 1: Example of smoothed daily and weekly patterns over two separate individuals’ email histories. a(t) on the yaxis represents an individual’s typical activity as a function of time. patterns that can be extracted and used predictively? How do our daily sleep and work patterns factor in to our response patterns? These are common issues that arise when modeling communication events, as the ego’s behavior typically changes significantly over the course of a day or week. Examples of such patterns are shown in Figure 1. We propose a novel method for parameterizing these circadian (daily) and weekly patterns, allowing the time dimension to be transformed according to such patterns. This transformation of time will allow models to describe and predict behavioral patterns which are invariant to the ego’s routine patterns. We apply this approach to predicting the ego’s response time to an event. Learning such response patterns is useful not only for identifying relationships between pairs of individuals (Halpin and De Boeck 2013), but also as features for prioritization of events, e.g., the priority inbox implemented in Gmail (Aberdeen, Pacovsky, and Slater 2010). Our experimental results show clear advantages in terms of predictive power when modeling response times in the transformed time domain. τ0 τ1 τ2 t0 t1 t2 ∆0

negative: Sentiment Classification through Semantic Orientation Using SentiWordNet Sentiment analysis is the procedure by which information is extracted from the opinions, appraisals and emotions of people in regards to entities, events and their attributes. In decision making, the opinions of others have a significant effect on customers ease in making choices regards to online shopping, choosing events, products, entities. In this paper, a rule based domain independent sentiment analysis method is proposed. The proposed method classifies subjective and objective sentences from reviews and blog comments. The semantic score of subjective sentences is extracted from SentiWordNet to calculate their polarity as positive, negative or neutral based on the contextual sentence structure. The results show the effectiveness of the proposed method and it outperforms the machine learning methods. The proposed method achieves an accuracy of 87% at the feedback level and 83% at the sentence level for comments. [Aurangzeb khan, Muhammad Zubair Asghar, Shakeel Ahmad, Fazal Masud Kundi, Maria Qasim, Furqan. Sentiment Classification through Semantic Orientation Using SentiWordNet. Life Sci J 2014; 11(10):309-315] (ISSN: 1097-8135). http://www.lifesciencesite.com. 44

negative: Automatic detection of users’ skill levels using high-frequency user interface events Computer users have different levels of system skills. Moreover, each user has different levels of skill across different applications and even in different portions of the same application. Additionally, users’ skill levels change dynamically as users gain more experience in a user interface. In order to adapt user interfaces to the different needs of user groups with different levels of skills, automatic methods of skill detection are required. In this paper, we present our experiments and methods, which are used to build automatic skill classifiers for desktop applications. Machine learning algorithms were used to build statistical predictive models of skill. Attribute values were extracted from high frequency user interface events, such as mouse motions and menu interactions, and were used as inputs to our models. We have built both task-independent and task-dependent classifiers with promising results.


query: Identification of Design Elements for a Maturity Model for Interorganizational Integration: A Comparative Analysis

positive: Capability Maturity Model for Software This paper provides an overview of the latest version of the Capability Maturity Model for Software, CMM v1.1. Based on over six years of experience with software process improvement and the contributions of hundreds of reviewers, CMM v1.1 describes the software engineering and management practices that characterize organizations as they mature their processes for developing and maintaining software. This paper stresses the need for a process maturity framework to prioritize improvement actions, describes the process maturity framework of five maturity levels and the associated structural components, and discusses future directions for the CMM.

positive: The Levels of Conceptual Interoperability Model Interoperability of systems is not a cookie-cutter-function. There are various levels of interoperability between two systems ranging from no interoperability to full interoperability. In the technical domain, various models for levels of interoperability already exist and are used successfully to determine the degree of interoperability between information technology systems. However, such models are not yet established in the domain of conceptual modeling. This paper introduces a general model dealing with various levels of conceptual interoperability that goes beyond the technical reference models for interoperable solutions. The model is intended to become a bridge between the conceptual design and the technical design for implementation, integration, or federation. It should also contribute to the standardization of V&V procedures as well as to the documentation of systems that are designed to be federated. It is furthermore a framework to determine in the early stages of the federation development process whether meaningful interoperability between systems is possible. To this end, the scope of the model goes beyond the implementation level of actual standards, which focus on the exchange of data using standardized formats and interfaces. Another practical application of the model is, that it enhances the only recently published DoD Net-Centric Data Strategy for the Global Information Grid (GIG) and is directly applicable to derive necessary metadata to reach the DoD Data Goal to “enable Data to be understandable.”

positive: Electronic Data Interchange and Small Organizations: Adoption and Impact of Technology Many EDI researchers and practitioners have recognized the importance of high penetration levels for the success of EDI. Unfortunately, such penetration is partly impeded by the resistance of small companies to become EDI capable. To investigate this issue, three major factors are identified that influence the EDI adoption practices of small firms. These factors are: organizational readiness (because of the low levels of IT sophistication and resource availability of small firms), external pressures to adopt (because of the weak market positions of small firms and the network nature of the technology), and perceived benefits (because of the limited impact that IT has on small firms due to under-utilization and lack of integration). By combining the anticipated effects of these factom, we developed a framework of EDI adoption by small businesses. The applicability of this framework is empirically demonstrated using the results of seven case studies. Finally, recommendations are made for the development of successful EDI partner expansion plans. These include the development of a long-term EDI partner expansion plan from the very beginning, the individual assessment of each partner’s EDI preparedness level, and the selection of appropriate influence tactics to expedite adoption by small partners. Specifically, it is suggested that EDI initiators pursue promotional efforts to improve partners’ perceptions of EDI benefits, provide financial and technological assistance to partners with low organizational readiness, and carefully select and enact influence strategies to reduce resistance.

positive: The Modeling Process for Stage Models The purpose of this paper is to present and test a modeling procedure because researchers have struggled for decades to develop stages-of-growth models that are both theoretically founded and empirically validated. This article presents the concept and hypothesis of stages, the history of stage models, and a procedure that may serve as a useful tool in modeling stages of growth. Based on previous research and lessons learned from case study experience of the government sector in Norway, a procedure for the stages of growth modeling process is suggested and demonstrated. The procedure is used for developing a stage model for e-government interoperability. This article provides new insight into issues and challenges faced when engaging in stages of growth research. The paper proposes a new approach to stages of growth modeling. The utility of the suggested procedure is to improve theory building and empirical validation. The contribution to academia is the modeling process that can be applied in future developments of stages of growth. The contribution to practice lies in the stage hypothesis of organizational development over time.

positive: The Case Research Strategy in Studies of Information Systems This article defines and discusses one of these qualitative methods--the case research strategy. Suggestions are provided for researchers who wish to undertake research employing this approach. Criteria for the evaluation of case research are established and several characteristics useful for categorizing the studies are identified. A sample of papers drawn from information systems journals is reviewed. The paper concludes with examples of research areas that are particularly wellsuited to investigation using the case research approach. ACM categories: H.O., J.O.

negative: Does a Water Protocol Improve the Hydration and Health Status of Individuals with Thin Liquid Aspiration Following Stroke? A Randomized Controlled Trial The benefit of water protocols for individuals with thin liquid aspiration remains controversial, with mixed findings from a small number of randomized controlled trials (RCTs). This study aimed to contribute to the evidence of the effectiveness of water protocols with a particular emphasis on health outcomes, especially hydration. An RCT was conducted with patients with known thin liquid aspiration post stroke randomized to receiving thickened liquids only or a water protocol. For the 14 participants in rehabilitation facilities whose data proceeded to analysis, there was no difference in the total amount of beverages consumed between the water protocol group (mean = 1103 ml per day, SD = 215 ml) and the thickened liquids only group (mean = 1103 ml, SD = 247 ml). Participants in the water protocol group drank on average 299 ml (SD 274) of water but offset this by drinking less of the thickened liquids. Their hydration improved over time compared with participants in the thickened liquids only group, but differences between groups were not significant. Twenty-one percent of the total sample was diagnosed with dehydration, and no participants in either group were diagnosed with pneumonia. There were significantly more diagnoses of urinary tract infection in the thickened liquids only group compared to the water protocol group (χ 2 = 5.091, p = 0.024), but no differences between groups with regard to diagnoses of dehydration (χ 2 = 0.884, p = 0.347) or constipation (χ 2 = 0.117, p = 0.733). The findings reinforce evidence about the relative safety of water protocols for patients in rehabilitation post stroke and provide impetus for future research into the potential benefits for hydration status and minimizing adverse health outcomes.

negative: A comparative study between RadViz and Star Coordinates RadViz and star coordinates are two of the most popular projection-based multivariate visualization techniques that arrange variables in radial layouts. Formally, the main difference between them consists of a nonlinear normalization step inherent in RadViz. In this paper we show that, although RadViz can be useful when analyzing sparse data, in general this design choice limits its applicability and introduces several drawbacks for exploratory data analysis. In particular, we observe that the normalization step introduces nonlinear distortions, can encumber outlier detection, prevents associating the plots with useful linear mappings, and impedes estimating original data attributes accurately. In addition, users have greater flexibility when choosing different layouts and views of the data in star coordinates. Therefore, we suggest that analysts and researchers should carefully consider whether RadViz's normalization step is beneficial regarding the data sets' characteristics and analysis tasks.

negative: The Role of Chatbots in Formal Education Chatbots appeared in large numbers at the beginning of the current decade. Interactive technology, often combined with artificial intelligence, has rapidly invaded and occupied the world of online chat. Chatbots are not just elements of virtual assistants, but are used by organizations and governments on websites, in applications, and instant messaging platforms to promote products, ideas or services. In this paper, the authors firstly present a theoretical and historical background, then discuss the issues of using chatbots as educational assistants, and finally describe the basic steps and challenges of programming a bot.

negative: Health data integration with Secured Record Linkage: A practical solution for Bangladesh and other developing countries Knowledge discovery from various health data repositories requires the incorporation of healthcare data from diversified sources. Maintaining record linkage during the integration of medical data is an important research issue. Researchers have given different solutions to this problem that are applicable for developed countries where electronic health record of patients are maintained with identifiers like social security number (SSN), universal patient identifier (UPI), health insurance number, etc. These solutions cannot be used correctly for record linkage of health data of developing countries because of missing data, ambiguity in patient identification, and high amount of noise in patient information. Also, identifiable health data in electronic health repositories may produce a significant risk to patient privacy and also make the health information systems security vulnerable to hackers. In this paper, we have analyzed the practical problems of collecting and integrating healthcare data in Bangladesh for developing national health data warehouse. We have proposed a privacy preserved secured record linkage architecture that can support constrained health data of developing countries such as Bangladesh. Our technique can anonymize identifiable private data of the patients while maintaining record linkage in integrated health repositories to facilitate knowledge discovery process. Experimental results show that our proposed method successfully linked records with acceptable accuracy for noisy data in the absence of any standard ID like SSN.

negative: with Emphasis to the Computer and Internet This article discusses the Roles of ICT in education. Information communication technologies (ICT) at present are influencing every aspect of human life. They are playing salient roles in work places, business, education, and entertainment. Moreover, many people recognize ICTs as catalysts for change; change in working conditions, handling and exchanging information, teaching methods, learning approaches, scientific research, and in accessing information. Therefore, this review article discusses the roles of ICTs, the promises, limitations and key challenges of integration to education systems. The review attempts in answering the following questions: (1) What are the benefits of ICTs in education? (2) What are the existing promises of ICT use in education systems of some developing countries? (3) What are the limitations and key challenges of ICTs integration to education systems? The review concludes that regardless of all the limitations characterizing it, ICT benefits education systems to provide quality education in alignment with constructivism, which is a contemporary paradigm of learning.

negative: Linear feedback control : analysis and design with MATLAB

negative: Adversarial Deep Structural Networks for Mammographic Mass Segmentation Mass segmentation is an important task in mammogram analysis, providing effective morphological features and regions of interest (ROI) for mass detection and classification. Inspired by the success of using deep convolutional features for natural image analysis and conditional random fields (CRF) for structural learning, we propose an end-to-end network for mammographic mass segmentation. The network employs a fully convolutional network (FCN) to model a potential function, followed by a CRF to perform structured learning. Because the mass distribution varies greatly with pixel position, the FCN is combined with a position priori. Due to the small size of mammogram datasets, we use adversarial training to control over-fitting. Four models with different convolutional kernels are further fused to improve the segmentation results. Experimental results on two public datasets, INBreast and DDSM-BCRP, demonstrate that our end-to-end network achieves the state-of-the-art results. 1

negative: Home, habits, and energy: examining domestic interactions and energy consumption This paper presents findings from a qualitative study of people's everyday interactions with energy-consuming products and systems in the home. Initial results from a large online survey are also considered. This research focuses not only on "conservation behavior" but importantly investigates interactions with technology that may be characterized as "normal consumption" or "over-consumption." A novel vocabulary for analyzing and designing energy-conserving interactions is proposed based on our findings, including: cutting, trimming, switching, upgrading, and shifting. Using the proposed vocabulary, and informed by theoretical developments from various literatures, this paper demonstrates ways in which everyday interactions with technology in the home are performed without conscious consideration of energy consumption but rather are unconscious, habitual, and irrational. Implications for the design of energy-conserving interactions with technology and broader challenges for HCI research are proposed.

negative: A new vector evaluated PBIL algorithm for reinsurance analytics The purpose of this paper is to evaluate the performance of a new multiobjective algorithm called Vector Evaluated Population Based Incremental Learning (VEPBIL). The new algorithm was applied in solving a real world application named Reinsurance Contract Optimization (RCO), which is a multiobjective problem consisting of maximizing two conflicting functions: expected return and risk. The VEPBIL was tested on two instances of the problem composed by 7 and 15 layers of real anonymized data. In order to evaluate the algorithm, metrics such as hyper volume, number of solutions and coverage were used. A comparisons against Vector Evaluated Differential evolution (VEDE) is also carried out. The comparison has shown that VEPBIL can dominate about 70% and 50% of solutions from VEDE using 7 and 15 layers respectively, whereas VEDE dominates about 10% and 30% of solutions in the way around.

negative: A service orchestration architecture for Fog-enabled infrastructures The development of Fog Computing technology is crucial to address the challenges to come with the mass adoption of Internet Of Things technology, where the generation of data tends to grow at an unprecedented pace. The technology brings computing power to the surrounds of devices, to offer local processing, filtering, storage and analysis of data and control over actuators. Orchestration is a requirement of Fog Computing technology to deliver services, based on the composition of microservices. It must take into consideration the heterogeneity of the IoT environment and device's capabilities and constraints. This heterogeneity requires a different approach for orchestration, be it regarding infrastructure management, node selection and/or service placement. Orchestrations shall be manually or automatically started through event triggers. Also, the Orchestrator must be flexible enough to work in a centralized or distributed fashion. Orchestration is still a hot topic and can be seen in different areas, especially in the Service Oriented Architectures, hardware virtualization, in the Cloud, and in Network Virtualization Function. However, the architecture of these solutions is not enough to handle Fog Requirements, specially Fog's heterogeneity, and dynamics. In this paper, we propose an architecture for Orchestration for the Fog Computing environment. We developed a prototype to prof some concepts. We discuss in this paper the implementation, and the tools chose, and their roles. We end the paper with a discussion on performance indicators and future direction on the evaluation of non-functional aspects of the Architecture.

negative: Design of a new counter-balancing stackable mechanism This paper describes design and application of a novel counter-balancing stackable parallel mechanism. The stackable mechanism is designed by combining a five-bar and a couple of parallelograms. This design allows a compact design and wide workspace as compared to prior parallel type counter-balancing mechanisms. A linear geometry of this mechanism guarantees static balancing in the whole workspace. This mechanism was successfully applied to otologic.

negative: A Language Modeling Approach to Predicting Reading Difficulty We demonstrate a new research approach to the problem of predicting the reading difficulty of a text passage, by recasting readability in terms of statistical language modeling. We derive a measure based on an extension of multinomial naïve Bayes classification that combines multiple language models to estimate the most likely grade level for a given passage. The resulting classifier is not specific to any particular subject and can be trained with relatively little labeled data. We perform predictions for individual Web pages in English and compare our performance to widely-used semantic variables from traditional readability measures. We show that with minimal changes, the classifier may be retrained for use with French Web documents. For both English and French, the classifier maintains consistently good correlation with labeled grade level (0.63 to 0.79) across all test sets. Some traditional semantic variables such as type-token ratio gave the best performance on commercial calibrated test passages, while our language modeling approach gave better accuracy for Web documents and very short passages (less than 10 words).

negative: Adopting a Digital Business Operating System The role of software in society and in industry in particular continues to grow exponentially. Most companies either have or are in the process of adoption continuous deployment of their software at products in the field and collect data concerning the performance of their systems. The continuous, fast feedback loops that companies now have available allow for a fundamentally different way of organizing. In fact, based on our work with dozens of companies, we have come to the conclusion that companies are moving towards a new, digital operating system. In this paper, we first present the key elements of the digital operating system and then discuss some of the challenges companies experience during the transformation.

negative: Preliminary Guidelines for Empirical Research in Software Engineering Empirical software engineering research needs research guidelines to improve the research and reporting processes. We propose a preliminary set of research guidelines aimed at stimulating discussion among software researchers. They are based on a review of research guidelines developed for medical researchers and on our own experience in doing and reviewing software engineering research. The guidelines are intended to assist researchers, reviewers and meta-analysts in designing, conducting and evaluating empirical studies. Editorial boards of software engineering journals may wish to use our recommendations as a basis for developing guidelines for reviewers and for framing policies for dealing with the design, data collection and analysis and reporting of empirical studies.

negative: The Multidimensional Work Motivation Scale : Validation evidence in seven languages and nine countries The Multidimensional Work Motivation Scale: Validation evidence in seven languages and nine countries Marylène Gagné, Jacques Forest, Maarten Vansteenkiste, Laurence Crevier-Braud, Anja Van den Broeck, Ann Kristin Aspeli, Jenny Bellerose, Charles Benabou, Emanuela Chemolli, Stefan Tomas Güntert, Hallgeir Halvari, Devani Laksmi Indiyastuti, Peter A. Johnson, Marianne Hauan Molstad, Mathias Naudin, Assane Ndao, Anja Hagen Olafsen, Patrice Roussel, Zheni Wang & Cathrine Westbye a School of Psychology, University of Western Australia, Perth, Australia b Department of Organization and Human Resource Management, UQAM School of Management Sciences, Université du Québec à Montréal, Montréal, Canada c Department of Developmental, Personality and Social Psychology, University of Gent, Gent, Belgium d Department of Psychology, Université du Québec à Montréal, Montréal, Canada e Department of Economy and Management, Hogeschool Universiteit Brussel, Brussels, and KU Leuven, Leuven, Belgium f Faculty of Economics and Social Science, Høgskolen i Buskerud og Vestfold, Kongsberg, Norway g Department of Psychology, Université de Montréal, Montréal, Canada h Psychology of Work in Organization and Society Research Group, Swiss Federal Institute of Technology, Zurich, Switzerland i Faculty of Economics and Business, Universitas Gadjah Mada, Yogyakarta, Indonesia j Institute of Work Psychology, University of Sheffield, Sheffield, United Kingdom k International Institute of Commerce and Development, Université Panthéon Assas — Paris II, Paris, France l Faculty of Economics and Management Sciences, Université Cheikh Anta DIOP, Dakkar, Sénégal m Department of Management and Human Resources, Université Toulouse 1, Toulouse, France Published online: 05 Feb 2014.

negative: Repeatable texture sampling with interchangeable patches Rendering textures in real-time environments is a key task in computer graphics. This paper presents a new parallel patch-based method which allows repeatable sampling without cache, and does not create visual repetitions. Interchangeable patches of arbitrary shape are prepared in a preprocessing step, such that patches may lie over the boundary of other patches in a repeating tile. This compresses the example texture into an infinite texture map with small memory requirements, suitable for GPU and ray-tracing applications. The quality of textures rendered with this method can be tuned in the offline preprocessing step, and they can then be rendered in times comparable to Wang tiles. Experimental results demonstrate combined benefits in speed, memory requirements, and quality of randomisation when compared to previous methods.

negative: Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using ℓ1 Regularization In recent years, online social media information has been the subject of study in several data science fields due to its impact on users as a communication and expression channel. Data gathered from online platforms such as Twitter has the potential to facilitate research over social phenomena based on sentiment analysis, which usually employs Natural Language Processing and Machine Learning techniques to interpret sentimental tendencies related to users’ opinions and make predictions about real events. Cyber-attacks are not isolated from opinion subjectivity on online social networks. Various security attacks are performed by hacker activists motivated by reactions from polemic social events. In this paper, a methodology for tracking social data that can trigger cyber-attacks is developed. Our main contribution lies in the monthly prediction of tweets with content related to security attacks and the incidents detected based on ℓ 1 regularization.

negative: The orbital angular momentum (OAM) multiplexing controversy: OAM as a subset of MIMO Recently the orbital angular momentum (OAM) multiplexing scheme has been proposed to increase without limit the channel capacity between a transmitter and a receiver node communicating in line of sight. A controversy arose about the far-field effectiveness of this method and about it being or not a subset of MIMO communication schemes. In this contribution we first illustrate that OAM multiplexing can be completely understood as line of sight MIMO, and hence that the technique cannot bring additional advantages in far field communications. Secondly, we show that it is possible to build similar line of sight near field MIMO systems based on lenses with similar or better performance than their OAM counterparts.

negative: Semantic Labeling: A Domain-Independent Approach Semantic labeling is the process of mapping attributes in data sources to classes in an ontology and is a necessary step in heterogeneous data integration. Variations in data formats, attribute names and even ranges of values of data make this a very challenging task. In this paper, we present a novel domain-independent approach to automatic semantic labeling that uses machine learning techniques. Previous approaches use machine learning to learn a model that extracts features related to the data of a domain, which requires the model to be re-trained for every new domain. Our solution uses similarity metrics as features to compare against labeled domain data and learns a matching function to infer the correct semantic labels for data. Since our approach depends on the learned similarity metrics but not the data itself, it is domainindependent and only needs to be trained once to work effectively across multiple domains. In our evaluation, our approach achieves higher accuracy than other approaches, even when the learned models are trained on domains other than the test domain.

negative: Internal Uniplanar Antenna for LTE/WWAN/GPS/GLONASS Applications in Tablet/Laptop Computers This letter presents an internal uniplanar small size multiband antenna for tablet/laptop computer applications. The proposed antenna in addition to common LTE/WWAN channels covers commercial GPS/GLONASS frequency bands. The antenna is comprised of three sections: coupled-fed, shorting and low frequency spiral strips with the size of 50 ×11 ×0.8 mm2. With the aid of spiral strip, lower band operation at 900 MHz is achieved. Two operating frequency bands cover 870-965 and 1556-2480 MHz. In order to validate simulation results, a prototype of the proposed printed antenna is fabricated and tested. Good agreement between the simulation and measurement results is obtained.

negative: A Novel Term Weighting Scheme Model The use of textual data has increased exponentially in recent years due to the networking infrastructure such as Facebook, Twitter, Wikipedia, Blogs, and so one. Analysis of this massive textual data can help to automatically categorize and label new content. Before classification process, term weighting scheme is the crucial step for representing the documents in a way suitable for classification algorithms. In this paper, we are conducting a survey on the term weighting schemes and we propose an efficient term weighting scheme that provide a better classification accuracy than those obtained with the famous TF-IDF, the recent IF-IGM and the others term weighting schemes in the literature.

negative: Cloud Data Patterns for Confidentiality Cloud computing enables cost-effective, self-service, and elastic hosting of applications in the Cloud. Applications may be partially or completely moved to the Cloud. When hosting or moving the database layer to the Cloud, challenges such as avoidance of disclosure of critical data have to be faced. The main challenges are handling different levels of confidentiality and satisfying security and privacy requirements. We provide reusable solutions in the form of patterns.

negative: Localization & Segmentation of Indian Car Number Plate system : A Projection based Multistage Approach Enormous incorporation of information technologies into all aspects of recent life caused demand for processing vehicles as conceptual resources in information systems. This can be achieved by a human agent, or by special intelligent equipment which is be able to recognize vehicles by their number plates in a real environment and reflect it into conceptual resources. Because of this, various recognition techniques have been developed and number plate recognition systems are today used in various traffic and security applications, such as parking, access and border control, or tracking of stolen cars. This work deals with problematic from field of artificial intelligence, machine vision and neural networks in construction of an automatic car number plate Recognition (ACNPR). This techniques deals with enhanced shadow removal algorithm, along with horizontal and vertical projection method for number plate localization & segmentation..Work comparatively deals with methods achieving invariance of systems towards image illumination, translations and various light conditions during the capture.

negative: The development of cross-language plagiarism detection tool utilising fuzzy swarm-based summarisation This work presents the design and development of a web-based system that supports cross-language similarity analysis and plagiarism detection. A suspicious document dq in a language Lq is to be submitted to the system via a PHP web-based interface. The system will accept the text through either uploading or pasting it directly to a text-area. In order to lighten large texts and provide an ideal set of queries, we introduce the idea of query document reduction via summarisation. Our proposed system utilised a fuzzy swarm-based summarisation tool originally built in Java. Then, the summary is used as a query to find similar web resources in languages Lx other than Lq via a dictionary-based translation. Thereafter, a detailed similarity analysis across the languages Lq and Lx is performed and friendly report of results is produced. Such report has global similarity score on the whole document, which assures high flexibility of utilisation.

negative: Revisiting Taxonomy Induction over Wikipedia Guided by multiple heuristics, a unified taxonomy of entities and categories is distilled from the Wikipedia category network. A comprehensive evaluation, based on the analysis of upward generalization paths, demonstrates that the taxonomy supports generalizations which are more than twice as accurate as the state of the art. The taxonomy is available at http://headstaxonomy.com.

negative: Workflow Automation: Overview and Research Issues Workflow management systems, a relatively recent technology, are designed to make work more efficient, integrate heterogeneous application systems, and support interorganizational processes in electronic commerce applications. In this paper, we introduce the field of workflow automation, the subject of this special issue of Information Systems Frontiers. In the first part of the paper, we provide basic definitions and frameworks to aid understanding of workflow management technologies. In the remainder of the paper, we discuss technical and management research opportunities in this field and discuss the other contributions to the special issue.

negative: The standard operating procedure of the DOE-JGI Microbial Genome Annotation Pipeline (MGAP v.4) The DOE-JGI Microbial Genome Annotation Pipeline performs structural and functional annotation of microbial genomes that are further included into the Integrated Microbial Genome comparative analysis system. MGAP is applied to assembled nucleotide sequence datasets that are provided via the IMG submission site. Dataset submission for annotation first requires project and associated metadata description in GOLD. The MGAP sequence data processing consists of feature prediction including identification of protein-coding genes, non-coding RNAs and regulatory RNA features, as well as CRISPR elements. Structural annotation is followed by assignment of protein product names and functions.

negative: Multi-Sensor Data Integration Using Deep Learning for Characterization of Defects in Steel Elements † Nowadays, there is a strong demand for inspection systems integrating both high sensitivity under various testing conditions and advanced processing allowing automatic identification of the examined object state and detection of threats. This paper presents the possibility of utilization of a magnetic multi-sensor matrix transducer for characterization of defected areas in steel elements and a deep learning based algorithm for integration of data and final identification of the object state. The transducer allows sensing of a magnetic vector in a single location in different directions. Thus, it enables detecting and characterizing any material changes that affect magnetic properties regardless of their orientation in reference to the scanning direction. To assess the general application capability of the system, steel elements with rectangular-shaped artificial defects were used. First, a database was constructed considering numerical and measurements results. A finite element method was used to run a simulation process and provide transducer signal patterns for different defect arrangements. Next, the algorithm integrating responses of the transducer collected in a single position was applied, and a convolutional neural network was used for implementation of the material state evaluation model. Then, validation of the obtained model was carried out. In this paper, the procedure for updating the evaluated local state, referring to the neighboring area results, is presented. Finally, the results and future perspective are discussed.

negative: Analysis of a Wavelength-Scaled Array (WSA) Architecture Wavelength-scaled array architectures use scaled elements to achieve ultrawideband performance with significantly fewer overall radiators than traditional ultrawideband arrays based on a single element type. Compared to a conventional ultrawideband array with 8:1 bandwidth, a wavelength-scaled array that uses elements of three different sizes creates an aperture with fewer than 16% of the original element count, i.e., 6.4-times fewer elements, and by extension a comparable reduction in electronics required to feed the array. In this paper, a study of an asymmetric wavelength-scaled array architecture is presented for finite arrays of offset-centered dual-polarized flared-notch radiators. The unique element transitions within the finite array structure are modeled via a non-matching grid Domain Decomposition-Finite Element Method that allows for rigorous impedance and radiation pattern prediction of full-sized wavelength-scaled arrays. This design study shows that the wavelength-scaled array has comparable performance to traditional ultrawideband arrays in terms of VSWR, radiation patterns, array mismatch efficiency, and cross-polarization levels.

negative: Coupling design and performance analysis of rim-driven integrated motor propulsor Coupling design of rim-driven integrated motor propulsor for vessels and underwater vehicle is presented in this paper. The main characteristic of integrated motor propulsor is that the motor is integrated in the duct of propulsor. So the coupling design of motor and propulsor is the key to the overall design. Considering the influence of the motor and duct size, the propeller was designed, and the CFD Method was used to analyze the hydrodynamic performance of the propulsor. Based on the air-gap magnetic field of permanent magnet motor and the equivalent magnetic circuit, the integrated motor electromagnetic model was proposed, and the finite element method was used to analyze the motor electromagnetic field. Finally, the simulation of the integrated motor starting process with the load of propulsor torque was carried out, and the results meets the design specifications.

negative:
A Software Gamification Model for Cross-Cultural Software Development Teams Gamification is the use of game design elements in non-game context platforms such as services and marketing to motivate people to participate in planned activities to increase engagement and loyalty to achieve goals.
 Gamification has been applied to academic fields including software engineering in recent years. Some studies show that gamification can motivate engineers in Software Engineering (SE) if applied appropriately. However, most gamification implementations are lacking well-defined frameworks or models.
 This paper develops a software gamification model (SGM) with a well-defined framework that provides a robust process for implementing gamification for SE. This model also contains elements from applied psychology, Social Software Engineering (SSE), and the Capability Maturity Model (CMM). Having those elements ensures that the model has a solid foundation in gamification and SE disciplines. The model is further tailored for cross-cultural software development teams (CCSDT).
 CCSDT have been popular in recent years due to the fact that software development has become a global business. However, as more and more CCSDT are formed, many challenges and issues have been raised in those cross-cultural environments stemming from miscommunication, misunderstanding, cultural differences and conflicts. This paper uses the SGM to help resolve the issues.

negative: Mapping learning and game mechanics for serious games analysis While there is a consensus on the instructional potential of Serious Games (SGs), there is still a lack of methodologies and tools not only for design but also to support analysis and assessment. Filling this gap is one of the main aims of the Games and Learning Alliance (GALA, www.galanoe.eu) European Network of Excellence on Serious Games (SGs), which has a focus upon pedagogy-driven SGs. This paper relies on the assumption that the fundamental aspect of SG design consists in the translation of learning goals/practices into mechanical element of game-play, serving to an instructional purpose beside that of play and fun. This paper proposes the Learning Mechanics-Game Mechanics (LM-GM) model, which supports SG analysis and design by allowing reflection on the various pedagogical and game elements in a SG. The LM-GM model includes a set of pre-defined game mechanics and pedagogical elements that we have abstracted from literature on game studies and learning theories. Designers and analysts can exploit these mechanics to draw the LM-GM map for a game, so as to identify and highlight its main pedagogical and entertainment features, and their interrelations. The tool may also be useful for teachers to evaluate the effectiveness of a given game and better understand how to implement it in educational settings. A case study is reported to illustrate the framework’s support in determining how game-play and pedagogy intertwine in a SG. Finally, the paper presents the results of two comparative user tests demonstrating the advantages of the proposed model with respect to a similar state-of-the-art framework.