title: FAIoT: Towards Building a Forensics Aware Eco System for the Internet of Things

abstract: The Internet of Things (IoT) involves numerous connected smart things with different technologies and communication standards. While IoT opens new opportunities in various fields, it introduces new challenges in the field of digital forensics investigations. The existing tools and procedures of digital forensics cannot meet the highly distributed and heterogeneous infrastructure of the IoT. Forensics investigators will face challenges while identifying necessary pieces of evidence from the IoT environment, and collecting and analyzing those evidence. In this article, we propose the first working definition of IoT forensics and systematically analyze the IoT forensics domain to explore the challenges and issues in this special branch of digital forensics. We propose a Forensics-aware IoT (FAIoT) model for supporting reliable forensics investigations in the IoT environment.


title: Maternal Mind-Mindedness and Attachment Security as Predictors of Theory of Mind Understanding

abstract: This study investigated relations between social interaction during infancy and children's subsequent theory of mind (ToM). Infant-mother pairs ( N � 57) were observed in a free-play context at 6 months. Interactions were coded for (a) mothers' use of mental state language that commented appropriately on the infants' mental states, and (b) mothers' use of mental state language that did not appropriately reflect their infants' minds. A third variable was (c) security of attachment, which was assessed using the Strange Situation procedure at 12 months. Performance on a battery of ToM tasks at 45 and 48 months was positively correlated with (a), but was not related to (b) or (c). A regression analysis showed that mothers' use of appropriate mental state comments independently predicted overall ToM performance, accounting for 11% of the variance. Children's verbal abil- ity was the only other independent predictor of ToM performance, accounting for 16% of the variance. These findings represent the earliest known social predictor of individual differences in ToM. It is now more than a decade since researchers first turned their attention to social influences on the de- velopment of children's theory of mind (ToM). Since Dunn and colleagues (Dunn, Brown, Slomkowski, Tesla, & Youngblade, 1991) published their initial findings relating family environment to children's ToM abilities, there has been a steadily growing body of evidence suggesting that specific features of the early social environment are associated with precocity in children's understanding of mind. In their landmark study, Dunn et al. (1991) reported a relation between certain types of family interac- tions and children's subsequent ToM performance. They found that children were more likely to suc- ceed on ToM tasks if their families had previously shown a tendency to discuss feelings and use causal state language, and if their mothers frequently at- tempted to control the behavior of older siblings. Subsequently, Perner, Ruffman, and Leekam (1994) reported that the mere presence of siblings had a fa- cilitatory effect on ToM performance, although a re- analysis of these data suggested that this was true only for the presence of older siblings (Ruffman, Perner, Naito, Parkin, & Clements, 1998). Indeed, con- tact with older children and adults beyond the nu- clear family appears to have a similar effect in aiding children's understanding of mind (Lewis, Freeman, Kyriakidou, Maridaki-Kassotaki, & Berridge, 1996). Recently, relations between global social factors and ToM performance have been reported. Children from families of higher socioeconomic status performed better on a range of ToM tasks than did peers from more disadvantaged backgrounds (Cole & Mitchell, 1998, 2000; Cutting & Dunn, 1999; Holmes, Black, & Miller, 1996), and mothers' educational attainment has been found to correlate positively with their children's ToM performance (Cutting & Dunn, 1999; Meins & Fernyhough, 1999). Despite this growing body of evi- dence, researchers have been somewhat cautious about making strong arguments for a causal role for any of these social factors in ToM development. Such caution appears justified, because the mecha- nisms through which children's social environment might influence their ToM are, as yet, poorly under- stood. Dunn (1994) argued that the reason why cer- tain types of social interaction (e.g., psychological dis- course, sibling conflict management, joint play, shared jokes, and moral reasoning) related to ToM perfor- mance was because they provide contexts in which children are confronted with conflicting views on the world. In turn, such conflicts in perspective facilitate children's developing understanding that reality may be represented and misrepresented. Fernyhough (1996) presented a broadly Vygotskian account of how the internalization of dialog relating to perspectival con- flict might underlie individual differences in ToM performance. In a different vein, Lewis et al. (1996) discussed an "apprenticeship" model of ToM devel- opment, whereby children's interactions with older kin provide them with informal tutoring about the mind. Although these approaches make intuitive sense, they have thus far been unsuccessful in specifying precise


title: A Shallow Varactor-Tuned Cavity-Backed Slot Antenna With a 1.9:1 Tuning Range

abstract: A shallow (0.025 wavelengths) microstrip-fed cavity-backed-slot (CBS) antenna has been demonstrated that tunes from 1 to 1.9 GHz with better than -20 dB reflection coefficient using a single varactor diode (0.45-2.5 pF). This is possible because the slot and the cavity combine to form a single resonance, and therefore, do not need to be tuned independently. The cavity is 72 × 72 × 3.18 mm3, and is dielectrically loaded (?r = 2.1). The impedance match over the tuning range has been achieved by searching for combinations of feed and varactor locations for which the impedance is matched to 50 ? by a single series inductor, and the input impedance is nearly identical whether the antenna is in free-space or conformally mounted on a 1.2 × 1.2 m2 ground plane. The cross-polarization is better than -25 dB at 1.0, 1.5, and 1.9 GHz. To the authors' knowledge, this is the first demonstration of a varactor-tuned CBS antenna.


title: STF-RNN: Space Time Features-based Recurrent Neural Network for predicting people next location

abstract: This paper proposes a novel model called Space Time Features-based Recurrent Neural Network (STF-RNN) for predicting people next movement based on mobility patterns obtained from GPS devices logs. Two main features are involved in model operations, namely, the space which is extracted from the collected GPS data and also the time which is extracted from the associated timestamps. The internal representation of space and time features is extracted automatically in the proposed model rather than relying on handcraft representation. This enables the model to discover the useful knowledge about people behaviour in more efficient way. Due to the ability of RNN structure to represent the sequences, it is utilized in the proposed model in order to keep track of user movement history. These tracks help the model to discover more meaningful dependencies and as consequence, enhancing the model performance. The results show that STF-RNN model provides good improvements in predicting people's next location compared with the state-of-the-art models when applied on a large real life dataset from Geo-life project.


title: Connected Through Crisis: Emotional Proximity and the Spread of Misinformation Online

abstract: During crises, the ability to access relevant information is extremely important for those affected. Previous research shows that social media have become popular for rapid information exchange between members of the online community after crisis events. This study focuses on the effects of proximity to a crisis on information sharing behaviors. Using constructivist grounded theory to guide our inquiry, we conducted interviews with eleven people who used social media in the aftermath of the 2013 Boston Marathon Bombings. Salient themes emerging from this study suggest that both physical and emotional proximity to a crisis influence online information seeking and sharing behaviors. Additionally, speed of information sharing and information access renders social media especially useful during crisis and particularly susceptible to the spread of misinformation. We view the latter as a consequence of the inevitable sensemaking process that occurs as individuals attempt to make sense of incomplete information.


title: A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting

abstract: Stock market price index prediction is regarded as a challenging task of the financial time series prediction process. Support vector regression (SVR) has successfully solved prediction problems in many domains, including the stock market. This paper hybridizes SVR with the self-organizing feature map (SOFM) technique and a filter-based feature selection to reduce the cost of training time and to improve prediction accuracies. The hybrid system conducts the following processes: filter-based feature selection to choose important input attributes; SOFM algorithm to cluster the training samples; and SVR to predict the stock market price index. The proposed model was demonstrated using a real future dataset - Taiwan index futures (FITX) to predict the next day's price index. The experiment results show that the proposed SOFM-SVR is an improvement over the traditional single SVR in average prediction accuracy and training time.


title: HAMLET: Hierarchical Harmonic Filters for Learning Tracts from Diffusion MRI

abstract: In this work we propose HAMLET, a novel tract learning algorithm, which, after training, maps raw diffusion weighted MRI directly onto an image which simultaneously indicates tract direction and tract presence. The automatic learning of fiber tracts based on diffusion MRI data is a rather new idea, which tries to overcome limitations of atlas-based techniques. HAMLET takes a such an approach. Unlike the current trend in machine learning, HAMLET has only a small number of free parameters HAMLET is based on spherical tensor algebra which allows a translation and rotation covariant treatment of the problem. HAMLET is based on a repeated application of convolutions and non-linearities, which all respect the rotation covariance. The intrinsic treatment of such basic image transformations in HAMLET allows the training and generalization of the algorithm without any additional data augmentation. We demonstrate the performance of our approach for twelve prominent bundles, and show that the obtained tract estimates are robust and reliable. It is also shown that the learned models are portable from one sequence to another.


title: Information technology and organizational learning in knowledge alliances and networks: Evidence from U.S. pharmaceutical industry

abstract: Recent years have witnessed a change in firms' innovation patterns, from closed to open, in which information technology (IT) has played an important role. This paper aims to open up the black box of IT-enabled absorptive capacity by theorizing and testing the role of IT in two organizational learning processes, which are either interactive with partners in the knowledge alliance or non-interactive with others in the knowledge network. In particular, we formulate a model explaining how a firm's IT investment moderates its organizational learning processes in knowledge alliances and networks, which sheds light on the role of IT as an enabler of absorptive capacity. Using a panel data set from the U.S. pharmaceutical industry, the results show the moderating role of IT in strengthening the organizational learning processes from knowledge alliance experience to coinvented knowledge and from knowledge network centrality to assimilated knowledge, which, in turn, improve firm competitiveness.


title: Toward the implementation of a topic specific dialogue based natural language chatbot as an undergraduate advisor

abstract: In this work, we explain the design of a chat robot that is specifically tailored for providing FAQBot system for university students and with the objective of an undergraduate advisor in student information desk. The chat robot accepts natural language input from users, navigates through the Information Repository and responds with student information in natural language. In this paper, we model the Information Repository by a connected graph where the nodes contain information and links interrelates the information nodes. The design semantics includes AIML (Artificial Intelligence Mark up Language) specification language for authoring the information repository such that chat robot design separates the information repository from the natural language interface component. Correspondingly, in the experiment, we constructed three experimental systems (a pure dialog systems associated with natural language knowledge based entries, a domain knowledge systems engineered with information content and a hybrid system, combining dialog and domain knowledge). Consequently, the information repository can easily be modified and focussed on particular topic without recreating the code design. Experimental parameters and outcome suggests that topic specific dialogue coupled with conversational knowledge yield the maximum dialogue session than the general conversational dialogue.


title: Retrospective Revaluation in Sequential Decision Making: A Tale of Two Systems

abstract: Recent computational theories of decision making in humans and animals have portrayed 2 systems locked in a battle for control of behavior. One system—variously termed model-free or habitual—favors actions that have previously led to reward, whereas a second—called the model-based or goal-directed system—favors actions that causally lead to reward according to the agent’s internal model of the environment. Some evidence suggests that control can be shifted between these systems using neural or behavioral manipulations, but other evidence suggests that the systems are more intertwined than a competitive account would imply. In 4 behavioral experiments, using a retrospective revaluation design and a cognitive load manipulation, we show that human decisions are more consistent with a cooperative architecture in which the model-free system controls behavior, whereas the model-based system trains the model-free system by replaying and simulating experience.


title: The Death of Object-Oriented Programming

abstract: Modern software systems are increasingly long-lived. In order to gracefully evolve these systems as they address new requirements, developers need to navigate effectively between domain concepts and the code that addresses those domains. One of the original promises of object-orientation was that the same object-oriented models would be used throughout requirements analysis, design and implementation. Software systems today however are commonly constructed from a heterogeneous "language soup" of mainstream code and dedicated DSLs addressing a variety of application and technical domains. Has object-oriented programming outlived its purpose? ::: ::: In this essay we argue that we need to rethink the original goals of object-orientation and their relevance for modern software development. We propose as a driving maxim, "Programming is Modeling," and explore what this implies for programming languages, tools and environments. In particular, we argue that: 1 source code should serve not only to specify an implementation of a software system, but should encode a queryable and manipulable model of the application and technical domains concerned; 2 IDEs should exploit these domain models to enable inexpensive browsing, querying and analysis by developers; and 3 barriers between the code base, the running application, and the software ecosystem at large need to be broken down, and their connections exploited and monitored to support developers in comprehension and evolution tasks.


title: On clustering heterogeneous social media objects with outlier links

abstract: The clustering of social media objects provides intrinsic understanding of the similarity relationships between documents, images, and their contextual sources. Both content and link structure provide important cues for an effective clustering algorithm of the underlying objects. While link information provides useful hints for improving the clustering process, it also contains a significant amount of noisy information. Therefore, a robust clustering algorithm is required to reduce the impact of noisy links. In order to address the aforementioned problems, we propose heterogeneous random fields to model the structure and content of social media networks. We design a probability measure on the social media networks which output a configuration of clusters that are consistent with both content and link structure. Furthermore, noisy links can also be detected, and their impact on the clustering algorithm can be significantly reduced. We conduct experiments on a real social media network and show the advantage of the method over other state-of-the-art algorithms.


title: Artificial Vision in Road Vehicles

abstract: The last few decades have witnessed the birth and growth of a new sensibility to transportation efficiency. In particular the need for efficient and improved people and goods mobility has pushed researchers to address the problem of intelligent transportation systems. This paper surveys the most advanced approaches to (partial) customization of the road following task, using on-board systems based on artificial vision. The functionalities of lane detection, obstacle detection and pedestrian detection are described and classified, and their possible application in future road vehicles is discussed.


title: Dynamic Probabilistic Volumetric Models

abstract: This paper presents a probabilistic volumetric framework for image based modeling of general dynamic 3-d scenes. The framework is targeted towards high quality modeling of complex scenes evolving over thousands of frames. Extensive storage and computational resources are required in processing large scale space-time (4-d) data. Existing methods typically store separate 3-d models at each time step and do not address such limitations. A novel 4-d representation is proposed that adaptively subdivides in space and time to explain the appearance of 3-d dynamic surfaces. This representation is shown to achieve compression of 4-d data and provide efficient spatio-temporal processing. The advances of the proposed framework is demonstrated on standard datasets using free-viewpoint video and 3-d tracking applications.


title: Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects

abstract: Brain-computer interfaces (BCIs) allow their users to communicate or control external devices using brain signals rather than the brain's normal output pathways of peripheral nerves and muscles. Motivated by the hope of restoring independence to severely disabled individuals and by interest in further extending human control of external systems, researchers from many fields are engaged in this challenging new work. BCI research and development have grown explosively over the past two decades. Efforts have recently begun to provide laboratory-validated BCI systems to severely disabled individuals for real-world applications. In this review, we discuss the current status and future prospects of BCI technology and its clinical applications. We will define BCI, review the BCI-relevant signals from the human brain, and describe the functional components of BCIs. We will also review current clinical applications of BCI technology, and identify potential users and potential applications. Finally, we will discuss current limitations of BCI technology, impediments to its widespread clinical use, and expectations for the future.


title: Intelligent value-based customer segmentation method for campaign management: A case study of automobile retailer

abstract: Most marketers have difficulty in identifying the right customers to engage in successful campaigns. So far, customer segmentation is a popular method that is used for selecting appropriate customers for a launch campaign. Unfortunately, the link between customer segmentation and marketing campaign is missing. Another problem is that database marketers generally use different models to conduct customer segmentation and customer targeting. This study presents a novel approach that combines customer targeting and customer segmentation for campaign strategies. This investigation identifies customer behavior using a recency, frequency and monetary (RFM) model and then uses a customer life time value (LTV) model to evaluate proposed segmented customers. Additionally, this work proposes using generic algorithm (GA) to select more appropriate customers for each campaign strategy. To demonstrate the efficiency of the proposed method, this work performs an empirical study of a Nissan automobile retailer to segment over 4000 customers. The experimental results demonstrate that the proposed method can more effectively target valuable customers than random selection.


title: Mining on Someone Else’s Dime: Mitigating Covert Mining Operations in Clouds and Enterprises

abstract: Covert cryptocurrency mining operations are causing notable losses to both cloud providers and enterprises. Increased power consumption resulting from constant CPU and GPU usage from mining, inflated cooling and electricity costs, and wastage of resources that could otherwise benefit legitimate users are some of the factors that contribute to these incurred losses. Affected organizations currently have no way of detecting these covert, and at times illegal miners and often discover the abuse when attackers have already fled and the damage is done.


Machine Learning & Knowledge Extraction ( MAKE ) for Health Informatics : Towards educating a new kind of graduates


title: Brute-Force k-Nearest Neighbors Search on the GPU

abstract: We present a brute-force approach for finding k-nearest neighbors on the GPU for many queries in parallel. Our program takes advantage of recent advances in fundamental GPU computing primitives. We modify a matrix multiplication subroutine in MAGMA libraryi?ź[6] to calculate the squared Euclidean distances between queries and references. The nearest neighbors selection is accomplished by a truncated merge sort built on top of sorting and merging functions in the Modern GPU libraryi?ź[3]. Compared to state-of-the-art approaches, our program is faster and it handles larger inputs. For instance, we can find 1000 nearest neighbors among 1 million 64-dimensional reference points at a rate of about 435 queries per second.


title: Systems and Experiment Paper: Construction of Panoramic Image Mosaics with Global and Local Alignment

abstract: This paper presents a complete system for constructing panoramic image mosaics from sequences of images. Our mosaic representation associates a transformation matrix with each input image, rather than explicitly projecting all of the images onto a common surface (e.g., a cylinder). In particular, to construct a full view panorama, we introduce a rotational mosaic representation that associates a rotation matrix (and optionally a focal length) with each input image. A patch-based alignment algorithm is developed to quickly align two images given motion models. Techniques for estimating and refining camera focal lengths are also presented. ::: ::: In order to reduce accumulated registration errors, we apply global alignment (block adjustment) to the whole sequence of images, which results in an optimally registered image mosaic. To compensate for small amounts of motion parallax introduced by translations of the camera and other unmodeled distortions, we use a local alignment (deghosting) technique which warps each image based on the results of pairwise local image registrations. By combining both global and local alignment, we significantly improve the quality of our image mosaics, thereby enabling the creation of full view panoramic mosaics with hand-held cameras. ::: ::: We also present an inverse texture mapping algorithm for efficiently extracting environment maps from our panoramic image mosaics. By mapping the mosaic onto an arbitrary texture-mapped polyhedron surrounding the origin, we can explore the virtual environment using standard 3D graphics viewers and hardware without requiring special-purpose players.


title: Clinical Nurse Leader Integrated Care Delivery to Improve Care Quality: Factors Influencing Perceived Success

abstract: PURPOSE ::: Clinical nurse leader(TM) (CNL)-integrated care delivery is a new model for organizing master's-level nursing clinical leadership at the microsystem level. While there is growing evidence of improved patient care quality and safety outcomes associated with CNL practice, organizational and implementation characteristics that influence CNL success are not well characterized. The purpose of this study was to identify organization and implementation factors associated with perceived success of CNL integration into microsystem care delivery models. ::: ::: ::: METHODS ::: A survey was developed and administered to a nationwide sample of certified CNLs and managers, leaders, educators, clinicians, and change agents involved in planning or integrating CNLs into a health system's nursing care delivery model. Items addressed organizational and implementation characteristics and perceived level of CNL initiative success. Generalized linear modeling was used to analyze data. ::: ::: ::: RESULTS ::: The final sample included 585 respondents. The final model accounted for 35% of variance in perceived CNL initiative success, and included five variables: phase of CNL initiative, CNL practice consistency, CNL instructor or preceptor involvement, CNL reporting structure, and CNL setting ownership status. ::: ::: ::: CONCLUSIONS ::: CNL initiative success is associated with modifiable organizational and implementation factors. ::: ::: ::: CLINICAL RELEVANCE ::: Study findings can be used to inform the development of successful implementation strategies for CNL practice integration into care delivery models to improve care quality outcomes.


title: A Brief Survey of Text Mining

abstract: The enormous amount of information stored in unstructured texts cannot simply be used for further processing by computers, which typically handle text as simple sequences of character strings. Therefore, specific (pre-)processing methods and algorithms are required in order to extract useful patterns. Text mining refers generally to the process of extracting interesting information and knowledge from unstructured text. In this article, we discuss text mining as a young and interdisciplinary field in the intersection of the related areas information retrieval, machine learning, statistics, computational linguistics and especially data mining. We describe the main analysis tasks preprocessing, classification, clustering, information extraction and visualization. In addition, we briefly discuss a number of successful applications of text mining.


title: Smoke Detection on Video Sequences Using Convolutional and Recurrent Neural Networks

abstract: The combination of a convolutional neural network (CNN) and recurrent neural network (RNN) is proposed to detect the smoke in space and time domains. CNN part automatically builds the low-level features, and RNN part finds the relation between the features in different frames of the same event. For this work, the new dataset was constructed with at least 64 sequential frames for each set giving the network ability to analyze the behavior of the smoke for at least 2 s. While being not too deep thus allowing fast processing, the proposed network outperformed state of the art deep CNNs which do not consider the change of the object in time.


title: Toward Personalised Gamification for Learning Environments

abstract: Many learning environments are deserted by the learners, even if they are effective. Gamification is a growing approach used to raise learners' motiva- tion by adding game elements in their environment, but it still pays few attention to the individual differences among learners' motivations. This paper presents a gamification system designed to be plugged on various learning environments. It can be automatically personalised, based on an analysis of the interaction traces.


title: How Should Rate Constraints be Implemented in Nonlinear Optimal Control Solvers?

abstract: Abstract This paper investigates the problem of implementing rate constraints when solving nonlinear optimal control problems with direct transcription methods. We generalize the approach of directly implementing rate constraints on the discretization mesh to all types of collocation methods (h, p and hp), for both state and input variables. This “on mesh” implementation replaces the additional dynamic equations and nonlinear path constraints in classical implementations with linear equations. Thus, there is no contribution to the Hessian and the contribution to the Jacobian can be precomputed, enabling faster iterations. Through an example, the benefits of the proposed approach are demonstrated, both in terms of obtaining singular arc-free solutions, as well as reductions in computation time of more than 20%.


title: Service chain modeling and embedding for NFV-based content delivery

abstract: Increasing over-the-top video consumption endangers the sustainability of content delivery over the Internet. Internet Service Providers (ISP) face difficulties in competing on value-added services with content providers and Content Delivery Network (CDN) operators. In this respect, we propose a new model for the collaboration between content delivery stakeholders, so that CDN operators can deploy their software in ISP infrastructures leveraging on Network Function Virtualization (NFV). As the ISP network topology and utilization is deemed confidential, we use a high-level Service Level Agreement (SLA) for the negotiation of both computing resources and connectivity, allowing the ISP to optimize server selection, while providing at the same time sufficient flexibility to the CDN operators for content delivery. Furthermore, we present a linear programming formulation for the VNF Service Chain Embedding and an heuristic to increase problem tractability with a small cost overhead. Finally, we validate the efficiency of the proposed service chain model for virtual CDN management.


title: Performance Studies of Underwater Wireless Optical Communication Systems With Spatial Diversity: MIMO Scheme

abstract: In this paper, we analytically study the performance of multiple-input multiple-output underwater wireless optical communication (MIMO UWOC) systems with on-off keying (OOK) modulation. To mitigate turbulence-induced fading, which is amongst the major degrading effects of underwater channels on the propagating optical signal, we use spatial diversity over UWOC links. Furthermore, the effects of absorption and scattering are considered in our analysis. We analytically obtain the exact and an upper bound bit error rate (BER) expressions for both optimal and equal gain combining. In order to more effectively calculate the system BER, we apply Gauss-Hermite quadrature formula as well as approximation to the sum of lognormal random variables. We also apply photon-counting method to evaluate the system BER in the presence of shot noise. Our numerical results indicate an excellent match between the exact and upper bound BER curves. Also {a good match} between {the} analytical results and numerical simulations confirms the accuracy of our derived expressions. Moreover, our results show that spatial diversity can considerably improve the system performance, especially for channels with higher turbulence, e.g., a $3\times1$ MISO transmission in a $25$ {m} coastal water link with log-amplitude variance of $0.16$ can introduce $8$ {dB} performance improvement at the BER of $10^{-9}$.


title: OpenTag: Open Attribute Value Extraction from Product Profiles

abstract: Extraction of missing attribute values is to find values describing an attribute of interest from a free text input. Most past related work on extraction of missing attribute values work with a closed world assumption with the possible set of values known beforehand, or use dictionaries of values and hand-crafted features. How can we discover new attribute values that we have never seen before? Can we do this with limited human annotation or supervision? We study this problem in the context of product catalogs that often have missing values for many attributes of interest. In this work, we leverage product profile information such as titles and descriptions to discover missing values of product attributes. We develop a novel deep tagging model OpenTag for this extraction problem with the following contributions: (1) we formalize the problem as a sequence tagging task, and propose a joint model exploiting recurrent neural networks (specifically, bidirectional LSTM) to capture context and semantics, and Conditional Random Fields (CRF) to enforce tagging consistency; (2) we develop a novel attention mechanism to provide interpretable explanation for our model's decisions; (3) we propose a novel sampling strategy exploring active learning to reduce the burden of human annotation. OpenTag does not use any dictionary or hand-crafted features as in prior works. Extensive experiments in real-life datasets in different domains show that OpenTag with our active learning strategy discovers new attribute values from as few as 150 annotated samples (reduction in 3.3x amount of annotation effort) with a high F-score of 83%, outperforming state-of-the-art models.


title: Fusion4D: real-time performance capture of challenging scenes

abstract: We contribute a new pipeline for live multi-view performance capture, generating temporally coherent high-quality reconstructions in real-time. Our algorithm supports both incremental reconstruction, improving the surface estimation over time, as well as parameterizing the nonrigid scene motion. Our approach is highly robust to both large frame-to-frame motion and topology changes, allowing us to reconstruct extremely challenging scenes. We demonstrate advantages over related real-time techniques that either deform an online generated template or continually fuse depth data nonrigidly into a single reference model. Finally, we show geometric reconstruction results on par with offline methods which require orders of magnitude more processing time and many more RGBD cameras.


title: DroidRA: taming reflection to support whole-program analysis of Android apps

abstract: Android developers heavily use reflection in their apps for legitimate reasons, but also significantly for hiding malicious actions. Unfortunately, current state-of-the-art static analysis tools for Android are challenged by the presence of reflective calls which they usually ignore. Thus, the results of their security analysis, e.g., for private data leaks, are inconsistent given the measures taken by malware writers to elude static detection. We propose the DroidRA instrumentation-based approach to address this issue in a non-invasive way. With DroidRA, we reduce the resolution of reflective calls to a composite constant propagation problem. We leverage the COAL solver to infer the values of reflection targets and app, and we eventually instrument this app to include the corresponding traditional Java call for each reflective call. Our approach allows to boost an app so that it can be immediately analyzable, including by such static analyzers that were not reflection-aware. We evaluate DroidRA on benchmark apps as well as on real-world apps, and demonstrate that it can allow state-of-the-art tools to provide more sound and complete analysis results.


title: Bounded rationality, ambiguity, and the engineering of choice

abstract: Rational choice involves two guesses, a guess about uncertain future consequences and a guess about uncertain future preferences. Partly as a result of behavioral studies of choice over a twenty-year period, modifications in the way the theory deals with the first guess have become organized into conceptions of bounded rationality. Recently behavioral studies of choice have examined the second guess, the way preferences are processed in choice behavior. These studies suggest possible modifications in standard assumptions about taste and their role in choice. This paper examines some of those modifications, some possible approaches to working on them, and some complications.


Harnessing TCP’s Burstiness with Flowlet Switching


Diagnostic and Therapeutic Aspects of Mucosal Leishmaniasis


title: Measuring and Predicting Search Engine Users’ Satisfaction

abstract: Search satisfaction is defined as the fulfillment of a user’s information need. Characterizing and predicting the satisfaction of search engine users is vital for improving ranking models, increasing user retention rates, and growing market share. This article provides an overview of the research areas related to user satisfaction. First, we show that whenever users choose to defect from one search engine to another they do so mostly due to dissatisfaction with the search results. We also describe several search engine switching prediction methods, which could help search engines retain more users. Second, we discuss research on the difference between good and bad abandonment, which shows that in approximately 30p of all abandoned searches the users are in fact satisfied with the results. Third, we catalog techniques to determine queries and groups of queries that are underperforming in terms of user satisfaction. This can help improve search engines by developing specialized rankers for these query patterns. Fourth, we detail how task difficulty affects user behavior and how task difficulty can be predicted. Fifth, we characterize satisfaction and we compare major satisfaction prediction algorithms.


title: Mobile Phone Forensics: An Investigative Framework based on User Impulsivity and Secure Collaboration Errors

abstract: This paper uses a scenario-based role-play experiment based on the usage of QR codes to detect how mobile users respond to social engineering attacks conducted via mobile devices. The results of this experiment outline a guided mobile phone forensics investigation method which could facilitate the work of digital forensics investigators while analysing the data from mobile devices. The behavioural response of users could be impacted by several aspects, such as impulsivity, smartphone usage and security or simply awareness that QR codes could contain malware. The findings indicate that the impulsivity of users is one of the key areas that determine the common mistakes of mobile device users. As a result, an investigative framework for mobile phone forensics is proposed based on the impulsivity and common mistakes of mobile device users. As a result, an investigative framework for mobile phone forensics is proposed based on the impulsivity and common mistakes of mobile device users. It could help the forensics investigators by potentially shortening the time spent on investigation of possible breach scenarios.


title: Privacy risk assessment and users' awareness for mobile apps permissions

abstract: Mobile applications have access to users' data. This raises a potential privacy concern since users' personal information is collected and revealed to third parties without their consent. In this paper we propose a proactive user-oriented approach towards users' awareness of the privacy risk involved with granting permissions to Android applications. We present a dynamic privacy scoring model that assesses the risk to users' privacy associated to an application which requires a set of permissions. The parameters of this model are the severity and the relative importance of permissions and their interactions. Severity is evaluated according to a standard severity assessment method. The relative importance is estimated according to an analytic method. An experimental study on a set of 64 applications has been conducted. Association rules between permissions have been identified by using a data mining algorithm.


title: Development and validation of a brief form of the Multidimensional Personality Questionnaire.

abstract: The Multidimensional Personality Questionnaire (MPQ; A. Tellegen, 1982, in press) provides for acomprehensive analysis of personality at both the lower order trait and broader structural levels. Its higherorder dimensions of Positive Emotionality, Negative Emotionality, and Constraint embody affect andtemperament constructs, which have been conceptualized in psychobiological terms. The MPQ thusholds considerable potential as a structural framework for investigating personality across varying levelsof analysis, and this potential would be enhanced by the availability of an abbreviated version. Thisarticle describes efforts to develop and validate a brief (155-item) form, the MPQ–BF. Success wasevidenced by uniformly high correlations between the brief- and full-form trait scales and consistency ofhigher order structures. The MPQ–BF is recommended as a tool for investigating the genetic, neurobi-ological, and psychological substrates of personality.


title: Assessment and Calibration of a RGB-D Camera (Kinect v2 Sensor) Towards a Potential Use for Close-Range 3D Modeling

abstract: In the last decade, RGB-D cameras - also called range imaging cameras - have known a permanent evolution. Because of their limited cost and their ability to measure distances at a high frame rate, such sensors are especially appreciated for applications in robotics or computer vision. The Kinect v1 (Microsoft) release in November 2010 promoted the use of RGB-D cameras, so that a second version of the sensor arrived on the market in July 2014. Since it is possible to obtain point clouds of an observed scene with a high frequency, one could imagine applying this type of sensors to answer to the need for 3D acquisition. However, due to the technology involved, some questions have to be considered such as, for example, the suitability and accuracy of RGB-D cameras for close range 3D modeling. In that way, the quality of the acquired data represents a major axis. In this paper, the use of a recent Kinect v2 sensor to reconstruct small objects in three dimensions has been investigated. To achieve this goal, a survey of the sensor characteristics as well as a calibration approach are relevant. After an accuracy assessment of the produced models, the benefits and drawbacks of Kinect v2 compared to the first version of the sensor and then to photogrammetry are discussed.


title: Backhaul-aware robust 3D drone placement in 5G+ wireless networks

abstract: Using drones as flying base stations is a promising approach to enhance the network coverage and area capacity by moving supply towards demand when required. However deployment of such base stations can face some restrictions that need to be considered. One of the limitations in drone base stations (drone-BSs) deployment is the availability of reliable wireless backhaul link. This paper investigates how different types of wireless backhaul offering various data rates would affect the number of served users. Two approaches, namely, network-centric and user-centric, are introduced and the optimal 3D backhaul-aware placement of a drone-BS is found for each approach. To this end, the total number of served users and sum-rates are maximized in the network-centric and user-centric frameworks, respectively. Moreover, as it is preferred to decrease drone-BS movements to save more on battery and increase flight time and to reduce the channel variations, the robustness of the network is examined as how sensitive it is with respect to the users displacements.


title: Pretreatment of Lignocellulosic Wastes to Improve Ethanol and Biogas Production: A Review

abstract: Lignocelluloses are often a major or sometimes the sole components of different waste streams from various industries, forestry, agriculture and municipalities. Hydrolysis of these materials is the first step for either digestion to biogas (methane) or fermentation to ethanol. However, enzymatic hydrolysis of lignocelluloses with no pretreatment is usually not so effective because of high stability of the materials to enzymatic or bacterial attacks. The present work is dedicated to reviewing the methods that have been studied for pretreatment of lignocellulosic wastes for conversion to ethanol or biogas. Effective parameters in pretreatment of lignocelluloses, such as crystallinity, accessible surface area, and protection by lignin and hemicellulose are described first. Then, several pretreatment methods are discussed and their effects on improvement in ethanol and/or biogas production are described. They include milling, irradiation, microwave, steam explosion, ammonia fiber explosion (AFEX), supercritical CO2 and its explosion, alkaline hydrolysis, liquid hot-water pretreatment, organosolv processes, wet oxidation, ozonolysis, dilute- and concentrated-acid hydrolyses, and biological pretreatments.


title: Improved algorithms for min cut and max flow in undirected planar graphs

abstract: We study the min st-cut and max st-flow problems in planar graphs, both in static and in dynamic settings. First, we present an algorithm that given an undirected planar graph and two vertices s and t computes a min st-cut in O(n log log n) time. Second, we show how to achieve the same bound for the problem of computing a max st-flow in an undirected planar graph. These are the first algorithms breaking the O(n log n) barrier for those two problems, which has been standing for more than 25 years. Third, we present a fully dynamic algorithm maintaining the value of the min st-cuts and the max st-flows in an undirected plane graph (i.e., a planar graph with a fixed embedding): our algorithm is able to insert and delete edges and answer queries for min st-cut/max st-flow values between any pair of vertices s and t in O(n(2/3) log(8/3) n) time per operation. This result is based on a new dynamic shortest path algorithm for planar graphs which may be of independent interest. We remark that this is the first known non-trivial dynamic algorithm for min st-cut and max st-flow.


title: The effect of high-resistance training on the strength and cross-sectional area of the human quadriceps

abstract: Seventeen volunteers performed unilateral strength-training of the quadriceps with high-resistance, low-repetition, dynamic exercise, thrice weekly for an average of 5 weeks. Both before and after the training period, bilateral measurements were made of isometric quadriceps strength, quadriceps cross-sectional area (by ultrasound scanning), and thigh circumference. There were no significant changes in the untrained thighs. The trained quadriceps increased their isometric strength by more than they changed their cross-sectional area (mean increments = 15% and 6% respectively). Quadriceps hypertrophy was underestimated by measurements of thigh circumference and could not be predicted from them. We conclude that studies of localized muscle growth require direct measurements of the size of the muscle(s) concerned. Nevertheless, these may still underestimate the improvements in strength produced by high-resistance training.


title: Automatic segmentation of text into structured records

abstract: In this paper we present a method for automatically segmenting unformatted text records into structured elements. Several useful data sources today are human-generated as continuous text whereas convenient usage requires the data to be organized as structured records. A prime motivation is the warehouse address cleaning problem of transforming dirty addresses stored in large corporate databases as a single text field into subfields like “City” and “Street”. Existing tools rely on hand-tuned, domain-specific rule-based systems. We describe a tool DATAMOLD that learns to automatically extract structure when seeded with a small number of training examples. The tool enhances on Hidden Markov Models (HMM) to build a powerful probabilistic model that corroborates multiple sources of information including, the sequence of elements, their length distribution, distinguishing words from the vocabulary and an optional external data dictionary. Experiments on real-life datasets yielded accuracy of 90% on Asian addresses and 99% on US addresses. In contrast, existing information extraction methods based on rule-learning techniques yielded considerably lower accuracy.


title: The COG database: an updated version includes eukaryotes

abstract: BackgroundThe availability of multiple, essentially complete genome sequences of prokaryotes and eukaryotes spurred both the demand and the opportunity for the construction of an evolutionary classification of genes from these genomes. Such a classification system based on orthologous relationships between genes appears to be a natural framework for comparative genomics and should facilitate both functional annotation of genomes and large-scale evolutionary studies.ResultsWe describe here a major update of the previously developed system for delineation of Clusters of Orthologous Groups of proteins (COGs) from the sequenced genomes of prokaryotes and unicellular eukaryotes and the construction of clusters of predicted orthologs for 7 eukaryotic genomes, which we named KOGs after euk aryotic o rthologous g roups. The COG collection currently consists of 138,458 proteins, which form 4873 COGs and comprise 75% of the 185,505 (predicted) proteins encoded in 66 genomes of unicellular organisms. The euk aryotic o rthologous g roups (KOGs) include proteins from 7 eukaryotic genomes: three animals (the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster and Homo sapiens), one plant, Arabidopsis thaliana, two fungi (Saccharomyces cerevisiae and Schizosaccharomyces pombe), and the intracellular microsporidian parasite Encephalitozoon cuniculi. The current KOG set consists of 4852 clusters of orthologs, which include 59,838 proteins, or ~54% of the analyzed eukaryotic 110,655 gene products. Compared to the coverage of the prokaryotic genomes with COGs, a considerably smaller fraction of eukaryotic genes could be included into the KOGs; addition of new eukaryotic genomes is expected to result in substantial increase in the coverage of eukaryotic genomes with KOGs. Examination of the phyletic patterns of KOGs reveals a conserved core represented in all analyzed species and consisting of ~20% of the KOG set. This conserved portion of the KOG set is much greater than the ubiquitous portion of the COG set (~1% of the COGs). In part, this difference is probably due to the small number of included eukaryotic genomes, but it could also reflect the relative compactness of eukaryotes as a clade and the greater evolutionary stability of eukaryotic genomes.ConclusionThe updated collection of orthologous protein sets for prokaryotes and eukaryotes is expected to be a useful platform for functional annotation of newly sequenced genomes, including those of complex eukaryotes, and genome-wide evolutionary studies.


title: Green Synthesis of Silver Nanoparticles Using Extracts of Ananas comosus

abstract: The biosynthesis of nanoparticles has been proposed as a cost effective and environmental friendly alternative to chemical and physical methods. Plant mediated synthesis of nanoparticles is a green chemistry approach that intercom nects nanotechnology and plant biotechnology. In the present study, synthesis of silver nanoparticles (AgNPs) or (Green-Silver) has been demonstrated using extracts of Ananas comosus reducing aqueous silver nitrate. The AgNPs were characterized by Ultraviolet-Visible (UV-vis) Spectrometer, Energy Dispersive X-ray Analysis (EDAX), Selected Area Diffraction Pattern (SAED) and High Resolution Transmission Electron Microscopy (HRTEM). TEM micrographs showed spherical particles with an average size of 12 nm. The XRD pattern showed the characteristic Bragg peaks of (111), (200), (220) and (311) facets of the face center cubic (fcc) silver nanoparticles and confirmed that these nanoparticles are crystalline in nature. The different types of antioxidants presented in the pineapple juice synergistically reduce the Ag metal ions, as each antioxidant is unique in terms of its structure and antioxidant function. The re- action process was simple for formation of silver nanoparticles and AgNPs presented in the aqueous medium were quite stable, even up to 4 months of incubation. This work proved the capability of using biomaterial towards the synthesis of silver nanoparticle, by adopting the principles of green chemistry.


title: A Survey of Machine Learning Applications for Energy-Efficient Resource Management in Cloud Computing Environments

abstract: Ensuring energy efficiency in data centers is a crucial objective in modern cloud computing because it reduces operating costs and complies with the goals of green computing. Researchers strive to develop optimal policies for resource management in the cloud, which has many components such as virtual machine placement, task scheduling, workload consolidation, and so on. Machine learning has a major role to play in these efforts. In this paper, we provide a detailed survey of recent works in the literature which have employed machine learning (ML) to offer solutions for energy efficiency in cloud computing environments. We also present a comparative classification of the proposed methods. Furthermore, we enrich this survey by studying non-ML proposals to energy conservation in data centers, and also how ML has been applied towards other objectives in the cloud.


title: Sensorless anti-swing control strategy for automatic gantry crane system: Soft sensor approach

abstract: To achieve a good control performance of automatic gantry crane system, sensors are indispensable instrument for feedback signals. However, sensing the payload motion of a real gantry crane, particularly swing motion, is troublesome and often costly. Therefore, a sensorless automatic gantry crane system is developed and proposed in this paper. A soft sensor based on mathematical model of the crane is introduced to substitute the real sensor. In the proposed method, the motion of the payload is estimated based on the mathematical of the crane and the measured trolley position. An experimental study using lab-scale automatic gantry crane is carried out to evaluate the effectiveness of the proposed soft sensor. The results show that the proposed sensorless anti-swing control strategy is effective for suppressing the swing motion since it gives similar performance to the sensor-based anti-swing control strategy. Moreover, the proposed method has robustness to deal with parameter variations.


title: Facial expression megamix: Tests of dimensional and category accounts of emotion recognition

abstract: Abstract We report four experiments investigating the perception of photographic quality continua of interpolated (`morphed') facial expressions derived from prototypes of the 6 emotions in the Ekman and Friesen (1976) series (happiness, surprise, fear, sadness, disgust and anger). In Experiment 1, morphed images made from all possible pairwise combinations of expressions were presented in random order; subjects identified these as belonging to distinct expression categories corresponding to the prototypes at each end of the relevant continuum. This result was replicated in Experiment 2, which also included morphs made from a prototype with a neutral expression, and allowed `neutral' as a response category. These findings are inconsistent with the view that facial expressions are recognised by locating them along two underlying dimensions, since such a view predicts that at least some transitions between categories should involve neutral regions or identification as a different emotion. Instead, they suggest that facial expressions of basic emotions are recognised by their fit to discrete categories. Experiment 3 used continua involving 6 emotions to demonstrate best discrimination of pairs of stimuli falling across category boundaries; this provides further evidence of categorical perception of facial expressions of emotion. However, in both Experiment 1 and Experiment 2, reaction time data showed that increasing distance from the prototype had a definite cost on ability to identify emotion in the resulting morphed face. Moreover, Experiment 4 showed that subjects had some insight into which emotions were blended to create specific morphed images. Hence, categorical perception effects were found even though subjects were sensitive to physical properties of these morphed facial expressions. We suggest that rapid classification of prototypes and better across boundary discriminability reflect the underlying organisation of human categorisation abilities.


title: Wideband, circularly polarized, crossed, bowtie dipole antenna for navigation satellite system

abstract: In the article, a wideband, high-efficient, circular polarized(CP) antenna for COMPASS navigation satellite system is presented. It works at B1(1.561098GHz) and B1–2(1.589742GHz) frequency band and it can also be used for global positioning system(GPS) at L1(1.57542GHz) frequency band. The radiating elements are two crossed bowtie dipole with rectangular gaps in the middle of the bowtie elements. A vacant-quarter printed ring is used to realize circularly polarized(CP) by and the gaps in the middle of the bowtie elements are designed to optimize CP and to produce resonances. The bowtie structure is used to widen the bandwidth. An inverted, rotary table, cavity-backed reflector is used to control the radiation pattern and acquire highly gain. The proposed antenna is fed by a single electricity port.


title: Performance study of LTE and mmWave in vehicle-to-network communications

abstract: A key enabler for the emerging autonomous and cooperative driving services is high-throughput and reliable Vehicle-to-Network (V2N) communication. In this respect, the millimeter wave (mmWave) frequencies hold great promises because of the large available bandwidth which may provide the required link capacity. However, this potential is hindered by the challenging propagation characteristics of high-frequency channels and the dynamic topology of the vehicular scenarios, which affect the reliability of the connection. Moreover, mmWave transmissions typically leverage beamforming gain to compensate for the increased path loss experienced at high frequencies. This, however, requires fine alignment of the transmitting and receiving beams, which may be difficult in vehicular scenarios. Those limitations may undermine the performance of V2N communications and pose new challenges for proper vehicular communication design. In this paper, we study by simulation the practical feasibility of some mmWave-aware strategies to support V2N, in comparison to the traditional LTE connectivity below 6 GHz. The results show that the orchestration among different radios represents a viable solution to enable both high-capacity and robust V2N communications.


title: Kernel Smoothing Method Applicable to the Dynamic Calibration of Traffic Flow Models

abstract: : Real time traffic flow simulation models are used to provide traffic information for dynamic traffic management systems. Those simulation models are supplied by traffic data in order to estimate and predict traffic conditions in unobserved sections of a traffic network. In general, most of recent real time traffic simulators are based on the macroscopic model because the macroscopic model replicates the average traffic behavior in terms of observable variables such as (time–space) flow and speed at a relatively fast computational time. Like other simulation models, an important aspect of the real time macroscopic simulator is to calibrate the model parameters online. The most conventional way of the online calibration is to add a random walk to the parameters to constitute an augmentation of the traffic variables and the model parameters to be estimated. Actually, this method allows the parameters to vary at every time step and, therefore, describes the adaptation of the model to the prevailing traffic conditions. However, it has been reported that the use of the random walk results in a loss of information and an increase of the covariance of parameters, which consequently leads to posteriors that are far more diffuse than the theoretical posteriors for the true parameters. To this end, this article puts forward a Kernel density estimation technique in the calibration process to handle the covariance issue and to avoid the information loss. The Kernel density estimation technique is embedded in the particle filter algorithm, which is extended to the calibration problems. The proposed framework is investigated using real-life data collected in a freeway in England.


title: Alignment and Adjustment of Synchronously Tuned Multiple-Resonant-Circuit Filters

abstract: A simple method of "tuning up" a multiple-resonant-circuit filter quickly and exactly is demonstrated. The method may be summarized as follows: Very loosely couple a detector to the first resonator of the filter; then, proceeding in consecutive order, tune all odd-numbered resonators for maximum detector output, and all even-numbered resonators for minimum detector output (always making sure that the resonator immediately following the one to be resonated is completely detuned). Also considered is the correct adjustment of the two other types of constants in a filter. Filter constants can always be reduced to only three fundamental types: f0, dr(1/Qr), and Kr(r+1). This is true whether a lumped-element 100-kc filter or a distributed-element 5,000-mc unit is being considered. dr is adjusted by considering the rth resonator as a single-tuned circuit (all other resonators completely detuned) and setting the bandwidth between the 3-db-down-points to the required value. Kr(r+1) is adjusted by considering the rth and (r+1)th adjacent resonators as a double-tuned circuit (all other resonators completely detuned) and setting the bandwidth between the resulting response peaks to the required value. Finally, all the required values for K and Q are given for an n-resonant-circuit filter that will produce the response (Vp/V)2=1 +(?f/?f3db)2n.


title: Location of Things (LoT): A Review and Taxonomy of Sensors Localization in IoT Infrastructure

abstract: Internet of Things (IoT) is a novel design paradigm, intended as a network of billions to trillions of tiny sensors communicating with each other to offer innovative solutions to real time problems. These sensors form a network named as wireless sensor networks (WSNs) to monitor physical environment and disseminate collected data back to the base station through multiple hops. WSN has the capability to collect and report data for a specific application. The location information plays an important role for various wireless sensor network applications. A majority of the applications are related to location-based services. The development of sensor technology, processing techniques, and communication systems give rise to a development of the smart sensor for the adaptive and innovative application. So a single localization technique is not adequate for all application. In this paper, a recent extensive analysis of localization techniques and hierarchical taxonomy and their applications in the different context is presented. This taxonomy of the localization technique is classified based on presence of offline training in localization, namely self-determining and training dependent approaches. Finally, various open research issues related to localization schemes for IoT are compared and various directions for future research are proposed.


title: FPGA Implementation of Convolutional Neural Networks with Fixed-Point Calculations

abstract: Neural network-based methods for image processing are becoming widely used in practical applications. Modern neural networks are computationally expensive and require specialized hardware, such as graphics processing units. Since such hardware is not always available in real life applications, there is a compelling need for the design of neural networks for mobile devices. Mobile neural networks typically have reduced number of parameters and require a relatively small number of arithmetic operations. However, they usually still are executed at the software level and use floating-point calculations. The use of mobile networks without further optimization may not provide sufficient performance when high processing speed is required, for example, in real-time video processing (30 frames per second). In this study, we suggest optimizations to speed up computations in order to efficiently use already trained neural networks on a mobile device. Specifically, we propose an approach for speeding up neural networks by moving computation from software to hardware and by using fixed-point calculations instead of floating-point. We propose a number of methods for neural network architecture design to improve the performance with fixed-point calculations. We also show an example of how existing datasets can be modified and adapted for the recognition task in hand. Finally, we present the design and the implementation of a floating-point gate array-based device to solve the practical problem of real-time handwritten digit classification from mobile camera video feed.


title: Resource Allocation for Multiple Device-to-Device Cluster Multicast Communications Underlay Cellular Networks

abstract: A practically efficient resource allocation scheme is designed for multiple device-to-device cluster (D2DC) multicast communications underlay cellular networks. The transmit power and the channel are allocated to D2DC to maximize the sum effective throughput provided that the cellular communications maintain a certain level of quality of service. We devise a channel allocation scheme using partial information of device locations. For this, we derive the outage probability and an effective throughput of D2DC communications in approximate forms. Numerical results reveal that, when the number of D2DC receivers is large, the proposed scheme achieves the near optimal sum effective throughput, which can be obtained by using full information of device locations.


A deblocking filter architecture for high efficiency video coding standard (HEVC).


title: Enhanced Prediction of Student Dropouts Using Fuzzy Inference System and Logistic Regression

abstract: Predicting college and school dropouts is a major problem in educational system and has complicated challenge due to data imbalance and multi dimensionality, which can affect the low performance of students. In this paper, we have collected different database from various colleges, among these 500 best real attributes are identified in order to identify the factor that affecting dropout students using neural based classification algorithm and different mining technique are implemented for data processing. We also propose a Dropout Prediction Algorithm (DPA) using fuzzy logic and Logistic Regression based inference system because the weighted average will improve the performance of whole system. We are experimented our proposed work with all other classification systems and documented as the best outcomes. The aggregated data is given to the decision trees for better dropout prediction. The accuracy of overall system 98.6% it shows the proposed work depicts efficient prediction.


title: Talent Development in Adolescent Team Sports: A Review

abstract: Traditional talent development pathways for adolescents in team sports follow talent identification procedures based on subjective games ratings and isolated athletic assessment. Most talent development models are exclusive rather than inclusive in nature. Subsequently, talent identification may result in discontentment, premature stratification, or dropout from team sports. Understanding the multidimensional differences among the requirements of adolescent and elite adult athletes could provide more realistic goals for potential talented players. Coach education should include adolescent development, and rewards for team success at the adolescent level should reflect the needs of long-term player development. Effective talent development needs to incorporate physical and psychological maturity, the relative age effect, objective measures of game sense, and athletic prowess. The influences of media and culture on the individual, and the competing time demands between various competitions for player training time should be monitored and mediated where appropriate. Despite the complexity, talent development is a worthy investment in professional team sport. No clear guidelines exist for the effective development of talented team sports athletes. Talent development issues are global and not exclusive to team sports athletes. 1 The one unifying factor is that because of the many factors associated with growth, development, and maturation, the same strategies employed with elite adult athletes are unlikely to be sustainable in adolescents.


title: Active Pedestrian Safety by Automatic Braking and Evasive Steering

abstract: Active safety systems hold great potential for reducing accident frequency and severity by warning the driver and/or exerting automatic vehicle control ahead of crashes. This paper presents a novel active pedestrian safety system that combines sensing, situation analysis, decision making, and vehicle control. The sensing component is based on stereo vision, and it fuses the following two complementary approaches for added robustness: 1) motion-based object detection and 2) pedestrian recognition. The highlight of the system is its ability to decide, within a split second, whether it will perform automatic braking or evasive steering and reliably execute this maneuver at relatively high vehicle speed (up to 50 km/h). We performed extensive precrash experiments with the system on the test track (22 scenarios with real pedestrians and a dummy). We obtained a significant benefit in detection performance and improved lateral velocity estimation by the fusion of motion-based object detection and pedestrian recognition. On a fully reproducible scenario subset, involving the dummy that laterally enters into the vehicle path from behind an occlusion, the system executed, in more than 40 trials, the intended vehicle action, i.e., automatic braking (if a full stop is still possible) or automatic evasive steering.


title: Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction

abstract: Object detection systems based on the deep convolutional neural network (CNN) have recently made ground-breaking advances on several object detection benchmarks. While the features learned by these high-capacity neural networks are discriminative for categorization, inaccurate localization is still a major source of error for detection. Building upon high-capacity CNN architectures, we address the localization problem by 1) using a search algorithm based on Bayesian optimization that sequentially proposes candidate regions for an object bounding box, and 2) training the CNN with a structured loss that explicitly penalizes the localization inaccuracy. In experiments, we demonstrate that each of the proposed methods improves the detection performance over the baseline method on PASCAL VOC 2007 and 2012 datasets. Furthermore, two methods are complementary and significantly outperform the previous state-of-the-art when combined.


title: Reinforcement Learning with Echo State Networks

abstract: Function approximators are often used in reinforcement learning tasks with large or continuous state spaces. Artificial neural networks, among them recurrent neural networks are popular function approximators, especially in tasks where some kind of of memory is needed, like in real-world partially observable scenarios. However, convergence guarantees for such methods are rarely available. Here, we propose a method using a class of novel RNNs, the echo state networks. Proof of convergence to a bounded region is provided for k-order Markov decision processes. Runs on POMDPs were performed to test and illustrate the working of the architecture.


title: A General Framework for Tracking Multiple People from a Moving Camera

abstract: In this paper, we present a general framework for tracking multiple, possibly interacting, people from a mobile vision platform. To determine all of the trajectories robustly and in a 3D coordinate system, we estimate both the camera's ego-motion and the people's paths within a single coherent framework. The tracking problem is framed as finding the MAP solution of a posterior probability, and is solved using the reversible jump Markov chain Monte Carlo (RJ-MCMC) particle filtering method. We evaluate our system on challenging datasets taken from moving cameras, including an outdoor street scene video dataset, as well as an indoor RGB-D dataset collected in an office. Experimental evidence shows that the proposed method can robustly estimate a camera's motion from dynamic scenes and stably track people who are moving independently or interacting.


DESvisual: A Visualization Tool for the DES Cipher


title: THE AIRBUS APPROACH TO OPEN INTEGRATED MODULAR AVIONICS (IMA): TECHNOLOGY, METHODS, PROCESSES AND FUTURE ROAD MAP

abstract: Since twenty years the rapid increase of micro electronics performance according to Moore’s Law pushed the respective customer demand for more and smarter functionality on modern Aircraft (A/C) systems. In order to keep the volume, weight, power consumption and cost of avionic within reasonable limits the classical concept: one function = one avionic controller can finally not be maintained. Concepts are due to get multiple software (SW) functions of different origin and criticality level to be integrated on single avionic controller devices. This is the step to “Integrated Modular Avionics (IMA)”. The first IMA solution was introduced 1995 on the Boeing 777. It was the “A/C Information Management System (AIMS)” from Honeywell, a backplane based modularized “cabinet”. The AIMS Cabinet integrated several flight guidance, data recording and maintenance applications. After several years of in service experience the new avionic concept proved to be at least one order of magnitude more reliable than conventional embedded controllers. Today the IMA cabinet technology is state of the art on different A/C programs. In Europe Airbus together with THALES-DIEHL took a further step in the development of an Open IMA technology standard on the A380 program. The features of “open” IMA go beyond the initial approach of AIMS by Honeywell in terms of applying open avionic and commercial communication standards as well as sharing knowledge between the IMA platform provider and the airframer on the technical features of the standardized computing resources. In order to achieve this, the proprietary cabinet solutions were abandoned and substituted by general purpose ARINC 600 Standard avionic controllers, labeled CPIOM (Core Processing & IO Module), which are applied to both cockpit and utility functions, i.e. quite across all A/C system domains. Further, an “A/C Full DupleX (AFDX)” switched Ethernet communication network was provided which connects all controller devices of the A/C. Ethernet switches and CPIOM are designed according to the common aeronautic ARINC 600 standards and thus, are open to all potential avionic manufacturers. Additionally Airbus and THALES-DIEHL developed the necessary processes, methods and tools that support the new Open IMA technology. The presentation will give a survey on the state of the art of the A380 IMA concept in terms of technical aspects and with a strong focus on the methodical features of this technology (configuration, incremental qualification, tools, industrial roles & responsibilities, liabilities etc.). Finally – derived from the Lessons learnt – a roadmap towards further needs and targets and the respective technology will be given.


title: The hustle and bustle of city life: monitoring the effects of urbanisation in the African lesser bushbaby

abstract: Urbanisation has become a severe threat to pristine natural areas, causing habitat loss and affecting indigenous animals. Species occurring within an urban fragmented landscape must cope with changes in vegetation type as well as high degrees of anthropogenic disturbance, both of which are possible key mechanisms contributing to behavioural changes and perceived stressors. We attempted to elucidate the effects of urbanisation on the African lesser bushbaby, Galago moholi, by (1) recording activity budgets and body condition (body mass index, BMI) of individuals of urban and rural populations and (2) further determining adrenocortical activity in both populations as a measure of stress via faecal glucocorticoid metabolite (fGCM) levels, following successful validation of an appropriate enzyme immunoassay test system (adrenocorticotropic hormone (ACTH) challenge test). We found that both sexes of the urban population had significantly higher BMIs than their rural counterparts, while urban females had significantly higher fGCM concentrations than rural females. While individuals in the urban population fed mainly on provisioned anthropogenic food sources and spent comparatively more time resting and engaging in aggressive interactions, rural individuals fed almost exclusively on tree exudates and spent more time moving between food sources. Although interactions with humans are likely to be lower in nocturnal than in diurnal species, our findings show that the impact of urbanisation on nocturnal species is still considerable, affecting a range of ecological and physiological aspects.


title: Recurrent Memory Networks for Language Modeling

abstract: Recurrent Neural Networks (RNN) have obtained excellent result in many natural language processing (NLP) tasks. However, understanding and interpreting the source of this success remains a challenge. In this paper, we propose Recurrent Memory Network (RMN), a novel RNN architecture, that not only amplifies the power of RNN but also facilitates our understanding of its internal functioning and allows us to discover underlying patterns in data. We demonstrate the power of RMN on language modeling and sentence completion tasks. On language modeling, RMN outperforms Long Short-Term Memory (LSTM) network on three large German, Italian, and English dataset. Additionally we perform in-depth analysis of various linguistic dimensions that RMN captures. On Sentence Completion Challenge, for which it is essential to capture sentence coherence, our RMN obtains 69.2% accuracy, surpassing the previous state-of-the-art by a large margin.


title: Healthcare Big Data Voice Pathology Assessment Framework

abstract: The fast-growing healthcare big data plays an important role in healthcare service providing. Healthcare big data comprise data from different structured, semi-structured, and unstructured sources. These data sources vary in terms of heterogeneity, volume, variety, velocity, and value that traditional frameworks, algorithms, tools, and techniques are not fully capable of handling. Therefore, a framework is required that facilitates collection, extraction, storage, classification, processing, and modeling of this vast heterogeneous volume of data. This paper proposes a healthcare big data framework using voice pathology assessment (VPA) as a case study. In the proposed VPA system, two robust features, MPEG-7 low-level audio and the interlaced derivative pattern, are used for processing the voice or speech signals. The machine learning algorithms in the form of a support vector machine, an extreme learning machine, and a Gaussian mixture model are used as the classifier. In the experiments, the proposed VPA system shows its efficiency in terms of accuracy and time requirement.


title: Kindergarten Social Assistive Robot (KindSAR) for children's geometric thinking and metacognitive development in preschool education: A pilot study

abstract: Abstract Kindergarten Social Assistive Robot (KindSAR) is an innovative tool promotes children’s development through social interaction. This pilot study demonstrates how KindSAR can assist educational staff in the teaching of geometric thinking and in promoting the metacognitive development by engaging children in interactive play activities. Children’s reactions and performances were video recorded for analysis. Most children exhibited positive interaction with the robot and a high level of enjoyment. Our results showed that their performances on geometric thinking and metacognitive tasks were improved while they “played” with the robot. To measure children’s learning we have developed a novel measure of cognitive learning, which we call “velocity of learning”. This study demonstrates the feasibility and expected benefits of incorporating KindSAR in preschool education.


title: Making words work: Using financial text as a predictor of financial events

abstract: We develop a methodology for automatically analyzing text to aid in discriminating firms that encounter catastrophic financial events. The dictionaries we create from Management Discussion and Analysis Sections (MD&A) of 10-Ks discriminate fraudulent from non-fraudulent firms 75% of the time and bankrupt from nonbankrupt firms 80% of the time. Our results compare favorably with quantitative prediction methods. We further test for complementarities by merging quantitative data with text data. We achieve our best prediction results for both bankruptcy (83.87%) and fraud (81.97%) with the combined data, showing that that the text of the MD&A complements the quantitative financial information.


title: On the security of a new ultra-lightweight authentication protocol in IoT environment for RFID tags

abstract: Recently, Tewari and Gupta proposed a ultra-lightweight mutual authentication protocol in IoT environments for RFID tags. Their protocol aims to provide secure communication with least cost in both storage and computation. Unfortunately, in this paper, we exploit the vulnerability of this protocol. In this attack, an attacker can obtain the key shared between a back-end database server and a tag. We also explore the possibility in patching the system with some modifications.


title: Ethical Challenges in Data-Driven Dialogue Systems

abstract: The use of dialogue systems as a medium for human-machine interaction is an increasingly prevalent paradigm. A growing number of dialogue systems use conversation strategies that are learned from large datasets. There are well documented instances where interactions with these system have resulted in biased or even offensive conversations due to the data-driven training process. Here, we highlight potential ethical issues that arise in dialogue systems research, including: implicit biases in data-driven systems, the rise of adversarial examples, potential sources of privacy violations, safety concerns, special considerations for reinforcement learning systems, and reproducibility concerns. We also suggest areas stemming from these issues that deserve further investigation. Through this initial survey, we hope to spur research leading to robust, safe, and ethically sound dialogue systems.


title: VizStruct: exploratory visualization for gene expression profiling

abstract: Motivation: DNA arrays provide a broad snapshot of the state of the cell by measuring the expression levels of thousands of genes simultaneously. Visualization techniques can enable the exploration and detection of patterns and relationships in a complex data set by presenting the data in a graphical format in which the key characteristics become more apparent. The dimensionality and size of array data sets however present significant challenges to visualization. The purpose of this study is to present an interactive approach for visualizing variations in gene expression profiles and to assess its usefulness for classifying samples. ::: ::: Results: The first Fourier harmonic projection was used to map multi-dimensional gene expression data to two dimensions in an implementation called VizStruct. The visualization method was tested using the differentially expressed genes identified in eight separate gene expression data sets. The samples were classified using the oblique decision tree (OC1) algorithm to provide a procedure for visualization-driven classification. The classifiers were evaluated by the holdout and the cross-validation techniques. The proposed method was found to achieve high accuracy. ::: ::: Availability: Detailed mathematical derivation of all mapping properties as well as figures in color can be found as supplementary on the web page http://www.cse.buffalo.edu/DBGROUP/bioinformatics/supplementary/vizstruct. All programs were written in Java and Matlab and software code is available by request from the first author.


title: The KISS Principle in Software-Defined Networking: A Framework for Secure Communications

abstract: The pace of adoption of secure mechanisms in software-defined networking (SDN) has been slow, largely due to traditional solutions’ performance overhead and their support infrastructure’s complexity. To address these challenges, we propose KISS, a secure SDN control plane communications architecture that includes innovative solutions in the context of key distribution and secure channel support.


title: Learning to Evaluate Image Captioning

abstract: Evaluation metrics for image captioning face two challenges. Firstly, commonly used metrics such as CIDEr, METEOR, ROUGE and BLEU often do not correlate well with human judgments. Secondly, each metric has well known blind spots to pathological caption constructions, and rule-based metrics lack provisions to repair such blind spots once identified. For example, the newly proposed SPICE correlates well with human judgments, but fails to capture the syntactic structure of a sentence. To address these two challenges, we propose a novel learning based discriminative evaluation metric that is directly trained to distinguish between human and machine-generated captions. In addition, we further propose a data augmentation scheme to explicitly incorporate pathological transformations as negative examples during training. The proposed metric is evaluated with three kinds of robustness tests and its correlation with human judgments. Extensive experiments show that the proposed data augmentation scheme not only makes our metric more robust toward several pathological transformations, but also improves its correlation with human judgments. Our metric outperforms other metrics on both caption level human correlation in Flickr 8k and system level human correlation in COCO. The proposed approach could be served as a learning based evaluation metric that is complementary to existing rule-based metrics.


title: Accelerating Recurrent Neural Networks: A Memory-Efficient Approach

abstract: Recurrent neural networks (RNNs) have achieved the state-of-the-art performance on various sequence learning tasks due to their powerful sequence modeling capability. However, RNNs usually require a large number of parameters and high computational complexity. Hence, it is quite challenging to implement complex RNNs on embedded devices with stringent memory and latency requirement. In this paper, we first present a novel hybrid compression method for a widely used RNN variant, long–short term memory (LSTM), to tackle these implementation challenges. By properly using circulant matrices, forward nonlinear function approximation, and efficient quantization schemes with a retrain-based training strategy, the proposed compression method can reduce more than 95% of memory usage with negligible accuracy loss when verified under language modeling and speech recognition tasks. An efficient scalable parallel hardware architecture is then proposed for the compressed LSTM. With an innovative chessboard division method for matrix–vector multiplications, the parallelism of the proposed hardware architecture can be freely chosen under certain latency requirement. Specifically, for the circulant matrix–vector multiplications employed in the compressed LSTM, the circulant matrices are judiciously reorganized to fit in with the chessboard division and minimize the number of memory accesses required for the matrix multiplications. The proposed architecture is modeled using register transfer language (RTL) and synthesized under the TSMC 90-nm CMOS technology. With 518.5-kB on-chip memory, we are able to process a $512 \times 512$ compressed LSTM in 1.71 $\mu {{{\text{s}}}}$ , corresponding to 2.46 TOPS on the uncompressed one, at a cost of 30.77-mm2 chip area. The implementation results demonstrate that the proposed design can achieve significantly high flexibility and area efficiency, which satisfies many real-time applications on embedded devices. It is worth mentioning that the memory-efficient approach of accelerating LSTM developed in this paper is also applicable to other RNN variants.


title: Sparse Coding From a Bayesian Perspective

abstract: Sparse coding is a promising theme in computer vision. Most of the existing sparse coding methods are based on either l0 or l1 penalty, which often leads to unstable solution or biased estimation. This is because of the nonconvexity and discontinuity of the l0 penalty and the over-penalization on the true large coefficients of the l1 penalty. In this paper, sparse coding is interpreted from a novel Bayesian perspective, which results in a new objective function through maximum a posteriori estimation. The obtained solution of the objective function can generate more stable results than the l0 penalty and smaller reconstruction errors than the l1 penalty. In addition, the convergence property of the proposed algorithm for sparse coding is also established. The experiments on applications in single image super-resolution and visual tracking demonstrate that the proposed method is more effective than other state-of-the-art methods.


title: Ability of troponin T to predict angiographic coronary artery disease in patients with chronic kidney disease.

abstract: Association between angiographic coronary artery disease and cardiac troponin T levels has been observed in patients with normal kidney function; however, this association remains unsettled in patients with chronic kidney disease (CKD) or end-stage renal disease (ESRD). Over a 12-month period we retrospectively reviewed coronary angiograms (CAs) performed in 194 hospitalized patients with presumed acute myocardial injury. About 50% of the ESRD and 30% of the CKD patients had normal CAs. Troponin T levels significantly correlated with CAs in patients with normal kidney function (r = 0.4, p = 0.005) but not in ESRD and CKD patients (r = 0.2, p = NS, respectively).


title: Contactless Energy Transfer System With FPGA-Controlled Resonant Converter

abstract: Power supply based on an inductive coupled contactless energy transfer system is presented in this paper. The energy is transferred using a rotatable transformer and a power electronic converter. To minimize total losses of the system, a series resonant compensation circuit is applied assuring zero-current switching condition for insulated-gate bipolar transistors. The analytical expression of the transfer dc voltage gain is presented and discussed. The novelty of the system lies in the application of a fully digital field-programmable-gate-array-based controller and a protection system. The resonant frequency is adjusted by a primary peak current regulator. Some simulation and experimental results illustrating the operation of the developed 3-kW 60-kHz laboratory prototype are given. Although the presented power supply with a rotatable transformer is constructed mainly for robotics and manipulators, the described design and control methodology has general validity and can be applied for a wide class of contactless power supply with core or coreless transformers.


title: NATIONAL CULTURE AND TECHNOLOGY ACCEPTANCE: THE IMPACT OF UNCERTAINTY AVOIDANCE

abstract: This study combined the results from 95 TAM articles to examine the impact of uncertainty avoidance on national culture and technology acceptance. This meta-analysis was based on 342 reviewed TAM articles, of which 95 met the selection criteria of both reported statistics and national culture. Each article was coded for national culture based on the rankings from the works of Hofstede and GLOBE. Overall, hypotheses about uncertainly avoidance practices (UAP) were supported for each correlation. Highlevel UAP confidence intervals were significantly lower than for the medium-level and low-level UAP groups for each of the TAM construct correlations.


title: Price discounts significantly enhance fruit and vegetable purchases when combined with nutrition education: a randomized controlled supermarket trial

abstract: Background: Reducing fruit and vegetable (FV 151 (76%) were included in the final analysis. FV 95% CI: 1.5, 6.3 kg) and the discount plus education intervention (+5.6 kg; 95% CI: 3.2, 7.9 kg) at 6 mo compared with control. Moreover, the percentage of participants who consumed recommended amounts of F&Vs ($400 g/d) increased from 42.5% at baseline to 61.3% at 6 mo in both discount groups (P = 0.03). Education alone had no significant effect. Conclusions: Discounting F&Vs is a promising intervention strategy because it resulted in substantially higher F&V purchases, and no adverse effects were observed. Therefore, pricing strategies form an important focus for future interventions or policy. However, the long-term effects and the ultimate health outcomes require further investigation. This trial was registered at the ISRCTN Trial Register as number ISRCTN56596945 and at the Dutch Trial Register (http://www.trialregister.nl/trialreg/index.asp) as number NL22568.029.08 Am J Clin Nutr doi: 10.3945/ajcn.112.041632.


title: An empirical comparison of supervised learning algorithms

abstract: A number of supervised learning methods have been introduced in the last decade. Unfortunately, the last comprehensive empirical evaluation of supervised learning was the Statlog Project in the early 90's. We present a large-scale empirical comparison between ten supervised learning methods: SVMs, neural nets, logistic regression, naive bayes, memory-based learning, random forests, decision trees, bagged trees, boosted trees, and boosted stumps. We also examine the effect that calibrating the models via Platt Scaling and Isotonic Regression has on their performance. An important aspect of our study is the use of a variety of performance criteria to evaluate the learning methods.


title: Augmenting Naive Bayes Classifiers with Statistical Language Models

abstract: We augment naive Bayes models with statistical n-gram language models to address short-comings of the standard naive Bayes text classifier. The result is a generalized naive Bayes classifier which allows for a local Markov dependence among observationss a model we refer to as the Chain Augmented Naive Bayes (CAN) Bayes classifier. CAN models have two advantages over standard naive Bayes classifiers. First, they relax some of the independence assumptions of naive Bayes—allowing a local Markov chain dependence in the observed variables—while still permitting efficient inference and learning. Second, they permit straightforward application of sophisticated smoothing techniques from statistical language modeling, which allows one to obtain better parameter estimates than the standard Laplace smoothing used in naive Bayes classification. In this paper, we introduce CAN models and apply them to various text classification problems. To demonstrate the language independent and task independent nature of these classifiers, we present experimental results on several text classification problems—authorship attribution, text genre classification, and topic detection—in several languages—Greek, English, Japanese and Chinese. We then systematically study the key factors in the CAN model that can influence the classification performance, and analyze the strengths and weaknesses of the model.


title: Finding similar questions in large question and answer archives

abstract: There has recently been a significant increase in the number of community-based question and answer services on the Web where people answer other peoples' questions. These services rapidly build up large archives of questions and answers, and these archives are a valuable linguistic resource. One of the major tasks in a question and answer service is to find questions in the archive that a semantically similar to a user's question. This enables high quality answers from the archive to be retrieved and removes the time lag associated with a community-based system. In this paper, we discuss methods for question retrieval that are based on using the similarity between answers in the archive to estimate probabilities for a translation-based retrieval model. We show that with this model it is possible to find semantically similar questions with relatively little word overlap.


title: Science and Human Behavior at fifty

abstract: The golden anniversary of Science and Human Behavior is cause for celebration. Toward that end, the present paper is largely an historical consideration of the book, its inception and reception, both at the time of its publication and in subsequent years. The range and intensity of reactions to S&HB mark its impact and show it to be among Skinner's most important works, if not the most important. S&HB was written as an introductory psychology text--a vigorous use of the book in our teaching could do much to benefit the dissemination of behavior analysis.