title: Orthogonally Evolved AI to Improve Difficulty Adjustment in Video Games

abstract: Computer games are most engaging when their difficulty is well matched to the player’s ability, thereby providing an experience in which the player is neither overwhelmed nor bored. In games where the player interacts with computer-controlled opponents, the difficulty of the game can be adjusted not only by changing the distribution of opponents or game resources, but also through modifying the skill of the opponents. Applying evolutionary algorithms to evolve the artificial intelligence that controls opponent agents is one established method for adjusting opponent difficulty. Less-evolved agents (i.e., agents subject to fewer generations of evolution) make for easier opponents, while highly-evolved agents are more challenging to overcome. In this publication we test a new approach for difficulty adjustment in games: orthogonally evolved AI, where the player receives support from collaborating agents that are co-evolved with opponent agents (where collaborators and opponents have orthogonal incentives). The advantage is that game difficulty can be adjusted more granularly by manipulating two independent axes: by having more or less adept collaborators, and by having more or less adept opponents. Furthermore, human interaction can modulate (and be informed by) the performance and behavior of collaborating agents. In this way, orthogonally evolved AI both facilitates smoother difficulty adjustment and enables new game experiences.


title: BGPmon: A Real-Time, Scalable, Extensible Monitoring System

abstract: This paper presents a new system, called BGPmon, for monitoring the Border Gateway Protocol (BGP). BGP is the routing protocol for the global Internet. Monitoring BGP is important for both operations and research; a number of public and private BGP monitors are deployed and widely used. These existing monitors typically collect data using a full implementation of a BGP router. In contrast, BGPmon eliminates the unnecessary functions of route selection and data forwarding to focus solely on the monitoring function. BGPmon uses a publish/subscribe overlay network to provide real-time access to vast numbers of peers and clients. All routing events are consolidated into a single XML stream. XML allows us to add additional features such as labeling updates to allow easy identification of useful data by clients. Clients subscribe to BGPmon and receive the XML stream, performing tasks such as archiving, filtering, or real-time data analysis. BGPmon enables scalable real-time monitoring data distribution by allowing monitors to peer with each other and form an overlay network to provide new services and features without modifying the monitors. We illustrate the effectiveness of the BGPmon data using the Cyclops route monitoring system.


title: Design and Implementation of Dual-Frequency Dual-Polarization Slotted Waveguide Antenna Array for Ka-Band Application

abstract: In this letter, a dual-frequency dual-polarization slotted waveguide antenna array at Ka-band is proposed. A 9 ×10 inclined slot array working at 35 GHz, cut in the narrow wall of rectangular waveguides, is employed for horizontal polarization (HP). An 8 ×10 longitudinal slot array working at 30 GHz, etched on the broadside of ridged waveguides, is employed for vertical polarization (VP). The two arrays are interlaced with each other and fed from the opposite sides. Two novel bend interconnected structures are invented to connect the radiating waveguides and feeding networks, which simplify and compact the antenna structure and improve the aperture efficiency. The measured -10-dB reflection coefficient bandwidth of HP array is 688 MHz, and 658 MHz for VP array. The maximum gain is 25.4 and 24.8 dB for HP array and VP array, which correspond to the aperture efficiency of 36.3% and 43.05%, respectively. The sidelobe levels of both arrays are below -17.6 dB.


62.Research on digital vectorization of artistic graphics and figures based on spline curve fitting and computer aided technology


title: Microplastics in the Marine Environment: A Review of the Methods Used for Identification and Quantification

abstract: This review of 68 studies compares the methodologies used for the identification and quantification of microplastics from the marine environment. Three main sampling strategies were identified: selective, volume-reduced, and bulk sampling. Most sediment samples came from sandy beaches at the high tide line, and most seawater samples were taken at the sea surface using neuston nets. Four steps were distinguished during sample processing: density separation, filtration, sieving, and visual sorting of microplastics. Visual sorting was one of the most commonly used methods for the identification of microplastics (using type, shape, degradation stage, and color as criteria). Chemical and physical characteristics (e.g., specific density) were also used. The most reliable method to identify the chemical composition of microplastics is by infrared spectroscopy. Most studies reported that plastic fragments were polyethylene and polypropylene polymers. Units commonly used for abundance estimates are "items per m(2)" for sediment and sea surface studies and "items per m(3)" for water column studies. Mesh size of sieves and filters used during sampling or sample processing influence abundance estimates. Most studies reported two main size ranges of microplastics: (i) 500 μm-5 mm, which are retained by a 500 μm sieve/net, and (ii) 1-500 μm, or fractions thereof that are retained on filters. We recommend that future programs of monitoring continue to distinguish these size fractions, but we suggest standardized sampling procedures which allow the spatiotemporal comparison of microplastic abundance across marine environments.


title: Look, My Baby Is Using an iPad! An Analysis of YouTube Videos of Infants and Toddlers Using Tablets

abstract: We know very little about the use of computers by children under the age of three. While few children in that age range used computers before the advent of smartphones and tablets, these devices have made computers much more accessible to infants and toddlers. In this paper, we provide a window into how these children are using tablets through an analysis of relevant YouTube videos. A majority of children aged 12 to 17 months in the videos in our dataset showed at least moderate ability to use the tablets. For children aged two, it was over 90 percent who displayed at least moderate ability. Our analysis also includes trends in interaction styles, child and device positioning, social aspects, and app genres. These findings point both to opportunities for research and starting points for design.


title: Audio-visual integration of emotion expression

abstract: Regardless of the fact that emotions are usually recognized by combining facial and vocal expressions, the multisensory nature of affect perception has scarcely been investigated. In the present study, we show results of three experiments on multisensory perception of emotions using newly validated sets of dynamic visual and non-linguistic vocal clips of affect expressions. In Experiment 1, participants were required to categorise fear and disgust expressions displayed auditorily, visually, or using congruent or incongruent audio-visual stimuli. Results showed faster and more accurate categorisation in the bimodal congruent situation than in the unimodal conditions. In the incongruent situation, participant preferentially categorised the affective expression based on the visual modality, demonstrating a visual dominance in emotional processing. However, when the reliability of the visual stimuli was diminished, participants categorised incongruent bimodal stimuli preferentially via the auditory modality. These results demonstrate that visual dominance in affect perception does not occur in a rigid manner, but follows flexible situation-dependent rules. In Experiment 2, we requested the participants to pay attention to only one sensory modality at a time in order to test the putative mandatory nature of multisensory affective interactions. We observed that even if they were asked to ignore concurrent sensory information, the irrelevant information significantly affected the processing of the target. This observation was especially true when the target modality was less reliable. Altogether, these findings indicate that the perception of emotion expressions is a robust multisensory situation which follows rules that have been previously observed in other perceptual domains.


title: A self-folding robot arm for load-bearing operations

abstract: Self-folding is capable of forming complex three-dimensional structures from planar sheets. However, existing self-folding machines cannot generate large forces or bear high loads. In contrast, traditional robots are expected to operate under large loads but these robots are generally dense, resulting in heavy machines with relatively low strength-to-weight ratios. In this paper, we present a new self-folding technique that is meter-scale and load-bearing. We demonstrate the design and fabrication of both structural beams and pneumatically actuated joints. We model and measure the stiffness and strength of self-folded structural beams. Finally, we integrate these results into a robotic arm that weighs 0.3 kg and can lift up to 1.0 kg. These results indicate that functional, load-bearing, self-folding robots are possible.


Supply chain management: a view of the distribution channel.


title: A desire to be taught: Instructional consequences of intrinsic motivation

abstract: This paper summarizes the results from a series of studies designed to test the hypothesis that making learning more fun will produce corresponding increases both in learning and retention and in subsequent interest in the subject matter itself. Each study examined the effects of two or more versions of an educational activity, each designed to involve identical instructional content, but to differ in motivational appeal. The data from the studies presented provide good general support for the hypothesized cognitive and motivational benefits of appropriately designed motivational embellishments of educational activities. Exceptions to this rule, however, and a more general theoretical analysis of the conditions under which such positive effects are (and are not) expected to occur, are also discussed.


title: An Active-Clamp Push–Pull Converter for Battery Sourcing Applications

abstract: This paper presents an active-clamp push-pull converter for battery sourcing applications. A pair of auxiliary switches, resonant inductors, and clamping capacitors is added to the primary side of the transformer to clamp voltage spike and recycle the energy trapped in the leakage inductors. In the proposed active-clamp push-pull converter, since both main and auxiliary switches can be turned on with zero-voltage switching, switching loss can be reduced and conversion efficiency therefore can be improved significantly. Furthermore, the proposed converter can eliminate potential flux-imbalance problems existing in the conventional push-pull converter. In this paper, a 1 kW active-clamp push-pull converter was implemented, from which experimental results have shown that efficiency improvement and surge suppression can be achieved effectively. It is relatively feasible for applications to battery sourcing converters.


title: Use of a dark object concept and support vector machines to automate forest cover change analysis

abstract: An automated method was developed for mapping forest cover change using satellite remote sensing data sets. This multi-temporal classification method consists of a training data automation (TDA) procedure and uses the advanced support vector machines (SVM) algorithm. The TDA procedure automatically generates training data using input satellite images and existing land cover products. The derived high quality training data allow the SVM to produce reliable forest cover change products. This approach was tested in 19 study areas selected from major forest biomes across the globe. In each area a forest cover change map was produced using a pair of Landsat images acquired around 1990 and 2000. High resolution IKONOS images and independently developed reference data sets were available for evaluating the derived change products in 7 of those areas. The overall accuracy values were over 90% for 5 areas, and were 89.4% and 89.6% for the remaining two areas. The user's and producer's accuracies of the forest loss class were over 80% for all 7 study areas, demonstrating that this method is especially effective for mapping major disturbances with low commission errors. IKONOS images were also available in the remaining 12 study areas but they were either located in non-forest areas or in forest areas that did not experience forest cover change between 1990 and 2000. For those areas the IKONOS images were used to assist visual interpretation of the Landsat images in assessing the derived change products. This visual assessment revealed that for most of those areas the derived change products likely were as reliable as those in the 7 areas where accuracy assessment was conducted. The results also suggest that images acquired during leaf-off seasons should not be used in forest cover change analysis in areas where deciduous forests exist. Being highly automatic and with demonstrated capability to produce reliable change products, the TDA-SVM method should be especially useful for quantifying forest cover change over large areas.


title: Modeling delayed feedback in display advertising

abstract: In performance display advertising a key metric of a campaign effectiveness is its conversion rate -- the proportion of users who take a predefined action on the advertiser website, such as a purchase. Predicting this conversion rate is thus essential for estimating the value of an impression and can be achieved via machine learning. One difficulty however is that the conversions can take place long after the impression -- up to a month -- and this delayed feedback hinders the conversion modeling. We tackle this issue by introducing an additional model that captures the conversion delay. Intuitively, this probabilistic model helps determining whether a user that has not converted should be treated as a negative sample -- when the elapsed time is larger than the predicted delay -- or should be discarded from the training set -- when it is too early to tell. We provide experimental results on real traffic logs that demonstrate the effectiveness of the proposed model.


A Practical Guide to Discrete Optimization Dynamic Programming , 29 December 2014


title: Software development practices, software complexity, and software maintenance performance: A field study

abstract: Software maintenance claims a large proportion of organizational resources. It is thought that many maintenance problems derive from inadequate software design and development practices. Poor design choices can result in complex software that is costly to support and difficult to change. However, it is difficult to assess the actual maintenance performance effects of software development practices because their impact is realized over the software life cycle. To estimate the impact of development activities in a more practical time frame, this research develops a two-stage model in which software complexity is a key intermediate variable that links design and development decisions to their downstream effects on software maintenance. The research analyzes data collected from a national mass merchandising retailer on 29 software enhancement projects and 23 software applications in a large IBM COBOL environment. Results indicate that the use of a code generator in development is associated with increased software complexity and software enhancement project effort. The use of packaged software is associated with decreased software complexity and software enhancement effort. These results suggest an important link between software development practices and maintenance performance.


title: Automatic Composition of Semantic Web Services Based on Fuzzy Predicate Petri Nets

abstract: Web service composition is a challenging research issue. This paper presents an automatic Web service composition method that deals with both input/output compatibility and behavioral constraint compatibility of fuzzy semantic services. First, user input and output requirements are modeled as a set of facts and a goal statement in the Horn clauses, respectively. A service composition problem is transformed into a Horn clause logic reasoning problem. Next, a Fuzzy Predicate Petri Net (FPPN) is applied to model the Horn clause set, and T-invariant technique is used to determine the existence of composite services fulfilling the user input/output requirements. Then, two algorithms are presented to obtain the composite service satisfying behavioral constraints, as well as to construct an FPPN model that shows the calling order of the selected services.


title: Broadband Printed-Circuit Yagi Array

abstract: Printed circuit board (PCB) antenna with useful bandwidth covering from 2.45 GHz to 3.80 GHz is suitable for WiMax, WLAN, and Bluetooth wireless communications applications. We present a low profile, seven elements Yagi array antenna etched on PCB of dielectric constant 4.2 achieving 43% bandwidth with return loss better than -10 dB, and directivity from 8.2dBi to 11.1dBi. Swept frequency return loss, impedance and radiation patterns of this antenna have been simulated and measured: The congruence between the simulated and measured results is satisfactory. In addition, current distribution on this broadband Yagi is also investigated.


Applying traditional animation principles for creating learning objects


title: 3D Role-playing Games as Language Learning Tools

abstract: Leveraging the experiential cognition and motivational factors of 3D games, we conduct a pilot study that utilizes Ever Quest 2 as pedagogical learning tool for English as a second language (ESL) students. We combine the benefits of massive multiplayer online role-playing games (MMORPGs) and second language methodology to create a digital learning environment for second language acquisition (SLA). Rather than using traditional computerassisted language learning (CALL) software, we explore the immersive, virtual environment of Ever Quest 2 as ESL participants assume virtual identities and engage in social interactions within the game world. We suggest that language becomes a crucial artifact for character development and completion of game tasks in the virtual world. Preliminary results demonstrate that Ever Quest 2 intermediate and advanced ESL students increase their English vocabulary by 40% as result of game play interactions with non-playing characters (NPCs). Furthermore, intermediate and advanced ESL students practice their conversational skills with playing characters (PCs), generating a 100% increase in chat messages during eight sessions of game play. These results lead to the conclusion that MMORPGs can provide motivation and adequate language learning support for intermediate and advanced ESL students.


title: The Influence of Project Managers on Project Success Criteria and Project Success by Type of Project

abstract: The importance attached by project managers to project success criteria and the associated rates of project success were assessed for different types of projects, industries and traits of project managers. 959 responses to a web-based survey showed that importance attached to project success criteria and project success rates differ by industry, project complexity and the age and nationality of the project manager. However, the underlying criteria are different and are explained herein. Modeling the relationship between importance assigned to success criteria and reported project success against these criteria showed a link between importance and actual achievements. Managerial and theoretical implications are discussed.


title: A visual attention-based keyword extraction for document classification

abstract: Document classification plays an important role in natural language processing. Among that, keyword extraction algorithm shows its great potential in summarizing the entire document. Attention is the process of selectively concentrating on a discrete aspect of information, while ignoring other perceivable information. A new probabilistic keyword extraction algorithm is proposed, which is inspired by the visual attention mechanism. An unsupervised neural network based pre-training method is proposed for training the semantic attention based keyword extraction algorithm, which is helpful in extracting keywords with rich contextual information from the document. A bidirectional Long short-term memory network combined with the proposed semantic keyword extraction algorithm is designed for both topic and sentiment classification tasks. Experiments on four large scale datasets show that the proposed visual attention based keyword extraction algorithm gives a better performance than the baseline methods. The semantic attention based keyword extraction method is significant in summarizing the content of a document, which is very useful for large scale document classification.


title: Mutual authentication in securing mobile payment system using encrypted QR code based on public key infrastructure

abstract: Payment transaction using mobile phone has some possibilities of risk. Mobile devices still not secure and need more attention in the security area. In this paper, we propose mutual authentication technique between client and merchant using encrypted QR Code. QR Code has been widely used in recent technology. Secure transmission of data transaction can be accommodated using encrypted QR Code. QR Code can store more information than a traditional barcode. This security system maintained by Public Key Infrastructure (PKI) will ensure an additional layer of security.


title: Revisiting Whittaker & Sidner's "email overload" ten years later

abstract: Ten years ago, Whittaker and Sidner [8] published research on email overload, coining a term that would drive a research area that continues today. We examine a sample of 600 mailboxes collected at a high-tech company to compare how users organize their email now to 1996. While inboxes are roughly the same size as in 1996, our population's email archives have grown tenfold. We see little evidence of distinct strategies for handling email; most of our users fall into a middle ground. There remains a need for future innovations to help people manage growing archives of email and large inboxes.


title: Fingerprint liveness detection using gradient-based texture features

abstract: Fingerprint-based recognition systems have been increasingly deployed in various applications nowadays. However, the recognition systems can be spoofed by using an accurate imitation of a live fingerprint such as an artificially made fingerprint. In this paper, we propose a novel software-based fingerprint liveness detection method which achieves good detection accuracy. We regard the fingerprint liveness detection as a two-class classification problem and construct co-occurrence array from image gradients to extract features. In doing so, the quantization operation is firstly conducted on the images. Then, the horizontal and vertical gradients at each pixel are calculated, and the gradients of large absolute values are truncated into a reduced range. Finally, the second-order and the third-order co-occurrence arrays are constructed from the truncated gradients, and the elements of the co-occurrence arrays are directly used as features. The second-order and the third-order co-occurrence array features are separately utilized to train support vector machine classifiers on two publicly available databases used in Fingerprint Liveness Detection Competition 2009 and 2011. The experimental results have demonstrated that the features extracted with the third-order co-occurrence array achieve better detection accuracy than that with the second-order co-occurrence array and outperform the state-of-the-art methods.


title: Multilingual Neural Machine Translation for Zero-Resource Languages

abstract: In recent years, Neural Machine Translation (NMT) has been shown to be more effective than phrase-based statistical methods, thus quickly becoming the state of the art in machine translation (MT). However, NMT systems are limited in translating low-resourced languages, due to the significant amount of parallel data that is required to learn useful mappings between languages. In this work, we show how the so-called multilingual NMT can help to tackle the challenges associated with low-resourced language translation. The underlying principle of multilingual NMT is to force the creation of hidden representations of words in a shared semantic space across multiple languages, thus enabling a positive parameter transfer across languages. Along this direction, we present multilingual translation experiments with three languages (English, Italian, Romanian) covering six translation directions, utilizing both recurrent neural networks and transformer (or self-attentive) neural networks. We then focus on the zero-shot translation problem, that is how to leverage multi-lingual data in order to learn translation directions that are not covered by the available training material. To this aim, we introduce our recently proposed iterative self-training method, which incrementally improves a multilingual NMT on a zero-shot direction by just relying on monolingual data. Our results on TED talks data show that multilingual NMT outperforms conventional bilingual NMT, that the transformer NMT outperforms recurrent NMT, and that zero-shot NMT outperforms conventional pivoting methods and even matches the performance of a fully-trained bilingual system.


title: Mechanism for Structuring the Data from a Generic Identity Document Image using Semantic Analysis

abstract: Nowadays, the enormous variety of identity documents that exist makes it difficult to standardize a system capable of extracting all the information of interest presented by them. Therefore, systems that use templates to classify information based on their positions are limited by the number of templates they could recognize. Thus, in this paper, a novel mechanism intended to automatically classify the major information of interest exposed by generic identity documents is presented. The proposal is created to be easily adaptable to any system capable of detecting and extracting text information from an identity document image. To assign meaning to the text extracted from the identity document, the proposal is based on a novel mechanism to structuring the data using semantic analysis. The mechanism consists of two main steps, first, all the text data are classified as sentences or near sentences based on the Euclidean distance between words; second, the sentences are analyzed to find keywords that allow structuring the information based on its semantic to show it as abstractions. The proposal has been designed to be able to store the data as abstractions of its meaning. This allows improving the scalability of the system and a better use of this information by different services, by the end user or to be interpreted by an automated process of decision-making.


title: Video Segmentation via Multiple Granularity Analysis

abstract: We introduce a Multiple Granularity Analysis framework for video segmentation in a coarse-to-fine manner. We cast video segmentation as a spatio-temporal superpixel labeling problem. Benefited from the bounding volume provided by off-the-shelf object trackers, we estimate the foreground/ background super-pixel labeling using the spatiotemporal multiple instance learning algorithm to obtain coarse foreground/background separation within the volume. We further refine the segmentation mask in the pixel level using the graph-cut model. Extensive experiments on benchmark video datasets demonstrate the superior performance of the proposed video segmentation algorithm.


title: First results in detecting and avoiding frontal obstacles from a monocular camera for micro unmanned aerial vehicles

abstract: Obstacle avoidance is desirable for lightweight micro aerial vehicles and is a challenging problem since the payload constraints only permit monocular cameras and obstacles cannot be directly observed. Depth can however be inferred based on various cues in the image. Prior work has examined optical flow, and perspective cues, however these methods cannot handle frontal obstacles well. In this paper we examine the problem of detecting obstacles right in front of the vehicle. We developed a method to detect relative size changes of image patches that is able to detect size changes in the absence of optical flow. The method uses SURF feature matches in combination with template matching to compare relative obstacle sizes with different image spacing. We present results from our algorithm in autonomous flight tests on a small quadrotor. We are able to detect obstacles with a frame-to-frame enlargement of 120% with a high confidence and confirmed our algorithm in 20 successful flight experiments. In future work, we will improve the control algorithms to avoid more complicated obstacle configurations.


title: A case study of algorithm selection for the traveling thief problem

abstract: Many real-world problems are composed of several interacting components. In order to facilitate research on such interactions, the Traveling Thief Problem (TTP) was created in 2013 as the combination of two well-understood combinatorial optimization problems. ::: With this article, we contribute in four ways. First, we create a comprehensive dataset that comprises the performance data of 21 TTP algorithms on the full original set of 9720 TTP instances. Second, we define 55 characteristics for all TPP instances that can be used to select the best algorithm on a per-instance basis. Third, we use these algorithms and features to construct the first algorithm portfolios for TTP, clearly outperforming the single best algorithm. Finally, we study which algorithms contribute most to this portfolio.


title: A Technical Approach to the Energy Blockchain in Microgrids

abstract: The present paper considers some technical issues related to the “energy blockchain” paradigm applied to microgrids. In particular, what appears from the study is that the superposition of energy transactions in a microgrid creates a variation of the power losses in all the branches of the microgrid. Traditional power losses allocation in distribution systems takes into account only generators while, in this paper, a real-time attribution of power losses to each transaction involving one generator and one load node is done by defining some suitable indices. Besides, the presence of P–V nodes increases the level of reactive flows and provides a more complex technical perspective. For this reason, reactive power generation for voltage support at P–V nodes poses a further problem of reactive power flow exchange, which is worth of investigation in future works in order to define a possible way of remuneration. The experimental section of the paper considers a medium voltage microgrid and two different operational scenarios.


title: Adversarial Machine Learning in Malware Detection: Arms Race between Evasion Attack and Defense

abstract: Since malware has caused serious damages and evolving threats to computer and Internet users, its detection is of great interest to both anti-malware industry and researchers. In recent years, machine learning-based systems have been successfully deployed in malware detection, in which different kinds of classifiers are built based on the training samples using different feature representations. Unfortunately, as classifiers become more widely deployed, the incentive for defeating them increases. In this paper, we explore the adversarial machine learning in malware detection. In particular, on the basis of a learning-based classifier with the input of Windows Application Programming Interface (API) calls extracted from the Portable Executable (PE) files, we present an effective evasion attack model (named EvnAttack) by considering different contributions of the features to the classification problem. To be resilient against the evasion attack, we further propose a secure-learning paradigm for malware detection (named SecDefender), which not only adopts classifier retraining technique but also introduces the security regularization term which considers the evasion cost of feature manipulations by attackers to enhance the system security. Comprehensive experimental results on the real sample collections from Comodo Cloud Security Center demonstrate the effectiveness of our proposed methods.


title: Gamification in Education: A Systematic Mapping Study

abstract: Introduction Traditional schooling is perceived as ineffective and boring by many students. Although teachers continuously seek novel instructional approaches, it is largely agreed that today's schools face major problems around student motivation and engagement (Lee & Hammer, 2011). The use of educational games as learning tools is a promising approach due to the games' abilities to teach and the fact that they reinforce not only knowledge but also important skills such as problem-solving, collaboration, and communication. Games have remarkable motivational power; they utilize a number of mechanisms to encourage people to engage with them, often without any reward, just for the joy of playing and the possibility to win. Creating a highly engaging, full-blown instructional game however is difficult, time consuming, and costly (Kapp, 2012a), while typically targeting only a single set of learning objectives as chosen by the game designer. In addition, their effective classroom adoption requires certain technical infrastructure and appropriate pedagogical integration. As opposed to using elaborate games requiring a large amount of design and development efforts, the "gamification" approach suggests using game thinking and game design elements to improve learners' engagement and motivation. Gamification, defined by Deterding et al. (2011) as the use of game design elements in non-game contexts, is a fairly new and rapidly growing field. The concept of gamification is different from that of an educational or serious game. While the latter describes the design of full-fledged games for non-entertainment purposes, "gamified" applications merely employ elements of games. The term "gamification" is quite recent: According to (Deterding et al., 2011) its first documented use is in 2008 but it did not see widespread adoption before the second half of 2010. Nevertheless, the concept itself is not new. For example, badges and ranks have been long used in the military, in the early Soviet era, game elements were used by the Soviet Union leaders as a substitute for monetary incentives for performing at work, etc. In recent years gamification has seen rapid adoption in business, marketing, corporate management, and wellness and ecology initiatives. This is driven by its potential to shape users' behavior in a desirable direction. Loyalty programs such as the frequent-flyer programs, Foursquare, and Nike+ are often given as examples of successful gamified mass-market products. Stackoverflow.com provides another example in which users' reputations increase as they answer questions and receive votes for their answers. Online education sites such as codeacademy.com and khanacademy.org use game elements to better engage users. The more courses and lessons that users complete, the more badges they earn. Sites like eBay and Fitocracy use game elements to keep people engaged and to encourage friendly competition between users. Gamification is still rising in popularity. According to Gartner's Hype Cycle (Gartner, 2013), a research methodology that outlines an emerging technology's viability for commercial success, gamification is at the peak of the Hype Cycle in 2013, with an expectation for reaching the productivity plateau in five to ten years. This position, however, mainly reflects its use in business contexts. The penetration of the gamification trend in educational settings seems to be still climbing up to the top, as indicated by the amount and annual distribution of the reviewed works. This paper presents the results of a study of the published works on the application of gamification to education, which aims to shed light on the tendencies and emerging practices in this area. There are few literature reviews on gamification (see Xu, 2012; Hamari, Koivisto, & Sarsa, 2014; Nah, Zeng, Telaprolu, Ayyappa, & Eschenbrenner, 2014), with only the last one focusing on education. This study differs from the latter by presenting a thematic analysis instead of narrative summaries that focus on a qualitative review. …


title: Estimating 3D hand pose from a cluttered image

abstract: A method is proposed that can generate a ranked list of plausible three-dimensional hand configurations that best match an input image. Hand pose estimation is formulated as an image database indexing problem, where the closest matches for an input hand image are retrieved from a large database of synthetic hand images. In contrast to previous approaches, the system can function in the presence of clutter, thanks to two novel clutter-tolerant indexing methods. First, a computationally efficient approximation of the image-to-model chamfer distance is obtained by embedding binary edge images into a high-dimensional Euclidean space. Second, a general-purpose, probabilistic line matching method identifies those line segment correspondences between model and input images that are the least likely to have occurred by chance. The performance of this clutter tolerant approach is demonstrated in quantitative experiments with hundreds of real hand images.


title: Building a motor simulation de novo: Observation of dance by dancers

abstract: Research on action simulation identifies brain areas that are active while imagining or performing simple overlearned actions. Are areas engaged during imagined movement sensitive to the amount of actual physical practice? In the present study, participants were expert dancers who learned and rehearsed novel, complex whole-body dance sequences 5 h a week across 5 weeks. Brain activity was recorded weekly by fMRI as dancers observed and imagined performing different movement sequences. Half these sequences were rehearsed and half were unpracticed control movements. After each trial, participants rated how well they could perform the movement. We hypothesized that activity in premotor areas would increase as participants observed and simulated movements that they had learnt outside the scanner. Dancers’ ratings of their ability to perform rehearsed sequences, but not the control sequences, increased with training. When dancers observed and simulated another dancer’s movements, brain regions classically associated with both action simulation and action observation were active, including inferior parietal lobule, cingulate and supplementary motor areas, ventral premotor cortex, superior temporal sulcus and primary motor cortex. Critically, inferior parietal lobule and ventral premotor activity was modulated as a function of dancers’ ratings of their own ability to perform the observed movements and their motor experience. These data demonstrate that a complex motor resonance can be built de novo over 5 weeks of rehearsal. Furthermore, activity in premotor and parietal areas during action simulation is enhanced by the ability to execute a learned action irrespective of stimulus familiarity or semantic label.


title: Change-Point Detection in Time-Series Data Based on Subspace Identification

abstract: In this paper, we propose series of algorithms for detecting change points in time-series data based on subspace identification, meaning a geometric approach for estimating linear state-space models behind time-series data. Our algorithms are derived from the principle that the subspace spanned by the columns of an observability matrix and the one spanned by the subsequences of time-series data are approximately equivalent. In this paper, we derive a batch-type algorithm applicable to ordinary time-series data, i.e. consisting of only output series, and then introduce the online version of the algorithm and the extension to be available with input-output time-series data. We illustrate the effectiveness of our algorithms with comparative experiments using some artificial and real datasets.


title: Differentiated pacing on multiple paths to improve one-way delay estimations

abstract: Several works in the literature show that accurate estimations of the actual One-Way Delays (OWD) could improve the performance of various network protocol, such as Transport Control Protocol (TCP) throughput. With the emergence of multipath transport protocols like Multipath TCP or the Stream Control Transport Protocol (SCTP), the potential impact can be even higher. Indeed, as multipath transport protocols send data concurrently on heterogeneous paths, the knowledge of corresponding OWDs can greatly help mitigating packet arrival disorder. Theoretically, clock synchronization protocols between endpoints could ensure OWD knowledge, but their efficiency at the Internet scale is debatable. In practice, TCP uses the Round Trip Time (RTT) to take into account congestion or to compute retransmission timeouts, and the OWD is assumed to be half the RTT. However, numerous studies show that a majority of Internet connections experience latency asymmetry. In this paper, we propose a technique based on differential pacing over multiple paths to obtain an estimation of the difference in OWDs between the different paths, motivated by its strong utility for multipath transport protocols such as MPTCP and SCTP. Simulations show which are the interesting scenarios of application.


title: Landing Strategies in Honeybees and Applications to Uninhabited Airborne Vehicles

abstract: An application of insect visuomotor behavior to automatic control of landing is explored. Insects, being perhaps more reliant on image motion cites than mammals or higher vertebrates, are proving to be an excellent organism in which to investigate how information on optic flow is exploited to guide locomotion and navigation. We have observed how bees perform grazing landings on a flat surface and have deduced the algorithmic basis for the behavior. A smooth landing is achieved by a surprisingly simple and elegant strategy: image velocity is held constant as the surface is approached, thus automatically ensuring that flight speed is close to zero at touchdown. No explicit knowledge of flight speed or height above the ground is necessary. The feasibility, of this landing strategy was tested by implementation in a robotic gantry. We also outline our current efforts at exploring the applicability of this and related techniques to the guidance of uninhabited airborne vehicles (UAVs). Aspects of the algorithm were tested on a small UAV using real imagery, to control descent rate.


title: Chelation in Metal Intoxication

abstract: Chelation therapy is the preferred medical treatment for reducing the toxic effects of metals. Chelating agents are capable of binding to toxic metal ions to form complex structures which are easily excreted from the body removing them from intracellular or extracellular spaces. 2,3-Dimercaprol has long been the mainstay of chelation therapy for lead or arsenic poisoning, however its serious side effects have led researchers to develop less toxic analogues. Hydrophilic chelators like meso-2,3-dimercaptosuccinic acid effectively promote renal metal excretion, but their ability to access intracellular metals is weak. Newer strategies to address these drawbacks like combination therapy (use of structurally different chelating agents) or co-administration of antioxidants have been reported recently. In this review we provide an update of the existing chelating agents and the various strategies available for the treatment of heavy metals and metalloid intoxications.


title: Highly reliable energy-saving mac for wireless body sensor networks in healthcare systems

abstract: Wireless body sensor networks (BSNs) in healthcare systems operate under conflicting requirements. These are the maintenance of the desired reliability and message latency of data transmissions, while simultaneously maximizing battery lifetime of individual body sensors. In doing so, the characteristics of the entire system, including physical, medium access control (MAC), and application layers have to be considered. The aim of this paper is to develop a new MAC model for BSNs to fulfill all these specific rigorous requirements under realistic medical settings. For that purpose, a novel cross-layer fuzzy-rule scheduling algorithm and energy-aware radio activation policies are introduced. The main idea is to integrate a fuzzy-logic system in each body sensor to deal with multiple cross-layer input variables of diverse nature in an independent manner. By being autonomously aware of their current condition, body sensors are able to demand a "collision-free" time slot, whenever they consider it strictly required (e.g. high system packet delay or low body sensor residual battery lifetime). Similarly, they may refuse to transmit, if there is a bad channel link, thus permitting another body sensor to do so. This results in improving the system overall performance. The proposed MAC model is evaluated by computer simulations in terms of quality of service and energy consumption under specific healthcare scenarios.


title: Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques

abstract: The many different techniques for maximum power point tracking of photovoltaic (PV) arrays are discussed. The techniques are taken from the literature dating back to the earliest methods. It is shown that at least 19 distinct methods have been introduced in the literature, with many variations on implementation. This paper should serve as a convenient reference for future work in PV power generation.


title: Semiautomated skeletonization of the pulmonary arterial tree in micro-CT images

abstract: We present a simple and robust approach that utilizes planar images at different angular rotations combined with unfiltered back-projection to locate the central axes of the pulmonary arterial tree. Three-dimensional points are selected interactively by the user. The computer calculates a sub- volume unfiltered back-projection orthogonal to the vector connecting the two points and centered on the first point. Because more x-rays are absorbed at the thickest portion of the vessel, in the unfiltered back-projection, the darkest pixel is assumed to be the center of the vessel. The computer replaces this point with the newly computer-calculated point. A second back-projection is calculated around the original point orthogonal to a vector connecting the newly-calculated first point and user-determined second point. The darkest pixel within the reconstruction is determined. The computer then replaces the second point with the XYZ coordinates of the darkest pixel within this second reconstruction. Following a vector based on a moving average of previously determined 3- dimensional points along the vessel's axis, the computer continues this skeletonization process until stopped by the user. The computer estimates the vessel diameter along the set of previously determined points using a method similar to the full width-half max algorithm. On all subsequent vessels, the process works the same way except that at each point, distances between the current point and all previously determined points along different vessels are determined. If the difference is less than the previously estimated diameter, the vessels are assumed to branch. This user/computer interaction continues until the vascular tree has been skeletonized.


title: Dermatoscopy of facial actinic keratosis, intraepidermal carcinoma, and invasive squamous cell carcinoma: A progression model

abstract: Background Little is known about the dermoscopic features of keratinocyte skin cancer. Objective We sought to determine the dermoscopic features of facial actinic keratosis (AK), intraepidermal carcinoma (IEC), moderately to poorly differentiated invasive squamous cell carcinoma (SCC), and well-differentiated SCC of the keratoacanthoma type. Methods This was a retrospective analysis of dermoscopic images of histopathologically diagnosed keratinocyte skin cancer. Results A total of 243 (70 AK, 71 IEC, 78 SCC, and 24 keratoacanthomas) tumors of the face from 243 patients (mean age: 71.1 years; range: 44-94 years) were analyzed. The majority of patients had a fair skin type, history of melanoma or nonmelanoma skin cancer, and multiple AK. A red pseudonetwork was significantly associated with AK ( P P P Limitations The retrospective design of our study and the lack of assessment of sensitivity and specificity of the dermoscopic criteria are limitations. Conclusions Based on our findings we propose a progression model of facial AK developing into IEC and invasive SCC.


title: Nurse Engagement in Shared Governance and Patient and Nurse Outcomes

abstract: OBJECTIVE: The objectives of this study were to examine differences in nurse engagement in shared governance across hospitals and to determine the relationship between nurse engagement and patient and nurse outcomes. BACKGROUND: There is little empirical evidence examining the relationship between shared governance and patient outcomes. METHODS: A secondary analysis of linked cross-sectional data was conducted using nurse, hospital, and Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey data. RESULTS: Engagement varied widely across hospitals. In hospitals with greater levels of engagement, nurses were significantly less likely to report unfavorable job outcomes and poor ratings of quality and safety. Higher levels of nurse engagement were associated with higher HCAHPS scores. CONCLUSIONS: A professional practice environment that incorporates shared governance may serve as a valuable intervention for organizations to promote optimal patient and nurse outcomes.


title: Critical Design Research and Information Technology: Searching for Empowering Design

abstract: Critical design arouses increasing interest in Human Computer Interaction (HCI). Critical Design is a relatively novel and a contentious design approach within this stream, while also other critical design approaches are emerging in HCI. Information systems research, then again, has been fascinated with critical research for decades and strongly integrated the critical lens into studies on systems development and use. However, critical information systems research is weak in the design practice -- very few studies actually involve design. We argue that combining the forces of these various critical traditions, an empowering approach to design can be developed. A categorization of critical design approaches is proposed and fascinating paths for future work are identified. Particularly we call for future developments on critical design research along two lines: expert-led critical design better integrating the tenets of the critical research tradition and user-led critical design truly advocating the empowerment of the power-weak.


A Survey of Unmanned Aerial Vehicles ( UAV ) for Traffic Surveillance


title: The Impact of Business Intelligence on Healthcare Delivery in the USA

abstract: Introduction To improve healthcare quality, safety, and efficiency is an economic and national necessity. The role of technology to ensure healthcare quality and control cost is an ongoing debate within the industry and a subject of interest to researchers. Delivering quality healthcare requires the integration of patient health information from many different sources and availing a diverse set of users; health providers must be able to readily access and use the right information at the right time and patients should be able to access their health information in order to be able to self-manage their conditions. Supporters of the adoption of advanced technology in healthcare consider it as an opportunity not only to enhance the quality of health services, but also transparency of economic activities and the availability of information in real time (Mettler, 2009). As technology has enhanced diagnosis and treatment options and since lifesaving medicines are entering the market at an increasing rate, life expectancy is on the rise. Healthcare organizations are investing millions in computer systems, diagnostic technology, and preventive care programs in an attempt to meet healthcare quality goals. These developments, however, come with a huge price tag. Health care costs now consume nearly 18 percent of the U.S. GDP (Ramsey, Ganz, Shankaran, Peppercorn, & Emanuel, 2013). Payers face difficulties compensating providers for high-cost treatments made possible by advances in technology. Claims that are inflated as well as outright fraudulent are intensifying the problem. The payers and providers in the healthcare industry, public and private, are looking into technology to reduce costs, while keeping the quality care intact. The predicament doesn't end with the notion of quality versus cost; the healthcare industry is experiencing more scrutiny and complexity than any other single industry in modern history. Health providers and the affiliates have to understand and respond to privacy laws and information security. In addition, a vast range of factors such as health care practice regulations, patient records and requirements, practice and staff management, training, financial stability, facilities and equipment management influence the holistic view of quality healthcare. Another force altering the current condition of healthcare in the United States is the passing of the Patient Protection and Affordable Care Act (PPACA). Healthcare industry is under pressure to reduce costs and better manage care. Burke and Ingraham (2008) note that healthcare in the U.S. is at the point of colossal change. The entire industry is struggling with the notion of management of quality and cost metrics. Intensified focus on compliance with evidence-based care protocols and, a staggering number of reimbursement programs affect revenue and the ability to compete. Healthcare industry executives must evaluate an increasing amount of information to best assess their organization's wellbeing and future. Furthermore, data overload is a common problem for many care providers and executive teams, who are grappling with too much information and looking to find ways to simplify acquiring knowledge from raw data (Byrnes, 2012). Coddington (2012) argues that decision-support capabilities allow collecting data from multiple sources, such as cost accounting systems, electronic health records and other sources, and make them available to physicians and other users. He suggests that a balance between cost control and the other priorities of healthcare organizations is necessary to provide quality care. The most important issue surrounding quality healthcare is the development of measurement goals to find validated metrics. Since usually high quality is perceived to be correlated with high cost, a statement such as "reduce costs, while keeping the quality care intact," sounds paradoxical. However, Process improvement initiatives facilitated by business intelligence solutions constitute a cost-effective option. …


title: Control-flow checking by software signatures

abstract: This paper presents a new signature monitoring technique, CFCSS (control flow checking by software signatures); CFCSS is a pure software method that checks the control flow of a program using assigned signatures. An algorithm assigns a unique signature to each node in the program graph and adds instructions for error detection. Signatures are embedded in the program during compilation time using the constant field of the instructions and compared with run-time signatures when the program is executed. Another algorithm reduces the code size and execution time overhead caused by checking instructions in CFCSS. A "branching fault injection experiment" was performed with benchmark programs. Without CFCSS, an average of 33.7 % of the injected branching faults produced undetected incorrect outputs; however, with CFCSS, only 3.1 % of branching faults produced undetected incorrect outputs. Thus it is possible to increase error detection coverage for control flow errors by an order of magnitude using CFCSS. The distinctive advantage of CFCSS over previous signature monitoring techniques is that CFCSS is a pure software method, i.e., it needs no dedicated hardware such as a watchdog processor for control flow checking. A watchdog task in multitasking environment also needs no extra hardware, but the advantage of CFCSS over a watchdog task is that CFCSS can be used even when the operating system does not support multitasking.


title: UTOPIAN: User-Driven Topic Modeling Based on Interactive Nonnegative Matrix Factorization

abstract: Topic modeling has been widely used for analyzing text document collections. Recently, there have been significant advancements in various topic modeling techniques, particularly in the form of probabilistic graphical modeling. State-of-the-art techniques such as Latent Dirichlet Allocation (LDA) have been successfully applied in visual text analytics. However, most of the widely-used methods based on probabilistic modeling have drawbacks in terms of consistency from multiple runs and empirical convergence. Furthermore, due to the complicatedness in the formulation and the algorithm, LDA cannot easily incorporate various types of user feedback. To tackle this problem, we propose a reliable and flexible visual analytics system for topic modeling called UTOPIAN (User-driven Topic modeling based on Interactive Nonnegative Matrix Factorization). Centered around its semi-supervised formulation, UTOPIAN enables users to interact with the topic modeling method and steer the result in a user-driven manner. We demonstrate the capability of UTOPIAN via several usage scenarios with real-world document corpuses such as InfoVis/VAST paper data set and product review data sets.


title: Explaining the Decline in Coronary Heart Disease Mortality in England and Wales Between 1981 and 2000

abstract: Background— Coronary heart disease mortality rates have been decreasing in the United Kingdom since the 1970s. Our study aimed to examine how much of the decrease in England and Wales between 1981 and 2000 could be attributed to medical and surgical treatments and how much to changes in cardiovascular risk factors. Methods and Results— The IMPACT mortality model was used to combine and analyze data on uptake and effectiveness of cardiological treatments and risk factor trends in England and Wales. The main data sources were published trials and meta-analyses, official statistics, clinical audits, and national surveys. Between 1981 and 2000, coronary heart disease mortality rates in England and Wales decreased by 62% in men and 45% in women 25 to 84 years old. This resulted in 68 230 fewer deaths in 2000. Some 42% of this decrease was attributed to treatments in individuals (including 11% to secondary prevention, 13% to heart failure treatments, 8% to initial treatments of acute myocardial infarction, and ...


title: On the usefulness of query features for learning to rank

abstract: Learning to rank studies have mostly focused on query-dependent and query-independent document features, which enable the learning of ranking models of increased effectiveness. Modern learning to rank techniques based on regression trees can support query features, which are document-independent, and hence have the same values for all documents being ranked for a query. In doing so, such techniques are able to learn sub-trees that are specific to certain types of query. However, it is unclear which classes of features are useful for learning to rank, as previous studies leveraged anonymised features. In this work, we examine the usefulness of four classes of query features, based on topic classification, the history of the query in a query log, the predicted performance of the query, and the presence of concepts such as persons and organisations in the query. Through experiments on the ClueWeb09 collection, our results using a state-of-the-art learning to rank technique based on regression trees show that all four classes of query features can significantly improve upon an effective learned model that does not use any query feature.


title: PatternNet: Visual Pattern Mining with Deep Neural Network

abstract: Visual patterns represent the discernible regularity in the visual world. They capture the essential nature of visual objects or scenes. Understanding and modeling visual patterns is a fundamental problem in visual recognition that has wide ranging applications. In this paper, we study the problem of visual pattern mining and propose a novel deep neural network architecture called PatternNet for discovering these patterns that are both discriminative and representative. The proposed PatternNet leverages the filters in the last convolution layer of a convolutional neural network to find locally consistent visual patches, and by combining these filters we can effectively discover unique visual patterns. In addition, PatternNet can discover visual patterns efficiently without performing expensive image patch sampling, and this advantage provides an order of magnitude speedup compared to most other approaches. We evaluate the proposed PatternNet subjectively by showing randomly selected visual patterns which are discovered by our method and quantitatively by performing image classification with the identified visual patterns and comparing our performance with the current state-of-the-art. We also directly evaluate the quality of the discovered visual patterns by leveraging the identified patterns as proposed objects in an image and compare with other relevant methods. Our proposed network and procedure, PatterNet, is able to outperform competing methods for the tasks described.


title: Phomoxanthone A--From Mangrove Forests to Anticancer Therapy.

abstract: Mangrove associated endophytes are treasure chests for bioprospecting especially in light of the need for new anticancer leads that are necessary to overcome drug resistance of cancer cells. This review highlights the potent anti-tumour compound phomoxanthone A (PXA), which represents a tetrahydroxanthone atropisomer derived from the mangrove-associated fungus Phomopsis longicolla. PXA displayed strong anti-tumour activity when tested against a panel of solid (including cisplatin resistant) tumour cell lines or of blood cancer cell lines with IC50 values in the submicromolar range whereas it was up to 100 folds less active against peripheral blood mononuclear cells (PBMC) from healthy donors. The anti-tumour activity of PXA was demonstrated to be due to an induction of caspase 3 dependent apoptosis. At the same time PXA was shown to activate immune cells such as murine T-lymphocytes, NK cells and macrophages which might help in fighting resistance during cancer chemotherapy. Structure activity studies that involved several naturally occurring as well as semisynthetic derivatives of PXA indicated the position of the biaryl linkage and the acetyl substituents as structural features that are important for the activity of this natural product.


title: Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention

abstract: This paper describes a novel text-to-speech (TTS) technique based on deep convolutional neural networks (CNN), without any recurrent units. Recurrent neural network (RNN) has been a standard technique to model sequential data recently, and this technique has been used in some cutting-edge neural TTS techniques. However, training RNN component often requires a very powerful computer, or very long time typically several days or weeks. Recent other studies, on the other hand, have shown that CNN-based sequence synthesis can be much faster than RNN-based techniques, because of high parallelizability. The objective of this paper is to show an alternative neural TTS system, based only on CNN, that can alleviate these economic costs of training. In our experiment, the proposed Deep Convolutional TTS can be sufficiently trained only in a night (15 hours), using an ordinary gaming PC equipped with two GPUs, while the quality of the synthesized speech was almost acceptable.


title: Efficient mining of emerging patterns: discovering trends and differences

abstract: We introduce a new kind of patterns, called emerging patterns (EPs), for knowledge discovery from databases. EPs are defined as itemsets whose supports increase significantly from one dataset to another. EPs can capture emerging trends in timestamped databases, or useful contrasts between data classes. EPs have been proven useful: we have used them to build very powerful classifiers, which are more accurate than C4.5 and CBA, for many datasets. We believe that EPs with low to medium support, such as 1%-20%, can give useful new insights and guidance to experts, in even “well understood” applications. The efficient mining of EPs is a challenging problem, since (i) the Apriori property no longer holds for EPs, and (ii) there are usually too many candidates for high dimensional databases or for small support thresholds such as 0.5%. Naive algorithms are too costly. To solve this problem, (a) we promote the description of large collections of itemsets using their concise borders (the pair of sets of the minimal and of the maximal itemsets in the collections). (b) We design EP mining algorithms which manipulate only borders of collections (especially using our multiborder-differential algorithm), and which represent discovered EPs using borders. All EPs satisfying a constraint can be efficiently discovered by our border-based algorithms, which take the borders, derived by Max-Miner, of large itemsets as inputs. In our experiments on large and high dimensional datasets including the US census and Mushroom datasets, many EPs, including some with large cardinality, are found quickly. We also give other algorithms for discovering general or special types of EPs. Permission to make digital or hard copies of all or part ol‘this work Iht personal or classroom USC is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the lirst page. To copy otherwise, to republish, to post on servers or to redistribute to lists. requires prior specific permission and/or a fee. KDD-99 San Diego CA USA Copyright ACM 1999 l-58113-143-7/99/08...$5.00 Jinyan Li Department of CSSE The University of Melbourne jyli@cs.mu.oz.au


title: Alert correlation in intrusion detection: Combining AI-based approaches for exploiting security operators' knowledge and preferences

abstract: Alert correlation is a crucial problem for monitoring and securing computer networks. It consists in analyzing the alerts triggered by intrusion detection systems (IDSs) and other security related tools in order to detect complex attack plans, discover false alerts, etc. The huge amounts of alerts raised continuously by IDSs and the impossibility for security operators to efficiently analyze them requires tools for eliminating false and redundant alerts on the one hand and prioritize them according the detected activities’ dangerousness and preferences of the analysts on the other hand. In this paper, we describe an architecture that combines AI-based approaches for representing and reasoning with security operators’ knowledge and preferences. Moreover, this architecture allows to combines experts’ knowledge with machine learning and classifier based tools. This prototype collects the alerts raised by security related tools and analyzes them automatically. We first propose formalisms for representing both background and contextual knowledge on the monitored network, known attacks and vulnerabilities. We then propose another logic-based formalism for representing and reasoning with operators’ preferences regarding the events and alerts they want analyze in priority. We after that propose probabilistic models for detecting and predicting attack plans and severe attacks. Finally, we provide further discussions and future work directions.


title: Staphylococcus aureus colonisation and its relationship with skin and soft tissue infection in New Zealand children

abstract: New Zealand children suffer from high rates of skin and soft tissue infection (SSTI). Staphylococcus aureus colonisation is known to increase the risk of nosocomial infection. We aimed to determine whether S. aureus colonisation also increased the risk of community-onset SSTI. This study, performed within the Growing Up in New Zealand cohort, used interview and administrative data, and bacterial culture results from the nose, throat, and skin swabs collected at 4½ years of age. Multivariable log-binomial regression was used to derive adjusted risk ratios. S. aureus was isolated from 2225/5126 (43.4%) children. SSTI affected 1509/5126 (29.4%) children before age five. S. aureus colonisation at any site was associated with SSTI (aRR = 1.09, 95%CI 1.01–1.19), particularly in the year prior to swab collection (aRR = 1.18, 95%CI 1.02–1.37). The strongest association was between skin colonisation and SSTI within the year prior to swab collection (aRR = 1.47, 95%CI 1.14–1.84). Socioeconomic and ethnic variables remained independent determinants of SSTI. S. aureus colonisation was associated with an increased risk of community-onset SSTI. Socioeconomic and ethnic factors and eczema had independent effects on SSTI risk. Interventions which reduce the prevalence of S. aureus colonisation may be expected to reduce the incidence of community-onset SSTI.


title: Fredholm Multiple Kernel Learning for Semi-Supervised Domain Adaptation

abstract: As a fundamental constituent of machine learning, domain adaptation generalizes a learning model from a source domain to a different (but related) target domain. In this paper, we focus on semi-supervised domain adaptation and explicitly extend the applied range of unlabeled target samples into the combination of distribution alignment and adaptive classifier learning. Specifically, our extension formulates the following aspects in a single optimization: 1) learning a cross-domain predictive model by developing the Fredholm integral based kernel prediction framework; 2) reducing the distribution difference between two domains; 3) exploring multiple kernels to induce an optimal learning space. Correspondingly, such an extension is distinguished with allowing for noise resiliency, facilitating knowledge transfer and analyzing diverse data characteristics. It is emphasized that we prove the differentiability of our formulation and present an effective optimization procedure based on the reduced gradient, guaranteeing rapid convergence. Comprehensive empirical studies verify the effectiveness of the proposed method.


title: Back Propagation is Sensitive to Initial Conditions

abstract: This paper explores the effect of initial weight selection on feed-forward networks learning simple functions with the back-propagation technique. We first demonstrate, through the use of Monte Carlo techniques, that the magnitude of the initial condition vector (in weight space) is a very significant parameter in convergence time variability. In order to further understand this result, additional deterministic experiments were performed. The results of these experiments demonstrate the extreme sensitivity of back propagation to initial weight configuration.


title: Micro suction cup array for wet/dry adhesion

abstract: We propose a micro suction cup array for adhesion to both dry and wet surfaces. We measured the peel-off forces of a PDMS micro suction cup array, a PDMS flat-tip micro structure array and a flat PDMS pad from both wet and dry glass surfaces. When the glass surface was wet, the peel-off forces of a PDMS flat-tip micro pattern and a flat PDMS pad decreased by more than 1.7 times and 7.0 times, respectively. On the other hand, peel-off forces of a PDMS micro suction cup array increased by over 1.1 times when the glass surface was wet. Also, in both wet and dry conditions, an array of PDMS micro suction cup adhered stronger to a glass surface than a PDMS flat-tip micro pattern and a flat PDMS pad. Furthermore, we demonstrated that the adhesion of a suction cup array can be enhanced by miniaturizing the size of the cups.


title: Advanced topical formulations (ATF).

abstract: Topical formulations aim to target the skin for a variety of cosmetic, protective or therapeutic needs. Despite the use of creams and ointments over the millennia, the bioavailability of actives from topical preparations remains quite low, often not exceeding 1-2% of the applied dose. In this review we examine the reasons underlying the poor performance of topical preparations. We also outline a rational approach, based on Fick's laws of diffusion, to develop advanced topical formulations. Methodologies which are currently used in research and development are critically examined and the importance of understanding the fate of the vehicle as well as the active is emphasised. Advanced topical formulation development will also be facilitated by emerging and sophisticated analytical techniques that are able to probe real time delivery of actives to the skin. A good understanding of the underlying physical chemistry of both the formulation and the skin is crucial in the development of optimised topical products.


title: Feedback control real-time scheduling: Framework, modeling, and algorithms

abstract: This paper presents a feedback control real-time scheduling (FCS) framework for adaptive real-time systems. An advantage of the FCS framework is its use of feedback control theory (rather than ad hoc solutions) as a scientific underpinning. We apply a control theory based methodology to systematically design FCS algorithms to satisfy the transient and steady state performance specifications of real-time systems. In particular, we establish dynamic models of real-time systems and develop performance analyses of FCS algorithms, which are major challenges and key steps for the design of control theory based adaptive real-time systems. We also present a FCS architecture that allows plug-ins of different real-time scheduling policies and QoS optimization algorithms. Based on our framework, we identify different categories of real-time applications where different FCS algorithms should be applied. Performance evaluation results demonstrate that our analytically tuned FCS algorithms provide robust transient and steady state performance guarantees for periodic and aperiodic tasks even when the task execution times vary by as much as 100% from the initial estimate.


title: Practical LR error recovery

abstract: We present a practical, language independent mechanism for error recovery in LR parsers. The method is easy to implement in existing parser generators. It uses only the normal parse tables and a small amount of symbol cost information. It is possible to use compressed parse tables with the method, as well as other LR augmentations such as precedence and associativity declarations. The method is efficient enough to be used in environments where space and time are at a premium. Our method utilizes a number of previous error recovery ideas: forward moves in the input after detection of errors to incorporate right context, recovery choice based on weighted costs, and the use of special productions to indicate major productions in the grammar for error recovery. The method also makes use of semantic information in choosing between possible recovery actions. We have implemented the method in a production Pascal translator which has been in use for instruction at a number of universities for two years. We report here on measurements of our system on a standard data base of Pascal programs with errors.


Acrania-Exencephaly Associated with Amniotic Band Syndrome in Newborn of a Post-Bariatric Pregnancy : Maternal Disabsorption ?


title: Automated industry classification with deep learning

abstract: In this paper, we present a novel technique for industry classification. Leveraging EverString's API to construct a database of companies labeled with the industries to which they belong, we train a deep neural network to predict the industries of novel companies. We examine the capacity of our model to predict six-digit NAICS codes, as well as the ability of our model architecture to adapt to other industry segmentation schemas. Additionally, we investigate the ability of our model to generalize despite the presence of noise in the labels in our training set. Finally, we explore the possibility of increasing predictive precision by thresholding based on the confidence scores that our model outputs along with its predictions. We find that our approach yields six-digit NAICS code predictions that surpass the precision of gold-standard databases.


title: Three-dimensional compression scheme based on wavelet transform

abstract: In this paper, a 3D compression method based on separable wavelet transform is discussed in detail. The most commonly used digital modalities generate multiple slices in a single examination, which are normally anatomically or physiologically correlated to each other. 3D wavelet compression methods can achieve more efficient compression by exploring the correlation between slices. The first step is based on a separable 3D wavelet transform. Considering the difference between pixel distances within a slice and those between slices, one biorthogonal Antoninin filter bank is applied within 2D slices and a second biorthogonal Villa4 filter bank on the slice direction. Then, S+P transform is applied in the low-resolution wavelet components and an optimal quantizer is presented after analysis of the quantization noise. We use an optimal bit allocation algorithm, which, instead of eliminating the coefficients of high-resolution components in smooth areas, minimizes the system reconstruction distortion at a given bit-rate. Finally, to remain high coding efficiency and adapt to different properties of each component, a comprehensive entropy coding method is proposed, in which arithmetic coding method is applied in high-resolution components and adaptive Huffman coding method in low-resolution components. Our experimental results are evaluated by several image measures and our 3D wavelet compression scheme is proved to be more efficient than 2D wavelet compression.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.


title: A modular 1mm3 die-stacked sensing platform with optical communication and multi-modal energy harvesting

abstract: Wireless sensor nodes have many compelling applications such as smart buildings, medical implants, and surveillance systems. However, existing devices are bulky, measuring >1cm3, and they are hampered by short lifetimes and fail to realize the “smart dust” vision of [1]. Smart dust requires a mm3-scale, wireless sensor node with perpetual energy harvesting. Recently two application-specific implantable microsystems [2][3] demonstrated the potential of a mm3-scale system in medical applications. However, [3] is not programmable and [2] lacks a method for re-programming or re-synchronizing once encapsulated. Other practical issues remain unaddressed, such as a means to protect the battery during the time period between system assembly and deployment and the need for flexible design to enable use in multiple application domains. To this end, we propose a 1.0mm3 general-purpose heterogeneous sensor node platform with a stackable multi-layer structure that includes a new, ultra-low power I2C (Inter-Integrated Circuit) interface for inter-layer communication. The system has an ultra-low-power optical wakeup receiver and GOC (Global Optical Communication), which enables re-programming and synchronization. It also includes an ultra-low power PMU (Power Management Unit) with BOD (BrownOut Detector) to prevent processor malfunctions and battery damage. The BOD also controls a POR (Power-On Reset) module in other layers to enable a proper reset sequence. Image and temperature sensors are implemented, but the modularity of the system allows end-users to easily replace or add layers to incorporate specific circuits in appropriate technologies as needed.


title: DLR-Hand II: next generation of a dextrous robot hand

abstract: This paper outlines the 2nd generation of multisensory hand design at DLR, based on the results of the DLR Hand I we analysed. An open skeleton structure for better maintenance with semi-shell housing and the new automatically reconfigurable palm have been equipped with more powerful actuators to reach 30 N on the fingertip. The newly designed sensors as the 6-DOF fingertip force torque sensor, the integrated electronics and the new communication architecture with a reduction of cabling to the hand to only 12 lines, are outlined. The Cartesian impedance control of all the fingers completes the new 13-DOF hand.


title: Predicting Cyber Events by Leveraging Hacker Sentiment

abstract: Recent high-profile cyber attacks exemplify why organizations need better cyber defenses. Cyber threats are hard to accurately predict because attackers usually try to mask their traces. However, they often discuss exploits and techniques on hacking forums. The community behavior of the hackers may provide insights into groups' collective malicious activity. We propose a novel approach to predict cyber events using sentiment analysis. We test our approach using cyber attack data from 2 major business organizations. We consider 3 types of events: malicious software installation, malicious destination visits, and malicious emails that surpassed the target organizations' defenses. We construct predictive signals by applying sentiment analysis on hacker forum posts to better understand hacker behavior. We analyze over 400K posts generated between January 2016 and January 2018 on over 100 hacking forums both on surface and Dark Web. We find that some forums have significantly more predictive power than others. Sentiment-based models that leverage specific forums can outperform state-of-the-art deep learning and time-series models on forecasting cyber attacks weeks ahead of the events.


title: An Improved Construction for Counting Bloom Filters

abstract: A counting Bloom filter (CBF) generalizes a Bloom filter data structure so as to allow membership queries on a set that can be changing dynamically via insertions and deletions. As with a Bloom filter, a CBF obtains space savings by allowing false positives. We provide a simple hashing-based alternative based on d-left hashing called a d-left CBF (dlCBF). The dlCBF offers the same functionality as a CBF, but uses less space, generally saving a factor of two or more. We describe the construction of dlCBFs, provide an analysis, and demonstrate their effectiveness experimentally.


title: Natural selection and animal personality

abstract: Summary Recent progress has been made on the study of personality in animals, both from a mechanistic and a functional perspective. While we start knowing more about the proximal mechanisms responsible for the consistent differences in behaviour between individuals in a population, little is known yet about the relationship between the phenotypic distribution of personality traits, or combinations of traits, and fitness. Here we provide an overview of the available literature on the fitness consequences of personality traits in natural populations. We start by a description of two case studies that have examined the role of natural selection on personality traits in the wild (i.e., the great tit, Parus major and bighorn sheep, Ovis canadensis), and review other studies that have reported some links between personality traits and fitness indices, in a large variety of animal species. We continue by outlining both direct approaches (i.e., measuring correlational selection on personality trait combinations) and indirect approaches (i.e., comparing correlations between personality traits within and between populations) to study suites of correlated traits from an adaptive perspective. This review, we hope, will be able to stimulate the use of the phenotypic selection analysis applied to the study of selection on personality traits in animals.


title: Data driven image models through continuous joint alignment

abstract: This paper presents a family of techniques that we call congealing for modeling image classes from data. The idea is to start with a set of images and make them appear as similar as possible by removing variability along the known axes of variation. This technique can be used to eliminate "nuisance" variables such as affine deformations from handwritten digits or unwanted bias fields from magnetic resonance images. In addition to separating and modeling the latent images - i.e., the images without the nuisance variables - we can model the nuisance variables themselves, leading to factorized generative image models. When nuisance variable distributions are shared between classes, one can share the knowledge learned in one task with another task, leading to efficient learning. We demonstrate this process by building a handwritten digit classifier from just a single example of each class. In addition to applications in handwritten character recognition, we describe in detail the application of bias removal from magnetic resonance images. Unlike previous methods, we use a separate, nonparametric model for the intensity values at each pixel. This allows us to leverage the data from the MR images of different patients to remove bias from each other. Only very weak assumptions are made about the distributions of intensity values in the images. In addition to the digit and MR applications, we discuss a number of other uses of congealing and describe experiments about the robustness and consistency of the method.


title: A miniaturized coplanar waveguide band pass filter using novel single split-ring resonators

abstract: In this letter, a novel structure of a single split-ring resonator (S-SRR) is presented and discussed. Two metallic vias are combined with the S-SRR to improve the performance. A coplanar waveguide (CPW) loaded with the new structures and series gaps, which works as a band pass filter is simulated and fabricated. The proposed band pass filter reaches a high level of compactness, which provides a good choice for designing composite right-left handed (CRLH) transmission lines.


title: Texture classification and segmentation using wavelet frames

abstract: This paper describes a new approach to the characterization of texture properties at multiple scales using the wavelet transform. The analysis uses an overcomplete wavelet decomposition, which yields a description that is translation invariant. It is shown that this representation constitutes a tight frame of l/sub 2/ and that it has a fast iterative algorithm. A texture is characterized by a set of channel variances estimated at the output of the corresponding filter bank. Classification experiments with l/sub 2/ Brodatz textures indicate that the discrete wavelet frame (DWF) approach is superior to a standard (critically sampled) wavelet transform feature extraction. These results also suggest that this approach should perform better than most traditional single resolution techniques (co-occurrences, local linear transform, and the like). A detailed comparison of the classification performance of various orthogonal and biorthogonal wavelet transforms is also provided. Finally, the DWF feature extraction technique is incorporated into a simple multicomponent texture segmentation algorithm, and some illustrative examples are presented. >


Towards the calibration of translation errors: The why and the how


title: Diurnal Surface-Pressure Variations over the Continental United States and the Influence of Sea Level Reduction

abstract: Abstract This paper presents the results of a detailed study of the diurnal and semidiurnal pressure variations at the surface across the continental United States. Using a larger dataset than preceding studies and a variety of analysis approaches, the spatial and temporal variations in the diurnal and semidiurnal components of station and sea level pressure are described, and the influence of current pressure reduction techniques on the characteristics of sea level pressure is evaluated. It is shown that the temperature-averaging scheme currently used in sea level pressure reduction creates a bogus semidiurnal signal in sea level pressure over high terrain. This paper also describes the summertime mesoscale evolution of station pressure over the northeast United States and presents 3-h pressure-change maps for the continental United States during the summer and winter seasons.


title: Discovering Latent Information By Spreading Activation Algorithm For Document Retrieval

abstract: Syntactic search relies on keywords contained in a query to find suitable documents. So, documents that do not contain the keywords but contain information related to the query are not retrieved. Spreading activation is an algorithm for finding latent information in a query by exploiting relations between nodes in an associative network or semantic network. However, the classical spreading activation algorithm uses all relations of a node in the network that will add unsuitable information into the query. In this paper, we propose a novel approach for semantic text search, called query-oriented-constrained spreading activation that only uses relations relating to the content of the query to find really related information. Experiments on a benchmark dataset show that, in terms of the MAP measure, our search engine is 18.9% and 43.8% respectively better than the syntactic search and the search using the classical constrained spreading activation.


Drag, Drop, and Clone: An Interactive Interface for Surface Composition


title: IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models

abstract: This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval focusing on predicting relevancy given a query-document pair. We propose a game theoretical minimax game to iteratively optimise both models. On one hand, the discriminative model, aiming to mine signals from labelled and unlabelled data, provides guidance to train the generative model towards fitting the underlying relevance distribution over documents given the query. On the other hand, the generative model, acting as an attacker to the current discriminative model, generates difficult examples for the discriminative model in an adversarial way by minimising its discrimination objective. With the competition between these two models, we show that the unified framework takes advantage of both schools of thinking: (i) the generative model learns to fit the relevance distribution over documents via the signals from the discriminative model, and (ii) the discriminative model is able to exploit the unlabelled data selected by the generative model to achieve a better estimation for document ranking. Our experimental results have demonstrated significant performance gains as much as 23.96% on Precision@5 and 15.50% on MAP over strong baselines in a variety of applications including web search, item recommendation, and question answering.


title: Decoding Decoders: Finding Optimal Representation Spaces for Unsupervised Similarity Tasks

abstract: Experimental evidence indicates that simple models outperform complex deep networks on many unsupervised similarity tasks. We provide a simple yet rigorous explanation for this behaviour by introducing the concept of an optimal representation space, in which semantically close symbols are mapped to representations that are close under a similarity measure induced by the model's objective function. In addition, we present a straightforward procedure that, without any retraining or architectural modifications, allows deep recurrent models to perform equally well (and sometimes better) when compared to shallow models. To validate our analysis, we conduct a set of consistent empirical evaluations and introduce several new sentence embedding models in the process. Even though this work is presented within the context of natural language processing, the insights are readily applicable to other domains that rely on distributed representations for transfer tasks.


title: TurboPixels: Fast Superpixels Using Geometric Flows

abstract: We describe a geometric-flow-based algorithm for computing a dense oversegmentation of an image, often referred to as superpixels. It produces segments that, on one hand, respect local image boundaries, while, on the other hand, limiting undersegmentation through a compactness constraint. It is very fast, with complexity that is approximately linear in image size, and can be applied to megapixel sized images with high superpixel densities in a matter of minutes. We show qualitative demonstrations of high-quality results on several complex images. The Berkeley database is used to quantitatively compare its performance to a number of oversegmentation algorithms, showing that it yields less undersegmentation than algorithms that lack a compactness constraint while offering a significant speedup over N-cuts, which does enforce compactness.


title: The topology of dark networks

abstract: Knowing the structure of criminal and terrorist networks could provide the technical insight needed to disrupt their activities.


title: A 28‐Year‐Old Woman With Branching Opacity and Chest Pain

abstract: A 28-year-old female patient presented through her primary care physician with symptoms of atypical chest pain and chronic cough. Her pain was described as pleuritic and intermittently radiating to the right arm. Her medical history was significant for recurrent respiratory infections, gastritis, and a left ovarian cyst treated with ipsilateral salpingo-oophorectomy. She denied any history of smoking, known lung disease, or extrapulmonary infections.


title: McTwo: a two-step feature selection algorithm based on maximal information coefficient

abstract: BackgroundHigh-throughput bio-OMIC technologies are producing high-dimension data from bio-samples at an ever increasing rate, whereas the training sample number in a traditional experiment remains small due to various difficulties. This “large p, small n” paradigm in the area of biomedical “big data” may be at least partly solved by feature selection algorithms, which select only features significantly associated with phenotypes. Feature selection is an NP-hard problem. Due to the exponentially increased time requirement for finding the globally optimal solution, all the existing feature selection algorithms employ heuristic rules to find locally optimal solutions, and their solutions achieve different performances on different datasets.ResultsThis work describes a feature selection algorithm based on a recently published correlation measurement, Maximal Information Coefficient (MIC). The proposed algorithm, McTwo, aims to select features associated with phenotypes, independently of each other, and achieving high classification performance of the nearest neighbor algorithm. Based on the comparative study of 17 datasets, McTwo performs about as well as or better than existing algorithms, with significantly reduced numbers of selected features. The features selected by McTwo also appear to have particular biomedical relevance to the phenotypes from the literature.ConclusionMcTwo selects a feature subset with very good classification performance, as well as a small feature number. So McTwo may represent a complementary feature selection algorithm for the high-dimensional biomedical datasets.


title: A Compact Dual-Band GPS Antenna Design

abstract: This letter discusses a small slot-loaded, proximity-fed patch antenna designed for GPS operation at L1 (1575 MHz) and L2 (1227 MHz) bands. High-dielectric substrate and meandered slots are employed to reduce the antenna size down to 25.4 mm in diameter and 11.27 mm in thickness. The thickness is important for achieving the wide bandwidth (>30 MHz) in support of modern GPS coding schemes. The dual-band coverage is achieved by utilizing the patch mode in L2 band and slot mode in L1 band. This design features additional slot stubs for independently tuning the L1 frequency. The right-hand circularly polarized (RHCP) field property is achieved by connecting two proximity probes to a small surface-mount 0°-90° hybrid chip. Simulated and measured antenna performance will be presented. This compact GPS antenna design is suitable for small GPS antenna arrays and portable GPS devices.


title: A 2.4GHz class-E power amplifier with high power control range

abstract: One of the most power consuming blocks in a transceiver is power amplifier. Power dissipation of power amplifier plays a critical role in mobile transmitters whose power supplies have been limited. In this paper, a high-power power amplifier with a high power control range is presented. The stacked topology along with a combination of two intermediate nodes matching technique are adopted to adjust the amplitude of the power amplifier voltages and eliminate their phase misalignment. As a result, the supply voltage increases, and high output power is achieved. Advanced cascode power control technique is employed to control the output power of the power amplifier and obtain optimum DC bias with a linear dynamic range of 40dB. The power amplifier which is simulated using 0.13µm CMOS technology delivers maximum output power of 26dBm at 2.4GHz frequency.