title: Malware Fingerprinting under Uncertainty

abstract: Malware detection and classification is critical for the security of IT infrastructure. Legacy detection of malware has been highly reliant on static signatures, so malware authors have evolved code polymorphic techniques to counteract these tools, thus rendering static malware detectors ineffective. While malware writers may easily use code rewriting techniques to scramble binary images; malware processes at runtime still must conduct a sequence of operational steps to achieve its design goal, indicating an approach based on behavioral analysis where the captured invariants form a new type of forensic fingerprint. Moreover these operational steps are constrained to occur within the computers' or mobile devices' abstract system interface - a finite basis of activities that submit to effective monitoring with a variety of tools. In this work, we propose a formalism for expressing these behaviors, learning them and analyzing them to form automated malware analysis tools. Thus motivated by a need to detect and classify malware, we root its foundation in formal verification, as well as methodology from statistical and machine learning. Specifically using trace data from malware we leverage formal verification methods (such as probabilistic model checking) to construct classifiers and evaluate their efficacy in supervised learning and cross-fold validation experiments. The results inform how a fully automated reasoning mechanism may be applied to unknown software by posing its system trace as a query to various classifiers as hypothesis testing, the outputs informing belief of membership. Finally, we demonstrate the method and results on real malware data.


title: Clustering Algorithms Applied in Educational Data Mining

abstract: Fifty years ago there were just a handful of universities across the globe that could provide for specialized educational courses. Today Universities are generating not only graduates but also massive amounts of data from their systems. So the question that arises is how can a higher educational institution harness the power of this didactic data for its strategic use? This review paper will serve to answer this question. To build an Information system that can learn from the data is a difficult task but it has been achieved successfully by using various data mining approaches like clustering, classification, prediction algorithms etc. However the use of these algorithms with educational dataset is quite low. This review paper focuses to consolidate the different types of clustering algorithms as applied in Educational Data Mining context.


title: A New Agro/Forestry Residues Co-Firing Model in a Large Pulverized Coal Furnace: Technical and Economic Assessments

abstract: Based on the existing biomass co-firing technologies and the known innate drawbacks of dedicated biomass firing, including slagging, corrosion and the dependence on fuel, a new model of agro/forestry residue pellets/shreds and coal co-fired in a large Pulverized Coal (PC) furnace was proposed, and the corresponding technical and economic assessments were performed by co-firing testing in a 300 MW PC furnace and discounted cash flow technique. The developed model is more dependent on injection co-firing and combined with co-milling co-firing. Co-firing not only reduces CO 2 emission, but also does not significantly affect the fly ash use in cement industry, construction industry and agriculture. Moreover, economic assessments show that in comparison with dedicated firing in grate furnace, agro/forestry residues and coal co-firing in a large PC furnace is highly economic. Otherwise, when the co-firing ratio was below 5 wt%, the boiler co-firing efficiency was 0.05%–0.31% higher than that of dedicated PC combustion, and boiler efficiencies were about 0.2% higher with agro/forestry residues co-firing in the bottom and top burner systems than that in a middle burner system.


title: Use and Misuse of the Likert Item Responses and Other Ordinal Measures

abstract: Likert, Likert-type, and ordinal-scale responses are very popular psychometric item scoring schemes for attempting to quantify people’s opinions, interests, or perceived efficacy of an intervention and are used extensively in Physical Education and Exercise Science research. However, these numbered measures are generally considered ordinal and violate some statistical assumptions needed to evaluate them as normally distributed, parametric data. This is an issue because parametric statistics are generally perceived as being more statistically powerful than non-parametric statistics. To avoid possible misinterpretation, care must be taken in analyzing these types of data. The use of visual analog scales may be equally efficacious and provide somewhat better data for analysis with parametric statistics.


title: The origins of probabilistic inference in human infants

abstract: Reasoning under uncertainty is the bread and butter of everyday life. Many areas of psychology, from cognitive, developmental, social, to clinical, are interested in how individuals make inferences and decisions with incomplete information. The ability to reason under uncertainty necessarily involves probability computations, be they exact calculations or estimations. What are the developmental origins of probabilistic reasoning? Recent work has begun to examine whether infants and toddlers can compute probabilities; however, previous experiments have confounded quantity and probability—in most cases young human learners could have relied on simple comparisons of absolute quantities, as opposed to proportions, to succeed in these tasks. We present four experiments providing evidence that infants younger than 12 months show sensitivity to probabilities based on proportions. Furthermore, infants use this sensitivity to make predictions and fulfill their own desires, providing the first demonstration that even preverbal learners use probabilistic information to navigate the world. These results provide strong evidence for a rich quantitative and statistical reasoning system in infants.


title: Amarasinghe,” Comparison of Lossless Data Compression Algorithms for Text Data”,Indian

abstract: Data compression is a common requirement for most of the computerized applications. There are number of data compression algorithms, which are dedicated to compress different data formats. Even for a single data type there are number of different compression algorithms, which use different approaches. This paper examines lossless data compression algorithms and compares their performance. A set of selected algorithms are examined and implemented to evaluate the performance in compressing text data. An experimental comparison of a number of different lossless data compression algorithms is presented in this paper. The article is concluded by stating which algorithm performs well for text data.


title: An efficient transaction processing method on the distributed database

abstract: Distributed system have shown its good robustness, extensibility and effectiveness in the processing, storage and transmission of large data. Now with the expansion of the amount of data and application, it has become a challenging task to ensure its transaction in a distributed and heterogeneous environment. The sub transactions of a distributed transaction not only need to be coordinated with the local other transactions, but also with other sub transactions that generated in the global manager. Based on the discussion of distributed transaction processing model and its transaction commit protocol, the failure reasons with the model analysis of general distributed transaction processing in the practical application in our information database system are given, based on the interface in a relational database management system and super text pre-treatment language of distributed transaction processing implementation method. The research results show that this distributed transaction processing method is reliable, and can simplify the implementation of global program.


title: It's about time: Online Macrotask Sequencing in Expert Crowdsourcing

abstract: We introduce the problem of Task Assignment and Sequencing (TAS), which adds the timeline perspective to expert crowdsourcing optimization. Expert crowdsourcing involves macrotasks, like document writing, product design, or web development, which take more time than typical binary microtasks, require expert skills, assume varying degrees of knowledge over a topic, and require crowd workers to build on each other's contributions. Current works usually assume offline optimization models, which consider worker and task arrivals known and do not take into account the element of time. Realistically however, time is critical: tasks have deadlines, expert workers are available only at specific time slots, and worker/task arrivals are not known a-priori. Our work is the first to address the problem of optimal task sequencing for online, heterogeneous, time-constrained macrotasks. We propose tas-online, an online algorithm that aims to complete as many tasks as possible within budget, required quality and a given timeline, without future input information regarding job release dates or worker availabilities. Results, comparing tas-online to four typical benchmarks, show that it achieves more completed jobs, lower flow times and higher job quality. This work has practical implications for improving the Quality of Service of current crowdsourcing platforms, allowing them to offer cost, quality and time improvements for expert tasks.


title: Microscopy cell segmentation via adversarial neural networks

abstract: We present a novel method for cell segmentation in microscopy images which is inspired by the Generative Adversarial Neural Network (GAN) approach. Our framework is built on a pair of two competitive artificial neural networks, with a unique architecture, termed Rib Cage, which are trained simultaneously and together define a min-max game resulting in an accurate segmentation of a given image. Our approach has two main strengths, similar to the GAN, the method does not require a formulation of a loss function for the optimization process. This allows training on a limited amount of annotated data in a weakly supervised manner. Promising segmentation results on real fluorescent microscopy data are presented. The code is freely available at: this https URL


title: Wideband RCS Reduction of a Microstrip Antenna Using Artificial Magnetic Conductor Structures

abstract: A design approach aimed at reducing the radar cross section (RCS) of microstrip antenna in wide frequency band is proposed. First, two different artificial magnetic conductor (AMC) unit cells are designed to obtain 180 $^\circ $ ( $ \pm 30^\circ $ ) reflection phase difference over broadband frequency range. Then, chessboard configuration is structured with the two AMC unit cells and is applied to a microstrip antenna for RCS reduction. The simulated results indicate that the proposed antenna possesses remarkable RCS reduction from 8.0 GHz to 20.0 GHz for both polarizations, covering the working band of the original antenna. The maximum reduction is 31.9 dB. Moreover, the radiation performance of the antenna has been well kept. Measured results of the fabricated prototype are in good agreement with the simulations.


title: Explicit Modelling of the Implicit Short Term User Preferences for Music Recommendation

abstract: Recommender systems are a key component of music sharing platforms, which suggest musical recordings a user might like. People often have implicit preferences while listening to music, though these preferences might not always be the same while they listen to music at different times. For example, a user might be interested in listening to songs of only a particular artist at some time, and the same user might be interested in the top-rated songs of a genre at another time. In this paper we try to explicitly model the short term preferences of the user with the help of Last.fm tags of the songs the user has listened to. With a session defined as a period of activity surrounded by periods of inactivity, we introduce the concept of a subsession, which is that part of the session wherein the preference of the user does not change much. We assume the user preference might change within a session and a session might have multiple subsessions. We use our modelling of the user preferences to generate recommendations for the next song the user might listen to. Experiments on the user listening histories taken from Last.fm indicate that this approach beats the present methodologies in predicting the next recording a user might listen to.


title: First leaps toward jumping microrobots

abstract: This paper presents the first results for jumping robots at millimeter size scales. Jumping at small sizes requires the quick release of stored energy to accelerate the robot to its takeoff velocity. Two approaches to this energy release are considered. The first is similar to the method used by most larger jumping robots and insects; energy is stored mechanically and generates thrust against the ground upon release. A new microfabrication process incorporates polymer springs for the robot leg and the resulting 4mm × 4mm × 0.3mm jumping mechanism has been launched over 32 cm into the air by hand. The second approach utilizes stored chemical energy and generates thrust by expelling gas from a chemical reaction. This energetic material has been combined with sensing, control, and power on a 4mm × 7mm × 4mm polymer chassis, and jumps in response to light stimuli have reached 8 cm in height.


title: Kodu Game Lab: a programming environment

abstract: Kodu Game Lab is a tile-based visual programming tool that enables users to learn programming concepts through making and playing computer games. Kodu is a relatively new programming language designed specifically for young children to learn through independent exploration. It is integrated in a real-time isometric 3D gaming environment that is designed to compete with modern console games in terms of intuitive user interface and graphical production values.


title: High-Voltage Gain Boost Converter Based on Three-State Commutation Cell for Battery Charging Using PV Panels in a Single Conversion Stage

abstract: This paper presents a novel high-voltage gain boost converter topology based on the three-state commutation cell for battery charging using PV panels and a reduced number of conversion stages. The presented converter operates in zero-voltage switching (ZVS) mode for all switches. By using the new concept of single-stage approaches, the converter can generate a dc bus with a battery bank or a photovoltaic panel array, allowing the simultaneous charge of the batteries according to the radiation level. The operation principle, design specifications, and experimental results from a 500-W prototype are presented in order to validate the proposed structure.


title: Unsupervised Word Polarity Tagging by Exploiting Continuous Word Representations Etiquetado no supervisado de la polaridad de las palabras utilizando representaciones continuas de palabras

abstract: alisis de sentimiento Abstract: Sentiment analysis is the area of Natural Language Processing that aims to determine the polarity (positive, negative, neutral) contained in an opinionated text. A usual resource employed in many of these approaches are the so-called polar- ity lexicons. A polarity lexicon acts as a dictionary that assigns a sentiment polarity value to words. In this work we explore the possibility of automatically generat- ing domain adapted polarity lexicons employing continuous word representations, in particular the popular tool Word2Vec. First we show a qualitative evaluation of a small set of words, and then we show our results in the SemEval-2015 task 12 using the presented method. Keywords: word embeddings, word polarity, sentiment analysis


title: Protecting entanglement from decoherence using weak measurement and quantum measurement reversal

abstract: The unavoidable coupling between a quantum state and its environment leads to decoherence. Weak measurements—indirectly observing a quantum state without disturbing it—are now shown to be a useful tool for reducing or even nullifying the effects of decoherence.


title: A Fast Synthesis Approach for Diplexer with E-Plane T-Junction Design

abstract: In this work a technique for designing microwave rectangular wave guide diplexers with E-plane T-junction is presented. The proposed approach is based on the exact synthesis of channel filters while the E-plane T-junction is taking into account at the same time. This approach represents a very good initial point for the full-wave optimization of the overall diplexer. It's particularly suited for small separation between the two diplexer channels.


title: Depth-Gated LSTM

abstract: In this short note, we present an extension of long short-term memory (LSTM) neural networks to using a depth gate to connect memory cells of adjacent layers. Doing so introduces a linear dependence between lower and upper layer recurrent units. Importantly, the linear dependence is gated through a gating function, which we call depth gate. This gate is a function of the lower layer memory cell, the input to and the past memory cell of this layer. We conducted experiments and verified that this new architecture of LSTMs was able to improve machine translation and language modeling performances.


title: MICROSTRIP SQUARE RING ANTENNA FOR DUAL-BAND OPERATION

abstract: This paper presents a generalized approach to design an electromagnetically coupled microstrip ring antenna for dual-band operation. By widening two opposite sides of a square ring antenna, its fractional bandwidth at the primary resonance mode can be increased significantly so that it may be used for practical applications. By attaching a stub to the inner edge of the side opposite to the feed arm, some of the losses in electrical length caused by widening can be regained. More importantly, this addition also alters the current distribution on the antenna and directs radiations at the second resonant frequency towards boresight. It has also been observed that for the dual frequency configurations studied, the ratio of the resonant frequencies (center dot r(2)center dot center dot r(1)) can range between 1.55 and 2.01. This shows flexibility in designing dual frequency antennas with a desired pair of resonant frequencies.


title: Principles for targeting RNA with drug-like small molecules

abstract: Recent studies have indicated the potential to develop small-molecule drugs that act on RNA targets, leading to burgeoning interest in the field. This article discusses general principles for discovering small-molecule drugs that target RNA and argues that the overarching challenge is to identify appropriate target structures in disease-causing RNAs that have high information content and, consequently, appropriate ligand-binding pockets.


title: Performance Evaluation of Different Data Mining Classification Algorithm and Predictive Analysis

abstract: Data mining is the knowledge discovery process by analyzing the large volumes of data from various perspectives and summarizing it into useful information; data mining has become an essential component in various fields of human life. It is used to identify hidden patterns in a large data set. Classification techniques are supervised learning techniques that classify data item into predefined class label. It is one of the most useful techniques in data mining to build classification models from an input data set; these techniques commonly build models that are used to predict future data trends. In this paper we have worked with different data mining applications and various classification algorithms, these algorithms have been applied on different dataset to find out the efficiency of the algorithm and improve the performance by applying data preprocessing techniques and feature selection and also prediction of new class labels.


title: Structured Sequence Modeling with Graph Convolutional Recurrent Networks

abstract: This paper introduces Graph Convolutional Recurrent Network (GCRN), a deep learning model able to predict structured sequences of data. Precisely, GCRN is a generalization of classical recurrent neural networks (RNN) to data structured by any arbitrary graph. Such structured sequences can be series of frames in videos, spatio-temporal measurements on a network of sensors, or random walks on a vocabulary graph for natural language modeling.The proposed model combines convolutional neural networks (CNN) on graphs to identify spatial structures and RNN to find dynamic patterns. We study two possible architectures of GCRN, and apply the models to two practical problems: predicting moving MNIST data, and modeling natural language with the Penn Treebank dataset. Experiments show that exploiting simultaneously graph spatial and dynamic information about data can improve both precision and learning speed.


title: Recommending Self-Regulated Learning Strategies Does Not Improve Performance in a MOOC

abstract: Many committed learners struggle to achieve their goal of completing a Massive Open Online Course (MOOC). This work investigates self-regulated learning (SRL) in MOOCs and tests if encouraging the use of SRL strategies can improve course performance. We asked a group of 17 highly successful learners about their own strategies for how to succeed in a MOOC. Their responses were coded based on a SRL framework and synthesized into seven recommendations. In a randomized experiment, we evaluated the effect of providing those recommendations to learners in the same course (N = 653). Although most learners rated the study tips as very helpful, the intervention did not improve course persistence or achievement. Results suggest that a single SRL prompt at the beginning of the course provides insufficient support. Instead, embedding technological aids that adaptively support SRL throughout the course could better support learners in MOOCs.


370 A GENOME-WIDE ASSOCIATION STUDY IDENTIFIES A LOCUS ON CHROMOSOME 7Q22 TO INFLUENCE SUSCEPTIBILITY FOR OSTEOARTHRITIS


title: A new type of entombment of Peronopsis (Agnostida) in a hyolithid conch

abstract: An enrolled exoskeleton of the holaspid specimen of a tiny agnostid Peronopsis integra (Beyrich, 1845) entombed inside a conch of the hyolithid ?Buchavalites sp. is described from the middle Cambrian (Drumian) Jince Formation of the Přibram-Jince Basin (Czech Republic). The agnostid is associated with an ichnofossil of the feeding trace classified as Arachnostega-type behaviour. The enrolled attitude of the agnostid exoskeleton suggests that the specimen is a carcass rather than moult. Either the storm disturbance and/or well-protected source of food hypotheses could explain the entombed agnostid. This additional example supports a benthic mode of life in the agnostid P. integra. The studied association of feeding tunnels of an unknown Arachnostega-strategist and Peronopsis preserved inside a hyolithid conch is a case of "frozen" behaviour.


title: Semi-Supervised Answer Extraction from Discussion Forums

abstract: Mining online discussions to extract answers is an important research problem. Methods proposed in the past used supervised classifiers trained on labeled data. But, collecting training data for each target forum is labor intensive and time consuming, thus limiting their deployment. A recent approach had proposed to extract answers in an unsupervised manner, by taking cues from their repetitions. This assumption however, does not hold true in many cases. In this paper, we propose two semi-supervised methods for extracting answers from discussions, which utilize the large amount of unlabeled data available, alongside a very small training set to obtain improved accuracies. We show that it is possible to boost the performance by introducing a related, but parallel task of identifying acknowledgments to the answers. The accuracy achieved by our approaches surpass the baselines by a wide margin, as shown by our experiments.


title: Gradient Descent Quantizes ReLU Network Features

abstract: Deep neural networks are often trained in the over-parametrized regime (i.e. with far more parameters than training examples), and understanding why the training converges to solutions that generalize remains an open problem. Several studies have highlighted the fact that the training procedure, i.e. mini-batch Stochastic Gradient Descent (SGD) leads to solutions that have specific properties in the loss landscape. However, even with plain Gradient Descent (GD) the solutions found in the over-parametrized regime are pretty good and this phenomenon is poorly understood. We propose an analysis of this behavior for feedforward networks with a ReLU activation function under the assumption of small initialization and learning rate and uncover a quantization effect: The weight vectors tend to concentrate at a small number of directions determined by the input data. As a consequence, we show that for given input data there are only finitely many,"simple"functions that can be obtained, independent of the network size. This puts these functions in analogy to linear interpolations (for given input data there are finitely many triangulations, which each determine a function by linear interpolation). We ask whether this analogy extends to the generalization properties - while the usual distribution-independent generalization property does not hold, it could be that for e.g. smooth functions with bounded second derivative an approximation property holds which could"explain"generalization of networks (of unbounded size) to unseen inputs.


Amazon Food Review Classification using Deep Learning and Recommender System


title: Slip Stacking for the Fermilab luminosity upgrade

abstract: Increasing the proton intensity available for antiproton production is part of a plan for increasing the luminosity in the Fermilab Tevatron in the near future. We intend to increase the proton intensity using a kind of momentum stacking in the Main Injector called Slip Stacking. We report the status of the effort towards its implementation.


title: Towards Controlled Transformation of Sentiment in Sentences

abstract: An obstacle to the development of many natural language processing products is the vast amount of training examples necessary to get satisfactory results. The generation of these examples is often a tedious and time-consuming task. This paper this paper proposes a method to transform the sentiment of sentences in order to limit the work necessary to generate more training data. This means that one sentence can be transformed to an opposite sentiment sentence and should reduce by half the work required in the generation of text. The proposed pipeline consists of a sentiment classifier with an attention mechanism to highlight the short phrases that determine the sentiment of a sentence. Then, these phrases are changed to phrases of the opposite sentiment using a baseline model and an autoencoder approach. Experiments are run on both the separate parts of the pipeline as well as on the end-to-end model. The sentiment classifier is tested on its accuracy and is found to perform adequately. The autoencoder is tested on how well it is able to change the sentiment of an encoded phrase and it was found that such a task is possible. We use human evaluation to judge the performance of the full (end-to-end) pipeline and that reveals that a model using word vectors outperforms the encoder model. Numerical evaluation shows that a success rate of 54.7% is achieved on the sentiment change.


title: Vision-based real-time traffic accident detection

abstract: Recently, traffic accident detection is becoming one of the interesting fields due to its tremendous application potential in Intelligent Transportation Systems. In this paper, we present a vision-based real time traffic accident detection method. We intend to extract foreground and background from video shots using the Gaussian Mixture Model (GMM) to detect vehicles; afterwards, the detected vehicles are tracked based on the mean shift algorithm. Then the three traffic accident parameters including the changes of the vehicles position, acceleration, and the direction of the moving vehicles are gathered to make the final accident decision. The experimental results on real video demonstrate the efficiency and the applicability of the proposed approach.


title: Comparing Children's Crosshair and Finger Interactions in Handheld Augmented Reality: Relationships Between Usability and Child Development

abstract: Augmented reality technology is a unique medium that can be helpful for young children's entertainment and education, but in order to achieve the benefits of this technology, augmented reality experiences need to be appropriately designed for young children's developing physical and cognitive skills. In the present study we investigated how 5-10 year-old children react to typical handheld augmented reality interaction techniques such as crosshair selection and finger selection, in AR environments that require them to change perspective or not. Our analysis shows significant impacts of age upon AR performance, with young children having slower selection times, more tracking losses, and taking longer to recover tracking. Significant differences were also found between AR interaction technique conditions, with finger selection being faster than crosshair selection, and interactions which required changes in perspective taking longer, generating more tracking losses, and more errors in selection. Furthermore, by analyzing children's performance in relation to metrics of physical and cognitive development, we identified correlations between AR interaction techniques performance and developmental tests of spatial relations, block construction and visuomotor precision. Gender differences were analyzed but no significant effects were detected.


title: Analyzing CUDA workloads using a detailed GPU simulator

abstract: Modern Graphic Processing Units (GPUs) provide sufficiently flexible programming models that understanding their performance can provide insight in designing tomorrow’s manycore processors, whether those are GPUs or otherwise. The combination of multiple, multithreaded, SIMD cores makes studying these GPUs useful in understanding tradeoffs among memory, data, and thread level parallelism. While modern GPUs offer orders of magnitude more raw computing power than contemporary CPUs, many important applications, even those with abundant data level parallelism, do not achieve peak performance. This paper characterizes several non-graphics applications written in NVIDIA’s CUDA programming model by running them on a novel detailed microarchitecture performance simulator that runs NVIDIA’s parallel thread execution (PTX) virtual instruction set. For this study, we selected twelve non-trivial CUDA applications demonstrating varying levels of performance improvement on GPU hardware (versus a CPU-only sequential version of the application). We study the performance of these applications on our GPU performance simulator with configurations comparable to contemporary high-end graphics cards. We characterize the performance impact of several microarchitecture design choices including choice of interconnect topology, use of caches, design of memory controller, parallel workload distribution mechanisms, and memory request coalescing hardware. Two observations we make are (1) that for the applications we study, performance is more sensitive to interconnect bisection bandwidth rather than latency, and (2) that, for some applications, running fewer threads concurrently than on-chip resources might otherwise allow can improve performance by reducing contention in the memory system.


title: Adversarial Active Learning for Deep Networks: a Margin Based Approach

abstract: We propose a new active learning strategy designed for deep neural networks. The goal is to minimize the number of data annotation queried from an oracle during training. Previous active learning strategies scalable for deep networks were mostly based on uncertain sample selection. In this work, we focus on examples lying close to the decision boundary. Based on theoretical works on margin theory for active learning, we know that such examples may help to considerably decrease the number of annotations. While measuring the exact distance to the decision boundaries is intractable, we propose to rely on adversarial examples. We do not consider anymore them as a threat instead we exploit the information they provide on the distribution of the input space in order to approximate the distance to decision boundaries. We demonstrate empirically that adversarial active queries yield faster convergence of CNNs trained on MNIST, the Shoe-Bag and the Quick-Draw datasets.


title: We’re Doing It Live: A Multi-Method Empirical Study on Continuous Experimentation

abstract: Abstract Context Continuous experimentation guides development activities based on data collected on a subset of online users on a new experimental version of the software. It includes practices such as canary releases, gradual rollouts, dark launches, or A/B testing. Objective Unfortunately, our knowledge of continuous experimentation is currently primarily based on well-known and outspoken industrial leaders. To assess the actual state of practice in continuous experimentation, we conducted a mixed-method empirical study. Method In our empirical study consisting of four steps, we interviewed 31 developers or release engineers, and performed a survey that attracted 187 complete responses. We analyzed the resulting data using statistical analysis and open coding. Results Our results lead to several conclusions: (1) from a software architecture perspective, continuous experimentation is especially enabled by architectures that foster independently deployable services, such as microservices-based architectures; (2) from a developer perspective, experiments require extensive monitoring and analytics to discover runtime problems, consequently leading to developer on call policies and influencing the role and skill sets required by developers; and (3) from a process perspective, many organizations conduct experiments based on intuition rather than clear guidelines and robust statistics. Conclusion Our findings show that more principled and structured approaches for release decision making are needed, striving for highly automated, systematic, and data- and hypothesis-driven deployment and experimentation.


title: Refined experts: improving classification in large taxonomies

abstract: While large-scale taxonomies--especially for web pages--have been in existence for some time, approaches to automatically classify documents into these taxonomies have met with limited success compared to the more general progress made in text classification. We argue that this stems from three causes: increasing sparsity of training data at deeper nodes in the taxonomy, error propagation where a mistake made high in the hierarchy cannot be recovered, and increasingly complex decision surfaces in higher nodes in the hierarchy. While prior research has focused on the first problem, we introduce methods that target the latter two problems--first by biasing the training distribution to reduce error propagation and second by propagating up "first-guess" expert information in a bottom-up manner before making a refined top down choice. Finally, we present an empirical study demonstrating that the suggested changes lead to 10--30% improvements in F1 scores versus an accepted competitive baseline, hierarchical SVMs.


Joint Embeddings of Chinese Words, Characters, and Fine-grained Subcharacter Components


title: Vaccine hypersensitivity--update and overview.

abstract: Concerns about possible reactions to vaccines or vaccinations are frequently raised. However, the rate of reported vaccine-induced adverse events is low and ranges between 4.8-83.0 per 100,000 doses of the most frequently used vaccines. The number of true allergic reactions to routine vaccines is not known; estimations range from 1 per 500,000 to 1 per 1,000,000 doses for most vaccines. When allergens such as gelatine or egg proteins are components of the formulation, the rate for serious allergic reactions may be higher. Nevertheless, anaphylactic, potentially life-threatening reactions to vaccines are still a rare event (approximately 1 per 1,500,000 doses). The variety of reported vaccine-related adverse events is broad. Most frequently, reactions to vaccines are limited to the injection site and result from a non specific activation of the inflammatory system by, for example, aluminium salts or the active microbial components. If allergy is suspected, an accurate examination followed by algorithms is the key for correct diagnosis, treatment and the decision regarding revaccination in patients with immediate-type reactions to vaccines.


title: Ecological genetics of rusty-necklaced partridge (Alectolis magna): environmental factors and population genetic variability correlations

abstract: Field studies of both genetic variability and natural selection have been central to ecological genetics. There have been a lot of studies on gene (or genotype) and environmental interaction, and we all know that environmental factors are very important to maintaining genetic variability. We do not discover the concrete mechanism by which environment can affect genetic variability. But we have many suggestions about the mechanism. This study used mitochondrial DNA (mtDNA) control-region to infer the relationship between population genetic diversity of rusty-necklaced partridge (Alectoris magna) and environmental factors. A 458bp fragment of the mitochondrial DNA control region was sequenced from 82 rusty-necklaced partridge throughout the subspecies range in northwestern China. Twenty-seven variable sites defined 25 mtDNA haplotypes. Statistically significant differences in mtDNA haplotypes; frequencies were observed among all populations. Rusty-necklaced partridge genetic diversity (pi- and h- values) decreased with longitude, temperature, rainfall, variation coefficients of temperature and rainfall(annual change) cline. However, genetic diversity increased with increasing latitude and altitude. The more stable the climate, the higher the genetic diversity observed. Natural selection and environmental factors may act to maintain the genetic diversity of rusty-necklaced partridge populations.


title: Online Narrative Inquiry as an Assessment Tool

abstract: Narrative Inquiry as an Authentic Assessment Tool for Library Education Structured self-reflective writing provides a rich data set for analysis, particularly when facilitated through technology. This assessment method is examined in light of online library education with applications for K-13 education as well.


title: A Network-Fusion Guided Dashboard Interface for Task-Centric Document Curation

abstract: Knowledge workers are being exposed to more information than ever before, as well as having to work in multi-tasking and collaborative environments. There is an increasing need for interfaces and algorithms to help automatically keep track of documents that are associated with both individual and team tasks. Previous approaches to the problem of automatically applying task labels to documents have been limited to small feature spaces or have not taken into account multi-user environments. Many different clues to potential task associations are available through user, task and document similarity metrics, as well as through temporal patterns in individual and team workflows. We present a network-fusion algorithm for automatic task-centric document curation, and show how this can guide a recent-work dashboard interface, which organizes user's documents and gathers feedback from them. Our approach efficiently computes representations of users, tasks and documents in a common vector space, and can easily take into account many different types of associations through the creation of edges in a multi-layer graph. We have demonstrated the effectiveness of this approach using labelled document corpora from three empirical studies with students and intelligence analysts. We have also shown how to leverage relationships between different entity types to increase classification accuracy by up to 20% over a simpler baseline, and with as little as 10% labelled data.


title: Anomaly detection using DBSCAN clustering technique for traffic video surveillance

abstract: Detecting anomalies such as rule violations, accidents, unusual driving and other suspicious action increase the need for automatic analysis in Traffic Video Surveillance (TVS). Most of the works in Traffic rule violation systems are based on probabilistic methods of classification for detecting the events as normal and abnormal. This paper proposes an un-supervised clustering technique namely Novel Anomaly Detection-Density Based Spatial Clustering of Applications with Noise (NAD-DBSCAN) which clusters the trajectories of moving objects of varying sizes and shapes. A trajectory is said to be abnormal if the event that never fit with the trained model. Epsilon (Eps) and Minimum Points (MinPts) are essential parameters for dynamically calculating the sum of clusters for a data point. The proposed system is validated using benchmark traffic dataset and found to perform accurately in detecting anomalies.


title: Multiple decision trees

abstract: This paper describes experiments, on two domains, to investigate the effect of averaging over predictions of multiple decision trees, instead of using a single tree. Other authors have pointed out theoretical and commonsense reasons for preferring the multiple tree approach. Ideally, we would like to consider predictions from all trees, weighted by their probability. However, there is a vast number of different trees, and it is difficult to estimate the probability of each tree. We sidestep the estimation problem by using a modified version of the ID3 algorithm to build good trees, and average over only these trees. Our results are encouraging. For each domain, we managed to produce a small number of good trees. We find that it is best to average across sets of trees with different structure; this usually gives better performance than any of the constituent trees, including the ID3 tree. Keywords: machine learning, transduction, empirical evaluation


title: Flowfence: a denial of service defense system for software defined networking

abstract: Most Denial of Service (DoS) attacks intend to generate a traffic pattern that is indistinguishable from legitimate traffic, making it hard to detect an attack. Conventional defenses for these attacks are not scalable, are slow to react or introduce an overhead to each routed packet. In this paper, we present FlowFence, a lightweight and fast denial of service detection and mitigation system for Software Defined Networking (SDN). The FlowFence architecture includes routers running daemons to monitor the average occupation of their interfaces to detect congestion conditions, and an SDN controller that coordinates bandwidth assignment of controlled links. The controller limits the flow transmission rate along a path to prevent users' starvation. The mitigation procedure of starvation state allocates an average bandwidth, while flows exceeding the mean are penalized. The penalization is proportional to the difference between the fair limit and the current bandwidth usage. A system prototype was implemented and evaluated in the Future Internet Testbed with Security (FITS). The results show that the proposal avoids users' starvation of network resources without adding much overhead in the network.


title: Emoticons convey emotions without cognition of faces: an fMRI study

abstract: In this paper, we describe the brain activities that are associated with emoticons by using functional MRI (fMRI). In communication over a computer network, we use abstract faces such as computer graphics (CG) avatars and emoticons. These faces convey users' emotions and enrich their communications. In particular, when we see some abstract faces, we feel that they are more vivid and lively than photorealistic faces. However, the manner in which these faces influence the mental process is as yet unknown. In this research, we conducted an experiment by using fMRI for the most abstract faces?emoticons. The experimental results show that emoticons convey emotions without the cognition of faces. This result is very important in order to promote an understanding of how abstract faces affect our behaviors.


title: Learning a Discriminative Model for the Perception of Realism in Composite Images

abstract: What makes an image appear realistic? In this work, we are answering this question from a data-driven perspective by learning the perception of visual realism directly from large amounts of data. In particular, we train a Convolutional Neural Network (CNN) model that distinguishes natural photographs from automatically generated composite images. The model learns to predict visual realism of a scene in terms of color, lighting and texture compatibility, without any human annotations pertaining to it. Our model outperforms previous works that rely on hand-crafted heuristics, for the task of classifying realistic vs. unrealistic photos. Furthermore, we apply our learned model to compute optimal parameters of a compositing method, to maximize the visual realism score predicted by our CNN model. We demonstrate its advantage against existing methods via a human perception study.


title: Parasitic-Aware Optimization and Retargeting of Analog Layouts: A Symbolic-Template Approach

abstract: Layout parasitics can significantly affect the performance of analog integrated circuits (ICs). In this paper, a systematic method of optimizing an existing analog layout considering parasitics is presented for technology migration and performance retargeting. This method represents the locations of layout rectangle edges as variables and extracts circuit and layout integrity such as device symmetry, matching, and design rules as constraints. To ensure the desired circuit performance, bounds of layout parasitics are determined first. These bounds are used to constrain the layout geometries while retargeting existing high-quality layouts across technologies and specification sets. The problem is then solved by a graph-based algorithm combined with nonlinear optimization. The proposed method has been implemented in a parasitic-aware automatic layout optimization and retargeting tool (intellectual property reuse-based analog IC layout). Its efficiency and effectiveness are demonstrated by successfully retargeting operational amplifiers within 1 min of CPU time.


title: State of the Art of Fingerprint Indexing Algorithms

abstract: Resumen en: Due to the large size that fingerprint databases generally have, the reduction of the search space is indispensable. In the resolution of this problem, i...


title: A comparison of B2B e-service solutions

abstract: The Internet is evolving not only to provide information and e-commerce transactions, but also to act as the platform through which services are delivered to businesses and customers. These electronic services or e-services could become a key part of the value provided by many businesses [2, 5, 10]. At the core of this evolution is Extensible Markup Language (XML), which has emerged as the foundation of all architectures suggested for such services. XML simplifies the exchange of information by letting users define their own syntax and use underlying technologies of the Internet. However, while organizations can define XML syntax to solve their specific problems, the multitude of syntax (schemas) creates incompatibility problems with schemas developed by others. This is one of the reasons why major organizations are creating business-to-business (B2B) XML framework standards to enable interoperability. To overcome these problems, efforts are underway to develop standards for e-services including eCo by a consortium led by CommerceNet; RosettaNet by a consortium that includes IBM, Microsoft, EDS, SAP, Cisco systems, Compaq and Intel; BizTalk by Microsoft; e-speak by Hewlett Packard (HP), and several others. Since these B2B interoperability standards are likely to be very important in the way businesses interact with each other in the future, an overview of these standardization efforts is certain to be of considerable importance to the IS community. This article describes the components (core modules of platforms for linking Internet-based service providers) of e-services and compares popular B-to-B e-commerce frameworks based on their support for e-service components.


title: A Novel Eye-Gaze-Controlled Wheelchair System for Navigating Unknown Environments: Case Study With a Person With ALS

abstract: Thanks to advances in electric wheelchair design, persons with motor impairments due to diseases, such as amyotrophic lateral sclerosis (ALS), have tools to become more independent and mobile. However, an electric wheelchair generally requires considerable skill to learn how to use and operate. Moreover, some persons with motor disabilities cannot drive an electric wheelchair manually (even with a joystick), because they lack the physical ability to control their hand movement (such is the case with people with ALS). In this paper, we propose a novel system that enables a person with motor disability to control a wheelchair via eye-gaze and to provide a continuous, real-time navigation in unknown environments. The system comprises a Permobile M400 wheelchair, eye tracking glasses, a depth camera to capture the geometry of the ambient space, a set of ultrasound and infrared sensors to detect obstacles with low proximity that are out of the field of view for the depth camera, a laptop placed on a flexible mount for maximized comfort, and a safety off switch to turn off the system whenever needed. First, a novel algorithm is proposed to support continuous, real-time target identification, path planning, and navigation in unknown environments. Second, the system utilizes a novel N-cell grid-based graphical user interface that adapts to input/output interfaces specifications. Third, a calibration method for the eye tracking system is implemented to minimize the calibration overheads. A case study with a person with ALS is presented, and interesting findings are discussed. The participant showed improved performance in terms of calibration time, task completion time, and navigation speed for a navigation trips between office, dining room, and bedroom. Furthermore, debriefing the caregiver has also shown promising results: the participant enjoyed higher level of confidence driving the wheelchair and experienced no collisions through all the experiment.


Music Playlist Recommendation via Preference Embedding


title: The RoboKnee: an exoskeleton for enhancing strength and endurance during walking

abstract: Exoskeletons that enhance human strength, endurance, and speed while being transparent to the wearer are feasible. In order to be transparent, the exoskeleton must determine the user's intent, apply forces when and where appropriate, and present low impedance to the wearer. We present a one degree of freedom exoskeleton called the RoboKnee which achieves a high level of transparency. User intent is determined through the knee joint angle and ground reaction forces. Torque is applied across the knee in order to allow the user's quadriceps muscles to relax. Low impedance is achieved through the use of series elastic actuators. The RoboKnee allows the wearer to climb stairs and perform deep knee bends while carrying a significant load in a backpack. The device provides most of the energy required to work against gravity while the user stays in control, deciding when and where to walk, as well as providing balance and control. Videos, photographs, and more information about the RoboKnee can be found at http://www.yobotics.com.


title: Beam-Scanning Continuous Transverse Stub Antenna Fed by a Ridged Waveguide Slot Array

abstract: A continuous transverse stub (CTS) array antenna is presented for beam-scanning applications. Rotatable ridged waveguide slot array (RWSA) is used as a line source to feed a CTS array for the first time. The proposed RWSA has easy design, compact structure, light weight, and easy sidelobe control. The CTS array and RWSA can be designed and optimized separately. A good beam-scanning capability of ±30° is achieved by rotating the RWSA at almost the same angles, thus obtaining a fine beam-scanning resolution. The measured aperture efficiency exceeds 60% in the entire beam-scanning range. Therefore, the proposed antenna can be a good alternative for radar systems and satellite communication terminals, which require low cost and high gain.


title: PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification

abstract: We present a new release of the Paraphrase Database. PPDB 2.0 includes a discriminatively re-ranked set of paraphrases that achieve a higher correlation with human judgments than PPDB 1.0’s heuristic rankings. Each paraphrase pair in the database now also includes finegrained entailment relations, word embedding similarities, and style annotations.


title: A majorization-minimization approach to the sparse generalized eigenvalue problem

abstract: Generalized eigenvalue (GEV) problems have applications in many areas of science and engineering. For example, principal component analysis (PCA), canonical correlation analysis (CCA) and Fisher discriminant analysis (FDA) are specific instances of GEV problems, that are widely used in statistical data analysis. The main contribution of this work is to formulate a general, efficient algorithm to obtain sparse solutions to a GEV problem. Specific instances of sparse GEV problems can then be solved by specific instances of this algorithm. We achieve this by solving the GEV problem while constraining the cardinality of the solution. Instead of relaxing the cardinality constraint using a l1-norm approximation, we consider a tighter approximation that is related to the negative log-likelihood of a Student's t-distribution. The problem is then framed as a d.c. (difference of convex functions) program and is solved as a sequence of convex programs by invoking the majorization-minimization method. The resulting algorithm is proved to exhibit global convergence behavior, i.e., for any random initialization, the sequence (subsequence) of iterates generated by the algorithm converges to a stationary point of the d.c. program. Finally, we illustrate the merits of this general sparse GEV algorithm with three specific examples of sparse GEV problems: sparse PCA, sparse CCA and sparse FDA. Empirical evidence for these examples suggests that the proposed sparse GEV algorithm, which offers a general framework to solve any sparse GEV problem, will give rise to competitive algorithms for a variety of applications where specific instances of GEV problems arise.


title: Attention network performance and psychopathic symptoms in early adolescence: an ERP study.

abstract: Reaction time (RT) and event-related potential (ERP) measures were used to examine the relationships between psychopathic symptoms and three major attention networks (alerting, orienting, and executive attention) among a community sample of youth. Antisocial Process Screening Device (APSD; Frick and Hare 2001) total and subscale scores were negatively correlated with ERP measures of attentional alerting, indicating that youth with psychopathic symptoms had difficulty using warning cues to prepare for upcoming targets. APSD total scores were not related to performance on measures of orienting or executive attention, although weaker executive attention was found among youth with higher scores on the Impulsivity subscale. These findings support attention-based models of psychopathy and provide evidence of specific deficits in attentional alerting among youth with psychopathic traits. Deficiencies in attentional alerting may be related to noradrenergic functioning and may have cascading effects on higher order cognitive and affective processing.


title: Pattern frequency representation for time series classification

abstract: The paper presents a new method for data transformation. The obtained data format enhances the efficiency of the time series classification. The transformation is realized in four steps, namely: time series segmentation, segment's feature representation, segments' binning and pattern frequency representation. The method is independent of the nature of the data, and it works well with temporal data from diverse areas. Its ability is proved by classification of different real datasets and is also compared with the results of other classification methods.


title: Semantics extraction from images

abstract: An overview of the state-of-the-art on semantics extraction from images is presented. In this survey, we present the relevant approaches in terms of content representation as well as in terms of knowledge representation. Knowledge can be represented in either implicit or explicit fashion while the image is represented in different levels, namely, low-level, intermediate and semantic level. For each combination of knowledge and image representation, a detailed discussion is addressed that leads to fruitful conclusions for the impact of each approach.


title: RAT selection games in HetNets

abstract: We study the dynamics of network selection in heterogeneous wireless networks (HetNets). Users in such networks selfishly select the best radio access technology (RAT) with the objective of maximizing their own throughputs. We propose two general classes of throughput models that capture the basic properties of random access (e.g., Wi-Fi) and scheduled access (e.g., WiMAX, LTE, 3G) networks. Next, we formulate the problem as a non-cooperative game, and study its convergence, efficiency, and practicality. Our results reveal that: (i) Singleclass RAT selection games converge to Nash equilibria, while an improvement path can be repeated infinitely with a mixture of classes. We next introduce a hysteresis mechanism in RAT selection games, and prove that with appropriate hysteresis policies, convergence can still be guaranteed; (ii) We analyze the Pareto-efficiency of the Nash equilibria of these games. We derive the conditions under which Nash equilibria are Paretooptimal, and we quantify the distance of Nash equilibria with respect to the set of Pareto-dominant points when the conditions are not satisfied; (iii) Finally, with extensive measurement-driven simulations we show that RAT selection games converge to Nash equilibria in a small number of steps, and hence are amenable to practical implementation. We also investigate the impact of noisy throughput measurements, and propose solutions to handle them.


title: Digital storytelling: a meaningful technology-integrated approach for engaged student learning

abstract: Although research emphasizes the importance of integrating technology into the curriculum, the use of technology can only be effective if teachers themselves possess the expertise to use technology in a meaningful way in the classroom. The aim of this study was to assist Egyptian teachers in developing teaching and learning through the application of a particular digital technology. Students were encouraged to work through the process of producing their own digital stories using MS Photo Story, while being introduced to desktop production and editing tools. They also presented, published and shared their own stories with other students in the class. Quantitative and qualitative instruments, including digital story evaluation rubric, integration of technology observation instruments and interviews for evaluating the effectiveness of digital storytelling into learning were implemented to examine the extent to which students were engaged in authentic learning tasks using digital storytelling. The findings from the analysis of students-produced stories revealed that overall, students did well in their projects and their stories met many of the pedagogical and technical attributes of digital stories. The findings from classroom observations and interviews revealed that despite problems observed and reported by teachers, they believed that the digital storytelling projects could increase students' understanding of curricular content and they were willing to transform their pedagogy and curriculum to include digital storytelling.


Changing Perspectives: Institutional Logics of Adoption and Use of Health Information Technology


title: Spread Spectrum Digital Beamforming (SSDBF) radar

abstract: Spread Spectrum Digital Beamforming (SSDBF) overcomes the CSWAP and the scalability in bandwidth-and-frequency limitations of Conventional Digital Beam Forming (CDBF) by eliminating the requirement of “one digital transceiver per element” while enabling fully capable digital beamforming with minimum hardware (and consequently minimum volume and heat dissipation) per element. SSDBF enables low-cost/low-profile/low-power digital beam forming phased arrays that scales in frequency (L through Ka and higher), bandwidth (100's MHz) and subarray size (100's elements per subarray) with a single down-converter, single digital receiver and single Nyquist-rate ADC for the entire subarray. It achieves this by replacing the MMIC T/R module at each element with much simpler bi-phase re-modulating hardware, and by re-modulating and aggregating the return signal incident at each element such that it can perfectly recover the complex baseband-equivalent of the RF signal of each element without mutual interference and with negligible noise performance degradation. The bi-phase re-modulator at each element can be extremely small and low power and amenable to implementations as simple as a single RF switch mounted directly at the antenna element with associated ON/OFF switch control logic built on the back. Thus SSDBF truly enables flat, thin, lightweight conformal phased arrays and has the potential of reducing phased array costs by, possibly, multiple orders of magnitude relative to CDBF state-of-the-art. The SSDBF parameters and waveforms are flexible, programmable and can be tailored and adapted, practically, to support any radar or communications applications. In a project for DARPA1, Applied Radar is developing an X-band SSDBF-based radar prototype in which the SSDBF features and performance have been demonstrated. In this paper, we describe the SSDBF method for a radar application including simulation, indoor testbed and open-range test results.


FLIGHT CONTROL OF 10 GRAM INSECTS BY IMPLANTED NEURAL STIMULATORS


title: Flexible and stretchable UHF RFID tag antennas for automotive tire sensing

abstract: UHF RFID (Radio Frequency IDentification) tag antennas are proposed for automotive tire applications, which are flexible and sufficiently broadband for a wide variety of tire materials and construction. The RFID tags may be mounted on or in the tires to monitor their condition and provide history data. The influence of the rubber material on the tag's performance is studied, and a broadband tag antenna is proposed which remains tuned on dielectrics of varying permittivity and thickness. Furthermore, a textile-based RFID antenna is fabricated and tested. The flexibility and durability of this antenna makes it a perfect candidate for RFID tire applications.


title: An Intent-Driven Planner for Multi-Agent Story Generation

abstract: The ability to generate narrative is of importance to computer systems that wish to use story effectively for entertainment, training, or education. We identify two properties of story - plot coherence and character believability - which play a role in the success of a story. Plot coherence is the perception by audience members that character actions have relevance to the outcome of the story. Character believability is the perception that character actions are motivated by agents' internal beliefs and desires. Unlike conventional planning in which plan goals represent an agent's intended world state, multiagent story planning involves goals that represent the outcome of a story. In order for the plans' actions to appear believable, multi-agent story planners must determine not only how agents' actions achieve a story's goal state, but must also ensure that each agent appears to be acting intentionally. We present a narrative generation planning system for multi-agent stories that is capable of generating narratives with both strong plot coherence and strong character believability. The planning algorithm uses causal reasoning and a simulated intention recognition process to drive plan creation.


title: RUSBoost: Improving classification performance when training data is skewed

abstract: Constructing classification models using skewed training data can be a challenging task. We present RUSBoost, a new algorithm for alleviating the problem of class imbalance. RUSBoost combines data sampling and boosting, providing a simple and efficient method for improving classification performance when training data is imbalanced. In addition to performing favorably when compared to SMOTEBoost (another hybrid sampling/boosting algorithm), RUSBoost is computationally less expensive than SMOTEBoost and results in significantly shorter model training times. This combination of simplicity, speed and performance makes RUSBoost an excellent technique for learning from imbalanced data.


title: The occurrence of Austrodiplostomum compactum (Lutz, 1928) (Digenea: Diplostomidae) metacercariae in the eyes of loricariid fish (Siluriformes: Osteichthyes: Loricariidae) from Brazil

abstract: The aim of this study was to report the occurrence of Austrodiplostomum compactum metacercariae in the eyes of 98 specimens of loricariid fish (Hypostomus ancistroides, H. hermanni, H. iheringii, H. margaritifer, H. regani, H. strigaticeps, Hypostomus sp. and Megalancistrus parananus) from the Chavantes reservoir (23807 0 36 00 S and 49837 0 35 00 W) located in the rio Paranapanema, upper Paranariver basin, municipality of Ipaussu, Sao Paulo State, Brazil. Fish were collected from October 2007 to February 2009 using nylon monofilament gill nets and transported to the field laboratory where they were euthanized and the eyes were taken and examined under a stereomicroscope. Hypostomus ancistroides and M. parananus were not infected by this diplostomid. Hypostomus hermanni and H. margaritifer were represented by only one specimen but both had a high intensity of A. compactum metacercarie (27 and 35, respectively). Hypostomus strigaticeps (n ¼ 45) and H. iheringii (n ¼ 28) were the most representative specimens and the prevalence, mean intensity of infection and mean abundance were 24.4%, 10.3 and 2.7, and 64.2%, 13.1 and 8.4, respectively. No correlation was observed between the intensity of infection and the standard length (r ¼ 2 0.223; P ¼ 0.827) and weight (r ¼ 0.03; P ¼ 0.779) of studied fish. Similarly, linear regression among these variables showed a poor correlation and indicated that the infection by A. compactum metacercariae occurs similarly in small and large fish specimens. A seasonal pattern of infection was not observed. Hypostomus hermanni, H. iheringii, H. margaritifer and H. strigaticeps were new hosts recorded for A. compactum metacercariae. A review of morphometric data of A. compactum metacercariae is presented.


Sodium Benzoate, a D-Amino Acid Oxidase Inhibitor, Increased Volumes of Thalamus, Amygdala, and Brainstem in a Drug-Naïve Patient With Major Depression


title: Novelty and Diversity Metrics for Recommender Systems: Choice, Discovery and Relevance

abstract: This is an electronic version of the paper presented at the International Workshop on Diversity in Document Retrieval, held in Dublin on 2011


title: Peter the Fashionista?: Computer Programming Games and Gender Oriented Cultural Forms

abstract: We present a study of two games designed to help elementary and middle school students learn computer programming concepts. The first game was intended to be "gender neutral", aligning with might be described as a consensus opinion on best practices for computational learning environments. The second game, based on the cultural form of dress up dolls was deliberately designed to appeal to girls. We recruited 70 participants in an international two-phase study to investigate the relationship between games, gender, attitudes towards computer programming, and learning. Our findings suggest that while the two games were equally effective in terms of learning outcomes, there were differences in motivation between players of the two games. Specifically, participants who reported a preference for girl-oriented games were more motivated to learn about computer programming when they played a game that they perceived as designed for girls. In addition, we describe how the two games seemed to encourage different types of social activity between players in a classroom setting. Based on these results we reflect on the strategy of exclusively designing games and activities as "gender neutral", and suggest that deliberately employing cultural forms, including gendered ones, may help create a more productive experience for learners


title: Performance Comparison between Sliding Mode Control (SMC) and PD-PID Controllers for a Nonlinear Inverted Pendulum System

abstract: The objective of this paper is to compare the time ::: specification performance between conventional controller PID and ::: modern controller SMC for an inverted pendulum system. The goal is ::: to determine which control strategy delivers better performance with ::: respect to pendulum's angle and cart's position. The inverted ::: pendulum represents a challenging control problem, which ::: continually moves toward an uncontrolled state. Two controllers are ::: presented such as Sliding Mode Control (SMC) and Proportional- ::: Integral-Derivatives (PID) controllers for controlling the highly ::: nonlinear system of inverted pendulum model. Simulation study has ::: been done in Matlab Mfile and simulink environment shows that both ::: controllers are capable to control multi output inverted pendulum ::: system successfully. The result shows that Sliding Mode Control ::: (SMC) produced better response compared to PID control strategies ::: and the responses are presented in time domain with the details ::: analysis.


title: Excitatory amino acid neurotoxicity and neurodegenerative disease.

abstract: The progress over the last 30 years in defining the role of excitatory amino acids in normal physiological function and in the abnormal neuronal activity of epilepsy has been reviewed in earlier articles in this series. In the last five years it has become clear that excitatory amino acids also play a role in a wide range of neurodegenerative processes. The evidence is clearest where the degenerative process is acute, but is more controversial for slow degenerative processes. In this article Brian Meldrum and John Garthwaite review in vivo and in vitro studies of the cytotoxicity of amino acids and summarize the contribution of such toxicity to acute and chronic neurodegenerative disorders.


title: Deep Kalman Filtering Network for Video Compression Artifact Reduction

abstract: When lossy video compression algorithms are applied, compression artifacts often appear in videos, making decoded videos unpleasant for human visual systems. In this paper, we model the video artifact reduction task as a Kalman filtering procedure and restore decoded frames through a deep Kalman filtering network. Different from the existing works using the noisy previous decoded frames as temporal information in the restoration problem, we utilize the less noisy previous restored frame and build a recursive filtering scheme based on the Kalman model. This strategy can provide more accurate and consistent temporal information, which produces higher quality restoration results. In addition, the strong prior information of prediction residual is also exploited for restoration through a well designed neural network. These two components are combined under the Kalman framework and optimized through the deep Kalman filtering network. Our approach can well bridge the gap between the model-based methods and learning-based methods by integrating the recursive nature of the Kalman model and highly non-linear transformation ability of deep neural network. Experimental results on the benchmark dataset demonstrate the effectiveness of our proposed method.


title: A Systematic Literature Review of Best Practices and Challenges in Follow-the-Sun Software Development

abstract: Follow-the-sun (FTS) software development is a strategy used to reduce the length of software projects that are developed across globally distributed locations. However, due to communication and collaboration challenges, software companies find it difficult to adopt this development strategy during task allocation and daily project handovers. In this study, we present results from a Systematic Literature Review (SLR) performed on papers published between 1990 and 2012. Our goal was to identify best practices and challenges for FTS implementation. We found 36 best practices and 17 challenges for FTS. These results are discussed in this paper in order to indicate opportunities for future research and make our results useful for the project managers.


title: In the Eye of the Beholder. Swedish Rural Tourism Operators' and Visitors' Views and Perceptions on Reused Agricultural Buildings

abstract: Reutilization of agricultural buildings for rural tourism (RT) is an area that is significantly expanding today. Buildings originally constructed for agricultural production, processing and housing are an essential part of most RT operations, both as a key resource and a significant cost centre. This study compared the perceptions of RT operators about visitors' opinions of buildings with visitors' actual opinions and attempted to identify the building feature preferences of tourists. The empirical evidence was collected using a 0–4 grade Likert-scale questionnaire that was either internet-based or provided in printed form. The results showed that RT operators partially misinterpreted how visitors view and experience reused buildings and the services therein. Although visitors found building technological factors to be less important than operators did, in a comparison of aggregated tourism-related and building-related factors both operators and visitors considered building-related factors to be of higher...


title: Gaussian process regression in vital-sign early warning systems

abstract: The current standard of clinical practice for patient monitoring in most developed nations is connection of patients to vital-sign monitors, combined with frequent manual observation. In some nations, such as the UK, manual early warning score (EWS) systems have been mandated for use, in which scores are assigned to the manual observations, and care escalated if the scores exceed some pre-defined threshold. We argue that this manual system is far from ideal, and can be improved using machine learning techniques. We propose a system based on Gaussian process regression for improving the efficacy of existing EWS systems, and then demonstrate the method using manual observation of vital signs from a large-scale clinical study.


Agile Retrospective Games for Different Team Development Phases.


title: Personal computer adventure games: their structure, principles, and applicability for training

abstract: Personal computer adventure games, in which the player assumes the role of a fantasy character to pursue an adventure, have enjoyed enormous popularity and commercial success. Beyond their entertainment value, these games also have an educational value, training users to become better problem solvers in the game domain and probably beyond.In order to understand better this type of game and determine its potential use for managerial training, we analyzed adventure games with respect to three issues. First, what makes a computer simulation an adventure game; second, what makes such a game enjoyable and challenging; and finally, how well does such a game facilitate learning? In search of answers, we analyzed a number of adventure games to determine game structures, and completed a small empirical study in which subjects played a game and had to answer both content and preference related questions.Overall, our analysis reconfirmed people's liking of adventure games. Players seem to enjoy the games' story and interface, and are challenged by the game tasks. Players also learn from gaming; yet levels of learning vary widely, based on the type of knowledge to be conveyed. Fact learning proved to be easiest; plan learning and learning of negative knowledge, the hardest.


title: Text Mining - Knowledge extraction from unstructured textual data

abstract: In the general context of Knowledge Discovery, specific techniques, called Text Mining techniques, are necessary to extract information from unstructured textual data. The extracted information can then be used for the classification of the content of large textual bases. In this paper, we present two examples of information that can be automatically extracted from text collections: probabilistic associations of key-words and prototypical document instances. The Natural Language Processing (NLP) tools necessary for such extractions are also presented.


title: The influence of empathy in human-robot relations

abstract: The idea of robotic companions capable of establishing meaningful relationships with humans remains far from being accomplished. To achieve this, robots must interact with people in natural ways, employing social mechanisms that people use while interacting with each other. One such mechanism is empathy, often seen as the basis of social cooperation and prosocial behaviour. We argue that artificial companions capable of behaving in an empathic manner, which involves the capacity to recognise another's affect and respond appropriately, are more successful at establishing and maintaining a positive relationship with users. This paper presents a study where an autonomous robot with empathic capabilities acts as a social companion to two players in a chess game. The robot reacts to the moves played on the chessboard by displaying several facial expressions and verbal utterances, showing empathic behaviours towards one player and behaving neutrally towards the other. Quantitative and qualitative results of 31 participants indicate that users towards whom the robot behaved empathically perceived the robot as friendlier, which supports our hypothesis that empathy plays a key role in human-robot interaction.


Determination of erosion on a small fairy chimney


Audio-visual preferences of water features used in open-plan offices


title: Replicability is not Reproducibility: Nor is it Good Science

abstract: At various machine learning conferences, at various times, there have been discussions arising from the inability to replicate the experimental results published in a paper. There seems to be a wide spread view that we need to do something to address this problem, as it is essential to the advancement of our field. The most compelling argument would seem to be that reproducibility of experimental results is the hallmark of science. Therefore, given that most of us regard machine learning as a scientific discipline, being able to replicate experiments is paramount. I want to challenge this view by separating the notion of reproducibility, a generally desirable property, from replicability, its poor cousin. I claim there are important differences between the two. Reproducibility requires changes; replicability avoids them. Although reproducibility is desirable, I contend that the impoverished version, replicability, is one not worth having.


title: Basic Properties and Design Principles of UWB Antennas

abstract: Basic principles for ultra-wide-band (UWB) radiation are presented and discussed in this paper. The discussion starts with a description of the influence of antennas on UWB transmission. The parameters characterizing antennas in time and in frequency domain are specified. Since the number of possible antenna structures is nearly unlimited, the focus will be on a classification according to different radiation principles. For each of these mechanisms, the typical advan- tages and disadvantages are discussed, and an example antenna and its characteristics are presented. For a wireless engineer, the problem to solve is the proper design of an antenna with the desired radiation characteristics. The final outcome of this paper is that there exist numbers of UWB antennas, but not each of them is suited for any application, especially in view of radar and communication systems requirements.


title: Dimensional Sentiment Analysis Using a Regional CNN-LSTM Model

abstract: Dimensional sentiment analysis aims to recognize continuous numerical values in multiple dimensions such as the valencearousal (VA) space. Compared to the categorical approach that focuses on sentiment classification such as binary classification (i.e., positive and negative), the dimensional approach can provide more fine-grained sentiment analysis. This study proposes a regional CNN-LSTM model consisting of two parts: regional CNN and LSTM to predict the VA ratings of texts. Unlike a conventional CNN which considers a whole text as input, the proposed regional CNN uses an individual sentence as a region, dividing an input text into several regions such that the useful affective information in each region can be extracted and weighted according to their contribution to the VA prediction. Such regional information is sequentially integrated across regions using LSTM for VA prediction. By combining the regional CNN and LSTM, both local (regional) information within sentences and long-distance dependency across sentences can be considered in the prediction process. Experimental results show that the proposed method outperforms lexicon-based, regression-based, and NN-based methods proposed in previous studies.


title: Bandwidth enhancement for transimpedance amplifiers

abstract: A technique for bandwidth enhancement of a given amplifier is presented. Adding several interstage passive matching networks enables the control of transfer function and frequency response behavior. Parasitic capacitances of cascaded gain stages are isolated from each other and absorbed into passive networks. A simplified design procedure, using well-known low-pass filter component values, is introduced. To demonstrate the feasibility of the method, a CMOS transimpedance amplifier (TIA) is implemented in a 0.18-/spl mu/m BiCMOS technology. It achieves 3 dB bandwidth of 9.2 GHz in the presence of a 0.5-pF photodiode capacitance. This corresponds to a bandwidth enhancement ratio of 2.4 over the amplifier without the additional passive networks. The transresistance gain is 54 dB/spl Omega/, while drawing 55 mA from a 2.5-V supply. The input sensitivity of the TIA is -18 dBm for a bit error rate of 10/sup -12/.


title: Why do people use Facebook

abstract: The social networking site, Facebook, has gained an enormous amount of popularity. In this article, we review the literature on the factors contributing to Facebook use. We propose a model suggesting that Facebook use is motivated by two primary needs: (1) the need to belong and (2) the need for self-presentation. Demographic and cultural factors contribute to the need to belong, whereas neuroticism, narcissism, shyness, self-esteem and self-worth contribute to the need for self-presentation. Areas for future research are discussed.


title: Visual classification of coarse vehicle orientation using Histogram of Oriented Gradients features

abstract: For an autonomous vehicle, detecting and tracking other vehicles is a critical task. Determining the orientation of a detected vehicle is necessary for assessing whether the vehicle is a potential hazard. If a detected vehicle is moving, the orientation can be inferred from its trajectory, but if the vehicle is stationary, the orientation must be determined directly. In this paper, we focus on vision-based algorithms for determining vehicle orientation of vehicles in images. We train a set of Histogram of Oriented Gradients (HOG) classifiers to recognize different orientations of vehicles detected in imagery. We find that these orientation-specific classifiers perform well, achieving a 88% classification accuracy on a test database of 284 images. We also investigate how combinations of orientation-specific classifiers can be employed to distinguish subsets of orientations, such as driver's side versus passenger's side views. Finally, we compare a vehicle detector formed from orientation-specific classifiers to an orientation-independent classifier and find that, counter-intuitively, the orientation-independent classifier outperforms the set of orientation-specific classifiers.


title: Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video

abstract: Automatic pain intensity estimation possesses a significant position in healthcare and medical field. Traditional static methods prefer to extract features from frames separately in a video, which would result in unstable changes and peaks among adjacent frames. To overcome this problem, we propose a real-time regression framework based on the recurrent convolutional neural network for automatic frame-level pain intensity estimation. Given vector sequences of AAM-warped facial images, we used a sliding-window strategy to obtain fixed-length input samples for the recurrent network. We then carefully design the architecture of the recurrent network to out-


title: Vocabulary size and its effect on topic representation

abstract: The impact of vocabulary reduction on topic modeling is explored for three data sets.Results are compared using four document and topic-centered measures.Removal of singly occurring terms has minimal influence on topics.Removal of frequently occurring terms greatly influences topic outcomes for three measures. This study investigates how computational overhead for topic model training may be reduced by selectively removing terms from the vocabulary of text corpora being modeled. We compare the impact of removing singly occurring terms, the top 0.5%, 1% and 5% most frequently occurring terms and both top 0.5% most frequent and singly occurring terms, along with changes in the number of topics modeled (10, 20, 30, 40, 50, 100) using three datasets. Four outcome measures are compared. The removal of singly occurring terms has little impact on outcomes for all of the measures tested. Document discriminative capacity, as measured by the document space density, is reduced by the removal of frequently occurring terms, but increases with higher numbers of topics. Vocabulary size does not greatly influence entropy, but entropy is affected by the number of topics. Finally, topic similarity, as measured by pairwise topic similarity and Jensen-Shannon divergence, decreases with the removal of frequent terms. The findings have implications for information science research in information retrieval and informetrics that makes use of topic modeling.


title: All-passive nonreciprocal metastructure

abstract: We introduce a systematic approach to design all-passive subwavelength high performance metasurfaces that exhibit nonreciprocal properties and achieve wave-flow isolation. Moreover we build upon those findings and propose a new paradigm for a quasi-2D metasurface that mimic the nonreciprocal property of Faraday rotation without using any magnetic or electric biasing. We envision that the proposed approaches may serve as a building block for all-passive time-reversal symmetry breaking with potential applications for future nonreciprocal systems and devices.


title: Frama-C: A software analysis perspective

abstract: Frama-C is a source code analysis platform that aims at conducting verification of industrial-size C programs. It provides its users with a collection of plug-ins that perform static analysis, deductive verification, and testing, for safety- and security-critical software. Collaborative verification across cooperating plug-ins is enabled by their integration on top of a shared kernel and datastructures, and their compliance to a common specification language. This foundational article presents a consolidated view of the platform, its main and composite analyses, and some of its industrial achievements.


title: High frequency walking pattern generation based on preview control of ZMP

abstract: This paper addresses a dynamically stable humanoid walking pattern generation system that can update the pattern at a short cycle such as 40 [ms]. Frequent update of the pattern enables a robot to reflect change of moving direction and upper body posture with short delay after the change of the command input. It also enables a robot to change the walking pattern according to the change of measurable applied force. Moreover short cycle generation is useful in constructing a system that reflects motion status modified by sensor feedback to the motion generation layer in order to maintain consistency of dynamics. Preview control theory is adopted to decide horizontal torso trajectory that realizes desired ZMP trajectory. The proposed system is implemented on a full-size humanoid HRP-2. Walking pattern is updated at every 40 [ms] with reflecting the current modified torso position in the system. Walking control is successfully carried out by the implemented system. Then an experiment of generating and executing walking pattern while variable force is applied to the hand is shown. The system generates walking pattern reflecting the force sensor information at the wrist while walking


title: Towards the semantic web: Collaborative tag suggestions

abstract: Content organization over the Internet went through several interesting phases of evolution: from structured directories to unstructured Web search engines and more recently, to tagging as a way for aggregating information, a step towards the semantic web vision. Tagging allows ranking and data organization to directly utilize inputs from end users, enabling machine processing of Web content. Since tags are created by individual users in a free form, one important problem facing tagging is to identify most appropriate tags, while eliminating noise and spam. For this purpose, we define a set of general criteria for a good tagging system. These criteria include high coverage of multiple facets to ensure good recall, least effort to reduce the cost involved in browsing, and high popularity to ensure tag quality. We propose a collaborative tag suggestion algorithm using these criteria to spot high-quality tags. The proposed algorithm employs a goodness measure for tags derived from collective user authorities to combat spam. The goodness measure is iteratively adjusted by a reward-penalty algorithm, which also incorporates other sources of tags, e.g., content-based auto-generated tags. Our experiments based on My Web 2.0 show that the algorithm is effective.


title: AnySee: Peer-to-Peer Live Streaming

abstract: Efficient and scalable live-streaming overlay construction has become a hot topic recently. In order to improve the performance metrics, such as startup delay, source-to-end delay, and playback continuity, most previous studies focused on intra-overlay optimization. Such approaches have drawbacks including low resource utilization, high startup and source-to-end delay, and unreasonable resource assignment in global P2P networks. Anysee is a peer-to-peer live streaming system and adopts an inter-overlay optimization scheme, in which resources can join multiple overlays, so as to (1) improve global resource utilization and distribute traffic to all physical links evenly; (2) assign resources based on their locality and delay; (3) guarantee streaming service quality by using the nearest peers, even when such peers might belong to different overlays; and (4) balance the load among the group members. We compare the performance of our design with existing approaches based on comprehensive trace driven simulations. Results show that AnySee outperforms previous schemes in resource utilization and the QoS of streaming services. AnySee has been implemented as an Internet based live streaming system, and was successfully released in the summer of 2004 in CERNET of China. Over 60,000 users enjoy massive entertainment programs, including TV programs, movies, and academic conferences. Statistics prove that this design is scalable and robust, and we believe that the wide deployment of AnySee will soon benefit many more Internet users.


title: Mathematical Modeling of Permanent Magnet Brushless DC Motor for Electric Scooter

abstract: Permanent magnet brushless dc (PMBLDC) motors are successfully developed to find the wide application in low power automotives due to high efficiency, silent operation, compact size, lower maintenance and high reliability. Due to the advancement in power electronics, PM Brushless dc motors have become potential drive for automotive applications. This paper mainly deals on simulation using Mat lab / Simulink for mathematical model of the PMBLDC motor for electric scooter application. The steady state and transient models of PMBLDC motor is analyzed for efficient performance of control. The transfer function of the motor is derived to arrive at the stability of the motor. The communication system and network topology of the motor drive is discussed and presented.


title: Agent based Tools for Modeling and Simulation of Self-Organization in Peer-to-Peer, Ad-Hoc and other Complex Networks

abstract: Agent-based modeling and simulation tools provide a mature platform for development of complex simulations. They however, have not been applied much in the domain of mainstream modeling and simulation of computer networks. In this article, we evaluate how and if these tools can offer any value-addition in the modeling & simulation of complex networks such as pervasive computing, large-scale peer-to-peer systems, and networks involving considerable environment and human/animal/habitat interaction. Specifically, we demonstrate the effectiveness of NetLogo - a tool that has been widely used in the area of agent-based social simulation.


title: Learning Feature Representations with K-means

abstract: Many algorithms are available to learn deep hierarchies of features from unlabeled data, especially images. In many cases, these algorithms involve multi-layered networks of features (e.g., neural networks) that are sometimes tricky to train and tune and are difficult to scale up to many machines effectively. Recently, it has been found that K-means clustering can be used as a fast alternative training method. The main advantage of this approach is that it is very fast and easily implemented at large scale. On the other hand, employing this method in practice is not completely trivial: K-means has several limitations, and care must be taken to combine the right ingredients to get the system to work well. This chapter will summarize recent results and technical tricks that are needed to make effective use of K-means clustering for learning large-scale representations of images. We will also connect these results to other well-known algorithms to make clear when K-means can be most useful and convey intuitions about its behavior that are useful for debugging and engineering new systems.


title: An Ontology for Satellite Databases

abstract: This paper demonstrates the development of ontology for satellite databases. First, I create a computational ontology for the Union of Concerned Scientists (UCS) Satellite Database (UCSSD for short), called the UCS Satellite Ontology (or UCSSO). Second, in developing UCSSO I show that The Space Situational Awareness Ontology (SSAO) (Rovetto & Kelso 2016)-—an existing space domain reference ontology—-and related ontology work by the author (Rovetto 2015, 2016) can be used either (i) with a database-specific local ontology such as UCSSO, or (ii) in its stead. In case (i), local ontologies such as UCSSO can reuse SSAO terms, perform term mappings, or extend it. In case (ii), the author’s orbital space ontology work, such as the SSAO, is usable by the UCSSD and organizations with other space object catalogs, as a reference ontology suite providing a common semantically-rich domain model. The SSAO, UCSSO, and the broader Orbital Space Environment Domain Ontology project is online at https://purl.org/space-ontology and GitHub. This ontology effort aims, in part, to provide accurate formal representations of the domain for various applications. Ontology engineering has the potential to facilitate the sharing and integration of satellite data from federated databases and sensors for safer spaceflight.


title: Exposure Fusion

abstract: We propose a technique for fusing a bracketed exposure sequence into a high quality image, without converting to HDR first. Skipping the physically-based HDR assembly step simplifies the acquisition pipeline. This avoids camera response curve calibration and is computationally efficient. It also allows for including flash images in the sequence. Our technique blends multiple exposures, guided by simple quality measures like saturation and contrast. This is done in a multiresolution fashion to account for the brightness variation in the sequence. The resulting image quality is comparable to existing tone mapping operators.


title: Retransmission steganography and its detection

abstract: The paper presents a new steganographic method called RSTEG (retransmission steganography), which is intended for a broad class of protocols that utilises retransmission mechanisms. The main innovation of RSTEG is to not acknowledge a successfully received packet in order to intentionally invoke retransmission. The retransmitted packet carries a steganogram instead of user data in the payload field. RSTEG is presented in the broad context of network steganography, and the utilisation of RSTEG for TCP (transmission control protocol) retransmission mechanisms is described in detail. Simulation results are also presented with the main aim of measuring and comparing the steganographic bandwidth of the proposed method for different TCP retransmission mechanisms, as well as to determine the influence of RSTEG on the network retransmission level.