title: Security practices in DevOps

abstract: DevOps focuses on collaboration between different teams in an organization to achieve rapid deployment of software and services to end-users by automating the software delivery infrastructure. According to Dyck et al. [1] DevOps is a software process that emphasizes collaboration within and between different teams involved in software development. According to a study from CA Technologies [5], 88% of 1425 organization executives stated that they have adopted DevOps, or are planning to adopt DevOps in the next five years. According to Puppet Labs' 2015 State of DevOps Report [2], organizations that have adopted DevOps experienced 60 times fewer failures and deploy 30 times more frequently than organizations that have not adopted DevOps. Despite the popularity, security aspects of DevOps remain a concern for organizations that want to adopt DevOps [5]. In organizations that use DevOps practices, developers can commit and deploy their software changes at a rapid rate using an automated pipeline. At such a rapid rate, if the security team operates in isolation without close collaboration with the development and operations teams, then the rapidly deployed software changes might not undergo the adequate security reviews, potentially leading to vulnerable software. Bringing security principles within the DevOps process can help the organization in achieving better quality of software by integrating security checks into the phases of development, testing, and deployment.


title: Standard Particle Swarm Optimisation 2011 at CEC-2013: A baseline for future PSO improvements

abstract: In this work we benchmark, for the first time, the latest Standard Particle Swarm Optimisation algorithm (SPSO-2011) against the 28 test functions designed for the Special Session on Real-Parameter Single Objective Optimisation at CEC-2013. SPSO-2011 is a major improvement over previous PSO versions, with an adaptive random topology and rotational invariance constituting the main advancements. Results showed an outstanding performance of SPSO-2011 for the family of unimodal and separable test functions, with a fast convergence to the global optimum, while good performance was observed for four rotated multimodal functions. Conversely, SPSO-2011 showed the weakest performance for all composition problems (i.e. highly complex functions specially designed for this competition) and certain multimodal test functions. In general, a fast convergence towards the region of the global optimum was achieved, requiring less than 10E+03 function evaluations. However, for most composition and multimodal functions SPSO2011 showed a limited capability to “escape” from sub-optimal regions. Despite this limitation, a desirable feature of SPSO-2011 was its scalable behaviour, which observed up to 50-dimensional problems, i.e. keeping a similar performance across dimensions with no need for increasing the population size. Therefore, it seems advisable that future PSO improvements be focused on enhancing the algorithm's ability to solve non-separable and asymmetrical functions, with a large number of local minima and a second global minimum located far from the true optimum. This work is the first effort towards providing a baseline for a fair comparison of future PSO improvements.


title: Late Fusion via Subspace Search With Consistency Preservation

abstract: In many real-world applications, data can be represented by multiple ways or multi-view features to describe various characteristics of data. In this sense, the prediction performance can be significantly improved by taking advantages of these features together. Late fusion, which combines the predictions of multiple features, is a commonly used approach to make the final decision for a test instance. However, it is ubiquitous that different features dispute the prediction on the same data with each other, leading to performance degeneration. In this paper, we propose an efficient and effective matrix factorization-based approach to fuse predictions from multiple sources. This approach leverages a hard constraint on the matrix rank to preserve the consistency of predictions by various features, and we thus named it as Hard-rank Constraint Matrix Factorization-based fusion (HCMF). HCMF can avoid the performance degeneration caused by the controversy of multiple features. Extensive experiments demonstrate the efficacy of HCMF for outlier detection and the performance improvement, which outperforms the state-of-the-art late fusion algorithms on many data sets.


title: Visual Question Answering Agent with Visual and Textual Attention

abstract: Advancements in computer vision, natural language processing and deep learning techniques have resulted in the creation of intelligent systems that have achieved impressive results in the visually grounded tasks such as image captioning and visual question answering (VQA). VQA is a task that can be used to evaluate a system's capacity to understand an image. It requires an intelligent agent to answer a natural language question about an image. The agent must ground the question into the image and return a natural language answer. One of the latest techniques proposed to tackle this task is the attention mechanism. It allows the agent to focus on specific parts of the input in order to answer the question. In this paper we propose a novel long short-term memory (LSTM) architecture that uses dual attention to focus on specific question words and parts of the input image in order to generate the answer. We evaluate our solution on the recently proposed Visual 7W dataset and show that it performs better than state of the art. Additionally, we propose two new question types for this dataset in order to improve model evaluation. We also make a qualitative analysis of the results and show the strength and weakness of our agent.


title: Robust Federated Learning Using ADMM in the Presence of Data Falsifying Byzantines

abstract: In this paper, we consider the problem of federated (or decentralized) learning using ADMM with multiple agents. We consider a scenario where a certain fraction of agents (referred to as Byzantines) provide falsified data to the system. In this context, we study the convergence behavior of the decentralized ADMM algorithm. We show that ADMM converges linearly to a neighborhood of the solution to the problem under certain conditions. We next provide guidelines for network structure design to achieve faster convergence. Next, we provide necessary conditions on the falsified updates for exact convergence to the true solution. To tackle the data falsification problem, we propose a robust variant of ADMM. We also provide simulation results to validate the analysis and show the resilience of the proposed algorithm to Byzantines.


title: Anticipating Visual Representations from Unlabeled Video

abstract: Anticipating actions and objects before they start or appear is a difficult problem in computer vision with several real-world applications. This task is challenging partly because it requires leveraging extensive knowledge of the world that is difficult to write down. We believe that a promising resource for efficiently learning this knowledge is through readily available unlabeled video. We present a framework that capitalizes on temporal structure in unlabeled video to learn to anticipate human actions and objects. The key idea behind our approach is that we can train deep networks to predict the visual representation of images in the future. Visual representations are a promising prediction target because they encode images at a higher semantic level than pixels yet are automatic to compute. We then apply recognition algorithms on our predicted representation to anticipate objects and actions. We experimentally validate this idea on two datasets, anticipating actions one second in the future and objects five seconds in the future.


title: Towards Accurate Binary Convolutional Neural Network

abstract: We introduce a novel scheme to train binary convolutional neural networks (CNNs) -- CNNs with weights and activations constrained to {-1,+1} at run-time. It has been known that using binary weights and activations drastically reduce memory size and accesses, and can replace arithmetic operations with more efficient bitwise operations, leading to much faster test-time inference and lower power consumption. However, previous works on binarizing CNNs usually result in severe prediction accuracy degradation. In this paper, we address this issue with two major innovations: (1) approximating full-precision weights with the linear combination of multiple binary weight bases; (2) employing multiple binary activations to alleviate information loss. The implementation of the resulting binary CNN, denoted as ABC-Net, is shown to achieve much closer performance to its full-precision counterpart, and even reach the comparable prediction accuracy on ImageNet and forest trail datasets, given adequate binary weight bases and activations.


Dynamic Real-time Replanning in Belief Space : An Experimental Study on Physical Mobile Robots


title: Deep Learning for Sensorless 3D Freehand Ultrasound Imaging

abstract: 3D freehand ultrasound imaging is a very promising imaging modality but its acquisition is often neither portable nor practical because of the required external tracking hardware. Building a sensorless solution that is fully based on image analysis would thus have many potential applications. However, previously proposed approaches rely on physical models whose assumptions only hold on synthetic or phantom datasets, failing to translate to actual clinical acquisitions with sufficient accuracy. In this paper, we investigate the alternative approach of using statistical learning to circumvent this problem. To that end, we are leveraging the unique modeling capabilities of convolutional neural networks in order to build an end-to-end system where we directly predict the ultrasound probe motion from the images themselves. Based on thorough experiments using both phantom acquisitions and a set of 100 in-vivo long ultrasound sweeps for vein mapping, we show that our novel approach significantly outperforms the standard method and has direct clinical applicability, with an average drift error of merely 7\(\%\) over the whole length of each ultrasound clip.


title: Lateral Nasal Artery Perforator Flaps: Anatomic Study and Clinical Applications

abstract: Background ::: Previous studies have investigated facial artery perforators, but have reported inconsistent results regarding lateral nasal artery (LNA) perforators. Although several authors have described the use of LNA perforators for ala nasi and nasal sidewall reconstruction, the literature contains little information regarding the cadaveric dissection of LNA perforators, and most previously published studies have focused on facial artery perforators.


title: A QoS Architecture for IOT

abstract: The application-oriented IoT is an integration of various kinds of technologies, rapidly developing in the globe. To ensure the quality its practical use, it is necessary to research its QoS architecture. We summarize QoS requirements through analysis of characteristic of IoT application service, transmission network, perception in this paper. Then we propose a QoS architecture based on IoT layered structure. This architecture sets QoS agent in lower layers then transmits QoS requirements, trying to guarantee the consistency as well as effectively use the existing QoS mechanisms in every layer.


title: On unifying key-frame and voxel-based dense visual SLAM at large scales

abstract: This paper proposes an approach to real-time dense localisation and mapping that aims at unifying two different representations commonly used to define dense models. On one hand, much research has looked at 3D dense model representations using voxel grids in 3D. On the other hand, image-based key-frame representations for dense environment mapping have been developed. Both techniques have their relative advantages and disadvantages which will be analysed in this paper. In particular each representation's space-size requirements, their effective resolution, the computation efficiency, their accuracy and robustness will be compared. This paper then proposes a new model which unifies various concepts and exhibits the main advantages of each approach within a common framework. One of the main results of the proposed approach is its ability to perform large scale reconstruction accurately at the scale of mapping a building.


title: A hybrid stock trading system using genetic network programming and mean conditional value-at-risk

abstract: This paper describes a hybrid stock trading system based on Genetic Network Programming (GNP) and Mean Conditional Value-at-Risk Model (GNP–CVaR). The proposed method, combining the advantages of evolutionary algorithms and statistical model, has provided useful tools to construct portfolios and generate effective stock trading strategies for investors with different risk-attitudes. Simulation results on five stock indices show that model based on GNP and maximum Sharpe Ratio portfolio performs the best in bull market, and that based on GNP and the global minimum risk portfolio performs the best in bear market. The portfolios constructed by Markowitz’s mean–variance model performs the same as mean-CVaR model. It is clarified that the proposed system significantly improves the function and efficiency of original GNP, which can help investors make profitable decisions.


title: Objective quality assessment for video retargeting based on spatio-temporal distortion analysis

abstract: This paper proposes a novel objective quality metric for video retargeting based on spatio-temporal distortion analysis. The proposed metric combines three indices: one spatial distortion index taking into account perceptual geometric distortion and spatial information loss, and two temporal distortion indices including temporal inconsistency distortion and temporal saliency similarity. Subjective tests are conducted to evaluate the performance of the proposed metric. Our experimental results show the good consistency between the proposed objective metric and the subjective rankings.


title: Expanding the Limits of Vision-based Localization for Long-term Route-following Autonomy

abstract: Vision-based, autonomous, route-following algorithms enable robots to autonomously repeat manually driven routes over long distances. Through the use of inexpensive, commercial vision sensors, these algorithms have the potential to enable robotic applications across multiple industries. However, in order to extend these algorithms to long-term autonomy, they must be able to operate over long periods of time. This poses a difficult challenge for vision-based systems in unstructured and outdoor environments, where appearance is highly variable. While many techniques have been developed to perform localization across extreme appearance change, most are not suitable or untested for vision-in-the-loop systems such as autonomous route following, which requires continuous metric localization to keep the robot driving. In this paper, we present a vision-based, autonomous, route-following algorithm that combines multiple channels of information during localization to increase robustness against daily appearance change such as lighting. We explore this multichannel visual teach and repeat framework by adding the following channels of information to the basic single-camera, gray-scale, localization pipeline: images that are resistant to lighting change and images from additional stereo cameras to increase the algorithm's field of view. Using these methods, we demonstrate robustness against appearance change through extensive field deployments spanning over 26 km with an autonomy rate greater than 99.9%. We furthermore discuss the limits of this system when subjected to harsh environmental conditions by investigating keypoint match degradation through time.


title: Benchmarking Internet of things devices

abstract: The use of commercial off-the-shelf components for implementing Internet of Things devices has become a common practice amongst researchers and solution providers. IOT solutions, based on the Raspberry Pi, BeagleBone and BeagleBone Black, offer cost effective, versatile and uncomplicated platforms for rapid application development. The devices are treated as black box devices and little work has been done to quantify the performance of these devices when the system architecture, software components or communication channels are varied. This paper introduces micro- and macro-benchmarking methods for these devices; quantifying the performance of each device for the varying hardware architectures. Micro-benchmarking was performed using lmbench — a cross platform benchmarking framework for UNIX devices. The macro-benchmarking was implemented using a custom developed CoAP benchmarking utility created using the libCoAP library. The results showed that the selection of the platform processor is a key design requirement and has the most potential to optimise CoAP server performance. The latency associated with the communication channels was found to be a dominating factor for round-trip times associated with CoAP requests.


title: Assessing the Readiness to Move into the Cloud

abstract: The race to keep software compatible and optimal with respect to the latest trends is hard. 90% of software cost can be due to maintenance, and 75% on developing new features to stay competitive and relevant. The industry progresses through periods of incremental development interspersed with true paradigm shifts. Legacy software must keep up the pace.


title: Learning to Account for Good Abandonment in Search Success Metrics

abstract: Abandonment in web search has been widely used as a proxy to measure user satisfaction. Initially it was considered a signal of dissatisfaction, however with search engines moving towards providing answer-like results, a new category of abandonment was introduced and referred to as Good Abandonment. Predicting good abandonment is a hard problem and it was the subject of several previous studies. All those studies have focused, though, on predicting good abandonment in offline settings using manually labeled data. Thus, it remained a challenge how to have an online metric that accounts for good abandonment. In this work we describe how a search success metric can be augmented to account for good abandonment sessions using a machine learned metric that depends on user's viewport information. We use real user traffic from millions of users to evaluate the proposed metric in an A/B experiment. We show that taking good abandonment into consideration has a significant effect on the overall performance of the online metric.


title: Pain as Social Glue : Shared Pain Increases Cooperation

abstract: Even though painful experiences are employed within social rituals across the world, little is known about the social effects of pain. We examined the possibility that painful experiences can promote cooperation within social groups. In Experiments 1 and 2, we induced pain by asking some participants to insert their hands in ice water and to perform leg squats. In Experiment 3, we induced pain by asking some participants to eat a hot chili pepper. Participants performed these tasks in small groups. We found evidence for a causal link: Sharing painful experiences with other people, compared with a no-pain control treatment, promoted trusting interpersonal relationships by increasing perceived bonding among strangers (Experiment 1) and increased cooperation in an economic game (Experiments 2 and 3). Our findings shed light on the social effects of pain, demonstrating that shared pain may be an important trigger for group formation.


title: Different Tumor Microenvironments Contain Functionally Distinct Subsets of Macrophages Derived from Ly6C(high) Monocytes

abstract: Findings deepen biological understanding of tumor-associated macrophages (TAMs), offering insights into the origins, molecular phenotype, intratumoral localization and functions of distinct TAM subpopulations in lung and breast tumors.


title: An Improved kNearest Neighbor Classification Using Genetic Algorithm.International

abstract: k-Nearest Neighbor (KNN) is one of the most popular algorithms for pattern recognition. Many researchers have found that the KNN algorithm accomplishes very good performance in their experiments on different data sets. The traditional KNN text classification algorithm has three limitations: (i) calculation complexity due to the usage of all the training samples for classification, (ii) the performance is solely dependent on the training set, and (iii) there is no weight difference between samples. To overcome these limitations, an improved version of KNN is proposed in this paper. Genetic Algorithm (GA) is combined with KNN to improve its classification performance. Instead of considering all the training samples and taking k-neighbors, the GA is employed to take k-neighbors straightaway and then calculate the distance to classify the test samples. Before classification, initially the reduced feature set is received from a novel method based on Rough set theory hybrid with Bee Colony Optimization (BCO) as we have discussed in our earlier work. The performance is compared with the traditional KNN, CART and SVM classifiers.


title: Stochastic Gradient Hamiltonian Monte Carlo

abstract: Hamiltonian Monte Carlo (HMC) sampling methods provide a mechanism for defining distant proposals with high acceptance probabilities in a Metropolis-Hastings framework, enabling more efficient exploration of the state space than standard random-walk proposals. The popularity of such methods has grown significantly in recent years. However, a limitation of HMC methods is the required gradient computation for simulation of the Hamiltonian dynamical system-such computation is infeasible in problems involving a large sample size or streaming data. Instead, we must rely on a noisy gradient estimate computed from a subset of the data. In this paper, we explore the properties of such a stochastic gradient HMC approach. Surprisingly, the natural implementation of the stochastic approximation can be arbitrarily bad. To address this problem we introduce a variant that uses second-order Langevin dynamics with a friction term that counteracts the effects of the noisy gradient, maintaining the desired target distribution as the invariant distribution. Results on simulated data validate our theory. We also provide an application of our methods to a classification task using neural networks and to online Bayesian matrix factorization.


title: An improved system for grading and treating tinnitus.

abstract: OBJECTIVE ::: Tinnitus is an auditory sensation that can cause discomfort or even pain. Because patients with tinnitus frequently have psychological problems, self-reporting of the severity of tinnitus is unreliable. We developed a new grading system and practical protocol for the systematic treatment of tinnitus that accounts for its severity, patients' psychological problems, and the frequency of catastrophic episodes. The aim of this study is to employ and validate the new system in patients with tinnitus. ::: ::: ::: METHODS ::: This study comprised two parts: (i) We identified 113 patients, who were then analyzed in terms of severity of tinnitus, psychological problems, and catastrophic episodes. They were then classified into 5 grades, and the records of their previous treatments were scrutinized. From these records, we designed a practical treatment protocol suitable for each of the 5 grades. (ii) We then identified 82 new patients, and graded and treated them according to the system developed in part (i). Patients were followed-up for at least 6 months; treatment efficacy was evaluated using the pre- and post-treatment scores on the Tinnitus Handicap Inventory (THI) and Hospital Anxiety and Depression Scale (HADS). Psychological status was also assessed with the DSM-IV. ::: ::: ::: RESULTS ::: (i) The overall patient group was categorized as follows: Grade I, 38 patients, average THI=37.6 points, average HADS=10.9 points, catastrophic episodes=0 points; Grade II, 24 patients, THI=70.6, HADS=13.1, catastrophic episodes=0; Grade III, 5 patients, THI=73.2, HADS=28.4, catastrophic episodes=0; Grade IV, 33 patients, THI=63.5, HADS=18.8, catastrophic episodes=1.0; Grade V, 13 patients, THI=73.2, HADS=22.4, catastrophic episodes=2.2. The treatment records revealed treatment via psychotropic drugs for 40% of Grade III, 45.5% of Grade IV, and 84.6% of Grade V patients; psychiatric consultation was provided for 20% of Grade III, 12.5% of Grade IV, and 53.8% of Grade V patients. (ii) THI scores improved significantly in Grades II, IV, and V after treatment using the new protocol; HADS scores improved significantly in Grades IV and V. Catastrophic episode scores improved significantly in Grades IV and V. ::: ::: ::: CONCLUSION ::: We found large enough differences in THI and HADS scores to successfully classify patients with tinnitus into 5 distinct grades that accounted for tinnitus severity, psychological problems, and catastrophic episodes. We found significant improvements in tinnitus severity and psychological problems in the higher (more severe) grades when this system was used to guide treatment. This system not only provided a reasonably reliable categorization system, it simplified treatment without sacrificing efficacy.


title: Brain Training in Children and Adolescents: Is It Scientifically Valid?

abstract: Background: Brain training products are becoming increasingly popular for children and adolescents. Despite the marketing aimed at their use in the general population, these products may provide more benefits for specific neurologically impaired populations. A review of Brain Training (BT) products analyzing their efficacy while considering the methodological limitations of supporting research is required for practical applications. Method: searches were made of the PubMed database (until March, 2017) for studies including: (1) empirical data on the use of brain training for children or adolescents and any effects on near transfer (NT) and/or far transfer (FT) and/or neuroplasticity, (2) use of brain training for cognitive training purposes, (3) commercially available training applications, (4) computer-based programs for children developed since the 1990s, and (5) relevant printed and peer-reviewed material. Results: Database searches yielded a total of 16,402 references, of which 70 met the inclusion criteria for the review. We classified programs in terms of neuroplasticity, near and far transfer, and long-term effects and their applied methodology. Regarding efficacy, only 10 studies (14.2%) have been found that support neuroplasticity, and the majority of brain training platforms claimed to be based on such concepts without providing any supporting scientific data. 36 studies (51.4%) have shown far transfer (7 of them are non-independent) and only 11 (15.7%) maintained far transfer at follow-up. Considering the methodology, 40 studies (68.2%) were not randomized and controlled; for those randomized, only 9 studies (12.9%) were double-blind, and only 13 studies (18.6%) included active controls in their trials. Conclusion: Overall, few independent studies have found far transfer and long-term effects. The majority of independent results found only near transfer. There is a lack of double-blind randomized trials which include an active control group as well as a passive control to properly control for contaminant variables. Based on our results, Brain Training Programs as commercially available products are not as effective as first expected or as they promise in their advertisements.


title: Asynchronous Bidirectional Decoding for Neural Machine Translation

abstract: The dominant neural machine translation (NMT) models apply unified attentional encoder-decoder neural networks for translation. Traditionally, the NMT decoders adopt recurrent neural networks (RNNs) to perform translation in a left-toright manner, leaving the target-side contexts generated from right to left unexploited during translation. In this paper, we equip the conventional attentional encoder-decoder NMT framework with a backward decoder, in order to explore bidirectional decoding for NMT. Attending to the hidden state sequence produced by the encoder, our backward decoder first learns to generate the target-side hidden state sequence from right to left. Then, the forward decoder performs translation in the forward direction, while in each translation prediction timestep, it simultaneously applies two attention models to consider the source-side and reverse target-side hidden states, respectively. With this new architecture, our model is able to fully exploit source- and target-side contexts to improve translation quality altogether. Experimental results on NIST Chinese-English and WMT English-German translation tasks demonstrate that our model achieves substantial improvements over the conventional NMT by 3.14 and 1.38 BLEU points, respectively. The source code of this work can be obtained from https://github.com/DeepLearnXMU/ABDNMT.


title: A stabilized multigrid solver for hyperelastic image registration

abstract: Summary ::: Image registration is a central problem in a variety of areas involving imaging techniques and is known to be challenging and ill-posed. Regularization functionals based on hyperelasticity provide a powerful mechanism for limiting the ill-posedness. A key feature of hyperelastic image registration approaches is their ability to model large deformations while guaranteeing their invertibility, which is crucial in many applications. To ensure that numerical solutions satisfy this requirement, we discretize the variational problem using piecewise linear finite elements, and then solve the discrete optimization problem using the Gauss–Newton method. In this work, we focus on computational challenges arising in approximately solving the Hessian system. We show that the Hessian is a discretization of a strongly coupled system of partial differential equations whose coefficients can be severely inhomogeneous. Motivated by a local Fourier analysis, we stabilize the system by thresholding the coefficients. We propose a Galerkin-multigrid scheme with a collective pointwise smoother. We demonstrate the accuracy and effectiveness of the proposed scheme, first on a two-dimensional problem of a moderate size and then on a large-scale real-world application with almost 9 million degrees of freedom.


title: Expression of Heat Shock Protein 27 in Melanoma Metastases Is Associated with Overall Response to Bevacizumab Monotherapy: Analyses of Predictive Markers in a Clinical Phase II Study

abstract: The aim of this study was to identify potential predictive biomarkers in 35 patients with metastatic melanoma treated with anti-angiogenic bevacizumab monotherapy in a clinical phase II study. The immunohistochemical expression of various angiogenic factors in tissues from primary melanomas and metastases as well as their concentration in blood samples were examined. Strong expression of Heat Shock Protein 27 (HSP27) in metastases correlated significantly with complete or partial response to bevacizumab (p = 0.044). Furthermore, clinical benefit, i.e., complete or partial response or stable disease for at least 6 months, was more frequent in patients with strong expression of HSP27 in primary tumors (p = 0.046). Tissue expression of vascular endothelial growth factor (VEGF-A), its splicing variant VEGF165b or basic fibroblast growth factor (bFGF) did not correlate with response, and the concentration of HSP27, VEGF-A or bFGF measured in blood samples before treatment did not show predictive value. Further, microvessel density, proliferating microvessel density and presence of glomeruloid microvascular proliferations were assessed in sections of primary tumors and metastases. Microvessel density in primary melanomas was significantly higher in patients with clinical benefit than in non-responders (p = 0.042). In conclusion, our findings suggest that strong HSP27 expression in melanoma metastases predicts response to bevacizumab treatment.


title: RACS: a case for cloud storage diversity

abstract: The increasing popularity of cloud storage is leading organizations to consider moving data out of their own data centers and into the cloud. However, success for cloud storage providers can present a significant risk to customers; namely, it becomes very expensive to switch storage providers. In this paper, we make a case for applying RAID-like techniques used by disks and file systems, but at the cloud storage level. We argue that striping user data across multiple providers can allow customers to avoid vendor lock-in, reduce the cost of switching providers, and better tolerate provider outages or failures. We introduce RACS, a proxy that transparently spreads the storage load over many providers. We evaluate a prototype of our system and estimate the costs incurred and benefits reaped. Finally, we use trace-driven simulations to demonstrate how RACS can reduce the cost of switching storage vendors for a large organization such as the Internet Archive by seven-fold or more by varying erasure-coding parameters.


title: Learning Longer-term Dependencies in RNNs with Auxiliary Losses

abstract: Despite recent advances in training recurrent neural networks (RNNs), capturing long-term dependencies in sequences remains a fundamental challenge. Most approaches use backpropagation through time (BPTT), which is difficult to scale to very long sequences. This paper proposes a simple method that improves the ability to capture long term dependencies in RNNs by adding an unsupervised auxiliary loss to the original objective. This auxiliary loss forces RNNs to either reconstruct previous events or predict next events in a sequence, making truncated backpropagation feasible for long sequences and also improving full BPTT. We evaluate our method on a variety of settings, including pixel-by-pixel image classification with sequence lengths up to 16\,000, and a real document classification benchmark. Our results highlight good performance and resource efficiency of this approach over competitive baselines, including other recurrent models and a comparable sized Transformer. Further analyses reveal beneficial effects of the auxiliary loss on optimization and regularization, as well as extreme cases where there is little to no backpropagation.


title: Sequence-to-Label Script Identification for Multilingual OCR

abstract: We describe a novel line-level script identification method. Previous work repurposed an OCR model generating per-character script codes, counted to obtain line-level script identification. This has two shortcomings. First, as a sequence-to-sequence model it is more complex than necessary for the sequence-to-label problem of line script identification. This makes it harder to train and inefficient to run. Second, the counting heuristic may be suboptimal compared to a learned model. Therefore we reframe line script identification as a sequence-to-label problem and solve it using two components, trained end-toend: Encoder and Summarizer. The encoder converts a line image into a feature sequence. The summarizer aggregates the sequence to classify the line. We test various summarizers with identical inception-style convolutional networks as encoders. Experiments on scanned books and photos containing 232 languages in 30 scripts show 16% reduction of script identification error rate compared to the baseline. This improved script identification reduces the character error rate attributable to script misidentification by 33%.


title: Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain

abstract: This paper presents a novel illumination normalization approach for face recognition under varying lighting conditions. In the proposed approach, a discrete cosine transform (DCT) is employed to compensate for illumination variations in the logarithm domain. Since illumination variations mainly lie in the low-frequency band, an appropriate number of DCT coefficients are truncated to minimize variations under different lighting conditions. Experimental results on the Yale B database and CMU PIE database show that the proposed approach improves the performance significantly for the face images with large illumination variations. Moreover, the advantage of our approach is that it does not require any modeling steps and can be easily implemented in a real-time face recognition system.


title: Justifications, Strategies, and Critical Success Factors in Successful ITIL Implementations in U.S. and Australian Companies: An Exploratory Study

abstract: Abstract A growing number of organizations are implementing the ITIL (IT Infrastructure Library) “best practice” framework in an attempt to improve their IT service management processes. However, not all ITIL implementations are successful and some companies have been disappointed with the outcomes. This exploratory research reports on four case studies of “successful” implementations of IT service management using the process-based ITIL V2 framework. Two companies are located in the U.S. and two in Australia. The cases demonstrate a mix of implementation justifications and strategies. Critical success factors (CSFs) suggested in the literature are compared against those attributed to these successful ITIL implementations. Some CSFs, including executive management support, interdepartmental communication and collaboration, use of consultants, training and careful software selection are confirmed. Three new CSFs are identified: creating an ITIL-friendly culture, process as a priority, and customer-focused metrics. Practitioner guidelines, to assist IT managers, who are contemplating adopting ITIL for process improvement and organisational transformation, are also provided together with some challenges encountered and their associated resolutions.


title: Improving the Performance and Endurance of Persistent Memory with Loose-Ordering Consistency

abstract: Persistent memory provides high-performance data persistence at main memory. Memory writes need to be performed in strict order to satisfy storage consistency requirements and enable correct recovery from system crashes. Unfortunately, adhering to such a strict order significantly degrades system performance and persistent memory endurance. This paper introduces a new mechanism, Loose-Ordering Consistency (LOC), that satisfies the ordering requirements at significantly lower performance and endurance loss. LOC consists of two key techniques. First, Eager Commit eliminates the need to perform a persistent commit record write within a transaction. We do so by ensuring that we can determine the status of all committed transactions during recovery by storing necessary metadata information statically with blocks of data written to memory. Second, Speculative Persistence relaxes the write ordering between transactions by allowing writes to be speculatively written to persistent memory. A speculative write is made visible to software only after its associated transaction commits. To enable this, our mechanism supports the tracking of committed transaction ID and multi-versioning in the CPU cache. Our evaluations show that LOC reduces the average performance overhead of memory persistence from 66.9% to 34.9% and the memory write traffic overhead from 17.1% to 3.4% on a variety of workloads.


title: Secure clustering and routing for heterogeneous mobile wireless sensor networks with dynamic key management

abstract: In mobile wireless sensor networks, the process of offering security to the network after computing dynamic key may result in misuse of information by malicious node. All the messages exchanged during the inter-cluster routing also need to be protected by providing integrity and confidentiality. The proposed architecture includes heterogeneous backbone nodes (BNs) deployed in the network using particle swarm optimisation technique. They perform secure clustering process using exclusion basis system. The cluster head is chosen based on weight value that is estimated using parameters such as the node degree (ND), distance to BN, node velocity and virtual battery power. Once the clusters are formed, the cluster members estimate the cost value based on location, ND and virtual battery power. This cost value is used by the member nodes to calculate the dynamic key for its data communication. Since the parameters used for clustering are further used during dynamic key generation, security has to be provided to ...


title: Exercise dependence, social physique anxiety, and social support in experienced and inexperienced bodybuilders and weightlifters

abstract: Objectives—To investigate psychological correlates of exercise dependence in experienced and inexperienced bodybuilders and weightlifters. Secondary objectives included measuring social physique anxiety, bodybuilding identity, and social support among bodybuilders and weightlifters. Methods—Thirty five experienced bodybuilders, 31 inexperienced bodybuilders, and 23 weightlifters completed the bodybuilding dependence scale, a bodybuilding version of the athletic identity measurement scale, the social physique anxiety scale, and an adapted version of the social support survey-clinical form. Results—A between subjects multivariate analysis of variance was calculated on the scores of the three groups of lifters for the four questionnaires. Univariate F tests and follow up tests indicated that experienced bodybuilders scored significantly higher than inexperienced bodybuilders and weightlifters on bodybuilding dependence (p<0.001), social identity and exclusivity subscales of bodybuilding identity (p<0.001), and social support scales (p<.001), and significantly lower on social physique anxiety (p<0.001). Conclusion—Experienced bodybuilders exhibit more exercise dependence, show greater social support behaviour, and experience less social physique anxiety than inexperienced bodybuilders and weightlifters. (Br J Sports Med 2000;34:431‐435)


title: A Game-Theoretic Approach for Runtime Capacity Allocation in MapReduce

abstract: Nowadays many companies have available large amounts of raw, unstructured data. Among Big Data enabling technologies, a central place is held by the MapReduce framework and, in particular, by its open source implementation, Apache Hadoop. For cost effectiveness considerations, a common approach entails sharing server clusters among multiple users. The underlying infrastructure should provide every user with a fair share of computational resources, ensuring that Service Level Agreements (SLAs) are met and avoiding wastes. In this paper we consider two mathematical programming problems that model the optimal allocation of computational resources in a Hadoop 2.x cluster with the aim to develop new capacity allocation techniques that guarantee better performance in shared data centers. Our goal is to get a substantial reduction of power consumption while respecting the deadlines stated in the SLAs and avoiding penalties associated with job rejections. The core of this approach is a distributed algorithm for runtime capacity allocation, based on Game Theory models and techniques, that mimics the MapReduce dynamics by means of interacting players, namely the central Resource Manager and Class Managers.


title: A saliency-based search mechanism for overt and covert shifts of visual attention

abstract: Most models of visual search, whether involving overt eye movements or covert shifts of attention, are based on the concept of a saliency map, that is, an explicit two-dimensional map that encodes the saliency or conspicuity of objects in the visual environment. Competition among neurons in this map gives rise to a single winning location that corresponds to the next attended target. Inhibiting this location automatically allows the system to attend to the next most salient location. We describe a detailed computer implementation of such a scheme, focusing on the problem of combining information across modalities, here orientation, intensity and color information, in a purely stimulus-driven manner. The model is applied to common psychophysical stimuli as well as to a very demanding visual search task. Its successful performance is used to address the extent to which the primate visual system carries out visual search via one or more such saliency maps and how this can be tested.


title: Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing

abstract: We present Memory Augmented Policy Optimization (MAPO), a simple and novel way to leverage a memory buffer of promising trajectories to reduce the variance of policy gradient estimate. MAPO is applicable to deterministic environments with discrete actions, such as structured prediction and combinatorial optimization tasks. We express the expected return objective as a weighted sum of two terms: an expectation over the high-reward trajectories inside the memory buffer, and a separate expectation over trajectories outside the buffer. To make an efficient algorithm of MAPO, we propose: (1) memory weight clipping to accelerate and stabilize training; (2) systematic exploration to discover high-reward trajectories; (3) distributed sampling from inside and outside of the memory buffer to scale up training. MAPO improves the sample efficiency and robustness of policy gradient, especially on tasks with sparse rewards. We evaluate MAPO on weakly supervised program synthesis from natural language (semantic parsing). On the WikiTableQuestions benchmark, we improve the state-of-the-art by 2.6%, achieving an accuracy of 46.3%. On the WikiSQL benchmark, MAPO achieves an accuracy of 74.9% with only weak supervision, outperforming several strong baselines with full supervision. Our source code is available at https://github.com/crazydonkey200/neural-symbolic-machines


title: Graph-Regularized Locality-Constrained Joint Dictionary and Residual Learning for Face Sketch Synthesis

abstract: Face sketch synthesis is a crucial issue in digital entertainment and law enforcement. It can bridge the considerable texture discrepancy between face photos and sketches. Most of the current face sketch synthesis approaches directly to learn the relationship between the photos and sketches, and it is very difficult for them to generate the individual specific features, which we call rare characteristics . In this paper, we propose a novel face sketch synthesis approach through residual learning. In contrast to traditional approaches, which aim to reconstruct a sketch image directly ( i.e ., learn the mapping relationship between the photo and sketch), we aim to predict the residual image by learning the mapping relationship between the photo and residual, i.e. , the difference between the photo and sketch, given an observed photo. This technique will render optimizing the residual mapping easier than optimizing the original mapping and deriving rare characteristic information. We also introduce a joint dictionary learning algorithm by preserving the local geometry structure of a data space. Through the learned joint dictionary, we transform the face sketch synthesis from an image space to a new and compact space; the new and compact space is spanned by learned dictionary atoms, where the manifold assumption can be further guaranteed. Results show that the proposed method demonstrates an impressive performance in the face sketch synthesis task on three public face sketch datasets and various real-world photos. These results are derived by comparing the proposed method with several state-of-the-art techniques, including certain recently proposed deep learning-based approaches.


title: Personal Navi: Benefits of an Augmented Reality Navigational Aid Using a See-Thru 3D Volumetric HUD

abstract: Augmented reality (AR) is an interface that can potentially provide more intuitive and immersive experiences, particularly for automotive applications. In this paper we present the Personal Navi, a vehicular AR navigational aid designed for use with a see-thru 3D volumetric Head Up Display (HUD). In one study, we found that the Personal Navi visuals helped participants recognize turn locations earlier than when provided with conventional navigation aids alone. The interface also helped keep users' eyes up and fixated on the driving environment more. In a second study, we tested participants' depth perception of AR visuals provided with our prototype 3D-HUD. We displayed the same visuals with a fixed focal depth display (2D-HUD) and found that for applications such as Personal Navi, where the spatial location of AR visuals are an important component of the interface, a dynamically adjustable focal plane (3D-HUD) is necessary for proper registration. We argue that AR-based HUD navigational aids should be implemented only with 3D-HUDs because of inferior depth perception with 2D-HUDs.


Deep Learning on Point Sets for 3 D Classification and Segmentation


title: Computation Offloading and Activation of Mobile Edge Computing Servers: A Minority Game

abstract: With the ever-increasing popularity of resource-intensive mobile applications, Mobile Edge Computing (MEC), e.g., offloading computationally expensive tasks to the cellular edge, has become a prominent technology for the next generation wireless networks. Despite its great performance in terms of delay and energy, MEC suffers from restricted power allowance and computational capability of the edge nodes. Therefore, it is imperative to develop distributed mechanisms for computation offloading, so that not only the computational servers are utilized at their best capacity, but also the users' latency constraints are fulfilled. In this paper, by using the theory of Minority Games, we develop a novel distributed server activation mechanism for computational offloading. Our scheme guarantees energy-efficient activation of servers as well as satisfaction of users' quality-of-experience (QoE) requirements in terms of latency.


title: Detection of the reaction intermediates catalyzed by a copper amine oxidase

abstract: To reveal the chemical changes and geometry changes of active-site residues that cooperate with a reaction is important for understanding the functional mechanism of proteins. Consecutive temporal analyses of enzyme structures have been performed during reactions to clarify structure-based reaction mechanisms. Phenylethylamine oxidase from Arthrobacter globiformis (AGAO) contains a copper ion and topaquinone (TPQ(ox)). The catalytic reaction of AGAO catalyzes oxidative deaminations of phenylethylamine and consists of reductive and oxidative half-reactions. In the reduction step, TPQ(ox) reacts with a phenylethylamine (PEA) substrate giving rise to a topasemiquinone (TPQ(sq)) formed Schiff-base and produces phenylacetaldehyde. To elucidate the mechanism of the reductive half-reaction, an attempt was made to trap the reaction intermediates in order to analyze their structures. The reaction proceeded within the crystals when AGAO crystals were soaked in a PEA solution and freeze-trapped in liquid nitrogen. The reaction stage of each crystal was confirmed by single-crystal microspectrometry, before X-ray diffraction measurements were made of four reaction intermediates. The structure at 15 min after the onset of the reaction was analyzed at atomic resolution, and it was shown that TPQ(ox) and some residues in the substrate channel were alternated via catalytic reductive half-reactions.


title: Heuristic Approaches to the Controller Placement Problem in Large Scale SDN Networks

abstract: Software Defined Networking (SDN) marks a paradigm shift towards an externalized and logically centralized network control plane. A particularly important task in SDN architectures is that of controller placement, i.e., the positioning of a limited number of resources within a network to meet various requirements. These requirements range from latency constraints to failure tolerance and load balancing. In most scenarios, at least some of these objectives are competing, thus no single best placement is available and decision makers need to find a balanced trade-off. This work presents POCO, a framework for Pareto-based Optimal COntroller placement that provides operators with Pareto optimal placements with respect to different performance metrics. In its default configuration, POCO performs an exhaustive evaluation of all possible placements. While this is practically feasible for small and medium sized networks, realistic time and resource constraints call for an alternative in the context of large scale networks or dynamic networks whose properties change over time. For these scenarios, the POCO toolset is extended by a heuristic approach that is less accurate, but yields faster computation times. An evaluation of this heuristic is performed on a collection of real world network topologies from the Internet Topology Zoo. Utilizing a measure for quantifying the error introduced by the heuristic approach allows an analysis of the resulting trade-off between time and accuracy. Additionally, the proposed methods can be extended to solve similar virtual functions placement problems which appear in the context of Network Functions Virtualization (NFV).


title: Evaluation of the Uncanny Valley in CG Characters

abstract: This article revisits the uncanny valley subject in order to evaluate its effects on people's perception of Computed Graphics (CG) characters from movies and games. We analyzed the "uncanny" rates given by the users and as result we obtained a graph quite similar to the original on proposed by Mori [1].


title: New Generation Sensor Web Enablement

abstract: Many sensor networks have been deployed to monitor Earth’s environment, and more will follow in the future. Environmental sensors have improved continuously by becoming smaller, cheaper, and more intelligent. Due to the large number of sensor manufacturers and differing accompanying protocols, integrating diverse sensors into observation systems is not straightforward. A coherent infrastructure is needed to treat sensors in an interoperable, platform-independent and uniform way. The concept of the Sensor Web reflects such a kind of infrastructure for sharing, finding, and accessing sensors and their data across different applications. It hides the heterogeneous sensor hardware and communication protocols from the applications built on top of it. The Sensor Web Enablement initiative of the Open Geospatial Consortium standardizes web service interfaces and data encodings which can be used as building blocks for a Sensor Web. This article illustrates and analyzes the recent developments of the new generation of the Sensor Web Enablement specification framework. Further, we relate the Sensor Web to other emerging concepts such as the Web of Things and point out challenges and resulting future work topics for research on Sensor Web Enablement.


title: Visual Analytics for Model Selection in Time Series Analysis

abstract: Model selection in time series analysis is a challenging task for domain experts in many application areas such as epidemiology, economy, or environmental sciences. The methodology used for this task demands a close combination of human judgement and automated computation. However, statistical software tools do not adequately support this combination through interactive visual interfaces. We propose a Visual Analytics process to guide domain experts in this task. For this purpose, we developed the TiMoVA prototype that implements this process based on user stories and iterative expert feedback on user experience. The prototype was evaluated by usage scenarios with an example dataset from epidemiology and interviews with two external domain experts in statistics. The insights from the experts' feedback and the usage scenarios show that TiMoVA is able to support domain experts in model selection tasks through interactive visual interfaces with short feedback cycles.


title: Understanding Generation Y and their use of social media: a review and research agenda

abstract: Purpose – The purpose of this paper is to review what we know – and don't know – about Generation Y's use of social media and to assess the implications for individuals, firms and society.Design/methodology/approach – The paper distinguishes Generation Y from other cohorts in terms of systematic differences in values, preferences and behavior that are stable over time (as opposed to maturational or other differences). It describes their social media use and highlights evidence of intra‐generational variance arising from environmental factors (including economic, cultural, technological and political/legal factors) and individual factors. Individual factors include stable factors (including socio‐economic status, age and lifecycle stage) and dynamic, endogenous factors (including goals, emotions, and social norms).The paper discusses how Generation Y's use of social media influences individuals, firms and society. It develops managerial implications and a research agenda.Findings – Prior research on the so...


title: Learning to guide task and motion planning using score-space representation

abstract: In this paper, we propose a learning algorithm that speeds up the search in task and motion planning problems. Our algorithm proposes solutions to three different challenges that arise in learning to improve planning efficiency: what to predict, how to represent a planning problem instance, and how to transfer knowledge from one problem instance to another. We propose a method that predicts constraints on the search space based on a generic representation of a planning problem instance, called score space, where we represent a problem instance in terms of performance of a set of solutions attempted so far. Using this representation, we transfer knowledge, in the form of constraints, from previous problems based on the similarity in score space. We design a sequential algorithm that efficiently predicts these constraints, and evaluate it in three different challenging task and motion planning problems. Results indicate that our approach perform orders of magnitudes faster than an unguided planner.


title: A Fuzzy logic controller for stabilization and control of Double Inverted Pendulum (DIP) using different Membership functions (MF's)

abstract: Double Inverted Pendulum (DIP) on cart is a highly non-linear system widely used as a testing bed for verification of newly designed control laws and controllers. In this study control of DIP is obtained using fuzzy logic controllers having different Membership functions(MF's) i.e. triangular, trapezoidal and gbell. The effects of shape of MF's on various controlling parameters i.e. stabilization time, maximum degree of overshoot and steady state error is also illustrated. Simulation results are shown with the help of graphs and tables which proves the validity of proposed method.


title: Realistic image composite with best-buddy prior of natural image patches

abstract: Realistic image composite requires the appearance of foreground and background layers to be consistent. This is difficult to achieve because the foreground and the background may be taken from very different environments. This paper proposes a novel composite adjustment method that can harmonize appearance of different composite layers. We introduce the Best-Buddy Prior (BBP), which is a novel compact representations of the joint co-occurrence distribution of natural image patches. BBP can be learned from unlabelled images given only the unsupervised regional segmentation. The most-probable adjustment of foreground can be estimated efficiently in the BBP space as the shift vector to the local maximum of density function. Both qualitative and quantitative evaluations show that our method outperforms previous composite adjustment methods.


title: PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud

abstract: In this paper, we propose PointRCNN for 3D object detection from raw point cloud. The whole framework is composed of two stages: stage-1 for the bottom-up 3D proposal generation and stage-2 for refining proposals in the canonical coordinates to obtain the final detection results. Instead of generating proposals from RGB image or projecting point cloud to bird's view or voxels as previous methods do, our stage-1 sub-network directly generates a small number of high-quality 3D proposals from point cloud in a bottom-up manner via segmenting the point cloud of the whole scene into foreground points and background. The stage-2 sub-network transforms the pooled points of each proposal to canonical coordinates to learn better local spatial features, which is combined with global semantic features of each point learned in stage-1 for accurate box refinement and confidence prediction. Extensive experiments on the 3D detection benchmark of KITTI dataset show that our proposed architecture outperforms state-of-the-art methods with remarkable margins by using only point cloud as input. The code is available at https://github.com/sshaoshuai/PointRCNN.


title: Introduction: Infectious diseases

abstract: In any discussion of the great challenges facing humanity in addressing global environmental problems, a small number of topics automatically rise to the top: climate change, the loss of biodiversity, and the sustainability of the services ecosystems provide us. But no threats to human welfare are more urgent than those posed by infectious diseases; we suffer already the devastating consequences of the emergence of new diseases such as HIV, the reemergence of old ones such as tuberculosis, and simply the increasing toll of endemic diseases such as malaria. Non-human animals play fundamental roles in the spread of many of these diseases – as reservoirs, as vectors, and as cauldrons for the creation of new types. Land-use practices and environmental management both affect the persistence and spread of endemic diseases, such as malaria. Furthermore, as animal populations increase their ranges, due to climate change and human-facilitated alien introductions, the potential for disease spread also increases. These factors, together with the increasing mobility of the human population, conspire to make these environmental problems of great and immediate concern.


title: Multi-loss Regularized Deep Neural Network

abstract: A proper strategy to alleviate overfitting is critical to a deep neural network (DNN). In this paper, we introduce the cross-loss-function regularization for boosting the generalization capability of the DNN, which results in the multi-loss regularized DNN (ML-DNN) framework. For a particular learning task, e.g., image classification, only a single-loss function is used for all previous DNNs, and the intuition behind the multiloss framework is that the extra loss functions with different theoretical motivations (e.g., pairwise loss and LambdaRank loss) may drag the algorithm away from overfitting to one particular single-loss function (e.g., softmax loss). In the training stage, we pretrain the model with the single-core-loss function and then warm start the whole ML-DNN with the convolutional parameters transferred from the pretrained model. In the testing stage, the outputs by the ML-DNN from different loss functions are fused with average pooling to produce the ultimate prediction. The experiments conducted on several benchmark datasets (CIFAR-10, CIFAR-100, MNIST, and SVHN) demonstrate that the proposed ML-DNN framework, instantiated by the recently proposed network in network, considerably outperforms all other state-of-the-art methods.


title: A game-theoretic model for medium access control

abstract: In this paper, a game-theoretic model for contention based medium access control (contention control) is proposed. We define a general game-theoretic model, called random access game, to capture the distributed nature of contention control and the interaction among wireless nodes with contention-based medium access. We study the design of random access games, characterize their equilibria, study their dynamics, and propose distributed algorithms to achieve the equilibria. This provides a unique perspective to understand existing MAC protocols and a general framework to guide the design of new ones to improve the system performance. As examples, a series of utility functions is proposed for games achieving the maximum throughput in a network of homogeneous nodes. The convergence of different variants (e.g., asynchronous and stochastic algorithms) of different dynamic algorithms such as gradient play are obtained. An equilibrium selection algorithm is also proposed to guarantee that the dynamic algorithms can actually achieve the desired operating point. Simulation results show that game model based protocols can achieve superior performance over the standard IEEE 802.11 DCF, and comparable performance as existing protocols with the best performance in literature.


title: Questionnaire Survey Nonresponse: A Comparison of Postal Mail and Internet Surveys

abstract: Rapid advances in computer technology, and more specifically the Internet, have spurred the use of the Internet surveys for data collection. However, there are some concerns about low response rates in studies that use the Internet as a medium. The question is whether the lessons learned in the past decades to improve rates in postal mail surveys can also be applied to increase response rates in Internet surveys. After all, the Internet is a completely new medium with its own “rules” and even its own (n)etiquette. This article examines 29 studies that directly compared different survey modes (postal mail, fax, e-mail, and Web-based surveys) with more than 15,000 respondents. Factors that can increase response rates and response quality when using Internet surveys, compared to mail surveys, are discussed. Finally, the research that can contribute to increase response rates in Internet surveys is examined.


title: Single image super-resolution using self-optimizing mask via fractional-order gradient interpolation and reconstruction

abstract: Image super-resolution using self-optimizing mask via fractional-order gradient interpolation and reconstruction aims to recover detailed information from low-resolution images and reconstruct them into high-resolution images. Due to the limited amount of data and information retrieved from low-resolution images, it is difficult to restore clear, artifact-free images, while still preserving enough structure of the image such as the texture. This paper presents a new single image super-resolution method which is based on adaptive fractional-order gradient interpolation and reconstruction. The interpolated image gradient via optimal fractional-order gradient is first constructed according to the image similarity and afterwards the minimum energy function is employed to reconstruct the final high-resolution image. Fractional-order gradient based interpolation methods provide an additional degree of freedom which helps optimize the implementation quality due to the fact that an extra free parameter $\alpha$-order is being used. The proposed method is able to produce a rich texture detail while still being able to maintain structural similarity even under large zoom conditions. Experimental results show that the proposed method performs better than current single image super-resolution techniques.


title: Smart: a MapReduce-like framework for in-situ scientific analytics

abstract: In-situ analytics has lately been shown to be an effective approach to reduce both I/O and storage costs for scientific analytics. Developing an efficient in-situ implementation, however, involves many challenges, including parallelization, data movement or sharing, and resource allocation. Based on the premise that MapReduce can be an appropriate API for specifying scientific analytics applications, we present a novel MapReduce-like framework that supports efficient in-situ scientific analytics, and address several challenges that arise in applying the MapReduce idea for in-situ processing. Specifically, our implementation can load simulated data directly from distributed memory, and it uses a modified API that helps meet the strict memory constraints of in-situ analytics. The framework is designed so that analytics can be launched from the parallel code region of a simulation program. We have developed both time sharing and space sharing modes for maximizing the performance in different scenarios, with the former even avoiding any copying of data from simulation to the analytics program. We demonstrate the functionality, efficiency, and scalability of our system, by using different simulation and analytics programs, executed on clusters with multi-core and many-core nodes.


title: Detecting rogue devices in bluetooth networks using radio frequency fingerprinting

abstract: Unauthorized Bluetooth devices or rogue devices can impersonate legitimate devices through address and link key spoofing. Moreover, they can infiltrate a Bluetooth network and initiate other forms of attacks. This paper investigates a novel intrusion detection approach, which makes use of radio frequency fingerprinting (RFF) for profiling, Hotelling’s T 2 statistics for classification and a decision filter, for detecting these devices. RFF is a technique that is used to uniquely identify a transceiver based on the transient portion of the signal it generates. Moreover, the use of a statistical classifier proves advantageous in minimizing requirements for memory. Finally, the detection rate is also improved by incorporating a decision filter, which takes the classification results of a set of events into consideration, prior to rendering the final decision. The average False Alarm Rate of five percent and Detection Rate of ninety-three percent support the feasibility of employing these components to address the aforementioned problem.


title: Security Issues in Vehicular Ad Hoc Networks (VANET): a survey

abstract: Vehicular Ad Hoc Networks (VANET) is a technology that has been recently emerged, and bring a lot of interests. VANET can be used to improve road safety, reduce road traffic, serve interests of its users, and provide emergency services. The security is one of the most important issues in VANET, it is considered a critical point in the development of robust VANET systems. In this paper, a diverse types of security challenges and requirements of VANET will be discussed, and a set of possible solutions for VANET security problems and attacks will be presented and analyzed. Also this paper will propose a new protocol that is called the reply protocol, this protocol aims to protect VANET against several attacks.


title: Reconstructing surfaces of particle-based fluids using anisotropic kernels

abstract: In this article we present a novel surface reconstruction method for particle-based fluid simulators such as Smoothed Particle Hydrodynamics. In particle-based simulations, fluid surfaces are usually defined as a level set of an implicit function. We formulate the implicit function as a sum of anisotropic smoothing kernels, and the direction of anisotropy at a particle is determined by performing Principal Component Analysis (PCA) over the neighboring particles. In addition, we perform a smoothing step that repositions the centers of these smoothing kernels. Since these anisotropic smoothing kernels capture the local particle distributions more accurately, our method has advantages over existing methods in representing smooth surfaces, thin streams, and sharp features of fluids. Our method is fast, easy to implement, and our results demonstrate a significant improvement in the quality of reconstructed surfaces as compared to existing methods.


title: HeteClass: A Meta-path based framework for transductive classification of objects in heterogeneous information networks

abstract: Abstract Transductive classification using labeled and unlabeled objects in a heterogeneous information network for knowledge extraction is an interesting and challenging problem. Most of the real-world networks are heterogeneous in their natural setting and traditional methods of classification for homogeneous networks are not suitable for heterogeneous networks. In a heterogeneous network, various meta-paths connecting objects of the target type, on which classification is to be performed, make the classification task more challenging. The semantic of each meta-path would lead to the different accuracy of classification. Therefore, weight learning of meta-paths is required to leverage their semantics simultaneously by a weighted combination. In this work, we propose a novel meta-path based framework, HeteClass, for transductive classification of target type objects. HeteClass explores the network schema of the given network and can also incorporate the knowledge of the domain expert to generate a set of meta-paths. The regularization based weight learning method proposed in HeteClass is effective to compute the weights of symmetric as well as asymmetric meta-paths in the network, and the weights generated are consistent with the real-world understanding. Using the learned weights, a homogeneous information network is formed on target type objects by the weighted combination, and transductive classification is performed. The proposed framework HeteClass is flexible to utilize any suitable classification algorithm for transductive classification and can be applied on heterogeneous information networks with arbitrary network schema. Experimental results show the effectiveness of the HeteClass for classification of unlabeled objects in heterogeneous information networks using real-world data sets.


title: Differential Evolution Using a Neighborhood-Based Mutation Operator

abstract: Differential evolution (DE) is well known as a simple and efficient scheme for global optimization over continuous spaces. It has reportedly outperformed a few evolutionary algorithms (EAs) and other search heuristics like the particle swarm optimization (PSO) when tested over both benchmark and real-world problems. DE, however, is not completely free from the problems of slow and/or premature convergence. This paper describes a family of improved variants of the DE/target-to-best/1/bin scheme, which utilizes the concept of the neighborhood of each population member. The idea of small neighborhoods, defined over the index-graph of parameter vectors, draws inspiration from the community of the PSO algorithms. The proposed schemes balance the exploration and exploitation abilities of DE without imposing serious additional burdens in terms of function evaluations. They are shown to be statistically significantly better than or at least comparable to several existing DE variants as well as a few other significant evolutionary computing techniques over a test suite of 24 benchmark functions. The paper also investigates the applications of the new DE variants to two real-life problems concerning parameter estimation for frequency modulated sound waves and spread spectrum radar poly-phase code design.


title: CDBFIP: Common Database Forensic Investigation Processes for Internet of Things

abstract: Database forensics is a domain that uses database content and metadata to reveal malicious activities on database systems in an Internet of Things environment. Although the concept of database forensics has been around for a while, the investigation of cybercrime activities and cyber breaches in an Internet of Things environment would benefit from the development of a common investigative standard that unifies the knowledge in the domain. Therefore, this paper proposes common database forensic investigation processes using a design science research approach. The proposed process comprises four phases, namely: 1) identification; 2) artefact collection; 3) artefact analysis; and 4) the documentation and presentation process. It allows the reconciliation of the concepts and terminologies of all common database forensic investigation processes; hence, it facilitates the sharing of knowledge on database forensic investigation among domain newcomers, users, and practitioners.


title: Lessons from the Netflix prize challenge

abstract: This article outlines the overall strategy and summarizes a few key innovations of the team that won the first Netflix progress prize.


title: Fidelity, Soundness, and Efficiency of Interleaved Comparison Methods

abstract: Ranker evaluation is central to the research into search engines, be it to compare rankers or to provide feedback for learning to rank. Traditional evaluation approaches do not scale well because they require explicit relevance judgments of document-query pairs, which are expensive to obtain. A promising alternative is the use of interleaved comparison methods, which compare rankers using click data obtained when interleaving their rankings. In this article, we propose a framework for analyzing interleaved comparison methods. An interleaved comparison method has fidelity if the expected outcome of ranker comparisons properly corresponds to the true relevance of the ranked documents. It is sound if its estimates of that expected outcome are unbiased and consistent. It is efficient if those estimates are accurate with only little data. We analyze existing interleaved comparison methods and find that, while sound, none meet our criteria for fidelity. We propose a probabilistic interleave method, which is sound and has fidelity. We show empirically that, by marginalizing out variables that are known, it is more efficient than existing interleaved comparison methods. Using importance sampling we derive a sound extension that is able to reuse historical data collected in previous comparisons of other ranker pairs.


title: A comprehensive leap motion database for hand gesture recognition

abstract: The touchless interaction has received considerable attention in recent years with benefit of removing the burden of physical contact. The recent introduction of novel acquisition devices, like the leap motion controller, allows obtaining a very informative description of the hand pose and motion that can be exploited for accurate gesture recognition. In this work, we present an interactive application with gestural hand control using leap motion for medical visualization, focusing on the satisfaction of the user as an important component in the composition of a new specific database. In this paper, we propose a 3D dynamic gesture recognition approach explicitly targeted to leap motion data. Spatial feature descriptors based on the positions of fingertips and palm center are extracted and fed into a support vector machine classifier in order to recognize the performed gestures. The experimental results show the effectiveness of the suggested approach in the recognition of the modeled gestures with a high accuracy rate of about 81%.


Advances in Knowledge Discovery and Data Mining


title: Age-Related Disease Association of Endogenous γ-H2AX Foci in Mononuclear Cells Derived from Leukapheresis

abstract: The phosphorylated form of histone H2AX (γ-H2AX) forms immunohistochemically detectable foci at DNA double strand breaks. In peripheral blood mononuclear cells (PBMCs) derived from leukapheresis from patients enrolled in the Baltimore Longitudinal Study of Aging, γ-H2AX foci increased in a linear fashion with regards to age, peaking at ~57 years. The relationship between the frequency of γ-H2AX foci and age-related pathologies was assessed. We found a statistically significant (p = 0.023) 50% increase in foci in PBMCs derived from patients with a known history of vitamin D deficiency. In addition, there were trends toward increased γ-H2AX foci in patients with cataracts (34% increase, p<0.10) and in sleep apnea patients (44%, p<0.10). Among patients ≥57 y/o, we found a significant (p = 0.037) 36% increase in the number of γ-H2AX foci/cell for patients with hypertension compared to non-hypertensive patients. Our results support a role for increased DNA damage in the morbidity of age-related diseases. γ -H2AX may be a biomarker for human morbidity in age-related diseases.


title: Camera Constraint-Free View-Based 3-D Object Retrieval

abstract: Recently, extensive research efforts have been dedicated to view-based methods for 3-D object retrieval due to the highly discriminative property of multiviews for 3-D object representation. However, most of state-of-the-art approaches highly depend on their own camera array settings for capturing views of 3-D objects. In order to move toward a general framework for 3-D object retrieval without the limitation of camera array restriction, a camera constraint-free view-based (CCFV) 3-D object retrieval algorithm is proposed in this paper. In this framework, each object is represented by a free set of views, which means that these views can be captured from any direction without camera constraint. For each query object, we first cluster all query views to generate the view clusters, which are then used to build the query models. For a more accurate 3-D object comparison, a positive matching model and a negative matching model are individually trained using positive and negative matched samples, respectively. The CCFV model is generated on the basis of the query Gaussian models by combining the positive matching model and the negative matching model. The CCFV removes the constraint of static camera array settings for view capturing and can be applied to any view-based 3-D object database. We conduct experiments on the National Taiwan University 3-D model database and the ETH 3-D object database. Experimental results show that the proposed scheme can achieve better performance than state-of-the-art methods.


title: A New Algorithm for Polygon Intersection Operation

abstract: Polygon Intersection is among the most fundamental operations in Computer Graphics, which is also broadly applied in GIS. The existing algorithms for polygon intersection may fail because of being specified ambiguously, or can not handling the concave polygons or those with holes. In this paper, introducing the concept of Monotone chain from Geometry, a rapid polygon intersection algorithm has been addressed. This algorithm could be adapted to any general polygons, with no need to process abnormal conditions; moreover, it could tolerate arithmetic errors. The algorithm has already been applied in GIS platform software, and proved of great stability and reliability.


title: A Generative Shape Regularization Model for Robust Face Alignment

abstract: In this paper, we present a robust face alignment system that is capable of dealing with exaggerating expressions, large occlusions, and a wide variety of image noises. The robustness comes from our shape regularization model, which incorporates constrained nonlinear shape prior, geometric transformation, and likelihood of multiple candidate landmarks in a three-layered generative model. The inference algorithm iteratively examines the best candidate positions and updates face shape and pose. This model can effectively recover sufficient shape details from very noisy observations. We demonstrate the performance of this approach on two public domain databases and a large collection of real-world face photographs.


title: VerbOcean: Mining The Web For Fine-Grained Semantic Verb Relations

abstract: Broad-coverage repositories of semantic relations between verbs could benefit many NLP tasks. We present a semi-automatic method for extracting fine-grained semantic relations between verbs. We detect similarity, strength, antonymy, enablement, and temporal happens-before relations between pairs of strongly associated verbs using lexicosyntactic patterns over the Web. On a set of 29,165 strongly associated verb pairs, our extraction algorithm yielded 65.5% accuracy. Analysis of error types shows that on the relation strength we achieved 75% accuracy. We provide the resource, called VERBOCEAN, for download at http://semantics.isi.edu/ocean/.


title: Automatic Coupling of Answer Extraction and Information Retrieval

abstract: Information Retrieval (IR) and Answer Extraction are often designed as isolated or loosely connected components in Question Answering (QA), with repeated overengineering on IR, and not necessarily performance gain for QA. We propose to tightly integrate them by coupling automatically learned features for answer extraction to a shallow-structured IR model. Our method is very quick to implement, and significantly improves IR for QA (measured in Mean Average Precision and Mean Reciprocal Rank) by 10%-20% against an uncoupled retrieval baseline in both document and passage retrieval, which further leads to a downstream 20% improvement in QAF1.


title: t-Distributed stochastic neighbor embedding spectral clustering

abstract: This paper introduces a new topological clustering approach to cluster high dimensional datasets based on t-SNE (Stochastic Neighbor Embedding) dimensionality reduction method and spectral clustering. Spectral clustering method needs to construct an adjacency matrix and calculate the eigen-decomposition of the corresponding Laplacian matrix [1] which are computational expensive and is not easy to apply on large-scale data sets. One of the issue of this problem is to reduce the dimensionality befor to cluster the dataset. The t-SNE method which performs good results for visulaization allows a projection of the dataset in low dimensional spaces that make it easy to use for very large datasets. Using t-SNE during the learning process will allow to reduce the dimensionality and to preserve the topology of the dataset by increasing the clustering accuracy. We illustrate the power of this method with several real datasets. The results show a good quality of clustering results and a higher speed.


Global Linear Neighborhoods for Efficient Label Propagation


title: Interaction control for postural correction on a riding simulation system

abstract: A horseback riding simulator is a robotic machine that simulates the motion of horseback riding. In this paper, we present an interaction control system for a horseback riding simulator. The proposed system provides a postural correction function suited to the user based on their historical log and specialized posture coaching data. The system has adopted certain schemes for posture detection and recognition of the identified user as a way to recommend customized exercise modes. Our experiments show that including these techniques will help users maintain good posture while riding. Categories and Subject Descriptors J.7 [Computer Applications]: Computers in Other Systems; H.5.m [Information Interfaces and Presentation]: Miscellaneous


title: Cognition from the bottom up: on biological inspiration, body morphology, and soft materials

abstract: Traditionally, in cognitive science the emphasis is on studying cognition from a computational point of view. Studies in biologically inspired robotics and embodied intelligence, however, provide strong evidence that cognition cannot be analyzed and understood by looking at computational processes alone, but that physical system–environment interaction needs to be taken into account. In this opinion article, we review recent progress in cognitive developmental science and robotics, and expand the notion of embodiment to include soft materials and body morphology in the big picture. We argue that we need to build our understanding of cognition from the bottom up; that is, all the way from how our body is physically constructed.


title: Non-Local Graph Convolutional Networks for Skeleton-Based Action Recognition

abstract: Traditional deep methods for skeleton-based action recognition usually structure the skeleton as a coordinates sequence or a pseudo-image to feed to RNNs or CNNs, which cannot explicitly exploit the natural connectivity among the joints. Recently, graph convolutional networks (GCNs), which generalize CNNs to more generic non-Euclidean structures, obtains remarkable performance for skeleton-based action recognition. However, the topology of the graph is set by hand and fixed over all layers, which may be not optimal for the action recognition task and the hierarchical CNN structures. Besides, the first-order information (the coordinate of joints) is mainly used in former GCNs, while the second-order information (the length and direction of bones) is less exploited. In this work, a novel two-stream nonlocal graph convolutional network is proposed to solve these problems. The topology of the graph in each layer of the model can be either uniformly or individually learned by BP algorithm, which brings more flexibility and generality. Meanwhile, a two-stream framework is proposed to model both of the joints and bones information simultaneously, which further boost the recognition performance. Extensive experiments on two large-scale datasets, NTU-RGB+D and Kinetics, demonstrate the performance of our model exceeds the state-of-the-art by a significant margin.


title: Convolutional Neural Network for Time Series Cattle Behaviour Classification

abstract: An experiment is introduced which demonstrates the application of supervised feature learning using a Convolutional Neural Network for cattle behaviour classification. The data set used contains observations from sensors attached to the cattle. Previously this problem was addressed by classifying features learned by a stacked autoencoder. Here we explore an alternative method for learning effective features. Convolutional Neural Networks have shown immense success in computer vision, natural language processing, speech recognition etc. The success of Convolutional Neural Network in so many applications has inspired us to verify how effective this network is on learning features from cattle data. A shallow Convolutional Neural Network such as we have developed in this experiment learns effective features for classification and is time-efficient compared to previous model.


title: Impact of single-microphone dereverberation on DNN-based meeting transcription systems

abstract: Over the past few decades, a range of front-end techniques have been proposed to improve the robustness of automatic speech recognition systems against environmental distortion. While these techniques are effective for small tasks consisting of carefully designed data sets, especially when used with a classical acoustic model, there has been limited evidence that they are useful for a state-of-the-art system with large scale realistic data. This paper focuses on reverberation as a type of distortion and investigates the degree to which dereverberation processing can improve the performance of various forms of acoustic models based on deep neural networks (DNNs) in a challenging meeting transcription task using a single distant microphone. Experimental results show that dereverberation improves the recognition performance regardless of the acoustic model structure and the type of the feature vectors input into the neural networks, providing additional relative improvements of 4.7% and 4.1% to our best configured speaker-independent and speaker-adaptive DNN-based systems, respectively. © 2014 IEEE.


title: A graph-based system for network-vulnerability analysis

abstract: This paper presents a graph-based approach to network vulnerability analysis. The method is flexible, allowing analysis of attacks from both outside and inside the network. It can analyze risks to a specific network asset, or examine the universe of possible consequences following a successful attack. The graph-based tool can identify the set of attack paths that have a high probability of success (or a low effort cost) for the attacker. The system could be used to test the effectiveness of making configuration changes, implementing an intrusion detection system, etc. The analysis system requires as input a database of common attacks, broken into atomic steps, specific network configuration and topology information, and an attacker profile. The attack information is matched with the network configuration information and an attacker profile to create a superset attack graph. Nodes identify a stage of attack, for example the class of machines the attacker has accessed and the user privilege level he or she has compromised. The arcs in the attack graph represent attacks or stages of attacks. By assigning probabilities of success on the arcs or costs representing level-of-effort for the attacker, various graph algorithms such as shortest-path algorithms can identify the attack paths with the highest probability of success.


title: Event Detection in Crowded Videos

abstract: Real-world actions occur often in crowded, dynamic environments. This poses a difficult challenge for current approaches to video event detection because it is difficult to segment the actor from the background due to distracting motion from other objects in the scene. We propose a technique for event recognition in crowded videos that reliably identifies actions in the presence of partial occlusion and background clutter. Our approach is based on three key ideas: (1) we efficiently match the volumetric representation of an event against oversegmented spatio-temporal video volumes; (2) we augment our shape-based features using flow; (3) rather than treating an event template as an atomic entity, we separately match by parts (both in space and time), enabling robustness against occlusions and actor variability. Our experiments on human actions, such as picking up a dropped object or waving in a crowd show reliable detection with few false positives.


title: User experience over time: an initial framework

abstract: A recent trend in Human-Computer Interaction (HCI) research addresses human needs that go beyond the instrumental, resulting in an increasing body of knowledge about how users form overall evaluative judgments on the quality of interactive products. An aspect largely neglected so far is that of temporality, i.e. how the quality of users' experience develops over time. This paper presents an in-depth, five-week ethnographic study that followed 6 individuals during an actual purchase of the Apple iPhone". We found prolonged use to be motivated by different qualities than the ones that provided positive initial experiences. Overall, while early experiences seemed to relate mostly to hedonic aspects of product use, prolonged experiences became increasingly more tied to aspects reflecting how the product becomes meaningful in one's life. Based on the findings, we promote three directions for CHI practice: designing for meaningful mediation, designing for daily rituals, and designing for the self.


title: An Overview of Comics Research in Computer Science

abstract: A wide variety of research about comics has been proposed in the recent years. One of the main focuses of researchers in document image analysis is about comics content analysis and segmentation such as balloons, text or panels. Research about user interaction or content generation has also been proposed. However, as these parts have been covered by different research fields (document image analysis, multimedia, artificial inteligence, human computer interaction, etc.) with different sets of values. We propose in this paper an overview about the previous research about comics in computer science. We will state what have been done and which are the main outlooks of current research.


title: A Chinese OCR spelling check approach based on statistical language models

abstract: This work describes an effective spelling check approach for Chinese OCR with a new multi-knowledge based statistical language model. This language model combines the conventional n-gram language model and the new LSA (latent semantic analysis) language model, so both local information (syntax) and global information (semantic) are utilized. Furthermore, Chinese similar characters are used in Viterbi search process to expand the candidate list in order to add more possible correct results. With our approach, the best recognition accuracy rate increases from 79.3% to 91.9%, which means 60.9% error reduction.


title: Fraud Detection Using an Adaptive Neuro-Fuzzy Inference System in Mobile Telecommunication Networks

abstract: GSM (Global Services of Mobile Communications) 1800 licenses were granted in the beginning of the 2000’s in Turkey. Especially in the installation phase of the wireless telecom services, fraud usage can be an important source of revenue loss. Fraud can be defined as a dishonest or illegal use of services, with the intention to avoid service charges. Fraud detection is the name of the activities to identify unauthorized usage and prevent losses for the mobile network operators’. Mobile phone user’s intentions may be predicted by the call detail records (CDRs) by using data mining (DM) techniques. This study compares various data mining techniques to obtain the best practical solution for the telecom fraud detection and offers the Adaptive Neuro Fuzzy Inference (ANFIS) method as a means to efficient fraud detection. In the test run, shown that ANFIS has provided sensitivity of 97% and specificity of 99%, where it classified 98.33% of the instances correctly.


title: An Approach to Pose-Based Action Recognition

abstract: We address action recognition in videos by modeling the spatial-temporal structures of human poses. We start by improving a state of the art method for estimating human joint locations from videos. More precisely, we obtain the K-best estimations output by the existing method and incorporate additional segmentation cues and temporal constraints to select the ``best'' one. Then we group the estimated joints into five body parts (e.g. the left arm) and apply data mining techniques to obtain a representation for the spatial-temporal structures of human actions. This representation captures the spatial configurations of body parts in one frame (by spatial-part-sets) as well as the body part movements(by temporal-part-sets) which are characteristic of human actions. It is interpretable, compact, and also robust to errors on joint estimations. Experimental results first show that our approach is able to localize body joints more accurately than existing methods. Next we show that it outperforms state of the art action recognizers on the UCF sport, the Keck Gesture and the MSR-Action3D datasets.